Browsing by Author "Selvakumar, S"

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    Accuracy of 64-slice coronary CT angiography in predicting percentage diameter stenosis.
    (2008-07-27) Harish, A; Khatri, Payal; Priyadarshini, H; Selvakumar, S; Arunkumar, N; Sivakumar, A; Ezhilan, J; Mullasari, Ajit S
    OBJECTIVE: Aim of our study was to evaluate the diagnostic accuracy of 64-slice CT coronary angiogram in measuring the percentage diameter stenosis compared to invasive angiography. METHODS AND RESULTS: 100 consecutive patients with more than 50% stenosis in at least one major coronary artery measured by 64-slice CT angiogram were included in the study. Patients with atrial fibrillation, history of allergy to contrast agent, acute coronary syndrome, renal insufficiency, history of previous coronary bypass surgery or percutaneous transluminal coronary stent, heart rate more than 70 per minute at the time of scan in spite of beta-blocker therapy, and calcium score >2000 Agaston units were not included in the study. 15-segment American Heart Association classification was used, and segments were compared using qualitative angiography. 192 segments (12.80%) could not be assessed due to poor image quality. The major cause for poor image quality was dense calcification precluding the luminal assessment (60.42%). Comparing the maximal percentage diameter stenosis by 64-slice CT versus invasive angiogram, the Spearman correlation coefficient between the two modalities was 0.788 and p value was <0.001. Bland-Altman analysis showed a mean difference in percentage stenosis of 2.1 +/- 16.22%. A total of 91.97% (401 of 436) of segments were within 1.96 standard deviations. CONCLUSION: This study shows that 64-slice CT coronary angiogram is accurate in detecting percentage diameter stenosis compared to coronary angiogram if the image quality is good. Calcifications and motion artifacts are the main culprits of poor image quality.
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    Assessment of Climate Change on Soil Erosion Using Geospatial Techniques: A Review
    (Ms. M. B. Mondal, Ph.D., 2025-06) Pazhanivelan, S; Lad, SU; Selvakumar, S; Ravikumar, V; Ramesh, AV; Karale, OS; Jadhav, RJ.
    Climate change is accelerating soil erosion, presenting a significant threat to food security and ecosystem health globally. This review investigates the impact of climate change on soil erosion using advanced geospatial methods and the Revised Universal Soil Loss Equation (RUSLE). An analysis of over 100 recent peer-reviewed articles (including research from 1990 to 2024) explores how factors such as shifting precipitation patterns, rising temperatures, extreme weather events, and land-use changes influence erosion across various scales. Climate change-induced shifts in precipitation patterns and intensifying weather events significantly affect soil erosion rates. Heavy rainfall events can cause substantial soil displacement, while droughts dry out the soil, leaving it vulnerable to wind erosion. Rising temperatures further exacerbate the problem by altering soil moisture levels and influencing vegetation cover, a crucial factor in erosion control. Land-use changes, including deforestation, urbanization, and agricultural practices, disturb soil stability and increase erosion rates. Remote sensing, GIS, and AI-machine learning are increasingly combined with advanced RUSLE variations to identify erosion hotspots with greater precision. These technologies enable monitoring of spatiotemporal patterns and assessment of future risks under various climate scenarios. Remote sensing techniques allow for high-resolution mapping of erosion-prone areas, while AI and machine learning enhance predictive models, providing more targeted and effective adaptation strategies. Despite potential temporary reductions in erosion in regions experiencing initial increases in vegetation cover, projections indicate a significant global soil loss increase by mid-century (2050). This increase is driven by heavier precipitation, intensifying droughts, and more frequent and severe floods. The resulting erosion can have devastating effects on agricultural productivity, water quality, and biodiversity. Integrated soil conservation practices, such as reduced tillage, cover cropping, and revegetation, are essential for building resilience against this growing threat. These practices help stabilize soil, improve water retention, and enhance the land's overall health. In addition, landscape management techniques, including contour farming and agroforestry, can further mitigate erosion risks. Advancements in AI-machine learning-based erosion prediction models offer promising opportunities for more precise and timely interventions. By integrating these models with remote sensing data, researchers can develop more accurate risk assessments and design more efficient mitigation strategies. High-resolution remote sensing allows for continuous monitoring and evaluation of erosion patterns, enabling adaptive management approaches. As results emerging technologies and innovative management practices offer new tools and approaches to address this challenge. By investing in research and adopting advanced geospatial methods, policymakers and stakeholders can work together to develop more effective strategies for mitigating soil erosion and safeguarding global food security and biological systems.