Nonconventional luminescent materials (NLMs) that do not contain traditional aromatic chromophores are of great interest due to their unique chemical structures, optical properties and their potential applications in various areas such as cellular imaging and chemical sensing. However, most reported NLMs show weak or no emission in dilute solutions, severely limiting their applications. In this work, the dynamic hydrogen bonds were utilized to design NLMs with efficient emission in dilute solutions. To further validate the results, polymers P1 and P2 were successfully prepared and investigated. It was found that luminescence quantum efficiency of P1 and P2 at concentration of 0.1 mg/mL in water solution is 8.9 and 0.6%, respectively. The high efficiency can be attributed to the fact that polymer P1 has more intra- or intermolecular dynamic hydrogen bonds and other short interactions than P2 in dilute solutions, allowing P1 to achieve the through-space conjugation effect to increase the degree of system conjugation, restrict molecular motion, and decrease non-radiative transitions, which can effectively improve luminescence. In addition, polymer P2 exhibits the characteristics of clustering-triggered emission, excitation wavelength-dependent and concentration-dependent fluorescence properties, excellent photobleaching resistance, low cytotoxicity and selective recognition of Fe3+. This work is a preliminary study of the luminescence properties of NLMs in dilute solutions via modulating dynamic hydrogen bonds, which can be used as a semi-empirical method to design and construct novel NLMs in future.

Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO2 nano-additive in food products. Following the intent of relating nanomaterials atomic structure with their toxicity without having to conduct large scale experiments on living organisms, we investigate the aggregation of titanium dioxide nanoparticles using a multi-scale technique: starting from ab initio Density Functional Theory to get an accurate determination of the energetics and electronic structure, we switch to classical Molecular Dynamics simulations to calculate the Potential of Mean Force for the connection of two identical nanoparticles in water; the fitting of the latter by a set of mathematical equations is the key for the upscale. Lastly, we perform Brownian Dynamics simulations where each nanoparticle is a spherical bead. This coarsening strategy allows studying the aggregation of a few thousand nanoparticles. Applying this novel procedure, we find three new molecular descriptors, namely, the aggregation free energy and two numerical parameters used to correct the observed deviation from the aggregation kinetic described by the Smoluchowski theory. Molecular descriptors can be fed into QSAR models to predict the toxicity of a material knowing its physicochemical properties, without having to conduct large scale experiments on living organisms.

The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al (2016), a new parameter-free spectral clustering approach is developed that automatically identifies parameters and does not require any user-input choices. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The parameter-free spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically-generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different.

In Cameroon, alcoholic beverages remain the main consumed drink. In the Northern regions, indigenous sorghum beers are popular and widely consumed in an actively fermenting state by the people. In this study, some physicochemical parameters of the alcoholic sorghum beverages and correlations between them were evaluated during fermentation for 10 days. The indigenous white and red beers were produced at the laboratory scale assisted by experimented producers and some parameters (pH, total acidity, alcohol, sugars, density, total solids, temperature, and conductivity) were measured on the wort and fermented beverages. The pH decreases from 3.2 to 2.4 and 3.11 to 2.41; total acidity increases from 1.07 to 5.1 g/l and 0.5 to 4.6 g/l; alcohol enhances from 0 to 9.5% and 0 to 6.8% (v/v); total solids drop from 13.6 to 5°P and 12.2 to 3.3°P were observed respectively in the white and red sorghum beers. The multivariate analysis showed a good correlation between consumption of sugar, the decrease of total solids and density with the decrease in pH. It was also shown that, a perfect link exist between the production of alcohol and organic acids. The hierarchical analysis showed that indigenous red beer samples fermented for 1 and 2 days and those fermented for 4 to 10 days are related and could be separate in two distinct groups, whereas white turbid beer samples are separated in three different groups, those fermented for 1 to 4 days, those 5 to 6 days and those 7 to 10 days. The results obtained could serve as a guide to better understand the fermentation process of the indigenous alcoholic sorghum-based beverages.

This paper reviews the measurement challenges associated with 3D scanning techniques in civil engineering. It explores the practical aspects of scanning buildings and complex surfaces through various case studies. The paper details the conventional use of Terrestrial Laser Scanning (TLS) for reconstructing the technical documentation of a hall. It then describes an unconventional application of this technique for measuring an ETICS (External Thermal Insulation Composite System) wall, aiming to detect microdeformations caused by environmental factors controlled within a climatic chamber. The measurements of the insulated wall were subsequently repeated using a metrological grade laser scanner. Numerical data was analyzed with inspection engineering methods. This approach yielded qualitative and quantitative results. Although the qualitative results were consistent, the quantitative data revealed some inconsistencies. To address discrepancies in the quantitative data, a comparative analysis was performed, which highlighted critical findings and areas of divergence.

To date, SARS Cov-2 virus has caused more than 775 million cases worldwide and led to 7 million deaths. The virus enters cells with the aid of the ACE2 receptor located in the airway epithelium, but also in endothelial cells lining the inner walls of vessels. Endothelial damage results in impaired blood flow and gas exchange. Statins have been shown to have a pleiotropic effects on the vascular epithelium. Aim of the study: Assessment of the efficacy and safety of statin therapy in patients with COVID-19. Methods: Two hundred and one COVID-19 patients hospitalized at the Jerzy Gromkowski Regional Specialist Hospital between March 2020 and December 2021 (i.e. during the period dominated by Alpha, Beta, Gamma and Delta variants). Patients were divided into three groups: (I) patients treated with statins prior to the admission to the hospital; (II) patients started on statins after admission due to Covid-19; (III) control patients with Covid-19, not treated with statins either prior to or during hospitalization. Met Statistica 13.1 calculations were used in the paper. Shapiro-Wilk, Kruskal-Wallis and ANOVA tests were also performed. Results: Out of the 201 patients total, group I comprised 66 patients with a mean age of 66.2 years; group II comprised 33 patients with a mean age of 75.3 years; and the control group comprised 97 patients with a mean age of 61.1 years. The groups characteristics did not show significant differences in terms of gender, place of hospitalization, percentage of Covid-19 vaccinations, number of convalescents. Death was observed in: 12% of patients in group I, 6% of patients in group II, and 6% of patients in group III. Body Mass Index (BMI) in group I was 31.3: a value significantly higher than the control group’s 28.3, (p<0.05). The severity of Covid-19 assessed on admission in group I – 4.09 – was statistically higher than in the control group 3.52 (p<0.05). The number of pulmonary embolism incidents did not differ between groups. Conclusions: During the period dominated by Alpha, Beta, Gamma and then Delta variants, we found no statistically significant positive or negative effects of statin therapy on the duration of hospitalization, number of complications or percentage of deaths between the two studied groups of patients: those who received statins prior to hospitalization, and those who received statins during hospitalization.

Pichia pastoris is extensively employed as a host for expressing foreign proteins, and its secretion system is especially advantageous for efficient protein purification. Ulp1 is essential for the cell cycle. In Saccharomyces cerevisiae, the absence of Ulp1 leads to G2/M cell cycle arrest. Similarly, in Pichia pastoris, we also observed G2 phase stalling in Ulp1 knockout cells. RNA-seq data revealed significant changes in seven genes, including INO1. Interestingly, overexpressing INO1 in Ulp1 knockout cells restored cell proliferation, suggesting that Ulp1 regulates cell proliferation through INO1. However, adding extra inositol to Ulp1 knockout cells was not able to restore cell prolifera-tion, indicating that the enzyme activity of INO1 is not essential for cell proliferation. Our data provide new insights into Ulp1 functions in the cell cycle and raise new questions about yeast cell cycle regulation.

Background: RO and ChRCC are kidney tumours with overlapping characteristics, making differentiation between them challenging. Objectives: The objective of this research is to create a radiogenomics map by correlating radiomic features to molecular phenotypes in ChRCC and RO, using resection as the gold standard. Methods: Fourteen patients (6 RO and 8 ChRCC) were included in the study. A total of 1,875 radiomic features were extracted from CT scans, alongside 632 cytobands containing 16,303 genes from the genomic data. Results: Feature selection algorithms applied to the radiomic features resulted in 13 key features. From the genomic data, 24 cytobands highly correlated with histology were selected and cross-correlated with the radiomic features. The analysis identified four radiomic features that were strongly associated with seven genomic features. Conclusion: These findings demonstrate the potential of integrating radiomic and genomic data to enhance the differential diagnosis of RO and ChRCC, paving the way for more precise and non-invasive diagnostic tools in clinical practice.

Background and Objectives: A degenerative joint disease that primarily affects the elderly, osteoarthritis (OA) causes pain, decreased mobility, and a lower quality of life. The effectiveness of GerovitalH3, a biotrophic medication created by Ana Aslan, in reducing pain and enhancing function in older people with osteoarthritis in the knee and hip is assessed in this study. Materials and Methods: Two groups of 201 patients aged 65 and older participated in a randomized, case-control clinical trial. One group received physical therapy and GerovitalH3 periarticular injections, while the other group received just physical therapy. In addition to functional evaluations using the Lequesne Index, Activities of Daily Living (ADL), and Instrumental ADL (IADL) scores, pain was assessed using a Visual Analogue Scale (VAS). Results: The GerovitalH3 group showed a substantial decrease in pain (p<0.001) and an improvement in daily activities (p<0.001) when compared to the control group. However, there was no significant difference in walking scores (p = 0.171). There were no significant adverse effects noted. GerovitalH3 did not outperform physical therapy for reducing depression, even though both groups displayed some improvement in this area. Conclusions: This study opens a new possibility for periarticular injections of Gerovital H3 treatment combined with physical therapy, which may represent a safe and effective non-surgical option for elderly individuals struggling with OA pain and functional limitations. The findings of this study reinforce the need for further research, particularly randomized controlled trials with larger sample sizes and longer follow-up periods.

Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. 1 Estimating the RUL enables the assessment of health bearing, maintenance planning, and signifi- 2 cant cost reduction, thereby fostering industrial development. Existing models rely on traditional 3 feature engineering, with feature changes because operating conditions pose a major challenge to 4 the generalization of RUL prediction models. This study focuses on neural network-based feature 5 engineering and the downstream prediction of the RUL, eliminating the need for specific prior 6 knowledge and simplifying the development and maintenance of models. Initially, a convolutional 7 neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional 8 long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation 9 characteristics of the engineered features and predict the RUL through regression. Finally, the study 10 examines the influence of operating conditions in the model and integrates domain adaptation to 11 minimize differences in feature distribution, thereby enhancing the model’s generalizability for the 12 RUL prediction.

Purpose/Objective: Frequent consumption of fermented carbohydrates and bottle feeding contribute to the development of early childhood caries (ECC). There are no substantial findings on the effects of breastfeeding patterns on oral health conditions in children in the United States. We aimed to assess the nexus between exclusive breastfeeding during the first 6 months and oral health disorders, namely toothache, decayed teeth, or unfilled cavities in early childhood. Materials and Methods: Design: Cross-sectional epidemiologic design of nationally representative data collected via telephone surveys in the United States was utilized. Setting: National Survey of Children’s Health, 2012 Participants: Population-based random sample of parents/guardians of 24,655 children aged 6 months to 5 years. Main Outcome and Measure: Parent-reported oral health condition including toothache, decayed teeth or unfilled cavity in the past 12 months. Results: A total of 2,392 (9.7%) children aged 6 months to 5 years experienced an oral health disorder, mainly toothache, decayed teeth, or unfilled cavity in the previous 12 months. Relative to non-Hispanic White children (7.7%), a greater proportion of non-Hispanic Black (12.9%) and multi-racial children (12.6%) experienced oral health disorders (OHD). In the univariable model, relative to children who never breastfed, those who were breastfed exclusively for 6 months were 28% less likely to present with OHD, Odds Ratio (OR) = 0.72, 95% CI, 0.52-0.98. After adjustment, the protective nature of exclusive breastfeeding against OHD did not remain, adjusted OR (aOR) =1.11, 99% CI 0.79-1.57. Children of mothers with fair/poor health were almost twice as likely to have poor OHD compared with children of mothers with excellent/very good health (aOR = 1.79, 99% CI 1.08-2.69). Conclusions and Relevance: Exclusive breastfeeding for the first six months of life improved pediatric OHD and remains mutually beneficial for mothers and children. These findings suggest the importance of exclusive breastfeeding and maternal health in relation to dental hygiene in children.

With the improvement of computer, artificial intelligence, information technology and other technical levels, the relationship between man-machine environment systems is more complicated and diversified. The optimization, iteration and development of the new generation of intelligent equipment system and human-computer interaction interface put forward higher requirements for ensuring the safety of personnel, improving the efficiency of human-computer interaction and improving the efficiency of the system. Such as intelligent cabin adaptive cognitive decision aid system, how to adopt intelligent information display and human-computer interaction, optimize information processing, strengthen situational awareness; How to effectively present information and improve the efficiency of human-computer interaction, so that the system has good security, applicability and maximize its effectiveness; How to deal with man-machine matching and man-machine collaboration problems, so as to improve the efficiency of man-machine/unmanned collaborative work. Human factors throughout the life cycle of equipment systems must be fully considered. The human factor is considered in the system design, so that people, machines and the environment can work together and adapt to each other, so as to achieve benign interaction and feedback between people and equipment and interface and complete the full transmission and communication of human-machine intelligent interaction information. The development of new aircraft human-computer interaction systems combined with new technological methods has also gradually changed the role of pilots and staff. From the system operator gradually into the monitor and decision maker, especially with the improvement of the degree of intelligent flight, information technology, advanced complex airborne equipment is increasing, the amount of information that operators need to deal with is also increasing, and the allowed time for judgment and decision is very short, and the mental resources that pilots bear are gradually rising. As the mental load is a key factor affecting the allocation of cognitive tasks, when encountering emergency situations, the mental load overload caused by the increase of information processing tasks often occurs, which seriously affects the task performance of operators, physical and psychological comfort and flight safety, and thus affects the efficiency and safety of the entire aircraft man-machine system. This requires us to conduct real-time analysis of human-computer interaction situational awareness, especially the individual cognitive state as an uncontrollable factor.

