Tien-Wen Sung¹, Wei Li¹, Chao-Yang Lee^2,*, Yuzhen Chen¹, Qingjun Fang¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 407-434, 2025, DOI:10.32604/cmc.2025.061694 - 26 March 2025

Abstract To transmit customer power data collected by smart meters (SMs) to utility companies, data must first be transmitted to the corresponding data aggregation point (DAP) of the SM. The number of DAPs installed and the installation location greatly impact the whole network. For the traditional DAP placement algorithm, the number of DAPs must be set in advance, but determining the best number of DAPs is difficult, which undoubtedly reduces the overall performance of the network. Moreover, the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the… More >

Stefan Ćirković¹, Vladimir Mladenović¹, Siniša Tomić², Dalibor Drljača², Olga Ristić^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4409-4430, 2025, DOI:10.32604/cmc.2025.059696 - 06 March 2025

Abstract With the increasing use of web applications, challenges in the field of cybersecurity are becoming more complex. This paper explores the application of fine-tuned large language models (LLMs) for the automatic generation of synthetic attacks, including XSS (Cross-Site Scripting), SQL Injections, and Command Injections. A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence. The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks. This approach not only improves the model’s precision and dependability but… More >

Hyeonbin Jeong¹, Young Tae Ryu², Byung Duk Song^1,*, Sang-Duck Lee^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2739-2769, 2025, DOI:10.32604/cmes.2025.061639 - 03 March 2025

Abstract This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems. The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints. Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions, if possible to be physically implemented. Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability. Experimental results demonstrate the extensive robustness of the proposed algorithm More >

Mingzhu Tang¹, Yujie Huang¹, Dongxu Ji², Hao Yu^2,*

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 95-129, 2025, DOI:10.32604/fhmt.2025.061143 - 26 February 2025

Abstract Load deviations between the output of ultra-supercritical (USC) coal-fired power units and automatic generation control (AGC) commands can adversely affect the safe and stable operation of these units and grid load dispatching. Data-driven diagnostic methods often fail to account for the imbalanced distribution of data samples, leading to reduced classification performance in diagnosing load deviations in USC units. To address the class imbalance issue in USC load deviation datasets, this study proposes a diagnostic method based on the multi-label natural neighbor boundary oversampling technique (MLNaNBDOS). The method is articulated in three phases. Initially, the traditional… More > Graphic Abstract

Ahmed M. Khalil¹, Samir Kamel^2,*

Journal of Renewable Materials, Vol.13, No.2, pp. 401-412, 2025, DOI:10.32604/jrm.2024.057419 - 20 February 2025

Abstract Developing favorable bio-based polymers that replace petroleum-based plastics is an essential environmental demand. Lignin is a by-product of the chemical pulping industry. It is a natural UV protection ingredient in broad-spectrum (UVA and UVB) sunscreens. It could be partially and selectively acetylated in a simple, fast, and more reliable process. In this work, a composite film was prepared with UV-resistant properties through a casting method. Bio-based cellulose acetate (CA) was employed as a major matrix while nano-acetylated kraft lignin (AL-NPs) was used as filler during synthesizing UV-shielding films loaded with various amounts (1–5 wt.%) of… More > Graphic Abstract

Usman Mahmood Malik¹, Muhammad Awais Javed², Ahmad Naseem Alvi², Mohammed Alkhathami^3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2255-2274, 2025, DOI:10.32604/cmc.2025.061254 - 17 February 2025

Abstract Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing, and data storage services which are required for several 6G applications. Artificial Intelligence (AI) algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability. In this paper, the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers (POMH) in which larger tasks are divided into smaller subtasks and processed in parallel, hence expediting task completion. However, using POMH presents challenges… More >

Ruoxin Li^1,*, Shaoxiong Wu¹, Fengping Deng¹, Zhongli Tian¹, Hua Cai¹, Xiang Li¹, Xu Xu¹, Qi Liu^2,3

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2969-2984, 2025, DOI:10.32604/cmc.2025.060230 - 17 February 2025

Abstract Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and… More >

Mohammad-Rahim Hematiyan^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3069-3090, 2025, DOI:10.32604/cmc.2025.060067 - 17 February 2025

Abstract The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using inverse methods in which displacement or strain measurements are taken at several points on the body. This paper presents an inverse method based on the method of fundamental solutions for the traction identification problem in two-dimensional anisotropic elasticity. The method of fundamental solutions is an efficient boundary-type meshless method widely used for analyzing various problems. Since the problem is linear, the… More >

Ru Hao^1,2,3, Chi Xu^2,3,*, Jing Liu¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3203-3222, 2025, DOI:10.32604/cmc.2024.059689 - 17 February 2025

Abstract Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely More >

Jiajia Liu^1,*, Peng Xie², Wei Li², Bo Tang², Jianhua Liu²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2609-2635, 2025, DOI:10.32604/cmc.2024.058810 - 17 February 2025

Abstract As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective… More >