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Purpose – This study was designed to investigate the impacts of work-family role conflict on job and life satisfaction among three major professionals: doctors, engineers and university teachers. Data were collected through a face to face survey on 60 doctors, 60 engineers and 60 university teachers of different public and private institutes of Bangladesh. Design/methodology/approach – Conducted data analysis were statistical analysis of questionnaires (mean, SD, max, min), descriptive analysis (%), t-test, analysis of variance test, correlation analysis and regression analysis. Findings – The results demonstrate that the university teachers had experienced more work-family conflict (WFC) on job satisfaction and family-work conflict (FWC) on job and life satisfaction than doctors and engineers; however, engineers experienced more WFC in the case of life satisfaction. The study also implied that control variables such as gender identification, reported number of children, marital status, education level and adhered religion had significant impact (p

Abstract In recent years, multi‐human parsing has become a focal point in research, yet prevailing methods often rely on intermediate stages and lacking pixel‐level analysis. Moreover, their high computational demands limit real‐world efficiency. To address these challenges and enable real‐time performance, low‐latency end‐to‐end network is proposed. This approach leverages vision transformer and convolutional neural network in a dual‐encoded network, featuring a lightweight Transformer‐based vision encoder) and a convolution encoder based on Darknet. This combination adeptly captures long‐range dependencies and spatial relationships. Incorporating a fuse block enables the seamless merging of features from the encoders. Residual connections in the decoder design amplify information flow. Experimental validation on crowd instance‐level human parsing and look into person datasets showcases the WNet's effectiveness, achieving high‐speed multi‐human parsing at 26.7 frames per second. Ablation studies further underscore WNet's capabilities, emphasizing its efficiency and accuracy in complex multi‐human parsing tasks.

Abstract This study aims to investigate the influence of Organizational Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) capabilities on Green Human Resource Management (GHRM) practices, Green Supply Chain Management (GSCM) practices, and also its direct impact on Sustainable Performance (SP) outcomes within the manufacturing landscape of Bangladesh. A quantitative approach was employed, gathering data from 395 employees in the Bangladeshi manufacturing sector through a survey questionnaire using a seven-point Likert scale. Structural equation modeling (SEM) was utilized to analyze the data, focusing on testing hypotheses related to the relationships between organizational STARA capabilities, GHRM, GSCM, and SP. The study found positive relationships between organizational STARA (OSC) capabilities and GHRM, GSCM, and SP. All hypotheses were accepted, GHRM ← STARA (β = 0.210), GSCM ← STARA (β = 0.247), and SP ← STARA (β = 0.135). Additionally, it revealed that GHRM and GSCM mediate the relationship between organizational STARA capabilities and SP. This research contributes to filling the existing gap in the literature by providing empirical evidence on the integration of STARA capabilities with GHRM and GSCM practices, specifically within the context of the Bangladeshi manufacturing sector. It also highlights the mediating role of GHRM and GSCM in enhancing sustainable performance through STARA capabilities. The findings of this study offer valuable insights for organizations operating in the manufacturing sector, suggesting strategies for leveraging STARA capabilities to enhance sustainability, efficiency, and competitiveness. Policymakers and stakeholders can use these insights to formulate policies and initiatives to promote the adoption of sustainable practices in manufacturing.

Abstract Objectives Undergoing women of pregnancy termination (PT) significantly faces the problem of physical and mental health. This study aims to assess the prevalence and socioeconomic disparity factors of PT in Bangladesh. This study analyzed data from the 2017–18 Bangladesh Demographic and Health Survey. Researchers employed chi-square tests to identify relationships between categorical variables and logistic regression to pinpoint factors associated with PT. To assess the socioeconomic variation of PT, the analysis utilized concentration curves, concentration indices, and decomposition techniques. Results The study found that 21.0% of reproductive-aged women in Bangladesh had ever terminated a pregnancy. Our study revealed that women from Chittagong and Sylhet regions, with wealthier backgrounds, aged 30 or older, employed in business, taking short birth intervals, and whose husband/partner was 35 or older, were more likely to have had a pregnancy termination with statistical significance (p

Abstract Methylenetetrahydrofolate reductase (MTHFR) plays a crucial role in regulating one-carbon metabolism. Polymorphisms within the MTHFR gene have been found to increase the risk of breast cancer in different populations. In this study, we evaluated the association of polymorphisms of the MTHFR gene (rs1801133 and rs1801131) with the risk of breast cancer in the Bangladeshi population. This case‒control study included 202 breast cancer patients and 104 healthy controls. After the organic extraction of DNA, genotyping was performed via the PCR-RFLP method. Sanger sequencing was performed to validate the RFLP data. Statistical analyses were performed to evaluate the associations of the polymorphisms. Different computational tools were used to predict the structural and functional consequences of the SNPs. Our study revealed that the MTHFR gene polymorphism rs1801131 is associated with an increased risk of developing breast cancer (p 0.05, OR = 1.57, 95% CI = 0.90–2.74 for the CT genotype; p > 0.05, OR = 1.35, 95% CI = 0.36–4.92 for the TT genotype). Computational analyses predicted rs1801131 to be tolerated and rs1801133 to be deleterious. Structural analyses demonstrated no significant changes in protein structure but revealed alterations in neighboring interactions according to both bond distances and angles. In conclusion, rs1801131 but not rs1801133 is significantly associated with breast cancer risk in the Bangladeshi population. Moreover, in silico analyses demonstrated changes in the interaction pattern of polymorphic residues with adjacent amino acids.

Abstract Paqr5b is a gene encoding membrane progesterone receptor γ (mPRγ), which is one of five mPR subtypes. Paqr5b belongs to the progestin and adipoQ receptor (PAQR) family, which consists of 11 genes. To elucidate the physiological functions of the mPR subtypes, we established gene knockout (KO) zebrafish strains by genetically editing seven paqr genes and analyzed their phenotypes. The null-mutant strain of paqr5b (paqr5b −/− ) that we established in this study showed low fecundity, reduced chorion elevation and a high percentage of abnormal embryos. Embryos showed curvature of the spine and an abnormal head morphology. Individuals with abnormal head morphology continued to develop a phenotype of markedly abnormal palatine bone. The length of the brain of paqr5b −/− zebrafish was short, and the position of the cerebellum moved to the front and overlapped with that of the midbrain. Micro-CT scans revealed that the olfactory rosettes (ORs) were so shrunken that they were difficult to identify and connected with the olfactory bulbs (OBs) by thread-like structures. Immunohistochemical staining of OR with an anti-Paqr5b antibody revealed that Paqr5b was extensively expressed in neurons in the OR in wild-type zebrafish, whereas signals were not detected in paqr5b −/− zebrafish. In histological sections, the neurons disappeared, and the lamellar layer of the OR became thinner. These results indicate that Paqr5b is required for the formation of neurons in the OR. This is the first report demonstrating a distinct role for the mPR gene.