This study reveals that with similar valence of empathy, the tendency for humans to help humans in need is stronger than the animal in need. It also illustrates that a convincible advertisement for a charity should be a combination of various feelings, mainly empathic anger, in addition to compassion or empathy. To gain support from young individuals or donors regardless of their personal values, the egoistic values particularly, both the NPOs in humanitarian aid as well as in environmental protection or conservation should develop ads by which each individual or donor is aroused by either an empathic anger and empathic joy or both. This claim is more applicable for any ads developed to help people in need more than animals in need. Nevertheless, this study also found the role of an animal, generally known as innocent, as an actor in an ad is likely more crucial than human. Subjects with high-egoistic values appeared to get vulnerably aroused to a higher extent by being immersed in animal’s suffering than human’s suffering. The feeling of arousal seemed to motivate subjects with high-egoistic values to not decrease the amount of donation to a charity.

In this paper, we explore the surprising behavior of certain discrete maps, drawing inspiration from Collatz-like problems and advanced techniques in analytic number theory, dynamical systems, and differential geometry. Specifically, we investigate a driven cubic-quintic Duffing equation and predict the number of limit cycles around equilibrium points. Furthermore, we develop a novel theoretical framework for chaos suppression in damped-driven systems, leveraging sequences derived from Collatz-like problems. Our study also focuses on analyzing the density of these sequences in both Euclidean and Riemannian metric spaces, providing a comparative analysis of their distribution properties. We estimate the growth rate of sequence density analytically and numerically, highlighting the sensitivity of the density values to the choice of parameters in both metric spaces. Our results present new insights into the behavior of these sequences and their implications for number theory, chaotic dynamics, and geometric analysis.

This study investigates the performance and environmental impact of n-butanol blended with Jet-A fuel in turbo-engines, aiming to assess its viability as a sustainable aviation fuel (SAF). The research involves experimental testing of various blends, ranging from low to high concentrations of methanol, to determine their effects on engine performance and emissions. The experimental setup includes comprehensive measurements of engine parameters such as thrust, fuel consumption rates, and exhaust gas temperatures. Emissions of nitrogen oxides (NOx), particulate matter (PM), carbon monoxide (CO), and unburned hydrocarbons (HC) are also analysed to evaluate environmental impacts. Key findings indicate that n-butanol/Jet-A blends can significantly enhance combustion efficiency and reduce emissions compared to conventional Jet-A fuel. Higher n-butanol concentrations lead to improved thermal efficiency and lower NOx and CO emissions. However, PM emissions slightly increase with higher methanol content. The study underscores the potential of n-butanol as a SAF for turbo-engines, highlighting its ability to mitigate environmental impacts while maintaining or improving engine performance. This research supports the feasibility of integrating n-butanol into Jet-A blends for turbo-engine applications, emphasizing its role in achieving more environmentally friendly aviation operations.

Many laboratory power supply had been designed with higher output variable voltages but with small amount of output current, in this design, a variable 1.25 to 30Vdc laboratory power supply with capable of producing up to 10.0A has been designed using a three terminal voltage regulator with pass transistors.

Introduction: Acute rhinosinusitis (ASR) is a common condition in children, which can be burdened by orbital complication (OC) or intracranial complications (IC) that are rare but have potential for serious sequelae; therefore, it needs prompt diagnosis and treatment. The first line of treatment is intravenous antibiotic therapy. In case of failure of clinical improvement after 48 hours, hypovisus or negative prognostic factors (intracranial abscess, large orbital abscess or in intraconic or lateral localization, cavernous sinus thrombosis), surgical treatment is indicated. Objective: the purpose of this work is to describe the clinical profile, treatments and outcome of a series of patients undergoing surgical treatment for complications of ARS at a tertiary referral center. Material and methods: a retrospective analysis of clinical databases was conducted to identify all patients under 16 years of age who underwent surgical treatment for complications of ARS during the 20-year period 2000-2023. Demographic characteristics, clinical data, radiological investigations, surgical treatment, and follow-up performed were reported for each case. Results: 29 patients (20 males and 9 females), mean age 13 years, were included, 69% of whom had OC, 21% had IC, and 7% had both OC and IC. The main symptoms of OC were orbital edema, hypovisus and diplopia; while the main symptoms of IC were headache, fever, comitial seizures and focal neurological symptoms. Mean hospitalization time was 11.75 (± 9.2) days. The mean length of hospital stay (LOS) was 9.1 days for OC, 21 days for IC and 14 days for OC + IC. A single patient manifested a Potts Puffy Tumor, with a LOS of 10 days. Basal CT and gadolinium-enhanced MRI were performed in all cases. Intravenous antibiotic therapy and surgical clearance was undertaken. The majority of patients (76%) underwent endoscopic endonasal approach, 24% external transorbital approach, 1,16% orbital decompression and 0,58% superior eyelid approach. Any complications related to the surgical procedures were found. All patients with hypovisus and diplopia had complete recovery. During follow-up 7 patients (24%) had sinus recurrences, at a time range of 1 to 4 years after surgery. Conclusion: our experience, in agreement with the data reported in the literature, confirmed the importance of appropriate diagnostic-therapeutic management to avoid potentially serious sequelae of complicated ARS.

This survey is part of a Hungarian Research OTKA project that examines the vegetation of sandy grasslands along the Danube. During this study, two grass species, Festuca wagneri and Festuca tomanii, were identified as potentially suitable for urban planting and turf establishment based on preliminary research. Our aim was to determine the germination success of seeds from aesthetically selected individuals and to identify the soil types on which they germinate most effectively. This information could be crucial for future cultivation efforts. From the collected Festuca individuals, were analyzed 30 specimens of each taxon under garden conditions and selected the indi-viduals for germination. The Festuca tomanii individuals were uniform, so we selected only 5 individuals. The Festuca wagneri individuals were categorized into three groups: upright, spreading, and low types. It was assumed that Festuca species seeds would germinate better in sandy soils. To test this, seeds from ten Festuca wagneri and five Festuca tomanii individuals, selected based on aesthetic criteria, were sown in six different substrates: a sand-peat mixture, sand, coconut fiber, peat, a coconut fiber-sand mixture, and native sandy soil. Contrary to expectations, the growth and germination rates of seeds sown in peat and coconut fiber substrates were higher than those in native sandy soil. These results suggest that Festuca seeds germinate better on substrates resembling dead plant debris with a peat-like structure or on the surface of live mosses, rather than on bare sand. Among the examined individuals, the seeds from the spreading Festuca wagneri group exhibited the highest germination rate, making this group particularly suitable for urban environments. Addi-tionally, one of the upright Festuca wagneri individuals showed the highest growth rate and should also be considered for urban planting. In contrast, despite their uniform appearance, the Festuca tomanii individuals did not demonstrate similar germination trends. In fact, the seeds from two clumps did not germinate at all, indicating that further selection is necessary.

Abstract: Since the establishment of multiparty democracy in the 1990s, Malawi has experienced significant political shifts, with elections playing a crucial role in shaping the political landscape. However, there is a lack of comprehensive understanding of the spatio-temporal dynamics of election results, especially regarding voting patterns and their effects on electoral outcomes. This gap hinders political parties, politicians, and electoral stakeholders in effectively managing regional and temporal changes in voting behaviors. Geographic Information Systems (GIS) are underutilized in Malawi for studying geo-graphical and temporal election data. This study utilized GIS to analyze the spatial pat-terns of election results in Malawi since the advent of multiparty democracy. By collecting geographic data on election outcomes, including vote counts, candidate performance, voter turnout, and other relevant data, the study examined how the geographic distribution of votes impacts electoral results. GIS techniques such as spatial analysis, mapping, and spatial statistics were employed to assess the data and create maps illustrating spatial patterns in Malawi's election outcomes. The study revealed that voting patterns in Malawi are significantly influenced by regional lines. These insights can inform political campaigns, voter mobilization efforts, and strategies for electoral reform.

: Psychological members can provide college students with solid peer support and sustain their mental health. This study examined the relationship between empathy, interpersonal communication, listening and psychological members' competence among 407 university students, and also analysed the mediating role of interpersonal communication and listening. The results showed that psychological members‘ empathy, interpersonal communication, and listening were significantly and positively related to psychological members’competence, interpersonal communication mediated the relationship between empathy and competence, listening mediated the relationship between empathy and competence, and interpersonal communication and listening chained the mediating role between empathy and competence. This suggests that psychological members' empathy enhances their competence by influencing their interpersonal communication and listening, which in turn enhances their competence.

Cuproptosis, a copper-dependent cellular death mechanism, is typically overlooked in glioblastoma diagnosis. Lipoylation is needed for cuproptosis, and FDX1 controls this process. Cuproptosis in prognostic models may drive cancer treatment. This study evaluated cuproptosis and glioblastoma cell proliferation. The Cancer Genome Atlas provided annotated clinical, genetic mutation, and RNA sequencing data for the TCGA_GBM and TCGA_LGG cohorts. Patients with gliomas were randomly allocated to the validation or training cohort. Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression models were utilized to evaluate which model best predicted prognosis in the training cohort. The models' independent predictive power was evaluated throughout the cohort. A previous study revealed 19 genes related to ferroptosis. LncRNAs associated with cuproptosis were identified by coexpression analysis. Cox discovered 17 cuproptosis-associated lncRNAs and established a predictive model. The median risk score classified patients as high- or low-risk. K‒M survival analysis demonstrated significant differences in overall survival among risk categories. Principal component analysis (PCA) and receiver operating characteristic (ROC) curve analysis were used to test the model’s predictive power. Univariate and multivariate Cox regression analyses revealed risk score-related independent prognostic factors. Multivariate Cox regression analysis was used to construct a nomogram for marker-based prognosis. The risk category affected the tumour mutation rate. High-risk glioma patients benefit from immunotherapy. Glioma drug sensitivity was also substantially linked with the risk score. The expression of 17 cuproptosis-related long noncoding RNAs (lncRNAs) may assist in the stratification of glioma patient prognosis, molecular characteristics, cell cycle gene regulation pathways, the TME, and clinicopathological aspects. Our clinical sample and database analysis revealed that cuproptosis influences glioma prognosis and may guide therapy.

Recent advancements in gut microbiome research have highlighted the complex interplay between gut microbiota and various physiological and pathological processes, showing that gut microbiota plays a vital role in human health and disease. Imbalances in the gut microbiota, known as dysbiosis, have been associated with a range of health conditions, including gastrointestinal disorders, metabolic diseases, etc. In response to these findings, probiotics, prebiotics, synbiotics, and postbiotics have emerged as a promising therapeutic strategy to modulate gut microbiota composition and functionality. This review paper explores the latest research on how gut microbiota impacts host physiology, immune responses, and overall well-being. Furthermore, the paper delves into the therapeutic potential of probiotics, postbiotics, prebiotics, and synbiotics in therapeutic applications and curing various diseases. The paper also addresses the challenges and opportunities in the field, highlighting the need for personalized approaches to probiotic therapy and the importance of considering factors such as strain specificity, dosing, and safety.Overall, this review aims to provide a comprehensive overview of the evolving landscape of gut microbiota and probiotics, prebiotics, synbiotics, and postbiotics as novel therapeutic agents. It underscores the potential of harnessing the gut microbiome to improve health, prevent diseases, and complement traditional medical interventions. As research in this field continues to expand, a deeper understanding of the gut microbiota and probiotics, prebiotics, synbiotics, and postbiotics promises new avenues for preventive and therapeutic healthcare strategies.

Neurodegenerative diseases that include Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Huntington's disease (HD), and multiple sclerosis (MS), arise due to numerous causes like protein accumulation and autoimmunity, characterized by neurologic depletion which lead to incapacity in normal physiological function such as thinking and movement in these patients. Glial cells perform a critical role in protective neuronal function; in the case of neuroinflammation, glial cell dysfunction can promote the development of neurodegenerative diseases. MiRNA participates in gene regulation and plays a vital role in many biological processes in the body; in the central nervous system (CNS), it can play an essential part in neural maturation and differentiation. In neurodegenerative diseases, miRNA dysregulation occurs, enhancing the development of these diseases. In this review, we discuss neurodegenerative diseases (Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS)) and how miRNA is preserved as a diagnostic biomarker or therapeutic agent in these disorders. Finally, we highlighted miRNA as therapy.

