Mody University of Science and Technology

Abstract. Srivastava A, Pandey V, Verma RK, Marwal A, Gupta R, Shahid MS, Gaur RK. 2025. Phylogeny and recombination of papaya begomoviruses in Northern and Central-East India. Nusantara Bioscience 17: 298-312. Papaya plants exhibiting characteristic leaf curl symptoms were surveyed in 2022 across Uttar Pradesh, Delhi, and Chhattisgarh, India. Begomovirus infection was suspected based on symptomatology and confirmed in 11 of 47 collected samples using PCR with Begomovirus-specific primers. This study aimed to elucidate the diversity and distribution of begomoviruses infecting papaya in these regions. Full-length viral genomes were amplified through Rolling Circle Amplification (RCA), and associated alphasatellites and betasatellites were detected using PCR with universal primers. The amplified viral genomes (~2.7 kb), betasatellites (~1.4 kb), and alphasatellites (~1.3 kb) were cloned and sequenced. Sequence analysis revealed 94.57-99.46% nucleotide identity of DNA-A with known isolates of Papaya Leaf Curl Virus (PaLCuV), Cotton Leaf Curl Virus (CLCuV), Croton Yellow Vein Mosaic Virus (CYVMV), Tomato Leaf Curl New Delhi Virus (ToLCNDV), and Cotton Leaf Curl Multan Virus (CLCuMuV). The DNA-B component exhibited 98.33% identity with ToLCNDV. Ten betasatellites shared 86.81-99.71% identity with related species, whereas two alphasatellites showed approximately 98.5% identity with PaLCuA and PaLCVSA. One betasatellite (PL36) displayed 86.81% identity and was identified as a novel Papaya Leaf Curl Raipur Betasatellite (PaLCuRPRB). Furthermore, six PaLCuV isolates showing <91% identity was classified as new PaLCuV variants.

Artificial Intelligence (AI) is changing the manufacturing process speedily; however, there is little empirical data on the adoption rates, technology emphasis, and performance results achieved, especially in developing economies. The study of AI adoption trends in developing economies is critical because these countries have special infrastructural and ability limitations that determine technology adoption and performance. This study fills this gap by surveying 209 manufacturing companies in India and examines the relationships between AI technologies, their areas of application, and multidimensional performance outcomes using descriptive, inferential, and multivariate analysis. Results indicate that robotics, IoT, computer vision, and machine learning are the most commonly utilized technologies, with the majority of their applications being in production, predictive maintenance, supply chain, and design activities. Firms with large size are more likely to adopt, but the size of a firm is not the decisive factor that determines performance improvement. Instead, market dynamics in an industry are more influential, particularly time-to-market, innovation, and responsiveness to customers. The results are relevant because they provide empirical associations between adoption tendency and technology orientation with quantifiable performance gains, which support the objective of the study to advise AI-context-dependent strategies in manufacturing. The novelty of the paper is not only an integrated empirical framework of the tools-applications–multidimensional outcomes but also a change of debate on adoption to an industry-centered view of adoption as opposed to size-centered view. The paper contains delicate organizational guidelines and precise actions concerning the spread of inclusive AI in emerging economies.

Darknet is a mysterious and often misunderstood on the internet. Unlike the Surface Web - the part we use through search engines like Google every day - darkness is hidden from public viewing. From data violations and ransomware attacks to the illegal distribution of trade and harmful software, cyber criminals utilize the confidentiality of the darkness without detection. What if we can change this privacy against them?This article suggests that cyber security can be strengthened by tracking hidden grid traffic, encrypted transactions and underground network networks. By analyzing the pattern of illegal activity, security experts can detect, predict and stop cyberattacks before they reach the mainstream network. Unlike the traditional methods that respond after an attack, the dark monitoring discovers active threats that help organizations and governments to be in front of cyber criminals.

Europe responded to Russia's invasion of Ukraine in February 2022 within a significant geopolitical context. Ukraine occupies a crucial strategic position between Europe and Russia, as most Russian energy supplies to European nations pass through Ukrainian territory. This war has severely impacted global and European energy security. The conflict's consequences for energy have reverberated worldwide, causing supply reductions, source diversification, price volatility, geopolitical tensions, and economic decline.

Healthcare systems around the world are facing huge challenges in responding to trends of the rise of chronic diseases. Early detection of breast cancer is essential for successful treatment since it is a common and potentially fatal condition. Based on clinical data, machine learning algorithms have shown potential in the categorization of breast cancer. This work aimed to build classification models Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Random Forest (RF), Logistic Regression (LR), and Artificial Neural Network (ANN) for Diagnostic Wisconsin Breast Cancer Dataset (WDBC) also improves classifier performance by using feature selection optimization and cross validation. The Particle Swarm Optimization (PSO) technique is used to select relevant, irredundant and most informative features that dependent on the performance of a classifier utilizing certain characteristics. The effectiveness of five distinct classifiers is assessed in this work. According to the findings, PSO-based SVM classifier has the greatest mean subset accuracy over a wide variety of training testing ratios.