Abstract This research investigates how the USA's load capacity factor (LCF) has been impacted by trade openness, financial development, stock market capitalization, and industrialization over the period 1990–2022. This study also tests the “Load Capacity Curve (LCC)” hypothesis. Various unit root tests were conducted to determine the stationarity of the dataset, revealing that the variables are free from unit root problems and exhibit mixed orders of integration. The “Autoregressive Distributed Lag (ARDL)” bounds test confirmed co-integration among the variables. The results from the ARDL model validated the existence of the LCC hypothesis in the USA. The findings also demonstrated that industrialization is positively correlated with LCF, whereas financial development, stock market capitalization, and trade openness are negatively correlated with LCF. To ensure the robustness of the ARDL estimations, the study also employed several regressions, all of which confirmed the validity of the ARDL results. Additionally, pairwise Granger causality tests revealed unidirectional causal relationships between GDP and LCF, stock market capitalization and LCF, and industrialization and LCF, while no causal relationships were found between financial development and LCF and trade openness and LCF. The findings advocate that the USA should advance eco-friendly industrial practices to ensure environmental sustainability, alongside regulated financial markets and institutions that mandate the utilization of green investments.

Abstract Air is one of the most significant elements of the environment. The increasing global air pollution crisis poses an unavoidable threat to human health, environmental sustainability, ecosystems, and the earth's climate. Air pollution has been referred to as a silent killer due to its insidious nature. Its indirect impact on human health further underscores its dangerous effects. Early detection of air quality can potentially save millions of lives globally. A unique and transformative approach can harness the power of machine learning to combat air pollution. This research presents a manual and web-based automatic prediction system that provides real-time alerts on air quality status and can help prevent premature deaths, chronic diseases, and other health problems. Air pollutants, including carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM 2.5), are used in this study for feature analysis and extraction. The system utilizes publicly available data from 23,463 different cities worldwide. Data preprocessing was performed before feeding the data into the machine learning models for feature correlation and evaluation. The proposed research uses various machine learning models to predict air quality, including Random Forest (100%), Logistic Regression (79%), Decision Tree (100%), Support Vector Machine (93%), Linear SVC (98%), K-Nearest Neighbor (99%), and Multinomial Naïve Bayes (52%). A user-friendly Django-based web interface offers an accessible platform for users to monitor air quality in real-time, based on the two best-performing models: Random Forest and Decision Tree techniques.

Abstract Nanoclays, a specific type of nanomaterial, have emerged as versatile and dynamic materials, with tremendous potential for advanced functional applications. Despite publishing a large number of research articles, there are relatively few review articles on this topic. This comprehensive review delves into the most widely used nanoclays and explores the diverse range of applications in different fields, such as aerospace, automobile, construction, biomedical, food packaging, and polymer composites. With their ability to enhance the performance of materials and products, nanoclays have become a highly desired material in various industries. The challenges associated with nanoclays like complex properties, difficulty in developing new synthesis methods, and challenges in investigating long‐term durability and stability have been summarized. The future research directions with the exciting possibilities to develop future innovative materials have been highlighted at the end of the article. Highlights This review provides an extensive examination of the most widely used nanoclays, detailing their properties, types, and limitations. A summary of publication trends over the last 15 years, based on Scopus data up to 2024, indicates growing interest and research output in nanoclays. Applications of nanoclays span across aerospace, automobile, construction, biomedical, food packaging, and polymer composites, showcasing their versatility. Key challenges discussed include complex properties, difficulties in new synthesis methods, and issues in long‐term durability and stability. Future research directions highlight the potential for developing innovative materials using nanoclays.

Abstract Chronic exposure to ultraviolet (UV) radiation can cause degenerative changes in the cells, fibrous tissues and blood vessels. Moreover, microbiological attacks pose severe risks to human health and are a major cause of sickness worldwide. The aim of this work is to develop UV protective and antimicrobial materials using zinc oxide (ZnO) colloidal solution with carrot and orange peel extract. Treated samples were subjected to color strength (K/S value) measurement, scanning electron microscope (SEM) image analysis, attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy analysis, ultraviolet protection factor (UPF) test, bacterial reduction rate analysis and bursting strength measurement. The study revealed that the treated fabric exhibited remarkable UV protection capabilities, as evidenced by low transmission percentages across UV wavelengths and an outstanding UPF rating of 50, signifying excellent defense against harmful ultraviolet radiation. Additionally, antibacterial tests revealed the significant effectiveness of the treatment against common bacterial strains, with impressive reduction rates for Staphylococcus aureus (99.99%) and Escherichia coli (99.17%). Furthermore, the fabric's bursting strength, both in dry and wet conditions, remained nearly unchanged after treatment. ATR‐FTIR analysis and SEM imaging provided insights into the phytochemical compounds that give protection against UV composition (44.63–49.91) and surface morphology of the treated fabric, elucidating the mechanisms behind its enhanced properties. Due to the availability of carrot and orange peel in nature and low cost of raw materials, this process can be considered to apply commercially for further research purposes. Highlights Utilization of carrot and orange peel waste. Successful application of zinc oxide for antibacterial and ultraviolet protective materials. Excellent defense against harmful ultraviolet radiation. Significant effectiveness against common bacterial strains. Low cost and commercially viable functional materials.

Abstract Background Before every sporting event, almost all athletes engage in a routine practice of warming up to prepare the body for peak performance. There has been a surge in popularity within the athletic world around the use of percussive massage therapy (PMT). Objectives The objectives of this study were to see if using percussive massage therapy, foam rolling (FR), and hamstring stretching (HStr) as part of a warm-up routine had any acute effects on flexibility, jumping performance, and range of motion in junior athletes, and if so, whether there was a significant difference in the acute effects of these treatment methods. Methods Thirty-nine junior athletes with at least 3 years of experience, male gender, age range above 17, seated flexion test

Abstract Eggplant (Solanum melongena) is moderately sensitive to salinity. Seed priming and exogenous supplementation are technique that enhances germination, growth, and crop yield by overcoming salt stress. Therefore, this study was designed to understand the role of seed priming and copper (Cu) supplementation in modulating salt tolerance in eggplant. When exposed to salt stress, eggplant seedlings showed significantly higher Na+ content, an increased Na/K ratio, prolonged mean germination time, higher relative water loss, more days to flower bud initiation and first flowering, along with decreased germination rate, growth factors, water content, photosynthetic pigments, ionic contents (K+, Ca2+, Mg2+), and yield. The results demonstrated that the germination rate, final germination percentage, germination index, germination energy, and seed vigor index significantly improved, while the mean germination time decreased in Cu-primed seeds. The results also revealed that Cu supplementations increased seedling traits, leaf water content, photosynthetic pigment contents, ionic contents (K+, Ca2+, and Mg2+), and yield while decreasing the contents of Na+, and Na/K ratio, mean germination time, relative water loss, days to flower bud initiation, and days to 1st flowering under salt stress. Germination of seeds, seedlings growth traits, plant water status, plant pigments, yield, and ionic contents with the NaCl and Cu treatments were found to substantially interact with each other according to both hierarchical clustering and PCA. Overall, Cu seed priming and exogenous supplementation emerged as a promising strategy to enhance salt tolerance and promote germination, growth, and yield by regulating water status, photosynthetic pigments, and ion homeostasis in eggplant seedlings under NaCl stress. These findings provide valuable insights into the mechanisms of Cu-mediated stress alleviation in eggplant, with implications for sustainable crop production in saline environments.

