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  • Open Access

    ARTICLE

    Design of Voltage Equalization Circuit and Control Method for Lithium-ion Battery Packs

    Qi Wang1,2,3, Lantian Ge1,*, Tianru Xie1, Yibo Huang1, Yandong Gu1, Tao Zhu1, Xuehua Gao1

    Energy Engineering, Vol.122, No.2, pp. 733-746, 2025, DOI:10.32604/ee.2024.059453 - 31 January 2025

    Abstract The active equalization of lithium-ion batteries involves transferring energy from high-voltage cells to low-voltage cells, ensuring consistent voltage levels across the battery pack and maintaining safety. This paper presents a voltage balancing circuit and control method. First, a single capacitor method is used to design the circuit topology for energy transfer. Next, real-time voltage detection and control are employed to balance energy between cells. Finally, simulation and experimental results demonstrate the effectiveness of the proposed method, achieving balanced voltages of 3.97 V from initial voltages of 4.10, 3.97, and 3.90 V. The proposed circuit is More >

  • Open Access

    ARTICLE

    Hybrid DF and SIR Forwarding Strategy in Conventional and Distributed Alamouti Space-Time Coded Cooperative Networks

    Slim Chaoui1,*, Omar Alruwaili1, Faeiz Alserhani1, Haifa Harrouch2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1933-1954, 2025, DOI:10.32604/cmes.2025.059346 - 27 January 2025

    Abstract In this paper, we propose a hybrid decode-and-forward and soft information relaying (HDFSIR) strategy to mitigate error propagation in coded cooperative communications. In the HDFSIR approach, the relay operates in decode-and-forward (DF) mode when it successfully decodes the received message; otherwise, it switches to soft information relaying (SIR) mode. The benefits of the DF and SIR forwarding strategies are combined to achieve better performance than deploying the DF or SIR strategy alone. Closed-form expressions for the outage probability and symbol error rate (SER) are derived for coded cooperative communication with HDFSIR and energy-harvesting relays. Additionally,… More >

  • Open Access

    REVIEW

    Hysteresis-Loop Criticality in Disordered Ferromagnets–A Comprehensive Review of Computational Techniques

    Djordje Spasojević1,*, Sanja Janićević2, Svetislav Mijatović1, Bosiljka Tadić3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1021-1107, 2025, DOI:10.32604/cmes.2024.057884 - 27 January 2025

    Abstract Disordered ferromagnets with a domain structure that exhibit a hysteresis loop when driven by the external magnetic field are essential materials for modern technological applications. Therefore, the understanding and potential for controlling the hysteresis phenomenon in these materials, especially concerning the disorder-induced critical behavior on the hysteresis loop, have attracted significant experimental, theoretical, and numerical research efforts. We review the challenges of the numerical modeling of physical phenomena behind the hysteresis loop critical behavior in disordered ferromagnetic systems related to the non-equilibrium stochastic dynamics of domain walls driven by external fields. Specifically, using the extended… More >

  • Open Access

    ARTICLE

    Exogenous Alpha-Ketoglutarate (AKG) Modulate Physiological Characteristics, Photosynthesis, Secondary Metabolism and Antioxidant Defense System in Peganum Harmala L. under Nickel Stress

    Marwa Rezgui1,#,*, Wided Ben Ammar1, Muhammad Nazim2,3,#, Walid Soufan4, Chiraz Chaffei Haouari1

    Phyton-International Journal of Experimental Botany, Vol.94, No.1, pp. 137-155, 2025, DOI:10.32604/phyton.2025.058851 - 24 January 2025

    Abstract Nickel (Ni) toxicity significantly impairs plant growth, photosynthesis, and metabolism by inducing oxidative stress. This study evaluates the potential of exogenous Alpha-Ketoglutarate (AKG) in mitigating Ni-induced stress in Peganum harmala L. Seedlings were exposed to 0, 200, 500, and 750 μM NiCl2, with or without AKG supplementation. Under 750 μM Ni stress, dry weight (DW) decreased by 33.7%, tissue water content (TWC) by 39.9%, and chlorophyll a and total chlorophyll levels were reduced by 17% and 15%, respectively. Ni exposure also significantly increased secondary metabolite production, with leaf anthocyanin content rising by 131%, and superoxide dismutase (SOD)… More >

