Excel Life Sciences (India)

Road accidents in hilly areas caused by hairpin bends account for a significant portion of India’s daily traffic accident statistics. These blind curves are extremely risky and frequently lead to car crashes that are fatal. Because of the poor visibility, the motorist is far more susceptible to head- on collisions. The system’s core function is processing data in real time. Traffic control systems need to be improved in order to prevent traffic congestion, as traditional systems are insufficient to manage the current traffic density. Traffic light controllers use fixed light signal sequences and durations that don’t take into account dynamically shifting traffic conditions. As a result, when emergency vehicles such as fire engines, police cars, ambulances, etc. get caught in traffic and take longer than expected to get at their location, lives and property may be lost. Traffic congestion, particularly near intersections, increases air pollution and fuel consumption in addition to causing delays for cars. We suggest an Internet of Things framework for self-organized traffic control management at intersections and emergency vehicle priority. Three parts make up the system: the cloud dashboard, the emergency radio-frequency identification (RFID) module, and the traffic light control module. Through the use of an ultrasonic sensor, the traffic light displays the density of vehicles. Using RFID, emergency vehicles may be found based on their priorities. At every road, our system gathers data on the number of vehicles and the locations of emergency vehicles. In order to prevent accidents and save the lives and property of innocent people, it modifies traffic light timings based on those real-time traffic data.

In recent years, the increasing adoption of wearable Internet of Things (IoT) devices in healthcare has accelerated the need for intelligent edge-fog systems by providing explainable decision support under changing energy constraints. However, the current Optimized Tiny Machine Learning (O-TML)-based energy-aware clustering is predominantly static and lacks adaptability to changing patient conditions and unseen health patterns. To overcome these limitations, this study proposes a Neuro-Adaptive Lightweight eXplainable Artificial Intelligence (NeoLight-XAI) framework that efficiently handles patient-specific and environmental context shifts. NeoLight-XAI integrates a hierarchical autoencoder-based feature encoder that enables dynamic weight consolidation to continuously learn and adapt to nonstationary data streams of uncertain health conditions. Then, a ContextAware Routing Mechanism (CARM) enables personalized decision-making by dynamically selecting task-specific pathways based on the patient's physiological and environmental states. Moreover, a sparsity-regularized SHapley Additive exPlanations (SHAP) module provides interpretable real-time feature attributions, whereas a drift-aware monitoring unit ensures model reliability over time. The experimental evaluation of multimodal health sensor data demonstrated that NeoLight-XAI achieved high classification accuracy (94.7%), precision (89.7%), recall (96.3%), and F1score (92.4%) under concept drift compared with the existing O-TML model.

Background: Hematohidrosis is an unusual anomaly in which intact skin allows the excretion of blood, with its origin mostly placed on emotional or physical stress. The pathophysiology of hematohidrosis remains mostly unknown. Autonomic dysfunction, microvascular fragility, and capillary rupture are all proposed hypotheses explaining the mechanism of this disorder. This report describes an 11-year-old girl with simultaneous hematohidrosis, hemolacria, and epistaxis, thus contributing to its knowledge and diagnosis together with differential considerations. Methodology: A retrospective case documentation is done with due permission and informed consent. Clinical details: these were obtained through history-taking and clinical examination. Laboratory workup including complete blood count, coagulation profile, and factor assays. Liver function tests were performed, and a computed tomography (CT) of the brain was done to exclude intracranial pathology. Slit-lamp examination was done for ocular involvement. No therapeutic or follow-up was done—this report serves as a clinical discussion. Results: The patient having irregular intermittent episodes of hematohidrosis, hemolacria, and epistaxis, all hematological, biochemical, and radiological workups were normal with the only exception of mild ethmoid sinusitis and over-pneumatization of the mastoid air cells on CT scan. Conclusion: This case shows diagnostic difficulties because of the absence of systemic abnormalities. Further studies should be performed to identify these biomarkers, neurohormonal influences, and potential treatment options, combining expertise from dermatology, neurology, and psychoneuroimmunology to achieve a clinical objective.

Abstract In this work, a study was undertaken to explore the possibility of using leftover tamarind fruit fibres as reinforcement in PLA and HDPE matrix. PLA and HDPE polymers form minimum 75% of the total polymers used in the composites. PLA and HDPE was mixed with natural fibres (5 wt.%, 10 wt.%, 15 wt.%, 20 wt.% and 25 wt.%,) individually and also as a hybrid filler to enhance its mechanical properties. Characterizations, mechanical behaviour and microscopic investigation were performed to understand the excellent mechanical properties and good chemical resistance of the prepared composites, which demonstrated potential suitability for semi-structural applications.