Views of aging include peoples’ assessment of their own aging process and their subjective age. Positive aging views relate to good psychological well-being which predicts better physical and mental health. While these were substantially studied, the moderating roles of self-aging attitudes and psychological well-being in the subjective age-age discrimination connection have been much less explored. The current study used a convenience sample of 568 participants (mean = 66.21y, SD = 11.95, age range 50-95), 55.8% women, 67.1% employed. In line with the hypotheses, young subjective age and psychological well-being were connected to less age discrimination in the workplace and higher psychological well-being mitigated the subjective age-age discrimination at work connection. When the perception of old age as a period of loss was added to the model, adults who perceived old age as a period of loss and reported lower levels of psychological well-being, demonstrated the strongest relationship between an increase in subjective age and an increase in age-related discrimination at work. The findings emphasize the importance of the psychological well-being of older employees as a resource for improving their attitudes to their last years at work.

Drug resistance in melanoma is a major hindrance in cancer therapy. Growth hormone (GH) plays a pivotal role in contributing to resistance to chemotherapy. Knocking down or blocking the GH receptor has been shown to sensitize the tumor cells to chemotherapy. Extensive studies have demonstrated that exosomes, a subset of extracellular vesicles, play an important role in drug resistance by transferring key factors to sensitize cancer cells to chemotherapy. In this study, we explore how GH modulates exosomal cargoes from melanoma cells and their role in drug resistance. We treated the melanoma cells with GH, doxorubicin, and GHR antagonist, pegvisomant, and analyzed the exosomes released. Additionally, we administered these exosomes to recipient cells. The GH-treated melanoma cells released exosomes with elevated levels of ABC transporters (ABCC1 and ABCB1), N-cadherin and MMP2, enhancing drug resistance and migration in recipient cells. GHR antagonism reduced these exosomal levels, restoring drug sensitivity and attenuating migration. Overall, our findings highlight a novel role of GH in modulating exosomal cargoes that drive chemoresistance and metastasis in melanoma. This understanding provides insights to the mechanisms of GH in melanoma chemoresistance and suggests GHR antagonism as a potential therapy to overcome chemoresistance in melanoma treatment.

There is growing interest in the discovery of novel xanthine oxidase inhibitors for gout prevention and treatment with fewer side effects. This study aimed to identify the xanthine oxidase (XO) inhibitory potential and drug-likeness of the metabolites present in the methanolic leaves extract of Anastatica (A.) hierochuntica L. using in vitro and in silico models. The extract-derived metabolites were identified by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Molecular docking predicted the XO inhibitory activity of the identified metabolites and validated the best scored in vitro XO inhibitory activities for experimental verification as well as predictions of their anticancer, pharmacokinetic and toxic properties, oral bioavailability, and endocrine disruption using SwissADMET, PASS, ProTox-II and Endocrine Disruptome web servers. A total of 12 metabolites with a majority of flavonoids were identified. Rutin, quercetin and luteolin flavonoids demonstrated the highest ranked docking score of -12.39, -11.15 and -10.43, respectively, while the half-maximal inhibitory concentration (IC50) values of these metabolites against XO activity were 11.35 µM, 11.1 µM and 21.58 µM, respectively. In addition, SwissADMET generated data related to physicochemical properties and drug-likeness of the metabolites. Similarly, PASS, ProTox-II and Endocrine Disruptome prediction models stated the safe and potential use of these natural compounds. However, in vivo studies are necessary to support the development of prominent and promising therapeutic use of A. hierochuntica methanolic leaves extract-derived metabolites as XO inhibitors for the prevention and treatment of hyperuricemic and gout patients. Furthermore, the predicted findings of the present study open a new paradigm for these extract-derived metabolites by revealing novel oncogenic targets for the potential treatment of human malignancies.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

Diabetes is one of the fastest spreading diseases of civilisation in the 21st century. The aim of the study was twofold: 1) to find differences in personality traits, self-esteem, body- esteem, and imagery between healthy women and women with diabetes 2) to check whether there are differences in analyzed factors in women with diabetes who participate in sports compared to women who do not. We used 3 questionnaires: Imagination: in sport - short form, Self-Esteem Scale (SES), and BFIS Personality, which were tested online. We found that women with diabetes were characterised by significantly higher neuroticism and lower extraversion and higher conscientiousness (marginally significant). We also found that women with diabetes who practice sport rated their bodies more highly in terms of sexual attractiveness and made better use of imagined affirmations than healthy women who were not active. Our study provides new insights into diabetics in terms of coping with the disease.

Postbiotic is a preparation of inanimate microorganisms and/or their components that confers a health benefit on the host. Different bacterial strain and preparation method result in different functions. Herein, a postbiotic preparation named Postbiotic-P by intercepting 1-5 KD of the cell-free supernatants of L. caseiparacasei P6 fermentation broth was prepared, and its multifunctional activities were evaluated. The results showed that Postbiotic-P exerted good antihemolytic (10%-50% Postbiotic-P resulted in 4.9%-48.1% antihemolytic rate), anti-inflammatory (8% to 40% Postbiotic-P is comparable to 259.1-645.4 μg/mL of diclofenac sodium equivalents), antioxidant [reducing power (1.25%-12.5% Postbiotic-P resulted in the increase of OD700 from 0.279-2.322), DPPH free radical scavenging activity (2.5%-50% Postbiotic-P resulted in 38.9%-92.4% free radical scavenging rate), and hydroxyl radical scavenging activity (0.5%-4% Postbiotic-P resulted in 4.66%-10.38% hydroxyl radical scavenging rate)], and antimicrobial activities (against 2 Gram-positive, 8 Gram-negative and 1 fungus). Furthermore, it was proved that Postbiotic-P could significantly inhibit the increase of the peroxide value and the malondialdehyde content in cookies, indicating the application potential of Postbiotic-P in food preservation. Taken together, Postbiotic-P has good application potential for the development of health care products and functional foods. However, further studies are needed to determine their precise mechanism of action, to clarify the active components responsible for the function, and to validate the biological activities in animal models or clinical trials.

Human endogenous retroviruses (HERV) are remnants of ancient retroviral infections that, over millions of years, became integrated into the human genome. While normally inactive, environmental stimuli such as infections have contributed to the transcriptional reactivation of HERV promoting pathological conditions including the development of autoimmunity, neurodegenerative disease and cancer. What infections then trigger HERV activation? Mycobacterium avium subspecies paratuberculosis (MAP) is a pluripotent driver of human disease. Aside from granulomatous diseases Crohn’s, sarcoidosis and Blau syndrome, MAP is associated with autoimmune disease: type one diabetes (T1D), multiple sclerosis (MS), rheumatoid arthritis and autoimmune thyroiditis. MAP is also associated with Parkinson’s disease. Autoimmune diabetes, multiple sclerosis and rheumatoid arthritis are the diseases with the strongest MAP/HERV association. There are several other diseases associated with HERV activation including diseases whose epidemiology and/or pathology would prompt speculation for a causal role of MAP. These include non-solar uveal melanoma, colon cancer, glioblastoma and amyotrophic lateral sclerosis (ALS). This article further points to MAP infection as a contributor to autoimmunity, neurodegenerative disease and cancer via unsilencing of HERV. We examine the link between the ever-increasing number of MAP- associated diseases, the MAP/HERV intersection with these diverse medical conditions and propose treatment opportunities based upon this association.

Background: Dietary quality and the consumption of antioxidant-rich foods has been shown to protect against memory decline. Therefore, this double-blind, randomized, placebo-controlled study aimed to investigate the effects of a nutritional supplement on changes in cognitive performance. Methods: In adults aged 40 to 70 years with subjective memory complaints, participants were randomly allocated to take a supplement containing vitamin E, astaxanthin, and grape juice extract daily for 12 weeks or a matching placebo. The primary outcomes comprised changes in cognitive tasks assessing episodic memory, working memory, and verbal memory. Secondary and exploratory measures included changes in the speed of information processing, attention, and self-report measures of memory, stress, and eye and skin health. Moreover, changes in plasma concentrations of brain-derived neurotrophic factor, malondialdehyde, tumor-necrosis factor-α, and interleukin-6 were measured, along with changes in skin carotenoid concentrations. Results: compared to the placebo, nutritional supplementation was associated with larger improvements in episodic memory, a self-report measure of everyday memory, increased plasma brain-derived neurotrophic factor, decreased plasma malondialdehyde, and increased skin carotenoid concentrations. However, there were no group differences in changes in the remaining outcome measures. Conclusions: Twelve weeks of supplementation with a nutritional supplement was associated with improvements in episodic memory and several biological markers associated with cognitive health. Future research will be essential to extend and validate the current findings.

Finding the roots of an equation is a fundamental problem in diverse fields. Optimization problem leads to non-linear equations for calculating the roots of the equation efficiently. Numerical iterative techniques are frequently applied when analytic solution is not available. Hybrid techniques to find roots of an equation is new to this fundamental problem. Recently, some attempts emerged to design hybrid algorithms for efficient solutions. A new heuristic algorithm is designed which is an intuitive approach to hybridization. In this paper, we implement two new algorithms: (1) a new BTsection algorithm blending the Bisection and Trisection algorithm, (2) a hybrid algorithm, that promises to be more efficient than the existing hybrid algorithms. The new hybrid algorithm is a blend of BTsection algorithm and Regula Falsi algorithm. The implementation results validate that the new algorithm, Hybrid4, surpasses the efficiency of existing hybrid algorithms Hybrid1, Hybrid2, and Hybrid3 algorithms. This paper contributes an essential hybrid algorithm to the repertoire of hybrid root finding algorithms.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

Chamazulene (CA) is an intensely blue molecule with a wealth of biological properties. In cosmetics chamazulene is exploited as a natural coloring and soothing agent. CA is unstable and tends to spontaneous degradation accelerated by light. We studied the photodegradation of CA upon controlled exposure to UVB-UVA irradiation by multi-ple techniques, including GC-MS, UHPLC-PDA-ESI-MS/MS and by direct infusion in ESI-MSn, which were matched to in silico mass spectral simulations to identify degra-dation products. Seven byproducts formed upon UVA exposure for 3 h at 70mW/cm2 (blue-to-green color change) were identified, including CA dimers and CA benzenoid which were not found on extended 6 h irradiation (green-to-yellow fading). Photosta-bility tests with reduced irradiance conducted in various solvents in the pres-ence/absence of air indicated highest degradation in acetonitrile in the presence of ox-ygen, suggesting a photo-oxidative mechanism. Testing in the presence of antioxidants (tocopherol, ascorbyl palmitate, hydroxytyrosol, bakuchiol, γ-terpinene, TEMPO and their combinations) indicated highest protection by tocopherol and TEMPO. Sun-screens ethylhexyl methoxycinnamate and particularly Tinosorb® S (but not octo-crylene) showed good CA photoprotection. Thermal stability tests indicated no degra-dation of CA in acetonitrile at 50°C in the dark for 50 days; however, accelerated deg-radation occurred in the presence of ascrorbyl palmitate.

Ionic liquids (ILs) have gained interest among researchers due to its tunable properties that can be used in wide applications. However, toxicity and bio-degradation studies of ILs proved that most of the aromatic ILs, such as imidazolium is highly toxic and non-biodegradable. Several advance oxidation processes (AOPs) have been investigated by researchers to evaluate the efficiency of the systems for the removal of ILs from wastewater. However, the issue on relative high cost and environmental concern has limited the application of these AOPs in industry. In this research, photocatalytic study using hybrid nano-materials was conducted to evaluate the efficiency of this system as an alternative AOP system for removal of ILs from wastewater. The synergistic effect of adsorption-photodegradation was introduced by depositing Fe-TiO₂ onto the functionalized activated carbon (AC). Nano-TiO₂ was synthesized using micromulsion method before modification with transition metal and deposited onto the oxidized AC. Photodegradation reaction of 1-butyl-3-methylimidazolium chloride [bmim]Cl was investigated under simulated visible light irradiation. It was observed that the overall efficiency of the system was increased with the increasing amount of Fe dopant. Investigation on the extrinsic factors such as solution pH, initial concentration of ILs and photocatalyst dosage showed to significantly affect the overall efficiency of the systems where the optimum condition for the system was observed at pH 10, with initial ILs at 1mM at 1 g/L of photocatalyst. The best performance photocatalyst was 0.2Fe-TiO₂/AC.