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

This paper presents the findings about implementing a machine learning (ML) technique to optimize the performance of 5 G mm wave applications utilizing multiple-input multiple-output (MIMO) antennas operating at the 28 GHz frequency band. This article examines various methodologies, including simulation, measurement, and the utilization of an RLC-equivalent circuit model, to evaluate the appropriateness of an antenna for its intended applications. In addition to its compact dimensions, the proposed design exhibits a maximum gain of 10.34 dBi, superior isolation exceeding 26 dB, and a broad bandwidth of 16.56 % Centered at 28 GHz and spanning from 25.905 to 30.544 GHz. Another supervised regression machine learning technique is utilized to predict the antenna's gain accurately. Machine learning (ML) models can be assessed by several measures, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and Mean Absolute Percentage Error (MAPE). Among the six machine learning models considered, it is seen that the Gaussian Process Regression (GPR) model exhibits the lowest error and achieves the highest level of accuracy in forecasting gain. The antenna under consideration has promising qualities for its intended use in high-band 5 G applications. This is evidenced by the modelling findings obtained from Computer Simulation Technology (CST) and Advanced Design System (ADS)and the measured and projected results derived using machine learning methodologies.

The industrial significance of stability analysis for dual solutions and heat transfer sets the stage for this research. Focusing on Maxwell ternary nanofluid flow, the study aims to enhance thermal conductivity and stability by delving into viscous dissipation and velocity slip effects on a stretching/shrinking sheet. Employing a mathematical model, refined with nondimensional transformations and MATLAB’s BVP4C solver, the research identifies dual solutions and examines the influence of key parameters on fluid dynamics and heat transfer. Results showcase a progressive improvement in convective heat transfer and skin friction from mono nanofluid (NF) to binary hybrid nanofluid (HNF), culminating with ternary hybrid nanofluid (THNF). These improvements are significantly associated with the suction/injection parameter (S), whereas the slip (σ) and elastic (K) parameters enhance thermal transfer but negatively affect skin friction efficiency at elevated levels. The robustness of the upper branch solutions underscores the reliability of these findings. Remarkably, at λ=−1.25 with a nanoparticle volume fraction of 0.04, ternary nanofluids achieve a 2.9% thermal efficiency leap over HNF, which itself surpasses NF by 0.46%. These findings hold potential for significant advancements in sectors such as electronics, manufacturing, energy, biomedical, environmental engineering, aerospace, and automotive, aiming at elevating thermal efficiency.

Objective: To evaluate anti-psoriatic activity of Coleus forskohlii in rats with imiquimod-induced psoriasis. Methods: Imiquimod was used to induce psoriasis in rats. Body weight, skin thickness, erythema, scaling, spleen weight, and histological alternations were measured to assess the effect of Coleus forskohlii. Furthermore, an emulgel formulation containing Coleus forskohlii 10% was prepared and characterized along with its ex vivo permeation studies. Results: The emulgel formulation containing Coleus forskohlii 10% had a pH of 5.40±0.36, with optimum spreadability of (31.67±2.08) g/(cms) and viscosity of (15 966.67±1 274.10) cps, and enhanced both the rate and the extent of drug permeation through psoriatic skin. In an ex vivo study, the quantity of drug permeated (19.18%), deposited (52.38%), and drug remaining in the donor compartments (28.31%) was satisfactory. Coleus forskohlii significantly alleviated imiquimod-induced psoriasis by increasing glutathione and superoxide dismutase activity, decreasing malondialdehyde and nitric oxide levels, and alleviating histological alternations in rat skin. Conclusions: Coleus forskohlii can alleviate imiquimod-induced psoriasis, which may be used as a therapeutic candidate for the treatment of psoriasis.

The untreated municipal solid wastes (MSWs) dumping and improper management causes major concerns of environmental degradation and human health risks. In this study, we collected soil and vegetable from the MSWs dumpsites of Khulna City Corporation (KCC) in southwestern Bangladesh. Trace metals (Pb, Cd, Co, Cr, Cu, Ni, Zn, Fe, Mn) were measured to explore the health risk of cultivated vegetables from dumpsites. Soil contamination was evaluated by geoaccumulation index (Igeo), enrichment factor (EF), contamination factor (CF), and pollution load index (PLI), while health risk was evaluated by transfer factor (TF), estimated daily intake (EDI), target hazard quotient (THQ). Results exhibited that the average trace metals in soil and vegetables followed in the order of Fe > Pb > Mn > Zn > Cu > Cr > Ni > Cd > Co. The Igeo, EF, CF, and PLI values revealed that the soil contamination was dominated by Pd, Cd, Mn, and Zn. The EDI of metals in vegetables were exceed the maximum daily intake only for Fe, Pb, and Cr. The total THQ was > 1, implying potential health hazards for the local people due to the long-term consumption of the cultivated vegetables. The multivariate analysis reveled that the sources of trace metals in the soils and vegetables of dumpsites were natural and anthropogenic. Overall, the findings suggest that growing vegetables in dumpsite is unsafe for long-term consumption by local inhabitants. Immediate action should be taken to protect the environment and human health from trace metal hazards.

Organizations are adopting green initiatives in response to global environmental crises and climate shifts. While previous research has examined how green HRM affects employees' in-role and extra-role working behaviors, less is known about how it influences employees' behavior outside of work due to their green values (GV) that develop because of implementing organizational green actions. The study addresses the issue, while also considering employees’ environmental knowledge (EK) as a mediating factor, utilizing social learning theory and psychological contract theory. Data were collected using the convenience sampling method, where the sample size was 320, from ten selected large-sized ceramic companies operating in Bangladesh. The structural equation modeling (SEM) was used to analyze the data. Results infer that green HRM effectively enhances EK among employees and redirects their GV beyond the workplace. Additionally, EK substantially and positively mediates between the green HRM and GV relationship. The conclusions correspond to the recommendations are indicated in the relational psychological contract theory and the social learning theory. The findings have imperative theoretical and managerial ramifications since they address the knowledge gap in the literature and outline knowledge-enhancing green HRM practices that organizations may adopt to improve their green performance. The study also outlines actions for adopting green HRM activities in organizations, aligning the scope for improving employees’ EK and giving them the opportunities to practice green actions for sustainable social outcomes through their increased sense of GV.

In any retail store, it is necessary to maintain ethical behavior by the retailers by providing accurate information to the customer about the usefulness and side effects of the product, not inducing people in an unethical way to the customer so that the retailer can attract the attention of the customers. In addition, the dishonest behavior of drugstore retailers may lead to a massive health crisis in the community. Considering the novelty and significance of this issue, the current study aims to analyze the ethical behavior disruption of drugstore retailers. Quantitative research design has been utilized to analyze drugstore retailers' ethical behavior disruption from the viewpoint of consumers. A structured questionnaire was delivered to 219 customers using a convenient sampling technique. The PLS-SEM was utilized for data analysis. It is identified that the independent variables like easy access to antibiotics, charging higher prices, and selling expired medicine are responsible for the disruption of the ethical behavior of the drugstore retailers. In conclusion, it is noteworthy that the drugstore retailers in Bangladesh are not maintaining ethical behavior towards the customers, which is unacceptable. The paper strives to assist policymakers, particularly the Ministry of Health, in formulating strategies to combat the unethical practices of drugstore sellers in Bangladesh. Furthermore, future research might be conducted from the standpoint of different retailers.

