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Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications. [ABSTRACT FROM AUTHOR]

This study investigates the potential of renewable energy sources to address the growing energy demands of developing nations while ensuring environmental sustainability. Here, a model-based analysis to assess the feasibility of utilizing rice straw pyrolysis as a renewable energy source is investigated. The thermogravimetric analysis identified the optimal temperature range for pyrolysis (230–400 °C) and the Coats–Redfern model was employed for kinetic analysis. Diffusion models, particularly D3, exhibited the best fit with an activation energy of 16–18 kJ/mol. Thermodynamic analysis confirmed the endothermic nature of the process, with positive enthalpy and negative entropy changes indicating an increase in energy and a more ordered product. These findings suggest that rice straw pyrolysis holds promise as a viable renewable energy option. This study contributes to understanding the rice straw pyrolysis mechanism which could be helpful for its effective utilization at commercial scale. [ABSTRACT FROM AUTHOR]

The atmosphere has been burdened by the augmented release of greenhouse gases, leading to the unfortunate consequences of global warming due to the combustion of fossil fuels. Consequently, the realms of energy have directed their attention toward sustainable alternatives like hydro energy in order to generate electricity. Methods for storing energy have the ability to ensure the provision of energy when it is needed, as long as the energy is stored at a time when renewable sources are available. Hydrogen is regarded as a promising substitute for conventional feedstocks due to its distinctive qualities such as being a clean and high-density energy source, devoid of harmful substances and environmentally friendly. Hydrogen serves as the primary fuel for fuel cells, and it can be generated through various means, including harnessing wind power or utilizing electrolysis methods to extract electricity from renewable sources. However, the secure, efficient, and cost-effective preservation of hydrogen remains a formidable obstacle that hinders the widespread adoption of hydrogen energy. While high-pressure hydrogen gas and cryogenic hydrogen liquid are two storage methods that have been employed, they fail to satisfy the aforementioned criteria. In order to surmount these limitations, the realm of research has predominantly focused on ceramic-based hydrogen storage materials in recent times. This article presents a comprehensive review of the innovative endeavors conducted on these systems, accompanied by a delineation of discoveries made in order to store the workable thresholds for the quantity of hydrogen that may be retained in hydrogen storage systems utilizing ceramic materials. [ABSTRACT FROM AUTHOR]

The demand for high capacity and high energy density lithium-ion batteries (LIBs) has drastically increased nowadays. One way of meeting that rising demand is to design LIBs with thicker electrodes. Increasing electrode thickness can enhance the energy density of LIBs at the cell level by reducing the ratio of inactive materials in the cell. However, after a certain value of electrode thickness, the rate of energy density increase becomes slower. On the other hand, the impact of associated limitations becomes stronger, reducing the practical applicability of LIBs with thicker electrodes. Hence, an optimum value of thickness is of utmost importance for the practicability of thicker electrode design. In this paper, both the cathode thickness and the anode thickness of an NCM LIB cell were optimized by applying response surface methodology (RSM) with a Box-Behnken design (BBD) to maximize the energy density. Moreover, the influence of electrode porosity, together with the interaction of porosity with cathode and anode thickness, was incorporated into the optimization. A full factorial design of 3-level, 3-factor was used to generate 15 simulation conditions in accordance with the design of experiment (DoE) achieved through BBD. Then, those conditions were used to achieve 15 responses by simulating a reduced-order electrochemical model. Finally, the statistical technique analysis of variance (ANOVA) was used to analyze and validate the results of RSM. The results show that the RSM-BBD optimization method, coupled with ANOVA, has successfully optimized the thicknesses of both positive and negative electrodes for maximum energy density, despite the nonlinearity of the electrochemical system. The findings suggest an optimized cathode thickness of 401.56 µm and anode thickness of 186.36 µm for a maximum energy density of 292.22 of an NCM LIB cell, while electrode porosity is preferred to be 0.2. [ABSTRACT FROM AUTHOR]

Rapid urbanization, industrialization, and population growth have led to significant changes in land use and land cover (LULC) across cities in developing countries, particularly in Bangladesh. This study investigates the LULC changes in Narayanganj District, one of the fastest-growing regions in Bangladesh, over a 20-year period (2000–2020). Using Google Earth Engine (GEE) and the Random Forest (RF) classifier, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were analyzed to classify five major LULC types: urban, bare land, water bodies, vegetation, and cropland, with an overall accuracy of 97.8%, 100%, 95.8%, 97.8%, and 96.4% for the years 2000, 2005, 2010, 2015, and 2020, respectively. The study demonstrates a marked increase in urban areas, with an 88.06% rise, accompanied by a 70.77% reduction in bare land and a 36.72% decrease in water bodies. Vegetation showed a significant growth of 74.64%, possibly due to reforestation efforts, natural regrowth of abandoned lands, or conversion of lower-intensity agricultural land. Meanwhile, cropland experienced minor changes, maintaining its area over time. The results highlight the rapid pace of urbanization, driven by Narayanganj’s proximity to Dhaka and its role as an economic hub. This expansion poses environmental challenges, such as the loss of critical land resources and the reduction of water bodies, which could exacerbate urban heat island effects and water scarcity issues. The analysis of urban expansion and its encroachment on agricultural and natural landscapes stresses the urgent need for sustainable land-use planning to balance urban growth with environmental conservation. The study offers a novel approach by leveraging GEE’s cloud-computing capabilities for large-scale LULC monitoring, making it an efficient and scalable tool for analyzing land cover changes in rapidly urbanizing areas. These findings contribute valuable insights for policymakers and urban planners, ensuring sustainable development while addressing environmental and socio-economic concerns. This methodology can be extended to other regions, facilitating informed decision-making in land management and urban planning globally. [ABSTRACT FROM AUTHOR]

This study aims to comprehensively assess the presence of aflatoxins, pervasive fungal toxins, and heavy metals (lead, chromium, cadmium) in the local feed of fish and shrimp. Forty-eight samples of the fish and shrimp feed were collected from the Dhaka and Sathkhira districts of Bangladesh respectively. Aflatoxins (B1, B2, G1, G2) were analyzed by high-performance liquid chromatography (HPLC) with fluorescence detector, and atomic absorption spectrophotometry (AAS) was used for the heavy metal detection after sample digestion. The standard analytical methods of the Association of Official Analytical Chemists were followed for analyzing the proximate composition of the feed samples. The findings revealed the high toxic concentration of total aflatoxins particularly in fish feed (196.25 μg/kg). Concurrently, varying concentrations of heavy metals, including lead (Pb) and chromium (Cr), were detected, with the highest levels found in shrimp feed at 3.324 and 174.6 mg/kg, respectively. In comparison, highest contamination of cadmium (Cd) was found in fish feed at 0.398 mg/kg. The outcomes are growing concerns regarding the quality of locally sourced feed and emphasizing the need for prompt corrective actions. Otherwise, neglecting this concern may lead to the possibility of both fish and, ultimately, humans who consume them being vulnerable to the absorption and accumulation of these assessed toxic compounds. [ABSTRACT FROM AUTHOR]