Immune responses to influenza (flu) antigens reflect memory to prior infections or vaccinations, which might influence immunity to new flu antigens. Memory of past antigens has been termed “original antigenic sin” or more recently “immune imprinting” and “seniority”. We have researched a comparison between the immune response to live flu infections and inactivated flu vaccina-tions. A brief history of antibody generation theories is presented culminating in new findings about the immune-network theory suggesting that a network of clones exists between an-ti-idiotypic antibodies and T cell receptors. Findings regarding the 2009 pandemic flu strain and immune responses to it are presented, including memory B cells and conserved regions within the hemagglutinin protein. The importance of CD4+ memory T cells and cytotoxic CD8+ T cells responding to both infections and vaccinations are discussed and compared. Innate immune cells like natural killer (NK) cells and macrophages are discussed regarding their roles in adaptive immune responses. Antigen presentation via macroautophagy processes are described. New vaccines in development are mentioned along with the results of some clinical trials. The manu-script concludes with how repeated vaccinations are impacting the immune system and a sketch of what might be behind the imprinting phenomenon, including future research directions.

An optical frequency comb (OFC) generator based on an electro-optic Talbot laser and cross-phase modulation (XPM) in a high nonlinear fiber (HNLF) is designed and demonstrated. The Talbot laser is an electro-optic frequency shifting loop to produce repetition rate multiplied pulses, and these pulses working as a pump signal induce XPM process in the HNLF to modulate the phase of a probe signal. At the output of the HNLF, OFCs with a multiplied line-spacing can be generated. The effects of the pump power and the HNLF length on the performance of the generated OFCs are theoretically analyzed. In the experiments, the line-spacing of the generated OFCs is respectively multiplied to be 10 GHz, 15 GHz and 20 GHz with a factor of 2, 3 and 4. The center of the OFCs is tuned in a 4-nm range by adjusting the wavelength of the probe signal.

In Kenya, reports of Rift Valley fever (RVF), one of the worst climate-sensitive zoonosis, have been common. Despite the fact that several empirical studies have demonstrated that Machine learning techniques perform better than time series models in forecasting time series data, there is little evidence of their use in predicting disease outbreaks in Africa. Recently, the literature has mentioned a number of other uses of machine learning to support intelligent decision-making in the healthcare industry and public health but there is limited knowledge on the use of the XGBoost model in the prediction of disease outbreaks. Among the Kenyan provinces, Rift valley fever cases were more pronounced in Rift valley (26.80%) and Eastern (20.60%) regions. The study explored the relationship be-tween RVF incidence and various climatic factors, such as humidity, clay content, elevation, slope, and rainfall. The strongest correlation, a meager 0.02903 for rainfall, was found in the correlation matrix, which showed weak linear relationships between various climatic factors and RVF cases. These climate variables were used to train the XGBoost model, which showed remark-able performance with an AUC of 0.8908, accuracy of 99.74%, precision of 99.75%, and recall of 99.99%. Rainfall was found to be the most important predictor in the feature importance analysis. These findings are consistent with other research showing how important weather conditions are to RVF outbreaks. According to the study's findings, the use of sophisticated machine learning models that take a variety of climatic factors into account can greatly improve RVF outbreak prediction and control.

In this study, AlSi12 alloy was fabricated using the selective laser melting (SLM) technique to produce high density components with complex and customized parts for railway applications. Nonetheless, the production of dense samples necessitates the optimization of production process parameters. As a statistical design of experimental technique, the response surface methodology was used to optimise different combination of SLM parameters. The results were analysed using analyse of variance (ANOVA) and the signal-to-noise(S/N) ratios. The relationship between the hardness response to the process parameters (scanning speed and lase power) for determining the optimal processing conditions were examined. A hardness value of 133 HV was obtained. The process parameters were successfully optimized and the relationship between the parameters and the structures of the fabricated samples were reported.

Abstract: Aim: This study aimed to evaluate the antibiotic-prescribing attitudes of dental students during the management of pulpal and periapical pathologies. Material and methods: This cross-sectional descriptive study concerned all the students in clinical training registered the 5 faculties of dental medicine in Morocco. A self-administered questionnaire was used, and completed via email addresses. Data were analyzed with Jamovi software. Quantitative variables are expressed as mean and standard deviation and qualitative variables as numbers and percentages. X2 and ANOVA tests were performed. The significance level was set at p < 0.05. Results: Three sixty-five students participated in this study. The average duration was 5.87 ±1.45 No statistical difference was found between the different universities p >0.05. Of most students 83.8% prescribe Amoxicillin first ; Amoxcilin in combination with clavulanic acid is prescribed by 9.2%. For patients with penicillin allergy, clindamycin was the most prescribed 53.9%, after followed by azitromycin at 20.9%. Antibiotics are prescribed for all pulp and periapical pathologies, whether acute or chronic. For acute pulpitis, a difference was statistically significant (P=0.01). Regarding acute apical periodontitis, a statistically significant difference between the different faculties was noted (P= 0.03). For the apical abscess, the antibiotic prescription was higher at the public faculty of Casablanca 92.8% than in the other faculties (P = 0.02) Conclusion: It appears from this study the need for faculties of dentistry to develop innovative teaching models to improve the level of knowledge of students on antibiotics and their indications in endodontics.

From the existing millions of fungal species only a few cause disease. In this study, we investigated invasive fungal infections in the head and neck over a 19-year period (2005 to 2024) at a large academic healthcare system. Among 413 cases, 336 were noninvasive, and 77 were invasive. The highest incidence of invasive infections occurred in the sinonasal cavities, with a 15-fold difference compared to other sites. Most infections affected adults over 40 years old. The most common organisms were Mucorales (51%), hyaline molds (29%), and Candida (11%). Risk factors included malignancy, transplant, diabetes, and illicit drug use. Mortality was high in patients with malignancy and/or transplant. Infections affecting the mandible were usually a complication of osteoradionecrosis and were associated with coinfection of Candida and Actinomyces. At other sites, infections were rare and were usually the result of penetrating injuries or immunosuppression. Treatment typically involved a combination of antifungals and surgical procedures.

In the context of the Korean building structure standard KDS2022, wind loads can be readily calculated in terms of the along-wind, across-wind, and torsional components, based on a single wind direction. However, this approach falls short in accurately and safely evaluating the impact of winds from various directions. Therefore, in this study, we developed a Wind Load Calculation Program (WCP) capable of predicting wind loads with relative precision during the preliminary design phase. First, wind tunnel tests were conducted to identify the essential factors necessary for calculating wind loads and the variables influencing these factors. The building shape considered was a square, and the wind force coefficients and power spectral density were measured by combining 4 ground roughness values, 11 side ratios (), 4 aspect ratios (), and wind directions ranging from 0° to 90°. Subsequently, a generalized formulation was devised to facilitate wind load calculations across diverse conditions and a method for calculating load combination coefficients, considering response correlation and load simultaneity, was presented. Finally, the wind loads obtained from the wind tunnel tests were compared with those predicted by the WCP using an actual project model.

This article deals with the denoising of signals from sensors (current and voltage) in a photovoltaic conversion chain using wavelet theory. The idea is to reduce the noise as much as possible without distorting or losing the original signal information. To see the reliability of this proposed method, a comparative study between our proposed method (WDD) and the classic low-pass filter method was presented. The contribution in this work is the improvement of the DC-DC boost converter via the use of wavelet theory, however, that’s why we set two objectives: the first is to obtain reliable control of the DC-DC boost converter via the proposed WDD and the second to increase the overall system efficiency. The experimental results show a very great improvement in the quality of the signals of the proposed method compared to the classic method of filtering by low-pass filter.

Intrusion Detection Systems play a crucial role in a network. They can detect different network attacks and raise warnings on them. Machine learning-based IDSs are trained on datasets that, due to the context, are inherently large, since they can contain network traffic from different time periods and often include a large number of features. In this paper, we present two contributions: the study of the importance of feature selection when using an IDS dataset, while striking a balance between performance and the number of features; and the study of the feasibility of using a low-capacity device, the Nvidia Jetson Nano, to implement an IDS in a low-capacity network. The results, comparing the GA with other well-known techniques in feature selection and reduction, show that the GA has a higher F1-score of 76%, although the time to find the optimal set of features surpasses other methods; the reduction of the number of features reduces the processing time without a significant impact in f1-score. The Jetson Nano allows the classification of network traffic with an overhead of 10 times in comparison to a traditional server.

Software project estimating (SPE) is a powerful but necessary step in the production of projects for software. Software project estimating includes several components, estimating software time, software resources, software costs, and software effort estimation (SEE). SEE seeks to estimate the number of "hours-person" and "months-person" of work required for developing or managing a software program. During the initial phase of the development of software, future efforts are impossible to predict. To estimate effort, many deep learning (DL) and machine learning (ML) models were previously created. For estimate, single-model techniques and ensemble methods were investigated in this research. Ensemble methods involve a combination of multiple separate models. Bagging, boosting, and voting were the ensemble methods looked into for estimation. Datasets considered for estimation were COCOMO81, COCOMONasa I, COCOMONasa II, Albrecht, Maxwell, China, Desharnais, Desharnais_1_1, Kitchenham, Boehm, and Belady. The evaluation criteria used MAE, RAE, RMSE, RRSE, and CC. The result from previous studies is that the total size of the set of data utilized is small in predicting the effort, as the largest size of the set of data used is the China dataset containing only 499 projects. So, in this study, the dataset has been merged to increase the total size of the set of data and increase the accuracy of prediction. The results indicated that the suggested merged dataset increased effort estimation accuracy.

When examining the crucial role of civic competence in fostering sustainable societies, economies, and ecosystems, its significance for cultivating responsible and engaged citizens in our ever more interconnected global landscape must be recognized. Notably, education influences the development of civic competence throughout the learning experience at all levels of education. This research aims to explore the evolution of students’ self-assessment in terms of civic competence and its three sub-competences. The objective is to comprehend the present status of students’ self-assessments and explore their potential impact on the formation of a sustainable society. Data were collected for this study using an assessment tool elaborated in the ESF project “Development and Implementation of the Education Quality Monitoring System” for students’ generic competences. The tool, presented in a digital form using the QuestionPro platform, measures six generic competences, including civic competence. A total of 1,166 bachelor students and 354 Master’s students participated in the study, representing 22 Latvian higher education institutions. The study’s findings indicate that civic competence in the sample is considerably lower than the other generic competences measured, with a greater dispersion of results. The results also highlight specific areas within universities that require improvement during the learning process to cultivate individuals who are sustainable and socially responsible members of society.

In this paper, we introduce and investigate a new family of sequences called the generalized Fibospinomials (or the generalized Fibonacci polynomial spinors or Horadam polynomial spinors). Being particular cases, we handle with $(r,s)$-Fibonacci and $(r,s)$-Lucas polynomial spinors. After a short history on spinors and quaternions, we present Binet's formulas, generating functions and the summation formulas for these polynomials. In addition, we obtain some identities of generalized Fibonacci polynomial spinors, $(r,s)$-Fibonacci polynomial spinors and $(r,s)$-Lucas polynomial spinors. Moreover, we give some special identities such as Catalan's and Cassini's identities and we present matrices related with these polynomials.

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash processes. Some of these systems have very limited flexibility due to their rigid structures and require considerable investment in hardware. As such, there is a need for an autonomous low-cost system (~€500) that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents a sensitivity analysis of time-lapse intervals for two low-cost cameras located in a remote coastal area, overlooking a dissipative beach-dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for intervals ranging between 1 second and 30 seconds (i.e., RMSDmax for Camera 1 = 2.8 m and Camera 2 = 1.3 m). The increase of the sampling interval from 1 s to 30 s is shown to have no significant adverse effect on the precision of shoreline detection. The research found that the limitations of battery life and memory storage substantially decrease when sampling intervals increase without an effect on the data quality. This also has a positive effect for low-cost systems and removes the requirement for external battery and memory storage. On the systems used, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s., As such, the current standard of producing Timex images from 1 s sampling intervals over 10 minutes can be changed depending on the application and requirements needed for analysis.

We developed a novel hybrid approach for solving global optimization, computer science, bio-medical and engineering real life applications that is based on the coupling of the Whale Optimizer and Sine Cosine Algorithms via a surrogate model. We relate the whale optimizer algorithm to balance between the exploitation and the exploration process in the proposed method. There exist confirmed techniques for searching approximate best optimal solutions, but our algorithm will further guarantee that such numerical and statistical solutions satisfy physical bounds of the standard and real life functions. Our experiments with the benchmark, bio-medical, computer science and engineering real life problems have illustrated the advantages of using a newly hybrid approach based on mixing Whale Optimizer and Sine Cosine algorithms. It holds considerable potential for reducing execution time for solving standard and real life problems and at the same time improving the quality of the solution.

Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science

This study aims to evaluate the financial performance of SOEs in Indonesia and compare it with private companies using financial ratio analysis and efficiency analysis approaches. Four construction sector SOEs were evaluated, and five private companies were compared using financial data from 2015 to 2022. The financial ratio analysis used the Peterson (2013) approach, while the efficiency analysis employed the data envelopment analysis (DEA) approach and the paired t-test. The study results show that the financial ratio performance of state-owned companies is relatively poor, with low profitability, critical liquidity, and a high debt ratio. Debt, as a source of capital in financing construction projects, makes companies face a greater debt risk. This study validates that SOEs have lower efficiency compared to private companies.

Development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods are either methods with adjustable parameters or machine learning algorithms, both requiring a database of experimentally measured folding and binding free energy changes. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residue on the above-mentioned thermodynamics characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variant (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases, may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases and the distribution of the free energy changes are not the same. Furthermore, the Pearson correlation coefficients (PCCs) obtained on cases involving SNVs are less impressive than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. All methods are found to underestimate the energy changes by roughly a factor of 2.

Regulation of the diet in terms of the content of soluble and insoluble fibre is of great importance from the point of view of dietetics, microbiology of the gastrointestinal tract and immunology of the organism. Dietary fibre plays an important role in maintaining the balance between the con-tinuity of gut mucus production (intestinal mucosa), the stability of the intestinal microbiome composition, the regeneration of the intestinal epithelium and the immune status within the gut-associated lymphoid tissue (GALT) are played by pectin and their metabolic products in the form of pectin oligosaccharides. The main purpose of this review was to focus on pectin, their functions, their impact on the human's health and on some of the most promising prebiotics due to their solubility in the gastrointestinal tract in the form of pectin oligosaccharides. They have the properties of pectin, but have also additional features and postbiotics obtained in fermentation process can affect the intestinal microbiome and GALT-related immunity. They can be obtained from plant agro-wastes.

The per capita availability of freshwater resources on a global scale is gradually decreasing due to population growth, rapid urbanization, industrialization, and modern lifestyles. In this situation, wastewater management becomes a major concern. Conventional treatment processes used to treat wastewater discharges are not adequate for the advanced removal of pollutants, especially refractory organic pollutants, which are mainly organic compounds that are not easily biodegradable in the environment. The use of photocatalytic membrane reactors (PMRs) for tertiary wastewater treatment has attracted much interest in recent periods, and various configurations have been investigated. The performance of a PMR using an iron doped titanium photo catalyst and Polysulfone-based polymeric membrane for municipal wastewater treatment was evaluated under natural and simulated solar light. Obtained results proved that solar driven PMRs can remove organic loading (COD) with high efficiencies and may represent a suitable alternative for the tertiary treatment step of municipal wastewater.

The members within the genus Thermoanerobacter have been much less investigated for protein and amino acid metabolism as compared with carbohydrates. The present study investigates the biotechnological potential of Thermoanaerobacter mathranii (DSM 11426) concerning its ability to produce high energy alcohols from amino and fatty acids. End-product formation was analyzed from glucose in the presence and absence of butyric acid as well as from selected proteins, and all amino acids. T. mathranii did not degrade any of the proteins tested to a large extent but de-grade several amino acids, namely serine and the branched-chain amino acids (leucine, isoleu-cine, valine) when cultivated in the presence of thiosulfate. The main end products from the branched-chain amino acids were a mixture of their corresponding branched-chain fatty acids and alcohols. 13C2-labeled leucine revealed that the strains degraded the amino acid in the pres-ence of thiosulfate, producing 3-methyl-1-butyrate, which was then used as an electron acceptor which led to the accumulation of 3-methyl-1-butanol. The strain is highly ethanologenic, pro-ducing more than 1.2 mol ethanol per mol of glucose degraded. The strain was able to reduce volatile fatty acids during glucose fermentation to their corresponding alcohol, further suggest-ing this strain may be of greater biotechnological value beyond bioethanol production.

Oncogenic protein E6 from Human Papilloma Virus 16 (HPV-16) mediates the degradation of Membrane-associated guanylate kinase with inverted domain structure-1 (MAGI-1), throughout the interaction of its protein binding motif (PBM) with the Discs-large homologous regions 1 (PDZ1) domain of MAG1-1. Generic variation in the E6 gene that translates to changes in the protein’s amino acidic sequence modifies the interaction of E6 with the cellular protein MAGI-1. MAGI-1 is a scaffolding protein found at tight junctions of epithelial cells, where it interacts with a variety of proteins regulating signaling pathways. MAGI-1 is a multidomain protein containing two WW (rsp-domain-9), one guanylate kinase-like, and six PDZ domains. PDZ domains played an important role in the function of MAGI-1 and served as targets for several viral proteins including the HPV-16 E6. The aim of this work was to evaluate, with an in silico approach, employing molecular dynamics simulation and protein-protein docking, the interaction of the intragenic variants E-G350 (L83V), E-C188/G350 (E29Q/L83V), E-A176/G350 (D25N/L83V), E6-AAa (Q14H/H78Y/83V) y E6-AAc (Q14H/I27RH78Y/L83V) and E6-reference of HPV-16 with MAGI-1. We found that variants E-G350, E-C188/G350, E-A176/G350, AAa and AAc increase their affinity to our two models of MAGI-1 compared to E6-reference.

A qualitative analysis of pressure acting within the human eye is presented. The situation emerging within the human eye in rest and human eye of accelerated/training person should be distinguished. When the person is in rest, the hydrostatic pressure is much larger than the Laplace pressure. The dynamic (Bernoulli pressure) emerging from intraocular flows in this case is negligible. The situation is rather different for accelerated or decelerated person. In this case, the dynamical pressure and the water hammer pressure become dominant. Under certain circumstances, the water hammer pressure may be larger than the ultimate tensile strength of retina, choroid and the Bruch membrane‐choroid complex, which may possibly affect the retina. As a recommendation: physical training, which is not accompanied with ultimate acceleration/acceleration of the body, may be performed after posterior vitreous detachment; however, potentially traumatic activities such as boxing or competitive diving should be avoided.

We will try to use the approach of labeled cycle cover sum to count the number of hamiltonian cycles in a special type of bipartite graphs, with 4k + 2 vertices which can also be considered as an alternative method to check hamiltonicity in such bipartite graphs. This graph is such that, there exists at least 1 vertex which is not a part of any cycle of size 4m + 2 where m < k, in O∗(1.414n) time. The algorithm is inspired by Bjorklund’s approach where instead of working on the field of GF(2k), we will be working on C.

Reactive oxygen species (ROS) play a pivotal role in cellular function, and their overproduction contributes to oxidative stress, fatigue, aging, and diseases. To counteract these effects, incorpo-rating antioxidant-rich nutrients is recommended. Functional waters, including electrolyzed hydrogen water (EHW), are gaining attention for potential antioxidant effects through daily in-take. This study employs aquaphotomics and near-infrared spectroscopy (NIRS) to elucidate the molecular mechanisms behind the antioxidant activity of EHW. Previous investigation reported a 100% loss in antioxidant functionality through autoclaving, and a 40% decrease with hydrogen gas removal, prompting the current investigation into the mechanisms of antioxidant effects and especially the role of water molecular structure in it. Aquaphotomics analysis of near infrared (NIR) spectral data revealed that the primary characteristic of EHW associated with anti-radical function is presence of water solvation shells. Autoclaving and degassing led to loss of these water molecular species and a significant increase in free water molecules. The study suggests that water shells (solvation shells) in EHW play a crucial role in neutralizing ROS, although the specific ions or nanoparticles responsible for creating these shells remain unidentified. This concept aligns well with existing proposals of the crucial role of water solvation shells in ROS neutralization.

The study was conducted at El-Sabahia Research Station (latitude 31°12'N, longitude 29°58'E), Alexandria, Egypt, to evaluate the effect of Spirulina platensis algae extract on the growth, yield, and juice quality of four sugarcane genotypes during the 2020/2021 and 2021/2022 seasons. The sugarcane genotypes tested (G.T. 54-9, G. 2003-47, G. 84-47, and G. 2004-27) were treated with four concentrations (0, 0.1, 0.2, and 0.3%) of Spirulina algae extract (SE) during their development as plant cane and first ratoon crops. At harvest, the growth, physiological, and juice quality characteristics were documented, while chlorophyll content was measured 210 days after sowing. Spraying canes with 0.2% of SE was the most effective treatment in enhancing all of the evaluated characteristics compared to those left without SE. Cane yield was more closely correlated with stalk weight (r = 0.88), followed by leaf area index (r = 0.82), chlorophyll content (r = 0.82), stalk length (r = 0.76), and number of tillers (r = 0.73), while recoverable sugar yield was closely correlated (P < 0.01) with sugar content% (r = 0.76).

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of Brain Computer Interfaces (BCI). This review paper provides a comprehensive overview of fNIRS, EEG, and fMRI, including their principles, advantages, limitations, and integration with other modalities for advanced BCIs. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Applications of advanced BCIs in clinical, assistive, gaming, and entertainment domains are also discussed. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with discussion on the available literature on data acquisition approaches and a comparison of the ease of use for each of the modalities.

Cognitive impairment affects 34-65% of People with Multiple Sclerosis (PwMS), significantly impacting their quality of life. Clinicians routinely address cognitive deficits with in-clinic neuro-behavioural interventions, but accessibility issues exist. Telerehabilitation is a promising solution that utilises digital tools for home-based cognitive remediation. This narrative review assessed the feasibility and efficacy of cognitive telerehabilitation in PwMS, identifying 13 relevant studies. Regarding the feasibility of cognitive telerehabilitation, evidence shows adherence rates are generally good, although, surprisingly, not all studies reported a measure of compliance with the cognitive training explored. Considering the efficacy of different rehabilitative techniques on cognitive performance of PwMS, findings are generally inconsistent, with only one study reporting consistently positive results. A range of methodological limitations are reported as potential factors contributing to the variability of results. Overcoming some of these issues will be key. Upcoming research is necessary to determine optimal intervention approaches, long-term effects of rehabilitation, and how to best integrate the use of telerehabilitation into routine clinical practice. By exploiting digital tools in the best way and addressing methodological concerns, home-based rehabilitation holds the potential for improving cognitive functions and enhancing the quality of life in PwMS.

Classroom resource allocation is a challenging problem in educational institutions. The use of cloud computing can aid in the optimization of this process by allowing resource providers to allocate resources to users in a timely and efficient manner. In this paper, we propose a modified Salp Swarm Algorithm (SSA) to optimize the allocation of classroom resources. The proposed approach involves generating a population of salps representing potential solutions to the resource allocation problem and updating the positions of the salps iteratively based on a fitness function and mathematical equations. We conducted simulations of the proposed algorithm on various numbers of tasks and sources to evaluate its performance. The results indicate that the modified SSA algorithm can efficiently allocate classroom resources, achieving lower completion time of tasks (makespan) with lower costs. This paper contributes to the literature by introducing an effective approach to solve the classroom resource allocation problem.

