APJ Abdul Kalam Technological University, formerly known as Kerala Technological University (KTU), is a state owned public university headquartered in Thiruvananthapuram, the state capital of Kerala, India. It is both a teaching and affiliation university, with more than 150 affiliated colleges and over 160,000 students enrolled, having jurisdiction over the entire area of Kerala. It is an AICTE (All India Council for Technical Education) and UGC (University Grants Commission) approved university that offers UG and PG courses under popular streams such as BTech, BArch, BHMCT, MTech, M.Arch, MBA, and MCA. Now, KTU also provides B.Tech Honours as well as Minor programs in addition to regular courses.APJ Abdul Kalam Technological University offers engineering and technology-related courses and has advanced courses and syllabi compared to the courses offered by its predecessor, the old Kerala University. It is a relatively new and reformed university, with its first batch in 2015.As of 2018, the state government has given administrative sanction to set up the headquarters of Kerala Technological University (KTU) at Vilappilshala, on the outskirts of Thiruvananthapuram.
Scrap tyre pad isolators, fabricated from discarded car tyres, offer a sustainable and economical alternative for seismic isolation due to their inherent energy dissipation and load-carrying capacity. However, limited clarity exists regarding the comparative behaviour of bonded and unbonded configurations, particularly when applied to masonry structures. This study presents an experimental and numerical investigation on properties of unbonded and bonded scrap tyre pad isolators developed from radial car tyres of size 175/65/R14. Finite element models are employed to evaluate the influence of key parameters, including aspect ratio, loading direction, vertical pressure, layer thickness, and embedded steel strands on the structural response. The present study measures and compares the impact of these parameters on the properties of unbonded and bonded isolators made from identical tyre pads. Based on the parametric study, it is observed that the aspect ratio and vertical pressure are the predominant parameters that govern the structural properties of both unbonded and bonded isolators. The compression behaviour of both the isolators is found to be comparable whereas shear properties indicate superior performance of unbonded isolators. The percentage reduction in horizontal stiffness is 80.24
PURPOSE:Accurate segmentation of glaucoma-related anatomical structures from retinal fundus images is crucial for reliable clinical assessment and early disease diagnosis. However, variations in illumination, low contrast, and complex structural patterns make precise boundary delineation of the optic disc (OD) and optic cup (OC) challenging. This study aims to improve the accuracy of OD and OC segmentation for glaucoma assessment. METHODS:An Enhanced SwinUNet model is proposed, integrating hierarchical transformer-based feature extraction with a Dual-Stage Context-Aware Feature Refinement (DCF-Refine) module embedded in skip connections. A preprocessing stage is applied using CLAHE-based contrast enhancement in LAB color space along with min-max normalization to improve image quality and stabilize training. The model employs Swin Transformer (ST) blocks to capture both local structural details and long-range dependencies. The DCF-Refine module enhances feature fusion through sequential Spatial Context Refinement (SCR) and Channel Context Refinement (CCR). RESULTS:Experimental evaluation on the Drishti-GS and REFUGE datasets demonstrates that the proposed Enhanced SwinUNet achieves superior performance compared to existing segmentation methods, attaining accuracies of 99.3% and 99.1%, respectively. CONCLUSION:The proposed model provides highly accurate and reliable segmentation of OD and OC structures, effectively addressing challenges in retinal image analysis. Its strong performance supports improved glaucoma-related structural assessment and has potential for clinical application.
The COVID 19 pandemic is highly contagious disease is wreaking havoc on people's health and well-being around the world. Radiological imaging with chest radiography is one among the key screening procedure. This disease contaminates the respiratory system and impacts the alveoli, which are small air sacs in the lungs. Several artificial intelligence (AI)-based method to detect COVID-19 have been introduced. The recognition of disease patients using features and variation in chest radiography images was demonstrated using this model. In proposed paper presents a model, a deep convolutional neural network (CNN) with ResNet50 configuration, that really is freely-available and accessible to the common people for detecting this infection from chest radiography scans. The introduced model is capable of recognizing coronavirus diseases from CT scan images that identifies the real time condition of covid-19 patients. Furthermore, the database is capable of tracking detected patients and maintaining their database for increasing accuracy of the training model. The proposed model gives approximately 97% accuracy in determining the above-mentioned results related to covid-19 disease by employing the combination of adopted-CNN and ResNet50 algorithms.
