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rabindra.tharu.np@gmail.com
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Predictive modelling and optimization of WEDM parameter for Mg-Li alloy using ANN integrated CRITIC-WASPAS approach
This work intended to improve the precision and machining efficiency of Magnesium alloy (Mg-Li-Sr) using Wire electrical discharge machining (WEDM). Mg-Li-Sr alloy is prepared through inert gas assisted stir casting route. Taguchi approach is used for experimental design for WEDM parameter such as pulse OFF time, pulse ON time, wire feed rate, servo voltage and…
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rabindra.tharu.np@gmail.com
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Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction
INTRODUCTION Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics.…
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rabindra.tharu.np@gmail.com
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Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer
BACKGROUND The role of Mast cells has not been thoroughly explored in the context of prostate cancer’s (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to…
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rabindra.tharu.np@gmail.com
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Deep learning-based electricity theft prediction in non-smart grid environments
In developing countries, smart grids are nonexistent, and electricity theft significantly hampers power supply. This research introduces a lightweight deep-learning model using monthly customer readings as input data. By employing careful direct and indirect feature engineering techniques, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), UMAP (Uniform Manifold Approximation and Projection), and resampling…
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rabindra.tharu.np@gmail.com
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The rise of Soybean in international commodity markets: A quantile investigation
The complex interplay between agricultural and energy commodities has been a subject of interest in past research, gaining more relevance recently due to geopolitical events such as the conflict between Ukraine and Russia. This conflict has systematically driven up the prices of both energy and agricultural commodities. Deeply understanding the dynamic interconnections between these commodities…
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rabindra.tharu.np@gmail.com
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Analysis of the interlink between glucose-6-phosphate dehydrogenase (G6PD) and lung cancer through multi-omics databases
Glucose-6-Phosphate Dehydrogenase (G6PD) is a crucial enzyme that executes the pentose phosphate pathway. Due to its critical nodal position in the metabolic network, it is associated with different forms of cancer tumorigeneses and progression. Nonetheless, its functional role and molecular mechanism in lung cancer remain unknown. The present study provides intricate information associated with G6PD…
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rabindra.tharu.np@gmail.com
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Adaptive machine learning for forecasting in wind energy: A dynamic, multi-algorithmic approach for short and long-term predictions
This study elucidates the formulation and validation of a dynamic hybrid model for wind energy forecasting, with a particular emphasis on its capability for both short-term and long-term predictive accuracy. The model is predicated on the assimilation of time-series data from past wind energy generation and employs a triad of machine learning algorithms: Artificial Neural…
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rabindra.tharu.np@gmail.com
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An enhanced proportional resonance controller design for the PMSM based electric vehicle drive system
Permanent magnet synchronous machine (PMSM) has proven to be a more economical traction drive system for electric vehicle (EV) applications owing to increased efficiency and high-power density. However, the drive system requires more efficient control schemes to deliver better dynamic performance irrespective of dynamic changes in the motor speed, machine parameters and disturbances. Hence, to…
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rabindra.tharu.np@gmail.com
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Evaluating the performance of ChatGPT-3.5 and ChatGPT-4 on the Taiwan plastic surgery board examination
BACKGROUND Chat Generative Pre-Trained Transformer (ChatGPT) is a state-of-the-art large language model that has been evaluated across various medical fields, with mixed performance on licensing examinations. This study aimed to assess the performance of ChatGPT-3.5 and ChatGPT-4 in answering questions from the Taiwan Plastic Surgery Board Examination. METHODS The study evaluated the performance of ChatGPT-3.5…
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rabindra.tharu.np@gmail.com
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An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows
Cow diseases are a major source of concern for people. Some diseases in animals that are discovered in their early stages can be treated while they are still treatable. If lumpy skin disease (LSD) is not properly treated, it can result in significant financial losses for the farm animal industry. Animals like cows that sign…