Soil microorganisms plays an important role on plant development and the homogenization of soil microbiome is agri-environmental harmful. It is essential that agricultural practices are carried out taking soil microbiome preservation in consideration. Agroforestry systems are one of the most environmentally friendly agrosystems and its plant diversity influence directly the soil microbiome diversity. In addition, many technologies have been developed and used by applying inoculants in agriculture. However, most of the inoculants are developed with only one or a little number of microbial species to supply a specific plant nutritional demand. In this study we tested the efficacy of the microbial consortium (MC) obtained from compost and the cyanobacteria Arthrospira platensis (AP) comparing with the application of the vermicompost tea (VT) and bokashi (BK) in arugula, lettuce, beetroot, and carrot in an agroforestry system in two seasons. Soil changes on chemical properties and β-glucosidase and arylsulfatase were measured over time. The vegetables yield was measured and for arugula and lettuce the nutritional traits and plant root system were also evaluated. Greater plant yield, nutritional values, and plant root development was observed in the CM treated plants, AP and BK had similar results. Our results show that both CM and AP have potential to become a sustainable product for agricultural production

Background: Autonomic balance measurement (ABM) is a valuable tool in clinical practice for assessing the functioning of the autonomic nervous system (ANS), which controls involuntary bodily functions. Here we summarize the current understanding of the utility of autonomic balance measurement in clinical practice. Methods: A narrative review of relevant literature was conducted, focusing on studies investigating the use of autonomic balance measures in various clinical settings. Results: ABM holds potential benefits in clinical practice, including aiding in diagnosing autonomic dysfunction, monitoring treatment efficacy, and predicting potential health complications. ABM can be crucial for diagnosing various conditions, including cardiovascular diseases (Heart failure, arrhythmias, and hypertension), neurological disorders (Parkinson's disease, Alzheimer's disease, and diabetic neuropathy), in sports medicine athletes can undergo ABM to optimize training regimens, enhance performance, assessing the impact of training stress, preventing overtraining, and promoting overall well-being and probably to assess potential health risks, in metabolic syndromes ABM can aid in the early detection of autonomic neuropathy, a diabetes-related complication, allowing for timely intervention and better management of the disease, and psychiatric disorders (Anxiety, depression, and post-traumatic stress disorder). In addition, ABM can be helpful in monitoring of treatment efficacy, by tracking changes in autonomic balance, clinicians can assess if treatments for conditions impacting the autonomic nervous system are effective. ABM can also be helpful in predicting outcomes by facilitating early recognition of future health complications in certain populations. However, limitations exist, including non-specificity, variability in measurements, and limited evidence for routine use in specific clinical settings. Conclusion: Incorporating autonomic balance measurement into routine clinical assessments provides a more comprehensive understanding of a patient's health. It complements traditional diagnostic methods, offering additional insights into the body's regulatory mechanisms. While autonomic balance measurement offers potential benefits, careful interpretation and consideration of its limitations are crucial for its effective use in clinical practice. ABM remains a valuable and versatile tool in clinical practice for a deeper understanding of the autonomic nervous system, enabling early detection, targeted interventions, and improved management of various medical conditions across different specialties.

The aim of the study was to assess the effects of an 8-week cognitive-motor training program on postural control and knee proprioception under single and dual-task conditions. Design: Randomized clinical trial. Methods: 20 healthy and physically active older adults (73.255.98 years) volunteered to participate and were randomly assigned into an experimental and a control group (EG and CG). Postural control was measured with the Romberg test, with open (RBOE), closed eyes (RBCE) and under unipodal dominant side (RUDL) conditions. Proprioception was assessed by measuring participants’ ability to reposition their dominant knee at 45º. Finally, performance on the cognitive task was measured through a subscale of the Barcelona Test called “categorical evocation in associations.” The EG and the CG completed 8-week training programme with two sessions the 30 minutes per week of postural control and proprioception exercises. The EG additionally included music in each session. Results: The results showed significant differences in both group at the postural control tests (RBOE and RBCE) and proprioceptive test post intervention. Conclusion: The 8-week training program had a positive impact on the post-intervention results for motor control and proprioception, but not on the results of the cognitive task. There were no significant differences between the groups that carried out sessions with or without music.

Abstract: Two novel low-dimensional molecular magnetic materials were prepared by a self-assembling of 3d- and 5d-metal complexes. These are the first neutral heterobimetallic cyanobridged compounds involving one anisotropic Mn(III) Schiff base complex and one octacyanotungstate(V) per molecular unit. A slow diffusion of the constituents’ solutions leads to a formation of the 0D crystalline complex 1 due to coordination of a water molecule to the Mn center prevents a polymer formation. A rapid mixing of reagents results in a precipitation of the microcrystalline powder of the complex 2, which on a totality of experimental data possess 1D polymeric structure. The magnetic studies have shown that antiferromagnetic exchange interactions are dominating in 1 J/k_B= -13.1(7) K, D=-3.0(1.3) K, zJ'= -0.16(20) K and g_av=2.00(1); while the presence of the significant intramolecular Mn(III)–W(V) ferromagnetic couplings through cyanide bridges is characteristic for 2 (J/k_B = 46.1(5) K g_Mn = 2.11(3), fixed g_W = 2.0). Due to the weak interchain interactions, zJ'/k_B = –0.8(2) K, compound 2 is a metamagnet with the Néel temperature of 9.5 K undergoing a spin-flip transition at 2 kOe. The slow magnetization dynamics of 2 was investigated at DС field of 0 and 2 kOe, giving the values of τ₀ 32(15) and 36(15) ps respectively, well within the range typical for SCMs. The respective ∆_τ/k_B values were 48.4(1.2) and 44.9(1.0) K.

The erosion of surface pipelines induced by proppant flowback during shale gas production is significant. The surface pipelines in a shale gas field in the Sichuan Basin experienced perforation failures after only five months of service. To investigate the erosion features of L360N, coatings, and ceramics and optimize the selection of two protective materials, a gas-solid two-phase flow jet erosion experimental device was used to explore the erosion resistance of L360N, coating, and ceramic under different impact velocities (15 m/s, 20 m/s, and 30 m/s). An energy dispersive spectroscope, a scanning electron microscope, and a laser confocal microscope were employed to analyze erosion morphologies. With the increase in flow velocity, erosion depth and erosion rate of L360N, coating, and ceramic increased and peaked under an impact velocity of 30 m/s. Maximum erosion rate and maximum erosion depth of L360N were respectively 0.0350 mg/g and 37.5365 µm. Its primary material removal mechanism was plowing of solid particles and microcracks were distributed on the material surface under high flow velocities. Maximum erosion rate and maximum erosion depth of coating were respectively 0.0217 mg/g and 18.9964 µm. Its primary material removal mechanism was the detachment of the binder phase Co caused by plowing. Maximum erosion rate and maximum erosion depth of ceramics were respectively 0.0108 mg/g and 12.4856 µm. The erosion mechanisms were micro-cutting and plowing. Under different particle impact velocity, different erosion morphologies were observed, but the primary erosion mechanism was the same. The erosion resistance of ceramics was higher than that of coating. Therefore, ceramic lining materials could be used to protect the easily eroded parts, such as pipeline bends and tees, and reduce the failure rate by more than 93%. The study provided the data and theoretical basis for the theoretical study on oil and gas pipeline erosion and pipeline material selection.

More and more works are using machine learning techniques while adopting supervisory control and data acquisition (SCADA) system for wind turbine anomaly or failure detection. While parameter selection is important for modelling a wind turbine’s health condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. Moreover, after proving that Copula, a multivariate probability distribution for which the marginal probability distribution of each variable is uniform is capable of simplifying the estimation of mutual information, an empirical copula based mutual information estimation method (ECMI) is introduced for an application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

For offshore wind power generation, accessibility is one of the main factors that has great impact on operation and maintenance due to constraints on weather conditions for marine transportation. This paper presents a framework to explore the accessibility of an offshore site. At first, several maintenance types are defined and taken into account. Next, a data visualization procedure is introduced to provide an insight into the distribution of access periods over time. Then, a rigorous mathematical method based on finite state Markov chain is proposed to assess the accessibility of an offshore site from the maintenance perspective. A five-year weather data of a marine site is used to demonstrate the applicability and the outcomes of the proposed method. The main findings show that the proposed framework is effective in investigating the accessibility for different time scales and is able to catch the patterns of the distribution of the access periods. Moreover, based on the developed Markov chain, the average waiting time for a certain access period can be estimated. With more information on the maintenance of an offshore wind farm, the expected production loss due to time delay can be calculated.

The gut microbiota constitutes a complex ecosystem, comprising trillions of microbes that have co-evolved with their host over hundreds of millions of years. Over the past decade, a growing body of knowledge has underscored the intricate connections among diet, gut microbiota, and human health. Bioactive polysaccharides (BPs) from natural sources like medicinal plants, sea-weeds, and fungi have diverse biological functions, including antioxidant, immunoregulatory, and metabolic activities. Their effects are closely tied to the gut microbiota, which metabolizes BPs into health-influencing compounds. Understanding how BPs and gut microbiota interact is critical for harnessing their potential health benefits. This review provides an overview of the human gut microbiota, focusing on its role in metabolic diseases like obesity, type II diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and cardiovascular diseases (CVD). It explores the basic characteristics of several BPs and their impact on gut microbiota. Given their significance for human health, we summarize the biological functions of these BPs, particularly in terms of im-munoregulatory activities, blood sugar, and hypolipidemic effect, providing a valuable reference for understanding the potential benefits of natural BPs in treating metabolic diseases. These properties make BPs promising agents for preventing and treating metabolic diseases. The comprehensive understanding of the mechanisms by which BPs exert their effects through gut microbiota opens new avenues for developing targeted therapies to improve metabolic health.

Background: Vitamin B 12 deficiency has been implicated in endothelial dysfunction and cardiovascular disease via hyperhomocysteinemia. Coronary tortuosity (CorT) is a common coronary angiography finding. The etiology, clinical implication and long term prognosis are still not well clarified. This study was conducted with the aim to evaluate the relationship between CorT and vitamin B12. Subjects and Method: The medical records of consecutive patients, who underwent coronary angiography, were retrospectively reviewed. The study group consisted of 1624 patients. Taking into consideration the inclusion criteria, 212 patients with CorT and 210 patients with normal coronary angiographies (control group) were included in the study. Vitamin B12, other biochemical parameters, clinical and echocardiographic parameters, and CorT score were evaluated in all patients. CorT is defined as fixed 3 bends during both systole and diastole, with each bend ≥45 °. Results: Patients with CorT had higher prevalence of older, female gender, hypertension, current smoking. Vitamin B12 was significantly decreased in patient with CorT (134.7±47.8 vs 239.6±53.8 p<0.001). On multivariate analysis age, female gender, hypertension, diabetes mellitus and vitamin B12 were independent predictors for CorT (OR 1.56; 95% CI: 1.247–1.962; p < 0.001, OR 1.628; 95% CI: 1.376-2.048; p<0.001, OR 1.865; 95% CI: 1.387-2.695; p<0.001, OR 1.362; 95% CI: 1.184-1.726; p<0.001, OR 1.862; 95% CI: 1.486-2.674; p<0.001, respectively). Conclusion: In our study, we have founded a significant relationship between vitamin B12 deficiency and CorT.

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

Presence detection is essential in many applications, including smart homes, office automation, and public space management. Traditional technologies like infrared sensors and cameras frequently encounter obstacles due to privacy concerns, high costs, and complexity. The study provides a novel way to detect presence based on Wi-Fi signals and machine learning algorithms, utilizing the ESP8266 microcontroller. The suggested system attempts to reliably detect the presence of people in a given place by evaluating fluctuations in Wi-Fi signal strength and patterns. The theoretical basis of this strategy is investigated, including the ESP8266’s capabilities for Wi-Fi data gathering and the use of machine learning methods for data analysis. The methodology includes data collecting with the ESP8266, preprocessing, feature extraction, and training and evaluating machine learning models. This system’s potential uses include smart home automation, office occupancy tracking, and crowd management in public settings. The paper also discusses potential issues such as signal interference, data privacy concerns, and the limitations of Wi-Fi detection. Future work is also mentioned, including the suggested system’s installation and testing and enhancement proposals to improve accuracy and scalability. This research aims to provide a cost-effective, non-intrusive, and scalable presence detection system by harnessing the power of Wi-Fi signals and machine learning, enhancing intelligent environments

This review is dedicated to sustainable practices in liquid chromatography. HPLC and UHPLC methods contribute significantly to routine analytical techniques. Therefore, the transfer of classical liquid chromatographic methods into sustainable ones is of utmost importance in moving toward sustainable development goals. Among other principles to render a liquid chromatographic method green, substitution of the organic solvent component in the mobile phase with a greener one received great attention. This review concentrates on choosing the best alternative green organic solvent to replace the classical solvent in the mobile phase for easy rapid transfer to a more sustainable normal phase or reversed-phase liquid chromatography. The main focus of this review will be on describing the transfer of non-green to green and white chromatographic methods in an effort to elevate the sustainability best practice in analytical chemistry. The greenness properties and greenness ranking in addition to the chromatographic suitability of seventeen organic solvents for liquid chromatography are mentioned to have a clear insight into the issue of rapidly choosing the appropriate solvent to transfer a classical HPLC or UHPLC method into a more sustainable one. A simple guide is proposed for making the liquid chromatographic method more sustainable.

The integration of Wireless Power Transfer (WPT) technologies with Artificial Intelligence (AI) is poised to revolutionize multiple industries, including consumer electronics and industrial applications. This study delves into the latest advancements in WPT, with a focus on AI-enhanced efficiency, range, and reliability. AI-driven algorithms optimize power transfer, improve device alignment, and predict maintenance needs, leading to smarter and more sustainable power solutions. Machine learning and computer vision are employed to achieve precise alignment, while predictive maintenance minimizes downtime and costs. Additionally, AI-powered power management systems adapt to varying demands, optimizing energy usage. This research highlights the transformative potential of AI in enhancing WPT technologies, with significant implications for next-generation applications. The findings emphasize the importance of ongoing research in this interdisciplinary field to address existing challenges and unlock new opportunities for innovation.

