PES University

Background: As frontline advocates for health and exercise, physiotherapists play a vital role in promoting physical activity (PA). Despite this responsibility, a persistent "knowledge-practice gap" is observed among physiotherapy students, who often report insufficient activity levels despite their training. This discrepancy highlights the need to formally evaluate their baseline understanding of PA principles to better address these shortcomings Methods: A descriptive cross-sectional survey was conducted among 170 undergraduate and postgraduate physiotherapy students in Pune, India. Data were collected using a validated, self-administered questionnaire, which showed excellent internal consistency (Cronbach’s alpha=0.9654$). Results: The mean knowledge score of the WHO guidelines was moderate at 2.4, standard deviation was 1.5 (Median: 2, IQR: 1-4). Key barriers reported were 'lack of time' (60%) and 'heavy academic workload' (55.3%). Primary facilitators included 'knowledge about the benefits of PA' (86.5%) and 'nutrition' (86.5%). Conclusions: A significant knowledge-practice gap exists among future physiotherapists, with the academic environment being the main obstacle to PA engagement. Curricular reform and institutional support are recommended to better integrate WHO guidelines and foster effective role models.

With e-commerce shaping the way people buy clothes today, correctly determining what fabrics are made of is now a key requirement. Conventional methods are often constrained by hardware dependencies (e.g, NIR sensors), limited accessibility and poor fabric classification. We propose a parameter-efficient multimodal system that uses instruction-tuned LLMs to determine the proportion of fibers directly from fabric images. We compare two different multimodal models. The first is Meta’s LLaMA 3.2-Vision 11B Instruct model, which builds on a separately trained image encoder and a vision adapter that integrates image-feature representations via cross-attention layers into a core language model. The second is Salesforce’s BLIP2, which connects a ViT-G/14 image encoder to an OPT language model. In both the models, a light-weight regression head is appended to the final pooled representations and trained alongside LoRA injected adapters targeting the query, key and value projections within the transformer layers. The LoRA configuration is implemented using the PEFT (Parameter-Efficient Fine-Tuning), reducing trainable parameters to a few million for efficient adaptation. Experiments span six fiber categories -cotton, wool, polyester, linen, silk, and other fibers using a custom dataset of labelled textile images. The BLIP2-based model achieved a mean absolute error of 13.40 percentage points, outperforming the LLaMA 3.2 model, which recorded 14.87 percentage points on the test set. The evaluation results validate the use of vision-language models for estimating fiber composition and point toward their potential adoption in both consumer and industrial applications.

<div class="section abstract"><div class="htmlview paragraph">Electric vehicles (EVs) are coming into usage quickly because of the environmental advantages and technological innovations. But among the most important issues in EV operation is effectively handling thermal loads, especially in the mobile air-conditioning (MAC) system. As opposed to internal combustion engine (ICE) vehicles, which have access to engine waste heat to use for climate control, EVs depend solely on the battery for propulsion and auxiliary systems. This renders the MAC system one of the primary energy consumers and directly influences vehicle range and overall efficiency. While MAC systems are inherently designed for energy efficiency, this study focuses on an addition to the controller-level optimization, providing an additional pathway to improve thermal management performance in existing EV architectures. The work uniquely implements and compares five rule-based supervisory controllers (RBCs) on an open-source Simulink-based electric vehicle thermal management (EVTM) model, demonstrating a simple and computationally efficient approach to compressor control. Five different RBC strategies are formulated, each of which controls the compressor depending on factors such as ambient temperature, cabin temperature variation, and battery thermal load. The controllers are tested over three varied driving cycles to determine their robustness: the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) Class 2 cycle, the New European Driving Cycle (NEDC), and Bangalore Drive Cycle. These varied test cycles allow for examination over different traffic patterns, speed profiles, and environmental conditions. Simulation results show the optimum RBC delivers an optimal compressor power saving of 4.38% compared to a baseline control strategy.</div></div>

Aluminium (Al) deposition in different parts of the brain contributes significantly for the progression of neurodegenerative changes. The present study was undertaken to find out the influence of Ficus religiosa leaves against Al induced deposition of neurofibrillary tangles (NFT) and amyloid plaque in Wistar rats. We used rats of around 12-week age for the study. The animals were divided into 7 groups classified as control, Al, T200 & 300 as two extract treatment groups, FR200 & 300 which served as FR extract control and PRL, the prophylactic group. Aluminium exposure increased the expression of NFT and amyloid plaques along with decreased locomotor activity in the present study. However, these deleterious effects were improved in the form of reduced number of NFT and amyloid plaques by Ficus religiosa leaf extract treatment in a dose dependent manner. The outcomes of our study reveal the therapeutic potential of FR leaves against neurological disorders by combating the amyloid plaque and NFT formation.