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Trend and Decomposition Analysis of Apple Production in Jammu and Kashmir

Trend and Decomposition Analysis of Apple Production in Jammu and Kashmir
Authors:-Assistant Professor Dr. R. Angamuthu

Abstract-In this paper examines the trend and decomposition analysis of apple production in Jammu and Kashmir in India. Apple fruits production in the world stood at around 2.4 million metric tons in the year 2022-23, making India the fifth largest producer. In India level, the Area, 321.90 in thousand hectares in the beginning year, nosedived to 312.60 in thousand hectares during the year 2020-21. It is exhibited a negative trend upto the end year. At the same time, the growth was not at notable level to area for apple fruits in India over the period. (CAGR = -0.28, t = – 0.59, P < 0.10). On the other hand, it is understood from the table that the production and productivity of the apple fruits in India with average of 2326.94 in thousand million tonnes and 7.57 million tonnes / hectares have reached to 2275.80 in thousand million tonnes and 7.30 in million tonnes / hectares after testing at as high as 2814.30 thousand million tonnes and 9.10 million tonnes / hectares in 2019-20 from 2203.40 thousand million tonnes and 6.80 million tonnes / hectares in 2011-12 at significant compound rate of 1.69 per cent (CAGR = 1.69, t = 1.58, P < 0.10) and 2.04 per cent (CAGR = 2.04, t = 1.55, P < 0.10). From the inferences of these results, it is found that negative growth in area of apple fruits, but notable growth in production and productivity of apple fruits in India level. Apple production in the Jammu and Kashmir region experienced substantial growth, with a notable upward trend during the period under consideration.

DOI: 10.61137/ijsret.vol.10.issue4.217

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Nonlinear Analysis of High-Rise Reinforced Concrete Buildings with Different Structural System and Floor Systems Using Fiber Model in Time History

Nonlinear Analysis of High-Rise Reinforced Concrete Buildings with Different Structural System and Floor Systems Using Fiber Model in Time History
Authors:-Rıza Torkan, Professor Dr. Mustafa Karaşahin, Professor Dr. Reha Artan, Professor Dr. Turgut Öztürk

Abstract-In order to ensure the safety of structures against earthquakes, it is stated that a structural system that takes into account nonlinear behavior should be installed. This is an approach mandated by TBDY2018, especially for tall buildings. Nonlinear behavior is important to ensure that structures behave realistically under earthquakes. This requires a detailed analysis of the structures to account for the elongation and shortening of the material fibers during an earthquake. These analyses are based on nonlinear solutions in terms of materials and geometry, taking into account second-order effects. Although nonlinear analysis is essential for realistic prediction of earthquake effects in tall buildings, comprehensive studies applying advanced nonlinear analysis techniques using Open Sees software to a large set of carefully selected earthquake records are lacking. Evaluation of the seismic performance of tall buildings in specific earthquake zones is not available. This study provides insights that previous studies have made more limited use of by uniquely combining advanced nonlinear analysis techniques in Open Sees with a total of 22 carefully selected earthquake records to provide a more accurate and realistic assessment of the performance of tall buildings in specific seismic zones by averaging these 22 earthquake records. The aim of this approach is to prevent loss of life and property by minimizing the destructive effects of earthquakes. Consideration of nonlinear behavior allows us to more realistically assess how structures will respond under real earthquakes. Thus, building design and assessment can be made safer.

DOI: 10.61137/ijsret.vol.10.issue4.216

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Leaveraging AI for Public Health Management

Leaveraging AI for Public Health Management
Authors:-Ganesh Ramalingam

Abstract-This whitepaper examines the use of artificial intelligence (AI) in managing population health. It discusses how AI can analyze population health data to identify trends, predict outbreaks, and optimize resource allocation. The paper covers the ethical considerations of using AI in public health and the regulatory measures needed to protect patient data. A novel algorithm called Geo Health AI is presented, along with Python code, to demonstrate how AI can be applied to geospatial population health analysis. Case studies and outcomes from AI implementations in population health management are reviewed. Finally, recommendations are provided for healthcare organizations looking to leverage AI for population health initiatives.

DOI: 10.61137/ijsret.vol.10.issue4.215

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