Cars are a highly inefficient mode of urban transport with significant negative impacts. Electric vehicles make it possible to eliminate direct CO2 emissions (though the total over a vehicle’s entire life cycle greatly offsets the advantages) but leave unchanged – or even aggravate – all the other negative effects on the quality of urban environments. Three visions for urban transport were presented: automobility, multimodality and accessibility. The first dominates North American cities, the second European one. The anomalies of these two visions have been examined. The automobility cities cannot manage the congestion and the attempts are self- defeating. The anomalies of multimodality city are related to the public space consumption for mobility and the impossibility to plan spatial proximity. The sustainable vision, that resolve the anomalies, is represented by the accessibility city in the Triple Access System (TAS) version which, however, still has a long way to go to be accepted and implemented.

The review explores the therapeutic potential of mangrove plants as sources of bioactive compounds, particularly focusing on their anti-inflammatory properties. Mangroves, found in tropical and sub-tropical tidal coasts, exhibit unique ecological and biological activities. The review compiles and examines 136 research findings published between 1982 and 2018, with 41 studies specifically addressing the anti-inflammatory properties of mangroves in Indian coastal regions, including the Andaman and Nicobar Islands. It summarizes the chemical structures, molecular weights, formulas, solvents, and biological activities of these mangroves, as reported by various researchers over time. The data underscore the significant potential of tropical Indian mangroves in anti-inflammatory applications. However, a notable research gap exists regarding the extraction of bioactive compounds from mangrove species in the Andaman and Nicobar Islands compared to other regions such as Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, West Bengal, and Puducherry. Overall, the compilation provides valuable insights into mangrove-based studies for drug discovery research, emphasizing the need for further exploration and exploitation of these natural resources.

CVDs are a leading cause of death globally. In CVDs, the heart is unable to deliver enough blood to other body regions. Since effective and accurate diagnosis of CVDs is essential for CVD prevention and treatment, machine learning (ML) techniques can be effectively and reliably used to discern patients suffering from a CVD from those who do not suffer from any heart condition. Namely, machine learning algorithms (MLAs) play a key role in the diagnosis of CVDs through predictive models that allow us to identify the main risks factors influencing CVD development. In this study, we analyze the performance of ten MLAs on two datasets for CVD prediction and two for CVD diagnosis. Algorithm performance is analyzed on top-two and top-four dataset attributes/features with respect to five performance metrics –accuracy, precision, recall, f1-score, and roc-auc – using the train-test split technique and k-fold cross-validation. Our study identifies the top two and four attributes from each CVD diagnosis/prediction dataset. As our main findings, the ten MLAs exhibited appropriate diagnosis and predictive performance; hence, they can be successfully implemented for improving current CVD diagnosis efforts and help patients around the world, especially in regions where medical staff is lacking.

In this paper we describe a nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions. Given $N$ independent observations, convexity theory shows that the NPML estimator is discrete with at most $N$ support points. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. The probability of the support points is found by a Primal-Dual Interior-Point method; the location of the support points is found by an Adaptive Grid method. Our method is able to handle high-dimensional and complex multivariate mixture models.An important application is discussed for the problem of population pharmacokinetics and a non-trivial example is treated.Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics.

Our study utilized density functional theory (DFT) to explore a 2D MoP2 surface for hydrogen storage. Optimization determined a gravimetric capacity for pristine MoP2 of 5.72%, with an adsorption energy of ‒ 0.13 eV/molecule. For this surface with 12.5% Mo vacancies, we achieved 6.02% and ‒ 0.14 eV/molecule. We then introduced a novel criterion to test the attraction forces in hydrogen storage at a given temperature. This involved a first-principles molecular dynamics approach at various temperatures. To determine the number of molecules adsorbed on the surface at temperature T1, we conducted an FPMD calculation at T1, using the optimization as the initial system configuration. Subsequently, we performed a second FPMD calculation at a temperature T2 (with T2 ‹‹ T1), using the stable configuration of the first FPMD calculation as the initial configuration. If there were attraction forces on the molecules, they would move towards the surface. We identified as adsorbed molecules at temperature T1, only those that moved back toward the surface at temperature T2. The defective surface gave the best gravimetric capacity, ranging from 5.27% at 300 K to 6.02% at 77 K. The latter met the requirement from the US-DOE, indicating the potential practical application of our research in hydrogen storage.

Abstract: Placenta accreta spectrum (PAS) is a severe complication of pregnancy associated with excessive invasion of cytotrophoblast cells at the sites of the endometrial-myometrial interface and the myometrium itself in cases of adherent (creta) and invasive (increta and percreta) forms, respectively. Despite advancements in ultrasound protocols and found associations of alpha-fetoprotein, PAPP-A, hCG, PLGF, sFlt-1, IL-8, and IL-33 peripheral blood levels with PAS, the condition remains undiagnosed before delivery because of the lacking clear evaluation criteria for each of the three grades of PAS. This leads to a high risk of massive blood loss, maternal hysterectomy, and preterm birth. While using deep sequencing, miRNA signatures of placental tissue, myometrium, and blood plasma from women with PAS in the third trimester of pregnancy, as well as miRNA profile in exosomes from the blood serum of women in the first trimester with physiologically progressing pregnancy, complicated by PAS or preeclampsia, were obtained. Two logistic regression models were constructed, both featuring statistically significant parameters related to the levels of miR-26a-5p, miR-17-5p, and miR-101-3p quantified by real-time PCR in native blood serum. These models demonstrated 100% sensitivity in detecting PAS during the first pregnancy screening. These miRNAs were identified as specific markers for PAS, showing significant differences in their blood serum levels during the first trimester in the PAS group compared to those in physiological pregnancies, early- or late-onset preeclampsia groups. Furthermore, these miRNAs exhibited differential expression in the PAS placenta and/or myometrium in the third trimester and, according to literature data, control angiogenesis. Significant correlations were found between extracellular hsa-miR-101-3p and nuchal translucency thickness, hsa-miR-17-5p and uterine artery pulsatility index, and hsa-miR-26a-5p and hsa-miR-17-5p with PLGF. The developed test system for early non-invasive PAS diagnosis based on the blood serum level of extracellular miR-26a-5p, miR-17-5p, and miR-101-3p can serve as an auxiliary method for 1st trimester screening pregnant women, subject to validation on independent test samples.

Delayed cancer detection is one of the common causes of poor prognosis in case of many cancers including the cancers of the oral cavity. Despite improvement and development of new and efficient gene therapy treatments, very little has been done to algorithmically assess the impedance of these carcinomas. In this work, we attempt to annotate viable attributes in oral cancer gene datasets for identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated to other forms of oral cancer detection.

Although researchers have investigated widely the impact of IOD phases on human lives, only a few have examined such impacts on fisheries. In this study, we analyzed the influence of negative (positive) of IOD on a chlorophyll a (Chl-a) concentration as an indicator of phytoplankton biomass and small pelagic fish production in the eastern Indian Ocean (EIO) off Java. We also conducted field surveys in the EIO off Palabuhanratu Bay at the peak (October) and the end (December) of the 2019 positive IOD phase. Our findings show that the Chl-a concentration had a strong and robust association with the 2016 (2019) negative (positive) IOD phases. The negative (positive) anomalous Chl-a concentration in the EIO off Java associated with the negative (positive) IOD phase induced strong downwelling (upwelling), leading to the preponderant decrease (increasing) of small pelagic fish production in the EIO off Java.

IoT system becomes hot topic nowadays for smart home, IoT helps devices to communicate together without human intervention inside home, so it is offering many challenges. A new smart home IoT platform powered using electromagnetic energy harvesting is proposed in this paper. It contains a high gain transmitted antenna array and efficient circular polarized array rectenna system to harvest enough power from any direction to increase life time of the batteries used in IoT system. Optimized energy consumption, the software with adopting the Zigbee protocol of the sensor node and low power microcontroller are used to operate in lower power modes. The proposed system has an 84.6 days lifetime which is approximately 10 times the lifetime for similar system. On the other hand, the proposed power management circuit operated at 0.3 V DC to boost the voltage to ~3.7V from radio frequency energy harvesting and manage battery level to increase the batteries lifetime. A predictive indoor environment monitoring system is designed based on a novel hybrid system to provide a non-static plan, approve energy consumption and avoiding failure of sensor nodes in smart home.

High frequency knowledge of the spatio-temporal distribution of the Downwelling Surface Shortwave Flux (DSSF) and its diffuse fraction (fd) at the surface is nowadays essential for understanding climate processes at the surface-atmosphere interface, plant photosynthesis and carbon cycle, and for the solar energy sector. The EUMETSAT Satellite Application Facility for Land Surface Analysis operationally delivers estimation of the MDSSFTD (Downwelling Surface Short-wave radiation Fluxes – Total and Diffuse fraction) product with an operational status since the year 2019. The method for the retrieval was presented in the companion paper [40]. The part 2 now focuses on the evaluation of the MDSSFTD algorithm and presents the comparison of the corresponding outputs, i.e. total DSSF and diffuse fraction (fd) components, against in-situ measurements acquired at four BSRN stations over a seven-month period. The validation is performed on an instantaneous basis. We show that the satellite estimates of DSSF and fd meet the target requirements defined by the user community for all-sky (clear and cloudy) conditions. For DSSF, the requirements are 20Wm^-2 for DSSF<200Wm^-2, and 10% for DSSF>=200Wm^-2. The MBE and rMBE compared to the ground measurements are 3.618Wm^-2 and 0.252%, respectively. For fd, the requirements are 0.1 for fd<0.5, and 20% for fd>=0.5. The MBE and rMBE compared to the ground measurements are -0.044 and -17.699%, respectively. The study also provides a separate analysis of the product performances for clear sky and cloudy sky conditions. The importance of representing the cloud-aerosol radiative coupling in the MDSSFTD method is discussed. Finally, it is concluded that the quality of the Aerosol Optical Depth (AOD) forecasts currently available is enough accurate to obtain reliable diffuse solar flux estimates. This quality of AOD forecasts was still a limitation a few years ago.

{\bf Abstract.} \\ {\bf Background:} Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 burden among the Rohingya refugees of Bangladesh to keep up with the disease outbreak's pace, health needs, and disaster preparedness. \noindent{\bf Methodology and Findings:} To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious Recovered (SEIR) transmission model. All the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and good fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be good control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number ($ \mathcal{R}_{0} $) has been estimated to be 0.7563. \noindent{\bf Conclusion:} Our analysis suggests that, due to the extensive precautions from the Bangladesh government as well as other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. Although detailed and pragmatic preparedness should be adopted for the worst scenario.

A self-propelled electric tea seed-planter is designed to address the lack of tea seed seeding equipment, low seeding efficiency, and poor seeding uniformity. Based on the physical characteristics of the tea seeds and the agronomic requirements of tea planting, the key structure of the planter is designed, and the structural parameters are determined. The motor and battery configurations are determined by calculating the power consumption of the entire machine. In a field experiment, the driving gear (A), ditching depth (B), and planting gear position (C) are used as test factors, and the acceptance index (AI), replay index (RI) and leakage index (LI) are used as test indexes. The test results showed that the driving speed and planting gear position significantly affect the acceptance index, replay, and leakage index, whereas the ditching depth has little effect on the performance indexes. When the driving speed was medium, ditching depth was 30 mm, and planting gear position was in the third gear, the corresponding acceptance index was 87.22%, the replay index was 8.89%, and the leakage index was 3.89%. The test prototype has a good working quality and can provide theoretical guidance for production practices.

P. palmata is a viable source of nutrients with bioactive properties. The present study determined the potential role of post-extraction ultrasonication on some compositional features and antioxidant properties of resulting extracts after sequential enzymatic/alkaline treatment. No significant difference was detected in terms of protein content and recovery as well as amino acid composition of the extracts. The nitrogen-to-protein conversion factor of 5 was found to be too high for the seaweed and its enzymatic/alkaline extracts. The extracts sonicated by bath for 10 minutes and not sonicated showed the highest and lowest total phenolic contents (p&lt;0.05), respectively. The highest radical scavenging and lowest metal chelating activities were observed for the non-sonicated sample, as evidenced by IC50 values. The extract sonicated by bath for 10 minutes showed the most favorable in vitro antioxidant properties since its radical scavenging was not significantly different from that of not sonicated sample (p&gt;0.05), whereas its metal chelating activity was significantly higher (p&lt;0.05). To conclude, post-extraction ultrasonication by an ultrasonic bath for 10 minutes is recommended to increase phenolic content and improve antioxidant properties of enzymatic-alkaline extracts from P. palmata.