Landslides represent a significant natural hazard in Sichuan Province, causing considerable property damage and loss of life. Utilizing machine learning (ML) for landslide susceptibility mapping (LSM) is an effective strategy for mitigating landslide risk. Nonetheless, individual ML models face certain limitations in interpreting landslide data, assigning weights to evaluation factors, and achieving high predictive accuracy. These limitations hinder the precision of landslide susceptibility classifications worldwide. To overcome these issues, this research proposes a hybrid ML model that combines information value (IVM) with ML, referred to as IVM-ML, to improve the precision of landslide susceptibility prediction (LSP). The dataset includes 7,840 historical landslide events and incorporates 14 landslide evaluation factors (LEFs), which were analyzed for their suitability through correlation and multicollinearity assessments. Low-susceptibility zones identified by the IVM model were used to select non-landslide samples, and six models—SVM, RF, LR, IVM-SVM, IVM-RF, and IVM-LR—were employed for LSP. The effectiveness of these models was evaluated based on Sensitivity (Se), Accuracy (Ac), Specificity (Sp), Precision(Pr), F1 Score(F1), Frequency ratio of landslide sites(Fr) and the area under the ROC curve (AUC). The findings reveal that the southeastern and eastern regions of the research region, which encompass approximately 30% of the entire area, exhibit a higher risk of landslides, while the western and northwestern areas, comprising approximately 45%, have a lower risk. The IVM-ML models, particularly IVM-RF, achieved notably higher predictive accuracy, with AUC values of 0.997, 0.996, and 0.998 for IVM-SVM, IVM-RF, and IVM-LR, respectively, outperforming the standard ML models. These results highlight the IVM-ML model’s potential for improving LSP accuracy, particularly in high-risk regions, contributing significantly to landslide hazard mitigation in Sichuan and globally. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNN) have seen significant growth recently for modeling temporal evolution in dynamic networks. Representation of complex networks in the form of graph data structures has enabled researchers to study how entities within these networks interact with each other. These interactions evolve over time. Developing a generic methodology for modeling this temporal evolution in complex networks for tracking evolving relationships has been a significant challenge. Most of the existing methods fail to extract contextual representations of historical neighborhood interactions for future link prediction. To address these challenges, this paper presents a novel method for modeling temporal evolution in complex networks using GNNs. A Context-Aware Graph Temporal Neural Network (CATGNN) method that uses dynamic neighborhood selection based on common neighbors for a given node is presented. The method uses dynamic neighborhood selection using contextual embeddings extracted from the historical interactions of the down-sampled set of neighbors of a central node based on a common neighborhood. Fixed-sized contextual memory modules are constructed for each node that store the historical interactions of its neighbors and are updated based on the recency and significance of interactions. The proposed method has been evaluated using six real-world datasets and has comparable performance against state-of-the-art methods, both in terms of accuracy and efficiency. It shows an improvement of 7.52 to 0.05% over the baselines in terms of average precision. The results demonstrate that the proposed CATGNN model can capture complex patterns of change that are difficult to identify using traditional techniques by propagating information over the graph structure. The model can be applied in various fields involving complex systems. [ABSTRACT FROM AUTHOR]

Understanding the complex relationship between crude oil futures and exchange rates is essential due to its profound implications for global economies and for making policy decisions worldwide. Previous studies have employed various methodologies to explore this dynamic, yet gaps in understanding persist. In this study, we address this gap by paying special attention to countries like Iran, Singapore, UAE, Venezuela, Iraq, Kazakhstan, Azerbaijan, Angola, Algeria, Pakistan, and Bangladesh. For this purpose, we use several methodologies on monthly dataset from January 1998 to February 2024. Our findings reveal that the exchange rates of Singapore and UAE are notably affected by net fluctuations, while results across other countries exhibit inconsistency. Furthermore, our analysis uncovers evidence of time-dependent and bilateral transmission of shocks between the oil and foreign exchange markets. These findings underscore the intricate interaction between crude oil futures and exchange rates, offering premium insights for policymakers and stockholders alike. [ABSTRACT FROM AUTHOR]

Dopamine receptor D4 (DRD4) plays a vital role in regulating various physiological functions, including attention, impulse control, and sleep, as well as being associated with various neurological diseases, including attention deficit hyperactivity disorder, novelty seeking, and so on. However, a comprehensive analysis of harmful nonsynonymous single nucleotide polymorphisms (nsSNPs) of the DRD4 gene and their effects remains unexplored. The aim of this study is to uncover novel damaging missense nsSNPs and their structural and functional effects on the DRD4 receptor. From the dbSNP database, we found 677 nsSNPs, and then we analyzed their functional consequences, disease associations, and effects on protein stability with fifteen in silico tools. Five variants, including L65ICL1P (rs1459150721), V1163.33D (rs761875546), I1293.46S (rs751467198), I1564.46T (rs757732258), and F2015.47S (rs199609858), were identified as the most deleterious mutations that were also present in the conserved region and showed lower interactions with neighboring residues. To comprehensively understand their impact, we docked agonist dopamine and antagonist nemonapride at the binding site of the receptor, followed by 200 ns molecular dynamics simulations. We identified the V116D and I129S mutations as the most damaging, followed by F201S in the dopamine-bound states. Both the V116D and I129S variants demonstrated significantly high RMSD, Rg, and SASA, and low thermodynamic stability. The F201S-dopamine complex exhibited lower compactness and higher motions, along with a significant loss of hydrogen bonds and active site interactions. By contrast, while interacting with nemonapride, the impact of the I156T and L65P mutations was highly deleterious; both showed lower stability, higher flexibility, and higher motions. Additionally, nemonapride significantly lost interactions with the active site, notably in the I156T variant. We also found the V116D-nemonapride complex as structurally damaging; however, the interaction patterns of nemonapride were less altered in the MMPBSA analysis. Overall, this study revealed five novel deleterious variants along with a comprehensive understanding of their effect in the presence of an agonist and antagonist, which could be helpful for understanding disease susceptibility, precision medicine, and developing potential drugs. [ABSTRACT FROM AUTHOR]

Diversity in the plant gene pool provides breeders with the opportunity to improve targeted traits. Enhancing the genetic background through the introgression of desired gene(s) is an effective strategy to combat stresses like heat. Two field experiments were carried out in 2019–20 and 2020–21 at the research fields of Genetics and Plant Breeding at Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh, to identify heat-tolerant wheat advance lines. In F4 generation, 60 advanced lines were selected from a pool of 160 line and in the F5 generation, 16 lines were selected from the previous selection. Analysis of variance revealed significant variation in both the F4 and F5 generations, with the F5 populations outperforming the check varieties. Grain weight demonstrated the highest heritability and genetic advance, while the plant yield exhibited moderate to high performance. Plant height had the most significant direct effect at the genotypic level, while grain weight had the highest impact at the phenotypic level. A total of 77 alleles were identified with an average of 4.53 alleles per locus. The genetic similarity analysis showed that Shatabdi and BARI Gom 25 had the highest similarity at 94%, followed by P3-18-10 with P3-11-3 at 90%, and P3-24-10 with P3-21-10 at 85%. The PCA, structural, and dendrogram analyses all indicated the same grouping of the selected lines. Therefore, selecting of these lines for the F6 generation would enhance resilience against heat stress. [ABSTRACT FROM AUTHOR]

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns, we employ a latent variable approach using bifactor analysis. We examine 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with 6192 participants. A curated subset of 37 maps with a balanced representation of RDoC domains constitute the training set, and the remaining held-out maps form the internal validation set. External validation is conducted using 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Here, we show that a bifactor model incorporating a task-general domain and splitting the cognitive systems domain better fits the examined corpus of tfMRI data than the current RDoC framework. We also identify the domain of arousal and regulatory systems as underrepresented. Our data-driven validation supports revising the RDoC framework to reflect underlying brain circuitry more accurately. Refining the Research Domain Criteria (RDoC) framework, a data-driven analysis of task fMRI contrast maps shows a bifactor model improves fit and suggests splitting the cognitive systems domain to better align with brain circuitry. [ABSTRACT FROM AUTHOR]

