Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been done on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. The architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of similar type with a character error rate of 13.2% and character error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

This case study examined performance of rural cassava SMEs managed by small scale farmers in Busia and Kisumu Counties in the lake region of Kenya. Formal survey, focus group discussions and key informant interviews were used to collect data from farmers and stakeholders in the region. The data were analysed descriptively using proportions (percentages and proportions), means and standard deviation and inferentially, using statistical tests of significance. The findings revealed gross inefficiencies in the management of cassava SMEs. Grass-root SMEs lack professional managers and exhibit inadequate organizational, quantity and quality management, high operational costs, lack of trust from farmers, inadequate financing, weak technical, agribusiness skills and weak linkages with markets. The findings challenge promotion of farmer-led organizations in rural areas to perform additional roles such as processing and marketing functions beyond their core business which is production. To remain relevant in a competitive value chain, grass-root farmer-led SMEs need to be re structured into viable economic entities with clear market-orientation. Ordinary smallholder farmers should specialize in producing sufficient quantity and quality of agricultural commodities, while processing and marketing functions are taken up by professional entrepreneurs. This study recommends an entrepreneur model rather than farmer-led SMEs as potentially best suited in creating efficiency in the cassava value chain in the lake region of Kenya.

Background. Of as yet, flow in the internal jugular veins has not been quantitatively studied in lateral decubitus position. Optimal venous outflow during sleep has been hypothesized to be potentially protective against neurodegeneration. Methods. Using ultrasonography, we measured the cross-sectional areas and flow volumes in middle parts of the internal jugular veins in group of 25 healthy individuals. These measurements were performed in the supine, upright and lateral decubitus positions. Results. In lateral decubitus positions we revealed collapse of the internal jugular vein located higher, and dilatation of the opposite vein; yet there was also a high heterogeneity of the cross-sectional area changes and in many participants this pattern was not observed. Regarding flow volumes, there was a shift of flow from one vein to the opposite one, depending on which side the person was lying, but in comparison with the supine position the total outflow through both internal jugular veins was not significantly different. Conclusions. In terms of venous outflow, the lateral decubitus position does not differ significantly from the supine one. Working hypothesis of potentially protective effect of this body position during sleep against neurodegeneration, through improved venous outflow, has not been proven, at least in healthy persons.

UVA1 phototherapy has been reported to mediate some of its effects via FICZ/AHR/MAPK signaling. A proof-of-concept study was performed to assess whether a topical solution of L tryptophan formulated to favor the generation 6-formylindolo[3,2-b] carbazole (FICZ) demonstrated activity at the aryl hydrocarbon receptor (AHR) as assessed by the intensity of cytochrome P450 1A2 staining. Ten participants applied a trial agent consisting of L- tryptophan, lecithin, polyvinyl alcohol and ethanol followed by 36 sessions of graduated sun exposure increasing to a total of 20 Joules per square centimetre. At the end of the trial, biopsies were taken from a treated site with photo-protected buttock skin used as a control. Assessment was via intensity of cytochrome P450 1A2 staining. An average baseline value of 53488923 units was obtained at the control site and 972214294 at the treatment site supporting the concept that this preparation displays activity at the AHR. As the components of this preparation are known to have UV absorbing properties and are potentially permissive of UVA-1 transmission, UV absorbance was assessed for natural sunlight and at 365 and 395nm confirming strong absorption in the UVB and UVA-2 spectra with partial transmission in the UVA-1 spectrum.

INTRODUCTION: This study focusses on diagnosis of stages of AD (Alzheimer’s disease) including MCI (Mild Cognitive Impairment) from two data modalities - gene expression and clinical data of ADNI (Alzheimer’s Disease Neuroimaging Initiative ) participants using multiclassification. The gene expression dataset is highly imbalanced and of HDLSS (high-dimensional and low-sample-size) characteristics. This is the only study where multiclassification based AD stage diagnosis is done to identify multiple stages of Alzheimer. We are able to achieve the best multiclassification result in both the modalities and identify new genetic biomarkers. METHODS: Combination of XGBoost and SFBS (“Sequential Floating Backward Selection”) methods is used to select features. We are able to select the most effective 95 gene probsets out of 49,386. For clinical study data, 8 most effective biomarkers could be selected using SFBS. For both genomic and clinical data, DL (‘Deep Learning’) classifier is used to identify stages - CN (Cognitive Normal), MCI (Mild Cognitive Impairment), AD (Alzheimer’s Disease / Dementia). Because of high data imbalance in genomic data, border line oversampling is used for model training and original data for validation. RESULT & DISCUSSION: With clinical data, we achieved ‘ROC AUC’ scores 0.97, 0.95, 0.94 for CN, MCI, Dementia stage respectively . We achieve ‘ROC AUC’ scores 0.75, 0.74, 0.70 for CN, MCI, Dementia stage respectively and 0.67 for both micro average F1 scores and micro weighted F1 score. This is the best result so far for AD stage diagnosis from gene expression profile data through multiclassification with ADNI data. Results reflect that our multiclassification model can efficiently handle the imbalanced data of HDLSS nature to identify samples of minority class. MAPK14, ZNF835, MID1, HLA-DQA1, TEP1 are some of the new genes found to be associated with AD risk. DRAXIN, HSPA12B, USP47 etc. are found to be AD preventive or suppressor.

Piezoelectric ultrasonic motors (USMs) are actuators that use ultrasonic frequency piezoelectric vibration generated waves to transform electrical energy into rotary or translating motion. USMs are paid more attention because they offer distinct qualities over traditional magnet-coil-based motors, such as miniaturization, great accuracy, speed, non-magnetic nature, silent operation, straightforward construction, broad temperature operations, and adaptability. This review study focuses on the principle of USMs and their classifications, characterization, fabrication methods, applications, and future challenges. Firstly, the classifications of USMs, especially, standing wave, traveling wave, hybrid mode, multi degree of freedom USMs are summarized, and their respective functioning principles are explained. Secondly, the finite element modelling analysis for design and performance predictions, the conventional and nano/micro fabrication methods, and, the various characterization methods are presented. Thirdly, their advantages, such as high accuracy, small size, and silent operation and their benefits over conventional motors for the different specific applications are examined. In addition, the substantial contributions to a variety of tech-nical fields like surgical robots, industrial, aerospace, and biomedical applications are introduced. Finally, their future prospects and challenges in USMs development, with an emphasis on downsizing, increasing efficiency, and new materials are outlined.

In the last few decades, we have witnessed an unprecedented transformation in the way information is created, disseminated, and consumed. The advent of the digital age has revolutionized not only our access to information but also the very fabric of our social, political, and cultural lives. Digital media, a term encompassing everything from online news portals and social media platforms to streaming services and mobile applications, now forms an integral part of the daily lives of billions of individuals worldwide.

In a multivariable system there are usually a number of relaxation times. When some of the relaxation times are shorter than others, the corresponding variables will decay to their equilibrium value faster than the others. After the fast variables have decayed, the system can be described with a smaller number of variables. From the theory of nonequilibrium thermodynamics, as formulated by Onsager, we know that the coefficients in the linear flux-force relations satisfy the so-called Onsager symmetry relations. The question we will address in this paper is how to eliminate the fast variables such that the coefficients in the reduced description for the slow variables still satisfy the Onsager relations. As the proof Onsager gave of the symmetry relations does not depend on the choice of the variables, it is equally valid for the subset of slow variables. Elimination procedures that lead to symmetry breaking are possible, but do not consider systems that satisfy the laws of nonequilibrium thermodynamics.

This study evaluates the preparedness of Siloam Hospital Bali for medical tourism using Actor-Network Theory (ANT) to analyze human and non-human interactions within social networks. The objective is to identify factors influencing the hospital's readiness. Data was collected through interviews with hospital administration, specialized physicians, local officials, and tourism organization members, supplemented by document and archival research. Network visualization with GraphCommons.com revealed interaction patterns, key actors, and potential impediments. The results show that effective hospital administration, professional care by doctors, and cooperation with tourism groups and local authorities significantly impact the hospital's readiness. However, dependencies on core nodes, lack of redundancy, and underutilization of peripheral nodes are vulnerabilities. Recommendations include enhancing integration with peripheral nodes, establishing network redundancy, involving educational institutions, and aligning with international insurance and immigration policies. This study contributes to a comprehensive understanding of medical tourism dynamics and offers strategies to enhance Siloam Hospital's readiness.

Many natural signals exhibit a quasi-periodic behavior and are conveniently modeled as a combination of several harmonic sinusoids whose relative frequencies, magnitudes and phases vary with time. The waveform shape of those signals reflects important physical phenomena underlying their generation, which requires that those parameters be accurately estimated and modeled. In the literature, accurate phase estimation and modeling has received much less research effort than frequency estimation, or magnitude estimation. First, this paper addresses accurate DFT-based phase estimation of individual sinusoids in six scenarios involving two DFT-based filter banks and three different windows. It is shown that bias in phase estimation is less than 1E-3 radians when the SNR is equal to or larger than 2.5 dB. Taking as a reference the Cramér-Rao Lower Bound, it is shown that one particular window offers a performance of practical interest by approximating better the CRLB when signal conditions are favorable, and by minimizing the performance deviation when signal conditions are adverse. Second, this paper explains how a shift-invariant phase-related feature can be devised that characterizes harmonic phase structure, which motivates a signal processing paradigm that greatly simplifies parametric modeling, transformation and synthesis of harmonics signals, in addition to facilitating the understanding and reverse engineering of the phasegram. Theory and results are discussed in a reproducible perspective using dedicated experiments that are supported with code allowing not only to replicate figures and results in this paper, but also to expand research.

Herbicides are widely used in agriculture to control weeds and enhance crop productivity. However, their application can have significant impacts on soil health and nutrient dynamics. This paper reviews the effects of herbicides on soil properties, microbial communities, and nutrient availability, with a particular focus on calcium deficiency. The review synthesizes findings from peer-reviewed journals and reports, including research conducted in Australia. The correlation between herbicide application and calcium deficiency is explored, highlighting both positive and negative impacts. The paper concludes with recommendations for sustainable herbicide use and alternative weed management strategies.

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to SPOT/VEGETATION (SPOT VGT). In past years, important changes have been implemented in the SYN VGT processing baseline. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle a continuous series of data products from the combined data archives of SPOT VGT (1998 – 2014), PROBA-V (2013-2020) and Sentinel-3 SYN VGT (from 2018 onwards) is available to users, consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. Spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and the intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for 3 distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have resulted in better quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive: a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.

Generalized Bayesian (GB) is a Bayesian approach based on the learning rate parameter (LRP) () as a fraction of the power of the likelihood function. In this paper, we consider the GB method to perform inference studies for a class of exponential distributions. Generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the considered distributions were obtained based on the censored type II samples. In addition, generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP) were considered using a one-sample prediction scheme. Monte Carlo simulations were performed to compare the performance of the GBE and GEBE estimation results and the GBP and GEBP prediction results for different values of the LRP.

Social Anxiety Disorder (SAD) is a common mental health condition characterized by an intense fear of social situations, leading to significant impairments in daily functioning. Traditional exposure therapies, including Cognitive-Behavioral Therapy (CBT), have shown efficacy in treating SAD but are often limited by logistical challenges and patient reluctance. Virtual Reality Exposure Therapy (VRET) has emerged as a promising alternative, providing a controlled and immersive environment for exposure to anxiety-provoking scenarios. This paper explores the integration of Generative Adversarial Networks (GANs) into VRET to enhance the personalization, realism, and adaptability of therapeutic scenarios. GANs, which consist of a generator and a discriminator network, allow for the creation of highly realistic virtual environments tailored to the specific fears and triggers of individual patients. By offering real-time adjustments and personalized exposure scenarios, GAN-enhanced VRET addresses key limitations of traditional therapies, increasing patient engagement and treatment adherence. This review synthesizes findings from key studies on GAN-enhanced VRET, evaluating its efficacy in reducing anxiety symptoms and discussing the potential for broader application across various anxiety disorders. The results indicate that GAN-enhanced VRET offers a significant advancement in the treatment of SAD, providing a more accessible, engaging, and effective approach to exposure therapy. Future directions include expanding accessibility through cost reduction, integrating additional AI techniques, and exploring applications beyond SAD to improve mental health outcomes across diverse patient populations.