Abstract Background Humanitarian crises increase the risk of mental health problems. Somalia has been affected by conflict, insecurity, and economic turmoil for over three decades, as well as climatic shocks. However, 80–90% of Somalis who have mental health problems do not have access to good-quality, and affordable mental health care. To develop an evidence-based, effective, equitable, and humane programme for mental health, we need to have a holistic understanding of mental health problems and care in relation to people’s perceptions, experiences, and behaviour related to mental health. Methods We undertook a qualitative study to explore Somalis’ perceptions and experiences of mental health problems. We conducted three key informant interviews, two in-depth interviews, nine focus group discussions, 12 observations in private and public health facilities and more than 12 informal discussions. We used case vignettes translated into Somali during our discussion. We also studied three cases with experience of mental health problems to understand care-seeking behaviour and the experiences with services available. Results Somalia has been moving from a traditional pastoral nomadic lifestyle to a settled one. A strong informal support system exists in the community within clans or family relations. Armed conflict often among clans, natural disasters, and khat use are the three main factors affecting mental health. The prevalence of mental problems is likely greater than is evident. It is perceived that about 95% of people suffering from mental illness remain outside of appropriate care. Few people seek care for mental health problems because they are not aware of it and because it is highly stigmatized and neglected. Those who do seek care usually go to traditional healers because of culture and cost. Resources for mental health care are grossly inadequate with a limited and often poorly trained workforce. At least two levels of barrier to mental health care exist, at the individual/family level (e.g. poor awareness of mental health and stigma) and service provider level (e.g. lack of staff and limited ability to diagnose, treat, or refer persons with mental health problems and stigma). No tool or evidence-based programme is available to address these barriers. Conclusion A qualitative data-driven mental health programme that addresses all these issues is needed with more trained mental health professionals. Given the stigma about mental health problems, there is also a need for a tool to raise awareness about mental health and the importance of mental health care among both the public and health workers.

A total of 200 day-old COBB-500 chicks were randomly allocated into four dietary treatments with five replications and ten birds per replicate for 28 days to select the most effective forms of plantain herb for broiler performance and meat quality. The control diet (CL-D) and different forms of plantain diets were based on corn-soybean meal that was iso-caloric and iso-nitrogenous. The different forms of plantain diets were fresh plantain-diet (FP-D): CL-D + fresh-plantain, shade-dried plantain-diet (SP-D): CL-D + shade-dried plantain powder, and blanched-dried plantain-diet (BP-D): CL-D + blanched-dried plantain powder. Plantain herb was given at a 1.0% dry matter basis. Daily feed consumption and weekly body weight were noted, whereas blood samples were taken and a meat panel test was confirmed by specialists in meat on day 28. Birds given the different forms of plantain diets revealed a 6-10% greater body weight gain compared to the CL-D (P < 0.05). Plantain diets in different forms showed a 3–6% better feed, crude protein, and metabolizable energy conversion ratio compared to the CL-D (P < 0.05), but no difference was observed between BP-D and SP-D (P > 0.05). Compared to the CL-D, birds fed the different forms of plantain diets reduced serum triglyceride and total cholesterol by 4–11% and 6–9%, respectively (P

Abstract Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

Cancer is a complex group of diseases characterized by uncontrolled cell growth that can spread throughout the body, leading to serious health issues. Traditional treatments mainly include chemotherapy, surgery, and radiotherapy. Although combining different therapies is becoming more common, predicting how these treatments will interact and what side effects they may cause, such as gastrointestinal or neurological problems, can be challenging. This research applies optimal control theory (OCT) to create precise and personalized treatment plans for cancer patients. OCT helps identify the most effective doses of chemotherapy and immunotherapy by forecasting how various treatment combinations will impact tumor growth and the immune response over time. It optimizes the integration of chemotherapy with immunotherapy to minimize side effects while maximizing therapeutic benefits. The study proposes a model for managing malignant tumors using a mix of immunotherapy, vaccines, and chemotherapy. The aim is to develop the best treatment plan that reduces new tumor growth while keeping healthy cells stable. It also takes into account individual differences among patients, including variations in tumor biology and immune responses in both younger and older individuals. To do this, we compared different optimal control strategies: interior point optimization (IPOPT), an open-source tool for nonlinear optimization; state-dependent Riccati equation (SDRE), which adapts linear control methods for nonlinear situations; and approximate sequence Riccati equation (ASRE), a globally optimal feedback control approach for nonlinear systems. The optimization criterion showed that the proposed work achieved a cost value of 52.3573 for IPOPT, compared with 52.424 for both SDRE and ASRE. For $ \mathrm{C}\mathrm{D}{8}^{+} $ T cells, the proposed method maintained a consistent value of 1.6499 for continuous (C) and dosed (D) across all techniques. Tumor cell counts had a C value of 0.0007 for IPOPT, compared with 0.0006 for ISDRE and ASRE, with D values remaining at 0 across all methods. This comparison demonstrates the successful use of control theory techniques and highlights their potential for developing personalized and effective treatment strategies for complex cancer cases. By optimizing treatment schedules and dosages, OCT can help minimize the side effects of cancer therapies, thereby enhancing patients' overall quality of life.

The southwest region of Bangladesh is particularly vulnerable to a range of climatic threats, including cyclones, prolonged flooding, sea level rise, saltwater intrusion, drought, and riverbank erosion. The study investigates how these different drivers affect aquaculture systems and aims to provide critical insights for sustainable management. The survey work focused on the problems, vulnerabilities, migration, and adaptive strategies of communities of the southwest region of Bangladesh, that rely heavily on shrimp, fish, and crab production for their livelihoods. Data were collected from the 80 respondents across four unions (Atulia, Burigoalini, Gabura, and Bhurulia) in Shyannagar Upazila, Satkhira District, using a structured questionnaire survey and focus group discussions. Findings reveal that climate change events have significantly changed shrimp farming in enclosures, pond aquaculture, and crab point management, and negatively impacted livelihoods. Pond aquaculture appeared to be the most vulnerable to climate change conditions, followed by shrimp farming in enclosures and crab points. Furthermore, the adverse effects of climate change compelled human migration within the study area, primarily driven by the search for employment. This study provides evidence of the effects of various climate change stressors on shrimp, fish, and crab production systems and the adaptive difficulties of the communities dependent on aquatic ecosystems. As the natural calamity like cyclone cannot be prevented, understanding the impact of previous events may help people of the affected area as well as the policy makers to plan for better survival.

Purpose – The purpose of the paper is to investigate the association between corporate social responsibility (CSR) and investment efficiency (INE) in Bangladeshi pharmaceutical companies and to explore the moderating role of corporate reputation in this relationship. Design/methodology/approach – The paper employs a two-step method, with stage 1 involving the development of a theoretical model using the literature's strategic framework and stage 2 using structural equation modelling (SEM) to investigate the relationships between variables. The data set used in the analysis includes 296 responses from senior executives/managers and subordinates at Bangladeshi pharmaceutical firms. Findings – The study finds that CSR activities that focus on customers, employees and the community significantly affect INE, as well as the extended stakeholders, and that company reputation moderates this relationship. The effect of CSR on INE differs between well-established companies and business firms with favourable reputations. Practical implications – The paper contributes to understanding the relationship between CSR and INE in a developing country context and highlights the importance of corporate reputation in this relationship. The findings suggest that companies can enhance their INE through CSR initiatives and that a positive reputation can strengthen this relationship further. Originality/value – The study adds to the limited literature on CSR and INE in developing countries and provides new insights into the moderating role of corporate reputation in this relationship.

