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ObjectiveIn China, the deep coalbed methane (CBM) industry has emerged as a key sector for the reserve growth and production addition of natural gas. However, the current CBM technical standard system, which was develope...

ObjectiveRescue drilling rigs undergo intense vibration at borehole mouths when operating in a confined underground space of coal mines. To address this issue, this study designed a compact multi-directional, passive dyn...

Regression testing plays a critical role in ensuring the reliability and quality of software following continuous integration and development. However, executing all test cases during regression testing can be time-consu...

Ultrasound-assisted interesterification is a new approach for biodiesel production, and in this study, coconut oil was selected as the raw material to produce biodiesel using this technology. Therefore, this study aims t...

Abstract The increasing penetration of photovoltaic distributed generation (PV-DG) in Radial Distribution Systems (RDSs) plays a vital role in achieving sustainable energy transition objectives; however, the inherent unc...

To address the issues of low solution accuracy, premature convergence, and susceptibility to local optima in the Parrot Optimization (PO) algorithm, a Reinforced Multi-strategy Parrot Optimization (RMPO) algorithm has be...

Abstract The emergence of Internet-of-Things devices and expanding data processing demands have led to the popularization of edge computing. But energy efficiency challenges and security problems also remain difficult in...

This paper proposes an innovative automated multiphysics Microelectromechanical systems (MEMS) co-optimization platform integrating the embedded reduced-order modeling offered by Coventor MEMS+ with MATLAB’s numerical ...

Abstract Active power distribution networks are evolving with the integration of distributed energy resources (DERs) and advanced optimization techniques to enhance grid flexibility and efficiency. However, radial distri...

Abstract Feature selection is a critical step in machine learning, as it helps minimize redundant features, thereby enhancing model performance and interpretability. In practice, partially labeled datasets are common. Wh...

Abstract In order to improve the operation efficiency and impact resistance of the robotic arm in unstructured environments such as orchards, this paper proposes an online trajectory planning method for multi-index compr...

Abstract Biochar yield prediction plays a critical role in optimizing pyrolysis processes and advancing sustainable biomass utilization. This study introduces a hybrid machine learning framework that integrates Decision ...

Abstract In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) play a vital role in enabling seamless communication between connected devices. In a WSN data prediction system based, clustering and...

This study compares seven quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), Quantum Amplitude Estimation (QAE), Quant...

Abstract Objective Current diagnostic paradigms for Crohn’s disease (CD) activity assessment remain fragmented across imaging and laboratory modalities. This study seeks to investigate the diagnostic efficacy of intest...

IntroductionInjury assessment and forensic decision support are pivotal challenges in sports medicine, requiring advanced methods to interpret complex biomechanical and medical imaging evidence under uncertainty. This st...

Modern edge–cloud deployments integrate devices, edge servers, private networks, and cloud platforms into heterogeneous, multi-layer systems. Each layer exposes distinct metrics, measurement granularities, and tim...

Abstract Accurate classification of phonocardiogram (PCG) signals is vital for the early detection of cardiovascular diseases (CVDs), a leading cause of global mortality. However, conventional machine learning and deep l...

Despite the impressive strides of large language models (LLMs) in text summarization, their effectiveness still hinges on well-crafted prompts, a process that remains labor-intensive, subjective, and difficult to scale. ...

Abstract The Broad Learning System (BLS) provides an effective framework for nonlinear mapping, offering advantages over traditional deep neural networks through its expanded input node architecture. While BLS has demons...

Abstract The primary characteristics of an electric drive include the ability to utilize the maximum torque throughout the speed range, minimal reliance on parameters, and, if feasible, the highest efficiency. This paper...

The high population growth rate, the accelerated industrialization and unpredictability of oil prices in the world have considerably raised the energy demand which has enhanced the necessity of alternative and sustainabl...

The aim of this work is to create the scientific foundations of microwave extractors implementing the effect of mechanodiffusion discovered by the authors, as well as visual, kinetic, and energy substantiation of the fun...

Introduction. Nowadays, the most widely used wind energy conversion system in wind farms is based on a doubly fed induction generator (DFIG); it has a large speed range and can function in multiple modes. Problem. Harmon...