Background: Understanding factors associated with protective immunity against emerging viral infections is crucial for global health. Pakistan reported its first COVID-19 case on February 26, 2020, but compared to other countries, it experienced relatively low COVID-19-related morbidity and mortality between 2020 and 2022. The underlying reasons remain unclear. Our research aims to shed light on this crucial issue. Methods: We conducted serial household surveys over 16 months (Rounds 1-4, July 2020 - November 2021) in urban (Karachi) and rural (Matiari) Sindh, sampling 1100 households and 3900 individuals. Our comprehensive approach included measuring antibodies in sera, testing a subset of respiratory samples for COVID-19 using RT-PCR and antigen tests, and measuring Hemoglobin, CRP, Vitamin D, and zinc in Round 1. Results Participants showed 23% (95% CI 21.9-24.5) antibody seroprevalence in round 1, increasing across rounds to 29% (95% CI 27.4-30.6), 49% (95% CI 47.2-50.9) and 79% (95% CI 77.4-80.8). Urban residents had 2.6 times (95% CI 1.9-3.6) higher odds of seropositivity than rural residents. Seropositivity did not differ between genders. Individuals aged 20-49 had 7.5 (95% CI 4.6-12.4) times higher odds of seropositivity compared to children aged 0-4. Most participants were asymptomatic, with no reported mortality. Vitamin D deficiency was linked to seroprevalence. COVID-19 was confirmed in 1.8% of individuals via RT-PCR and antigen tests. Conclusions: The data suggests a steady increase in humoral immunity in Pakistan, likely due to increased transmission and potential cross-protective immunity, which may be associated with asymptomatic disease, leading to the reduction of morbidity and mortality observed.

The comorbidity of osteoarthritis and type 2 diabetes mellitus represents a complex clinical challenge and complicates patient management due to the interplay of overlapping pathophysiological mechanisms. The research aimed to analyze the exacerbation of clinical symptoms and biochemical markers in patients with osteoarthritis and type 2 diabetes mellitus compared to those with osteoarthritis. We utilized various assessment methods to measure the patient cohorts' inflammation, oxidative stress, and glycemic control. The study also involved the administration of alpha-lipoic acid to patients with comorbid osteoarthritis and type 2 diabetes mellitus and monitored its effects on joint function, inflammatory markers, oxidative stress levels, and glycemic control. Our findings revealed that the presence of type 2 diabetes mellitus significantly intensified osteoarthritis symptoms and altered biochemical markers, including increased inflammation, elevated oxidative stress, and disrupted immunological balance. The treatment with alpha-lipoic acid resulted in improved joint function, reduced inflammation and oxidative stress levels, and better glycemic control. Additionally, it was identified several potential predictors for developing osteoarthritis and type 2 diabetes mellitus comorbidity. The results underscore the detrimental impact of type 2 diabetes mellitus on osteoarthritis and highlight the beneficial effects of alpha-lipoic acid in alleviating some of the adverse consequences associated with this comorbidity. Nonetheless, further research is necessary to comprehensively evaluate the therapeutic efficacy of alpha-lipoic acid and its long-term effects on managing comorbidity of osteoarthritis and type 2 diabetes mellitus.

Peptide-based inhibitors represent a promising approach for the treatment of HIV-1, offering a range of potential advantages including specificity, low toxicity, and the ability to target various stages of the viral lifecycle. This review outlines the current state of research on peptide-based anti-HIV therapies, highlighting key advancements and identifying future research directions. Over the past few years, there has been significant progress in developing synthetic peptide-based drugs that target various stages of the viral life cycle, including entry and replication. These approaches aim to create effective anti-HIV therapies. Additionally, peptides have proven valuable in the development of anti-HIV vaccines. In the quest for effective HIV vaccines, discovering potent antigens and designing suitable vaccine strategies are crucial for overcoming challenges such as low immunogenicity, safety concerns, and increased viral load. Innovative strategies for vaccine development through peptide research are, therefore, a key focus area for achieving effective HIV prevention. This review aims to explore the strategies for designing peptides with anti-HIV activity and to highlight their role in advancing both therapeutic and preventive measures against HIV.

The defining characteristic of Alzheimer's disease (AD) is the aberrant deposition of pathogenic proteins, particularly a massive build-up of amyloid-β peptides, specifically in the brain. Amy-loid-β aggregation is neurotoxic and ultimately results in dysfunction of the nervous system. The present investigation aims to predict potential amyloid-β fibrils disaggregating molecules from plant sources through molecular modeling techniques, this appears to be a very promising and appealing treatment philosophy. Here, 500 flavonoids from various plants were considered, ini-tially undergoing ADME studies to screen molecules that could cross the blood-brain barrier. Later, potential Amyloid-β-disaggregating molecules were identified by molecular docking and dynamics (MD) simulation studies. Five molecules, prenylmethoxy flavonol (-7.3 kcal×mol-1), isopentenyl flavonol (-7.3 kcal×mol-1), 7,3'-Dihydroxyflavone (-7.2 kcal×mol-1), 7-Hydroxy-5-methyl-4'-methoxyflavone (-7.2 kcal×mol-1), 8-hydroxy-7-methoxyflavone (-7 kcal×mol-1) showed higher binding affinity score against Alzheimer's Aβ (1-42) fibrils, these are very close to the standard drug (Donepezil) (-7.90 kcal×mol-1). Further, the MD simulation studies established the intermolecular interaction and stability of the five selected ligands-Amyloid-β oligomer protein complexes. These results suggest that the chosen five flavonoid molecules could be used as possible agents to disaggregate amyloid-β fibrils; however, more in vitro and in vivo investigations are required to verify the therapeutic effectiveness.

Public food trees are increasingly popular among researchers, urban planners and citizens, for their diversity of social, provisional and environmental benefits. However, the practice still faces several barriers, including the limited share of the population who uses them, and long-term management, studies to date suggesting municipality-citizen co-management partnerships as the most effective form, but still rare. Though participatory projects usually involve new plantations, several green spaces already harbor food trees which could be used to rapidly develop and test approaches for improving public usage and for co-management. Here we used a qualitative approach to explore usage and perception of public fruit trees and attitude towards public usage and participation, among green space visitors, staff and administrators of two urban green spaces harboring fruit trees in Birjand, Iran: one public park (Tohid Park) and one historic garden (Akbarieh Garden). Semi-structured interviews were conducted with twelve visitors of both spaces, four administrators and eight workers. We found that despite their great personal experience and interest in picking fruit, as well as a culture of strong relationships with fruit trees, visitors’ usage of the fruit was low, with a social norm against harvesting the fruit of others appearing as a main but easily remediable reason. Nevertheless, most visitors had a deep level of appreciation of these public fruit trees due in particular to the diverse pleasures that they provided (sensory, emotional, cognitive, experiential, social), and were greatly emotionally attached to them. They also had great interest in participating in their management and care, and more so in Tohid Park due to a greater potential social impact. According to some participants, education is needed to reduce damage that some citizens cause while picking fruit, or access should be restricted if maximizing production is the goal. These findings expand our understanding of the ways in which people can relate to existing urban fruit trees and suggest that they could be used to develop effective co-management schemes, thereby contributing to building smart and participatory edible cities.

(1) The objective of this study is to describe and compare the levels of physical activity, preferences for leisure-time physical activity and the frequency of non-sedentary behaviors of Portuguese, Italian and Spanish students attending, respectively, higher education in Portugal, in Italy and Spain; (2) Methods: 1354 Portuguese, Spanish and Italian students (21.2 ± 2.9 years) participated in the study. An online questionnaire was used to collect data and a priori power analysis statistical procedures were used using G∗Power (3.1.9.7) and one-way Anova; (3) Results: The highest levels of sedentary lifestyles are found among Spanish students, followed by the Portuguese and lastly the Italians. Regarding the levels of physical activity Spanish students perform more minutes of low and moderate activity compared to Italians and Portuguese. Italians students are the ones who perform more vigorous activity; (4) Conclusions: We can conclude that Spanish students have a higher level of sedentary lifestyle with the majority of physical activity being low and moderate, while Italians are those who have more time spent on vigorous physical activity and naturally a lower level of sedentary lifestyle. However, it is worth highlighting that all three countries reach the minimum levels of physical activity recommended by the WHO.

Approximately 10–20% of individuals suffering from COVID-19 develop prolonged symptoms known as long COVID or post-COVID condition (LC). This review aimed to assess healthcare resource use (HCRU) and healthcare costs associated with LC. Because LC is not clearly defined and often remains undiagnosed, studies reporting on long-term follow-up of individuals with a COVID-19 diagnosis were also included. Among the 41 publications included, 36 reported on HCRU and 16 on costs. Individuals with LC had significantly elevated HCRU and healthcare cost vs controls without a COVID-19 diagnosis over ≥15 months, with a 7.6%–13.1% increase in total healthcare costs per person per month as assessed by difference-in-difference analysis. Among studies that did not specifically refer to LC, having a COVID-19 diagnosis was associated with a significant 4–10% increase in long-term total HCRU over 6–8 months and a 1.3- to 2.9-fold relative increase in total healthcare costs over 6 months. Due to the heterogeneity of included studies, high-quality evidence is needed to better understand the economic burden of LC. In the absence of effective treatments, prioritizing prevention of acute COVID-19, e.g., through vaccination, may be crucial for preventing LC and the associated long-term HCRU and medical spending.

The architecture, Engineering, and Construction (AEC) industry is dealing with serious challenges on a global scale, i.e., project delays, budget overruns, and insufficient stakeholder collaboration. In this context, Value Engineering (VE) and Building Information Modeling (BIM) have been used recently to address these challenges and cope with large-scale and complex construction projects. An extensive body of literature highlights significant advantages of integrated VE within BIM technology. Despite this, it is full practical potential has not yet been realized. For this reason, this paper aims to investigate the integration of Value Engineering (VE) and Building Information Modeling (BIM) within a Common Data Environment (CDE) for the Architecture, Engineering, and Construction (AEC) industry. In doing this research, a comprehensive framework was developed and systematically implemented into practice through the use of four interconnected modules: (M. 1); Creating CDE, (M. 2); Design BIM Model Development, (M. 3); Value Analysis / Engineering Model, and (M. 4); Value Engineering Study. Introducing a new term the ""VEIDEA"" databank based on the OmniClass classification system provides a structured database utilized to store, manage, and accumulate VE ideas. Moreover, the study incorporates the Analytical Hierarchy Process (AHP) to automate the evaluation phase. Such a framework seeks to assist designers and VE team members make informed decisions when choosing appropriate design alternatives. Lastly, a case study method was adopted to examine the proposed framework for an office building. Quantitative data showed a significant link between cost savings and VR integration with BIM technology. For instance, cost of the RC slabs dropped by 20%, while flooring materials cost dropped by a 39%. Empirically, evidence from the case study supports literature regarding t the potential for significant enhancements in both cost-effectiveness and overall project performance through this innovative integration. In the context of knowledge contribution, this study provides an innovative way to assist designers and VE team members in making well-informed decisions when choosing appropriate design alternatives.

This study addresses the gap in methodological guidelines for Neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices including the Engagement Index (EI) and Beta/Alpha ratio alongside subjective ratings. A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, the study successfully demonstrated the effectiveness of the paradigm. Results indicate that both Beta/Alpha ratio and EI are sensitive to changes in attentional demands, with Beta/Alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to accurately monitor attention fluctuations and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining high attention level is crucial.

This study investigates the use of flours derived from the leaves of Pereskia aculeata and Pereskia grandifolia, commonly known as ora-pro-nóbis, in pasta products. The motivation stems from the search for nutritious and affordable dietary alternatives that promote food security. The objective is to produce flours from the leaves of these species through convection drying and to assess their physicochemical quality and potential application in spaghetti-type pasta. The leaves were dehydrated and processed into flour, which was then incorporated into pasta formulations in varying concentrations from 1% to 7%. The resulting samples underwent physicochemical, antioxidant, toxicity, and mineral composition analyses. Significant differences were observed between the flours of P. aculeata and P. grandifolia in terms of protein, lipid, carbohydrate, and bioactive compound content. P. grandifolia was noted for its higher protein and mineral content, while P. aculeata exhibited superior antioxidant activity. The pasta enriched with these flours demonstrated improvements in nutrient content and changes in colorimetric properties. The study concludes that ora-pro-nóbis flours possess high nutritional value and are viable for application in pasta products, contributing to a healthier and more diversified diet without compromising the sensory characteristics of the products.

The combination of nanoemulgel and phytochemistry has resulted in several recent discoveries in the field of the topical delivery systems. The present study aimed to prepare turmeric (Curcuma longa) and neem (Azadirachta indica) based nanoemulgel against microbial infection as topical drug delivery. Olive oil (oil phase), Tween 80 (surfactant), and PEG600 (co-surfactant) were used for the preparation of nanoemulsion. Carbopol 934 was used as gelling agent to convert the nanoemulsion to nanoemulgel and promote to control the release of biological properties of turmeric and neem. The nanoemulsion were characterized based on particle size distribution, PDI values and compatibility using FTIR analysis. In contrast, the nanoemulgel was evaluated based on pH, viscosity, spreadability, plant extract and excipients compatibility or physical state, in-vitro study, ex-vivo mucoadhesive study, antimicrobial properties and stability. The resulting nanoemulsion was homogeneous, stable during the centrifugation process, with the smallest droplets and low PDI values. FTIR analysis also confirmed good compatibility and absence of phase separation between the oil substance, surfactant, co-surfactant with both plant extracts. The improved nanoemulgel also demonstrated a smooth texture, good consistency, pH, desired viscosity, ex-vivo mucoadhesive strength with highest spreadability, and an 18-hour in-vitro drug release study. Additionally, it exhibited better antimicrobial properties against different microbial strain. Stability studies also revealed that the product had good rheological properties and physicochemical state for a period of over 3 months. The present study has affirmed that turmeric and neem based nanoemulgel as a promising alternative for microbial infection particularly associated by microorganisms via topical application.

