PSG Institute of Advanced Studies

Computational Neuroscience is an interdisciplinary field leveraging mathematical and computational methodologies to develop theoretical models for the diagnosis of various brain disorders via neural connectivity analysis. Positive Point-wise Mutual Information (PPMI), a non-linear measure traditionally used in Natural Language Processing, is introduced in this study as an innovative analytical tool for assessing functional brain connectivity in electroencephalography (EEG) data. This work discusses the application of PPMI in neurological signal analysis to characterize altered connectivity patterns in children diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD) compared with age-matched healthy controls. By quantifying the probability of strong associations between EEG electrode pairs, PPMI provides a robust framework for identifying connectivity alterations that conventional linear measures may overlook. The study employs multiple visualization methods-including line graphs to represent connectivity probabilities, bar graphs illustrating pairwise association strengths, network graphs depicting global organization, and topoplot-based spatial projections-to reveal region-specific connectivity deviations. Results demonstrate significant reorganization of functional networks in the ADHD group. Several electrodes, particularly Fp2, C3, C4, P3, and Fz, consistently exhibit elevated probabilities of forming strong associations, indicating localized hyperconnectivity. In contrast, evaluation of specific electrode pairings such as Fp1-Pz, F3-F7, and Fp2-F4 uncovers a complex interplay of both heightened and reduced connectivity, reflecting heterogeneous network disruptions. Overall, the findings highlight PPMI as a promising biomarker for capturing subtle yet meaningful connectivity variations in ADHD. Its capacity to elucidate non-linear functional interactions positions it as a valuable tool for advancing computational neurodiagnostics and improving understanding of ADHD-related neural dysregulation. c

This study examines the role of reverse logistics within the industrial sector of Coimbatore, focusing on optimizing return management, enhancing customer satisfaction, and promoting sustainable business practices. By pinpointing local challenges and potential strategic enhancements, this research offers practical insights into how reverse logistics can enhance customer loyalty and yield environmental advantages in this specific industrial setting. A descriptive study with a sample size of 269 is conducted in Coimbatore's e-commerce industry across food and beverages, apparel, electronics, and engineering sectors. Targeting logistics personnel, the study explores and focuses on five main variables: environment, return management, customer satisfaction, and performance effectiveness. Through statistical analysis, this research aims to identify significant factors impacting reverse logistics in these categories, offering insights into optimizing practices for improved customer and operational outcomes. The study highlights actionable measures that can improve operational performance and elevate customer satisfaction, all while cutting costs and promoting sustainability.

Abstract This study examines the use of generative artificial intelligence and multimodal analytics to create a more personalised experience of English language instruction for the pertinent diverse learners in higher education institutions. Using a large major research university as the site of an extensive case study, and with a sizeable contingent of disciplinary English as a Second Language (ESL) instructors, who worked at the behest of the principal investigator, unquestioningly, for 12 months, the discipline-agnostic experimental classroom was populated with upward of 200 ESL students, each of whom was subject to varying degrees and types of private AI supervision. The study makes a substantial contribution to research on educational technologies by establishing a robust framework for integrating multimodal AI into education. It is clear that the comfortable pedagogical fit of AI in language education is the result of: (1) fostering instructor agency through careful and inclusive planning; (2) placing personnel training at the center of implementation efforts; (3) enacting strong support throughout all levels of the institution; and (4) keeping the ethics of AI use at the forefront of decision-making.