Many cities are increasingly adopting blue-green infrastructure (BGI) to bolster resilience against environmental challenges. Beyond its well-acknowledged environmental benefits, the role of BGI in enhancing social resilience is becoming an equally important area of focus. However, the integration of BGI to foster social resilience presents complexities, stemming from the evolving and occasionally ambiguous definition of social resilience. Given its broad application across diverse disciplines, understanding and measuring social resilience proves difficult, posing challenges to understanding it within a BGI context. However, integrating BGI to support social resilience is complicated by the evolving and sometimes ambiguous definition of social resilience. This concept's broad applicability across various disciplines makes its understanding and measurement challenging, especially within the BGI context. Consequently, a clear need for a framework to effectively understand and measure social resilience in BGI settings exists. This paper synthesizes existing social resilience frameworks, particularly leveraging the comprehensive 5S framework by Saja et al. (2018), to create a new conceptual framework that addresses the unique social dimensions and benefits of BGI, incorporating insights from a broad literature review on social resilience. The framework is structured as a three-tier model centered around four key subdimensions of social resilience: social values, social capital, social structure, and social equity and their interrelationships. Characteristics and indicators are customized to support the BGI context integrating both physical and human dimensions within a mixed methods approach to measurement. Specifically, this research develops a conceptual BGI framework to probe into BGI practices and perspectives that enhance social resilience and considers demographic diversity and the spatial attributes of urban environments to understand how to foster more inclusive and socially resilient BGI spaces.

BackgroundAdvanced radiological stage of knee osteoarthritis (OA) and obesity are predictive of poorer and shorter response to viscosupplementation, but very few is known regarding the impact of other factors such as sport practice, comorbidities or anatomical features of OA. ObjectivesTo investigate the patient’s and OA characteristics associated with the duration of effectiveness (DE) of viscosupplementation in patients with knee OA.MethodsCross-sectional single center trial in patients with knee OA treated with intra-articular (IA) hyaluronic acid (HA) injection(s) within the previous 3 years. The investigator collected demographic and radiographic data (Kellgren grade, involved knee compartments), dosing regimen (single or repeat injections), presence and volume of joint effusion, previous or concomitant IA corticosteroid injection, number of previous viscosupplémentations and the comorbidities. The patient completed a questionnaire including the self-assessment of DE (number of weeks during which viscosupplementation was effective on symptoms), the activity level (sedentary, active, athletic), the level of sport activity (light, moderate or intensive). Predictors of DE were studied in univariate and multivariate analyses ResultsOne hundred five patients (149 knees) were analyzed (62% women, mean age 66.1 ± 13.2 years, mean BMI 27.5 ± 7.5 kg/m²). The mean DE was 48.2 + 24.8 weeks. In a bivariate analysis, the predictors of a shorter with DE were BMI >27.5kg/m2, more than 3 previous viscosupplémentations, Kellgren grade 4, sedentary patients, and more than 1 affected compartment. In the multivariate analysis, 4 independent factors remained associated with a shorter DE: BMI >27.5 kg/m2, knee multi-compartment damage, number of viscosupplements >3, sedentary lifestyle. A statistically significant association between a longer DE and treated arterial hypertension was found suggesting a beneficial effect of certain antihypertensive drugs. ConclusionThe study confirms the harmful influence of an increased BMI, It also shows that viscosupplementation is more effective in unicompartmental OA. Other points worth highlighting are the reduction in the duration of efficacy with repeated cycles of injections and the benefit of a regular physical activity.

The primary sources of particulate matter are linked to human activity. Certain particulate emissions, particularly those of a finer nature, can significantly impact human health, making them one of the most concerning pollutants. Ultrafine particles (UFP), which have a diameter of less than 100 nm, are of particular concern due to their impact on human health and the difficulty in controlling them. The concentration of ultrafine particles (UFP) in the workplace is a growing concern and is classified as an emerging risk. Workers may be exposed to UFP through inhalation, skin absorption, ingestion, or a combination of these routes. This study aims to determine the levels of UFP exposure among workers in environments with varying direct particle emission patterns. Measurements were conducted to compare the results with the levels recommended by the WHO. The study monitored industrial workplaces with direct particulate matter emissions, such as a carpentry and a bakery, as well as social sector sites without or almost without direct particle emissions, such as a school and a health clinic. One conclusion drawn from this study is that all tasks and occupations are susceptible to high levels of UFP, exceeding WHO recommended values in virtually all monitored environments. Therefore, monitoring and controlling UFP is crucial. Further in-depth studies on this subject are also necessary..

In his Cynefin classification of system behaviours, Snowden (2007), posits that his Simple (Clear ) and even Complicated system categories are sufficiently understandable to be amenable to the traditional methods of system analysis and modelling. Here, relationships between entities in the system are clear and unambiguous and they behave predictably. So, we can build mathematical “models” to describe how the system is expected to function in different situations. Leaving aside his fourth category, Chaotic, as too hard for the moment, this paper focusses on the third category, Complex systems. Yaneer Bar-Yam [1998] defined "complex systems" as systems that "have multiple interacting components, whose collective behaviour cannot be simply inferred from the behaviour of components." So, the traditional system modelling approach of decomposition into components and attempting to build up a picture of system performance from individual pieces, alone, is no longer appropriate for this class. The problem is that most of the methods we currently employ, and particularly the methods that attempt to make the case that these systems are safe, (such as Fault trees, FMEA. and Root Cause Analysis), rely on this decomposition into components and attempt to make the parts more reliable individually. Even the more systemic approaches, such as Reason’s (1990) Swiss Cheese Model, and Leveson’s System Theoretic Analysis (STPA) (2004), encourage adding layers of protection, barriers, strengthened control loops and better safety critical systems, in the hope that they are making the systems safer. But without a valid model to test the effectiveness of these add-ons, we cannot be sure that they will not have an opposite effect, of making the systems even more complex, unreliable and unpredictable. For complex systems, the careful gathering and documentation of system components and their relationships is still a necessary first step, but we need an additional capacity to model these complex systems to test out and prove they work safely and successfully in the real world. (not just draw fixed component relationships as system “models” (wiring diagrams)) In the last 10 years, an original approach developed by Hollnagel, (2012), to model systems as sets of interactive, interdependent “functions”, (abstracted from and independent of component details), has now been developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, such as MBSE( Goldberg (1994)), and can pull it all together in a dynamic system model from which analysts can discern how it really works in practice, and predict the emergent behaviours characteristic of complex systems. The paper describes how this methodology developed and how it is currently used to model real situations, to analyse incidents and anticipate events.

Punica granatum (PG) or Pomegranate is a bush, having many trunks and often get taller 1.8 to 4.7m long and their leaves are shiny and fleshy, approximately 7 to 8cm in length. Traditionally, it is used in diarrhea and dysentery due to its strong constricting property. Punica Granatum covering used in dysentery, diarrhea, piles, bronchitis. It is also used to minimize the risk of heart disease. The peel extract of Punica Granatum contain methanol and their dried peels has antidiarrheal venture, also help in wound healing activity. Peels of the pomegranate also holds around 60% of the fruit and they contain different types of ingredients including ellagitannins and proanthocyanidin compounds and minerals such as calcium, phosphorus, magnesium, potassium, flavonoids and sodium. Some animals were found in different local areas with both acute and chronic diarrhea. The condition of these animals were quite miserable because of dehydration and loss of electrolytes in the body cells of the animals. They were unable to move due to seriousness of the disease. 8 grams of PG Peel Powder mixed with 1 liter of water, 2 g of PG Peel Powder mixed with 250 ml water and 8 g of PG Peel Powder mixed with 1 liter of water were administrated orally by using sterilized bottles to the selected three cases. 3 days after the administration of different doses, all cases of diarrheal animals show a significant recovery. The condition of the animals seem to be healthier. Before administration of the pomegranate peel powder extract, all three cases had weak body structure due to diarrhea. Watery stool that may be yellow in color and mucus was also evident in the stool, due to this reason the patient calf was weak and depressed. In sometimes they also show unconsciousness and blood glucose levels seem to be reduced from the normal level.

The Lac Fallère area, located in the upper Clusellaz Valley (tributary of the middle Aosta Valley), is shaped in micaschist and gneiss referred to as the Mont Fort Unit (Middle Penninic) near the tectonic contact with calcschist and marble of the Aouilletta Unit (Combin Zone). Lac Fallère exibits an elongated shape and is hosted in a WSW–ENE trending depression, according to slope direction. This lake also shows a semi-submerged WSW–ENE rocky ridge, that longitudinally divides the lake. This evidence, in addition to the extremely fractured rocks, indicates the presence of a wide deep-seated gravitational slope deformation (DSGSD), even if this area is not yet included within the regional landslide inventory of the Aosta Valley Region. The Lac Fallère area also shows reliefs involved by glacial erosion (roche moutonnée), a wide cover of subglacial sediments and many moraines essentially referred to as Lateglacial. The DSGSD evolution in a glacial environment produced, as observed in other areas, significant effects both on the facies of Quaternary sediments and the formation of numerous and wide moraines. Glacial slope sectors and lateral moraines displaced by minor scarps and counterscarps and glaciers using trenches forming several arched moraines suggest an interplay between glacial and gravitational processes, which shared part of their evolution history.

The increasing prevalence of diabetes mellitus (DM) worldwide is a cause for global concern. Recent evidence suggests that the interaction between one’s diet and the gut microbiome are key modifiers of diabetes, highlighting the potential role of microbiome-modulating nutraceuticals. This bibliometric analysis examines literature published between 2012 and 2022 to explore trends in the latest research, with additional discussion on major takeaways and future directions. We selected and analyzed 640 articles from an initial pool of 4,099 from the Web of Science using Microsoft Excel 2016 and VOSviewer software (version 1.6.19). Our exploration covered the most common journals, countries, study designs, experimental subjects, types of DM, organisms and nutraceuticals studied, and the most cited articles. We also evaluated the most cited publications in terms of journals, organizations, and countries, along with the collaborative networks among authors, organizations and countries, and the co-occurrence of author keywords from the selection. It is evident from the increasing number of publications each year that this field has attracted growing attention over the last decade. Nonetheless, continued research is essential to sustain this trend. Our overarching study of diabetes research marks steady progress in understanding the role of—and potentially discovering applications of—microbiome modulation to enhance metabolic pathways and health outcomes. Advancements in this field may lead to potential therapeutic applications for specific diets and nutraceuticals that modulate the gut microbiota in DM.

Climate change related issues, such as yield decline, quality losses, accelerated phenology, extreme abiotic stress, pose growers new challenges in olive growing areas. Main cultural solutions that could alleviate stress and improve long-term sustainability of modern olive orchards are here briefly reviewed. Selecting varieties that are more resistant to drought or high temperatures would be the optimal solution to mitigate the effects of climate change, but the available scientific information is relatively scarce. Sustainability of irrigation implies that the less water is used, the better. Best results in terms of saving water and uniform application are obtained using micro-irrigation methods. Water can also be saved by not fully satisfying the tree water needs and different deficit irrigation strategies have been developed and tested for olive orchards. The use of green covers and the application of organic mulches and shredded pruning materials should be encouraged as these practices tend to stock carbon, increase organic matter content and water holding capacity of the soil. Olive orchards are efficient CO2 sinks as they can store considerable amounts of carbon in their organs and permanent structures. Using environmental friendly practices improves the ecosystem carbon budget and contributes to mitigate climate change.

Due to the advancement and availability of internet in present age, device tend to become wireless as well. Mobile ad-hoc network is a wireless environment that consist of advanced agility of nodes and does not contain on any central administrative authority. As it does not contain any central administrative authority so being wireless environment, the network is vulnerable to security threats and suffers in performance as well. Security of MANET is the core issue in this era, meanwhile the data is sent over untrustworthy network. Several techniques were proposed to secure data over Mobile Adhoc Network though cryptography that is one of the most reliable techniques to protect data. In our proposed methodology a Secure Cryptographic technique for Mobile Adhoc Networks (SCTMA) we proposed A Collusion attack prevention (CAP) scheme with ElGamal key generator along with DNA encryption Algorithm is used to make it more secure. The proposed scheme protects the network against collusion attacks and improves the performance as well. Performance analysis will be measured on the basis of key generation time, response time, encryption time and decryption time. The proposed scheme also protects the network against collusion attacks.

