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COVID-19 pandemic has forced educational institutions to use e-learning systems. Bangladesh is no exception; many students come from underprivileged families who are not well-off. This study aimed to explore the antecedents to the underprivileged undergraduate students' intention to participate in online classes in Bangladesh through the integration of the Technology Acceptance Model, Information Systems Success Model, and Theory of Planned Behaviour. We used confirmatory factor analysis (CFA) to test the hypotheses. The non-probability sampling method was used to select 394 respondents by dint of the subjective judgment of the researchers. Using smart PLS software, the data were analyzed with Structural Equation Modeling (SEM). It was divulged that e-Learning usage intention (BI) is influenced by attitude (ATT), perceived usefulness (PU), students' online learning satisfaction (SOS) and subjective norms (SN). But perceived ease of use (PEU) and system quality (SQ), internet service quality (ISQ) and perceived behavioral control (PBC) do not influence BI. Even ISQ does not influence SOS. It was also revealed that PEU mediated attitude and PU, and PEU and SQ also influenced SOS. The study contributes to e-Learning literature by incorporating three models which may guide policymakers in understanding how to integrate students from all social classes into e-learning systems to eliminate academic digital discrimination.

Abstract In this paper, we assess the impact of credit on rural transformation in Bangladesh. District-level rural data from three waves of the Bangladesh Household Income and Expenditure Survey (HIES) were analyzed using ordinary least squares and fixed-effects panel regression analysis. The findings indicate that increasing household credit significantly stimulated the production of high-value agricultural commodities and expanded opportunities for non-farm employment in rural areas. Robustness analysis using access to credit instead of average household credit confirmed the positive relationship between credit and rural transformation at the district level. Access to education and electricity was also found to be positively related to rural transformation; on the other hand, the decline in land per capita was directly related to rural non-agricultural employment opportunities. Our findings highlight the necessity of improving credit accessibility, enhancing capacity-building skills and knowledge of both institutions and individuals, and implementing financial literacy programs for an inclusive, sustainable rural transformation in Bangladesh.

Abstract Background SARS-CoV-2 and other viruses rely on the protease function of the TMPRSS2 protein to invade host cells. Despite cancer patients often experience poorer outcomes following SARS-CoV-2 infection, the role of TMPRSS2 in different cancer types has not yet been analyzed in detail. Therefore, the aim of the study was to determine the expression, function and clinical relevance of TMPRSS2 in tumors. Methods Publicly accessible RNA sequencing data from tumors, adjacent tissues and whole blood samples of COVID-19 patients as well as data from human tumor epithelial and endothelial cells infected with SARS-CoV-2 were analyzed for TMPRSS2 expression and correlated to the expression of immune-relevant genes and clinical parameters. In vitro models of cells transfected with TMPRSS2 (TMPRSS2high), siTMPRSS2 or mock controls (TMPRSS2low cells) were analyzed by qPCR, flow cytometry, ELISA and Western blot for the expression of immune response-relevant molecules. Co-cultures of TMPRSS2 model systems with blood peripheral mononuclear cells were employed to evaluate immune cell migration, cytotoxicity and cytokine release. Results Higher expression levels of TMPRSS2 were found in blood from patients infected with SARS-CoV-2, while TMPRSS2 expression levels significantly varied between the tumor types analyzed. TMPRSS2high tumor cells exhibit increased activity of the interferon (IFN) signal pathway accompanied by an increased expression of class I human leukocyte antigens (HLA-I) and programmed cell death ligand 1 (PD-L1) elevated interleukin 6 (IL-6) secretion and reduced NK cell-mediated cytotoxicity compared to TMPRSS2low mock controls. Treatment with a Janus kinase (JAK) 2 inhibitor or TMPRSS2-specific siRNA decreased TMPRSS2 expression. Co-cultures of the in vitro TMPRSS2 models with peripheral blood mononuclear cells in the presence of the immune checkpoint inhibitor nivolumab resulted in a significantly increased migration and infiltration of immune cells towards TMPRSS2high cells and a reduced release of the innate immunity-related cytokines CCL2 and CCL3. Conclusions This study provides novel insights into the role of TMPRSS2 in various tumor systems and the impact of SARS-CoV-2 infection on the host immunogenicity via the activation of immune-relevant pathways. These findings were linked to the efficacy of immune checkpoint inhibitor therapy, offering a potential alternative strategy to mitigate the severity of COVID-19.