Intimate Partner Violence (IPV) during pregnancy poses a serious threat to maternal health, particularly in low- and lower-middle-income countries (LMICs). Despite these known risks, the role of spousal educational differences in IPV during pregnancy remains poorly understood. This study aimed to examine this influence, analyzing data from multiple countries across five continents. This study utilized data from Demographic and Health Surveys (DHS) focusing on lower and LMIC countries. DHS employs two-stage sampling to gather comprehensive health data. Thirty countries from five regions were selected. Covariates like husband’s age, residence, wealth, education, husband’s working status, husband’s education, and spousal educational gap were considered. Cross-sectional survey design was considered. Chi-square test was done to find the association between IPV and covariates. Binary logistic regression was used to assess whether the independent variable is related to spousal educational disparity and other covariates of IPV during pregnancy. Out of 152,643 (weighted) pregnant women from all five continents, 8357 (weighted) experienced IPV during pregnancy. IPV is most prevalent in Papua New Guinea (17.01%; 95% CI 15.76–18.38%)), while least prevalent in Cambodia (0.99%; 95% CI 0.88–1.10%)). Overall, the IPV prevalence was (5.47%; 95% CI 5.30–5.65%)). Educational disparity and socioeconomic factors play a significant role in encountering IPV during pregnancy. This study revealed complex, region-specific effects on violence likelihood, emphasizing implications for policymakers and practitioners addressing IPV. Education disparity emerged as a significant factor; lower-educated couples exhibit increased abusive behavior. [ABSTRACT FROM AUTHOR]

Enhancement of dynamic characteristics of sand through bio-cementation is one of the prospective ground improvement techniques for sustainable development considering seismic loading scenarios. Microbially induced calcite precipitation (MICP) has already been established as an efficient and low-cost and sustainable bio-cementation technique. In the present study, engineering characteristics of poorly graded standard Ennore sand of India have been improved through the bio-cementation effects of Sporosarcina pasteurii bacteria using the MICP technique. Microstructure images obtained through scanning electron microscope and crystalline form of minerals present obtained through X-ray diffraction analyses are presented for the bio-cemented sand. Finally, dynamic characteristics of the bio-cemented sand are investigated through strain-controlled unconsolidated undrained cyclic triaxial testing varying the shear strain range from 0.3 to 1.5% with 1.0-Hz frequency. Variations of shear modulus and damping ratio of bio-cemented sand with shear strain are reported considering treatment durations of 7 and 14 days with treatment intervals of 12 and 24 h, respectively. It is observed that the improvement in shear modulus of bio-cemented sand for lesser strain rate approximately ranges from 37% to more than 80% compared to untreated sand. Furthermore, for strain higher than 1.0%, the margin of improvement varies from 50 to 70%. Moreover, the damping of bio-cemented sand was found to be lesser than untreated sand, and the variation is more significant for the higher number of loading cycles. This study is helpful in the assessment of dynamic characteristics of bio-cemented sand specifically applicable to seismic design. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) were investigated in four fish species viz., paradise threadfin (Polynemus paradiseus), corsula mullet (Rhinomugil corsula), flathead sillago (Sillaginopsis panijus) and king gobyeel (Taenioides sandwipi), which were collected from the Meghna River estuary in Bangladesh. The gastrointestinal tracts (GITs) of fishes (n = 80 in total) were examined for the presence of MPs following alkali digestion, microscopic observation and μ-FTIR analysis. A total of 573 MPs were detected across all four fish species, indicating the presence of MPs in 81.3% of the fish samples. On average, there were 7.16 ± 4.33 MP items/fish, 2.16 ± 1.76 items/g of GIT and 0.15 ± 0.12 items/g of body weight. Polyethylene, polypropylene and nylon were the most abundant polymers, comprising 47%, 23% and 19%, respectively, according to μ-FTIR analysis. The majority of MPs were fibers (62.48%), with white being the dominant color. R. corsula demonstrated the highest number of MPs in the < 500 μm size range, suggesting that smaller particles are more bio-available to pelagic fish. The findings confirm the contamination of the Meghna River estuary by MPs, and their presence in local fish raises concerns about potential public health risks through the food chain. [ABSTRACT FROM AUTHOR]

The study aims to investigate the clinicopathological significance of MRPL24 in human cancers, with a particular focus on breast cancer (BC). Comprehensive bioinformatics analyses were conducted using data from The Cancer Genome Atlas (TCGA) and various advanced database, including cBioPortal, UALCAN, TIMER, Prognoscan, TISIDB, KM Plotter, and The Human Protein Atlas, to provide a detailed evaluation of MRPL55's role in cancer. The findings were further validated through experimental studies. Pan-cancer analysis of TCGA/ICGC data revealed significant amplification of MRPL24 across multiple cancer types, with the highest amplification rate of 60% observed in metastatic breast cancer. MRPL24 was found to be overexpressed in primary breast tumors, metastatic, and various molecular subtypes of breast cancer. High MRPL24 expression was associated with poor prognosis and lower survival rates in breast cancer patients. RT-PCR and western blot confirmed MRPL24 depletion in breast cancer cells. Knockdown of MRPL24 was shown to suppress proliferation, and clonogenic potential in breast cancer cells and inhibit cell migration. Additionally, MRPL24 depletion sensitized breast cancer cells to PD0325901 and 5-FU treatment. Mechanistic studies revealed that MRPL24 knock-down downregulates mRNA levels of oncogenic genes, including c-MYC, BRD4, WNT3, and STAT3. Positive correlations were observed between MRPL24 and key genes involved in ferroptosis regulation, such as ERBB2, ERBB3, GRB2, PIK3CA, AKT1, MAPK3, and MAPK1. Finally, through virtual screening and molecular dynamics simulations, we have identified three FDA-approved drugs with strong binding affinities and interactions with MRPL24. These findings underscore MRPL24's oncogenic role in breast cancer and suggest potential therapeutic strategies targeting this protein. [ABSTRACT FROM AUTHOR]

This study investigates the health status of China's elderly population, emphasizing the influence of socioeconomic status (SES) and the mediating role of exercise frequency. Using data from the 2021 Chinese General Social Survey (CGSS) and an ordered logistic regression model, SES is assessed through income, education, social class, and social insurance. The findings reveal that income, education, and social class positively correlate with health outcomes, with income showing a slightly stronger impact on women. Urban elderly populations particularly benefit from higher education and social class, while the effects of medical and pension insurance are mixed, suggesting that insured individuals often have pre-existing health issues. Exercise frequency yielded limited effectiveness in improving health for those from lower socioeconomic backgrounds. Gender and marital status also influenced outcomes: men reported better health than women, and marriage positively affected rural but not urban elderly individuals. These results highlight the critical role of socioeconomic factors in shaping elderly health and underscore the need for policies addressing urban–rural gaps and improving socioeconomic conditions to enhance overall health in this demographic. [ABSTRACT FROM AUTHOR]

The environmental burden of tannery wastewater, characterized by high levels of total dissolved solids (TDS) and other contaminants, presents a significant challenge for sustainable water management. This study addresses this issue by developing a novel polyvinyl alcohol (PVA) and polyvinyl chloride (PVC) composite membrane optimized for efficient TDS removal from tannery effluent. The membrane was fabricated using a solution casting technique, with glutaraldehyde employed as a crosslinking agent to enhance mechanical properties and stability. Characterization techniques, including Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential analysis, and contact angle measurements, were used to evaluate the membrane's surface chemistry, morphology, and hydrophilicity, which are crucial for pollutant separation. Performance testing demonstrated that the membrane achieved a TDS removal efficiency of 91.73% at an optimal pH of 8 and a transmembrane pressure of 3.5 bar, with a permeability of 194 Lm−2 h−1 bar−1. Additionally, substantial reductions in turbidity (94.51%), chemical oxygen demand (COD, 91.91%), biological oxygen demand (BOD, 89.70%), salinity (80.57%), and total suspended solids (TSS, 96.45%) were observed. The membrane exhibited impressive mechanical properties, with a tensile strength of 44 ± 0.43 MPa, 150 ± 0.67% elongation at break, Young's modulus of 750 ± 0.47 MPa, and flexibility of 23 ± 0.53%, indicating its flexibility and durability. Its partial biodegradability and potential for scalable production contribute to its environmental sustainability. This work establishes the PVA-PVC composite membrane as a promising and cost-effective solution for industrial wastewater treatment, offering a sustainable approach to mitigating water pollution in the leather industry. Fabrication of PVA-PVC Composite Membrane for TDS removal from tannery wastewater [ABSTRACT FROM AUTHOR]