This study introduces an efficient strategy for synthesizing polyhydroxyurethane-based multicomponent hydrogels with enhanced rheological properties. In a single-step process, 3D materials composed of Polymer 1 (PHU) and Polymer 2 (PVA or gelatin) were produced. Polymer 1, a crosslinked polyhydroxyurethane (PHU) grew within a colloidal suspension of Polymer 2, forming an interconnected network. The synthesis of Polymer 1 utilized a Non-Isocyanate Polyurethane (NIPU) methodology based on the aminolysis of bis(cyclic carbonate) (bisCC) monomers derived from 1-thioglycerol and 1,2-dithioglycerol (monomers MA and ME, respectively). This method, applied for the first time in Semi Interpenetrating Network (SIPN) formation, demonstrated exceptional orthogonality, since the functional groups in Polymer 2 do not interfere with Polymer 1 formation. Optimizing PHU formation involved a twenty-trial methodology, identifying influential variables such as polymer concentration, temperature, solvent (an aprotic and a protic solvent), and the organo-catalyst used [a thiourea derivative (TU) and 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU)]. The highest molecular weights were achieved under near-bulk polymerization conditions using TU-protic and DBU-aprotic as catalyst-solvent combinations. ME-based PHU exhibited higher (M_w ) ̅ than MA-based PHU (34,100 Da and 16,400 Da, respectively). Applying the enhanced methodology to prepare ten multicomponent hydrogels using PVA or gelatin as the polymer scaffold revealed superior rheological properties in PVA-based hydrogels, exhibiting solid-like gel behavior. Incorporating ME enhanced mechanical properties and elasticity (with loss tangent values of 0.09 and 0.14). SEM images unveiled distinct microstructures, including a sponge-like pattern in certain PVA-based hydrogels when MA was chosen, indicating the formation of highly superporous interpenetrated materials. In summary, this innovative approach presents a versatile methodology for obtaining advanced hydrogel-based systems with potential applications in various biomedical fields.

This paper analyzes the causal effect between domestic private investment, public investment, foreign direct investment and economic growth in Tanzania during the 1970-2014 period. The modified neo-classical growth model is used to estimate the ieffect of investment on economic growth. Also, the economic growth models based on Phetsavong and Ichihashi (2012) [1], and Le and Suruga (2005) [2]are used to estimate the crowding out effect of public investment on domestic private investment on one hand and foreign direct investment on the other hand. In the same way, the crowding out effect of foreign direct investment on domestic private investment is estimated. A correlation test is applied to check the correlation among independent variables, and the results show that there is very low correlation suggesting that multicollinearity is not a serious problem. Moreover, the diagnostic tests including RESET regression errors specification test, Breusch-Godfrey serial correlation LM test, Jacque-Bera-normality test and white heteroskedasticity test reveal that the model has no signs of misspecification and that, the residuals are serially uncorrelated, normally distributed and homoskedastic. Broadly, the empirical results show that the domestic private investment and foreign direct investment play an important role in economic growth in Tanzania. Besides, a revealed negative, albeit weak, association between public and private investment suggests that the positive effect of domestic private investment on economic growth becomes smaller when public investment-to-GDP ratio exceeds 8-10 percent. Similarly, foreign direct investment tends to marginally reduce the impact of domestic private investment on growth. These results suggest that public investment and foreign direct investment need to be considered carefully in order to avoid a reduced positive impact of domestic private investment on growth. Domestic saving may be promoted to encourage domestic investment for economic growth.

Mothers of children with a specific clinical condition such as type 1 diabetes mellitus (T1DM) may experience higher levels of stress predisposing them to a poorer perception of quality of life and alterations in nutritional status. The aim of this research was to evaluate the relationship between nutritional status and quality of life perception of mothers caring for children with Type 1 Diabetes Mellitus. This research was conducted from July to September 2022, with mothers of children aged 5 to 10 years with diabetes. Data were collected through an online questionnaire including soci-oeconomic information, anthropometric data, clinical data of the children, and the WHO-QOL-abbreviated quality of life questionnaire. For statistical analysis, Statistical Package for Social Science 24 was used. Fifty-five mothers of children with diabetes were evaluated. Over 33% of the mothers were overweight and reported a regular perception of quality of life. Higher BMI was associated with poorer perception of quality of life in the psychological and self-assessment domains, and longer diagnosis time was associated with better perception of quality of life in the physical, psychological, and environmental domains. Therefore, it is understood that actions aimed at improving the quality of life of these mothers are necessary.

Myocardial fibrosis is a pivotal pathophysiological step of various cardiovascular diseases, which eventually leads to heart failure and death. Cardiac fibroblasts activation and subsequently differentiate into myofibroblasts are pivotal pathophysiological step in myocardial fibrosis. Phosphoglycerate mutase 1 (PGAM1) directly interacts with α-smooth muscle actin (ACTA2) and participate in fibrosis regulation. Allosteric inhibitor of PGAM1 could attenuates fibrosis through break the interaction of PGAM1 and ACTA2. In this study, we aimed to screen and identify novel ligand of PGAM1 in natural product library. Firstly, various natural products were screened via DARTS assay and Carthamus tinctorius L. was identified as a target pool for molecular docking. Then, in silico screening assay identified baicalein as a novel ligand. In addition, baicalein could directly bind to the PGAM1 protein with a KD value of 34.6 μM. Molecular dynamics simulation revealed that baicalin and PGAM1 interactions were stable. These findings indicate that PGAM1 is a direct pharmacological target of baicalein. Immunoblot analysis further demonstrated that baicalin inhibits the activation of fibroblasts in vitro. Finally, the anti-myocardial fibrosis effect of baicalein was verified in vivo. Taken together, our findings suggest that baicalein is a novel PGAM1 templates for developing new therapeutics against myocardial fibrosis.

B-1 cells, a subtype of B lymphocytes, are found in lymphoid tissues and peritoneal and pleural cavities. They play diverse roles, releasing cytokines, generating antibodies, regulating the immune response, engaging in phagocytosis, and moving to inflammatory sites. However, their physiology remains partially understood, especially in stressful conditions like sleep restriction. This study aimed to investigate murine B-1 cell activation and differentiation under sleep restriction. Male C57BL/6J mice underwent an 18-hour sleep restriction for 21 days, followed by Candida albicans or Paracoccidioides brasiliensis infection. Purified peritoneal B-1 cells were evaluated for activation markers, microbicidal molecules, cytokine gene expression, Toll-like receptors, and commitment factors. Results showed that sleep restriction minimally influenced activation marker expression in B-1 cells across infection models. However, under sleep restriction, microbicidal molecule production increased in P. brasiliensis (NO) and C. albicans (ROS) infections. Gene expression revealed elevated TLR-2 and cytokines (IL-12, IL-6, TNF-α) in sleep-restricted animals. Lineage commitment analysis indicated myeloid polarization in sleep-restricted B-1 cells for all infection models. These findings underscore the impact of sleep restriction on B-1 cell activation and differentiation, with outcomes varying based on infection type. This study highlights the importance of investigating immunological changes resulting from sleep restriction.

Herpesvirus is a prevalent pathogen that primarily infects human epithelial cells and has the ability to reside in neurons. In the field of otolaryngology, herpesvirus infection primarily leads to hearing loss and vestibular neuritis, and is considered the primary hypothesis regarding the pathogenesis of vestibular neuritis. In this review, we provide a summary of the effects of herpes virus on cellular processes in both host cells and immune cells, with a focus on HSV-1 as illus-trative examples.

In recent years, with the popularity of smart mobile devices, the interaction between devices and users, especially in the form of voice interaction, has become increasingly important. If smart devices can understand more users' emotional states through voice data, more customized services can be provided for users. This paper proposes a novel machine learning model for speech emotion recognition, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN), called CLDNN. To make the designed system can recognize the audio signal closer to the human auditory system does, this article uses the Mel frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal is extracted as the input of the model, and the feature values of the data are calculated using several local feature learning blocks (LFLB) composed of one-dimensional CNN. Because the audio signals are time-series data, the feature values obtained from LFLBs then input into LSTM layer to enhance the learning on time-series level. Finally, fully connected layers are used for classification and prediction. Three databases RAVDESS, EMO-DB and IEMOCAP are used for the experiments in this paper. The experimental results show that the proposed method can improve the accuracy compared to other related researches in speech emotion recognition.

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a simple statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

Cardiovascular Diseases (CVDs) are still menacing and killing adults worldwide, notwithstanding the tremendous effort, to decrease their related mortality and morbidity. Lately, a growing body of evidences indicated that inflammation plays a pivotal role in the pathogenesis and complications of CVDs. A receptor of the immunoglobulin superfamily, triggering receptor expressed on myeloid cells -1 (TREM-1) was shown to induce and to amplify the inflammation in both acute and chronic diseases pathogenesis and progression and hence it is one of the important factors that complicates CVDs. Thus, studies endeavored to investigate the role played by TREM-1 in CVDs with respect to their etiologies, complications and possible therapeutics. We examined here, for the first time, the most relevant studies regarding TREM-1 involvement in CVDs. We summarized the finding after critically analyzing them and made some suggestions for furtherance of the investigations with the aim to utilize TREM-1 and its pathways for diagnostic, management and prognosis of CVDs. Overall, TREM-1 was found to be involved in the pathogenesis of acute and chronic cardiovascular conditions like Acute myocardial infraction (AMI) and atherosclerosis as well. Although most therapeutic approaches are yet to be elucidated, present research outcome displays a promising future to utilize TREM-1 pathway as potential target to understand and manage CVDs.

The first part of the paper was aimed at analyzing the given nonlinear problem by different methods of computation of the Lyapunov exponents (Wolf method [1], Rosenstein method [2], Kantz method [3], method based on the modification of a neural network [4, 5], and the synchronization method [6, 7]) for the classical problems governed by difference and differential equations (Hénon map [8], hyper-chaotic Hénon map [9], logistic map [10], Rössler attractor [11], Lorenz attractor [12]) and with the use of both Fourier spectra and Gauss wavelets [13]. It was shown that a modification of the neural network method [4, 5] makes it possible to compute a spectrum of Lyapunov exponents, and then to detect a transition of the system regular dynamics into chaos, hyper-chaos, hyper hyper-chaos and deep chaos [14-16]. Different algorithms for computation of Lyapunov exponents were validated by comparison with the known dynamical systems spectra of the Lyapunov exponents. The carried out analysis helps comparatively estimate the employed methods in order to choose the most suitable/optimal one to study different kinds of dynamical systems and different classes of problems in both this and the next paper parts.

Novel SARS-CoV-2, a bat based virus originated in Wuhan, China that caused a global pandemic in December, 2019 belongs to the Betacorona virus family and contains single stranded genome of ~29Kbp. The host cell invasion of SARS-CoV-2 is facilitated by interaction of C-Terminal Domain (CTD) of Spike (S) protein of virus and host ACE2 receptor in the presence of TMPRSS seine protease secreted by the host cell. In this study the mutation hotspots of S-protein will be identified and the impact of such mutation in the binding affinity will be studied. Additionally, the lead molecule which can bind to the mutated protein also will be identified. Multiple sequence alignment of the spike protein sequence of SARS-CoV-2 shows the number of single amino acid mutation hotspots such as L5F, R214L, R408I, G476S, V483A, H519Q, A520S, T572I, D614G and H655Y. Among these mutations D614G has 57.5% occurrence and G476S, V483A has 7.5% occurrence. The mutated proteins were modelled based on wild type homolog and docked to ACE2 receptor. When the mutated S protein is docked, the ∆G (binding free energy) value is very minimal in mutated protein showed the stability of variants. By the drug repurposing method, 1000 FDA approved drugs were virtually screened for its binding to RBD of S1 domain. Among these drugs Digitoxin, Gliquidone and Zorubicin Hcl binds to spike proteins with higher docking score (lesser than -8.5 Kcal/mol) to both wild type and mutants.

