Government Medical College Thoothukudi

Interstitial lung diseases comprise a heterogeneous group of diffuse parenchymal lung disorders with substantial morbidity and mortality. Early diagnosis and accurate severity assessment are very important for making predictions and managing the disease. High-resolution computed tomography has become the preferred imaging technique for detection, pattern recognition, and disease quantification

Artificial Intelligence (AI) is transforming multiple sectors; however, its environmental implications require critical examination. The training and deployment of large-scale AI models demand significant computational power, resulting in high energy consumption, increased carbon emissions, and growing electronic waste. Hardware production further intensifies ecological degradation through resource-intensive processes. At the same time, AI presents substantial opportunities for sustainable growth, including energy-efficient algorithms, renewable-powered data centres, precision agriculture, climate modelling, and optimised resource management. This study provides a structured review of the positive and negative environmental implications of AI and analyses how these implications influence sustainable development pathways. The findings highlight the dual role of AI as both a contributor to environmental pressure and a strategic tool for sustainability transformation. The paper concludes that responsible AI governance, energy-efficient innovation, and renewable integration are essential to ensure that AI supports long-term sustainable growth

One of the major problems which the world faces today is water scarcity, as every life needs it for their day-to-day activity. Pollution and salinity are the two major causes of most waterbodies being unfit for use. Removing pollutants or salt from water became essential for overcoming the water shortage. Desalination technology is being used to achieve this, but the major disadvantage is its lower production. Applying solar energy and nanofluids may tackle the problem and increase the production. In this work, some physical and chemical parameters, in particular isoelectric point (pH), total dissolved solids (TDS), electric conductivity (EC), chloride (Cl–), fluoride (F–), iron(II) (Fe2+) content, were studied before and after desalination. Also, the influence of various water nanofluids (WNF) on increasing the output of glass-made double slope solar stills was studied. Physical and chemical parameter analysis revealed a reduction in values after desalination. Single and double-basin glass solar stills of 0.81 m2 basin area were fabricated, and the experiments were conducted at Kovilpatti. The experiments were carried out at a depth of 1 cm with borewell saline water and nanofluids under insulated conditions. A maximum productivity of 4350 mL per 0.81 m2/day (5405 mL/m2/day) was obtained while using Al2O3 nanofluid in a double basin glass solar still, which was higher than that obtained using water, SnO2 and ZnO WNF. This confirms the efficiency of Al2O3 aqueous nanofluid, which yielded more distillate than other two nanofluids used.

The identification and mapping of palaeochannels offer an essential understanding of past fluvial processes and groundwater potential, particularly in arid and semi-arid regions facing severe water stress. This study focuses on the Gundar River Basin within Kadaladi Taluk, Ramanathapuram District, Tamil Nadu, aiming to delineate palaeochannel networks using integrated geospatial techniques. Remotely sensed datasets, including Landsat 9 imagery and ASTER GDEM, were used to generate topographic derivatives such as slope, aspect, curvature, flow accumulation, and Topographic Wetness Index (TWI) along with spectral indices like NDVI, NDWI, and NDMI. False Colour Composites (FCC and SWIR-FCC) were prepared to enhance visual interpretation of buried channel patterns. A fuzzy overlay analysis, using a gamma value (γ = 0.8), synthesized these inputs to classify palaeochannel suitability. Results revealed that nearly 18.3% of the basin, predominantly in the central and southeastern sectors, exhibits high palaeochannel potential. These zones are characterized by gentle slopes (< 3°), concave curvature (<-5), high flow accumulation (> 2000), and TWI values above 15, along with NDVI (0.35-0.50), NDWI (> 0.25), and NDMI (0.30-0.41). These findings align with fluvial orientations from northwest to southeast, consistent with regional drainage patterns that flow towards the Bay of Bengal. Despite the absence of field validation, the results correspond well with previous studies in similar Indian basins. This research provides a robust, non-invasive framework for palaeochannel detection, supporting future groundwater recharge planning and paleohydrological reconstructions in coastal, water-stressed environments, such as Kadaladi Taluk.

This article explores the ethical and moral implications of scientific discovery in Mary Shelley’s Frankenstein (1818), focusing on the destructive potential of unregulated ambition. Through Victor Frankenstein, the first modern scientist, the narrative captures the moral void of creation. He gives life to a being and yet pays no thought to the moral obligations of care. The absence of responsibility and rejection culminate in destruction. The pain of knowledge, when coupled with an absence of compassion, manifests as tragedy. This paper addresses the ethical void in the scientific endeavours portrayed in Frankenstein and links Shelley’s caution to the present-day scenario with artificial intelligence, high-end biotechnology, and genetic engineering. The essence of Shelley’s caution demonstrates the contemporary need to guide scientific pursuits with compassion, ethics, and responsibility. The article demonstrates Shelley's prophetic awareness of the perils of science lacking empathy, illustrating how her caution is relevant to the ethical challenges of the twenty-first century.

This research investigates the optimization of downlink performance metrics, including spectral efficiency, data rates, and coverage, for cell-edge user equipment (UE) in Beyond 5G (B5G) networks using a hybrid relaying Cooperative Non-Orthogonal Multiple Access (CNOMA) framework.The system architecture consists of a gNodeB (gNB) located at the cell center, serving both an intra-cell cluster (ICC) near the gNB and a cell-edge cluster (CEC) positioned farther away.To address the challenges faced by UEs in these clusters, particularly random signal strength deprivation due to deep Rayleigh fading, two novel resource allocation strategies are proposed: Channel-Aware Adaptive Fair (CAAF) Power Allocation and Resource-Free Best Positioned Opportunistic Relay Selection (RFBP-ORS).The CAAF method ensures fair and dynamic power allocation by leveraging real-time channel state information (CSI), while RFBP-ORS selects the optimal relay based on resource availability and spatial positioning, thereby maintaining uninterrupted connectivity.Simulation results reveal that the proposed methodologies significantly enhance the achievable data rates by up to 140% compared to conventional CNOMA, reaching 5.8 bps/Hz at 12.5 dB SINR.They also improve energy efficiency to 1 Gb/J and achieve a 10 times reduction in outage probability for cell-edge users at 12.5 dB SINR, thereby demonstrating strong suitability for B5G deployment requirements.The proposed CNOMA-based hybrid relaying scheme outperforms conventional resource allocation approaches, offering a reliable and efficient communication framework for B5G networks, particularly benefiting UEs near the cell edge.These findings underline the potential of CNOMA-based adaptive and opportunistic resource allocation in addressing the evolving demands of next-generation wireless networks.