Abstract The rapid advancement of artificial intelligence (AI) has brought significant benefits across various domains, yet it has also led to increased energy consumption and environmental impact. This paper positions Green AI as a crucial direction for future research and development. It proposes a comprehensive framework for understanding, implementing, and advancing sustainable AI practices. We provide an overview of Green AI, highlighting its significance and current state regarding AI’s energy consumption and environmental impact. The paper explores sustainable AI techniques, such as model optimization methods, and the development of efficient algorithms. Additionally, we review energy-efficient hardware alternatives like tensor processing units (TPUs) and field-programmable gate arrays (FPGAs), and discuss strategies for designing and operating energy-efficient data centers. Case studies in natural language processing (NLP) and Computer Vision illustrate successful implementations of Green AI practices. Through these efforts, we aim to balance the performance and resource efficiency of AI technologies, aligning them with global sustainability goals.

Abstract Economic growth in leading tourist destinations faces challenges requiring investment, technological advancement, and resource management. This study examines the impact of tourism, Fintech, FDI, and digitalization on economic growth in Austria, France, Germany, Greece, Italy, Mexico, Spain, Turkey, the UK, and the USA. These countries, identified by UNWTO for 2022, represent 46% of the global tourism market. Utilizing yearly data from 2010 to 2022 from sources like the World Bank, Crunchbase, and UNWTO, we apply Pooled OLS, PCSEs, FGLS, and Two-Step GMM models using Stata v17.0. Additionally, Necessary Condition Analysis (NCA) was conducted using SmartPLS v.4.3 software. Our findings reveal that Fintech, tourism, FDI, and digitalization significantly contribute to economic growth, with varying degrees of impact across different models. Specifically, digitalization and tourism demonstrate the most substantial positive effects, while FDI and Fintech also play crucial roles. The study highlights the necessity of integrating these factors to foster sustainable economic growth in top tourist destinations. These results provide actionable insights for policymakers, stakeholders, and government officials, emphasizing the importance of strategic investments in Fintech, tourism, FDI, and digital infrastructure to enhance economic performance. The implications of this research extend to informing policy formulation and strategic decision-making aimed at leveraging technological advancements and investments for sustained economic development in major tourist economies.

Abstract A large uniaxial negative thermal expansion (NTE) along the c-axis has recently been observed in the transition metal (Tr) zirconides TrZr2 with a tetragonal CuAl2-type structure. A recent study on FeZr₂ [M. Xu et al., Nat. Commun. 14, 4439 (2023)] suggests that optical phonons play a critical role in inducing the NTE along the c-axis. In this study, we investigate the thermophysical properties of TrZr₂ compounds (Tr = Fe and Co1− x Ni x (x = 0, 0.2, 0.4, 0.6, 0.8, 1)) using specific heat measurements, sound velocity data, and theoretical phonon calculations to achieve our aim of clarifying the contribution of optical phonons to the uniaxial NTE along the c-axis observed in both FeZr₂ and CoZr₂. We found that FeZr2 shows a lattice-specific heat peak structure at 8.90 meV, which corresponds to optical phonon energy with a high population of negative Grüneisen parameter along the c-axis in the phonon dispersion curves in FeZr2. In an examination of a chemical substitution effect on the Co1− x Ni x Zr2, we found that the lattice-specific heat peak structure disappeared for x ≥ 0.4 and the oscillator intensity decreased. Phonon calculations revealed the existence of low-energy optical phonon branches at the Γ point for CoZr2 and FeZr2 with uniaxial NTE along the c-axis. However, the low-energy phonon branches were not found in NiZr2 with uniaxial positive thermal expansion along the c-axis. The increase in phonon density of states near the above optical phonon energy in CoZr2 and FeZr2 is consistent with the lattice-specific heat analyses, and we propose that low-energy optical phonons are essential for the exhibiting of uniaxial NTE along the c-axis in TrZr2.

Abstract The purpose of this study was to investigate the effects of different electrical stimulation methods (bilateral electroacupuncture (BEA), unilateral EA (UEA), transcutaneous electrical acustimulation (TEA, stimulation via surface electrodes placed at acupoints), and sacral nerve stimulation (SNS)) on visceral pain in a rodent model of irritable bowel syndrome (IBS). Ten-day-old male and female pups were treated with 0.2 ml of 0.5% acetic acid (AA) solution. Visceral sensitivity was assessed using an electromyogram (EMG) in response to graded colorectal distension. In the first experiment, bilateral EA at ST36 acupoint was performed with different parameters in male rats to determine the best stimulation parameters. In the second experiment, male rats were randomly assigned into the Sham, BEA, UEA, TEA, and SNS groups to determine the best stimulation method. Lastly, the AA-treated female rats were randomly assigned into the BEA and sham groups to investigate a potential treatment difference between the sexes. Two distinct sets of stimulation parameters were used: Set 1 (100 Hz, 0.5 ms pulse width, 0.1 s ON, 0.4 s OFF, 0.4–3.0 mA current) and Set 2 (25 Hz, 0.5 ms pulse width, 2 s ON, 3 s OFF, 0.4–3.0 mA current). Results (1) The parameter set of 100Hz was found to be most effective in reducing visceral pain. (2) Both acute UEA and TEA effectively relieved visceral pain, whereas acute SNS did not exhibit such an effect. (3) Acute BEA improved visceral pain in both male and female rats. Conclusions These findings suggest that transcutaneous ST36 stimulation is as effective as direct ST36 stimulation and unilateral ST36 stimulation is comparable to bilateral stimulation. Development of a novel therapy using unilateral transcutaneous ST36 stimulation is warranted.

Abstract Background Metabolic dysfunction associated steatotic liver disease (MASLD), closely linked to excessive lipogenesis, induces chronic liver disease. MASLD often cause other metabolic diseases, such as cardiovascular disease, diabetes and obesity. However, the mechanism of N-acetyltransferase 10 (NAT10)-mediated N4-acetylcytidine (ac4C) mRNA modification in lipogenesis of MASLD has not been fully elucidated. This study investigated the role of NAT10 in lipogenesis targeting mRNA ac4C modification. Methods The expression of NAT10 in mouse liver was assessed after a 12-week high-fat diet. In addition, the expression of NAT10 also was detected after AML12 hepatocytes cells were treated with 150 µmol/L palmitic acid (PA). The ac4C mRNA modification was performed by dot blotting. Oil red O staining and the mRNA expression of Srebf1, Acaca and Fasn were used to assess lipogenesis in AML12 cells with NAT10 overexpression or knockdown. acRIP-PCR and NAT10 RIP-PCR were used to verify the Srebf1 and Scap mRNA ac4C modification by NAT10. Furthermore, the liver lipogenesis was evaluated by AAV-mediated target knockdown of NAT10 in mouse liver and treating a specific inhibitor, Remodelin. Results This study revealed that NAT10 is significantly upregulated in liver lipogenesis after a 12-week high-fat diet. NAT10 and ac4C mRNA modification were also drastically increased in AML12 cells after treated with 150 µmol/L PA. Silencing of NAT10 notably inhibited the lipogenesis in AML12 cells and AAV-mediated target knockdown of NAT10 in mouse liver. The acRIP-PCR and NAT10-RIP-PCR revealed that NAT10 ac4C modified Srebf1 and Scap mRNA, the critical modulator of liver lipogenesis, to regulate liver lipogenesis. Besides, Remodelin strongly inhibited liver lipogenesis, including liver TG, serum ALT, AST, TG and TC level and glucose metabolism. Conclusions NAT10 mediates ac4C modification of Srebf1 and Scap mRNA, thereby affecting lipogenesis in the liver. This study provided a new target for the treatment of MASLD.