Although research exists on traditional and virtual internships, there is a significant gap in understanding virtual instructional technology (IT) internships for graduate students. Specifically, this study addresses three key areas: (1) the unique benefits and challenges experienced by graduate students in virtual IT internships, (2) the application and effectiveness of authentic learning principles in the virtual internship context, and (3) how these virtual experiences contribute to the development of skills crucial for future IT professionals. This primarily qualitative study with supplementary quantitative data investigates the experiences of 14 graduate students in a virtual IT internship program through participant journals and an end-of-semester survey. Employing the authentic learning framework, which proposes that learning is most effective when situated in real-world contexts, the study examines the efficacy of virtual internships in developing critical skills and competencies. Findings revealed that participants highly valued applying theoretical knowledge to real-world projects, developing essential professional skills, and receiving personalized mentorship. Interns appreciated the flexibility of the virtual format but identified a need for more robust pre-internship training and clearer expectations. The study highlights the importance of proactively developing time management, organization, and communication skills in virtual settings. The study provides valuable insights for designing virtual internships that align with authentic learning principles, contributing to the growing body of knowledge on effective remote professional development in IT education. [ABSTRACT FROM AUTHOR]

Steganography is used to hide sensitive types of data including images, audio, text, and videos in an invisible way so that no one can detect it. Image-based steganography is a technique that uses images as a cover media for hiding and transmitting sensitive information over the internet. However, image-based steganography is a challenging task due to transparency, security, computational efficiency, tamper protection, payload, etc. Recently, different image steganography methods have been proposed but most of them have reliability issues. Therefore, to solve this issue, we propose an efficient technique based on the Least Significant Bit (LSB). The LSB substitution method minimizes the error rate in the embedding process and is used to achieve greater reliability. Our proposed image-based steganography algorithm incorporates LSB substitution with Magic Matrix, Multi-Level Encryption Algorithm (MLEA), Secret Key (SK), and transposition, flipping. We performed several experiments and the results show that our proposed technique is efficient and achieves efficient results. We tested a total of 165 different RGB images of various dimensions and sizes of hidden information, using various Quality Assessment Metrics (QAMs); A name of few are; Normalized Cross Correlation (NCC), Image Fidelity (IF), Peak Signal Noise Ratio (PSNR), Root Mean Square Error (RMSE), Quality Index (QI), Correlation Coefficient (CC), Structural Similarity Index (SSIM), Mean Square Error (MSE), Entropy, Contrast, and Homogeneity, Image Histogram (IH). We also conducted a comparative analysis with some existing methods as well as security analysis which showed better results. The achieved result demonstrates significant improvements over the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hypsometric or area-altitude analysis unveils the geomorphic evolution of a river basin and its interactions with tectonics, lithology, and climate. The results of hypsometric analysis i.e., hypsometric curve (HC) along with the hypsometric parameters (HP) namely, hypsometric integral (HI), skewness (SK), kurtosis (KU), density skewness (DSK), and density kurtosis (DKU) can also be further analyzed to evaluate the geological erosional status of a river basin. However, the spatial scale’s significance in such analyses is often underestimated, assuming consistency across various resolutions of Digital Elevation Models (DEMs). To address this oversight, this study compares HP values obtained from DEMs of different sources and resolutions—Shuttle Radar Topographic Mission (30 m), Global Multi-resolution Terrain Elevation Data 2010 (225 m), and Global 30 Arc-Second Elevation Data Set (1000 m). A total of 108 sub-watersheds spanning four large Indian River basins (Mahanadi, Godavari, Krishna, and Cauvery) underwent analysis using both parametric and non-parametric statistical tests, namely Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests, to assess the distribution of hypsometric parameters. Subsequently, the null hypothesis regarding the robustness of HP across varying DEM resolutions was tested using non-parametric statistical tests such as the Mann-Whitney U test, Kruskal-Wallis H test, and Friedman test. The study’s findings challenge the prevalent notion of robustness, revealing that DEM resolution significantly impacts the results of hypsometric analysis. The Friedman test, in particular, highlighted significant differences in the full distribution of hypsometric parameters across different DEM resolutions, suggesting that variations in DEM resolution can alter the shape and variability of these parameters, not just their central tendencies. Higher-resolution DEMs, such as SRTM (30 m), provided more accurate representations of landscape features, especially in capturing subtle variations that influence erosion rates, sediment transport, and geomorphic classification. [ABSTRACT FROM AUTHOR]

A reader of the peer-reviewed neuroscience literature will often encounter expressions like the following: ‘the brain is a dynamic system’, ‘the brain is a complex network’, or ‘the brain is a highly metabolic organ’. These expressions attempt to define the essential functions and properties of the mammalian or human brain in a simple phrase or sentence, sometimes using metaphors or analogies. We sought to survey the most common phrases of the form ‘the brain is…’ in the biomedical literature to provide insights into current conceptualizations of the brain. Utilizing text analytic tools applied to a large sample (> 4 million) of peer-reviewed full-text articles and abstracts, we extracted several thousand phrases of the form ‘the brain is…’ and identified over a dozen frequently appearing phrases. The most used phrases included metaphors (e.g., the brain as a ‘information processor’ or ‘prediction machine’) and descriptions of essential functions (e.g., ‘a central organ of stress adaptation’) or properties (e.g., ‘a highly vascularized organ’). Comparison of these phrases with those involving other bodily organs (e.g. the heart, liver, etc.) highlighted common phrases between the brain and other organs, such as the heart as a ‘complex, dynamic system’. However, the brain was unique among organs in the number and diversity of analogies ascribed to it. The results of our analysis underscore the diversity of qualities and functions attributed to the brain in the biomedical literature and suggest a range of conceptualizations that defy unification. [ABSTRACT FROM AUTHOR]