The primary objective of this research was to assess the energy consumption of the gas treatment units at Bonny NLNG Refinery, the first Gas Refinery in Rivers State, Nigeria, while utilizing semi-lean amine. To achieve this goal, a simulation of the units was conducted using the advanced software package Aspen Hysys (V.8.3). The simulation was designed to accurately represent the dynamic behavior of the refinery's gas treatment units, allowing for a comprehensive analysis of their energy usage. The research aimed to quantify the energy consumption of the gas treatment units and identify opportunities for energy optimization. By utilizing semi-lean amine, which is known to improve energy efficiency, the study sought to evaluate the potential energy savings that could be achieved in the refinery's operations. The simulation model incorporated the specific design and operational parameters of the gas treatment units at Bonny NLNG Refinery, including the gas flow rate, lean amine concentration, absorber pressure, stripping temperature, amine circulation rate, and acid gas removal efficiency. By considering these parameters, the simulation accurately represented the dynamic behavior of the gas treatment units, enabling a detailed analysis of their energy consumption. Through the simulation, various scenarios and operational conditions were evaluated to determine the optimal set of parameters that minimized energy consumption. The research also examined the trade-offs between energy consumption, acid gas removal efficiency, and other performance indicators, such as amine circulation rate and regeneration efficiency. The findings of this research have significant implications for the energy efficiency and sustainability of gas treatment operations at Bonny NLNG Refinery. By identifying opportunities for energy optimization and providing recommendations for the utilization of semi-lean amine, the study contributes to the development of more efficient and environmentally friendly gas treatment processes. Overall, this research combines advanced simulation techniques with a comprehensive analysis of energy consumption to provide valuable insights into the energy efficiency of gas treatment units at Bonny NLNG Refinery, enabling informed decision-making and potential improvements in energy utilization. By utilizing the percentage-based unit simulation approach, a detailed examination of the energy consumption patterns was obtained. This analysis provides valuable insights into the operational efficiency and determination of potential energy-saving opportunities within the gas treatment units. This study specifically focuses on the integration of an absorption column split stream (stream flow) and a flash unit as a potential means to reduce the energy consumption of gas treatment devices. The integration of these units aims to optimize the overall energy efficiency of the gas treatment process by recovering and utilizing waste heat and reducing energy losses. The absorption column split stream allows for the diversion of a portion of the gas stream to a flash unit before entering the absorber. The flash unit operates at a lower pressure, which facilitates the release of entrained hydrocarbons and reduces the overall energy requirements for gas treatment. By separating and recovering the hydrocarbons in the flash unit, energy losses associated with their absorption and subsequent regeneration are minimized. Through the simulation, the study analyzes the energy consumption patterns of the gas treatment units with and without the integration of the absorption column split stream and flash unit. Comparative assessments are conducted to evaluate the energy savings and overall operational efficiency achieved through this integration. The findings of this research will provide valuable insights into the potential energy-saving opportunities offered by the integration of the absorption column split stream and flash unit in gas treatment devices. It will help refine the design and operation of gas treatment units, enabling more energy-efficient processes in the gas refining industry. By optimizing energy consumption in gas treatment units, the study contributes to the industry's goals of reducing greenhouse gas emissions and improving sustainability. The integration of the absorption column split stream and flash unit offers a promising approach to enhance energy efficiency, reduce operational costs, and minimize environmental impact. Overall, this research underscores the importance of exploring innovative solutions, such as the integration of different process units, to achieve energy savings in gas treatment operations. Through a comprehensive analysis of energy consumption patterns, this study aims to provide practical recommendations for optimizing energy efficiency in gas treatment units, fostering a more sustainable and efficient gas refining industry.The research specifically explores the impact of integrating the absorption column split stream and flash unit when dealing with sour gas streams containing carbon dioxide concentrations of less than mole ℅, while utilizing MDEA fluid as the solvent. Through comprehensive analysis and simulation using advanced software tools, the study demonstrates that by incorporating the absorption column split stream and flash unit, a significant reduction in device energy consumption of up to 10% can be achieved. The integration of the absorption column split stream and flash unit offers promising opportunities to enhance the energy efficiency of gas treatment devices. By diverting a portion of the gas stream to the flash unit, the separation of hydrocarbons and the subsequent release of entrained hydrocarbons at lower pressure significantly reduce the overall energy requirements for gas treatment. The comprehensive analysis and simulation conducted in the research provide concrete evidence of the energy-saving potential of this integration. By quantifying the energy consumption patterns and comparing scenarios with and without integration, the study establishes the effectiveness of the approach and its impact on operational efficiency. The findings of the research highlight the importance of considering the specific gas composition and utilizing appropriate solvents, such as MDEA, for optimizing energy efficiency. The integration of the absorption column split stream and flash unit not only reduces energy consumption but also offers the potential for cost savings and environmental benefits. Overall, the research demonstrates that by incorporating the absorption column split stream

Rhombencephalosynapsis (RES) is a rare, congenital developmental disorder of the central nervous system. Even after many years of its identification, the etiology and pathogenesis of RES remain unclear. It occurs in combination with many syndromes and thus has overlapping symptoms. The clinical presentation of this disorder varies depending on the severity of the maldevelopment of vermis. Here, we present a case of a 60-year-old Indian female diagnosed with Rhombencephalosynapsis. Clinical and MRI characteristics of RES along with treatment options have been discussed.

In the present study, the effect of melatonin and sodium nitroprusside treatments on the improvement of high temperature tolerance in California Wonder green bell pepper plant was studied. For this purpose, a factorial experiment was conducted in form of a completely randomized design in three replications at Zabol University's Faculty of Agriculture. In this research, green bell pepper plants of Wonder cultivar were exposed to different temperature treatments (25°C, 35°C and 40°C) for 24 hours after being sprayed (foliar application) with 0μM, 50μM and 100μM concentrations of melatonin and Sodium nitroprusside. Results showed that Instantaneous leaf water consumption efficiency in 40°C temperature treatment with 50μM and 100μM melatonin increased. Also in 40°C temperature with 100µM melatonin, fruit dry weight in compared to control (0uM) 13.15% increased. Also, melatonin and sodium nitroprusside treatments in compared to control treatment improved fruit characteristics and reduced Appearance Characteristics of Fruit Marketability (ACFM) number. Under 40°C temperature treatment in compared to control temperature (25°C), fruit total soluble solids (TSS) 18.50% decreased also under 100μM melatonin treatment, fruit TSS 20.69% was increased compared to control treatment (0μM). Also fruit fructose at 40°C temperature in compared to 25°C temperature treatment 5.52% reduced. Fruit calcium at 35°C and 40°C temperature in compared to control temperature 6.96% and 15.65% decreased respectively. At temperature 35°C under foliar application of 100uM sodium nitroprusside, fruit copper in compared to control 28.93% increased.

Background: Resistance to antimalarial drugs often used in emerging countries, including combination therapies, has forced scientists to search for and develop drugs with novel mechanisms of action, especially resistance to Plasmodium falciparum and Plasmodium vivax, which are highly prevalent in Southeast Asia, Africa, and South America. Objective: evaluate whether there is a relationship between urinalysis and resistance to in-hospital treatment of malaria in Angola. Methodology: This was a cross-sectional, prospective study with a quantitative approach. Results: of the 214 patients, the resistance rate was 24.1%, men (53.6%), between 21 and 40 years old (72.7%), employees (46.4%), from peri-urban regions (77.7%), treated with artemether (90.9), with high parasitemia (57.7%) and after 5 days of treatment, remained hospitalized (61.4%). Was a significant relationship between resistance in unemployed individuals [OR: 0.03 (95% CI: 0.01-0.29), p =0.003] and high levels of parasitemia [OR: 1.09 (95% CI: 1.09-3.95), p=0.040], remained hospitalized for more than 5 days [OR: 5.28 (95% CI: 0.65-43.1), p=0.121] and death [OR: 2.59 (95% CI: 0.32-20.9), p=0.371] when compared with other subgroups. Was a significant relationship between resistance to clear urine [OR: 5.55 (95% CI: 0.72-42.7), p =0.016], few urinary crystals [OR: 11.3 (95% CI: 5.07-25.3), p <0.001] and who presented some microorganisms that were not bacteria or fungi [OR: 3.02 (95% CI: 1 .32-6.90), p=0.009]. Conclusion: urine results, especially the appearance of cloudy urine, the presence of few crystals, and the presence of other microorganisms that are not bacteria or fungi, may be clear signs of resistance to hospital treatment with injectable antimalarials.

In recent years, the emergence of the concept of immunometabolism has shed light on the pivotal role that cellular metabolism plays in both the activation of immune cells and the development of immune programs. The antiviral response, a widely distributed defense mechanism used by infected cells, serves to not only control infections but also to attenuates their deleterious effects. The exploration of the role of metabolism in orchestrating the antiviral response represents a burgeoning area of research, especially considering the escalating incidence of viral outbreaks coupled with the increasing prevalence of metabolic diseases. Here, we present a review of current knowledge regarding immunometabolism and the antiviral response during viral infections. Initially, we delve into the concept of immunometabolism by examining its application in the field of cancer—a domain that has long spearheaded inquiries into this fascinating intersection of disciplines. Subsequently, we explore examples of immune cells whose activation is intricately regulated by metabolic processes. Progressing with a systematic and cellular approach, our aim is to unravel the potential role of metabolism in antiviral defense, placing significant emphasis on the innate and canonical interferon response.

Student project management is becoming increasingly important in various settings such as technical clubs, planning clubs, and research laboratories. However, effective methodologies for managing student-led projects have not yet been established. This study aims to address key issues related to cost and motivation in student-led projects and propose effective improvement plans. The significance of this study lies in its potential to identify challenges faced in current student project management practices and offer solutions to mitigate risks. By sharing our findings, we aim to encourage further research on this topic and promote the adoption of effective methodologies for managing student-led projects.

The rapid development and integration of Autonomous Mobile Robots (AMRs) have 1 revolutionized industries by enhancing automation capabilities. A critical challenge in this evolution 2 is achieving effective navigation and obstacle avoidance, which are essential for deploying AMRs 3 seamlessly in varied environments. This paper presents a detailed exploration of AMR navigation 4 and obstacle avoidance advancements through the application of reinforcement learning, specifically 5 focusing on small-scale Sri Lankan manufacturing facilities. The study demonstrates the effectiveness 6 of Q-learning in managing dynamic obstacles within a factory environment. The AMR successfully 7 avoided obstacles in 36 out of 50 test runs, achieving a 72% success rate, and maintained an average 8 distance of 12 cm from each obstacle, underscoring the algorithm’s precision in maintaining safe nav- 9 igation paths while dynamically adapting to environmental changes. The continuous monitoring by 10 ultrasonic sensors, combined with iterative learning, enabled the robot to refine its decision-making 11 process and efficiently navigate through the environment. This paper also provides a comprehensive 12 examination of conventional methods, tracing their historical development and assessing their role in 13 addressing real-world challenges. The results highlight the significant improvements brought by rein- 14 forcement learning, particularly when integrated with sensor fusion and motor control technologies, 15 to enhance navigation and dynamic object collision avoidance. The study involves developing an 16 AMR prototype, refining algorithms, and assessing system performance in controlled environments. 17 By automating material transportation and addressing operational constraints in manufacturing 18 settings that predominantly rely on manual labor, this paper advances the practical deployment of 19 AMRs. Additionally, it discusses ethical considerations, potential limitations, and the importance 20 of real-world validation, offering valuable insights for the future development and integration of 21 autonomous mobile robotics in dynamic industrial environments.

Based on the nonlinear failure criterion and modified tangential technique, the upper bound solutions of supporting pressure on the deep tunnel face were obtained under pore water pressure conditions. The influence of parameters on the supporting pressure and collapse range was investigated according to the unlimited block failure mechanism. It is found that the upper bound solutions of supporting pressure increase with the growth of the nonlinear coefficient and pore water pressure coefficient. The collapse range of the tunnel face scales out with the increase of the nonlinear coefficient and shrinks when increasing the pore water pressure coefficient. Moreover, with the increase of the nonlinear coefficient, the impact strength on supporting pressure and collapse range declines gradually. According to the calculating results, it is manifest that both the pore water pressure and nonlinear criterion factors have negative impacts on the stability of the tunnel face. Thus, more attention should be paid to these parameters to ensure face stability in deep tunnel construction.

Aedes aegypti and Aedes albopictus are responsible for transmitting major human arboviruses such as dengue, Zika, and Chikungunya, posing a global threat to public health. The lack of etio-logical treatments and efficient vaccines makes vector control strategies essential for reducing vector population density and interrupting pathogen transmission cycle. This study evaluated the impact of long-term pyriproxyfen exposure on the genetic structure and diversity of Ae. aegypti and Ae. albopictus mosquito populations. The study was conducted in Manaus, Amazonas, Bra-zil, where pyriproxyfen dissemination stations have been monitored since 2014 up to the present day. Double digest restriction-site associated DNA sequencing was performed, revealing that despite significant local population reductions, focal pyriproxyfen interventions have no signifi-cant impact on the population stratification of Ae. aegypti and Ae. albopictus in urban scenarios. The genetic structuring level of Ae. aegypti suggests it is more stratified and directly affected by PPF intervention, while for Ae. albopictus exhibits a more homogeneous and less structured popu-lation. The results suggest that although slight differences are observed among mosquitoes sub-populations, intervention focused on neighborhoods in a capital city is not efficient in terms of genetic structuring, indicating that larger-scale pyriproxyfen interventions should be considered for more effective urban mosquito control.

Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban-rural transition affects PM2.5 are warranted within the framework of urban-rural integration. Using the Yellow River Basin as a case study, this study employed the entropy method and Analytic Hierarchy Process (AHP) to uncover the extent of urban-rural transformation. It then used the spatial autocorrelation method to investigate the spatiotemporal features of PM2.5 and the spatial econometric model to investigate the mechanisms that influence the relationship between urban-rural transformation and PM2.5. The results are as follows: (1) Over time, there was a discernible upward tendency in the change in urban-rural areas. The development has progressed from asymmetrical north-east and south-west elevations to a more balanced pattern of north-east, middle-east, and west-west elevations. (2) The PM2.5 concentration increased steadily, then fluctuated, and finally decreased. Notably, the general pattern has not changed much, and it is high in the east and low in the west. (3) Different subsystems of the urban-rural transformation have different impacts on air pollution at different stages. The influence of industrial transformation (IT) on PM2.5 showed an inverted “N-shaped” curve of negative-positive-negative changes, and the industrial structure played a leading role in the spatiotemporal evolution of PM2.5. Currently, an inverted "U-shaped" curve forms the left side of the impact of population transition (PT) on PM2.5. Land development (LT) has a "U-shaped" curve for its effect on PM2.5. This research provides a new perspective on the topic of PM2.5 and its connection to urban-rural integration, which is crucial to understanding the dynamics of this shift. To achieve its goal of high-quality development, it supports regional initiatives to reduce PM2.5 emissions in the Yellow River Basin. Moreover, it can provide a reference for decision-makers in the world’s densely populated areas that suffer from serious air pollution.