Clinical trial is the key approach for studying the effectiveness of a drug or a medical equipment. In the recent years several drugs which were available commercially, were found to be inefficient and they were banned in many countries. These insights point towards the need for cross checking the credibility of clinical trials being conducted. This paper considers the drawbacks in medical data collection. In the proposed work, we have used an improved lion optimized SDN (Software Defined Network) controller for the efficient data collection by the IoT medical devices. A possible architecture for addressing problems related to the data evaluation and analysis is edge computing. For improving the data processing speed of IoT devices in a clinical trial system, edge computing is affordable and can offer low latency data services. Load balancing, network optimisation, and effective usage of resources are precisely carried out in the proposed clinical trial scenario employing adaptive software-defined network (SDN). The low-powered medical IoT IOT devices are susceptible to many safety hazards, as are the information that they are linked to (personal, confidential patient information). The Edge processors of the suggested architecture employ a straightforward method of authentication to confirm the IoT devices and users in order to establish a secure environment for SDN-facilitated computation in an internet of things medical facility. Following identification, the gadgets gather clinical information then send it to nearby servers for handling, storage, and evaluation. The Edge servers have connections to an intelligent SDN controller that controls distribution of load, system optimization, and efficient resource use in the medical sector. Improved Lion Optimization (ILO) algorithm helps the SDN to make intelligent decisions. Performance of the suggested framework is evaluated using software simulations.
We introduce WimPyC, a Python code for the calculation of the capture rate of Weakly Interacting Massive Particles (WIMPs) by celestial bodies through nuclear scattering in the optically thin regime. WimPyC is an extension of the WimPyDD code, that calculates WIMP–nucleus scattering signals in direct detection (DD) experiments, and allows to combine DD and capture in celestial bodies in virtually any scenario within the framework of Galilean–invariant non–relativistic effective theory (NREFT), including inelastic scattering, an arbitrary WIMP spin and a generic WIMP velocity distribution in the Galactic halo. WimPyDD and WimPyC are suitable for both top–down approaches, where the interaction operators of a high–energy physics model are matched to those of the NREFT, and to bottom–up studies, where the Wilson coefficients of the NREFT are explored in a model–independent way and/or where the velocity distribution is written in terms of a superposition of streams taken as free parameters. As in the case of WimPyDD WimPyC exploits the factorization of the three main components that enter in the calculation of the capture rate: i) the Wilson coefficients that encode the dependence of the signals on the ultraviolet completion of the effective theory; ii) a response function that depends on the nuclear physics; iii) the halo function that depends on the WIMP velocity distribution. In WimPyC these three components are calculated and stored separately for later interpolation and combined together only as the last step of the signal evaluation procedure. This makes the phenomenological study of the capture rate with WimPyC transparent and improves computational speed.PROGRAM SUMMARYModule Title: WimPyCCPC Library link to program files: https://doi.org/10.17632/bg5wxh6mgj.1”Licensing provisions: MITProgramming language: Python3Developer’s repository link: wimpydd.hepforge.orgNature of problem:Stars and planets in our galaxy are expected to be embedded in an extended dark matter halo made of WIMPs. When they cross a celestial body Dark Matter (DM) particles can lose energy and become gravitationally captured by the same WIMP–nucleus scattering process driving direct detection in terrestrial detectors. The rate at which this process occurs is called the capture rate, and, under proper conditions, can lead to a dense population of DM particles inside the celestial body with a consequent enhancement of their annihilation rate. This can produce indirect observational signatures such as a flux of high–energy neutrinos from the celestial body, or an increase in its temperature/luminosity. Combining direct and indirect signals can potentially improve our chance to detect WIMPs, but can be cumbersome especially in generalized scenarios.Solution method:The WimPyC module seamlessly integrates with the WimPyDD code, that calculates WIMP–nucleus scattering signals in direct detection experiments. It allows to calculate the WIMP capture rate in virtually any scenario, including an arbitrary spin, inelastic scattering, and a non-standard halo function. Each ingredient, including the celestial body, can be set up independently in an easy and intuitive way.