Target detection in an unknown environment is a crucial aspect of reconnaissance using a swarm of unmanned aerial vehicles (UAVs). An efficient target detection technique is required to minimize the number of iterations for searching and maximize the coverage area with respect to the number of iterations and detected targets. This paper proposes a gravitational search algorithm (GSA) swarm-based UAV reconnaissance scheme to detect targets in an unknown environment. Additionally, different GSA-based searching methods are analyzed to identify the most efficient one with the minimum number of iterations and maximum coverage. Extensive simulations are performed, and the results of the proposed scheme are compared with existing search schemes. The results demonstrate that the proposed GSA swarm-based detection scheme requires fewer iterations and provides greater area coverage than existing UAV reconnaissance schemes for target detection in an unknown environment.

Nano-cellulose is a biodegradable polysaccharide which has multifunctional uses due to its fascinating properties (i.e., antibacterial, antifungal, anticarcinogenic activity) in wastewater treatment, drug design, food packaging, etc. CNC usually extracted from primary plant (such as jute, hemp, flax, cotton, etc.) which has other significant uses (for example preparation of yarn, rope, tissue paper, etc.). To reduce the pressure on primary plant the use of waste biomass of secondary plant could be an effective and economic route of CNC isolation. In this study a series of chemical treatment i.e. scouring (5% soap solution), alkali treatment (16% NaOH solution), bleaching (2% NaClO2 and 2% Na2S2O5 solution at pH = 4.0), acid hydrolysis (60% H2SO4) was conducted to isolate CNC from the waste maize cob. Characterization of the specimens were conducted by FTIR-ATR (Fourier Transform Infrared-Attenuated Total Reflection), FESEM (Field Emission Scanning Electron Microscopy), XRD (X-ray Diffraction), EDX (Energy Dispersive X-ray), DLS (Dynamic Light Scattering), Thermal analysis (TGA/DTG/DTA), and Zeta potential analysis. Different functional groups (>C=O, C≡C, C-O-C, C-O, -OH, etc.) were identified by FTIR-ATR. Crystal structure, crystallite size, and crystallinity index of CNC (around 84.63±0.03%) were observed by XRD analysis. Produced CNC showed enhanced thermal stabilities in TGA/DTG/DTA curves (about 40% residual mass at 600 °C) with the appearance of a peculiar 2D honey comb like void surface microstructure in FESEM micrographs and the surface charge (around -7.09mV) was measured by zeta potential. The newly produced CNC was perfectly nano sized (around 100 nm according to DLS analysis and FESEM micrograph). Hence, this newly produced CNC would be beneficially used to fabricate bio-nanocomposites for potential applications in various sectors such as biomedical, engineering, and industrial wastewater treatment as an appropriate substitute of the unsafe fossil based synthetic ones to develop legitimate environment.

This study aims to investigate the strategies employed by halal food manufacturers in Malaysia to mitigate operational supply risks. This study selected a sample of 369 respondents using a simple random sampling method to participate in the main survey. The collected dataset was analysed using covariance-based software (AMOS-SEM)) to test the study hypotheses. The findings of this research highlight that halal food manufacturers in Malaysia proactively adopt measures to manage operational supply risks from suppliers. Notably, they utilize behaviour-based and buffer-based strategies to effectively minimize the impact of these risks. This study focused on supply-related risks. To secure the integrity of halal, the firms must address demand-related risks and governmental and organizational risks to ensure the halalness of halal products. Therefore, it is crucial to consider risk management for all parts of the supply chain to guarantee the halal compliance of food products. The study highlights halal firms' need for behaviour-based and buffer-based risk management strategies to mitigate price, quality, and delivery risks while ensuring brand reputation and consumer trust through collaboration, information sharing, and supplier performance evaluation. This study presents a comprehensive analysis of the operational supply risks faced by halal food manufacturers, offering insights into the unique challenges and vulnerabilities within the halal food supply chain. By specifically focusing on Malaysia, this research contributes to the limited existing literature in this specific context, further enriching our knowledge in this field.

This study introduces a real-time traffic density monitoring system utilizing YOLOv8-based digital image processing to improve traffic management efficiency. By leveraging YOLOv8’s enhanced speed and precision, the system detects and classifies five types of vehicles and displays traffic data through a web interface developed with OpenCV and Flask. Key implementation features include real-time video streaming and accurate detection metrics, with the system achieving 96% Precision, 84% Recall, and an F1 Score of 90% during field testing in Bogor. This indicates the system’s potential for minimizing manual traffic monitoring and aiding traffic authorities in making data-driven decisions. The research also discusses the system’s integration into urban traffic management and its scalability for diverse environments.