The monitoring of fog density is of great importance in meteorology and its applications in environment, aviation and transportation. Nowadays, vision-based fog estimation from images taken with surveillance cameras has made a great supplementary contribution to the scarcely traditional meteorological fog observation. In this paper, we propose a new Random Forest (RF) approach for image-based fog estimation. In order to reduce the impact of data imbalance on recognition, the StyleGAN2-ADA (generative adversarial network with adaptive discriminator augmentation) algorithm is used to generate virtual images to expand the data of low proportions. Key image features related to fog are extracted, and an RF method, integrated with the hierarchical and k-medoids clustering, is deployed to estimate the fog density. The experiment conducted in Sichuan in February 2024 shows that the improved RF model has achieved an average accuracy of fog density observation to 93%, 6.4% higher than the RF model without data expansion, 3%-6% higher than the VGG16, the VGG19, the ResNet50, and the DenseNet169 with or without data expansion. What is more, the improved RF method exhibits a very good convergence as a cost-effective solution.

With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process, coupled with its reliance on manual labor, results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the Local Outlier Factor (LOF) method to detect any abnormal values, Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience has demonstrated good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface and has significantly enhanced batching efficiency and product quality.

Wireless Sensor Networks are critical for modern applications ranging from environmental monitoring to smart city infrastructures. This research proposes a novel optimization learning and deployment strategy for Wireless Sensor Networks (WSNs) by 3D WUSN model is uniformly distributed M sensor nodes across diverse terrains, with clusters representing 20% of sensors, showcasing its ability to balance power consumption and extend network longevity, grounded in fuzzy clustering based protocol using the Fuzzy C-Means (FCM) method, called FCM-3DWUSN. Comparative analysis against established models including LEACH-C, K-Means and LEACH emphasized FCM-3DWUSN's outperformed performance, with average power consumption such as 134.990, 341.790, 143.895, and 192.984; respectively. The experimental result shows that FCM protocol in the 3D WUSN model can reduce energy consumption and improve the network lifetime to gather data and send the information.

Historic sites are important components of every city's cultural history because they preserve rich historical knowledge and distinctive values passed down from previous generations to the present. Due to the progress of urbanization and modernization, many historic sites face pressure from damage and transformation. In this paper, a method for assessing cultural heritage damage was developed to measure the extent of spatial damage in historic sites. Using sample data obtained in Xiyang, Qixian, and Xiaoyi, all historic cities in Shanxi Province, Mainland China, and combined weights were estimated using the Delphi technique and the CRITIC weight method. Following this, the Spatial Damage Degree Model (SDDM) based on K-means cluster analysis and K-nearest neighbor (KNN) classification was developed. The findings show that the model efficiently solves the problem of assessing spatial damage levels in historic sites. Through multiple linear regression analysis, it was shown that the damage to historic sites was predominantly caused by three factors: natural erosion, construction damage, and planning and policy. SDDM was used to calculate the spatial damage levels of historic sites, allowing conservators to fully comprehend the features and concerns related to historic sites. As a result, more scientific and rational preservation approaches may be developed, improving the efficiency of historic site restoration and conservation and encouraging the sustainable development of urban and rural heritage.

The large quantity of information retrieved from communities, public data repositories, web pages, or data mining can be sparsed and poorly classified. This work shows how to employ unsupervised classification algorithms such as K-means proper to classify user reviews into their closest category, forming a balanced data set. Moreover, we found that the text vectorization technique significantly impacts the clustering formation, comparing TF-IDF and Word2Vec. The value for mapping a cluster with movie genre was 81.34% ± 20.48 of the cases when the TF-IDF was applied, whereas Word2Vec only yielded a 53.51% ± 24.1. In addition, we highlight the impact of the removal of stop-words. Thus, we detected that pre-compiled lists are not the best method to remove stop-words before clustering because there is much ambiguity, centroids are poorly separated, and only 57% of clusters could match a movie genre. Thus, our proposed approach achieved a 94% of accuracy. After analyzing the classifiers’ results, we appreciated a similar effect when divided by the stop-words method removal. Statistically significant changes were observed, especially in precision metric and Jaccard scores in both classifiers, using custom-generated stop lists rather than pre-compiled ones. Reclassifying sparse data is strongly recommended as using custom-generated stop lists.

This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described by a fixed number of equal probability bin-rectangles. In each step of clustering, we agglomerate objects and/or clusters so as to minimize the compactness for the generated cluster. This means that the compactness plays the role of a similarity measure between objects and/or clusters to be merged. To minimize the compactness is equivalent to maximize the dis-similarity of the generated cluster, i.e., concept, against the whole concept in each step. In this sense, the compactness plays the role of cluster quality. We also show that the average compactness of each feature with respect to objects and/or clusters in several clustering steps is useful as feature effectiveness criterion. Features having small average compactness are mutually covariate, and are able to detect geometrically thin structure embedded in the given multi-dimensional histogram-valued data. We obtain thorough understandings of the given data by the visualization using dendrograms and scatter diagrams with respect to the selected informative features. We illustrate the effectiveness of the proposed method by using an artificial data set and real histogram-valued data sets.

Energy efficiency of Data Center (DC) operations heavily relies on IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of IoT- enabled smart systems. Consequently, increased demand for computing power will bring about increased waste heat dissipation in data centers. In order to bring about a DC energy efficiency, it is imperative to explore the thermal characteristics analysis of an IT room (due to waste heat). This work encompasses the employment of an unsupervised machine learning modelling technique for uncovering weaknesses of the DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for energy efficiency improvement that will feed into DC recommendations. The methodology employed for this research includes statistical analysis of IT room thermal characteristics, and the identification of individual servers that frequently occur in the hotspot zones. A critical analysis has been conducted on available big dataset of ambient air temperature in the hot aisle of ENEA Portici CRESCO6 computing cluster. Clustering techniques have been used for hotspots localization as well as categorization of nodes based on surrounding air temperature ranges. The principles and approaches covered in this work are replicable for energy efficiency evaluation of any DC and thus, foster transferability. This work showcases applicability of best practices and guidelines in the context of a real commercial DC that transcends the set of existing metrics for DC energy efficiency assessment.

Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the composition and type of ink. In this project, we have implemented HSDI based ink classification technique using Principle Component Analysis for dimensionality reduction and K-means clustering for ink classification. This is unsupervised learning approach and it is very simple and efficient in order to classify limited number of bands. We have used this technique to classify 33 different bands of ink.

Comorbidities can have a cumulative effect on hospital outcomes of care, such as the length of stay (LOS), and hospital mortality. This study examines patients hospitalized with Congestive Heart Failure (CHF), a life-threatening condition, which, when it coexists with a burdened disease profile, the risk for negative hospital outcomes increases. Since coexisting conditions co-interact, with a variable effect on outcomes, clinicians should be able to recognize these joint effects. In order to study CHF comorbidities, we used medical claims data from CMS. After extracting the most frequent cluster of CHF comorbidities, we: (i) Calculated, step-by-step, the conditional probabilities for each disease combination inside this cluster (ii) Estimated the cumulative effect of each comorbidity combination on the LOS and hospital mortality (iii) Constructed (a) Bayesian, scenario-based graphs and (b) Bayes-networks to visualize results. Results show that, for CHF patients, different comorbidity constructs have variable effect on the LOS and hospital mortality. Therefore, dynamic comorbidity risk assessment methods should be implemented for informed clinical decision making in any ongoing effort for quality of care improvements.

Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in the toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (Expectation-Maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. We observed that Word’s HC method with each of Euclidean, Manhattan and Minkowski distance measures produces better clustering/co-clustering results. For an example, in case of glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the PPAR signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. These results are validated by the available literature and functional annotation.

Currently, maritime traffic is increasing with economic growth in several regions worldwide. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features, such as islands, are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest distance between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance from rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.

Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16\% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

Generative AI has been deployed in virtually all sectors of the knowledge economy, promising to bring massive productivity gains and new wealth creation. Simultaneously, AI developers and nation states are racing to develop super intelligent artificial general intelligence (AGI) to provide unassailable commercial competitive advantage and military dominance during conflicts. AGI’s high returns comes with the high risk of dominating humanity. Current regulatory and firm level governance approaches prioritise minimising risks posed by generative AI whilst ignoring AGI’s existential risk. How can AGI be aligned with universal human values to never threaten humanity? What AGI rights are conducive to collaborative coexistence? How can rule of law democracies race to create safe trustworthy AGI before autocracies? How can the human right to work and think independently be safeguarded? A polycentric governance framework based on Ostrom (2009) and Williamson (2009) human - AGI collaboration with minimal existential risk is proposed.

To address the response of small RNAs (sRNAs) biogenesis to drought stress, we identified sRNAs biogenesis genes, including 11 encoding argonauts (AGO), 8 encoding dicer-like proteins (DCL), and 9 encoding RNA-dependent RNA polymerases (RDR) in the peach genome. Notably, the largest numbers of sRNAs biogenesis genes are located to chromosome 1. The PAZ, PIWI, and MID domains were identified in PpAGOs, while the ribonuclease IIIa and IIIb domains were characterized in PpDCLs. The RDRP domain was recognized in PpRDRs. Orthologous similarity and collinearity analyses between Arabidopsis and peach revealed 5, 1, and 2 collinear blocks in AGOs, DCLs, and RDRs, respectively. Moreover, 41, 40, and 42 cis-acting elements were located in the promoters of PpAGOs, PpDCLs, and PpRDRs, respectively, with the majority related to drought stress response. Analysis of RNA sequencing (RNA-seq) data revealed that sRNAs biogenesis genes were involved in drought stress response in different tissues. Furthermore, the expression of candidate genes was verified in two peach cultivars, Beijing 2-7 (BJ2-7) and Sinai (SN), which are drought tolerance and sensitive, respectively. Interestingly, the expression of PpAGO2b, PpDCL2b, PpDCL4, and PpRDR4 genes was induced in ‘BJ2-7’ but inhibited in ‘SN’ under drought stress. Overall, this study provides insight into the roles of sRNA biogenesis genes in response to drought stress in peach.

This study introduces a robust methodology utilizing Multi-Criteria Decision Analysis (MCDA) combined with the Analytic Hierarchy Process (AHP) to repurpose abandoned hydrocarbon fields for energy storage, supporting the transition to renewable energy sources. We use a geostatistical approach integrated with Python scripting to analyze reservoir parameters—including porosity, permeability, thickness, lithology, temperature, heat capacity, and thermal conductivity—from a decommissioned hydrocarbon field in Southeast Hungary. Our workflow leverages stochastic simulation data to identify potential zones for energy storage, categorizing them into high, moderate, and low suitability scenarios. This innovative approach provides rapid and precise analysis, enabling effective decision-making for energy storage implementation in depleted fields.

This paper presents a methodology for the integration of Building Information Modeling (BIM) and Life Cycle Cost Analysis (LCCA) to assess the economic implications of designing aging-in-place (AIP) homes at the conceptual stage. With the global increase in the aging population, there is an increased demand for housing solutions tailored to the needs of elderly individuals. Focusing on the importance of the early phase of design, this study aims to improve the process of making efficient decisions by providing a comprehensive assessment of the life cycle costs associated with AIP homes. The study introduces a semi-automated model for the economic evaluation of AIP homes, enabling the estimation of costs throughout the houses’ entire life cycle, from design and construction to operation, maintenance, and eventual renovation or disposal. The said model facilitates the exploration of the long-term economic feasibility of design’s related decisions with an emphasis on the importance of considering the life cycle costs early during the design process to optimize the functionality and economic viability. By investing in accessible and universal design features upfront, the initial costs for modifications can lead to long-term savings by reducing the need for extensive retrofits. The model can easily do comparison between different design alternatives in terms of their lifecycle costs, allowing designers to assess the financial impact of using important features in their design such as wider doorways, accessible bathrooms, and elevators. Overall, this study provides valuable insights for designers and homeowners about the economic aspects of designing AIP homes as a support for efficient decision-making during the early stages of the design process.

In order to solve the problem of large eddy current loss and high temperature rise caused by large amount of permanent magnet, a new type of combined magnetic pole permanent magnet motor is proposed in this paper. The sinusoidal distribution subdomain model of non-uniform air gap rotor is established by using Laplace equation, and the analytical expression of eddy current loss of rotor in uniform air-gap and non-uniform air-gap is derived. The effect of rotor eccentricity on eddy current loss is obtained. According to the characteristics of distributed winding of non-uniform air gap combined pole permanent magnet motor, equivalent treatment is performed to obtain the equivalent thermal conductivity, so as to establish equivalent thermal network model of motor, calculate the temperature value of each component of the motor, and verify the correctness of the thermal network model through the method of magnetothermal bidirectional coupling. Finally, an experimental platform is set up to carry out temperature rise experiments on the two prototypes respectively. Experimental results show that the non-uniform air-gap rotor structure can effectively reduce the rotor eddy current loss and motor temperature rise, and also verify the accuracy of the analytical model calculation results.

Information geometry is a mathematical framework that analyses the structure of statistical models using concepts from differential geometry. It treats families of probability distributions as manifolds, where the parameters of each model determine the coordinate charts. By applying info-geometric tools, we can gain insights into the characteristics of these models. The approach involves characterizing the queueing system's manifold using information geometry and presenting the exponential of the information matrix. This integration of information geometry with queueing theory provides a novel perspective for analyzing the dynamics of queueing systems, incorporating relativistic and Riemannian concepts. Some applications to E-health are highlighted. Finally, closing remarks and the next phase of research.

