Exosomes are nano-sized extracellular vesicles that perform a variety of biological functions linked to the pathogenesis of various neurodegenerative disorders. In Alzheimer's disease (AD), for examples, exosomes are responsible for the release of Aβ oligomers, and their extracellular accumulation, although the underpinning molecular machinery remains elusive. We propose a novel model for Alzheimer's Aβ accumulation based on Ca2+-dependent exosome release from astrocytes. Moreover, we exploit our model to assess how temperature dependence of exosome release could interact with Aβ neurotoxicity. We predict that voltage-gated Ca2+ channels (VGCCs) along with the transient-receptor potential M8 (TRPM8) channel are crucial molecular components in Alzheimer's progression.

This study investigates the factors such as knowledge management capacities and their positive influence on firm competitive advantage or the supply chain agility of the firm and the underlying mechanisms (supply chain agility) that facilitate the firm's performance and leads to firm competitive advantage. It also explores the moderating role of inter-functional integration. We have collected the data from the 308 supply chain managers of pharmaceutical firms in Pakistan and questionnaires were used for data collection with multi-item scales already developed and validated. The findings suggest that knowledge management capacities significantly influence a firm's competitive advantage or supply chain agility. The supply chain agility fully mediates between absorptive capacity, transformative capacity, and firm competitive advantage. Further, supply chain agility partially mediates between inventive capacity and firm competitive advantage. Meanwhile, inter-functional integration moderates the relationship between supply chain agility and firm competitive advantage, with their positive relationship strengthening when inter-functional integration is high. The study provides empirical evidence that knowledge management capacities (such as absorptive capacity, transformative capacity, and inventive capacity), supply chain agility, and inter-functional can be important factors in improving firm performance.

No abstract

No abstract

No abstract

This research focuses on studying the classification performance of a Machine Learning-based Intrusion Detection System (IDS) using the UNSW-NB15 dataset. The effectiveness of three classifiers - Decision Tree, Multilayer Perceptron (MLP), and XGBoost - was analyzed to determine their accuracy in identifying attacks and normal network traffic. The experimental results revealed that Decision Tree achieved an accuracy of 85%, MLP achieved an accuracy of 89.83%, and XGBoost achieved an accuracy of 89.9%. Additionally, the explanability of the machine learning models was analyzed, highlighting the differences in interpretability among the classifiers. It was observed that Decision Tree provided better explanability, but lower accuracy compared to MLP and XGBoost. Overall, this research contributes to our comprehension of the performance and explanability of three different machine learning classifiers for intrusion detection, The findings can provide valuable insights for choosing suitable classifiers that align with the specific priorities and requirements of the IDS system.

No abstract

No abstract