Polychlorinated phenoxathiins (PCPTs), polychlorinated dibenzothiophenes (PCDTs), and polychlorinated thianthrenes (PCTAs) are sulfur analogues of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/DFs). Chlorothiophenols(CTPs) and chlorophenols (CPs) are key precursors to form PCTA/PT/DTs, which can form chloro(thio)phenoxy radical, sulfydryl/hydryl-substituted phenyl radical and (thio)phenoxyl diradicals. The available radical/radical PCTA/DT formation mechanism failed to explain the higher concentration of PCDTs than that of PCTAs under the pyrolysis or combustion conditions. Thus in this work, a detailed thermodynamics and kinetic calculations were carried out to investigate the pre-intermediates formation for PCTA/PT/DTs from radial/molecule coupling of 2-C(T)P with their key radical species. Our study found that the radial/molecule mechanism can thermodynamically and kinetically contribute to the gas-phase formation of PCTA/PT/DT/s. The S/C coupling modes to form thioether-(thio)enol intermediats are preferable over the O/C coupling modes to form ether-(thio)enol intermediats. Thus, although the radial/molecule coupling of chlorophenoxy radical with 2-C(T)P have no effect on the PCDD/PTs formation, the radial/molecule coupling of chlorothiophenoxy radical with 2-C(T)P play an important role in the PCDT/PT formation. Most importantly, the pre-PCDT intermediates formation pathways from the coupling of sulfydryl/hydryl-substituted phenyl radical with 2-C(T)P and the coupling of (thio)phenoxyl diradicals with 2-C(T)P are more favorable to pre-PCTA/PT intermediates formation pathways from the coupling of chlorothiophenoxy radical with 2-C(T)P, which can give reasonable explanation for the high PCDT-to-PCTA ratio in the environment.

In an effort to discover an effective and selective antitumour agent, synthesis and anti-cancer potential of 4-(pyridin-4-yl)-6-(thiophen-2-yl)pyrimidin-2(1H)-one (SK-25), which has been reported earlier by us with significant cytotoxicity towards MiaPaCa-2 malignant cells, with an IC₅₀ value of 1.95 μM and was found to instigate apoptosis. In the present study, the antitumour efficacy of SK-25 was investigated on Ehrlich ascites tumour (solid), Sarcoma 180 (solid) tumour and Ehrlich ascites carcinoma. The compound was found to inhibit tumour development by 94.71% in Ehrlich ascites carcinoma (EAC), 59.06% in Ehrlich tumour (ET, solid) and 45.68% in Sarcoma-180 (solid) at 30 mg/kg dose. Additionally, SK-25 was established to be non-toxic at a maximum tolerated dose of 1000 mg/kg in acute oral toxicity in Swiss-albino mice. Computer-based predictions also show that the compounds could have an interesting DMPK profile. The current study provides insight for further investigation of the antitumour potential of the molecule.

The replication of species A rotaviruses (RVAs) and other RNA viruses involves recruitment of and inter-action with the cellular organelles lipid droplets (LDs), both physically and functionally. Inhibition of en-zymes involved in the cellular fatty acid biosynthesis pathway or of cellular lipases that degrade LDs re-duces the functions of ‘viral factories’ (viroplasms for rotaviruses or replication compartments of other RNA viruses) and decreases the production of infectious progeny virus. Similarly, disturbance of the cellular lipid homeostasis in various ways blocks the replication of flaviviruses (hepatitis C viruses, Zikavirus, others), HIV-1, SARS-CoV-2, picornaviruses, noroviruses, influenza viruses and other negative-strand RNA viruses.

Pollution spillover is an important issue to improve the water environment of transboundary rivers, which has been aggravated by the decentralization of China's pollution control and promotion system.This paper analyzes the evolution of the pollution reduction mandates and the possible change of water environment in China which are tested with the water quality data of state key monitoring sections in 2004-2014 .In terms of research methods, this paper mainly uses Propensity Score Matching reference with group difference test and OLS. Empirical findings support the association between decentralization and pollution levels. The pollution levels of the monitoring points located at the boundary are significantly higher than that of interior counties. The pollution of tributary is more serious than trunk stream，which quickly reversed after the system changed. Water pollution levels rapidly changes when we compare the monitoring site in front of jurisdictional boundaries with that after the jurisdictional boundaries. We draw the following conclusions that local goverments may manipulate pollution within their jurisdictions and total pollutant control system will exacerbate border pollution, while water quality inspection can reduce marginal pollution.

Since the beginning of the COVID-19 pandemic, extensive drug repurposing efforts sought to identify small molecule antivirals with various mechanisms of action. Here we aim to review research progress on small molecule viral entry and fusion inhibitors that directly bind to the SARS CoV-2 Spike protein. Early in the pandemic, numerous small molecules were identified in drug repurposing screens and reported to be effective in vitro SARS CoV-2 viral entry or fusion inhibitors. However, given minimal experimental information regarding the exact location of small molecule binding sites on Spike, it was unclear what the specific mechanism of action was, or where the exact binding sites were on Spike for some inhibitor candidates. The work of countless researchers has yielded great progress, with the identification of many viral entry inhibitors that target elements on the S1 receptor binding domain (RBD) or N-terminal domain (NTD) and disrupt S1 receptor binding function. In this review, we will also focus on highlighting fusion inhibitors that target inhibition of the S2 fusion function, either by disrupting the formation of the postfusion S2 conformation or alternatively by stabilizing structural elements of the prefusion S2 conformation to prevent conformational changes associated with S2 function. We highlight experimentally validated binding sites on the S1/S2 interface and on the S2 subunit. While most mutations to the Spike protein to date in variants of concern (VOCs) have been localized to the S1 subunit, the S2 subunit sequence is more conserved with only a few observed mutations in proximity to S2 binding sites. Several recent small molecules targeting S2 have been shown to have robust activity over numerous recent VOC mutant strains and/or greater broad spectrum antiviral activity for other more distantly related coronaviruses.

Proteases catalyse irreversible posttranslational modifications that often alter a biological function of the substrate. The protease dipeptidyl peptidase 4 (DPP4) is a pharmacological target in type 2 diabetes therapy primarily because it inactivates glucagon-like protein-1. DPP4 also has roles in steatosis, insulin resistance, cancers and inflammatory and fibrotic diseases. In addition, DPP4 binds to the spike protein of MERS virus, causing it to be the human cell surface receptor for that virus. DPP4 has been identified as a potential binding target of SARS-CoV-2 spike protein, so this question requires experimental investigation. Understanding protein structure and function requires reliable protocols for production and purification. We developed such strategies for baculovirus generated soluble recombinant human DPP4 (residues 29-766) produced in insect cells. Purification used differential ammonium sulphate precipitation, hydrophobic interaction chromatography, dye affinity chromatography in series with immobilised metal affinity chromatography, and ion exchange chromatography. The binding affinities of DPP4 to the SARS-CoV-2 full-length spike protein and its receptor binding domain (RBD) were measured using surface plasmon resonance and ELISA. This optimised DPP4 purification procedure yielded 1 to 1.8 mg of pure fully active soluble DPP4 protein per litre of insect cell culture with specific activity >30 U/mg, indicative of high purity. No specific binding between DPP4 and CoV-2 spike protein was detected by surface plasmon resonance or ELISA. In summary, a procedure for high purity high yield soluble human DPP4 was achieved and used to show that, unlike MERS-CoV, SARS-CoV-2 does not bind human DPP4.

Polymer-based composites are widely used in the automotive, security, aeronautical and space industries, to mention a few. This is because of their good mechanical properties, which are similar to those of metals but with the attraction of being lightweight. Kevlar® as a reinforcement is extensively used in the security industry owing to its good ballistic properties. This investigation presents the mechanical characterization based on in-plane quasi-static tensile testing of Kevlar® composite using a universal testing system. The methodology developed for the preparation of coupons is based on pressure, temperature and time. As a result of this work, elastic moduli (EL and ET), Poisson’s ratio (νLT), shear modulus (GLT) and strengths (XT, YT, S) were obtained. It is worth mentioning that there is scarce or no characterization of this material in the literature, and those studies that do characterize it do not present the coupon thermoforming conditions or the reasons of the specimen dimensions (width, length and thickness).

Predicting ground reaction forces (GRFs) and ground reaction moments (GRMs) through a biomechanical model-based approach offers advantages for biomechanical analysis, particularly in situations where the application of a statistical model is limited by insufficient training data. However, the current prediction method is unsuitable for clinical applications due to its long computational time. The present study developed a new optical motion capture (OMC)-based method to predict GRFs, GRMs, and joint torques. The proposed approach performed the estimation process by distributing external forces, as determined by the equation of motion, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on machine learning or optimization techniques. We investigated the prediction accuracy during walking by comparing a conventional analysis using a force plate with the OMC results. The comparison revealed excellent or strong correlations between the prediction and measurement for all GRFs, GRMs, and lower limb joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, thereby increasing its applicability for kinetics estimation in clinical settings.

The paper combines two new original concepts about eco-energetic systems. The first one is related to the M.E.N. (Mega-Eco-Nega-Watt) paradigm, which is based on three different but complementary ecological economic spaces: MEGAWATT, as needed energy, ECOWATT, as ecological energy, and NEGAWATT, as preserved energy, even the renewable energies and technologies, in the context of electrical energy production. The second concept presented in this paper is the eco-energetic efficiency, introduced in order to facilitate a correlation between the energetic efficiency of the system and a necessary, new defined ecological coefficient. The proposed formula for eco-energetic efficiency enables an interesting form of reporting to the different situations in which the input energy, output energy, lost energy and externalities, involved in an energetic process interact to produce energy in a specific energetic system, in connection with the circular economy model. Finally, is presented an original diagram of the energetic chains to produce electricity in a resilience regim, with high eco-energetic efficiency from originating in different primary energetic sources as external (gravitation & solar sources), fuels (classical & radioactive), internal sources and others. Even what kind of energetic sources are used to obtain electricity, as coals, gas, wood, hydropower, nuclear power, wind power, biomass, solar systems, and others, the entire process should be sustainable in what is the transdisciplinary integration of the different representative spheres as energy, socio-economy, ecology (environment), with the main core, sustainable education, inclueding the law and administrative aspects, as necessary fields of the knowledge based society/economy.

Proton Exchange Membrane Fuel Cell (PEMFC) is a fast-developing battery technology, and the key to its reliability and lifespan improvement lies in the accurate assessment of durability. However, the degradation mechanism of PEMFC is hard to determine and its internal parameters are highly coupled. Thus, the development of the more accurate life prediction model that meets the actual scenarios is urgent to investigate. To solve this problem, a multi-feature fusion life prediction method based on Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) is proposed. A TCN algorithm is used as the prediction base model, and two GRU modules are included to the model to strengthen the model expression ability and improve its predictive accuracy. Two widely recognized datasets and two operating conditions are utilized for model training and prediction, respectively. Comparisons are made with single-feature parameter models in terms of Root Mean Square Error (RMSE) and Determination Coefficient (R2). The results show that the prediction accuracy of the TCN-GRU multi-feature fusion model is higher than that of the single-feature models in terms of stability and anti-interference under both operating conditions. Furthermore, with the increase of input feature parameters, the TCN-GRU model is closer to the real value, which proves once again that the proposed model can meet the accuracy requirements of the life prediction of PEMFC.

The problem of energy poverty, when energy becomes unaffordable for some population group, is not only a problem for developing countries, but this phenomenon is appearing more and more often in European countries as well. In Europe, it is estimated that 50 to 125 million people are at risk of energy poverty. We hear more and more about energy poverty in connection with the current energy crisis and rising energy prices, but also as a result of insufficient use of renewable energy sources. Due to rising energy prices, we are increasingly hearing about deepening energy poverty in Slovakia as well. The aim of the contribution is to examine the development of energy poverty in Slovakia compared to other EU countries. The situation is studied from the view of the number of days of heating and cooling, the percentage of the population that cannot maintain adequate heat at home, the percentage of the population that has a lack of heat and the percentage of residents without enough heat. During the research, we used distribution analysis, trend analysis, analysis of variance and one-way analysis. The main results show that HDD index recorded decrease, CDD index recorded increase, and Energy poverty is most obvious in low-income group of inhabitants, having shortage of heat, when RES use contribute to mitigation of energy poverty. Solving the unfavorable situation with energy poverty is possible by increasing the share of Renewable energy sources in the gross final energy consumption for heating and cooling, primarily in residential buildings. The results provide information for policy makers in area of economic, social and environmental decision-making.

Walls-like structured monoclinic WO3 thin films were obtained by spray deposition method for further integration in gas sensors. For sensing applications, it is very important to achieve a high surface-to-volume ratio surface together with a proper thin films structure. The present communication reports on the effect of the sprayed solution volume variation (as a thickness variation element) on the detailed Raman spectroscopy for WO3 thin films with different thicknesses grown from precursor solutions with two different concentrations. Detailed analysis of the two series of samples shows that the growing thickness strongly affects the films morphology while their crystalline structure is only slightly affected. The Raman analysis contribute to refining the structural features clarifications. It was observed that, for both concentrations, the increase in thickness seems to slightly affect the stoichiometry of material and, in association with surface morphology variations, provide new possibilities for fine tuning of films properties towards enhanced as sensing applications. Further studies are ongoing.