Key message: Total 60-QRC for FLM traits were detected by meta-genomics analysis, nine major and stable QTL identified by DH population and validated, and a novel QTL Qflw.sxau-6BL was fine mapped. The flag leaf is an “ideotypic” morphological trait providing photosynthetic assimilates in wheat. Although flag leaf morphology (FLM) traits had been extensively investigated through genetic mapping, there is a desire for FLM-related loci to be validated in multi-environments and fine mapping. In order to identify the stable genomic regions for FLM traits, we conducted a meta-genomic analysis based on reports from 2008 to 2024. Experimentally, a doubled haploid (DH) population was used to assess the genetic regions associated with FLM traits in nine environments. The meta-genomic analysis extracted 60 QTL-rich clusters (QRC), 45 of which were verified in marker-trait association (MTA) study. Nine major and stable QTL were found being associated with FLM traits across three-to-seven environments including BLUP, with phenotypic variance explained (PVE) ranging from 5.05 to 34.95%. The KASP markers of the nine QTL were validated (P < 0.005) in more than three environments using a panel of diverse wheat collections from Shanxi Province in China. Two co-located major and stable QTL viz. Qflw.sxau-6B.5 and Qfla.sxau-6B.4 were found novel and contributed to increase FLW by 12.09–19.21% and FLA by 5.45–13.28%. They also demonstrated high recombination rates in LD analysis based on the resequencing of 145 wheat landmark cultivars. The fine mapping of Qflw.sxau-6BL narrowed it down to a 1.27 Mb region as a result of the combined genotypic and phenotypic analysis for secondary mapping population. Comparing to NIL-ND3338, the NIL-LF5064 showed higher FLW by 20.45–27.37%, thousand-grain weight by 1.88–2.57% and grain length by 0.47–2.30% across all environments. The expression analysis of 11 tissues revealed seven highly expressed genes within the fine map region. This study provides a genetic basis for the FLM traits for further map-based cloning of FLW genes in wheat. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to evaluate the symptomatic efficacy and tolerability of three different radiotherapy (RT) regimens for patients with vertebral metastases in a low-middle-income country setting, focusing specifically on the effectiveness of single-fraction radiotherapy. Methods: Conducted at the National Institute of Cancer Research and Hospital, Bangladesh, from July 1, 2020, to June 30, 2021, this prospective, non-randomized study enrolled 90 patients aged 18 to 75 years with histologically confirmed primary malignancies and vertebral metastases. Patients were allocated to one of three treatment arms: 8 Gy in a single fraction (Arm A), 20 Gy in 5 fractions (Arm B), or 30 Gy in 10 fractions (Arm C). The primary endpoint was pain response at 12 weeks, assessed by the Visual Analogue Scale and International Bone Metastases Consensus. Secondary endpoints included toxicity, measured by the Common Terminology Criteria for Adverse Events, and overall survival. Results: Pain control at 12 weeks showed no significant differences among the treatment groups, with 70% of patients in Arm A, 67% in Arm B, and 70% in Arm C experiencing either partial or complete pain relief (p = 0.95). The overall survival rates were comparable across the groups (median survival, 7 months for arms A and C, 6 months for Arm B). Skin toxicity was significantly lower in Arm A (10% incidence) compared to arms B (30%) and C (47%) (p = 0.017). There were no reports of Grade 3 or higher toxicities. Conclusion: The study confirms the efficacy and safety of single-fraction RT for spinal bone metastases, providing significant pain relief and lower skin toxicity relative to multiple fraction regimens. These results confirm the efficacy of single-fraction RT in the treatment of vertebral metastases also in resource-limited settings, suggesting its broader adoption to reduce toxicity and treatment burdens in low-middle-income countries. [ABSTRACT FROM AUTHOR]

In order to plan and facilitate the culture of personalized / precision medicine in medical practices within any healthcare institution, it is requisite for healthcare professionals like clinicians to have a clear understanding and approach towards the practices of personalized genetic testing. This nationwide cross-sectional study aimed to measure the perceptions and knowledge of clinicians towards personalized genetic testing and assess their current practices of personalized genetic testing in clinical settings through an online self-administered questionnaire in Saudi Arabia. The results of the study revealed that almost two-fifths of participants were responsible for ordering genetic tests directly (39.0%). The main concerns of the participants were the lack of clinical practice guidelines on personalized genetic tests (50.0%) and the cost of these tests (50.9%). The participants revealed that the genetic tests had influenced the patients' treatment plans (50.0%) and helped them understand the risks of their patients' disorders (52.3%). The study revealed a gap in understanding and implementing personalized genetic testing in Saudi Arabia, with enthusiasm tempered by challenges like insufficient knowledge, lack of guidelines, and practical applications' barriers. Effective integration of genetic testing into clinical practice should focus on enhancing medical education, developing clinical guidelines, and improving patient awareness about genetic tests that can lead to an improved personalized healthcare approach. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a metabolic disease marked by an excessive rise in blood sugar (glucose) levels caused by a partial or total absence of insulin production, combined with alterations in the metabolism of proteins, lipids, and carbohydrates. The International Diabetes Federation estimates that 425 million individuals globally had diabetes in 2017 which will be 629 million by 2045. Several medications are used to treat DM, but they have limitations and side effects including weight gain, nausea, vomiting, and damage to blood vessels and kidneys. Therefore, it is essential to identify anti-diabetic drugs that have less or no side effects. Hence, the current study employed in silico approaches to discover new DPP-IV inhibitors that might be associated with diabetes. Thirty-four (34) co-crystalized DPP-IV enzymes were found from the protein data bank and the co-crystal ligands were docked into the active-site 6B1E protein to find out the hit compounds. From the docking results, we found two hit compounds (5T4E and 4J3J) which were used to find out the analogs from the experimental drug database using the DrugRep software. According to the results, twenty (20) analogs were found from the experimental drug database with the similarity score of ≥ 0.790 and docked once again into the active site of the DPP-IV (PDB ID: 6B1E) enzyme. Interestingly, DB02226 showed the best binding affinity (−10.3 kcal/mol) and prime MM/GBSA (−68.73 kcal/mol) compared to the reference drug (co-crystal ligand; −7.4 kcal/mol and −47.49 kcal/mol, respectively). Additionally, DB02226 has shown excellent reactivity, efficacy, and structural stability in the binding region of target proteins in studies using MD simulation, MM/GBSA, DFT, and MESP analysis. These findings can be utilized to support further in vitro, in vivo, pre-clinical and clinical research rather than definitively confirming anti-diabetic effectiveness. [ABSTRACT FROM AUTHOR]

Social media has become a platform for people to express emotions, including happiness and sadness, to their followers. Major Depressive Disorder (MDD), a common mental health disorder, is characterized by sadness and loss of interest in activities, leading to physical, emotional, cognitive, and social suicidal thoughts. Early detection and intervention of MDD are crucial for effective management and treatment. The study investigates the potential of detecting MDD on social media platforms like Facebook, Twitter and Reddit by analyzing text using advanced machine learning and deep learning algorithms. In order to collect dataset, we employed both web scraping techniques and publically existing datasets (Twitter, Reddit) that are available on the Kaggle website. Natural language processing (NLP) techniques are applied to preprocess and excerpt meaningful features from the textual data. Several machine learning algorithms are employed to make prophetic models for MDD discovery grounded on verbal patterns, sentiment analysis, and verbal labels associated with depressive symptoms. We analyse our models using three datasets. The two online datasets for which the LSTM algorithm performs best are Reddit with 93.72% accuracy, Twitter with 99.85% accuracy, and our dataset which is extracted using web scraping technologies from Reddit gets 96.47% accuracy utilizing Stacking ensemble. The model's performance is thoroughly assessed using a variety of criteria, such as accuracy, precision, recall, and F1-score. Additionally, We find an approach with a more effective ML framework for enhancing MDD detection. [ABSTRACT FROM AUTHOR]

