Tsu-Yang Wu¹, Chengyuan Yu¹, Yanan Zhao², Saru Kumari³, Chien-Ming Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069139 - 12 January 2026

Abstract With the increasing complexity of logistics operations, traditional static vehicle routing models are no longer sufficient. In practice, customer demands often arise dynamically, and multi-depot systems are commonly used to improve efficiency. This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand. New customers appear in the delivery process at any time and are periodically optimized according to time slices. Then, we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem. The classical ant colony More >

Jiajun Sun, Shunshun Ji, Chao Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068668 - 09 December 2025

Abstract Asparagus stem blight is a devastating crop disease, and the early detection of its pathogenic spores is essential for effective disease control and prevention. However, spore detection is still hindered by complex backgrounds, small target sizes, and high annotation costs, which limit its practical application and widespread adoption. To address these issues, a semi-supervised spore detection framework is proposed for use under complex background conditions. Firstly, a difficulty perception scoring function is designed to quantify the detection difficulty of each image region. For regions with higher difficulty scores, a masking strategy is applied, while the… More >

Chunyong Yin, Williams Kyei^*

Journal of Cyber Security, Vol.7, pp. 615-635, 2025, DOI:10.32604/jcs.2025.072740 - 24 December 2025

Abstract The evolving sophistication of network threats demands anomaly detection methods that are both robust and adaptive. While autoencoders excel at learning normal traffic patterns, they struggle with complex feature interactions and require manual tuning for different environments. We introduce the Adaptive Robust AutoEncoder (ARAE), a novel framework that dynamically balances reconstruction fidelity with latent space regularization through learnable loss weighting. ARAE incorporates multi-head attention to model feature dependencies and fuses multiple anomaly indicators into an adaptive scoring mechanism. Extensive evaluation on four benchmark datasets demonstrates that ARAE significantly outperforms existing autoencoder variants and classical methods, More >

A. Z. Mahmoud^a,b, L. G. Amin^c,*, S. A. Mahmoud^d, M. M. Bashier^e, M. E. M. Eisac^e, N. Dhahri^c, A. Mohamed^e, A. A. Al-Dumiri^c, S. E. I. Yagoub^c, M. A. Abdel-Rahim^b

Chalcogenide Letters, Vol.22, No.4, pp. 441-450, 2025, DOI:10.15251/CL.2025.224.441

Abstract The electrical and structural characteristics of Bi_xSe_100-x glasses (where x=5, 10, 15, and 25 at. %) were systematically investigated. Using the traditional Quenching of melts process, the amorphous Bi_xSe_100-x materials were created. Thin films of Bi_xSe_100-x have formed onto ultrasonically glass substrates that have been cleaned using thermal evaporation in a vacuum of approximately 10^-5 Torr. Here we show and discuss the results of four bulk glasses of Bi_xSe_100-x (where x=5, 10, 15, and 25 at. %) that were subjected to differential thermal analysis (DTA) under non-isothermal conditions. For the compositions under consideration, five separate methods were used More >

Fei Wu¹, Ziyang Zeng^1,2, Kunliang Xie¹, Yuqiang Liu¹, Jiang Ding^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1689-1710, 2025, DOI:10.32604/cmes.2025.071256 - 26 November 2025

Abstract The level set method (LSM) is renowned for producing smooth boundaries and clear geometric representations, facilitating integration with CAD environments. However, its inability to autonomously generate new holes during optimization makes the results highly dependent on the initial design. Although topological derivatives are often introduced to enable hole nucleation, their conversion into effective shape derivatives remains challenging, limiting topological evolution. To address this, a level set topology optimization method with autonomous hole formation (LSM-AHF) is proposed, integrating the material removal mechanism of the SIMP (Solid Isotropic Material with Penalization) method into the LSM framework. First,… More >

He Wang, Shiqiang Li^*, Yiqi Liu, Jing Bian

Energy Engineering, Vol.122, No.11, pp. 4497-4521, 2025, DOI:10.32604/ee.2025.065244 - 27 October 2025

Abstract With the evolution of DC distribution networks from traditional radial topologies to more complex multi-branch structures, the number of measurement points supporting synchronous communication remains relatively limited. This poses challenges for conventional fault distance estimation methods, which are often tailored to simple topologies and are thus difficult to apply to large-scale, multi-node DC networks. To address this, a fault distance estimation method based on sparse measurement of high-frequency electrical quantities is proposed in this paper. First, a preliminary fault line identification model based on compressed sensing is constructed to effectively narrow the fault search range… More >

Pasi Fränti^1,*, Claude Cariou², Qinpei Zhao³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4687-4704, 2025, DOI:10.32604/cmc.2025.066534 - 23 October 2025

Abstract K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances. We show that, as a consequence, it also maximizes between-cluster variances. This means that the two measures do not provide complementary information and that using only one is enough. Based on this property, we propose a new objective function called cluster overlap, which is measured intuitively as the proportion of points shared between the clusters. We adopt the new function within k-means and present an algorithm called overlap k-means. It is an alternative way to design a k-means algorithm. A localized variant is also More >

Mariette Coetzee^*, Linda Naidoo

Journal of Psychology in Africa, Vol.35, No.5, pp. 627-634, 2025, DOI:10.32604/jpa.2025.065791 - 24 October 2025

Abstract This study developed and tested the internal reliability of a 27-item Organisational Citizenship Behaviour (OCB) scale for higher education institutions. Participants were a probability sample of 452 (N = 452) university staff of a South African open-distance higher education institution (academics 46%, administrative staff 33%, professional and managerial staff 21%). The participants completed the Organisational Citizenship Behaviour questionnaire. Exploratory factor analysis identified a four-construct measurement model for organisational citizenship behaviour: altruism, civic virtue, sportsmanship, and sense of duty and consideration. The sense of duty and consideration is the only factor not previously identified as a factor More >

Siham Essahraui¹, Ismail Lamaakal¹, Yassine Maleh^2,*, Khalid El Makkaoui¹, Mouncef Filali Bouami¹, Ibrahim Ouahbi¹, May Almousa³, Ali Abdullah S. AlQahtani⁴, Ahmed A. Abd El-Latif^5,6

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3151-3183, 2025, DOI:10.32604/cmc.2025.067359 - 23 September 2025

Abstract The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments, as traditional proctoring methods fall short in preventing cheating. The increase in cheating during online exams highlights the need for efficient, adaptable detection models to uphold academic credibility. This paper presents a comprehensive analysis of various deep learning models for cheating detection in online proctoring systems, evaluating their accuracy, efficiency, and adaptability. We benchmark several advanced architectures, including EfficientNet, MobileNetV2, ResNet variants and more, using two specialized datasets (OEP and OP) tailored for online proctoring contexts. Our findings More >

Shah Zeb Khan¹, Muhammad Rahim², Adel M. Widyan^3,*, A. Almutairi³, Njood Shaher Ethaar Almutire³, Hamiden Abd El-Wahed Khalifa³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2185-2211, 2025, DOI:10.32604/cmes.2025.063929 - 30 May 2025

Abstract This study introduces a novel distance measure (DM) for spherical fuzzy sets (SFSs) to improve decision-making in complex and uncertain environments. Many existing distance measures either fail to satisfy essential axiomatic properties or produce unintuitive outcomes. To address these limitations, we propose a new three-dimensional divergence-based DM that ensures mathematical consistency, enhances the discrimination of information, and adheres to the axiomatic framework of distance theory. Building on this foundation, we construct a multi-criteria decision-making (MCDM) model that utilizes the proposed DM to evaluate and rank alternatives effectively. The applicability and robustness of the model are More >