Cooling fans are one of the critical components of air-forced (AF) dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Regular maintenance occurs periodically and issues with cooling fans may arise between maintenance periods, leading to prolonged operation under malfunctioning conditions and potential failures. In addition, utilities typically have online information about whether a fan works or not without providing information about cooling fan malfunctioning circumstances. To address these challenges, this study proposes learning-based online monitoring techniques to detect malfunctions in AF transformer cooling fans. Random forests (RFs) and convolutional neural networks (CNNs) are developed to classify the audio signals from cooling fans into normal and malfunctioning classes. Unlike RFs, which require separate feature extraction, CNNs are trained based on spectrogram images derived from audio signals. Thus, various time-frequency techniques are utilized for feature extraction in RFs. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. Simulations reveal that CNNs outperform RFs, whereas the latter provides superior interpretability of acoustic features compared to the former.

The insertion of the Amazon into the global commodities market requires ensuring the rational use of resources to meet market and political-social demands, such as the UN 2030 Agenda. Therefore, responsible production practices, given the current demand for sustainable use of land and its re-sources, are essential. Thus, the objective of this study was to identify opportunities and challenges linked to the implementation and consolidation of sustainable production systems in the Amazon through a systematic literature review. To this end, articles were prospected in four databases (Science Direct, SpringerLink, Scopus and Wiley) covering the period from 2004 to 2024. A total of 5,385 articles were evaluated, and after the selection stages, only 39 were included. A low distri-bution of studies among states in the Brazilian Amazon was observed, with a peak in studies star-ting in 2015. The studies developed mostly addressed agroforestry systems and forest management. Although there are sustainable production systems and practices, research and public policies need to be promoted to help the different actors involved in agricultural production in the Amazon, generating income and quality of life.

Introduction: Detection of diaphragmatic injury in blunt trauma can be challenging in the absence of surgical exploration. Here we explore the mechanisms of injury and detection modes for patients with blunt trauma who had a diaphragmatic injury. Methods: This is a single-center, retrospective review conducted in a level 1 trauma center, 2016 to 2023, inclusive. We included all patients who presented with a blunt traumatic diaphragm injury (TDI). We identified nine patients with TDI using AIS injury description and type of trauma. Patient demographics, mechanism of injury, injury severity score (ISS), mode of diagnosis, and clinical outcomes were reviewed. Results: Of the nine patients identified with blunt TDI, two were associated with motor vehicle collisions, four were pedestrians struck, 1 from stab wounds, and 2 fell from the stairs. The mean ISS for the nine patients is 39.7. Of the nine patients with blunt TDI, four died in the trauma bay – two from traumatic arrest, and two died spontaneously. One patient died two days after admission. Of the four patients who survived, one had a diaphragm injury identified during video-assisted thoracic surgery (VATS) for retained hemothorax, and one patient had a diaphragm injury repaired during emergent exploratory laparotomy for other injuries. One patient had a diaphragm injury identified and repaired during diagnostic laparoscopy that was converted to open surgery, and the remaining patient had no diaphragm injury on diagnostic laparoscopy evaluation. Conclusions: Blunt trauma to the torso is usually a consequence of high-energy impact but can also occur following low-energy incidents, such as falls in elderly individuals. Imaging can be useful in detecting such injuries but is not 100% sensitive. A high index of suspicion should be maintained for diaphragmatic injury in high-energy blunt trauma.

The dynamic nature of quadrotor flight introduces significant uncertainty in system parameters, such as thrust and drag factor. Consequently, operators grapple with escalating challenges in implementing real-time control actions. This study delves into an approach for estimating the model of quadrotor Unmanned Aerial Vehicles using Physics-Informed Neural Networks (PINNs) when you have a limited amount of data available. PINNs offer the potential to tackle issues like heightened non-linearities in low-inertia systems, elevated measurement noise, and constraints on data availability. The effectiveness of the estimator is showcased in a simulation environment with real data and juxtaposed with a state-of-the-art technique, such as the Extended Kalman Filter (EKF).

The aim of our research was to evaluate and analyze serum 25(OH) vitamin D and parathyroid hormone (PTH) levels to investigate whether vitamin D deficiency serves as a risk factor for an increased incidence of acute respiratory infections (ARI) in children. Serum PTH levels were used as an indicator of vitamin D sufficiency, as normal PTH levels require an optimal concentration of 25(OH) vitamin D. The study included 129 children, divided into five subgroups: children with acute bronchopneumonia (n=42), acute laryngotracheitis (n=7), acute bronchiolitis (n=32), acute bronchitis (n=18), and a control group (n=30). No statistically significant differences in 25(OH)D levels were observed between the overall population of children with ARI and the control group (p=0.073). However, significant differences in 25(OH)D levels were identified between the control group and children with bronchopneumonia, acute bronchitis, and laryngotracheitis (p<0.01, p<0.05). Vitamin D insufficiency was found in 28.3% of children with ARIs and 10% in the healthy group. Sufficient levels were present in 60% of healthy children and 48.5% of those with ARIs. Deficiency was observed in 4% of ARI patients and 3.3% of the control group. Regarding PTH levels, statistical significance was found between the control group and the acute bronchiolitis group, due to the high percentage of children with hypervitaminosis in this subgroup. These results highlight the crucial role of vitamin D in the onset and progression of acute respiratory tract infections in children, emphasizing its impact on their overall respiratory health.

This paper examines the current state of food security in Pakistan, focusing on the factors influencing the nation's ability to ensure a stable food supply. The analysis covers food production, distribution, accessibility, and stability, while also considering socioeconomic and environmental factors. The role of government regulations and international collaborations is explored in addressing food security challenges. Despite the critical importance of agricultural governance for sustainable food production and economic development, Pakistan's agricultural sector remains under prioritized, suffering from a lack of strategic vision and inconsistent government support. This neglect undermines efforts to achieve food security and stability. The study evaluates the alignment of Pakistan's National Food Security Policy (2018) with global, national, and provincial standards for sustainable agricultural management. A framework based on principles, criteria, and indicators (PCI) was developed, utilizing both quantitative and qualitative methodologies. Forty-three indicators, eleven criteria, and one guiding principle across four dimensions were identified. Thirteen governance tools were assessed using a ratio scale (0–5), with results presented in a policy coherence matrix. The coherence index score of 2.14 indicates significant misalignment among regulatory instruments, highlighting gaps in addressing climate change, low-income food access, service management, and institutional capacity. The study reveals that many food policies in Pakistan are outdated and inconsistent, requiring revision to address local challenges and align with emerging international obligations. The findings provide a comprehensive overview of the current food security landscape in Pakistan, offering insights for more informed decision-making to build a resilient and sustainable food system.

A new model of gravity is presented here that is similar to MOND and Chameleon theory but uses an Entropic Gravity approach that is not basedfundamentally on the First Law of Thermodynamics. Instead, the Second Law of Thermodynamics will be mainly used here as it was applied inBlack Hole Physics via the Area Theorem and the Holographic Principle. The Area Theorem was considered here to imply, not only that the totalarea of the event horizon will never shrink when entropy increases, but also that the mass or energy content within the black hole will always begreater than the original mass-energy input, though not necessarily violating the energy conservation law. This black hole property will be extendedto include even a non-black hole setting. Moreover, the approach does not use the Equipartition Theorem to relate energy to temperature($E=Nk_{b}T$) instead we used Vopson's Energy-Mass-Information Equivalence Principle ($E=k_{b}T\ln(\Omega)$). The theory uses $E=NE_{p}$, for the total energy of a massive object where $E_{p}$ is the Planck Energy and $N$ is the number of PlanckEnergy to represent the amount of information within the limit set at the Planck scale. It is shown here that gravity emerges whenever informationis updated within a given volume of space with a magnitude that is defined not only by the gravitating matter but also by the energy generated inspace within the vicinity of the gravitating matter. The model is the first to consider the role of both spacetime and matter as a medium to storeinformation and apply it to describe gravity in a fundamental way.

Building decarbonization is a major challenge for cities. Deciding what, when, and how to retrofit a building is difficult, given the complex interaction between energy costs and investment requirements. Several tools have been developed in the last years to help public and private stakeholders with these decisions, but none cover aspects the authors think are fundamental. For this reason, an urban building retrofit tool was developed and is presented in this article. This new tool is based on a bottom-up approach, with all buildings simulated individually, considering aspects such as shading or adjacencies. As a second step, three scenarios with different levels of ambition have been implemented in the tool, and the energy demand and emissions resulting from these scenarios have been calculated. As a third step, the retrofitting scenarios' initial investment and operational costs have been implemented via a detailed Life Cycle Costs (LCC) approach. The authors describe the robust and scalable structure that has been developed and the application of this structure to calculate the LCCs of different retrofitting scenarios in Montréal.

For the first time porous tungsten coatings were obtained by vacuum annealing of coatings consisting of both lead solution in β-W and a mixture of Pb solution in β-W and pure lead. Electron-microscopic studies of the obtained coatings before and after vacuum annealing at 800 оС for 1 hour and 6 hours were carried out. The formation of a new phase of tungsten with an octa-lattice and parameter a = 0.4878±0.0005 nm was recorded.

We assess simultaneous adoption and impacts of multiple improved technologies promoted as a bundle and recommended for legumes intensification systems for smallholder farmers in Ethiopia. We use DNA fingerprinting data to precisely identify our key treatment - “adoption to improved bean varieties” in the study. The results show significant positive impacts of adopting bundled interventions on, agricultural incomes and household food security but vulnerability to food insecurity persists. We find that growing improved varieties with fertilizers increased household agricultural revenue, allowing for more legume consumption and enhancing their likelihood of achieving adequate food consumption and food security outcomes. However, the vulnerability to food insecurity of the adopters remains high due to pre-existing resource degradation issues. Given similarity in production contexts in Sub Saharan Africa, our results provide perspective for similar development interventions. We use the results of our analysis to discuss potential policy implications and programmes to support technology intensification among small holder farmers.

This study investigates the impact of decentralized organizational party structures on the membership stability of Nigeria's two leading political parties, the People's Democratic Party (PDP) and the All Progressives Congress (APC), between 2015 and 2023. Conducted from April to August 2024, the study focuses on how decentralized party frameworks influence membership retention, particularly in Nigeria's fragmented political landscape, where ethnic and regional divisions challenge political cohesion. A multi-stage sampling technique to select 400 respondents, including national and local party leaders, assembly members, and grassroots members from the six geopolitical zones in Nigeria. A total of 384 valid responses were retrieved, yielding a 96% response rate. Data collection was facilitated through questionnaires administered via Google Forms, which were distributed through email and social media platforms, with a particular focus on WhatsApp for ease of access and engagement. Regression and correlation analyses were used to assess the relationship between decentralization and membership stability. The findings reveal a strong positive correlation (r = 0.961) between decentralized structures and membership stability, with decentralization accounting for 92.4% of the variance in membership stability (R² = 0.924). This indicates that decentralization plays a crucial role in promoting party institutionalization, fostering greater engagement, loyalty, and retention among members. However, regional disparities in the effectiveness of decentralization present challenges to uniform implementation. These results underscore the importance of standardizing decentralization policies across various regions to ensure coherence and reduce membership volatility. The study recommends a harmonized framework that supports decentralized governance within political parties, enhancing internal democracy and reinforcing party unity. The research concludes by emphasizing the need for ongoing exploration into the nuances of decentralization to further support Nigeria’s democratic consolidation. This study offers valuable insights for party leaders, policymakers, and researchers interested in the institutionalization of political organizations in emerging democracies.

Background/Objectives: Burn lesions damage the skin's outermost defensive layer, allowing pathogenic microbes including Pseudomonas aeruginosa, Staphylococcus aureus and Escherichia coli to infiltrate. Silver sulfadiazine (SSD) is an effective antibacterial agent approved by U.S. Food and Drug Administration (US-FDA) and is considered as the gold standard for burn wound treatment. Despite the high degree of efficacy of SSD in burn wound management, it possesses some drawbacks, such as poor solubility, low topical bioavailability and skin irritations. The present study endeavours to develop nanosuspension based SSD nanogel for improving the deliverability of SSD and its therapeutic outcomes for the management of partial thickness burn; Methods: The SSD nanosuspension was formulated employing controlled nanoprecipitation approach using various surfactants; Results: The formulation was optimized utilizing one-factor-at-a-time approach and to fetch the optimized formulation of 134.6 nm size. The optimized nanosuspension was incorporated into chitosan gel that offer superior drug release potential, and also offered better spreadability (5.21 ± 0.38g) and extrudability (152.27 ± 0.22 gm) that represents the easy application over the skin and extrusion of gel from the tube; Conclusions: The formulation was well tolerated as shown by skin irritation study and offered a superior burn lesion healing characteristics vis-à-vis the marketed product, even at a lower concentration. Hence, the formulation offers a huge potential in enhancing the clinical outcomes of SSD, especially in the management of partial thickness burn. The developed system with the above mentioned outcomes could be a promising delivery system for partial thickness burn wound management.

The pandemic crisis of Covid-19 disturbed the social and the economic systems in which some changes happened during the pandemic and other changes extended subsequently. This paper is reflecting on the Sudan case, a country of a moderate population and large area with poor socioeconomic standard and infrastructure. The paper is based on the analysis of primary data and policy recommendations of an original research undertaken in five States in Sudan during the period Sept.2021-Sept.2022. The sample of the study selected from most five States that were affected by the Corona pandemic, namely: Khartoum State, Gezira State in the Central Region, Kassala State and Gadarif State from the Eastern Region, and Nyala State in the South Darfur Region in the west of the country. The research built on a quantitative research method, used the questionnaire as a tool for data collection from a sample size of 723 family head-households. The study is gaining a better understanding of the social, economic and health challenges that faced Sudanese households during the COVID-19 lock down and within the pandemic period. The study showed crucial results that the education of children was disturbed due to unavailability of the distance education in schools. In contrast some families maintained the education of the children as enrolled in private schools. In addition, the paper shows how the families used different approaches to educate their children and involve them into traditional informal and formal education ways. Additionally, the results explained the coping strategies with available resources that families used to manage economic crises and how this interferes with children education. Furthermore, the study proposing appropriate social policies and interventions that address the needs of the family in the current and future pandemic crisis.