The shaft hydropower plant (SHPP) is a novel hydraulic concept for low-head hydropower sites with several environmental and operational advantages over conventional layouts. However, the first two projects implementing this concept have shown comparatively high construction costs and project risks. Therefore, further optimization is required to the economic attractiveness and enabling broader market adoption. Initial model tests recommend a square-shaped shaft inlet with a three-sided approach flow for low-loss and fish-friendly inflow conditions. Yet, this design requires significant space for structural implementation and may be unsuitable for use with multiple shafts or as an extension of non-powered dams and weirs. This research paper presents the application of a computational fluid dynamics simulation setup to evaluate the hydraulic per-formance of various design configurations, especially, alternative design layouts with one-sided approach flow without further physical model tests,. The simulation setup is calibrated against observations including head loss and velocity measurements from the physical model tests, and its satisfactory performance enables the analysis of alternative design layouts. This aims to derive the most significant design parameters for achieving the desired hydraulic conditions at the intake. Increasing the flow depth before the intake and enlarging the inlet area have the most significant impact, while increasing the overflow of the front gate has the least significant effect.. The chosen CFD application is deemed suitable for hydraulic design optimization and provides guidance on the key parameters to focus on for tailored site-specific design development.

This paper offers an integrated approach for correlated storage assignment strategy (CSAS) to optimize travel distance by considering item correlation, picking frequency, zoning, and slot constraints to improve operational efficiency in distribution centers. We simplify the mixed integer non-linear programming (MINLP) model and incorporate integrated heuristic procedures for faster convergence. We introduce positive and negative centroid deviations as techniques to guide the model convergence and explore different scenarios. In the second stage of item assignment, we prioritize items within zones using a ranking formula that is optimal for both single-item and multiple-item orders. By analyzing the distribution of correlated item across zones and the impact of their picking frequency on optimizing travel distance, we propose an improved CSAS that minimize travel distance by strategically placing items based on their proximity to the depot. Our model reduces trips in distant zones by prioritizing larger average order sizes, while maximizing trips for frequently ordered smaller lists in closer zones. This method significantly reduces travel distance by minimizing the need for pickers in distant zones to frequently travel to the depot. Simulation results show reductions of up to 36.75% for fitted data and 33.59% for recent data.

A theoretical framework for agricultural landscape pattern transitions in rural areas is proposed to fill the gap of current research with an empirical study in the Alabata farm settlement in Ogun State, Southwest Nigeria. Adopting the use of ArcGIS 10.8 and Surfer 27.3 to calculate the surface area and volume of the ground surface degraded and exposed to harsh weather conditions due to both natural and man-made anthropogenic activities causing runoffs and continuous soil erosion, which has affected agricultural production and sent some of the farmers packing and abandoning their farmlands. The farm settlement within the total sampled frame area of 1.50 km² covers a total distance of 5.11 km. The sample frame was divided into 36 frames with similar ecological terrain and soil types, which fell into either identified soil types 1 and 2. The study completed a questionnaire onsite using three major criteria. Only 23 farmers responded positively to the few questions with farmers either living on the farm or working as hired laborers on-site at the visit. The transition characteristics of agricultural landscape patterns as represented in abandoned land, and orchards are analyzed from a local perspective.

In Africa, the urban population and informal settlements are increasing, resulting in additional public health and environmental risks and challenges related to the need of basic and sustainable infrastructures and services. In Maputo, the capital of Mozambique, at least 80% of the city is composed of informal settlements, and although some areas are regularized and have approved urbanization plans, the municipality has major deficiencies in meeting current demands in terms of basic infrastructures, such as water supply, sewerage, solid wastes, energy, roads and communications. Most of the peri-urban areas is occupied with small but overcrowding houses, mostly self-built, served by unpaved roads, and with access limitations. The water network is almost non-existent, and the same occurs for stormwater drainage, sanitation, waste collection and public illumination. Despite the improvements made in the last few years, some households still do not have safe sanitation, and in some neighborhoods, open defecation still prevails. In this study, the authors try to understand and explore the interdependences among the infrastructures of the different sectors and how they may jointly contribute for adequate services and a better life of the urban poor, and how to identify the combination of infrastructures to be implemented at short term in face of the limited budgets for investments.

This study examines change detection techniques in dense point clouds for the purpose of Cultural Heritage preservation, with a particular focus on the San Pietro Barisano Bell Tower in Matera, Italy. Dense point clouds, obtained via advanced laser scanning, offer detailed 3D representations of heritage structures, facilitating the precise monitoring changes over time. The investigation uses a variety of change detection algorithms, including the Iterative Closest Point (ICP) algorithm, which is renowned for its robust registration capabilities in aligning point clouds with high accuracy. The combination of ICP with deviation analysis and feature-based methods allows for the effective identification of alterations, including deformations, material loss, and surface degradation. This methodology establishes a comprehensive framework for the monitoring of Cultural Heritage, thereby enabling timely and targeted preservation efforts. The results emphasise the substantial contribution of dense point cloud analysis to the enhancement of heritage management and the safeguarding of vulnerable architectural sites.

(1) Background: The increasing demand for sustainable practices in the textile industry has led to the exploration of natural dyes and eco-friendly dyeing processes. This study focuses on the potential of used coffee grounds (UCG) as an eco-friendly natural dye for Nylon 6.6 woven fabrics. (2) Methods: Five dyeing processes were evaluated, varying in the use of mordants and acids, to assess their impact on color saturation, colorfastness to laundering, and crocking resistance of Nylon 6.6. fabric. The processes included a control with no mordant or acid and others that incorporated tannic acid, acetic acid, and ferrous sulfate heptahydrate. (3) Results: The results demonstrated that Process 4, which combined tannic acid pre-mordanting with acetic acid in the dye bath, provided the best balance between color saturation and colorfastness. Process 2, utilizing only tannic acid, offered some durability in laundering and crocking tests. Process 5, being the least eco-friendly process demonstrated high color saturation, but it performed poorly in colorfastness to crocking, which means that it releases the UCG-based dye after rubbing of the dyed Nylon 6.6. fabric. (4) Conclusions: The findings confirm that UCG can be an effective and sustainable natural dye for Nylon 6.6, with pre-mordanting and acid treatment significantly enhancing dye uptake and retention. However, further research is needed to optimize color intensity and expand the application of UCG in textile dyeing.

Children on farms face high risks of work and non-work-related fatalities, with tractors being a significant contributor. This study examines children's involvement in fatal tractor-related accidents within agriculture in Iceland in 1950-2024, explores adult reflections on childhood tractor-driving experiences, and analyses Members of Parliament´s arguments against setting a minimum age for off-road tractor driving. Data rests on fatal tractor-related accidents using newspaper archives and supplementary sources, narrative interviews with former summer children who stayed at farms in childhood, and parliamentary debates on tractor-related legislation. Over half of the 81 registered accidents involved children, primarily boys, with 75% occurring between 1958 and 1988 when no minimum age for off-road tractor driving existed. Children's fatality incidence rate was more than four times higher than adults. Arguments against minimum age requirements for off-road driving included the need for child labour, children's superior driving skills, and a denial that children were more often victims than adults. Since 1988, no child has died when driving a tractor. A human-centred approach focusing on working conditions, driver capacity, and adherence to safety procedures and legal frameworks is needed to prevent future accidents.

Despite the technological advancements of 3-D city models, the 3-D shape abstraction methods for urban vegetation are recent and still limited to support spatial analysis of individual or groups of trees and forest patches. In this paper, a scheme for individual urban tree abstraction, which retains the semantic complexity of the object, is proposed. Our contribution is three-fold. First, an initial tree structure based on a new 3-D aggregation operator is proposed. Second, we developed Gestalt rules for 3-D urban vegetation mapping. Third, current state-of-the-art deep learning is adapted for the abstraction of individual urban trees. Quantitative and qualitative results show the effectiveness of our proposed approach in accurately reducing the spatial density of trees and the degree of fragmentation of the total green area, which can access information about the location, size, and spatial distribution of mapped urban trees.

Hydroperoxides (ROOH) are known as damaging agents capable of mediating mutation, while a role as signaling agents through oxidation of protein sulfhydryls that can alter cancer-related pathways has gained traction. Glutathione peroxidase 2 (GPX2) is an antioxidant enzyme that reduces ROOH at the expense of glutathione (GSH). GPX2 is noted for a tendency of large in-creases or decreases in expression levels during tumorigenesis that leads to investigators focusing on its role in cancer. However, GPX2 is only one component of multiple enzyme families that metabolize ROOH and GPX2 levels are often very low in the context of these other ROOH reducing activities. Colorectal cancer (CRC) was selected as a case study for examining GPX2 function as colorectal tissues and cancers are sites where GPX2 is highly expressed. A case can be made for a significant impact of changes in expression levels. There is also a link between GPX2 and NADPH oxidase 1 (NOX1) from earlier studies that is seldom addressed and is discussed, presenting data on a unique association in colon and CRC. Tumor-derived cell lines are quite commonly used for pre-clinical studies involving the role of GPX2 in CRC. Generally, selection for this type of work is limited to identifying cell lines based on high and low GPX2 expression with the standard re-search scheme of overexpression in low expressing lines and suppression in high expressing lines to identify impacted pathways. This overlooks CRC subtypes among cell lines involving a wide range of gene expression profiles and a variety of driver mutation differences along with a large difference in GPX2 expression levels. A trend for low and high GPX2 expressing cell lines to segregate into different CRC subclasses, indicated in this report, suggests that choices based solely on GPX2 levels may provide misleading and conflicting results by disregarding other properties of cell lines and failing to factor in differences in potential protein targets of ROOH. CRC and cell line classification schemes are presented here that were intended to assist workers in performing pre-clinical studies but are largely unnoted in work on GPX2 and CRC. Studies are often initiated on the premise that the transition from normal to CRC is associated with up-regulation of GPX2. This is probably correct. However, the source normal cells for CRC could be almost any colon cell type, some with very high GPX2 levels. These factors are addressed in this study.

Sect. Tuberculata in the genus Camellia, is named after the "tubercle-like projections on the surface of the capsule and ovary". Due to complex morphological variations in these taxon and insufficient field investigations, the interspecific relationships are unclear, the species definitions are vague, and the names are confusing. This is not conducive to the conservation and study of these species. Therefore, herein, we systematically explored the taxonomic status of five sect. Tuberculata species using morphological, pollen morphology, and molecular phylogenetic methods. The result showed that (1) morphological characteristics for the flower, fruit, and leaves of C. anlungensis and C. leyeensis were similar. Furthermore, the pollen characteristics and pollen wall ornamentation showed that it was no significant difference between the two species. (2) there were significant differences between C. acutiperulata and C. anlungensis in terms of leaf shape (elliptic vs. obovate), calyx characteristics (sepal apex pointed vs. sepal oblong), and fruit shape (subglobose folds with shallow verruculose vs. flat folds and verruculose protuberances with pronounced internal cleavage). (3) C. pyxidiacea and C. rubituberculata differed in flower color (white or few light vs. red), fruit verrucae (obviously deeply cleft vs. shallowly uncracked). (4) a phylogenetic tree based on the chloroplast genome showed that C. anlungensis and C. leyeensis form a single clade (BS = 100%, PP = 1.0) and were on a different branch with C. acutiperulata in clade II, (BS = 100%, PP = 1.0); C. pyxidiacea and C. rubituberculata were clustered on different branches of clade I (BS = 99%, PP = 1.00). Integrated above results, we consider that C. leyeensis should be treated as a homonym of C. anlungensis; C. acutiperulata, C. pyxidiacea, C. rubituberculata respectively as a separate species. Clarifying the taxonomic status of these five species not only advances our understanding of the significance and complexity of the systematic classification of the genus Camellia but also has important implications for diversity conservation and population genetics.

This research focuses on optimization of coupled solar production and battery storage system to achieve Nearly Zero-Energy Building (nZEBs) based on building load behavior patterns. Moni-toring data from an office building with 120kW peak and 40kW average load consumption equipped with a 160kW solar photovoltaic (PV) system and a 50kW-45kWh energy storage system (ESS). The primary objective is to the primary objective is to analyze collected data from existing system and setup guideline to optimize ESS capacity to maximize clean energy consumption and thus minimize excess production losses, progressing the building toward Nearly ZEB Level 1 status (87.5%-100% energy usage). The study outcome has identified parameter impacts and demonstrates additional optimized cases with various PV and ESS installed capacity. Further analysis is performed based on working day load profile and appliance patterns. This ap-proach aims to learn the usage HVAC, lighting, and electronics, enabling the optimization scheme to be applied to buildings with different load patterns. Additionally, the analysis predicts other building behavior patterns and optimized clean energy system. The findings are then validated against actual data and the results demonstrate good model accuracy, especially daytime predic-tion. These insights also provide guidelines and strategies for further interventions on HVAC operations and adjusting concerned schedules to achieve desired ZEB level.

