Yijun Gao^1,2, Yong Wang^1,*, Qing Na¹, Jiawei Zhang¹, Aixiang Wu¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2443-2458, 2024, DOI:10.32604/fdmp.2024.051385 - 28 October 2024

Abstract Filling methods in the mining industry can maximize the recovery of mineral resources and protect the underground and surface environments. In recent years, such methods have been widely used in metal mines where pipeline transportation typically plays a decisive role in the safety and stability of the entire filling system. Because the filling slurry contains a large percentage of solid coarse particles, the involved pipeline is typically eroded and often damaged during such a process. A possible solution is the so-called nesting repair technology. In the present study, nesting a 127 mm outer diameter pipeline… More >

Mustafa Mohammed Alhassow^1,*, Oguz Ata², Dogu Cagdas Atilla¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 749-771, 2024, DOI:10.32604/cmc.2024.055592 - 15 October 2024

Abstract This study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict-Based Search (CBS) approach, introducing a unique hierarchical search mechanism for planning paths for multiple robots.… More >

Lin Ma¹, Liyong Wang¹, Shuang Zeng¹, Yutong Zhao¹, Chang Liu¹, Heng Zhang¹, Qiong Wu^2,*, Hongbo Ren²

Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332 - 21 May 2024

Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

Farhan Ullah¹, Xuexia Zhang^1,*, Mansoor Khan², Muhammad Abid^3,*, Abdullah Mohamed⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3373-3395, 2024, DOI:10.32604/cmc.2024.048656 - 15 May 2024

Abstract Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data… More >

FREDERICK H. SILVER^1,2,*, TANMAY DESHMUKH²

BIOCELL, Vol.48, No.4, pp. 525-540, 2024, DOI:10.32604/biocell.2024.047965 - 09 April 2024

Abstract All tissues in the body are subjected externally to gravity and internally by collagen fibril and cellular retractive forces that create stress and energy equilibrium required for homeostasis. Mechanotransduction involves mechanical work (force through a distance) and energy storage as kinetic and potential energy. This leads to changes in cell mitosis or apoptosis and the synthesis or loss of tissue components. It involves the application of energy directly to cells through integrin-mediated processes, cell-cell connections, stretching of the cell cytoplasm, and activation of the cell nucleus via yes-associated protein (YAP) and transcriptional coactivator with PDZ-motif… More >

Deni Ainur Rokhim^1,2, Isma Yanti Vitarisma¹, Sumari Sumari^1,*, Yudhi Utomo¹, Muhammad Roy Asrori¹

Journal of Renewable Materials, Vol.12, No.2, pp. 275-284, 2024, DOI:10.32604/jrm.2023.043419 - 11 March 2024

Abstract The high consumption of electricity and issues related to fossil energy have triggered an increase in energy prices and the scarcity of fossil resources. Consequently, many researchers are seeking alternative energy sources. One potential technology, the Microbial Fuel Cell (MFC) based on rice, vegetable, and fruit wastes, can convert chemical energy into electrical energy. This study aims to determine the potency of rice, vegetable, and fruit waste assisted by Cu/Mg electrodes as a generator of electricity. The method used was a laboratory experiment, including the following steps: electrode preparation, waste sample preparation, incubation of the… More >

Bilal Khan¹, Saifullah Jan^1,*, Wahab Khan¹, Muhammad Imran Chughtai²

Journal on Big Data, Vol.6, pp. 1-20, 2024, DOI:10.32604/jbd.2023.046223 - 26 January 2024

Abstract The extraction, transformation, and loading (ETL) process is a crucial and intricate area of study that lies deep within the broad field of data warehousing. This specific, yet crucial, aspect of data management fills the knowledge gap between unprocessed data and useful insights. Starting with basic information unique to this complex field, this study thoroughly examines the many issues that practitioners encounter. These issues include the complexities of ETL procedures, the rigorous pursuit of data quality, and the increasing amounts and variety of data sources present in the modern data environment. The study examines ETL… More >

Mohammed S. M. Gismalla^1,2, Asrul I. Azmi^1,2, Mohd R. Salim^1,2, Farabi Iqbal^1,2, Mohammad F. L. Abdullah³, Mosab Hamdan^4,5, Muzaffar Hamzah^4,*, Abu Sahmah M. Supa’at^1,2

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2189-2204, 2023, DOI:10.32604/cmc.2023.035250 - 29 November 2023

Abstract Visible light communication (VLC) has a paramount role in industrial implementations, especially for better energy efficiency, high speed-data rates, and low susceptibility to interference. However, since studies on VLC for industrial implementations are in scarcity, areas concerning illumination optimisation and communication performances demand further investigation. As such, this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication performances. The proposed model was evaluated based on various semi-angles at half power (SAAHP) and different height levels for several parameters, including received power, signal to noise ratio More >

Muzi Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2465-2493, 2023, DOI:10.32604/cmes.2023.026827 - 03 August 2023

Abstract Modular construction as an innovative method aids the construction industry in transforming to off-site construction production with high efficiency and environmental friendliness. Despite the obvious advantages, the uptake of modular construction is not booming as expected. However, previous studies have investigated and summarized the barriers to the adoption of modular construction. In this research, a Large-Scale Group Decision Making (LSGDM)- based analysis is first made of the severity of barriers to modular construction adoption from the perspective of construction stakeholders. In addition, the Technology-Organization-Environment (TOE) framework is utilized to identify the barriers based on three More >

Popuri Srinivasarao, Aravapalli Rama Satish^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1527-1547, 2023, DOI:10.32604/csse.2023.038951 - 28 July 2023

Abstract Responding to complex analytical queries in the data warehouse (DW) is one of the most challenging tasks that require prompt attention. The problem of materialized view (MV) selection relies on selecting the most optimal views that can respond to more queries simultaneously. This work introduces a combined approach in which the constraint handling process is combined with metaheuristics to select the most optimal subset of DW views from DWs. The proposed work initially refines the solution to enable a feasible selection of views using the ensemble constraint handling technique (ECHT). The constraints such as self-adaptive… More >