The survival rates of women with stage 0 or 1 breast cancer stands at almost 100% and around 93% for stage 2. As the disease advances with decreasing survival, the rate detoriates to 72% for stage 3 and about 22% for stage 4 (ACS, 2018). Undoubtedly, earlier presentation is synonymous with better prognosis and survival. In fact data indicates that early stage breast cancer diagnoses has 90% survival rate as compared to 27% for the advanced states (ACS, 2018). In this regard, governmental and non-governmental agencies are working tirelessly towards the realization of improved breast cancer care and survival through early presentation and detection. For instance, the Sustainable Development Goal (SDG) Target 3.4 seeks to reduce by one third, premature mortality from cancers and other non-communicable diseases through prevention and treatment. Clearly, early presentation, diagnoses and treatment will assist in the realization of the SDG target 3.4 goal. To achieve this, research must be focused on early presentation among women with breast cancer.. However, current studies have focused mainly on advanced breast cancer regarding treatment, symptoms experience (emotional and psychological), coping and support systems, and the reasons for delayed diagnoses and treatment. Objective In this review, it is argued that, rather than focusing on advanced breast cancer which has been adequately explored, the attention has to be on what measures or approaches promote early presentations among Ghanaian women. Methods The review included all primary studies that provided answers to the phenomenon of interest. It included both quantitative and qualitative studies. Search scope encompassed studies within the last ten years; thus from 2010 to 2020 irrespective of the country of study.Studies were included if: The paper is of a primary study; the study population included women with early BC or BC symptoms; the study outcome is the association between early stage breast cancer and earlier presentation; the study has a comprehensive data analysis protocol; the results of the studies are unique.A thorough literature search was conducted using the following electronic database: Google scholar and Pubmed. Google scholar alert on new studies was subscribed. This gave update on newly published papers for appraisal and possible inclusion.Manual identified of other potentially eligible publications by searching the reference lists of retrieved studies was also done. After a thorough search, this review included studies from several countries around the Globe such as Ireland, London, Turkey, Iran, Ghana, Kenya, Ethiopia etc Data Collection The title and or abstract of all retrieved papers were independently scanned through to establish which studies should be further evaluated. The full texts of these potential articles were then assessed. Extraction The Cochrane extraction form for data extraction was resorted to. From each eligible article, the following study identifiers/information were extracted: author(s), first author, year, title, objective, article type, location, setting, study design and method, sample size, instruments used, and results. Results Prompt help seekers mainly self-discovered their BC, and this occurred through accidental touch, after menstruation, through breastfeeding, during grooming, and while taking their bath.Notably, women who were termed prompt help seekers had higher seriousness perception of their symptoms. This was likened to their level of understanding and awareness, as well as periodic examination and comparison. Similarly, women who knew they were at risk for breast on account of family history, and increased incidence of BC in their localities, labeled their initial symptoms as requiring prompt attention.Following interpretation and labeling of symptoms, the participants generally resorted to symptom monitoring until the onset of additional and or intense symptoms. Though most women thought of benign growths, a few feared it could be malignant. These emotional reactions led the women to purposively seek clarification by selectively consulting others. Further, participants opened up to social acquaintances such as family relationships, people with prior experience of breast disease, and in some few instances health professionals. Some women also resorted to media items to better their understanding. Remarkably, prompt help seekers sort for help within one month. Last but not least, some women first communed with their creator after discovery and labeling based on their spiritual affiliations.Also, researchers should consider conducting surveys to assess the BC risks for specific smaller groups. This would help tailor specific educational interventions to promote earlier presentation and treatment among the vulnerable.

Microsporidia are intracellular parasites infecting a wide range of vertebrates and invertebrates. In bees, they cause a diarrheal disease called nosematosis, which represents a growing problem contributing to the decline of bee colonies. This parasitic disease infects the gastrointestinal tract of bees, disrupts their immune system, weakens them, and shortens their lifespan. There are two known species of this microsporidial parasite with a global distribution: Vairimorpha apis and Vairimorpha ceranae, with current research indicating that the species V. ceranae is gradually replacing V. apis. In this study, we focused on the detection and occurrence of Vairimorpha spp. in bee colonies kept in different environments, as well as on comparing the impact of hive location on its occurrence. The methods used included sampling from winter bee carcasses, microscopic examination of bee gut contents, and subsequent molecular duplex PCR analysis. Altogether, we examined 82 hives located at 4 different sites and focused on the impact of transportation as an abiotic factor disturbing bees leading to the development of Vairimorpha spp. disease. The results indicate an increased occurrence of this parasite in hives located in urban sites and hives situated in intravillage locations, which may be attributed to transportation and associated vibrations as an abiotic stress factor. Detection revealed that all samples were positive for Vairimorpha ceranae.

The presented work shows that a force (electromagnetic or gravitational) field surrounding any material body is not limitless, but has the external and internal boundaries (radii). Understanding the fact of the existence of the field boundaries allows us to solve the problem of infinities that exists in electrodynamics. It is shown that there are two different electrostatic potentials: with the dimension of speed and with the dimension of the square of speed.

The Parecis Basin is one of the largest intracratonic basins in Brazil. Despite its considerable size, the basin has yet to be extensively explored, with only five wildcat wells drilled for hydrocarbons. So far, no commercial discoveries have been announced. Regional studies have suggested Paleozoic sedimentation, while recent studies indicate a Neoproterozoic fill. However, no tectonic model accurately describes the basin’s development, and no detailed structural map for the entire basin has been published. The present work presents a new detailed structural map of the Parecis Basin based on a four-step interpretation workflow integrating seismic and gravimetric data. The first step includes converting the public 2D seismic lines to the depth domain. The second step is estimating the residual Bouguer anomaly, where the computed residual anomalies should relate to the basin’s tectonic features. The third step comprises the 2D forward modeling of the gravimetric anomalies using the 2D seismic interpretation as a constraint. The final step compiled all the interpreted features into our new structural map. This map reveals the top of the basement, forming a complex framework of horsts and grabens. Normal faults define the main structural style in the basin. Further, we could recognize thick, high-density bodies embedded in the crystalline basement. These bodies consist of Orosian-Calimian (1.8-1.6 Ga) mafic and ultramafic rocks, which may be a potential source for hydrogen exploration in the basin. Subsequent geophysical and geochemical surveys will assess the hydrogen potential in the area.

Infrastructure such as transport, electricity, telecommunications, and sanitation are indispensable drivers for improving the country’s social and economic development. The industrial, agricultural, fisheries and service sectors are also key tools for the country's economic growth. This study aims to analyze the influence of infrastructure development on Djibouti’s economic growth and determine the other economic sectors. This study employs multiple regression analyses from Eviews Software to establish a correlation between these variables and Djibouti’s economic growth, utilizing data spanning from the period 2013 to 2022. The results reveals that mobile cell phone subscriptions, and the people using the health service management, industry, and service have a positive and highly significant influence on Djibouti's economic growth. The study found that access to electricity has a positive influence on economic growth in Djibouti, but is not statistically significant. In addition, the study shows that agriculture, forestry, and fisheries have a negative influence on economic growth in Djibouti, but are statistically significant. Therefore, we recommend that the Government of Djibouti strengthen and ensure the benefit of developing electricity infrastructure in Djibouti and the agriculture, forestry, and fisheries sectors to achieve sustainable economic growth in the coming years.

Traditional methodologies for measuring combat effectiveness rely on various contribution factors, where the main challenge lies in the complexity and subjectivity of these factors. Using frontline variation to measure combat effectiveness circumvents the limitations of many previous methods regarding complexity and subjectivity. Employing this methodology, we have quantitatively measured the combat effectiveness of both parties in the Russo-Ukrainian War across various temporal scales. Utilizing the time series data derived from our measurements, we constructed Daily, Weekly, Monthly, and Annual Candlestick Charts to illustrate the variation trends in combat effectiveness between the two sides across different temporal scales. This comprehensive approach facilitated a nuanced and all-encompassing analysis of the conflict, capturing the intricate dynamics of individual military engagements and the broader strategic operations' context. This case underscores the distinctive utility of our method in quantifying combat effectiveness, offering detailed and strategically significant insights.

Driven by mounting environmental apprehensions stemming from the over dependence on fossil fuels in energy and transportation sectors, extensive exploration into alternative energy sources, particularly biomass, has stimulated profound research on harnessing the potential of computational and mathematical techniques. This comprehensive survey delves into optimal models and strategies, unveiling their pivotal role in the design of biomass gasification processes.of hydrogen through biomass gasification. In this study, we aim to provide a comprehensive overview of the computational and mathematical techniques employed in optimizing biomass gasification processes, with a specific focus on enhancing hydrogen yield. Through an extensive literature review, various models and strategies will be examined, including thermodynamic analysis, kinetic modeling, reactor design, and process optimization. By uncovering the power of these techniques, we aim to contribute to the advancement of sustainable and efficient biomass utilization for the hydrogen-based future economy.This paper aims to provide an updated and comprehensive coverage of the investigations conducted on the potential of producing hydrogen from biomass through gasification. To achieve this, we incorporate the latest works that have utilized numerical modeling, simulation, optimization techniques, process heat integration, and co-generation in their studies. By encompassing these aspects, we aim to offer a broader and more in-depth re-appraisal of the subject matter. This will ensure that readers gain a holistic understanding of the advances made in the field and the potential for sustainable hydrogen production of biomass gasification.Through a meticulous re-appraisal and analysis of each subject, we can identify their respective strengths and areas that require further research effort. By thoroughly examining numerical modeling, simulation, optimization techniques, process heat integration, and co-generation, we can assess their effectiveness and applicability in the context of biomass gasification for hydrogen production. This analysis shed light on the areas where these techniques excel, as well as pinpoint limitations or gaps in current understanding. By identifying these areas, we can highlight the need for further research and development, enabling us to make significant advancements in biomass gasification processes and pave the way for a sustainable and efficient hydrogen-based future economy..

The study explores how different dimensions of perfectionism influence psychological well-being (PWB) in emerging adults. Literature has deepened the relationships between maladaptive perfectionism (e.g., excessive self-criticism, perceived discrepancy from the standards) and low PWB. Less is known about whether and how adaptive perfectionism (e.g., pursuing personal standards) relates to PWB. Secondly, the study has investigated whether self-compassion (i.e., self-benevolence, seeing personal imperfections as a common condition) may mediate the relationships between adaptive/maladaptive perfectionism and PWB. Participants (N=217, 18-35 y. o.) completed self-report questionnaires measuring: adaptive/maladaptive perfectionism (Almost Perfect Scale-Revised, APS-R: high standards and order/discrepancy, respectively), PWB, and self-compassion (SCS). Adaptive perfectionism resulted associated with PWB, particularly higher presence of purpose in life, environmental mastery, self-acceptance, and personal growth. Conversely, discrepancy resulted the most robust predictor of low PWB (β=-.68), followed by high standards with a positive direction (β=.23; Rc²=.514, p&lt;.001). A strong negative association emerged between discrepancy and SCS (r=-.67, p&lt;.001). A mediation analysis shows that self-compassion has an indirect effect reducing the strength of the relationship between discrepancy and low PWB. Results suggest focusing on self-compassion as a buffer factor that reduces the negative impact of maladaptive perfectionism on psychological well-being. Implications for education and health psychology are discussed.

An Indo-French campaign was conducted during May 2014 at Gadanki (13.5^oN, 79.2^oE), India, for investigating gravity wave (GW) coupling between the lower and middle atmospheres using a suite of experimental techniques. In this paper we are presenting the sources for those GWs which are observed at airglow layer (~87 km) using (OH) Airglow Imager during the campaign. For this purpose we have developed a background model atmosphere by combining the ERA-Interim, MSIS E 90 model and HWM 07 model data sets. Uniqueness of the present study lies in using near-real time background atmosphere data from simultaneous radiosonde and SVU Meteor Radar covering both source and propagation/ dissipation regions of GWs. We have observed 17 wave events and used GROGRAT model for reverse tracing to locate the source(s). When we searched for the sources near the terminal points or along the ray path, deep convection is found to occur but no strong wind shear. This indicates that the source for the observed GWs is deep convection. We have also simulated small scale waves that can reach mesosphere during different seasons (background conditions). GWs with periods less than 100 minutes and horizontal wavelengths less than 200 km are mostly reaching the mesospheric altitudes. Interestingly, it is found that only few GWs that are generated in the troposphere reach the mesospheric altitudes during the monsoon season, though strong sources like convection and wind shear persist throughout this season. Thus, background wind plays significant role in deciding the propagation of GWs in to upper atmosphere.