Megaselia scalaris and Senotainia tricuspis are two parasitoid flies of the honeybee Apis mellifera and are known to infest apiaries of different European and Mediterranean countries. However, their prevalence is still little known and no systematic control of their presence and countermeasures are currently adopted. To achieve a better knowledge about their distribution in apiaries, quantitative PCR (qPCR) based methods for their rapid detection directly in hive matrices were developed in this study. These were applied to samples of adult honeybees and hive debris collected in the summer season in Central Italy and Kosovo. In total 16 samples per type per region were analyzed. Both flies were detected in the two geographical contexts but more frequently in Central Italy. The positive apiaries in Central Italy were re-sampled one year later by capturing viable honeybees and isolating emerging flies after differentiation into adults. These were genotyped by sequencing regions of the mitochondrial cytochrome oxidase subunit I gene COI for M. scalaris and cytochrome B gene cytB for S. tricuspis amplified with specific primers designed in this study. Different genotypes of M. scalaris were detected and these were related to biotypes from distant nations. Some fly isolates were shown to harbor DNA or RNA of honeybee microbial pathogens. Results suggested to implement efficient control against these parasites in apiaries to limit their detrimental effects. The qPCR tests that have been developed can facilitate larger screening studies and early detection.

Climate change poses a significant threat to European security, implicitly to Romania’s naval forces in the Black Sea. Rising sea levels, extreme weather events, and ocean acidification impact maritime operations, infrastructure, and personnel, with potential ramifications for regional stability. This paper explores the implications of climate change for the Romanian Navy and proposes "Green Defence" strategies to enhance resilience and sustainability. These strategies encompass energy efficiency, renewable energy integration, sustainable procurement, and the application of advanced functional materials in naval operations and infrastructure. By adopting a proactive and multifaceted approach, the Romanian Navy can adapt to the changing climate, ensuring operational effectiveness while minimizing environmental impact. This approach aligns with broader European security concerns and contributes to a more sustainable and resilient defence posture in the face of climate-related challenges.

Humulus lupulus, or hops, is a vital ingredient in brewing, contributing bitterness, flavor, and aroma. The female plants produce strobiles rich in essential oils and acids, along with bioactive compounds like polyphenols, humulene, and myrcene, which offer health benefits. This study examined the aromatic profiles of five hop varieties grown in Brazil versus their countries of origin. Fifty grams of pelletized hops from each strain were collected and analyzed using HS-SPME/GC-MS to identify volatile compounds, followed by statistical analysis with PLS-DA and ANOVA. The study identified 330 volatile compounds and found significant aromatic differences among hops from different regions. For instance, H. Mittelfrüher grown in Brazil has a fruity and herbaceous profile, while the German-grown variety is more herbal and spicy. Similar variations were noted in Magnum, Nugget, Saaz, and Sorachi Ace varieties. The findings underscore the impact of terroir on hop aromatic profiles, with Brazilian-grown hops displaying distinct profiles compared to their counterparts from their countries of origin, including variations in aromatic notes and α-acid content.

The emergence of "green" materials has been attracting significant attention along the last few years for various applications. Here, we report a rapid, efficient and reproducible microwave-assisted synthesis of graphene quantum dots GQDs of identical features whatever the nature of biomass wastes investigated. The synthesized GQDs were fully characterized by X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, transmission electron microscopy and dynamic light scattering. The nanoparticles display narrow size of some nanometers with crystal lattices. Furthermore, the water-soluble particles show excitation-dependent photoluminescence ranging from blue to orange emission wavelength in solution. Interestingly, thin films display white light emission under 325 nm UV-light excitation, while aggregation-induced quenching is usually observed.

Flood protection infrastructures are crucial for enhancing the resilience of societies exposed to natural hazards. Newly designed infrastructures are evaluated for sustainability using a coherent and internationally recognized method defined by the International Hydropower Association (IHA). However, older structures require a different assessment approach. Popescu et al. (2024) proposed a modified IHA protocol, mHSAP, which identifies opportunities for improvement and develops a sustainability evaluation framework for existing infrastructures. This paper applies the modified protocol to evaluate the sustainability of two types of flood protection structures: a unique canal system for flood-drought protection of an urban area and a flood protection dyke. The time of operation of over 250 years, and respectively over 50 years. The application of the modified framework demonstrates its advantages in identifying areas for improvement in operating the flood protection structure, such that it is maintained sustainable. It also illustrates how Romanian water boards can use such tools to facilitate collaboration between structure owners and stakeholders, allowing them to assess the risks and effects of flooding on society. Potentially water boards, could use the results of the method to account for the effects of climate change and addressing the issue in a coordinated and efficient manner. Through these two examples from Romania, we also show that the mHSAP framework has the potential to actively support the fulfillment of the United Nations Agenda 2030 Sustainable Development Goals (SDGs).

Public-Private Partnerships (P3 or PPPs) have been utilized and studied more frequently over the past couple of decades for both infrastructure projects and building development projects. Based upon the records of the outcomes of these projects there is a plethora of data that demonstrates the benefits and challenges that this delivery method poses. As the name suggests, these partnerships allow for public goals to become realized while also providing opportunities for the private sector to gain business success. These two often divergent interests are finding mutual benefit but the process of compromises also comes with some disadvantages that should be understood in order to make decision of whether the risk/reward equation works for all stakeholders. In particular, this paper will focus on how PPPs affect the construction aspects of projects and will assert how to understand the risks and evaluate the benefits from a construction perspective.

Background: The development of new technologies, interpretation of amputations as therapeutic failures by society, and high morbidity and mortality associated with external hemipelvectomies make these mutilating surgical procedures appear obsolete. Herein, we review the scientific literature on the topic and present two cases of high-grade ulcerated soft tissue sarcomas in the gluteal region showing exceptional behavior and different outcomes. Methods: We performed a literature review of the PubMed databases from 2022 to April 2024. Additionally, we present two cases of soft tissue sarcomas in 18- and 71-years old female patients, which were treated with extended external hemipelvectomies with anterior flap and in combination with abdomino-perineal amputation and colostomy in one case. Results: At 4 years of follow-up, case one is living a relatively normal life. She had an uncomplicated pregnancy and a cesarean section delivery. Case two underwent emergency surgery for intestinal perforation and sepsis. She died 2.5 months following the surgery. Conclusion: External hemipelvectomy for soft tissue sarcoma treatment is a demanding surgical procedure with purpose in selected cases after review by multidisciplinary committees and with informed patient consent. This should be similarly individualized and extended to other pathologies when possible

Background: Delis-Kaplan Executive Function System (D-KEFS) is a neuropsychological tool used in clinical practice to measure executive function in clinical and non-clinical populations, in individuals aged 8-89 years, and consists of nine subtests that can be administered either as a single system or separately. Aims: In the present study, were established norms for the Verbal Fluency Test as well as the Design Fluency Test of the D-KEFS in a Greek population aged 20 to 49 years, since there is no previous study conducted in Greece for these age clusters. Methods: The study sample consisted of 100 healthy adults (44.4% male and 55.6% female), aged 20 to 49 years (M=32.81 SD=11.04) with education from 12 to 18 years (M=14.73, SD= .91). First, Spearman and Pearson tests explored the associations of gender, age and education with D-KEFS subtests’ performance. Afterwards, we calculated normative data using raw scores and transformed to percentile scores. Results: The present study is the first attempt to calculate norms for the two fluency subtests of the D-KEFS in Greek adult population stratified by age and educational level. Conclusion: Future research will promote the use of D-KEFS, in order to introduce a widely used tool which measures all basic executive functions subtypes for Greek adult population. Given that the majority of neuropsychological tools are adapted for older adult population, the current study aims at filling the gap in neuropsychological assessment of young adults as well as those who have co existing neurological or psychiatric disorders.

Abstract Background The advent of monoclonal antibodies (mAbs) targeting calcitonin gene-related peptide (CGRP) pathway has transformed the management of migraine, offering newfound optimism for clinicians and individuals with episodic migraine (EM) and chronic migraine (CM). While randomized controlled trials (RCTs) have provided crucial insights into the effectiveness and safety profiles of these treatments, their translation into real-world clinical practice remains a e challenge. Objective This review aims to conduct a comprehensive assessment of real-world studies, offering valuable insights tailored for practical application in clinical settings. Methods We conducted a comprehensive literature search in PubMed, SCOPUS, and MEDLINE for real-life studies on erenumab, fremanezumab, and galcanezumab. Abstracts underwent rigorous screening by two reviewers for relevance. Data extraction from selected articles was performed using a standardized form, with verification by a second reviewer. Data synthesis was narrative, following PRISMA guidelines. Results Our search included 55 pertinent studies conducted between 2019 and 2023. Real-world study designs demonstrated notable variability in the selection and inclusion of migraine patients, influenced by factors such as attack frequency, data collection criteria, and primary/secondary objectives. Key findings commonly reported considerable improvements in efficacy outcomes (migraine frequency, analgesic use, pain severity, disability), high responder rates, and optimal safety and tolerability profiles. Conclusion Real-world evidence underscores the role of anti-CGRP mAbs as targeted therapies for both CM and EM patients. The overall results indicate that the effectiveness and tolerability of anti-CGRP mAbs in real-world applications may exceed those observed in RCTs, an unprecedented finding in clinical neurology.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in some sections we present concerning numerical examples and the respective softwares.

Porcine metabolomics research has been extensively conducted, primarily focusing on conventional cohorts or animal models. However, the composition and functions of fecal metabolites in pigs at various ages, particularly in the elderly, remain poorly understood. In the present study, an untargeted metabolomics approach was employed to analyze the fecal metabolomes of pigs at three distinct age stages, with the aim of elucidating age-associated changes in metabolite composition and functionality under standardized rearing conditions. Through the untargeted metabolomic analysis of fecal samples from young (one year), middle-aged (four years), and elderly (eight years) pig cohorts, a variety of age-related metabolites were identified. For example, L-methionine sulfoxide was observed to increase with age, while cytidine-5-monophosphate exhibited a concomitant gradual decrease during the aging process. These metabolites were found to undergo alterations across multiple biological pathways, including energy metabolism, pyrimidine metabolism, lipid metabolism, and amino acid metabolism. Furthermore, the age-dependent metabolic changes were associated with known senescence biomarkers, offering new insights into the mechanistic understanding of aging in pigs.

Popular, industry, and academic attention is increasingly being paid to Electrically-Assisted Pedal Cycles (EAPCs), also known as electric bikes, or e-bikes. Attention has also begun to be given to ideas of the electric bicycle as a component of “electric velomobility” or “e-velomobility” and to the particular benefits of electric bicycles over conventional bicycles. We present here the effects of EAPCs on the tangible quantitative levels of greenhouse gas emission reductions, increased mobility performance, and greater accessibility of cycling; and the intangible qualitative level of perceptions of electric cycling and those who engage in it, harmonized with transhumanist lines of thought in order to articulate a sociotechnical imaginary of the electric bicycle as an augmentation to a cyclist’s physical body. Through a combination of original research and drawing on prior literature, we articulate the idea of a “cyborg cyclist” as a vision for present and future e-velomobilities, and emphasize the ability of appropriate technology to augment the capabilities of cyclists, viewing rider and machine as a single unit, while also warning of the possible risks of inappropriate types of electric bicycle. Such a view, we believe, adds a much-needed technology-positive view to present debates, and highlights especially the ability of the EAPC as a rehabilitative and augmentative mobility tool, and as a means of displacing more carbon-intensive means of transportation, particularly the private car.

Diabetic wound (DW) is the most devastating complication resulting in significant mortality and morbidity in diabetic patients. Although the pathophysiology of DWs is multifaceted, evidence revealed that prolonged inflammation with infections, extracellular matrix (ECM) degradation and unnecessary NETosis impair DW healing. This theoretical problem commands the necessity of developing a novel strategy focused on targeting the 'specific' molecular modalities of DW. The primary culprits, matrix metalloproteinase (MMP)-9 and protein kinase C (PKC)-βII, are responsible for impaired angiogenesis, NETosis, and ECM degradation. Thus, there is reinvigorated interest in identifying selective inhibitors for the effective management of DW. The current study exemplified human β-defensin-2 (HBD-2), a biological macromolecule as a dual-inhibitor of MMP-9 and PKC-βII using protein-protein docking and molecular dynamics simulation studies. Residue-residue interactions and binding affinity were investigated using Z-dock. Chemical bonding behavioral changes, structural dynamics, and functional changes were determined using GROMACS by analyzing RMSD, ROG, RMSF, SASA, FEL, and PCA. Overall, the data analysis revealed that HBD-2 possesses strong binding affinity and stability against MMP-9 and PKC-βII, which can be an ideal therapeutic for the accelerated healing of DW. The study provides valuable insights into the molecular mechanisms underlying hBD-2's potential therapeutic effects, offering new avenues for the development of innovative treatment strategies. The clinical relevance of our findings lies in their potential to inform the development of new approaches for diabetic wound management, addressing a significant healthcare concern.

