Punjab Technical University

Wear-induced deterioration in additively manufactured Ti6Al4V components restricts their long-term performance in biomedical and structural applications due to severe frictional contact and abrasive conditions. Therefore, this study aimed to enhance the surface durability and tribological reliability of wire arc additively manufactured (WAAM) Ti6Al4V substrates by depositing hydroxyapatite (HAP) coatings through plasma spraying. The optimized HAP coatings exhibited a uniform lamellar microstructure with thicknesses ranging from 134 to 169 µm and strong interfacial bonding. Tribological performance was evaluated through slurry abrasion and pin-on-disk tests against an EN31 hardened steel counterface, supported by response surface methodology (RSM)-based optimization. Slurry abrasion testing demonstrated that the HAP-coated specimens achieved an average reduction in mass loss of approximately 35-45

The development of dual inhibitors against histone deacetylases (HDACs) and vascular endothelial growth factor receptor 2 (VEGFR2) is a promising approach in the treatment of cancers. A detailed computational screening of 36 quinazoline analogues was conducted in the context of identifying any possible dual HDAC VEGFR2 inhibitors in this study. A detailed in-silico workflow was used, comprising of molecular docking, molecular dynamics (500 ns), MMGBSA binding free energy, density functional theory (DFT), pharmacophore modelling and 3D-QSAR (PLS, MLR and GFA). The predications of ADME and toxicity were made to determine the drug-likeness and safety profiles. Compound 20 also exhibited high binding affinities to HDAC and VEGFR2, which are better than the reference drugs Panobinostat and Pazopanib. RMSD, RMSF and hydrogen-bond assays were used to ascertain stable protein and ligand complexes. Calculation of MMGBSA showed positive binding free energies. Strong predictivity has been demonstrated by robust 3D-QSAR models (maximum R2 = 0.981), whereas DFT analysis has revealed the presence of good electronic characteristics to facilitate dual inhibition. All the interaction analyses, dynamic stability, electronic properties and structure-activity relationships sustained the dual inhibitory nature of Compound 20. Its toxicity, limited penetration into BBB and good ADME characteristics further enhanced its therapeutic relevance. The present integrative computational research confirms that Compound 20 is an effective dual HDAC-VEGFR2 that has high anticancer potential. The results offer a logical basis on the continued experimental validation and development of dual-target anticancer therapeutics.

Transformer insulation oil deterioration critically impacts transformer reliability and lifespan, often leading to operational disruptions and financial losses. Accurate forecasting of oil ageing is therefore essential for proactive maintenance. This study presents a novel approach that combines a Deep Convolutional Neural Network (DCNN) with a Variance-Controlled Hunger Game Search (VC-HGS) algorithm to optimise the DCNN’s dense layers, reducing prediction errors. Experimental results show that the VC-HGS-DCNN model outperforms traditional methods, achieving 7

Particle Swarm Optimization (PSO) is widely recognized in the literature as a leading swarm-based algorithm. Since its inception in the mid-1990s, PSO has undergone significant advancements, including various enhancements, extensions, and modifications, particularly in the years following the turn of the century. As a result, research in this area has reached a remarkable level of sophistication. This paper presents a comprehensive and systematic review that organizes and synthesizes current knowledge on PSO. It offers an in-depth examination of the core concepts of the algorithm, neighborhood topologies, and historical and recent variants of the PSO. In addition, it highlights the significant engineering applications of PSO and discusses ongoing challenges in the field. By systematically arranging and summarizing the latest research, this review serves as a valuable resource for both researchers and practitioners interested in the development and application of PSO.

Agriculture is vital for global food security and economic stability. Technological advancements can significantly enhance productivity and sustainability. Mango, one of the world’s most cherished fruits, holds immense commercial and nutritional value. However, its cultivation is threatened by diseases such as Alternaria (causing dark leaf spots), Anthracnose (leading to black, sunken spots), Black Mould Rot (characterized by sooty fungal growth), Stem End Rot (causing decay from the stem end), and the crucial Healthy category. These diseases compromise fruit quality and yield, causing substantial economic losses. Manual diagnosis is subjective, time-consuming, and requires expertise, making it impractical for large-scale use. Artificial intelligence offers a transformative solution by enabling automated, accurate disease identification. In deep learning, conventional models such as Convolutional Neural Networks (CNNs) have a limited receptive field and struggle with long-range dependencies. Also, vision transformers are data-hungry and computationally exhaustive. Both of the above approaches lack focus on disease-specific features in complex agricultural settings. To address these gaps, we propose SAAEffNet, a synthetic-augmented attention-based EfficientNet deep learning model for classifying mango diseases. The model begins with a curated dataset, expanded using CycleGAN to generate high-fidelity synthetic images for enhanced diversity. In the previous pre-trained model, i.e., EfficientNetB3, we integrate the Convolutional Block Attention Module (CBAM) approach to refine features such as dark leaf spots, sunken spots, sooty fungal growth, etc. The model is trained with adaptive scheduling and early stopping. Our model achieves superior generalization, offering a robust tool for automated mango disease diagnosis. The model has undergone rigorous training and validation with a comprehensive dataset that includes images of both diseased and healthy mangoes, ensuring its effectiveness in accurately detecting and diagnosing mango health conditions. The results clearly illustrate our model’s exceptional performance, achieving remarkable metrics: accuracy 98.40