The striped snakehead, Channa striata, is commercially and nutritionally important due to its medicinal properties, such as wound healing and antimicrobial abilities. This study investigated the reproductive biology of C. striata in relation to hydro-climatic changes using a fuzzy logic approach for long-term management in the wetland ecosystem (Gajner beel), Bangladesh. A total of 1200 C. striata individuals were collected monthly during January to December 2019 using various fishing methods. Measurements of total length (TL), body weight (BW), and gonad weight (GW) were taken with a precision of 0.01 cm and 0.01 g, respectively. The GSI (gonadosomatic index in %), MGSI (modified gonadosomatic index in %), and DI (Dobriyal index) were used to determine size at sexual maturity (Lm) and the spawning season. Results revealed a wide range of TL, ranging from 12.7 to 46 cm. The Lm was estimated between 21.7 and 25.33 cm based on various model as TL vs. GSI, MGSI, and DI; TL-fork length (FL) regressions; and maximum length (Lmax) for this species. The spawning period was observed from April to July, peaking in May, indicated by higher GSI, MGSI, and DI values. Eco-climatic factors showed a significant relationship with GSI, except for total dissolved solids (TDS). Rising temperatures and declining rainfall could delay spawning of C. striata. A fuzzy logic approach was employed to predict future GSI changes over extended periods (e.g., 50 or 60 years), enabling anticipation of shifts in the timing and duration of the spawning season. Finally, the findings of our research might be effective to carry out specific conservation efforts of C. striata in response to climate variability in sub-tropical water. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) poses a major global healthcare challenge, highlighting the need for new treatments beyond current options. Currently available drugs have side effects including weight gain, nausea, vomiting, diarrhea, insulin resistance etc. Therefore, given the benefits of indole derivatives in diabetes and the lack of computational studies on bis-indole-based triazine derivatives with aldose reductase (AR), this study employs in-silico analysis to explore their potential as type-2 diabetes treatments. Based on the Differential Expression analysis, the human aldose reductase (HAR) encoding gene AKR1B1 showed overexpression in GSE30122 diabetes patients (Log2FC = 0.62, P < 0.01). Moreover, the compounds 2-((5,6-di(1H-indol-3-yl)-1,2,4-triazin-3-yl)thio)-1-(3-hydroxy-5-methylphenyl)ethan-1-one (4) and 2-((5,6-di(1H-indol-3-yl)-1,2,4-triazin-3-yl)thio)-1-(4-nitrophenyl)ethan-1-one (8) were identified as leading candidates, showing binding energies of − 62.12, − 81.73 kcal/mol and − 57.19, − 85.97 kcal/mol, respectively. Docking, MM/GBSA screening, molecular dynamics (MD) simulations, PCA, and post-MM/GBSA analysis confirmed their stability and favorable binding compared to the apo protein and control. Further in-vitro, in-vivo, and clinical studies are required to validate their therapeutic potential. [ABSTRACT FROM AUTHOR]

The bean aphid is a serious pest due to its ability to drain plant sap and spread viruses and controlling it remains challenging in Bangladesh. Ants are known to establish antagonistic and mutualistic interactions with honeydew-producing sucking pests, and their potential varies with the presence of different cooccurring herbivores. This study aims to infer the role that such interactions play in the population dynamics of ants, ladybird beetles, and aphids in country beans in a multilayered correspondence toward developing an effective biocontrol strategy. The molecular characterization of aphids was performed via the use of the cytochrome oxidase subunit-1 gene of mtDNA along with a taxonomic study of ants. The seven treatments (T1 to T7) included periodic culture and mass release of the ladybird beetle (LBB), which induced ants and aphids. Two subspecies of bean aphids, namely, Aphis fabae fabae (Scopoli, 1763) and Aphis fabae cirsiiacanthoidis (Scopoli 1763), were identified in reference to the GenBank accessions OQ892174-OQ892178 and OQ892199-OQ89220. This is the first sequencing of aphid colonies in Bangladesh, which identified two subspecies of black bean aphids. Among the identified ants, Tapinoma simrothi (Emery, 1925) and Monomorium libanicum (Emery, 1925) were found to be effective under both natural and induced LBB conditions. These findings hold promise for the development of sustainable pest management strategies for Bangladesh's country bean ecosystems. [ABSTRACT FROM AUTHOR]

Background: Rice is subjected to various environmental stresses, resulting in significant production losses. Abiotic stresses, particularly drought and salinity, are the leading causes of plant damage worldwide. The High-affinity Potassium Transporter (HKT) gene family plays an important role in enhancing crop stress tolerance by regulating physiological and enzymatic functions. Methods and results: This study investigates the effect of overexpressing the rice HKT1;5 gene in Arabidopsis thaliana on its tolerance to salinity and drought. The OsHKT1;5 gene was introduced into Arabidopsis under the control of 35 S promoter of CaMV via floral dip transformation method. PCR confirmed the integration of the transgene into the Arabidopsis genome, while qPCR analysis showed its expression. Three transgenic lines of OsHKT1;5 were used for stress treatment and phenotypic studies. The overexpressed lines showed considerably higher germination rates, increased leaf counts, greater fresh and dry weights of the roots and shoots, higher chlorophyll contents, longer root lengths, and reduced Na+ levels together with increased K+ ions levels after salt and drought treatments, in comparison to wild-type plants. Furthermore, overexpressed lines exhibited higher antioxidant levels than wild-type plants under salinity and drought conditions. In addition, transgenic lines showed higher expression levels of the OsHKT1;5 gene in both roots and shoots compared to wild-type plants. Conclusions: In conclusion, this study revealed OsHKT1;5 as a promising candidate for enhancing tolerance to salinity and drought stresses in rice, marking a significant step toward developing a new rice variety with improved abiotic stress tolerance. [ABSTRACT FROM AUTHOR]

Azoospermia, a severe form of male infertility characterized by the complete absence of sperm in the ejaculate, affects about 1% of the male population, with most cases attributed to nonobstructive azoospermia (NOA) caused by gametogenic failure. NOA has various genetic origins, including chromosomal abnormalities, Y chromosome microdeletions, and monogenic mutations. Although whole-exome sequencing (WES) has identified over thirty candidate genes associated with NOA, the genetic causes of most cases have yet to be elucidated. In our study, we selected seven consanguineous families diagnosed with azoospermia from a total of 21 male infertile families recruited from the rural area of Pakistan. Blood samples were collected from both patients and fertile controls for DNA extraction, followed by WES to identify potential causative recessive monogenic variants linked to male infertility. We successfully identified five deleterious variants among five of the seven families, including three missense biallelic substitutions in WWC2, RPL10L, and SOHLH1, a hemizygous deletion in ESX1, and a homozygous deletion in TXNDC2, which have potentially pathogenic relevance to the azoospermia of human male infertility. These novel findings enhance our understanding of the molecular mechanisms underlying the complex etiology of azoospermia, offering valuable insights for genetic counseling and diagnostics and paving the way for future therapeutic approaches. [ABSTRACT FROM AUTHOR]

Mucuna (Family: Fabaceae), a herbaceous, indigenous plant, has been widely used worldwide due its nutritional value and medicinal properties. Mucuna gained recognition due to presence of L-DOPA (L-3, 4-dihydroxyphenylalanine), which is used in the treatment of Parkinson's disease and mental disorders. Traditionally, the plant is used for cuts, wound healing, and pain relief. Standardization of herbal drugs is of immense value to estimate drug efficacy. In the present work, an effective tincture of Mucuna imbricata (MI) DC. ex Baker was prepared through leaf maceration (for 10, 20, 30 days) using varying ethanol-water solvent systems (30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%). A maximum yield (195 mg/10mL) was recorded on the 30th day of maceration in 30% v/v ethyl alcohol concentration, where the yellowish-black tincture had a pH of 5.2, conductivity of 900 µS sec− 1, and specific gravity of 0.99. The results of fluorescence analysis depicted the multifaceted fluorescence attribute of Mucuna imbricata (MI) leaves. Interestingly, the total crude protein (95 mg g− 1 dwt), total flavonoids (1.35 ± 0.51 mg g− 1 dwt) and total alkaloids (111.8 mg g− 1 dwt) contents were highest in 100% grain tincture after 30 days, while the total phenolics were found to be maximum (13.44 ± 0.53 mg g− 1) in 60% grain tincture after 30 days of maceration. 50% scavenging activity was observed at 50 µg mL− 1 of crude tincture after 30 days in 30% grain alcohol. Thus, the best physico-chemical attributes and moderate antioxidant activity were revealed with the longest duration of leaf tincture maceration. [ABSTRACT FROM AUTHOR]