Class attendance in institutions of higher learning plays a major role in the success of students and as such putting in measures to curb absenteeism of students would go a long way to help improve their grades. This research sought to design a ‘Bring Your Own Device’ System which will be very suitable for the post-COVID era as well as the fast-growing university community. The research results in a mobile-based attendance infrastructure used by students to prove their presence for class sessions. Flutter, a Software Development Kit (SDK) was used to program the mobile platform and Firebase was used for both the authentication and database. This system also comes in as an improvement on most existing systems as it functions on both Android and iOS. After the testing phase, the models achieved high accuracy and the proposed design has proven to have great potential. This solution stands beyond a research model as a ready-to-use platform.

This report provides a comprehensive analysis of portfolio risk management using Value at Risk (VaR) for a banking sector investment portfolio. We evaluate a portfolio composed of SBI, HDFC, Axis Bank, and ICICI Bank, using historical data from July 1, 2023, to June 30, 2024. The report covers the calculation of VaR, portfolio variance, and standard deviation, highlighting the impact of different weightage adjustments on the portfolio’s risk profile. Two scenarios—one with increased weightage in SBI and another with increased weightage in HDFC—demonstrate how changes in asset allocation affect potential losses. The findings emphasize the importance of strategic asset allocation for both speculators and hedgers. Speculators are advised to focus on high volatility and market trends, while hedgers should prioritize stability and risk management. This analysis aids investors in making informed decisions to balance risk and return in their banking sector investments.

Excessive use of plastic mulches, has triggered a series of environmental problems, this is due to the large volumes generated and their low or non-existent degradability. For this reason, materials with similar characteristics to synthetic mulches are sought, but with a biodegradable character. In this work, mulches films were produced from gelatin (GC) and chitosan (ChC), reinforced with mango cellulose. Its biodegradation time in soil and a photographic analysis with scanning electron microscopy (SEM) were determined. GC sample presented a biodegradability of 97% at 30 days, and ChC lost 95% of its weight after 70 days exposed to soil. The SEM results for both mulch films presented some color changes after 30 days, completely fractured, the growth of mycelium on the surface and the presence of pores are observed. Obtaining polymers from waste materials, such as mango, represents an important task to obtain cellulose that can be used both to reinforce and to provide biodegradable character to biobased materials which can be degraded by microorganisms present in the soil. FTIR spectra revealed a decrease in hydroxyl groups, amides and carbonyls bands as the days of degradation increased.

Software is increasingly vital, with automated systems regulating critical functions. As development demands grow, manual code review becomes more challenging, often making testing more time-consuming than development. A promising approach to improving defect detection at the source code level is the use of artificial intelligence combined with natural language processing (NLP). Source code analysis, leveraging machine-readable instructions, is an effective method for enhancing defect detection and error prevention. This work explores source code analysis through NLP and machine learning, comparing classical and emerging error detection methods. To optimize classifier performance, metaheuristic optimizers are used, and a modified algorithm is introduced to meet the study’s specific needs. The proposed two-tier framework uses a convolutional neural network (CNN) in the first layer to handle large feature spaces, with AdaBoost and XGBoost classifiers in the second layer to improve error identification. Additional experiments using Term Frequency-Inverse Document Frequency (TF-IDF) encoding in the second layer demonstrate the framework’s versatility. Across five experiments with public datasets, the CNN achieved an accuracy of 0.768799. The second layer, using AdaBoost and XGBoost, further improved these results to 0.772166 and 0.771044, respectively. Applying NLP techniques yielded exceptional accuracies of 0.979781 and 0.983893 from the AdaBoost and XGBoost optimizers.

Acid-producing bacteria in pit mud play a crucial role in the fermentation of Chinese liquor, significantly influencing its flavor, taste, and overall quality. This study investigates three strains of anaerobic acid-producing bacteria from pit mud to optimize both single and combined bacterial fermentations for enhanced acid production. Acid production levels were quantitatively measured using gas chromatography-mass spectrometry. The Design Expert software was employed to identify significant nutritional factors affecting the production of hexanoic and butyric acids. These factors were optimized through climbing tests and response surface analysis. The Box-Behnken Design (BBD) experimental design and variance analysis were used to determine the optimal levels of each significant factor. The SJ-1/SJ-3/SJ-8/enriched solution exhibited the highest acid production, with a total acid production of 2216.07±0.10 mg/100 ml. The yields of caproic acid and butyric acid were 801.94±0.11 mg/100 ml and 1167.33±0.14 mg/100 ml, respectively. The optimized fermentation medium for caproic and butyric acid production consisted of 5.1 g/L peptone, 5.04 g/L sodium acetate, and 4.45 g/L sodium butyrate, with a pH of 6.4. Under these conditions, the production of caproic acid and butyric acid reached 1172.73 mg/100 ml and 2382.45 mg/100 ml, respectively. These values represent significant increases (P<0.05) of approximately 46.24% for caproic acid and 104.09% for butyric acid compared to initial levels. The model predicts caproic and butyric acid production to be 1184.21 mg/100 ml and 2387.84 mg/100 ml, respectively, which closely aligns with experimental results. This indicates the model’s effectiveness and provides valuable insights for enhancing acidity in liquor production.

Considering the prevalence of cropping pattern in many Saffron cultivation regions, the continuous cropping obstacle caused the decline in growth and development. These effects justify the necessity of studying agronomic strategies to promote plant quality and production. Herein, the mono cropping, intercrop of Paulownia and Saffron, and intercrop of Soybean and Saffron patterns have been performed. The metabolites of Saffron under three cropping patterns were determined and identified using by an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method. Uni and multivariate statistical analysis were applied to examine the difference between groups and select the significant differential accumulated metabolites. The results showed that a total of 315 compounds were significantly changed in three comparison groups. Phenylalanine metabolism, linoleic acid metabolism, and flavonoid biosynthesis pathway has been greatly enriched by the changed metabolites. The phenyl lactate, trans-2-Hydroxy-cinnamate, γ-Linolenate, 9-oxo-octadecadienoic acid, 13-0xo-0ctadecadienoic acid, 9,12,13-TriHOME of those metabolites showed significant differential accumulation in Saffron under three cropping patterns. And the accumulation of safranal and picrocrocin related compounds, such as Crocetin, Crocin showed a similar variation regularity. These indicate that there may be a close internal relationship between the cropping pattern and the biosynthesis of metabolites related to endogenous substances and Saffron quality formation.

Cardiovascular diseases (CVDs) and their risk factors are a major cause of illness and death worldwide, especially in low- and middle-income countries like Cameroon. Physiotherapist-led health promotion (PLHP) has proven effective in improving health and reducing CVD risks. Understanding patient perspectives is crucial for designing effective, context-specific PLHP interventions. This study explored patients' views, experiences, perceived usefulness, acceptability, and preferred methods of PLHP through a sequential explanatory mixed-methods approach. The quantitative data highlights a significant burden of CVD conditions and risk factors among patients seen in physiotherapy services. Qualitatively, three themes were identified and included: 1) perspectives and experiences of people at risk or with CVDs (pwCVDs) on PLHP; 2) Perceived usefulness and acceptability of PLHP; 3) Preferred delivery methods of PLHP. Participants reported positive feedback on PLHP and physiotherapy services. Barriers to effective PLHP included high workloads for physiotherapists, limited service access in rural areas, and prohibitive costs. Despite these challenges, participants expressed strong confidence in physiotherapists' competence, though they also called for improved regulation and ongoing professional development. PLHP components, especially physical treatment and dietary advice, were deemed highly useful and acceptable. Patients suggested various delivery methods, including peer support groups, home visits, and mass media interventions. This study highlights the need to improve the scope of practice, competence of physiotherapists and accessibility of physiotherapy services in Cameroon for pwCVDs. It is necessary to adopt multidisciplinary approaches to achieve better outcomes for risk factors like diabetes and hypertension in context.

Background and Objectives: Fibrillinopathies have different phenotypic expression: Marfan- like skeleton features, MLSF; mitral valve prolapse syndrome, MVPS; MASS phenotype, M= mitral valve prolapse, MVP, A= aortic root dilation, S= skeletal features, and the second S= (cutaneous) striae; Marfan syndrome, MS. These are inherited connective tissue diseases, provoked by a mutation of the encoding gene for a protein(fibrillin 1, FBN1) which has major importance in the formation of elastic fibers.The study had the following main objectives: the correlation between disc displacement, DD( a major sign for temporomandibular joint dysfunction, TMD) on magnetic resonance imaging, MRI, and aortic Z score(a major sign for aortic root dilation) on echocardiography; the predictive value of DD towards aortic Z score. DD had 2 types of severity: DD with reduction(the mild type, DDwR) , and DD without reduction( the severe type, DDwoR). Materials and Methods: The type of fibrillinopathy was precised by clinical exam( systemic score), ophthalmic exam(for ectopia lentis), and echocardiography(aortic Z score, MVP). Medical treatment consisted in betablockers, BB(for patients with tachycardia), or angiotensin II receptor blockers, ARB. Surgical treatment was addressed to aortic root aneurysm, and severe MR. DD benefited by dental conservatory treatment, or surgical restoration.Results: DD- Z score had a powerful correlation in MASS(Rho= 0.787, p&lt; 0.01), and in MS patients(Rho=0.819, p&lt; 0.01). For the entire sample, both DDwR- Z score, and DDwoR- Z score had a moderate correlation(Rho= 0.469, and respectively 0.669). Muchmore, DD was an important predictor for Z score. DDwoR had a double power of prediction for Z score, comparative with DDwR(B coefficient= 1.661 for DDwR, and 3.281 for DDwoR). Conclusions: TMD had a powerful correlation with aortic root dilation, in MASS, and MS patients from the sample. Likewise, TMD was a major predictor for aortic root dilation, in the entire sample. We can utilize in clinical practice, an extracardiac finding, TMD, obtained by a non-invasive technique, MRI, for cardiac severity stratification of fibrillinopathies.

This study investigates the evolution of the vegetation and beach dune system at Enebrales Beach in Huelva, Spain, from 2003 to 2022. Utilizing orthophotographs and advanced GIS techniques, we analyzed changes in natural surface areas, vegetation cover, and dune stability. The results demonstrate significant ecological improvements: a recovery of 25,300 m² in dunes and 7,400 m² in beach areas, and a total vegetation cover increase of nearly 10,000 m². Interventions, including reforestation with native species and the stabilization of dune ridges, have been effective in enhancing the coastal ecosystem's resilience. Additionally, the beach's carrying capacity increased, allowing for approximately 2,000 more users without compromising ecological integrity. These findings underscore the importance of continued conservation efforts and adaptive management to maintain and further improve the ecological health and sustainability of Enebrales Beach Dune.

Faba bean is grown globally for human consumption and animal feed and is a significant rotation crop in cereal dominant areas in many countries including Australia. It is sensitive to several abiotic stresses, such as drought, frost and high temperatures, especially at the reproductive stage. However, little is known of the upper and lower temperature limits for successful podding. Experiments were conducted over two years at Breeza and Narrabri in north-western New South Wales to assess the impact of temperature on flowering and pod set of three genotypes; Doza, Cairo and IX1148f under field conditions. Flowering nodes were tagged from designated plants in the field and scored for podding, field temperatures were recorded. Optimum maximum temperature for podset ranged from 23.7-26.1 °C and the equivalent minimum temperature was 3.2-3.8 °C. Optimum average daily temperature for podset ranged from 12.7-13.9 °C. The strongest correlations between temperature and podding were observed at minimum temperatures, indicating that low temperatures are more important than high temperatures in determining yield in faba bean.

According to evolutionary cancer cell biology ECCB, cancer stem cells do not arise from normal stem cells, but rather from the functional ACD phenotype of the germline. This phenotype proliferates through asymmetric cell division, producing self-renewing cells and non-proliferating daughter cells with cancer stem cell qualities (CSCs). ECCB posits that cancer stem cells themselves do not proliferate. Similar to protists, there is a close reciprocal relationship between both sister cells that collectively form a germ and stem cell lineage. These sister cells share functional roles: the proliferating cancer germline cells generate stem cells, while the non-proliferating cancer stem cells accumulate progenitor cells for new germline clones. ECCB distinguishes between primary CSCs, which are associated with carcinogenesis and primary tumors, and secondary CSCs, which are linked to metastases. This unicellular stem cell system is homologous to that of parasitic protists, such as amoebae. Both cancer stem cells and amoebae stem cells are generated by an oxygen-sensitive germline and are vulnerable to damage when oxygen levels exceed 6.0% (germline hyperoxia). Oxygen concentrations above this threshold can damage the germline genome. Dysfunctional germline cells do not undergo senescence; instead, they continue to cycle through defective symmetric cell cycles However, to regain functionality, the germline genome requires repair by hyperpolyploid giant cells known as native PGCCs, which are homologous to the multinucleated genome repair structures MGRS found in protists. Native PGCCs differ in some respects from genotoxic-induced PGCCs

The primary purpose of this research article is to study the famous K$\ddot{o}$the's Conjecture for the special cases - when a ring $\mathfrak{R}$ is commutative, and when $\mathfrak{R}$ is an Algebra over a field. Furthermore, some open problems involving tensor product of Algebras over a field has also been discussed extensively along with their proofs. Subsequently, an equivelent statement of K$\ddot{o}$the's problem in terms of algebra of matrices has been established.

