http://hdl.handle.net/2080/17 Tue, 16 Jun 2026 18:57:33 GMT 2026-06-16T18:57:33Z http://hdl.handle.net/2080/5816 Title: Listening to Stress: A Multimodal Approach towards Analysis of Mindfulness Meditation Effects Authors: Gupta, Sakshi; Sengupta, Anwesha Abstract: Stress monitoring and management have become increasingly critical owing to the rising prevalence of mental health challenges in contemporary society. Mindfulness meditation has demonstrated potential as an effective intervention. However, objective and scalable frameworks for evaluating its impact remain limited. This study presents a multimodal approach for stress assessment that integrates speech-based biomarkers, psychological measures, and cognitive performance indicators to evaluate the effectiveness of guided mindfulness meditation. A web-based platform was developed to facilitate automated data collection and longitudinal monitoring. It incorporated speech recordings, Perceived Stress Scale (PSS) responses, and cognitive assessments, including reaction-time and Stroop color-word tasks. Acoustic features such as fundamental frequency, Mel-frequency cepstral coefficients (MFCCs), and local jitter were extracted from speech signals to investigate their relationship with stress levels. Results show consistent reductions in stress indicators, accompanied by improved behavioral performance and decreased self-reported stress. Speech-derived features exhibit strong potential as complementary biomarkers. The proposed multimodal framework supports objective, scalable, and non-invasive stress assessment. Description: Copyright belongs to proceeding publisher. Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/2080/5816 2026-06-01T00:00:00Z http://hdl.handle.net/2080/5815 Title: Cumulative Learning-Based Innovative Optimization Framework for Brain Tumor Classification Authors: Jena, Pranshu; Pati, Umesh C. Abstract: The accurate classification of brain tumors using MRI scans has been a crucial part of medical imaging analysis. However, class imbalance in the data, overfitting, and unstable convergence of the optimizer are significant concerns in a conventional deep learning framework. The proposed framework introduces an innovative optimization approach that combines Nadam and Particle Swarm Optimization (PSO), achieving a balance between adaptive and robust convergence. This approach leverages the global search ability of PSO across the entire data space while employing a cumulative learning strategy to efficiently train the ResNet-50 model. The proposed framework is trained using a cumulative learning framework, which improves generalization by gradually improving learned representations across a series of learning phases. The proposed classification framework is evaluated using the Figshare, Br35H, and Sartaj datasets, yielding accuracies of 97.44%, 99.33%, and 99.70%, respectively. Description: Copyright belongs to proceeding publisher. Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/2080/5815 2026-06-01T00:00:00Z http://hdl.handle.net/2080/5814 Title: Organotin(IV) Azo Hydrazonates as Lysosome-Targeted Anticancer Agents Authors: Das, Sanchita; Dinda, Rupam Abstract: Following the success of cisplatin, several platinum-based anti-cancer metallodrugs were discovered, including carboplatin, oxaliplatin, and nedoplatin. Nevertheless, the severe adverse side effects associated with these Pt-based drugs have limited their widespread clinical use and prompted the hunt for non-Pt-based medication alternatives.[1] Metallodrugs with organotin(IV) compounds emerging as a potential alternative to platinum-based drugs have revolutionised the field of both diagnosis and therapy, offering enhanced anticancer efficacy and bio-imaging capabilities for targeting intracellular organelles.[2,3] Even though non-platinum metal-based complexes have been extensively studied as anticancer agents, their fast hydrolysis, reduced hydrolytic stability, and cellular absorption render them unsuitable for the development of standard anticancer metallodrugs.4 In this presentation, we embarked on an effort to explore the theranostic potential of a new class of azo hydrazone-based organotin(IV) complexes [SnIVL1–4(Ph)2] (1–4). The aqueous stability, hydrophobic character, and interaction of 1−4 with biomolecule (BSA and DNA) were examined. The speciation studies suggested the complexes possess exceptional hydrolytic stability. Further in-depth mechanistic studies revealed that they preferentially accumulate in the lysosome, damage lysosomal membrane potential, and upregulate intracellular reactive oxygen species (ROS), leading to apoptotic-mediated cancer cell death. Description: Copyright belongs to the proceeding publisher. Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/2080/5814 2026-06-01T00:00:00Z http://hdl.handle.net/2080/5813 Title: Dynamic Stability Analysis of Pinned-Pinned Underwater Pipelines Conveying Fluids: Effects of Internal Flow Velocity on Natural Frequencies and Critical Thresholds Authors: Panda, Siddharth; S, Bala Murugan; Behera, Rabindra Kumar Abstract: Underwater pipelines carry oil and gas all over the world. Their functioning in aggressive marine environments, however, raises fundamental engineering challenges. This study presents the dynamic performance of fluid-conveying underwater pipelines. It addresses the role of fluid-structure interaction (FSI) and hydrodynamic loading on external pipeline behaviour. We establish a pinned-pinned pipeline mathematical model based on Euler-Bernoulli beam theory. It provides closed-form solutions of natural frequency and mode shape. Analytical and numerical analysis prove that internal fluid velocity lowers natural frequency considerably. This increase in internal fluid velocity enhances dynamic instability danger, with critical velocities determined for each vibration mode. Sensitivity study proves that pipe length negatively affects natural frequency, while wall thickness and Young's modulus increase structural stiffness. These findings highlight the importance of incorporating FSI and material properties during pipeline design to avoid resonance and ensure safety. This study enriches our knowledge of underwater pipeline dynamics and encourages further research on nonlinear effects, multiphase flows, and real-time monitoring using machine learning. Description: Copyright belongs to the proceeding publisher. Wed, 01 Oct 2025 00:00:00 GMT http://hdl.handle.net/2080/5813 2025-10-01T00:00:00Z