Abstract Global warming has led to a notable increase in heatwaves globally and regionally. In spring 2022, South Asia witnessed an unprecedented heatwave with temperatures breaking historical records and exceeding 5° C from climatological mean at several locations in the northern Indian subcontinent. Here, using 3D tracking, this heatwave ranked the most severe in the past 64 years, characterized by its protracted duration and wide spatial extent. The excess atmospheric heat, represented by temperature anomalies, during the mega-heatwave triggered rapid snowmelt across the snow-covered areas in the region, leading to an average loss of 42% in snow cover and 57% in snow depth of the regional snowpack. This rapid melting resulted in the complete disappearance of low-level snowpack in the western Himalayas, Pir Panjal, and Afghanistan highlands, leading to the lowest snowpack observed in the last six decades. Moreover, during the heatwave, the amount of snowfall received was only 29% of the long-term average. This combination of excessive melting and reduced snowfall culminated in a severe regional snow drought in spring 2022. The heatwave genesis lay a persistent high-pressure system over Northwest South Asia, reinforced by a quasi-stationary Rossby wave packet over Europe during the initial spell. Continuous heat from high-pressure ridges associated with circumglobal Rossby waves combined with the physical barrier of the Himalayas, lent staying power to this system during the second phase.

Vision-language models (VLMs) have shown remarkable potential in various domains, particularly in zero-shot learning applications. This research focuses on evaluating the performance of notable VLMs—CLIP, PLIP, and BiomedCLIP—in the classification of blood cells, with a specific emphasis on distinguishing between normal and malignant (cancerous) cells datasets. While CLIP demonstrates robust zero-shot capabilities in general tasks, this study probes its biomedical adaptations, PLIP and BiomedCLIP, to assess their effectiveness in specialized medical tasks, such as hematological image classification. Additionally, we investigate the impact of prompt engineering on model performance, exploring how variations in prompt construction influence accuracy across these biomedical datasets. Extensive experiments were conducted on a variety of biomedical images, including microscopic blood cell images, brain MRIs, and chest X-rays, providing a comprehensive evaluation of the VLMs' applicability in medical imaging. Our findings reveal that while CLIP, trained on general datasets, performs well in broader contexts, PLIP and BiomedCLIP—optimized for medical imagery—demonstrate enhanced accuracy in medical settings, particularly in hematology. The results underscore the strengths and limitations of these models, offering valuable insights into their adaptability, precision, and potential for future applications in medical image classification.

The primary aim of this investigation is to examine the historical (1989–2020) and future trends and magnitude of Reference Evapotranspiration (ET0) in terms of spatiotemporal measures, considering its significance as a hydro-meteorological parameter influenced by changing climate. The FAO-56 Penman-Monteith method is employed to analyze ET0, while the Modified Mann Kendall test is utilized to assess trends and Sen’s Slope Estimator is used for magnitude analysis. The future prediction is conducted using Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) models. Results show an increasing ET0 trend annually in the southeastern and northeastern zones, while decreased values are observed in other regions, particularly in the northwest (0.83 mm). Sen’s slope estimator reveals distinct fluctuations in ET0, with notable disparities in the Southeastern, Northeastern, Southwestern, and South-Central zones. Notably, Chattogram and Sitakunda experience an ET0 magnitude of 0.02 mm/Year, while other zones show a magnitude of 0.01 mm/Year. SVM outperformed other models, predicting rising ET0 in various seasons. These findings offer insights for optimizing irrigation systems and sustainable water management under changing climatic conditions.

Introduction. Hepatotropic viruses (HAV, HBV, HCV, HDV, and HEV) significantly impact global health, with varying prevalence across regions. Objective. This study aims to systematically consolidate data from diverse meta-analyses to provide a contemporary reference on virus distribution and prevalence. Materials and methods. Adhering to PRISMA guidelines, the study utilized a mixed effects model for data integration. Quality evaluation was carried out with QUOROM and AMSTAR tools, with heterogeneity assessed via the Higgins I2 statistic, Q-statistic and Tau squared (τ2) values. Results. The study analyzed 86 meta-analyses from 56 studies (2017–2022) with minimal overlap. Prevalence rates by region were as follows: MENA – 29.2%, Afghanistan – 9.14%, Africa – 8.10%. Prevalence rates by virus type: HAV – 82.5%, HBV – 8.6%, HCV – 15.1%, HDV – 8.9%, HEV – 13.9%, dual HBV-HCV coinfection – 2.2%. Prevalence rates by risk groups: general population – 8.3%, healthcare workers – 4.0%. Continent-specific HBV-HCV prevalence rates: Africa – 9.2%, China – 6.9%, others. HCVprevalence rates among at-risk groups: healthcare workers – 5.58%, hemodialysis patients – 34.8%. Regional HCV rates: Africa – 7.42%, Middle East – 25.30%. Conclusion. Diverse global hepatotropic virus prevalence patterns are influenced by multifaceted factors. MENA faces higher rates due to healthcare challenges, while Africa struggles with limited resources. Tailored public health strategies, including vaccination and awareness campaigns, are essential to alleviate burdens and enhance global health. This consolidated data serves as a valuable resource for informed decision-making.

Abstract Background Inflammatory bowel disease (IBD) poses a significant and growing global health challenge, affecting both humans and domestic cats. Research on feline IBD has not kept pace with its widespread prevalence in human populations. This study aimed to develop a model of feline IBD by incorporating dextran sulfate sodium (DSS) to evaluate the therapeutic potential of MSCs and to elucidate the mechanisms that enhance their action. Methods We conducted a comprehensive clinical assessment, including magnetic resonance imaging (MRI), endoscopy, and histopathological examination. Additionally, alterations in intestinal microbiota were characterized by 16 S rDNA sequencing, and the influence of MSCs on IBD-related gene expression was investigated through transcriptome analysis. Results According to our findings, MSC treatment significantly mitigated DSS-induced clinical manifestations, reduced inflammatory cell infiltration, decreased the production of inflammatory mediators, and promoted mucosal repair. Regarding the intestinal microbiota, MSC intervention effectively corrected the DSS-induced dysbiosis, increasing the presence of beneficial bacteria and suppressing the proliferation of harmful bacteria. Transcriptome analysis revealed the ability of MSCs to modulate various inflammatory and immune-related signaling pathways, including cytokine-cytokine receptor interactions, TLR signaling pathways, and NF-κB pathways. Conclusion The collective findings indicate that MSCs exert multifaceted therapeutic effects on IBD, including the regulation of intestinal microbiota balance, suppression of inflammatory responses, enhancement of intestinal barrier repair, and modulation of immune responses. These insights provide a solid scientific foundation for employing MSCs as an innovative therapeutic strategy for IBD and pave the way for future clinical explorations.