Abstract Increasing awareness of the potential health risks associated with microplastics (MPs) presence in the environment has led to significant rise in research focused on these particles over the past few years. This review focuses on research on MPs presence and spread, pathways of exposure and toxicological effects on human health and legal framework related to MP challenges. Several research projects have aimed to assess their potential harm to human health. Toxicological effects have been noticed at high concentrations of MPs, specifically polystyrene, the most widespread typical MP, but only short-term effects have been mostly studied. Significant quantities of consumed MP have been discovered to have diverse detrimental effects, posing a threat to human welfare. The exact concentrations of microplastics that are inhaled and swallowed and then build up in the human body are still not known. Further investigation is necessary to evaluate the impact of MP contamination at minimal concentrations and for prolonged durations.

Based on the minimum conduction band edge caused by the minimum channel potential resulting from the quasi-3D scaling theory and the 3D density of state (DOS) accompanied by the Fermi-Dirac distribution function on the source and drain sides, a unified semiconductor-device-physics-based ballistic model is developed for the threshold voltage of modern multiple-gate (MG) transistors, including FinFET, W-gate MOSFET, and Nanosheet (NS) MOSFET. It is shown that the thin silicon, thin gate oxide, and high work function will alleviate the ballistic effects and resist the threshold voltage degradation. Besides, as the device dimension is further reduced to give rise to the 2D/1D DOS, the lowest conduction band edge is hence increased to resist the threshold voltage degradation. The nanosheet MOSFET exhibits the largest threshold voltage among the three transistors due to the smallest minimum conduction band edge caused by the quasi-3D minimum channel potential. Compared to P-type MOSFET (P-FET), the N-FET shows more threshold voltage because the electron has a more effective mass than the hole

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierachical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace are delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm is employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trains foot points to facilitate robot movement along predefined paths. Experimental results demonstrate the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieves a 100% success rate, enhances average overall scores by 6.27%, reduces average stride lengths by 39.04%, and attains a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

The landscape of clinical management for metastatic melanoma (MM) and other solid tumors has been modernized by the advent of immune checkpoint inhibitors (ICI), including programmed cell death-1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors. While these agents demonstrate efficacy in suppressing tumor growth, they also lead to immune-related adverse events (irAEs), resulting in exacerbation of autoimmune diseases such as Rheumatoid arthritis (RA), Ulcerative colitis (UC), and Crohn’s disease (CD). The immune checkpoint inhibitors offer promising advancements in the treatment of melanoma and other cancers, but they also present significant challenges related to irAEs and autoimmune diseases. Ongoing research is crucial to better understand these challenges and develop strategies for mitigating adverse effects while maximizing therapeutic benefits. In this manuscript, we addressed this challenge using network-based approaches by constructing and analyzing molecular and signaling networks associated with tumor-immune cross-talk.. Our analysis revealed that IL6 is the key regulator responsible for irAEs during ICI therapies. Furthermore, we conducted an integrative network and molecular-level analysis, including virtual screening, of drug libraries, such as the Collection of Open Natural Products (COCONUT) and the Zinc15 FDA-approved library, to identify potential IL-6 inhibitors.. Subsequently, the compound amprenavir was identified as the best molecule that may disrupt essential interactions between IL6 and IL6R responsible for initiating signaling cascades underlying irAEs in ICI therapies.

The object of this research project is the investigation of the forgotten landscape of transhumance in mainland Greece, focusing on areas abandoned by modern society and their historical passage. The objective is to revive these lands by restoring their cultural heritage and creating a sustainable community that balances the needs of people, livestock, and the environment. The investigation employs a dual approach: studying transhumance as a cultural and natural practice and analyzing anonymous architecture to preserve collective memory. This involves extensive historical research and a simultaneous analysis of spatial, social, symbolic, and architectural elements. The study identifies convergence points between cultural practices and architectural heritage, applying these insights to modern redevelopment scenarios. Findings reveal a significant interplay between transhumance practices and the region’s anonymous architecture. Key characteristics of the landscape and its spatial identity are documented, providing insights into effective preservation and redevelopment strategies. Specific case studies validate these strategies, demonstrating their relevance to current and future efforts. The study concludes that understanding the historical context and spatial identity of transhumance landscapes is crucial for sustainable redevelopment. By preserving cultural practices and architectural heritage, this project offers a framework for revitalizing these forgotten landscapes, ensuring balanced coexistence of people, livestock, and the environment.

The Aspergillus genus comprises fungi that are widely distributed in nature. The Terrei section is particularly important due to its functions in recycling organic matter. Aspergillus hortae is a species from this section that has been isolated from clinical samples, but its role as a pathogenic agent is unclear. In this study, three clinical A. hortae isolates from Colombia, initially identified as A. terreus, were characterised by micro and macro-morphological analysis at 26°C and 37°C. A bioinformatic pipeline was used for molecular typing, which included analysis of whole genome sequences for accurate species identification. Susceptibility assays were also conducted using the microdilution broth method (EUCAST). It was confirmed that this species is intrinsically resistant to Amphotericin B with a minimum inhibitory concentration (MIC) of 2-4 mg/L, and is also thermotolerant. We obtained the in vitro susceptibility profiles for three azole drugs (MIC, 0.25-1 mg/L). The three isolates showed phylogenetic divergence from the reference genome strain. A mutation (M769K) in the MelA ortholog of a hypopigmented isolate, which had the lowest MIC value for AmB (2 mg/L), was identified. This study presents the morphological characterization, molecular typing through whole-genome analysis and identification of susceptibility profiles to azoles and amphotericin B of three clinical isolates of A. hortae from Colombia.

Cancer, a complex group of diseases marked by uncontrolled cell growth and invasive behavior, is characterized by distinct hallmarks acquired during tumor development. These hallmarks, first proposed by Douglas Hanahan and Robert Weinberg in 2000, provide a framework for un-derstanding cancer's complexity. Targeting them is a key strategy in cancer therapy. It includes inhibiting abnormal signaling, reactivating growth suppressors, preventing invasion and me-tastasis, inhibiting angiogenesis, limiting replicative immortality, modulating the immune sys-tem, inducing apoptosis, addressing genome instability, and regulating cellular energetics. Usnic acid (UA) is a natural compound found in lichens that has been explored as a cytotoxic agent against cancer cells of different origins. Although the exact mechanisms remain incom-pletely understood, UA presents a promising compound for therapeutic intervention. Under-standing its impact on cancer hallmarks provides valuable insights into the potential of UA in developing targeted and multifaceted cancer therapies. This article explores UA activity in the context of disrupting hallmarks in cancer cells of different origins based on articles that emphasize the molecular mechanisms of this activity.

I present in this paper Diatomic, an open-source Excel application that calculates molar ther-modynamic properties for diatomic ideal gases. This application is very easy to use and requires only a limited number of molecular constants, which are freely available online. Despite its sim-plicity, Diatomic provides methodologies and results that are usually unavailable in general quantum chemistry packages. This application uses the general formalism of statistical mechanics, enabling two models to describe the rotational structure and two models to describe the vibra-tional structure. In this work, Diatomic was used to calculate standard molar thermodynamic properties for a set of fifteen diatomic ideal gases. Special emphasis was placed on the analysis of four properties (standard molar enthalpy of formation, molar heat capacity at constant pressure, average molar thermal enthalpy and standard molar entropy), which were compared with ex-perimental values. The results obtained lead to the following main conclusions: (i) the theoretical model used to describe rotational structure has a small effect on the accuracy of the results, (ii) at moderate and high temperatures, the Morse model gives better results than the harmonic model, and (iii) at the lowest temperatures both models give similar results.

In this paper, we study the asymptotic properties of slowly varying functions of one real variable in the sense of Karamata. We establish analogs of fundamental theorems on uniform convergence and integral representation for slowly varying functions with remainder depending on the types of remainder. We also prove several important theorems on the asymptotic representation of integrals of Karamata functions. Under certain conditions, we observe a “narrowing” of classes of slowly varying functions concerning the types of remainder. At the end of the paper, we discuss the possibilities of application of slowly varying functions in the theory of stochastic branching systems. In particular, under the condition of the finiteness of the moment of the type Exlnx for the particle transformation intensity, it is established that the property of slow variation with remainder is implicitly present in the asymptotic structure of a non-critical Markov branching random system.

This study presents the design and implementation of a piezoelectric power harvesting device to capture vibrational strength from pipelines to autonomous strength IoT devices. The gadget utilizes key components along with the PPA-1001 piezoelectric sensor, STM32F103C8T6 microcontroller, and LTC-3588 energy harvesting strength delivery. Experimental results verified the gadget's performance in harvesting strength within a specific frequency variety of 10 Hz to 50 Hz, with the foremost overall performance at 30 Hz. The device generated the highest voltage of 3.3V, delivering a strength output of 2.18 mW, which is sufficient to power low-strength electronic gadgets. The device maintained solid performance across a temperature range of 20°C to 35°C, underscoring its robustness in various environmental situations. The findings highlight the capacity of this generation to offer a sustainable power source for far-off pipeline tracking, contributing to stronger protection and operational efficiency.

In today’s data-driven marketing landscape, accurately predicting customer responses to marketing campaigns is critical for optimizing engagement and return on investment (ROI). This study utilizes a Decision Tree (DT) model to identify key factors influencing customer behaviour. Initially, the model achieved a high accuracy of 87.3% but struggled with precision and recall due to class imbalance. By applying a resampling technique, the model’s performance improved significantly, with a recall increase from 44% to 83.1% and an F1-score improvement from 49% to 74.2%. Key influential features identified include how recently a customer made a purchase, the number of days they have been a customer, and the number of previous campaigns they responded to. The study highlights the DT model’s interpretability, making it a practical tool for marketing professionals to improve campaign effectiveness and customer targeting.

Objectives: Teachers’ intention understanding ability reflects their professional insight, which is the basis for effective classroom teaching activities. However, the cognitive process and brain mechanism of how teachers understand students' action intention in class are still unclear; Methods: This study used event-related potential (ERP) technology to explore the cognitive neural differences in intention understanding ability among teachers with different levels of knowledge and experience. The experiment used the comic strips paradigm to examine the ability of expert and novice teachers to understand students’ normative and non-normative classroom actions under different text prompts (“how” and “why”); Results: The results revealed that in the late time window, expert teachers induced larger P300 and LPC amplitudes when they understood students' classroom action intentions, while the N250 amplitudes induced by novice teachers in the early time window were significantly larger. In addition, for both types of teachers, when understanding the intentions behind students’ normative actions, the N250 amplitude was the most significant, while the P300 and LPC amplitudes were more significant for non-normative actions.; Conclusions: This study found that teachers at varying professional development stages had different time processing processes in intention understanding ability, which supported teachers' brain electrophysiological activities related to social ability.

Balancing the levels of ammonia, in poultry facilities plays a role in enhancing the welfare and productivity of animals. Various environmental factors such as temperature, humidity, ammonia concentrations, carbon dioxide concentration and exposure to light have an impact on the wellbeing and efficiency of poultry. It is essential to monitor these elements to achieve outcomes necessitating practical, reliable and cost-effective solutions. Leveraging the Internet of Things (IoT) in conjunction with LoRa-WAN (Long Range Wide Area Network) technology presents an opportunity in the industry by enhancing connectivity among devices and facilitating real time data transmission. This research introduces a hardware and software setup for monitoring conditions at poultry farms through LoRa WAN and IoT technologies. More-over researchers employed Random Forest Machine Learning prediction models to tune the concentration of Ammonia (NH3) in poultry facilities across a wide area or multiple Poultry houses. Comparative assessments were conducted to evaluate the proposed system against devices revealing a correlation exceeding 0.9830 between the sensors and standard equipment while costing 28% less than traditional tools. The prototype displayed outcomes underscoring its potential, for managing poultry facilities across multiple farms with centralized oversight.

In ocean engineering, accurate and efficient numerical simulations are crucial. Leveraging axisymmetry vastly enhances the efficiency of a simulation, reducing a three-dimensional scenario to a two-dimensional simulation. Exploiting axisymmetry enables a significant reduction in computational demand while maintaining simulation fidelity. In this paper, axisymmetry is leveraged to perform a large ensemble of simulations, in order to evaluate and maximise the accuracy and efficiency of a Computational Fluid Dynamics (CFD) simulation. In particular, the free decay motion and wave radiation from a heaving semi-submerged sphere are simulated, using the Reynolds Averaged Navier Stokes (RANS) solver, interFOAM, in the opensource finite volume CFD software OpenFOAM. Validated against highly accurate experimental data, extensive parametric studies are conducted, previously limited by computational constraints, which facilitate the refinement of simulation setups. More than 50 iterations of the same heaving sphere simulation are performed, informing efficient trade-offs between computational cost and accuracy across various simulation parameters and mesh configurations. Ultimately, by employing axisymmetry, this research contributes to the development of more accurate and efficient numerical modeling in ocean engineering.

