Government Vidarbha Institute of Science and Humanities

Introduction. Wear is a critical factor in assessing the performance and durability of tungsten-copper (W-Cu) composites. These composites are widely used in electrical contacts, electrodes, and high-temperature applications. Tungsten provides high hardness and wear resistance, while copper ensures excellent electrical and thermal conductivity. The purpose of the work. This study aims to quantify the wear rate based on pin mass loss and to develop a statistically robust procedure for minimizing the dry-sliding wear rate of W-Cu metal matrix composites. The investigation focuses on determining the optimal process parameters for achieving minimal wear. The methods of investigation. In this study, a Taguchi L9 orthogonal array was employed to design the experiments, with the following key parameters: reinforcement percentage (20–40%), temperature (160–200 °C), and load (80–100 N). Wear tests were conducted using a pin-on-disc tribometer. The results were analyzed using the signal-to-noise (S/N) ratio approach (smaller-the-better characteristic) and analysis of variance (ANOVA). The experiments followed the 'One Variable At A Time' (OVAT) principle, varying only one parameter while keeping the others constant. Furthermore, ANOVA was used to assess the individual influence of each control factor – reinforcement percentage, temperature, and load – on the wear performance of the W-Cu composites. Results and Discussion. The experimental results were analyzed using signal-to-noise (S/N) ratios (smaller-the-better characteristic) and analysis of variance (ANOVA). The optimum parameter combination – 30% reinforcement, 200°C, and 80N – resulted in the lowest wear rate of 3.498×10-7 mm³/(N•m). ANOVA identified temperature as the most influential factor, contributing 90.6% to the performance variation, followed by reinforcement percentage (7.5%) and load (1.8%). Validation experiments confirmed the prediction accuracy, with an error of 4.6%. This study demonstrates the effectiveness of the Taguchi method in identifying a robust set of process parameters for enhancing the wear performance of W-Cu composites, offering practical guidance for industrial applications.

Equus kiang is a robust equid and the only odd-toed wild ungulate endemic to the Tibetan Plateau. It belongs to the order Perissodactyla, the family Equidae, and the genus Equus. In India, it is found exclusively in the Ladakh region within the Changthang Wildlife Sanctuary. In our research, we employed a non-invasive genetic approach to unravel the genetic diversity and to resolve the phylogeny of species by using partial mtDNA D-loop sequence information. A total of 33 samples were amplified and sequenced, and a final 27 D-loop partial sequences of ~230 bp were used for further analysis. The E. kiang population from Changthang Wildlife Sanctuary harbours moderate genetic diversity, displaying haplotype diversity of 0.501 with the discovery of two haplotypes. The phylogenetic tree constructed indicated the monophyletic clade of the two haplotypes with other E. kiang sequences. The median-joining network analysis revealed a gene flow across the border where the haplotypes from Ladakh displayed haplotype sharing with the Southern Tibet haplotype. Based on our results, we can conclude that the E. kiang population in the Changthang Wildlife Sanctuary showcases a moderate genetic diversity, supporting the IUCN Least-Concern status of the species, and demonstrates gene flow with the nearby regions of Tibet.

The exponential growth of interconnected systems across the Internet of Things (IoT), Software-Defined Networks (SDN), and cloud environments has led to a drastic increase in the scale and advancement of Distributed Denial of Service (DDoS) attacks. Conventional defences based on machine learning are often static, traffic-centric, and lack adaptivity to dynamic network behaviour, resulting in high false-positive rates and delay in mitigation responses. To address these challenges, this paper presents a conceptual framework for an AI-driven, context-aware DDoS detection and mitigation system. The proposed approach employs a optimized Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) model for efficient, real-time detection of DDoS attacks. The model captures spatial and temporal correlations in traffic while incorporating contextual parameters such as Quality of Service (QoS), Service Level Agreement (SLA) priority, and workload telemetry. These contextual attributes enable the model to distinguish legitimate network fluctuations from malicious activities, so that the false positive rate will be reduced without compromising accuracy. For adaptive response, a Reinforcement Learning (RL)-based mitigation layer—exploiting Deep Q-Network (DQN) and Actor–Critic (AC) variants—continuously learns optimal mitigation actions based on real-time feedback. The interaction between detection and mitigation components forms a context-aware feedback loop, enabling dynamic policy refinement and continuous system improvement. Comprehensive literature analysis reveals that existing AI-based DDoS defense mechanisms lack integration between detection and mitigation, and often overlook contextual intelligence. The proposed framework bridges this gap by introducing a self-adaptive, feedback-driven defense system capable of maintaining detection precision, low latency, and QoS preservation in heterogeneous environments.

Artificialintelligence continues to advance rapidly and reshape many areas of modern life, the human brain remains one of the most complex and least understood systems. Among its many mental states, the flow state is particularly notable. Marked by deep focus, high productivity, and seemingly effortless task performance, it represents a fascinating yet still insufficiently explored phenomenon in neuroscience. This research paper examines the key brain regions involved in achieving and sustaining the flow state, with focused attention on the precuneus, posterior cingulate gyrus, middle cingulate gyrus, and the inferior temporal gyrus. It also studies how neurosymbolic AI an approach that integrates neural learning with symbolic reasoning can be used to examine and interpret patterns of activity across these regions. By bringing together insights from cognitive neuroscience and hybrid AI methodologies, this work aims to deepen the understanding of the neural foundations of flow and to propose computational approaches that may ultimately help enhance understanding of human brain.

Sesame seeds and molasses as vegetable-derived ingredients in the manufacture of calcium-fortified premix drink. Sesame seeds (Sesamum indicum L.) contain calcium, protein, beneficial fats, and lignans but have their calcium bioavailability limited by anti-nutrients phytates and oxalates. Molasses, being a by-product of the sugar industry, contributes other minerals like iron and potassium to support the nutritional content. The article discusses the determinants of calcium absorption, methods for increasing bioavailability such as dehulling, soaking, fermentation, sprouting, and fortification with calcium salts and the role of vitamin D and acidic foods in increasing calcium absorption. Product development research utilizing sesame, such as fortified bars, doughnuts, and milk substitutes, is a testimony to its status in functional foods. Industrial applications have been found to range from nutraceuticals, to dairy alternatives, and calcium-fortified snacks, addressing worldwide needs for plant-based and sustainable nutrition. Despite sensory, standardization, and economic challenges, sesame–molasses compositions have great potential to increase dietaries of calcium, particularly in lactose-intolerant and vegan groups. Emerging research has to be focused to maximize bioavailability, consumer acceptability, and clinical effectiveness to support their uses in the development of functional and fortified foods.