Natural material-based non-volatile resistive switching (RS) memory devices are considered as one of the emerging technologies for the sustainable electronics. Here, Moringa Oleifera Lam. (MO) locally called Sajna leaves are used as an active switching layer for write-once-read-many (WORM) devices. Spin coating technique has been utilized to fabricate the MO leaves based active layer upon ITO. Observed WORM memory devices showed a high memory window (103), reliable read endurance (350 times) and stable data retention behaviour up to 1.5 × 103 s at room temperature. Fitting models revealed the switching mechanisms such as Schottky Emission (SE), trap-filled space charge limited conduction (TSCLC) and Ohmic conduction are involved in the RS process. Based on the obtained results, this work suggests that MO leaves based bio-WORM could be a promising substitute for the next generation non-volatile memory applications. [ABSTRACT FROM AUTHOR]

Garment products are international trade items and essential consumption items. The readymade garment industry of Bangladesh is the second largest garment exporter in the world. The garment industry of Bangladesh consumes one-third of the country's total industrial energy. Such industry consumes significant energy and contributes greenhouse gas (GHG) emissions into the atmosphere. Assessment of energy consumption and GHG emission of garment products helps to understand the baseline status of the production and benchmarking the energy and emission leveling of the product. This paper aims to investigate the energy consumption and GHG emissions of readymade garment (T-shirt) production in Bangladesh. Gate-to-gate production stages have been considered for the investigation. The strategies for the energy and GHG emission-saving potential have been also analyzed. The result showed that the total energy consumption in terms of natural gas, oil, and electricity found are 15,105 m3, 49.93 kg, and 36,265 kWh respectively per day. The total CO2 emission found is 56.65 tons per day. The specific energy consumption found is 12 MJ/piece and the specific emissions of the CO2, CO, CH4, NOx, and SOx are 0.94 kg/piece, 1.69 gm/piece, 3.53 gm/piece, 4.1 gm/piece, and 0.89gm/piece, respectively. The proposed strategies could save energy in the machinery section in the range of 22–70%. The energy saving potential of 25% is possible in the steam generation sector by employing the proposed electric steam generator. The steam generation section gives the highest CO2 emission and the compressor section gives the lowest emission. The specific energy consumption and carbon footprint of Bangladesh-manufactured garments are in very close agreement with the literature studied. [ABSTRACT FROM AUTHOR]

Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R 2 ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) act as key gene expression regulators, influencing intracellular biological and pathological processes. They are of significant interest in animal genetics as potential biomarkers for animal selection and health. This study aimed to unravel the complex miRNA signature involved in mastitis in in vitro cell culture. For this purpose, we constructed a control and treatment model in ovarian mammary epithelial cells to analyze miRNA responses upon Staphylococcus aureus (S. aureus) stimulation. The high-throughput Illumina Small RNA protocol was employed, generating an average of 7.75 million single-end reads per sample, totaling 46.54 million reads. Standard bioinformatics analysis, including cleaning, filtering, miRNA quantification, and differential expression was performed using the miRbase database as a reference for ovine miRNAs. The results indicated differential expression of 63 miRNAs, including 33 up-regulated and 30 down-regulated compared to the control group. Notably, miR-10a, miR-10b, miR-21, and miR-99a displayed a significant differential expression (p ≤ 0.05) associated to signal transduction, transcriptional pathways, diseases of signal transduction by growth factor receptors and second messengers, MAPK signaling pathway, NF-κB pathway, TNFα, Toll Like Receptor 4 (TLR4) cascade, and breast cancer. This study contributes expanding miRNA databases, especially for sheep miRNAs, and identifies potential miRNA candidates for further study in biomarker identification for mastitis resistance and diagnosis. [ABSTRACT FROM AUTHOR]

Using the two rounds of the Survey of Manufacturing Industries, this paper empirically investigates the likely scenarios of the impacts of technological progress on sectoral employment by divisions in the manufacturing sector in Bangladesh. We performed multiple linear regression of output on labour, capital stock, region fixed effects, year fixed effect, and cluster our robust standard errors at the industrial classification to obtain the Solow residual where the technology parameter comes from the residual of this regression. Then, we introduce advancements in the technology parameter and estimate the impacts on the labour, holding other things fixed. The manufacturing industry in Bangladesh has a total of 5.3 million employees, with the majority in the textile and apparel sector. A 15% increase in productivity due to technological advancement would eliminate 688,000 jobs in Bangladesh, a 30% increase would eliminate 1.22 million, and a 50% increase would result in a dire consequence, with a total job loss of 1.8 million. The largest affected sectors are textiles and apparel. Dhaka would be affected most, followed by Chattogram, Rajshahi, and Khulna. Assuming a 10% annual growth in the industry, the net increase in total manufacturing employment would be 2.02 million in 2025. The unemployment we have forecasted will not occur with the rise in technology if the technology is augmenting instead of labour replacing. [ABSTRACT FROM AUTHOR]

Tungsten carbide (WC) is one of the most robust mold materials in precision glass molding owing to its excellent hardness at high temperatures. However, the fabrication of a microlens array (MLA) shape on a WC mold is challenging. Electrical discharge machining (EDM) is highly effective in fabricating conductive materials regardless of their hardness. However, due to electrode wear, the machining accuracy and consistency of the MLA are gradually reduced in the EDM process. In this study, a micro copper ball–assembled array electrode (BAAE) was proposed and developed to achieve high efficiency, accuracy, and consistency in MLA mold manufacturing. The copper balls aligned in an array on the position template were used as the electrode to fabricate MLA in EDM. The general calculation method for the maximum service cycles of copper balls was developed to achieve maximum utilization of the copper balls. The in situ rotation of the copper ball updated the processing area, significantly improving the consistency of the lenslet morphology. The effects of two polarity processing were investigated, and the positive processing was chosen. The efficiency of BAAE was significantly improved through the implementation of array alignment and path planning. The effectiveness and efficiency of the ball rotation were confirmed by comparing the lenslet morphology with the ball end–formed electrode. The EDM using BAAE is proven to be capable of improving the efficiency, accuracy, and consistency of MLA mold manufacturing. [ABSTRACT FROM AUTHOR]

To assess the sources, levels, spatial distributions and exposure to human health, the concentration of heavy metals Pb, Cu, Mn, Zn, and Fe in the sand/sediment of the Parki Beach area of Anowara, Chattogram, Bangladesh are determined using Atomic Absorption Spectroscopy (AAS) for the first time. A total of 40 surface and subsurface sand and sediment samples were collected from 20 different sampling points along the 15 km long Parki Beach area, Bangladesh. Average concentrations of Pb, Cu, Mn, Zn and Fe in surface samples are 14.60, 10.10, 283, 407 and 25,256 mg/kg respectively and 9.95, 4.20, 193, 156.6 and 24,404 mg/kg for sub-surface samples, respectively, which shows that the values are higher in surface samples than those in sub-surface samples. According to the Consensus-Based Sediment Quality Guidelines (CBSQG), the northern part of the beach becomes moderately polluted by Mn and Fe, and a smaller area of the southern part is highly polluted by Zn. The average Contamination Factor (CF) of Zn was greater than 1(CF > 1), while the CF of other metals was less than 1(CF < 1). CF of Zn in some sampling points was exceptionally high. Geo-accumulation Index (Igeo) also shows that Zn slightly pollutes some sampling points. The enrichment Factor (EF) of Fe and Mn in samples in the northern part of the study area is quite high and the study reveals that high values of Fe and Mn are mainly derived from geogenic sources. Ecological risk factor (Er) indicates low ecological risk for all sampling points. The Pollution Load Index (PLI) was measured at all sampling stations, and the results showed that the overall level of heavy metal pollution is low. The health quotient (HQ), health index (HI), total health index (THI) and incremental life time cancer risk (ILCR values suggest that adults are safe from any health risk while children may experience non carcinogenic health risk due to the combined effect of the metals. Reduction of heavy metal in the beach is possible with the adaptation of multiple strategies. This data can be used by policymakers to develop strategies to reduce the potential impacts of soil contamination on the environment and public health. [ABSTRACT FROM AUTHOR]

