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  • Open Access

    ARTICLE

    Growth and Yield Responses of Soledad Chili Pepper (Capsicum annuum L.) to the Application of Chitosan and Bacillus subtilis

    Adolfo Amador Mendoza1,*, Rosalba Guadalupe Gomez Raymundo2, Ana Rosa Ramírez Seañez1, Hipolito Hernández Hernández1, Rogelio Enrrique Palacios Torres1, Nelda Xanath Martínez Galero3, Miguel Ángel García Muñoz3, Saribel Zilli Gutiérrez4,*

    Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.073856 - 30 January 2026

    Abstract The cultivation of Soledad pepper (Capsicum annuum L.) is essential in Oaxaca and Veracruz, but it faces issues with pests and diseases, which affect yield and cause economic losses. To mitigate these impacts, farmers have started using biostimulants such as chitosan and plant growth promoting bacteria instead of agrochemicals due to their environmental and health benefits. This study evaluated the effect of Bacillus subtilis and chitosan, both individually and combined, on the growth, yield, and fruit quality of Soledad pepper under greenhouse conditions. Four treatments were applied at different stages of the crop cycle: Q (Chitosan), BS (Bacillus More >

  • Open Access

    REVIEW

    GNN: Core Branches, Integration Strategies and Applications

    Wenfeng Zheng1, Guangyu Xu2, Siyu Lu3, Junmin Lyu4, Feng Bao5,*, Lirong Yin6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075741 - 29 January 2026

    Abstract Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways More >

  • Open Access

    ARTICLE

    Real-Time Mouth State Detection Based on a BiGRU-CLPSO Hybrid Model with Facial Landmark Detection for Healthcare Monitoring Applications

    Mong-Fong Horng1,#, Thanh-Lam Nguyen1,#, Thanh-Tuan Nguyen2,*, Chin-Shiuh Shieh1,*, Lan-Yuen Guo3, Chen-Fu Hung4, Chun-Chih Lo1

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075064 - 29 January 2026

    Abstract The global population is rapidly expanding, driving an increasing demand for intelligent healthcare systems. Artificial intelligence (AI) applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend. Among these applications, mouth motion tracking and mouth-state detection represent an important direction, providing valuable support for diagnosing neuromuscular disorders such as dysphagia, Bell’s palsy, and Parkinson’s disease. In this study, we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices. The proposed system integrates the Facial… More >

  • Open Access

    REVIEW

    A Comprehensive Literature Review of AI-Driven Application Mapping and Scheduling Techniques for Network-on-Chip Systems

    Naveed Ahmad1, Muhammad Kaleem2, Mourad Elloumi3, Muhammad Azhar Mushtaq2, Ahlem Fatnassi4, Mohd Fazil5, Anas Bilal6,*, Abdulbasit A. Darem7,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074902 - 29 January 2026

    Abstract Network-on-Chip (NoC) systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture. As a result, application mapping has become an important aspect of performance and scalability, as current trends require the distribution of computation across network nodes/points. In this paper, we survey a large number of mapping and scheduling techniques designed for NoC architectures. This time, we concentrated on 3D systems. We take a systematic literature review approach to analyze existing methods across static, dynamic, hybrid, and machine-learning-based approaches, alongside preliminary AI-based dynamic models in recent works. We classify them… More >

  • Open Access

    ARTICLE

    Noninvasive Radar Sensing Augmented with Machine Learning for Reliable Detection of Motor Imbalance

    Faten S. Alamri1, Adil Ali Saleem2, Muhammad I. Khan3, Hafeez Ur Rehman Siddiqui2, Amjad Rehman3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074679 - 29 January 2026

    Abstract Motor imbalance is a critical failure mode in rotating machinery, potentially causing severe equipment damage if undetected. Traditional vibration-based diagnostic methods rely on direct sensor contact, leading to installation challenges and measurement artifacts that can compromise accuracy. This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar. A dataset of 1802 experimental trials was sourced, covering four imbalance levels (0, 10, 20, 30 g) across varying motor speeds (500–1500 rpm) and load torques (0–3 Nm). Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second… More >

  • Open Access

    ARTICLE

    Bias Calibration under Constrained Communication Using Modified Kalman Filter: Algorithm Design and Application to Gyroscope Parameter Error Calibration