Bilberry (Vaccinium myrtillus) encroachment in subalpine and alpine ecosystems is increasing due to climate change and reduced land management. This review examines bilberry traits, environmental responses, and ecosystem impacts. As a stress-tolerant chamaephyte, bilberry thrives in acidic, nutrient-poor soils across various habitats. It propagates effectively through rhizomes and demonstrates a phalanx growth form. Bilberry's growth and distribution are influenced by elevation, soil structure, pH, water availability, and nitrogen content. Mycorrhizal associations play a crucial role in nutrient uptake. The species modifies microclimate, facilitates litter accumulation, and influences soil microbial communities, affecting nutrient turnover and biodiversity. Bilberry shows moderate tolerance to herbivory and frost, with the ability to recover through rapid emergence of new ramets. However, severe or repeated disturbances can significantly impact its abundance and reproductive success. Climate warming and atmospheric nitrogen deposition have accelerated bilberry growth in treeline ecotones. Management of bilberry encroachment requires a nuanced approach, considering its resilience, historical land-use changes, and environmental factors. The goal should be to limit, not eliminate, bilberry as it's a natural part of subalpine communities. Long-term comparative monitoring and experimental manipulation are necessary for effective management strategies.

This study presents a reproducible and scalable method for synthesizing silica nanoparticles (SNPs) with controlled sizes below 200 nm, achieved by systematically varying three key reaction parameters: ammonium hydroxide concentration, water concentration, and temperature. SNPs with high monodispersity and controlled dimensions were produced by optimizing these factors. The results indicated a direct correlation between ammonium hydroxide concentration and particle size, while higher temperatures resulted in smaller particles with increased polydispersity. Water concentration also influenced particle size, with a quadratic relationship observed. This method provides a robust approach for tailoring SNP sizes, with significant implications for biomedical applications, particularly in drug delivery and diagnostics. Using eco-friendly solvents such as ethanol further enhances the sustainability and cost-effectiveness of the process.

This study investigates the cooling effects of water facilities installed in urban parks, focusing on Sangju Namsan Park. The park features a water path and a cooling fog system, introduced as part of an environmental adaptation project to mitigate urban heat. Microclimate data were collected during the hot summer months of 2023 to assess the impact of these facilities on air temperature, humidity, and perceived thermal comfort. Results indicate that the water path and fog system reduced surface temperatures by up to 1.08°C, with additional benefits in reducing fine particulate matter and CO2 levels. The findings suggest that strategically placed water features can enhance thermal comfort and air quality in urban parks, offering a practical approach to climate change adaptation in urban planning. These insights are crucial for urban planners and policymakers aiming to improve public health and quality of life in the face of climate change.

Optical wireless video transmission technology combines the advantages of high data rates, enhanced security, large bandwidth capacity, and strong anti-interference capabilities inherent in optical communication, establishing it as a pivotal technology in contemporary data transmission networks. However, video data comprises a large volume of image information, resulting in substantial data flow with significant redundant bits. To address this, we propose an adaptive block sampling compressive sensing algorithm that overcomes the limitations of sampling inflexibility in traditional compressive sensing, which often leads to either redundant or insufficient local sampling. This method significantly reduces the presence of redundant bits in video images. First, the sampling mechanism of the block-based compressive sensing algorithm was optimized. Subsequently, a wireless optical video transmission experimental system was developed using a Field-Programmable Gate Array chip. Finally, experiments were conducted to evaluate the transmission of video optical signals. The results demonstrate that the proposed algorithm improves the Peak Signal-to-Noise Ratio by over 3 dB compared to other algorithms, with an enhancement exceeding 1.5 dB even in field tests, thereby significantly optimizing video transmission quality. This research contributes essential technical insights for the enhancement of wireless optical video transmission performance.

This study investigates the comparative performance of autoencoders and traditional machine learning (ML) algorithms in predicting tumor dynamics following Gamma Knife radiosurgery (GKRS) for brain metastases (BMs). The research retrospectively analyzed clinical data from 77 patients (median age: 64 years; 45 males, 32 females) treated at the “Prof. Dr. NicolaeOblu” Emergency Clinic Hospital-Iasi. The dataset comprised 12 variables, including socio-demographic, clinical, treatment, and radiosurgery-related factors. The primary outcome was tumor progression or regression within three months post-GKRS, with 71 cases of regression and 6 cases of progression observed. Traditional ML models—Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost—were evaluated, achieving accuracy rates ranging from 0.91 (KNN) to 1.00 (Extra Trees). When incorporating features extracted by autoencoders, the performance of these models generally improved, especially with compression in the bottleneck layer. For instance, Logistic Regression's accuracy increased from 0.91 to 0.94, while SVM's accuracy improved significantly from 0.85 to 0.96. XGBoost consistently performed well across both traditional and hybrid setups, maintaining an accuracy of 0.94 with an AUC of 0.98. The findings suggest that hybrid approaches combining deep learning with traditional ML techniques can significantly enhance predictive accuracy and robustness, particularly in the context of GKRS for BMs. This improved performance has potential implications for more informed clinical decision-making in personalized medicine.

Background: Various vitamins and minerals have been implicated in the aetiology of depression. Objective: To estimate the effects of micronutrient exposures on major depressive disorder (MDD) and recurrent depression (rMDD) using Mendelian randomization (MR). Methods: We undertook a comprehensive bidirectional MR study of multiple micronutrient exposures on MDD and rMDD. Summary statistics were obtained from the Psychiatric Genomics Consortium (PGC) genome wide association studies (GWASs) of MDD (cases = 116,209; controls = 314,566), and rMDD (cases=17,451; controls=62,482). Results: None of the micronutrients with available genetic instruments were strongly associated with MDD or rMDD using traditional MR methods. However, using methods to increase analytical power by accounting for genetically correlated variants (e.g. cIVW), highlighted five micronutrients with possible causal effects. Point estimates for rMDD were the largest magnitude, with three micronutrients suggestive of a protective effect: serum iron (OR_cIVW 0.90 per SD increase; 95% CI 0.85- 0.95; p=0.0003); erythrocyte copper (OR_cIVW 0.97; 95% CI 0.95- 0.99; p=0.0004); and 25(OH) vitamin D (OR_cIVW 0.81; 0.66- 0.99; p=0.04). Apparent adverse effects of increased selenium on the risk of MDD (OR_cIVW 1.03; 95% CI 1.02-1.05; p=0.0003) and rMDD (OR_cIVW 1.08; 95% CI 1.00-1.08; p=0.06), and serum magnesium on rMDD (OR_cIVW 1.21; 1.01- 1.44; p=0.04); were less consistent between methods and may be driven by pleiotropy. Conclusions: Our results suggest weak evidence for a protective effect of iron, copper and 25(OH)D on major depressive outcomes, with mixed evidence for selenium and magnesium. There was no evidence to support a causal effect of any other micronutrients on MDD or rMDD, although genetic instruments were lacking, with insufficient power to detect small but important effects. Future micronutrient supplementation trials should ensure ample statistical power given modest causal effect estimates, and consider potential risks of supplementation, as some micronutrient effect estimates suggested potential harm in excess.

Selection is a fundamental part of the plant breeding process, enabling the identification and development of varieties with desirable traits. Thanks to advances in genetics and biotechnology, the selection process has become more precise and efficient, resulting in faster breeding progress and better adaptation of crops to environmental challenges. Genetic parameters related to gene additivity and epistasis play a key role and can influence decisions on the suitability of breeding material. In this study, 188 rapeseed doubled haploid lines were assessed in field conditions for resistance to Leptosphaeria spp. Through next-generation sequencing, a total of 133,764 molecular markers (96,121 SilicoDArT and 37,643 SNP) were obtained. The similarity of the DH lines at the phenotypic and genetic levels was calculated. The results indicate that the similarity at the phenotypic level was markedly different than the similarity at the genetic level. Genetic parameters related to additive gene action effects and epistasis (double and triple) were calculated using two methods: based on phenotypic observations only and using molecular marker observations. All evaluated genetic parameters (additive, additive-additive and additive-additive-additive) were statistically significant for both estimation methods. The parameters associated with the interaction (double and triple) had opposite signs depending on the estimation method.

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarante. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with power and energy balance. Drawing upon the principles of conditional value at risk theory, the risk indexes of load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment and the risk cost of load shedding, a distributed robust optimal scheduling model based on flexible ramp reserve of thermal power is established.

Bipolar disorder (BD) is a complex mental health condition characterized by alternating periods of mania and depression. This review provides a comprehensive overview of BD, focusing on its genetics, causal hypotheses, clinical signs and evolution, potential biomarkers with an emphasis on electroencephalography (EEG), and treatment options. Genetic studies have identified several susceptibility genes and polygenic risk scores, highlighting the heritable nature of BD. Various causal hypotheses, including monoamine dysregulation, kindling, circadian rhythm disruptions, and neuroinflammation, have been proposed to explain its pathophysiology. The clinical course of BD is characterized by recurrent episodes of mania/hypomania and depression, with significant heterogeneity among individuals. EEG and other biomarkers hold promise for improving diagnosis, predicting treatment response, and elucidating the neurobiological underpinnings of BD. Current treatment options include pharmacotherapy, psychotherapy, and neuromodulation techniques, which can be tailored to individual needs based on clinical presentation and response to treatment. Future research should focus on identifying more specific biomarkers, developing novel treatments, and optimizing existing therapies to improve outcomes for individuals with BD.

Ransomware, a combination of "ransom" and "malware," is a type of malicious software designed to encrypt or block access to a victim's data or system, demanding a ransom for its release Initially targeting individuals, ransomware has evolved to attack businesses for greater financial gain. It mainly exists in two forms: encrypting ransomware, which holds data hostage, and non-encrypting ransomware, which blocks system access and displays a ransom note. The energy sector has been notably targeted by ransomware, exemplified by the 2021 attack on Colonial Pipeline by the DarkSide group, which led to a temporary shutdown and a significant fuel shortage on the US East Coast. Similarly, in 2023, Russian hackers using LockBit ransomware disrupted Royal Mail, halting international deliveries and incurring millions in recovery costs despite no ransom payment. These incidents underscore the need for comprehensive cybersecurity strategies that combine human vigilance with advanced technologies like AI and machine learning. By adopting a multi-layered protection approach, organizations can better prepare for and mitigate the risks posed by ransomware attacks safeguarding sensitive data and ensuring business continuity.

This study x-rays the philosophical meanings, interpretations, and intersections of death and wealth in Igbo cosmology. Using such tools as oral interviews, ethnography and participant observations, the researcher extracted data from both primary and secondary sources to clarify some misconceptions surrounding wealth and death in Igbo religious and philosophical thought. This research is timely because amidst a growing literature on the Igbo people and culture, discourses on the complementarity and convergence of good death and wealth have lacked attention over the years. This work fills this gap through the application of hermeneutic approach which enabled an informed conclusion that the Igbo recognise two categories of death: the bad death and the good death. While The bad death is abhorred and avoided, the good death is the expectation of every Igbo human person as it is a strong component of wealth. The findings provide a better understanding of African Igbo culture, death, wealth, and the meanings they attach to life which is equally important for a harmonious relationship and a stable social order. The paper calls for a reexamination of the narratives surrounding peripheral interpretations given to this aspect of Igbo culture. It further contributed to the growing volumes of literature on Igbo history, indigenous philosophy, and religion, among others.

Since the beginning of the 21th century, infrared Nd:YVO4 pulsed lasers have been widely applied especially in some actual industrial processes. In the working process of a laser-aided etching device, the “match-head” effect must be effectively controlled by suppressing the first giant pulse for a solid-state Q-switched laser. In the process of optimizing the infrared Nd:YVO4 pulsed laser by adjusting the slope parameters of the radio frequency (RF) modulation to suppress the first giant pulse, it has been come across that some abnormal horizontally polarized emission with a very short time appears before the formal vertically polarized emission when the gate signal is artificially started. Actually, abnormal horizontally polarized emission will bring some unexpected machining traces during the production process and even greater dangers. The experimental results show that with the increase of the slope duration of a RF signal, the existence time of abnormal output horizontal polarized light will be shortened, and the horizontal giant pulse and vertical giant pulse are well suppressed. When the slope duration is greater than 0.18 ms both horizontal and vertical giant pulses will disappear. The horizontal polarized light can be thoroughly suppressed when the slope duration is greater than 13.7 ms. Compared with the method of adding polarizer to eliminate abnormal output horizontal polarized light, this method does not add elements in the laser, ensuring that the laser volume is relatively small, and does not affect the quality of the normal output laser. The research conclusion is thought to be of great practical significance especially for processing transparent materials.

