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Doppler weather radars as a game changer in desert locust swarm tracking.
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India Meteorological Department, New Delhi, 110003, India.
Desert locusts, notorious for their ruinous impact on agriculture, threaten over 20% of Earth's landmass, prompting billions in losses and global food scarcity concerns. With billions of these locusts invading agrarian lands, this is no longer a thing of the past. Recent invasions, such as those in India, where losses reached US$ 3 billion in 2019-20 alone, underscore the urgency of action.
View Article and Find Full Text PDFResearch on truck-drone collaborative route planning for rural logistics delivery services.
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Department of Production Engineering, KTH Royal Institute of Technology, 11428, Stockholm, Sweden.
This study investigates the implementation of collaborative route planning between trucks and drones within rural logistics to improve distribution efficiency and service quality. The paper commences with an analysis of the unique characteristics and challenges inherent in rural logistics, emphasizing the limitations of traditional methods while highlighting the advantages of integrating truck and drone technologies. It proceeds to review the current state of development for these two technologies and presents case studies that illustrate their application in rural logistics.
View Article and Find Full Text PDFOptimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework.
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Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Kyiv, Ukraine.
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.
View Article and Find Full Text PDFWeb application using machine learning to predict cardiovascular disease and hypertension in mine workers.
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This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFA novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction.
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College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Accurate prediction of runoff is of great significance for rational planning and management of regional water resources. However, runoff presents non-stationary characteristics that make it impossible for a single model to fully capture its intrinsic characteristics. Enhancing its precision poses a significant challenge within the area of water resources management research.
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