The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a reflection is developed on the dual attention network that applies the learning equations and coordinates subnetworks toward purposeful behaviors. Speculation is made on possible developments of functionalities that could provide additional skills and at the same time shed light on competencies typical of ``natural intelligence".

SerpinB3, a member of the serine-protease inhibitor family, has emerged as a crucial player in various physiological and pathological processes. Initially identified as an oncogenic factor in squamous cell carcinomas, SerpinB3's intricate involvement extends from fibrosis progression and cancer to cell protection in acute oxidative stress conditions. This review explores the multi-faceted roles of SerpinB3, focusing on its implications in fibrosis, metabolic syndrome, carcino-genesis, and immune system impairment. Furthermore, its involvement in tissue protection from oxidative stress and wound healing underscores its potential as diagnostic and therapeutic tool. Recent studies have described the therapeutic potential of targeting SerpinB3 through its upstream regulators, offering novel strategies for cancer treatment development. Overall, this review underscores the importance of further research to fully elucidate the mechanisms of ac-tion of SerpinB3 and to exploit its therapeutic potential across various medical conditions.

This study empirically analyzes the effectiveness of resource optimization utilizing artificial intelligence (AI) within cloud computing environments. It compares systems that have been transitioned from a Monolithic structure to Microservices Architecture (MSA) with those adopting a simple Rehost strategy, to validate the operational efficiency of two systems whose resource efficiency has been enhanced compared to traditional legacy systems. The research is based on systems that determined resource efficiency post-cloud migration using APM tools. It points out the limitations of traditional heuristic operation methods in resource optimization, which lead to issues such as application performance degradation, resource over-allocation, and increased operational costs. This study aims to overcome the conventional resource optimization standards that depended on the average peak usage of CPU and memory infrastructure, by introducing AI-based optimization tools, and applying on-premise standards to cloud and container environments to achieve resource optimization. The paper emphasizes the importance of resource optimization through AI-based automation tools, exploring the possibilities of resolving real-world application performance issues in cloud environments, reducing unnecessary resource usage costs, and cutting down on labor risks and operational expenses. Through this, it presents strategic directions on how AI technology can offer financial and operational advantages in managing cloud resources.

The EASY-NET network program (NET-2016-02364191 - Effectiveness of Audit and Feedback (A&F) strategies to improve health practice and equity in various clinical and organizational set-tings), piloted a new and more structured A&F strategy. This study compared the effectiveness of the new strategy versus the periodic publication of indicators alone in improving the appropri-ateness and timeliness of emergency health interventions in patients with Acute Miocardial In-farction (AMI) and ischemic stroke in the Lazio Region. Efficacy was evaluated according to a prospective quasi-experimental design of pre-post (2021-2022) intervention type with control group. The exposed are hospitals in the Lazio Region where professional teams voluntarily par-ticipated in the intervention. The control group is involved only in the usual reporting activities. Effectiveness analysis was conducted at the patient level. Regional health information systems were used to calculate process and outcome indicators. The effectiveness of the intervention was assessed with Difference In Difference models, comparing pre-post intervention periods between exposures and controls. Estimates were calculated in terms of the difference in percentage points (pp) between absolute risks. Sixteen facilities for the IMA pathway and 13 for the stroke pathway joined the intervention. The intervention reduced the proportion of 30-days readmissions after a hospitalization for ischemic stroke by 0.54 pp in the exposed patients with a significant difference of -3.80 pp (IC95% -6.57; -1.03; 5453 patients, 63.7% cases) in the exposed compared to controls. There were no statistically significant differences attributable to the implemented A&F interven-tion in the other indicators considered. The results show, for the first time in Italy, the impact of A&F interventions in an emergency setting using aggregated data from hospitals involved in the Lazio Region emergency network.

Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and diseases. Several methods have emerged to pinpoint these influential entities in networks that are represented as graphs. In these graphs, nodes corre-spond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We categorize structural central-ity into two: local, considering a node's immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both mi-cro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network's broader features, especially paths of different lengths. Through Spearman and Pearson's correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the SIR model, portraying disease spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model's progression. Our results indicate a nota-ble correlative efficacy across various real-world networks relative to other centrality metrics.

The study of coherent vortices remains an urgent field of singular optics of vortex beams. In this paper, coherent properties of partially coherent vortex Bessel—Gaussian optical beams propagating through a uniform medium (free space) or a turbulent atmosphere are examined theoretically. The consideration is based on analytical solution of the equation for the transverse second-order mutual coherence function of the field of a partially coherent optical radiation in a turbulent atmosphere. For the partially coherent Bessel—Gaussian beam, the second-order mutual coherence function of the source field is taken as a Gaussian Shell-model. In this approximation, we analyze the behavior of the coherence degree and the integral coherence scale of these beams as a function of the propagation pathlength, propagation conditions, and beam parameters, such as the radius of the Gauss factor of the beam, parameter of the Bessel factor of the beam, topological charge, and correlation width of the source field of partially coherent radiation. In addition, the integral coherence scale of the partially coherent vortex Bessel—Gaussian beam is compared with that of the partially coherent Gaussian beam. It is found that as a partially coherent vortex Bessel—Gaussian beam propagates through a turbulent atmosphere, there appear not two (as might be expected: one due to atmospheric turbulence and another due to partial coherence of the source field), but only one ring dislocation of the coherence degree (due to the simultaneous effect of both these factors on the optical radiation). In addition, it is shown that the dislocation of the coherence degree that affects significantly the beam coherence level is formed only for beams, for which the coherence width of the source field is larger than the diameter of the first Fresnel zone.

Introduction: Unicompartmental knee arthroplasty (UKA) is gaining traction as a treatment for knee osteoarthritis due to faster recovery, better range of motion, and reduced healthcare costs compared to total knee arthroplasty (TKA). UKA preserves healthy parts of the knee, leading to a quicker return to function and less pain. However, TKA offers potentially longer-lasting results with lower revision rates. This study investigates the National Inpatient Sample (NIS) database for UKA and TKA patients to understand their relative benefits and limitations, aiming to inform patient care and resource allocation. Methods: This retrospective analysis examined outcomes following UKA and TKA using the Nationwide Inpatient Sample (NIS) database (2016-2019). Patients undergoing elective UKA or TKA (n=2,606,925) were identified using ICD-10 codes (Appendix). To minimize confounding factors, propensity score matching was performed using patient demographics, hospital characteristics, and comorbidities, resulting in a balanced cohort of 136,890 patients. In-hospital mortality, length of stay, postoperative complications, and hospitalization costs were compared (p < 0.05). This study received exempt IRB approval due to de-identified data. Results: The prevalence of UKA procedures significantly increased over the study period, with primary osteoarthritis being the leading cause for both UKA and TKA. Patients undergoing UKA were younger and had less comorbidities compared to those undergoing TKA. Propensity score matching effectively balanced these baseline characteristics between the two groups. Examining hospitalization outcomes in propensity-matched cohorts revealed low in-hospital mortality for both groups (0.015%). However, UKA demonstrated a clear advantage in terms of shorter length of stay (1.53 days vs. 2.47 days) and lower total hospitalization charges ($55,976 vs. $61,513). While UKA had slightly higher rates of intraoperative fracture and pulmonary edema compared to TKA, TKA was associated with a significantly increased risk of various complications. These included blood transfusion, blood loss anemia, acute coronary artery disease, pulmonary embolism, pneumonia, and acute kidney injury. Conclusion: UKA shows promise as a less-invasive option for younger patients with one-sided knee osteoarthritis, potentially offering shorter hospital stays, lower costs, and potentially fewer complications.

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the industrial revolutions which have continued expansion and progress into the fourth industrial revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. The significance of standardized data and its mode of collection cannot be overstated as it has a major impact on the process outcomes. In order to achieve a better understanding of the impact of this, this study methodically reviewed the literature on the subject of the data collection and processing mechanism of 4IR technologies in the context of the transport industry with an emphasis on the areas of accomplishment of several studies and drawing attention to their limitations. Although the industry has advanced significantly as a result of the adoption and application of various research studies in this field, considerable focus needs to be placed in the areas of standardized data collection and computational approaches, which will ultimately assist in achieving various benefits in the transportation industry.

In India, particularly within its North-Eastern territories, the occurrence of landslides triggered by post-dry period rainfall events is a significant concern, with the phenomena persistently causing extensive damage to both life and infrastructure. Despite the inevitability of such natural disasters, it is understood that the impact they exert can be substantially mitigated through the implementation of pre-emptive measures. Critical to the endeavour of landslide prevention is the application of a spectrum of mitigation strategies, predicated upon a foundational analysis of slope stability to ascertain critical surfaces for intervention. This investigation delves into the dynamics of partially saturated soils, a condition commonly resultant from the prolonged dry periods preceding the monsoonal rains. The study's focal point is an experimental examination conducted on soil samples to assess parameters such as soil matric suction and unconfined compressive strength across varying degrees of saturation and density, alongside performing analyses of slope failure incidents after dry period during the 2017 monsoon in the Lunglei district, Mizoram based on some established data. The Soil-Water Characteristic Curve (SWCC), derived from soil suction measurements employing the ASTM D5298-10 standard, utilizes a contact filter paper method and serves to delineate the moisture transition phases of the slope. To replicate the desiccation of the soil slope after monsoonal rains, a microwave drying technique based on the ASTM standard was employed to prepare unsaturated soil samples for Unconfined Compressive Strength (UCS) testing. The integrity of the slope was evaluated in a temporal context, before and after rainfall events of varied intensities throughout the calendar year, utilizing a blend of numerical modeling and empirical laboratory investigations. The persistence of real onsite slope failures underscores the necessity for a numerical analysis approach to unravel the underlying factors causing slope instability, especially given the absence of prior stability assessments for the landslide in question. An evaluation of SWCC curves, UCS tests on partially saturated soil at different densities and saturation and the utilization of the benchmark data collected in 2017 on the shallow transitional landslide in Lunglei, Mizoram caused by intense monsoonal precipitation, are central key data and information used to carry out this research. Rainfall data for Lunglei, sourced from the Mizoram Meteorological Department, facilitated a real-time monsoonal data-driven numerical analysis. This analysis elucidated the pivotal role of rainfall in inducing slope failure, indicating a marked destabilization of the slope after precipitation events. The implications of these findings are far-reaching, suggesting a foundational basis for the development of an advanced landslide early warning system, thereby enhancing disaster preparedness, and mitigating potential damages.

The high transmissibility and replication of SARS-CoV-2 have been attributed to enhanced protein functions which are dependent on protein folding. Our in silico study endeavored to scrutinize SARS-CoV-2 ORF1ab by analyzing the conserved folding patterns of its transcribed proteins. Accordingly, the findings indicated that SARS-CoV-2 ORF1ab shares domain-specific fold-fingerprints with a spectrum of unrelated organisms. Closer observation revealed slight changes in folding patterns engendered with small variation in the intrinsic amino acid sequence. By correlating with the evolvability-potential of RNA-viruses and COVID-19 pandemic, we hypothesize that SARS-CoV-2 could undergo fast recombination with the host, SARS-CoV-2 minor variants and other viral species resulting in a reservoir of SARS-CoV-2 quasispecies. It is highly possible that natural selection will cause a future emergence of evolved SARS-CoV-2-descendants. Nonetheless, we hope that this insightful study will assist in elucidating SARS-CoV-2 protein functionalities, development of vaccines, and the possibility and nature of future emergence.

In recent years, extensive research has been conducted on the IoT, one of its challenges has been the field security and penetration in these networks. security solutions it Require more planning and awareness to ensure system security and privacy protection.it has also been shown that adjusting the weights of neural networks can help improve the accuracy of detection to what extent. the challenge in the attack detection field is to increase the accuracy of attack detection by machine learning methods, in this paper, a new method is presented to adjust weights of the random neural network for attack detection. Evaluations of the proposed method show that compared to random neural network methods, nearest neighbor, and support vector machine, the accuracy of attack detection improves up to 99.49 percent, and the random neural network method has been able to improve by 99.01 percent, placing the best methods together in these experiments in the form of a multi-learning model showed that the results also had an increase in accuracy up to 99.56 percent, while the training time in the proposed model is less compared to the random neural network method.