Annai Vailankanni College of Engineering (AVCE) is a private co-educational Engineering College in the Indian state, AVK Nagar, Pothayadi Salai, Pottalkulam, Azhagappapuram Post, Kanyakumari District, Tamil Nadu. It was established in 2008. The college is accredited by AICTE and affiliated to Anna University..
The present study focuses on the green synthesis of zirconia-doped cadmium sulfide (CdS) nanocomposites using Moringa oleifera seed extract as a reducing and stabilizing agent. The prepared nanocomposites, denoted CZ1, CZ2, and CZ3, were characterized by XRD, EDX, and UV-Vis spectroscopy to confirm their structural, elemental, and optical properties. The XRD analysis revealed a phase transition from the cubic to the hexagonal structure of CdS with increasing zirconia content, accompanied by a reduction in crystalline size from 31.18 nm for CdS to 18.4 nm for CZ3. The photocatalytic activity of the nanocomposites was evaluated using Rhodamine B (RhB) and Eosin Yellow (EY) under sunlight irradiation. The CZ3 sample exhibited the highest degradation efficiency, achieving 92.3% for RhB and 88.7% for EY within 120 min, attributed to the improved optical and electronic properties of the material. Antibacterial activity assessed by the zone of inhibition method showed superior efficacy of the nanocomposites against Staphylococcus aureus compared to Escherichia coli, with CZ3 demonstrating the largest inhibition zone of 18.4 mm at 100 mu g/mL. Additionally, antioxidant activity, measured as DPPH free radical scavenging, showed concentration-dependent enhancement, with CZ3 achieving 89.6% scavenging efficiency at 100 mu g/mL. The results highlight the potential of zirconia-doped CdS nanocomposites synthesized via a green approach as multifunctional materials for environmental remediation and biomedical applications.
The increasing demand of digital technologies and their integration with wearable health devices provides an efficient trigger for next-generation wearable healthcare devices for long-term physiological monitoring. The advancement of energy harvesting mechanism, nanomaterial-based sensor fabrication and their integration with digital technologies have emerged as a promising solution for transforming future of digital health. This study provides a comprehensive summary and framework for wearable self-powered electronic devices, enabling continuous, battery-free health monitoring and advancing the development of sustainable, next-generation digital healthcare systems. This review paper presents a broad and detailed overview of current technologies and sensors advancement in developing low-power wearable, self-powered electronic devices suitable for healthcare applications. The importance and reliable use of key energy harvesting approaches including triboelectric, piezoelectric, thermoelectric, and photovoltaic approaches are systematically presented which focused on development of energy efficient wearable devices. This review further examines the low-power circuit design strategies for flexible electronics focusing personalized healthcare monitoring. Current challenges and limitations related to advanced manufacturing of wearable health devices focusing on large-scale deployment are also analyzed. Finally, the key future research directions are outlined for advancing a next-generation intelligent digital health system.
The performance of natural fiber-reinforced composites is often constrained by weak interfacial bonding and limited durability. This study investigates the effect of chitosan incorporation on the mechanical, tribological, thermal, and moisture absorption behavior of Hennep 16 hybrid short fiber-reinforced vinyl ester composites fabricated via hand layup. Composites were developed with varying chitosan contents (1–5 vol
Waiting time is a critical indicator of healthcare operational performance and patient-centered service quality. Although outpatient pharmacies represent the final service node in the care continuum, systematic quantitative evaluation of congestion dynamics remains limited in tertiary care settings in India. This study integrates analytical queuing theory, discrete-event simulation (DES), and cost-effectiveness analysis (CEA) to evaluate waiting time performance in the outpatient pharmacy of a tertiary care teaching hospital in Kerala. Empirical time–motion observations (N = 1,584 encounters) were conducted to estimate arrival and service parameters. The system was modelled as an M/M/4 queue under first- come-first-served discipline and validated using 100 simulation replications. Statistical comparison across three scenarios—baseline (four counters), temporary peak-hour expansion (five counters), and staff redeployment—revealed significant reductions in mean waiting time, F(2, 297) = 184.63, p < .001, η² = .55. Incremental cost-effectiveness analysis demonstrated superior efficiency of peak-hour expansion (Rs.187 per patient-hour saved) compared to permanent staffing expansion (Rs.349 per hour). Findings support demand- responsive staffing strategies and demonstrate the value of integrating operational analytics with economic evaluation in hospital management.
Thoracic radiotherapy for lung cancer patients followed by radiation pneumonitis (RP) has significant clinical side effects. Risk-adaptive treatment planning can be supported by accurate early RP prediction. Using thoracic CT scans, this study suggests an efficient deep learning algorithm for RP prediction. An analysis was conducted on a retrospective cohort of 548 patients with lung cancer who received thoracic radiotherapy between 2010 and 2021. According to established toxicity criteria, clinically significant RP was classified as Grade ≥ 2 and evaluated during post-treatment follow-up. Clinically accessible radiation outlines were used to separate bilateral lung regions, and an improved ResNet101-based XceptionNet architecture was used to extract deep features from CT images. Cauchy Lotus Optimization (CLO) was used for feature selection in order to minimize redundancy after an autoencoder was used for compact feature representation. At the patient level, the dataset was divided into cohorts for independent training (80%) and testing (20%). To avoid information leaking, only the training data was used for feature selection and model training. Precision, specificity, sensitivity, accuracy, and ROC-AUC were used to assess performance on the independent test set. Emperor Penguins Colony Algorithm (EPCA), Sea Lion Optimization (SLO), Spotted Hyena Optimization (SHO), Marine Predator Optimization (MPO), and other optimization-based techniques were compared to representative deep learning baselines. On the independent test set, the suggested framework demonstrated excellent predictive performance with high precision, accuracy, sensitivity, and specificity. Strong discriminative ability was shown by ROC analysis, and the suggested approach produced the highest AUC when compared with competing techniques. While maintaining discriminative power, the optimized feature selection technique significantly decreased feature dimensionality.