  • Open Access

    ARTICLE

    Loss Aware Feature Attention Mechanism for Class and Feature Imbalance Issue

    Yuewei Wu1, Ruiling Fu1, Tongtong Xing1, Fulian Yin1,2,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 751-775, 2025, DOI:10.32604/cmc.2024.057606 - 03 January 2025

    Abstract In the Internet era, recommendation systems play a crucial role in helping users find relevant information from large datasets. Class imbalance is known to severely affect data quality, and therefore reduce the performance of recommendation systems. Due to the imbalance, machine learning algorithms tend to classify inputs into the positive (majority) class every time to achieve high prediction accuracy. Imbalance can be categorized such as by features and classes, but most studies consider only class imbalance. In this paper, we propose a recommendation system that can integrate multiple networks to adapt to a large number… More >

  • Open Access

    ARTICLE

    A Scalable and Generalized Deep Ensemble Model for Road Anomaly Detection in Surveillance Videos

    Sarfaraz Natha1,2,*, Fareed A. Jokhio1, Mehwish Laghari1, Mohammad Siraj3,*, Saif A. Alsaif3, Usman Ashraf4, Asghar Ali5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3707-3729, 2024, DOI:10.32604/cmc.2024.057684 - 19 December 2024

    Abstract Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack… More >

  • Open Access

    ARTICLE

    RE-SMOTE: A Novel Imbalanced Sampling Method Based on SMOTE with Radius Estimation

    Dazhi E1, Jiale Liu2, Ming Zhang1,*, Huiyuan Jiang2, Keming Mao2

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3853-3880, 2024, DOI:10.32604/cmc.2024.057538 - 19 December 2024

    Abstract Imbalance is a distinctive feature of many datasets, and how to make the dataset balanced become a hot topic in the machine learning field. The Synthetic Minority Oversampling Technique (SMOTE) is the classical method to solve this problem. Although much research has been conducted on SMOTE, there is still the problem of synthetic sample singularity. To solve the issues of class imbalance and diversity of generated samples, this paper proposes a hybrid resampling method for binary imbalanced data sets, RE-SMOTE, which is designed based on the improvements of two oversampling methods parameter-free SMOTE (PF-SMOTE) and… More >

  • Open Access

    ARTICLE

    A DDoS Identification Method for Unbalanced Data CVWGG

    Haizhen Wang1,2,*, Na Jia1,2, Yang He1, Pan Tan1,2

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3825-3851, 2024, DOI:10.32604/cmc.2024.055497 - 19 December 2024

    Abstract As the popularity and dependence on the Internet increase, DDoS (distributed denial of service) attacks seriously threaten network security. By accurately distinguishing between different types of DDoS attacks, targeted defense strategies can be formulated, significantly improving network protection efficiency. DDoS attacks usually manifest as an abnormal increase in network traffic, and their diverse types of attacks, along with a severe data imbalance, make it difficult for traditional classification methods to effectively identify a small number of attack types. To solve this problem, this paper proposes a DDoS recognition method CVWGG (Conditional Variational Autoencoder-Wasserstein Generative Adversarial… More >

  • Open Access

    ARTICLE

    Genome-Wide Identification of the GST Gene Family in Loquat (Eriobotrya japonica Lindl.) and Their Expression under Cold Stress with ALA Pretreatment

    Guanpeng Huang1,#, Ti Wu1,2,#, Yinjie Zheng3, Qiyun Gu2, Qiaobin Chen2, Shoukai Lin2,*, Jincheng Wu2,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.11, pp. 2715-2735, 2024, DOI:10.32604/phyton.2024.056484 - 30 November 2024

    Abstract Loquat (Eriobotrya japonica Lindl.), a rare fruit native to China, has a long history of cultivation in China. Low temperature is the key factor restricting loquat growth and severely affects yield. Low temperature induces the regeneration and metabolism of reduced glutathione (GSH) to alleviate stress damage via the participation of glutathione S-transferases (GSTs) in plants. In this study, 16 GSTs were identified from the loquat genome according to their protein sequence similarity with Arabidopsis GSTs. On the basis of domain characteristics and phylogenetic analysis of AtGSTs, these EjGSTs can be divided into 4 subclasses: Phi, Theta, Tau… More >

  • Open Access

    REVIEW

    Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

    Suneel Kumar Rath1, Madhusmita Sahu1, Shom Prasad Das2, Junali Jasmine Jena3, Chitralekha Jena4, Baseem Khan5,6,7,*, Ahmed Ali7, Pitshou Bokoro7

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024

    Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >

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