Advancements in prestressed concrete bridge technology have increasingly focused on sustainability in response to growing environmental concerns. This review examines recent innovations in integrating recycled concrete aggregates (RCA) and supplementary cementitious materials (SCMs) within prestressed concrete to conserve resources, reduce waste, and lower carbon emissions. Sustainable prestressing techniques, including the use of fiber-reinforced polymer (FRP) tendons and shape memory alloys (SMAs), increase the durability of prestressed concrete bridges, extend service life, and minimize maintenance needs, thereby reducing environmental impact. Key methodologies, such as lifecycle assessment (LCA) and performance-based design, are highlighted for their roles in optimizing structural performance while reducing the ecological footprint. Despite the benefits, barriers to widespread adoption remain, including technical limitations, economic challenges, and regulatory constraints. To address these issues, this review proposes further research on material development, updated design guidelines, cost‒benefit analyses, and supportive policy initiatives. The findings confirm that integrating sustainable materials and advanced technologies in prestressed concrete bridge construction offers environmental advantages without compromising structural integrity. Collaborative efforts among engineers, researchers, policy-makers, and educators are essential to overcoming these barriers and advancing sustainable, resilient infrastructure.

Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory” decoder for word prediction and sentence construction, and a “convolutional neural network” as an encoder that is skilled at object recognition and spatial information retention. The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. For both training and testing, the study uses a variety of photos from open‐access datasets, such as Flickr8k, Flickr30k, and MS COCO. The Python platform is used for implementation, with Keras and TensorFlow as backends. The experimental findings, which were assessed using the “bilingual evaluation understudy” metric, demonstrate the effectiveness of the suggested methodology in automatically captioning images. By addressing spatial relationships in images and producing logical, contextually relevant captions, the paper advances image captioning technology. Insightful ideas for future study directions are generated by the discussion of the difficulties faced during the experimentation phase. By establishing a strong neural network architecture for automatic picture captioning, this study creates opportunities for future advancement and improvement in the area.

Abstract This research explores the contribution of Blockchain Technology and Industry 5.0 in driving sustainability within Bangladeshi Ready-Made Garments (RMG) industry, with a focus on alignment with key Sustainable Development Goals (SDGs). The study employs Interpretive Structural Modeling (ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods to identify and analyze 14 critical synergies that can drive sustainability. The ISM analysis categorizes the synergies into independent, dependent, and linkage variables, providing insights into their roles and significance within the system. Fuzzy DEMATEL further refines this understanding by evaluating the direct and indirect relationships among the linkage synergies. Key findings reveal the importance of synergies such as reverse logistics and recycling, supply chain collaboration & visibility and ethical practices in driving sustainability. This research contributes by offering a detailed analysis of how the synergy between Blockchain technology and Industry 5.0 can enhance sustainability practices in the RMG industry, providing actionable insights into the technological transformation of supply chain dynamics in support of global sustainability targets.

Abstract Prostaglandin D2 (PGD2) signals via the DP1 and DP2 receptors. In Phase II trials, DP2 antagonism decreased airway inflammation and airway smooth muscle (ASM) area in moderate-to-severe asthma patients. However, in Phase III, DP2 antagonism failed to lower the rate of exacerbations, and DP2 as a target was shelved. Here, using a preclinical model of chronic experimental asthma, we demonstrate that rhinovirus-induced exacerbations increase PGD2 release, mucus production, transforming growth factor (TGF)-β1 and type-2 inflammation. DP2 antagonism or DP1 agonism ablates these phenotypes, increases epithelial EGF expression and decreases ASM area via increased IFN-γ. In contrast, dual DP1-DP2 antagonism or dual corticosteroid/DP2 antagonism, which attenuates endogenous PGD2, prevented ASM resolution. We demonstrate that DP2 antagonism resolves ASM remodelling via PGD2/DP1-mediated upregulation of IFN-γ expression, and that dual DP2 antagonism/corticosteroid therapy, as often occurred in the human trials, impairs the efficacy of DP2 antagonism by suppressing endogenous PGD2 and IFN-γ production.

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.