The fruit of the jujube tree is high in nutrients and has various health benefits. China is a major producer of jujube, and it is now cultivated all around the world. Numerous studies have demonstrated the nutritional value and potential health advantages of bioactive compounds found in the jujube tree. Furthermore, the jujube tree has a remarkable 7000-year agricultural history. The jujube plant has developed a rich gene pool, making it a valuable resource for germplasm. Different studies have focused on the developmental stages of jujube fruits to identify the optimal time for harvest and to assess the changes in their bioactive natural compounds or products during the process of development but the molecular mechanism underlying the production of bioactive natural products in Z. jujuba is still poorly understood. Moreover, the potential differential expressed genes (DEGs) identified as responsible for the synthesis of these compounds should be further functionally verified. It has been noticed that the contents of total flavonoids, total phenolic, and vitamin C increase significantly during the ripening process, while the contents of soluble sugars and organic acids decrease gradually. In this review, we have also scrutinized the challenges that hinder the utilization of jujube fruit resources and suggested potential areas for further research. As such, our review serves as a valuable resource for the future development of jujube-based nutritional compounds and the incorporation of their nutritional elements into the functional foods industry. [ABSTRACT FROM AUTHOR]

Density Functional Theory (DFT) is incorporated in this study to examine the thermodynamic, electronic, mechanical, and optical characteristics of antiperovskite compounds A3BO (A = K, Rb and B = Au, Br). The purpose of the study is to demonstrate a comprehensive understanding of these materials and their potential applications across various fields emphasizing their stability and energetic profiles. The electronic properties, including band structures, and density of states are also analyzed to understand the electrical behavior of these materials, which enables predicting their conductive and semiconductor nature. The band gaps of K3AuO, K3BrO, Rb3AuO, and Rb3BrO are 0.72 eV, 0.80 eV, 0.15 eV, and 0.29 eV, respectively. The study also investigated the mechanical properties of the antiperovskite structures, including elastic constants, bulk modulus, and shear modulus to provide insights into their mechanical stability and durability. Their Pugh's ratio (B/G) is below 1.75 and negative Cauchy pressure indicates these compounds are brittle. And machinability index B/C44 > 1.5 implies excellent lubricating properties. This phenomenon extends the potential industrial application of materials with specific mechanical integrity to their structural components. Additionally, the study investigated the optical properties of the A3BO antiperovskite compounds, including the dielectric function, loss function, reflectivity, conductivity, refractive index, and absorption spectra. These findings provide a comprehensive understanding of how these materials interact with light, which could be useful in the development of optoelectronic devices. Overall, this DFT study provides significant insights into the multifaceted properties of A3BO antiperovskite compounds, laying the groundwork for further experimental exploration and facilitating the targeted design of materials with tailored properties for specific technological applications. [ABSTRACT FROM AUTHOR]

Dhaka city is experiencing tremendous growth in traffic. Until recently, the city's demand for traffic was entirely served by public buses, a mix of motorized and non-motorized paratransit, and private personalized transport. The first ever rail-based metro, mass rapid transit (MRT), namely MRT Line 6, was partially inaugurated on 28 December 2022. Authority expects that there will be visible modal shift. However, MRT systems in many Asian and European countries are attracting much lower private motorists than what is expected. Moreover, in Dhaka, a unique mix in road-based transport with public transit and varieties of paratransit and private personalized vehicles intensifies the uncertainties involved in modal shift. Therefore, based on a field survey done before four (04) months of the partial inauguration of MRT Line 6, this study intends to explore the modal shift potentials of different mode users to a completely new mode and the modal choice factors. The analysis of the results finds overall, 75% of all mode users are willing to shift, while para and public transit users are comparatively more willing than private personalized vehicle users. However, such willingness comes up with one or more conditions: comfort, reasonable fare, reduced total travel time, less crowd, etc. The study finds that affordability, availability, and accessibility factors have an influence on their modal shift behavior. Also, results from binary logistic model identify significant impact of several sociodemographic, trip- and accessibility-related factors influencing modal shift choice. Findings from this study explain the optimism regarding MRT by different mode users and inform the decision-makers about their course of actions including different interventions, strict and carrot approaches to hold on to the potential shifters and attract more. [ABSTRACT FROM AUTHOR]

The aim of this paper is to solve second order nonlinear boundary value problems (BVPs) approximately. To do so, we utilize Thakur et al. iterative scheme (Filomat 3(10):2711- 2720, 2016) involving a weak contraction mapping in an arbitrary Banach space. Firstly, we study the convergence behaviour and the two numerical examples to show the better rate of convergence. Further, we apply this method to find the approximate solution of nonlinear boundary value problems (BVPs) and two illustrative numerical examples for BVPs are provided. Thus, several corresponding results in the literature are generalised and improved. [ABSTRACT FROM AUTHOR]

The purpose of this study was to identify potential novel drug targets for Bacteroides fragilis infections using bioinformatics techniques, such as subtractive and comparative genomics. Bacteroides fragilis is a frequently isolated anaerobic pathogen, particularly in the human digestive tract, where its pathogenesis and persistence are influenced by various virulence factors. By understanding these factors, the study aims to explore alternative therapeutic strategies and provide insights for the development of treatments against B. fragilis infections, particularly as alternatives to antibiotic therapy. A comparative subtractive genomic analysis was performed against the B. fragilis (strain CL07T12C05) to identify unique drug targets. The analysis includes the identification of non-paralogous, non-homologous, essential, and drug target like proteins. Moreover, a comprehensive structural analysis of the protein was conducted utilizing structure modeling and validation techniques, along with network topology analysis. Furthermore, a library comprising approximately 9000 FDA-approved compounds accessible in the DrugBank database was employed to conduct virtual screenings for compounds effective against the designated drug target. The top shortlisted compounds were further studied by employing MD simulations using GROMACS. This approach was chosen due to the established safety, efficacy, pharmacokinetics, and toxicity profiles of these compounds. As a result, B. fragilis (strain CL07T12C05) was found to possess 4595 proteins. Among these, 3518 were identified as non-homologous, 1508 deemed essential for bacterial viability, 348 exhibited drug-like properties, 203 were implicated in virulence, and 135 displayed antibiotic resistance. Following an extensive literature review, the protein Sialic acid O-acetyltransferase was chosen through a hierarchical shortlisting process as a potential therapeutic target. The ongoing research facilitated the repurposing of drug compounds: DB12411, DB02112, DB03591, and DB00192, as cost-effective medications against B. fragilis related infections. MD simulations analysis showed that DB12411 may be a potential drug candidate against Sialic acid O-acetyltransferase from B. fragilis. Through subtractive and comparative genomic analysis, Sialic acid O-acetyltransferase was identified as a promising drug target against Bacteroides fragilis. The findings indicate that compounds targeting this protein could potentially be effective in treating B. fragilis infections. However, further experimental validation is required to conclusively confirm their efficacy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic may have exacerbated depression, anxiety, and executive function (EF) difficulties in children with autism spectrum disorder (ASD). EF skills have been positively associated with mental health outcomes. Here, we probed the psychosocial impacts of pandemic responses in children with and without ASD by relating pre-pandemic EF assessments with anxiety and depression symptoms several months into the pandemic. We found that pre-pandemic inhibition and shifting difficulties, measured by the Behavior Rating Inventory of Executive Function, predicted higher risk of anxiety symptoms. These findings are critical for promoting community recovery and maximizing clinical preparedness to support children at increased risk for adverse psychosocial outcomes. [ABSTRACT FROM AUTHOR]