Daffodil International University

Abstract Traditional healing practices hold significant social and cultural significance in Bangladesh. While numerous traditional jaundice treatments exist across the country, detailed documentation of these methods is lacking. Therefore, this study aimed to explore and examine the various traditional healing methods used to treat jaundice in Bangladesh. The study was conducted in two phases. The first phase involved interviewing 400 jaundiced patients at two healthcare facilities, while the second phase consisted of overt naturalistic observations of traditional healers in their practice settings. Among the 400 jaundiced patients, 211 (52.75%) sought treatment from traditional healers. The study revealed a diverse range of traditional healing practices for jaundice, spanning ritualistic and spiritual approaches to physical interventions. The healing procedures were categorized as either noninvasive or invasive. Common noninvasive procedures included scalp and hand cleansing rituals, jaundice garlands, talismans or amulets, chewing herbs, roots, bark, or leaves, liver katano , ear candling, pet tana, and the application of chanted substances such as bananas, coconut water, or lemons. Two invasive procedures— liver khilano and cauterization—were found to cause dry burns on the skin, leading to severe complications. Upon thorough investigation, certain healing techniques appeared to result from chemical reactions or the application of physical processes rather than being supported by scientific evidence of their medical effectiveness. Understanding these practices is crucial for modern healthcare providers to facilitate culturally sensitive communication with patients and guide communities toward scientifically validated jaundice treatments. Further research is recommended to evaluate the efficacy and safety of these traditional methods from a scientific perspective.

Heat stress is a key environmental factor that adversely affects plant growth, development, and productivity. Nitrate reductase (NR), encoded by AtNIA1 and AtNIA2, plays a crucial role in nitric oxide (NO) biosynthesis, which mediates stress responses in plants. In this study, we investigated the roles of AtNIA1 and AtNIA2 in regulating plant heat stress tolerance. Under heat stress conditions, Arabidopsis thaliana plants maintained higher relative water content and chlorophyll levels, whereas atnia1 and atnia2 mutants exhibited greater physiological damage. Oxidative stress markers such as MDA and H2O2 accumulated to higher levels in nitrate reductase mutants than in Col-0, indicating increased heat sensitivity. Gene expression analysis further revealed a pronounced late-phase induction of MBF1c in atnia2 plants, accompanied by altered expression of heat shock proteins. These results suggest that nitrate reductase-dependent pathways contribute to heat stress tolerance by regulating water status, membrane stability, ROS detoxification, and heat shock gene expression. This study provides new insights into NR-mediated NO signaling in thermotolerance and highlights potential targets for improving crop resilience under rising temperatures.

ABSTRACT The research paper aims to examine the direct effects of exploratory supply chain innovation (ERSCI) and exploitative supply chain innovation (ETSCI) on digital supply chain practices (DSCP) in a sample of manufacturing SMEs in Jordan. The current study also presented other objectives and contributions in examining the potential causal relationships of industry 4.0 technologies (I4Ts) and studying their moderating effect between ERSCI, ETSCI, and DSCP. To achieve the research mentioned above objectives, 203 responses were collected from the organizational and managerial levels in manufacturing SMEs; purposive sampling was used in collecting data, and then the partial least squares‐structural equation modelling (PLS‐SEM) approach was employed in analyzing the data obtained. The empirical results reached a set of results, some of which were expected and others surprising. It was found that there was a positive and strong effect of ERSCI, ETSCI, and I4Ts on DSCP, and these variables were able to explain the variance occurring in DSCP by 54.8%. I4Ts also played a moderating role in the relationship between ETSCI and DSCP, while the results revealed that the interaction between ERSCI and I4Ts was not positive; in addition, this effect was not statistically significant. The results of this study had a clear contribution to filling the research gaps based on ambidexterity theory, as the current paper addressed a set of ambiguous causal relationships in the SC literature that had not previously been revealed in past literature. Thus, the current study expanded the discussion space about the interplay nexus between these relationships.

The leather industry plays a crucial role in the production of high-quality leather products to remain competitive in the market. Due to various reasons such as material handling and turning, leather defects can appear at different stages of the production process. This paper proposed deep learning techniques, specifically transfer learning, deep convolutional neural networks (D-CNN), and ensemble learning for automating leather defect detection and classification. The dataset was taken from the open source Kaggle. The dataset was preprocessed from the beginning. However, we performed Error Level Analysis (ELA) to detect possible changes or fraud in the image. We also used data augmentation techniques (resizing, rescaling, flipping, rotation, zooming, and contrasting) on the dataset to improve the model performance. This study investigated deep learning architectures for automatic leather defect detection, comparing them with seven deep convolutional neural networks (D-CNN) models and transfer learning architectures. Furthermore, we introduce and evaluate two novel ensemble models: MER built on transfer learning architecture (MobileNetV2, EfficientNetB0, and ResNet50) and AZL built on D-CNN architecture (AlexNet, ZfNet, and LeNet). A key contribution of this study is the demonstration of significant performance improvements achieved by strategically integrating squeeze and excitation and label smoothing techniques into transfer learning, D-CNN, and ensemble frameworks. Our proposed MER ensemble method achieves a state-of-the-art of 96.39% for leather defect detection. The proposed model was evaluated using performance metrics including precision, recall, F1-score, AUC, ROC, and Matthews Correlation Coefficient (MCC). Additionally, Grad-CAM was employed for explainable AI to visualize and interpret the model’s decision-making process. Comparative analysis against separate transfer learning and D-CNN models fails to establish the superior performance of our MER ensemble, which highlights the potential to significantly advance automated quality inspection in leather production. This study provides insights into evaluating the best deep learning techniques for leather defect classification, paving the way for more accurate, efficient, and reliable industrial applications. Proposes a robust deep learning-based framework for automated leather defect detection using transfer learning, deep CNNs, and two novel ensemble models (MER and AZL), enhanced with squeeze-and-excitation and label smoothing techniques. Achieves state-of-the-art performance with the proposed MER ensemble model, achieve 96.39% accuracy, and demonstrates superior results across precision, recall, F1-score, AUC–ROC, and MCC. Integrates Error Level Analysis (ELA), extensive data augmentation, and Grad-CAM–based explainable AI to ensure data reliability, improve generalization, and provide transparent visual interpretation of defect regions for industrial quality inspection.