    Qi Li, Yifan Wang*, Yuxi Liu, Xingjing She, Yixuan Wu

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074066 - 29 January 2026

    Abstract In data communication, limited communication resources often lead to measurement bias, which adversely affects subsequent system estimation if not effectively handled. This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system. An output-based event-triggered scheme is first employed to alleviate transmission burden. Accounting for the limited-communication-induced measurement bias, a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation, thereby achieving accurate system state estimates. Subsequently, the Field Programmable Gate Array (FPGA) implementation More >

  • Open Access

    ARTICLE

    Design and Development of a Forced-Convection Solar Dryer: Application to Beetroot Cultivated in Béchar, Algeria

    Benali Touhami1, Bennaceur Said1, Atouani Toufik1, Lammari Khelifa2, Ouradj Boudjamaa2, Bounaama Fateh2, Belkacem Draoui2, Lyes Bennamoun3,*

    Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073329 - 27 January 2026

    Abstract The aim of this study is to design, build, and evaluate an indirect forced convection solar dryer adapted to semi-arid climate, such as that of Béchar situated in the west south region of Algeria. The tested drying system consists of a flat-plate solar collector, an insulated two-chamber drying unit, and an Arduino-controlled device that ensures uniform temperature distribution and real-time monitoring using DHT22 sensors. Drying tests were conducted on locally grown beet slices at air temperatures of 45°C, 60°C, and 80°C, with a constant air velocity of 1.2 m/s and a mass flow rate of… More > Graphic Abstract

    Design and Development of a Forced-Convection Solar Dryer: Application to Beetroot Cultivated in Béchar, Algeria

  • Open Access

    ARTICLE

    Mechanically Stable, Thermodynamic, Photo-Catalytic and Ferromagnetic Characteristic of Ferrites Al2Mn(S/Se)4 for Energy Storage Applications: DFT-Calculations

    Hosam O. Elansary1, Naveed A. Noor2, Syed M. Ahmad3, Humza Riaz3, Sohail Mumtaz4,*

    Chalcogenide Letters, Vol.23, No.1, 2026, DOI:10.32604/cl.2026.076592 - 26 January 2026

    Abstract Ferrites are remarkable compounds for energy harvesting and spintronic applications. For this purpose, mechanically stable, thermodynamic, photo-catalytic, and ferromagnetic characteristics of ferrites Al2Mn(S/Se)4 have been investigated significantly using PBEsol-GGA and modified Becke Johnson potential (TB-mBJ). In order to determine structural stability, we calculate formation energy (Ef) and Born stability criteria that confirm the structural stability of the Al2Mn(S/Se)4. 2D and 3D plots of Poisson’s ratio (υ) and linear compressibility are also used to indicate the stability of these materials. Additionally, thermodynamic characteristics reveal that both ferrites are stable. Spin-polarized electronic properties indicate that both ferrites are ferromagnetic More >

  • Open Access

    REVIEW

    Recent Advances in Hydrothermal Carbonization of Biomass: The Role of Process Parameters and the Applications of Hydrochar

    Cheng Zhang, Rui Zhang, Yu Shao, Jiabin Wang, Qianyue Yang, Fang Xie, Rongling Yang, Hongzhen Luo*

    Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0157 - 23 January 2026

    Abstract Biomass is a resource whose organic carbon is formed from atmospheric carbon dioxide. It has numerous characteristics such as low carbon emissions, renewability, and environmental friendliness. The efficient utilization of biomass plays a significant role in promoting the development of clean energy, alleviating environmental pressures, and achieving carbon neutrality goals. Among the numerous processing technologies of biomass, hydrothermal carbonization (HTC) is a promising thermochemical process that can decompose and convert biomass into hydrochar under relatively mild conditions of approximately 180°C–300°C, thereby enabling its efficient resource utilization. In addition, HTC can directly process feedstocks with high… More >

  • Open Access

    ARTICLE

    Energy Aware Task Scheduling of IoT Application Using a Hybrid Metaheuristic Algorithm in Cloud Computing

    Ahmed Awad Mohamed1, Eslam Abdelhakim Seyam2,*, Ahmed R. Elsaeed3, Laith Abualigah4, Aseel Smerat5,6, Ahmed M. AbdelMouty7, Hosam E. Refaat8

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073171 - 12 January 2026

    Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >

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