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 Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This study suggested GBM-causing ten key genes (ASPM, CCNB2, CDK1, AURKA, TOP2A, CHEK1, CDCA8, SMC4, MCM10, and RAD51AP1) from nine transcriptomics datasets by combining supervised and unsupervised learning results. Differential expression patterns of key genes (KGs) between GBM and control samples were verified by different independent databases. Gene regulatory network (GRN) detected some important transcriptional and post-transcriptional regulators for KGs. The KGs-set enrichment analysis unveiled some crucial GBM-causing molecular functions, biological processes, cellular components, and pathways. The DNA methylation analysis detected some hypo-methylated CpG sites that might stimulate the GBM development. From the immune infiltration analysis, we found that almost all KGs are associated with different immune cell infiltration levels. Finally, we recommended KGs-guided four repurposable drug molecules (Fluoxetine, Vatalanib, TGX221 and RO3306) against GBM through molecular docking, drug likeness, ADMET analyses and molecular dynamics simulation studies. Thus, the discoveries of this study could serve as valuable resources for wet-lab experiments in order to take a proper treatment plan against GBM.

Tujuan dari penelitian ini adalah untuk melihat jihad nilai-nilai kepemimpinan Rasulullah dengan Jihad membela Palestina dan memberikan wawasan bagi pembaca mengenai jihad-jihad dalam islam dan mengenai jihad dalam membela palestina dari penjajahan yang dilakukan oleh Israel. Jihad adalah tindakan seseorang yang menggunakan semua keahliannya untuk menangkis serangan lawan. Al-Asfahani lebih lanjut menekankan bahwa ada tiga jenis jihad, diantaranya perang melawan musuh nyata, jihad melawan godaan setan, dan jihad melawan nafsu. Metode yang digunakan dalam penelitian ini adalah Jenis metode penelitian yang penulis gunakan dalam penulisan jurnal ini adalah studi kepustakaan (library research) yaitu sebuah penelitian yang dilakukan dengan cara mencari dan membahas literatur, buku-buku jurnal serta dokumen yang berkaitan dengan penelitian dengan menggunakan pendekatan kualitatif deskriptif. Dalam penelitian ini dapat disimpulkan bahwa jihad yang digunakan dalam peperangan di Palestina adalah jihad Al-Harb, Al-Harb diartikan sebagai pengerahan segala kekuatan, senjata, alat atau sarana apa pun yang dilakukan sekelompok orang melawan kelompok lain, seperti antar suku, perang antar gang, antar geng. Dapat kita pahami dan bedakan antara jihad dengan harb, sebab jihad adalah sebuah pengertian agama, memiliki tujuan, motif, cara, aturan, dan etika, berbeda dengan perang bermakna harb.

Decarbonising transport is a crucial element of the UK’s strategy to achieve net-zero carbon emissions by 2050, as the transport sector is currently the largest contributor to the UK’s greenhouse gas emissions. Rural communities face distinct challenges in this effort due to their reliance on internal combustion engines (ICEs) across vehicles and machinery essential for daily life, including farming equipment and private transport. While the upcoming ban on new petrol and diesel vehicles paves the way for the adoption of Electric Vehicles (EVs), this solution may not fully address the unique needs of rural areas where infrastructure limitations and specific mobility requirements pose significant barriers. In this context, synthetic fuels, produced using renewable energy sources, offer a potential alternative. These fuels can be used directly in existing internal combustion engines without requiring major modifications and have the added benefit of reducing overall greenhouse gas emissions by capturing CO2 during production. This entry explores the potential advantages of adopting synthetic fuels, particularly in rural areas, and examines how community-based buying cooperatives could support their wider use through bulk purchasing, cost reduction, and community empowerment.

STEM (Science, Mathematics, Engineering and Technology) education has widely been considered to have the potential to prepare students with 21st-century skills. Though the Government of Bangladesh (GoB) is concerned with developing human resources for the 21st-century, STEM education has not yet achieved a strong position in the national curriculum system. This study aims to provide a guideline to the GoB to take necessary steps to integrate STEM initiatives aligned with the current science and mathematics curriculum. Adopting a literature survey on the integrated STEM approach, document analysis (primary math and science curriculum, textbook, and teachers' guide of grade 3, 4, and 5) and semi-structured interviews of the primary mathematics teachers, head teachers, STEM experts and curriculum developers of Bangladesh, a framework of integrated STEM approach for primary level has been developed. Besides, the study identified some chapters from science and mathematics textbooks where there are some scopes to integrate the STEM approach and also developed some STEM activities for grade 3, 4, and 5 as future guidelines to the practitioners. This study contributes to the field of STEM-focused primary education in the context of Bangladesh by offering practical guidelines for academic institutions, curriculum developers, teachers' trainers, and policy makers of the country.