Lualaba Province, located in the southeastern Democratic Republic of the Congo, consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The unique miombo woodlands of Lualaba are threatened by deforestation, posing risks to biodiversity and local livelihoods that depend on these woodlands for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba's landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. Our provincial-level analysis revealed a pronounced deforestation trend, with miombo woodland cover declining from 62,90 % to less than 25 % over the 34-year period. This trend was characterized by the fragmentation and dissection of woodland patches and a decline in remaining woodland fragments, due to their ongoing replacement by savannas, agriculture, and urbanization. The average distance between miombo woodland patches increased significantly, indicating heightened fragmentation and spatial isolation. Agricultural areas such as Sandoa and Kapanga were particularly vulnerable to deforestation. On the other hand, the miombo forest cover in the mining areas is still representative, with over 30% of the landscape covered. Notably, the reduction in woodland cover within protected areas was substantial, with significant losses observed across both agricultural and mining territories. The loss of miombo woodland cover in Lualaba and its territories was accompanied by an increase in landscape patch diversity, as indicated by the Shannon diversity index, suggesting a shift to more heterogeneous landscapes. These findings underscore a complex deforestation pattern, highlighting the intense local impact on miombo woodland cover loss. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen miombo woodland regulation enforcement, and actively support protected areas. Involvement from both local and international stakeholders is imperative to preserve the ecological richness and functionality of Lualaba Province's miombo woodland ecosystems.

One of the most common shoulder injuries is rotator cuff tear (RCT). The risk of RCTs increases with age, with a prevalence of 9.7% in those under 20 years old to 62% in individuals aged 80 and older. In this article, we present first Microwave Digital Twin Prototype (MDTP) for RCT detection, based on machine learning (ML) and advanced numerical modeling of the system. We generate a generalizable dataset of scattering parameters through flexible numerical modeling, in order to bypass real-world data collection challenges. This involves solving the linear system in result of finite element discretization of the forward problem with use of domain decomposition method to accelerate the computations. We use support vector machine (SVM) to differentiate between injured and healthy shoulder models. This approach is more efficient in terms of required memory resources and computing time compared to the traditional imaging methods.

Chronic noncommunicable diseases (NCDs) are responsible for approximately 74% of deaths globally. Medicinal plants have traditionally been used to treat NCDs, including diabetes, cancer, and rheumatic diseases, and are a source of anti-inflammatory compounds. This study aimed to evaluate the anti-inflammatory effects of Rhus trilobata (Rt) extracts and fractions in lipopolysaccharide (LPS)-induced inflammation models in vitro and in vivo. The aqueous extract (RtAE) and five fractions (F2 to F6) were obtained via C18 solid-phase separation and tested in murine LPS-induced J774.1 macrophages. Key inflammatory markers such as IL-1β, IL-6, TNF-α, and COX-2 gene expression were measured using RT-qPCR, and PGE2 production was assessed via HPLC-DAD. The in vivo effects were tested in an LPS-induced paw edema model in Wistar rats. Results showed that RtAE at 15 μg/mL significantly decreased IL-1β and IL-6 gene expression in vitro. Fraction F6 further reduced IL-1β, TNF-α, and IL-6 gene expression, COX-2 expression, and PGE2 production. In vivo, F6 significantly reduced LPS-induced paw edema, inflammatory infiltration, and IL-1β and COX-2 protein expression. These findings support the anti-inflammatory potential of RtAE and F6, reinforcing the medicinal use of Rt.

Reticular and cluster materials often feature complex formulas, making a comprehensive overview challenging due to the need to consult various resources. While datasets have been collected for metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and zeolites, among others, there remains a gap in systematically organized information for polyoxometalates. This paper introduces a carefully curated dataset of 1984 polyoxometalate (POM) and related cluster metal oxide formula instances, currently connecting over 2,500 POM material instances. These POM instances incorporate 75 different chemical elements, with compositions ranging from binary to octonary element clusters. This dataset not only enhances accessibility to polyoxometalate data but also aims to facilitate further research and development in the study of these complex inorganic compounds.

Clarifying the distribution of temperature and pressure in the wellbore and cavern during hydrogen injection and extraction is crucial for quantitatively assessing cavern stability and wellbore integrity. This paper establishes an integrated flow and heat transfer model for the cavern and wellbore during hydrogen injection and production, analyzing the variations in temperature and pressure in both the wellbore and the cavern. The specific conclusions are as follows. (1) The temperature and pressure parameters of hydrogen and natural gas within the chamber and wellbore were compared. Under identical injection and production conditions, the temperature of hydrogen in the chamber was 10°C higher than that of natural gas, and 16°C higher in the wellbore. The pressure of hydrogen in the chamber was 2.9 MPa greater than that of natural gas, and 2.6 MPa higher in the wellbore. (2) A comparative analysis was conducted on the impact of surrounding rock’s horizontal and numerical distance on temperature during hydrogen and natural gas injection processes. As the distance from the cavity increases, from 5 to 15 meters, the temperature fluctuation in the surrounding rock diminishes progressively, with the temperature effect in the hydrogen storage chamber extending at least 10 meters. (3) The influence of rock thermal conductivity parameters on temperature during the processes of hydrogen injection and natural gas extraction is compared. The better the thermal conductivity, the deeper the thermal effects penetrate the rock layers, with the specific heat capacity having the most significant impact.

Background: Dietary fiber is a key component of a health-promoting diet, and it is acknowledged as a short-fall nutrient in most Western diets. Some sources of dietary fiber, such as hemp hull fiber, also include inherent small molecule bioactives that further promote health and well-being. While dietary fiber is a key component that supports digestive health, it also can cause digestive discomfort. Digestive symptoms such as gas, bloating, belly pain, and the resulting decrease in digestive quality of life are common among adults. The aim of this study was to evaluate changes in digestive comfort, in individuals who currently experience digestive discomfort, after consuming either a proprietary dietary fiber supplement from hemp hull, a dietary fiber supplement made from inulin, or a digestible control (maltodextrin). Methods: This study was a completely virtual, double-blind, placebo-controlled, randomized clinical trial of healthy adults. The parallel arm study tested two fiber supplements at a practical dose yielding a “Good Source of Fiber” based on United States Dietary Reference Intakes (2.8 grams of fiber per day) for six weeks. Results Five hundred seventy nine randomized participants provided follow-up outcomes data. Digestion-associated quality of life questionnaire, belly pain, and gas and bloating scores were assessed and found to be superior after hemp hull fiber intake compared to inulin intake at multiple time points in a post-hoc analysis (p&lt;0.05). Conclusion This study demonstrated that a practical dose of bioactive-rich fiber from hemp hull is well-tolerated, provides superior digestive comfort relative to both placebo and inulin fiber, and can aid in reaching total fiber intake goals.

The rise of antibiotic-resistant pathogens represents a significant global challenge in infectious disease control, which is amplified by the decline in the discovery of novel antibiotics. Staphylococcus aureus continues to be a highly significant pathogen causing infections in multiple organs and tissues in both healthcare institutions and community settings. The bacterium has become increasingly resistant to all available antibiotics. Consequently, there is an urgent need for novel small molecules that inhibit growth or impair survival of bacterial pathogens. Given their large structural and chemical diversity as well as often unique mechanisms of action, natural products represent an excellent avenue for the discovery and development of novel antimicrobial treatments. Anaephene A and B are two such naturally occurring compounds with significant antimicrobial activity against gram-positive bacteria. Here, we report the rapid syntheses and biological characterization of five novel anaephene derivatives, which display low cytotoxicity against mammalian cells but potent antibacterial activity against various S. aureus strains, including methicillin resistant S. aureus (MRSA) and the multi drug-resistant community acquired strain USA300LAC. A Sonogashira cross-coupling reaction served as the key step for the synthesis of the alkyl pyridinol products. Using the compound JC-01-074, which displays bactericidal activity already at low concentrations (MIC: 16 mg/ml), we provide evidence that alkyl pyridinols target actively growing and biofilm-forming cells and show that these compounds cause disruption and deformation of the staphylococcal membrane, indicating a membrane associated mechanism of action.

In the present research, it has been tried to use the applied research bases in the field of business management with marketing orientation. Since there were wide and diverse spectrums in the field of economic activities in Isfahan province and Isfahan city, the model of economic sectors was used to categorize these activities. In this regard, the specific goals of the research in all five sectors of the model of economic sectors, drawing and prioritizing the components (indices) of the 7P marketing mix model in each of these economic sectors by the related individuals and members of subgroups of guilds and non-governmental organizations in Isfahan province, have been formed, specified and determined. Collecting data for processing and answer the question of this research, after designing a questionnaire in accordance with the AHP model standard, by spending a period of 2 months from September 2022 and ended in November 2022 , from the statistical population has been done.

We provide representations for the generalized Drazin inverse of an anti-triangular matrix of the form $\left( \begin{array}{cc} a&b\\ 1&0 \end{array} \right)$ in a Banach algebra $\mathcal{A}$, under the condition that $ab=ba$. Specifically, we present the representation of Drazin inverse for these types of anti-triangular matrices in Banach algebras.

The BAFF-APRIL system is crucial for the pathogenesis of systemic lupus erythematosus (SLE) by promoting B cell survival, differentiation and the maintenance of humoral autoimmunity. Here, we investigated the relationship of BCMA expression on B cell subsets with its ligands BAFF and APRIL, together with soluble BCMA, and with clinical and serologic variables in a cohort of 100 SLE patients (86 under conventional and 14 under belimumab therapy) and 30 healthy controls (HC) using multicolor flow cytometry and ELISA. We found that BCMA expression in SLE patients was significantly increased on all B cell subsets compared to HCs, with all examined components of the BAFF-APRIL system being upregulated. BCMA expression was significantly increased on switched and unswitched memory B cells compared to naïve B cells, both in HCs and SLE. BCMA expression on B cells correlated with plasmablast frequencies, serum anti-dsDNA antibodies and complement consumption, while soluble BCMA correlated with plasmablast frequency, highlighting its potential as clinical biomarker. Belimumab treatment significantly reduced BCMA expression on most B cell subsets and soluble TACI, and contributes to the inhibition of almost the entire BAFF-APRIL system and restoration of B cell homeostasis. These results provide insights into the complex dysregulation of the BAFF-APRIL system in SLE and highlight the therapeutic potential of targeting its components, particularly BCMA, in addition to its use as biomarker for disease activity.

With Generation Z youths growing up with the Internet, the problem of mobile phone addiction is becoming increasingly worse. This paper administered questionnaires based on measures such as the Mobile Phone Addiction Index Scale, Research Self-Efficacy Scale, The Depression Anxiety Stress Scale, and Stress Mindset Measure. The survey targeted 2,278 graduate students and explored the mechanism of mobile phone addiction on their research self-efficacy (RSE). The results revealed that such addiction has significant negative effects on self-efficacy, with mental stress playing a mediating role in this process. That is, mobile phone addiction lowers RSE by increasing mental stress. Meanwhile, the aforementioned negative impacts caused by addiction can be moderated through stress mindsets: the “stress-is-enhancing” mindset reduces the negative effects of addiction on scientific RSE, whereas the “stress-is-debilitating” mindset amplifies these negative effects by enhancing the mediating effect of mental stress.

In this paper, we integrate the Finite Element Method (FEM) with orthogonal polynomial approximation in high-dimensional spaces to innovatively model the Moon’s surface gravity anomaly. Our aim is to approximate solutions to Laplace’s classical differential equations of gravity, employing classical Chebyshev polynomials as basis functions. Using classical Chebyshev polynomials as basis functions, the least-squares approximation was used to approximate discrete samples of the approximation function. These test functions provide an understanding of errors in approximation and corresponding errors due to differentiation and integration. These test functions provide an understanding of errors in approximation and corresponding errors due to differentiation and integration. The first application of this project is to substitute the globally-valid classical spherical harmonic series of approximations with locally-valid series of orthogonal polynomial approximations (i.e. using the FEM approach). With an error tolerance set at 10−9ms−2, we use this method to adapt the gravity model radially upwards from the lunar surface. The results showcase a need for a higher degree of approximation on and near the lunar surface, with the necessity decreasing as the radius increases. Notably, this method achieves a computational speedup of five orders of magnitude when applying the method to radial adaptation. More intrinsically, our second application involves using the methodology as an effective tool in solving boundary value problems. Specifically, we implement this approach to solve classical differential equations involved with high precision, long-term orbit propagation. This application provides a four-order of magnitude speedup in computational time while maintaining an error within the 10−10ms−2 error range for various orbit propagation tests. Alongside the advancements in orthogonal approximation theory, the FEM enables revolutionary speedups in orbit propagation without compromising accuracy.

Accurate assessment of bone age (BA) is crucial for pediatric clinical practice. This study validated the Radiological Reference Atlas for Bone Age in the Canary Islands Population with a total of 203 anteroposterior left hand and wrist radiographs from healthy children (91 females, 112 males) across preschool, school-age, and adolescent stages. Intra-observer agreement, assessed by intraclass correlation coefficients (BAA1: ICC = 1.000, BAA2: ICC = 0.995, BAA3: ICC = 0.943), showed differing levels of precision among assessors. Inter-observer agreement was high (Pearson’s r = 0.905 - 0.951), suggesting consistent evaluations across different expertise levels. Accuracy analysis revealed significant BA underestimation compared to chronological age (CA) in preschool (MD = -17.036 months, p < 0.001) and school-age (MD = -8.165 months, p < 0.001) groups, but closer agreement in teenagers (MD = -0.33 months, p = 0.550). Furthermore, a relevant underestimation in the teenage stage for girls compared to boys was also noted. These findings underscore the atlas's precision; however, its accuracy for BA assessment in younger children and adolescent girls needs to be approached with caution.