Hajee Mohammad Danesh Science & Technology University

Accurate and early detection of brain tumors is essential for effective treatment planning in medical diagnosis. However, deep learning (DL) models often struggle with MRI-based tumor detection due to significant variability in tumor size, shape, and location. Traditional diagnostic techniques are limited by subjectivity and low interpretability, while many DL models operate as black boxes, reducing clinical trust. Incorporating attention mechanisms can help by directing the model's focus to the most informative regions of an image, thus improving both accuracy and interpretability. However, existing attention methods often fail to capture the complex spatial and contextual features present in medical images such as MRI scans. In this study, we propose a novel attention-based, explainable DL framework designed to improve the performance and transparency of brain tumor diagnosis. We introduce the Strip-Style Pooling Attention Network (SSPANet), which combines the strengths of channel and spatial attention mechanisms to more effectively capture intricate imaging features. We evaluated SSPANet using VGG16 and ResNet50 as backbone architectures, integrating it alongside existing attention methods for comparison. Among all configurations, ResNet50 combined with SSPANet achieves the best results, with 97% accuracy, precision, recall, and F1-score, along with 95% Cohen's Kappa and Matthews Correlation Coefficient. For interpretability, we employ GradCAM, GradCAM++, and EigenGradCAM across attention-guided DL models. The ResNet50 + SSPANet + GradCAM++ combination consistently provides superior visual explanations, highlighting SSPANet's ability to capture complex spatial-contextual information effectively. We also offer a theoretical analysis to support the efficiency and effectiveness of the proposed attention mechanism.

In today's complex, multi-echelon supply chain (SC) management landscape, the timely production and delivery of goods while upholding environmental sustainability and adhering to delivery schedules is a critical priority. Supply chain disruptions, such as the COVID-19 pandemic and the 2022 floods in Australia, pose significant challenges to the transportation sector and production processes. These events underscore the need for resiliency in SC design. Our model tackles these challenges using a mathematical framework that captures production disruptions, imperfections, and carbon emissions. The proposed model captures complexities, such as adjusting production speed and shipments under sequential disruptions, while maintaining delivery commitments. Manufacturers adjust production speeds to match third party logistics (3PL) schedules amid disruption variability, while 3PLs adapt shipment quantities to retailer demand during transportation disruptions, helping reduce carbon emissions. The supply chain allocates resources toward social welfare investments to offset environmental impacts from operations. This not only mitigates the societal impacts but also enables firms to benefit from tax rebates as incentives for environmental responsibility (without social investment, total integrated profit decreases by 1.292% ( $ N = 1, {N_t} = 4 $ N=1,Nt=4)). Our approach jointly addresses production and transportation disruptions previously studied in isolation, providing strategic insights for enhancing SC resilience, fulfilling customer demand, and achieving sustainability objectives.

Despite the extensive use of social media networks (USMNs) and celebrity endorsers (CEs) to influence consumers' green consumption intentions (GCI), there is a lack of thorough examination of the efficacy of USMNs and CEs on consumer attitudes towards green consumption (ATGC) and GCI. Additionally, how GCI can be transformed into actual green consumption (AGC) has also been neglected. To address this issue, this research introduces a conceptual framework grounded in Source Credibility Theory and the Theory of Planned Behavior and conducted three studies via an e-questionnaire survey from Malaysia and Bangladesh (n = 1667), who had engaged with green campaigns on YouTube and Facebook featuring CEs of green consumption. Structural Equation Modeling (SEM) analysis of the data revealed positive and significant effects of USMNs and CEs on ATGC and GCI that lead to AGC. It also provided valuable insights for managers and policymakers.

Lead-free double halide perovskites, specifically Cs₂TlAX₆ (A = As, Sb; X = Cl, Br, I), offer key advantages over lead-based versions, including strong optical absorption, high structural and thermal stability, superior carrier mobility, tunable band gaps, non-toxicity, and cost-effectiveness. Their mechanical, electrical, optical, and thermal properties were investigated using DFT with the PBE functional under ambient and hydrostatic pressures. Stability was confirmed through formation enthalpy, tolerance factor, and elastic constants. Pugh’s and Poisson’s ratios suggest these compounds are generally ductile (except Cs₂TlAsBr₆), with pressure-enhanced machinability, reduced friction, and increased plastic strain. Band structure analysis using the GGA-PBE approximation shows the direct band gap semiconducting nature of Cs₂TlAX₆ (A = As, Sb; X = Cl, Br, I), where the band gap values are ranging from 0.957 to 1.697 eV. However, the TB-mBJ functional efficiently moderates the GGA-PBE underestimation, yielding refined band gaps between 1.22 and 2.17 eV—values which are more efficient solar applications. Pressure-induced band gap tuning highlights potential for optoelectronic device applications. Their narrow band gaps and strong absorption make them suitable for solar cells, while high infrared reflectivity and low thermal conductivity indicate potential as thermal barrier coatings (TBCs).

The emergence and the growing influence of contaminants in wastewater has driven the development of advanced and efficient treatment technologies. Catalysts based on biochar have become a promising material because of their cheapness, adjustable physicochemical characteristics, and environmental compatibility. This study comprehensively reviews recent developments in biochar-based catalytic processes to treat wastewater with an emphasis on AOPs and photocatalysis. The main categories of catalysts including metal-loaded biochar, heteroatom-doped biochar, biochar-supported semiconductor composites, and magnetic biochar are extensively discussed with regard to their synthesis, structure, and performance in the elimination of organic, emerging, and heavy metal contaminants. Emphasis is placed on catalytic reactions, radical (•OH, SO4•−) and non-radical (singlet oxygen and electron transfer) reactions, as well as the effect of functional groups on the surface, defects, and electronic features in the control of activity. Engineered biochar has a better performance in charge separation, reactive species generation, and synergistic interactions between adsorption and degradation. Nevertheless, there are issues such as heterogeneity in biochar properties, insufficient understanding of structure–activity interactions, catalyst stability, and the absence of studies of biochar under real wastewater conditions. The future perspectives focus on rational catalyst design, integration of processes, and scaling up to practical applications. Overall, biochar-based catalysts have emerged as a sustainable platform for advanced wastewater treatment, but additional studies are needed to enable their large-scale use.