Abstract The study of whether life exists, is extinct, or not depends on various sophisticated experimental studies, as many different signatures of life can be used. The experimental procedures that can be performed to identify life can be further restricted by time, resources, and mobility constraints. Therefore, any research analyzing the presence of extraterrestrial life must be precise and unambiguous. This research focuses on the objective of the extraterrestrial life detection domain and seeks to provide an efficient protocol that can produce life detection decisions based on empirical data obtained through chemical analysis under time and resource-constrained conditions. While the majority of existing frameworks in this field are designed to identify biomolecules, our goal is to accomplish the same with minimal operational expense and mission complexity. We argue that the thoughtful integration of multiple biomolecular detections with lesser complexity and a robust framework can improve overall mission performance by satisfying the necessary time and resource constraints. In this study, a rapid multiple biomolecules-based life detection protocol (MBLDP-R) from soil samples is developed from scratch and embedded in an operational scientific rover subsystem targeted for planetary analysis missions. The study uses artificial biomolecule samples and simulated extraterrestrial environments to illustrate the suggested protocol’s end-to-end process. First, we list a few significant biomolecules, including lipids, proteins, carbohydrates, nucleic acids, ammonia, and pigments. Then, a weighted qualitative test scoring is applied to sort out the best test method for the finally selected biomolecules which are used as operational analogue to showcase the protocol’s in-situ analysis and decision-making capabilities. Based on the suitable biomolecules, a scientific exploration subsystem is developed, and the implemented protocol is built to perform onboard sample analysis. Evaluation results show that: (1) the proposed MBLDP-R protocol could effectively predict the classes with an average f1-score of 98.65% (macro) and 90.00% (micro), (2) the area under the Receiver Operating Characteristics (AUC-ROC) curve shows the sample categories to be correctly predicted 92% of the time (97% for Extant, 88% for Extinct, and 92% in the case of NPL), and (3) the protocol is time-efficient with an average completion time of 17.60 min, demonstrating the protocol’s rapid nature in detecting biosignatures in soil samples. The research outcome yields useful additional data for related future studies, particularly in the design of scientific frameworks for mission-specific requirements with limited resources while also serving as a reference point for constraint evaluation methods for similar systems.

Abstract Subarachnoid hemorrhage (SAH) is the deadliest form of hemorrhagic stroke; however, effective therapies are still lacking. Perfluorocarbons (PFCs) are lipid emulsion particles with great flexibility and their much smaller size as compared to red blood cells (RBCs) allows them to flow more efficiently within the blood circulation. Due to their ability to carry oxygen, a specific PFC-based emulsion, PFC-Oxygent, has been used as a blood substitute; however, its role in cerebral blood flow regulation is unknown. Adult C57BL/6 wildtype male mice were subjected to an endovascular perforation model of SAH followed by an intravenous (i.v.) injection of 9 ml/kg PFC-Oxygent or no treatment at 5 h after SAH. At 48 h after SAH, functional and anatomical outcomes were assessed. We found that SAH resulted in significant neurologic and motor deficits which were prevented by PFC-Oxygent treatment. We found that SAH-induced vasospasm, reduced RBC deformability, and augmented endothelial dysfunction were also restricted by PFC-Oxygent treatment. Moreover, mitochondrial activity and fusion proteins were also markedly decreased as assessed by oxidative phosphorylation (OXPHOS) after SAH. Interestingly, PFC-Oxygent treatment brought the mitochondrial activity close to the basal level. Moreover, SAH attenuated the level of phosphorylated AMP-activated protein kinase (pAMPK), whereas PFC treatment improved pAMPK levels. These data show the beneficial effects of PFC-Oxygent in limiting the severity of SAH. Further studies are needed to fully understand the mechanism through which PFC-Oxygent exerts its beneficial effects in limiting SAH severity.

Abstract This paper presents the detailed design configuration and investigation of a small-scale dual-band metamaterial absorber (MTMA) for solid and liquid sensing applications. The overall dimension of the MTMA unit cell is 10 × 10 × 1.57 mm3 and constitutes an affordable FR-4 substrate. The absorber exhibits dual absorption peaks at 3.470 GHz for the S-band and 7.219 GHz for the C-band, respectively. Both absorption characteristics have been validated through comprehensive simulation and experimental procedures. The dual-band absorption rate exceeded 99% during simulations, and experimental validation showed an absorption rate above 98%. For sensing applications, various solid materials, including different Rogers substrates ( RT 5880, RT 5870, RT 4003 and RT 4835) and liquids such as sunflower and crown oil, were utilized. Our findings indicate that the proposed MTMA achieves a maximum Q-factor of 191 and a sensitivity of up to 2.5 for both solid and liquid sensing applications compared to previous studies. The simulation and experimental validation of the result indicate that suggested MTMA can be effectively used in different sensing applications such as the medical and communications industry.

Abstract Generalized anxiety disorder (GAD) is a common and persistent mental illness accompanied by uncontrollable worries for daily staff. Physiological, environmental, genetic, and daily stress from surroundings is involved in developing GAD. Here, we aimed to assess the association of stromal cell-derived factor-1 (SDF-1) and epidermal growth factor (EGF) in the pathophysiology of GAD and their role as diagnostic tools. This case-control study recruited 50 GAD patients from the psychiatry department of a tertiary care teaching hospital in Dhaka city and 38 age-sex-matched healthy controls (HCs) from the surrounding areas. A qualified psychiatrist conducted standardized psychiatric interview to diagnose GAD patients and evaluate HCs by applying the diagnostic and statistical manual for mental disorders, 5th edition (DSM-5) and used the GAD-7 scale to assess the severity of the GAD symptoms. After confirming the inclusion and exclusion criteria, we collected 5 ml blood samples from each participant. We measured serum SDF-1 and EGF levels using ELISA techniques. We observed increased serum levels of EGF in GAD patients compared to HCs (5.91 ± 3.90 ng/ml vs. 1.59 ± 1.08 ng/ml; p

Abstract This study aimed to determine the longitudinal predictors of lifestyle behaviours among stroke survivors in New South Wales, Australia. This longitudinal study utilised data from the baseline survey (2005–2009) and a sub-study survey (2017) of the 45 and Up Study. Physical activity, alcohol consumption, smoking status, and supplement use were included as dependent variables. Generalised estimating equation models were employed to assess the longitudinal association between the dependent variable and demographic and health status measures. The average age of the participants (n = 576) was 67 (SD = 9) years at baseline and 76 (SD = 9) years at the sub-study survey time, with 54.9% being male. The longitudinal analysis revealed that the likelihood of moderate/high physical activity significantly declined over time and was lower among participants with diabetes, but was higher among those with university education. The likelihood of smoking was significantly higher in females, moderate/high-risk alcohol consumers, and those with depression, but was lower among supplement users. The likelihood of moderate/high-risk alcohol consumption significantly declined with time, and was lower among females, but higher among smokers. The likelihood of supplement use significantly declined over time, but was higher among females and/or those with asthma. Our findings help illustrate that many stroke survivors may benefit from further support in adopting and maintaining a healthy lifestyle as part of their stroke management and long-term rehabilitation, which is crucial to optimising their quality of life and successful secondary stroke prevention.