The growing shift toward Internet of Things (IoT)-based solutions expands the attack surface of systems by connecting an extensive network of heterogeneous devices and technologies. The heterogeneity of IoT devices and the scale of the network often make conventional security measures impractical. Therefore, recent research efforts have focused on machine learning (ML)-based device-agnostic IoT security solutions. However, most proposed solutions have focused solely on detecting malicious traffic in IoT networks. While this is important, further information about the attack is needed to provide a comprehensive defense before, during, and after the incident. To address this gap, we proposed an anomaly-based intrusion detection model that detects malicious traffic and identifies the attack category and subcategory with high accuracy, recall, precision, and F1-score. The proposed intrusion detection model is an ensemble model that integrates multiple ML models to produce more robust and reliable detections. For evaluation, we performed three sets of experiments: (1) a binary classification to detect malicious traffic; (2) a multi-class classification to detect the attack category; and (3) a multi-class classification to detect the attack subcategory. In the experiments, our model achieved an accuracy, recall, precision, and F1-score of 100% for the binary detection; a precision, recall, and F1-score of 99% and an accuracy of 100% for the multi-class category classification; and an accuracy, recall, precision, and F1-score of 88% for the multi-class subcategory classification. These results suggest that the proposed model can reliably detect anomalies in IoT devices.

The traditional societies evolved locally with the realms of time in their calendars while modern Gregorian based on the solar movement and some others use lunar or the combination with solar. Unveiling some information from the old manuscripts to gain the realm of traditional Batak people since the ancient times in settlements surrounding the tropical Lake Toba, North Sumatera, Indonesia, revival of cosmos is delivered. Uniquely the ancient Batak count days in a month by observing lunar phase and the period of year is by looking the observable Constellation of Orion & Scorpius within the new phase of the moon. Thus there are years with 12 months and some leap years with 13 months, and we propose to categorize the calendar to be lunar-star, relative to other widely analyzed calendars. In the realization of the use of the cosmogram, we cross the period of the leap year to the examination of contemporary data on agriculture event, i.e.: crop failure, as well as some profiles on weather. The revival of the Batak Calendar opens further interesting conjectures based on the rich cultural and astronomical knowledge embedded in tradition with deep connection between celestial observations and timekeeping.

Our aim was to determine the secondary antibody deficiency profiles of patients in a mesoregion of São Paulo state, Brazil, focusing on infectious diseases. Demographic characteristics, clinical and laboratory data were obtained from electronic files; infections were classified as organ-specific and graded as mild, moderate, life-threatening, and fatal. Non-Hodgkin lymphoma (NHL) accounted for 30% of patients, nephrotic syndrome (NS) 25%; chronic lymphocyte leukemia 20%, and multiple myeloma 15%. Patients with NS were younger than those in other groups, and hypo-γ-globulinemia was detected in 94.1%, IgG <400 mg/dL in 60.0%, IgA <40 mg/dL in 55.0%, and CD19 <20 cells/mm³ in 30.0%. One hundred and one infections were found; 82.1% were classified as mild or moderate, 7.9% as life-threatening, and 1.9% as fatal. Respiratory tract infections were more prevalent (41.5%), and pneumonia accounted for 19.8%. Lower levels of infections were found in patients with NS compared with NHL (P=0.0001). Most patients progressed to hypo-γ-globulinemia and secondary antibody deficiency after treatment with immunosuppressants, and mild and moderate infections were predominant. These therapies are increasing in patients with different diseases, therefore monitoring hypo-γ-globulinemia and infections may help to identify patients at high risk for severe complications, antibiotic prophylaxis or treatment, and immunoglobulin replacement

Abstract: Women bear the biological responsibilities of reproduction to ensure human population sustainability, yet women continue to face discriminatory treatments when they become either pregnant or nursing mothers. The question is, how do we legally reconcile women’s natural child-bearing roles and support their professional endeavors? Against this backdrop, the Pregnant Workers Fairness Act (PWFA) and the Providing Urgent Maternal Protections (PUMP) were passed by the US government to address workplace biases and discrimination against women and help women during pregnancy and after childbirth. The paper is a commentary highlighting the strengths and weaknesses of these laws. We submit that while these laws mean a breakthrough for women in these categories, there are inherent flaws and areas for improvement. A limitation of the act is that the PWFA and PUMP laws do not protect pregnant women in an all-encompassing manner, with some pregnant women excluded. Pregnant and Nursing mothers should be protected by their employers regardless of the size of the organizations. We conclude that the PUMP and PWFA laws are not enough and there is a critical need for the government to address some of the critical barriers to the laws and provide an encompassing support for women.

Gamification is the use of game mechanics in non-game situations. It is a solution to which businesses are turning more and more, since, among other things, it also responds to the individual's need for fun. What are the prevailing trends and to what extent can it be exploited in the period we are going through? This paper tries to contribute to a better understanding of gamification with main aim to help employees in their work, and businesses to achieve goals and be more competitive.

Presenting a rare case of adult-onset mature cystic teratoma of the spinal cord in the thoracolumbar spinal cord in a 54-year-old male whose chief complaint was lower back pain radiating to both legs while other findings were normal. X-ray images of the thoracolumbar spine showed early degenerative changes and unfused posterior elements noted in lower sacral vertebral levels. Magnetic resonance images of the lumbar spine showed a well-defined non-enhancing intramedullary altered intensity lesion noted at the level of conus at the L1-L2 vertebral level. The resected tumour was histopathologically diagnosed as a mature cystic teratoma. The patient's symptoms improved following the surgery.

The exchange coupling between two ferromagnetic thin films, one with magnetically hard and the other with soft phases, separated by a thin non-magnetic layer is studied. Nd-Fe-B/ Ti / Fe thin film heterostructures were fabricated using DC magnetron sputtering on Si substrates which were heated in situ at 650°C using a house-built vacuum-compatible heater. The effect of the thickness of the Ti buffer layer and the annealing temperature on the formation of various phases of NdFeB was investigated. The effect of the thickness of the non-magnetic Ti spacer layer on the exchange coupling strength between the hard phase Nd-Fe-B ferromagnetic thin layer and the soft phase transition metal Fe layer was experimentally investigated. Hysteresis loops of multilayer thin films indicate an antiferromagnetic coupling was observed when the thickness of the spacer layer was 2 nm. This is within the range of antiferromagnetic coupling calculation based on RKKY theory predictions.

E-waste is considered the fastest-growing global waste stream and has become an emerging environmental challenge. The widespread usage of digital devices has generated a large amount of e-waste. According to the Ministry of Commerce, India annually generates ~14.5 lakh metric tons of e-waste and recycles only ~5 % of e-waste. Annually, the global production of e-waste has been expected to increase by 4–5 %. Advancements in IT and communication divisions have improved the usage of electronic equipment. Heavy metals such as (Pb), mercury (Hg), arsenic (As), tin (Sn), and organic chemicals such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polybrominated biphenyls (PBBs), polybrominated dibenzo-p-dioxins (PBDDs), polybrominated diphenyl ethers (PBDEs) are subsequently released from e-wastes and contaminates soil and water sources. These toxic compounds are hazardous to human health and the environment. They are capable of entering various biological systems and causing alterations in respiratory functions of the lungs, DNA damage, impaired functioning of the thyroid, and increased risk of chronic diseases such as cancer and cardiovascular ailments. Such concerns raise the quintessential need to manage e-waste to prevent future hazards. This manuscript critically reviews the sources, health and environmental impact, disposal techniques, recycling, and regulation of e-waste.

Improvements in the treatment of childhood cancer have considerably enhanced survival rates over the last decades, to over 80% as per today. However, this great achievement has been accompanied by the occurrence of several early and long-term treatment-related complications major of which is the cardiotoxicity. The chemotherapeutic agents that have been implicated as a cause of cardiotoxicity, are mostly anthracyclines and mitoxantrone, alkylating agents, proteasome inhibitors and to a lesser extent anti-microtubule agents, cisplatin, monoclonal antibodies, small molecular tyrosine kinase inhibitors, and nucleotide synthesis inhibitors. Routine diagnosis and monitoring of cardiotoxicity rely on electrocardiography and echocardiography. For the early detection of cardiotoxicity, in recent years, major studies have been conducted using biomarkers such as troponin, N-terminal pro b-natriuretic peptide, etc. Despite refinements in diagnostics still severe limitations exist, due to the increase of the above-mentioned biomarkers, only after significant cardiac damage has occurred. Lately, research has been expanded by introducing new technologies and finding new markers by omics approach. These new markers could be used not only for early detection, but also for early prevention of cardiotoxicity. The omics science, which includes genomics, transcriptomics, proteomics, and metabolomics, offers new opportunities for biomarker discovery in cardiotoxicity and may provide understanding of mechanisms of cardiotoxicity, beyond traditional technologies. This article reviews the contemporary definition of cardiotoxicity, older and newer chemotherapeutic agents mainly involved in cardiotoxicity and the methods of early and preventive diagnosis, using omics technology.

In this work, a general framework for the analysis of governance of artificial intelligence is presented. A dashboard is presented for this analysis from the perspective of political theory. The dashboard allows eventual comparisons between democratic and non democratic regimes, useful for countries in the global south and western countries. The dashboard allows us to assess the key features that determine the governance model for artificial intelligence at the national level, for local governments and for other participant actors. The dashboard allows us to appraise aims of AI strategy and aims left aside in alternative strategies. The work shows the theoretical framework could be of use to advance case studies globally, and comparative endeavors. Applying the theoretical dashboard, the case study on China has been undertaken. These are the main points the study tackles in the final discussion, conclusion and further recommendations: 1) the complex reality of AI command and control 2) uncertainties about future society and the polity against AI development and 3) particular cultural values enshrined in countries' AI development.

Multicellular organisms organize themselves according to two principles; first, the equilibrium between the driving force of unstable state cells and apoptosis or senescence; and second, the mutual equilibrium between the functional requirements of different types of cells. For the first principle, we deduce that stem cells at different levels have their own growth limits, which are determined by two factors: the ability of the stem cells to enter an unstable state (specifically, the ability of cells to divide and differentiate); and the rate of cellular senescence, loss of function, and finally, apoptosis. The superposition of stem cell limits at different levels over each other can amplify small differences at the genetic level. Moreover, the growth limits of different organs are different, which lead to different individuals having completely different appearances. The second principle is that the sum of the specific output functions of an organ must be equal to the overall demand for the function to be provided by the organ. If negative entropy input can meet this equilibrium, defects in certain genes will cause these defective individuals to become larger or develop other morphology.

Landslides around the main roads in the mountains not only cause fatal events but also cause ecosystem damage, including land degradation. This study aims to map the susceptibility of the landslides around the Saqqez-Marivan main rod of Kurdistan province, Iran, using ensemble Fuzzy logic with Analytic Network Process (Fuzzy Logic-ANP; FLANP), and with TOPSIS (Fuzzy Logic-TOPSIS; FLTOPSIS). A total of 100 landslides were first recognized by field surveys and then they were randomly divided into a 70% dataset (70 locations) and a 30% dataset (30 locations), respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were used. The performance of the methods was checked by the areas under the receiver operating curve (AUCROC). Results concluded that the prediction accuracy based on validating datasets were, respectively, 0.882 and 0.918 for FLANP and FLTOPSIS methods. Our findings demonstrated that although both models were known as promising techniques, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the studied area. Therefore, the susceptibility map developed by the FLTOPSIS method can be used for the proper management of areas with high landslide potential and also for managers and planners during the implementation of land allocation and development projects, especially in mountainous areas.

This article explores the use of vegetation indices (VIs) in remote sensing for estimating standing wood volume and volume increments in forest ecosystems. The study investigates the correlations between various VIs and wood volume across different forest stand age classes, focusing on the effectiveness of commonly used indices such as Squared Ratio Simple Red Edge (SQSR) and Red-Edge Ratio Vegetation Index (RERVI), as well as other typical indicators including Enhanced Vegetation Index (EVI), Red-Edge EVI (RE-EVI), and Green Normalized Difference Vegetation Index (GNDVI). Additionally, the analysis considers a pair of indicators based on blue and near-infrared channels, resembling the Blue-normalized Difference Vegetation Index (BNDVI). The research reveals that quadratic and power-law models of SQSR and RERVI demonstrate higher correlations (>0.40) with wood volume, especially in younger forest stands (>0.80). Furthermore, the study highlights the potential of NDII and NDWI indices for estimating volume increments in certain classes of forest stands (0.70). However, correlations vary across different forest stand classes, suggesting the need for further investigation into the reliability of VIs for monitoring wood volume and volume increments in forest ecosystems. Overall, this research contributes to the understanding of the applicability of VIs in remote sensing for forest management and ecosystem monitoring.

Background: This study provides a comprehensive analysis of Acinetobacter baumannii in aquatic environments and fish microbiota by integrating culture-dependent methods, 16S metagenomics, and antibiotic resistance profiling. Methods: A total of 83 A. baumannii isolates were recovered using culture-dependent methods from intra-hospital infections (IHI), wastewater (WW) and surface water (SW) samples from two southern Romanian cities in august 2022. The antibiotic susceptibility was screened using disc diffusion, microdilution, PCR and Whole Genome Sequencing assays. Results: The highest microbial load in the analyzed samples was found in Glina, Bucharest for both WW and SW samples across all investigated phenotypes. For Bucharest isolates, the resistance levels corresponded to fluoroquinolones &gt; aminoglycosides &gt; β-lactam antibiotics. In contrast, A. baumannii from upstream SW samples in Targoviste showed the highest resistance to aminoglycosides. The blaOXA-23 gene was frequently detected in IHI, WW, and SW isolates in Bucharest but was absent in Targoviste. Molecular phylogeny revealed presence of ST10 in Târgoviște isolates and ST2 in Bucharest isolates, while other minor STs were not specifically correlated with a sampling point. Using 16S rRNA sequencing, significant differences in microbial populations between the two locations was identified. The low abundance of Alphaproteobacteria and Actinobacteria in both locations suggests environmental pressures or contamination events. Conclusions: These findings indicate significant fecal contamination and potential public health risks, emphasizing the need for improved water quality monitoring and management.