Abstract Viral nervous necrosis (VNN) poses a significant threat to the aquaculture industry, causing substantial losses and economic burdens. The disease, attributed to nervous necrosis viruses within the Betanodavirus genus, is particularly pervasive in the Mediterranean region, affecting various fish species across all production stages with mortality rates reaching 100%. Developing effective preventive measures against VNN is imperative. In this study, we employed rigorous immunoinformatics techniques to design a novel multi-epitope vaccine targeting VNN. Five RNA-directed RNA polymerases, crucial to the lifecycle of Betanodavirus, were selected as vaccine targets. The antigenicity and favorable physicochemical properties of these proteins were confirmed, and epitope mapping identified cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte epitopes essential for eliciting a robust immune response. The selected epitopes, characterized by high antigenicity, non-allergenicity, and non-toxicity, were further enhanced by adding PADRE sequences and hBD adjuvants to increase immunogenicity. Two vaccine constructs were developed by linking epitopes using appropriate linkers, demonstrating high antigenicity, solubility, and stability. Molecular dynamics simulations revealed stable interactions between the vaccine constructs and Toll-like receptors (TLRs), essential for pathogen recognition and immune response activation in fish. Notably, vaccine construct V2 exhibited superior stability and binding affinity with TLR8, suggesting its potential as a promising candidate for VNN prevention. Overall, our study presents a comprehensive approach to VNN vaccine design utilizing immunoinformatics, offering safe, immunogenic, and effective solutions across multiple Betanodavirus species. Further experimental validation in model animals is recommended to fully assess the vaccine’s efficacy. This research contributes to improved vaccine development against diverse fish pathogens by addressing emerging challenges and individualized immunization requirements in aquaculture.

Abstract Focusing on Rhenium disulfide (ReS2), a group VII transition metal di-chalcogenides (TMDC), being a promising contender system for future nanoelectronics and optoelectronics, here, we present an innovative pathway to experimentally achieve an almost barrier-free contact for the ReS2 field effect transistors (FETs) by using few layered graphene as contact electrodes, further supported by comparative first-principles analysis. Such barrier-free contacts enable the observation of metal-to-insulator transition with enhanced room temperature carrier mobility up to 25 cm2/Vs, linear Ids-Vds characteristic down to 80 K, along with the reduction of 1/f noise by more than two orders of magnitude. We further demonstrate a highly responsive gate- tunable phototransistor (R > 106 A/W) at an illumination wavelength of 633 nm. This work demonstrates a straightforward strategy to unlock the full potential of ReS2 for CMOS compatible future electronic and optoelectronic devices.

Abstract Background Current methods for oral health workforce planning lack responsiveness to dynamic needs, hampering efficiency, equity and sustainability. Effective workforce planning is vital for resilient health care systems and achieving universal health coverage. Given this context, we developed and operationalised a needs-adaptive oral health workforce planning model and explored the potential of various future scenarios. Methods Using publicly available data, including the Special Eurobarometer 330 Oral Health Survey, we applied the model in a hypothetical context focusing on the Dutch population’s dental needs from 2022 to 2050. We compared current and future provider supply and requirement and examined, in addition to a base case scenario, several alternative scenarios. These included epidemiological transition scenarios with different oral health morbidity trajectories, skill-mix scenarios with independent oral hygienists conducting check-ups and multiple dental student intake and training duration (5 instead of 6 years) scenarios. Results Based on the aforementioned historical data, our model projects that provider requirement will exceed supply for the planning period. If the percentage of people having all natural teeth increases by 10% or 20% in 2032, 34 or 68 additional full-time equivalent (FTE) dentists will be required, respectively, compared to the base case scenario. In the skill-mix scenario, the model indicates that prioritising oral hygienists for check-ups and shifting dentists’ focus to primarily complex care could address population needs more efficiently. Among the student intake and training duration scenarios, increasing intake to 375 and, to a lesser extent, reducing training to 5 years is projected to most effectively close the provider gap. Conclusions The study underscores the importance of understanding oral health morbidity trajectories for effective capacity planning. Due to limited dental epidemiological data, projections carry substantial uncertainty. Currently, demand for FTE dentists seems to exceed supply, though this may vary with epidemiological changes. Skill-mix strategies could offer efficiency gains by redistributing tasks, while adjustments in dental intake and training duration could also help address the requirement-supply gap. Resolving dentistry workforce challenges requires a multifaceted approach, including strengthening oral epidemiology projections, addressing the root causes of dental health issues and prioritising harmonious dental public health and general practice prevention measures.

Abstract Sea level rise affects the long-term psychological health of coastal communities. However, research on long-term and seasonal impacts on vulnerable communities’ psychological health is limited. Here, we explore the effect of sea-level rise on the psychological health of the coastal population in Satkhira and Khulna districts of southwest Bangladesh across two seasons: before monsoon (March to April) and post-monsoon months (October to November) in 2021. We leveraged the longitudinal research that involved 1144 participants. We collected data on psychological health using established scales for distress, depression, anxiety, and stress and also measured environmental factors and resource losses. Results indicate that psychological health, particularly distress, depression, anxiety, and stress, increased during the post-monsoon months in communities more vulnerable to sea-level rise. Highly vulnerable communities showed increased psychological distress post-monsoon. Environmental stressors and resource loss escalated during the post-monsoon period, especially in moderate and highly vulnerable communities. Our findings emphasize the urgent need for targeted support and resilience-building interventions in affected communities to alleviate the psychological health impacts of sea-level rise.

Abstract Although single‐stage inverters are popular for simpler design they face difficulties in shoot‐through mode operation. Insufficient DC voltage regulation and complicated LC network are principal drawbacks of these topologies. A new split source inverter (SSI) can solve problems with single‐stage inverters. In this paper, SSI replaced the voltage source inverter (VSI) of a shunt active power filter (SAPF) which alleviates power quality (PQ) issues in power system. Furthermore, a VSI‐based SAPF faces challenges to maintain consistent voltage across DC‐link capacitor, exactly compensating current generation, and switching loss reduction in inverter. This study examined efficacy of two inverter‐based SAPFs considering a modified synchronous reference frame approach for generating reference current; fuzzy logic controller for regulating DC‐link voltage; and adaptive fuzzy hysteresis current controller for generating switching pulses. Ideal and non‐ideal sources were considered when running the simulations for various load circumstances. The proposed SSI‐based SAPF with an advanced control approach can eradicate source current harmonics to 0.52% and 0.45% under ideal source conditions, and 0.65% and 0.54% under non‐ideal source conditions for non‐linear loads. Similarly, the ripples in the DC‐link voltage are also reduced to 2–3 V for the ideal case and 4–5 V for non‐ideal case.

Abstract Unbalanced voltages are becoming increasingly common in distribution networks due to the growing integrations of distributed renewable energy resources as well as large loads such as electric vehicle chargers. Such unbalanced voltages introduce significant control challenges as they produce ripples in the controlling signals, impacting the operation of inverters during the unbalanced conditions. Therefore, this paper proposes a controlling algorithm for three‐level inverters aimed at suppressing these ripples on controlling signals; improving the overall performance of inverters, and consequently enhancing the fault ride‐through capability under unbalanced conditions and faults. The proposed controlling algorithm enables inverters to operate in either a balanced current output mode or a suppressed‐ripple active power output mode, depending on the specific event scenario and demands from the grid operators. Current harmonics are suppressed even under severe faults. For improving the voltage balance on the DC capacitors in three‐level inverters, an enhanced method of neutral point current prediction is presented. The proposed method ensures higher balancing accuracy through the use of weighted switching costs, avoiding the requirement of a balancing controller or current extrapolation. Simulation and experimental results demonstrate the effectiveness of the proposed controller for reliable grid operation.