Anfeng Yang, Fei Kang, Wenjuan Bu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073979 - 12 March 2026

Abstract Large language models (LLMs) have demonstrated significant capabilities in semantic understanding and code generation. However, cybersecurity tasks often require prompting the adaptation of open-source models to this domain. Despite their effectiveness, large-parameter LLMs incur substantial memory usage and runtime costs during task inference and downstream fine-tuning for cybersecurity applications. In this study, we fine-tuned six LLMs with parameters under 4 billion using LoRA (Low-Rank Adaptation) on specific cybersecurity instruction datasets, employing evaluation metrics similar to Hackmentor. Results indicate that post-fine-tuning, smaller models achieved victory or parity rates up to 85% against larger models like Qwen-1.5-14B… More >

Abdullah Esmeray, Huseyin Burak Yazili^*, Mucahit Gelmis, Nazim Furkan Gunay, Caglar Dizdaroglu, Faruk Ozgor, Yasar Pazir, Ufuk Caglar

Canadian Journal of Urology, Vol.33, No.1, pp. 165-171, 2026, DOI:10.32604/cju.2025.069657 - 28 February 2026

Abstract Objectives: Supracostal access during percutaneous nephrolithotomy (PCNL) increases the risk of pulmonary complications. Although routine postoperative thoracic imaging is commonly performed to detect these events, its clinical necessity remains controversial. This study aimed to assess the necessity of routine postoperative thoracic imaging for detecting pulmonary complications in patients undergoing supracostal mini percutaneous nephrolithotomy (m-PCNL) surgery. Methods: A retrospective analysis was conducted on data from patients who underwent supracostal m-PCNL between 2017 and 2022 in a tertiary center. Excluding patients under 18, with kidney/skeletal anomalies, or active thoracic disease, 112 eligible patients were included. Patients were… More >

Dexiecia Anak Francis¹, Jalal Tavalaei¹, Hadi Nabipour Afrouzi^2,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070501 - 27 January 2026

Abstract Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions. Despite Malaysia’s extensive coastline, the exploration of wave energy in Sarawak remains limited due to economic, technical, and environmental challenges that hinder its implementation. Compared to other renewable energy sources, wave energy is underutilized largely because of cost uncertainties and the lack of local performance data. This research aims to identify the most suitable coastal zone in Sarawak that achieves an optimal balance between energy potential, cost-effectiveness, and environmental impact, particularly in relation to… More >

Si-Woo Kim, Yong Soo Kim^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073492 - 12 January 2026

Abstract Predictive maintenance (PdM) is vital for ensuring the reliability, safety, and cost efficiency of heavy-duty vehicle fleets. However, real-world sensor data are often highly imbalanced, noisy, and temporally irregular, posing significant challenges to model robustness and deployment. Using multivariate time-series data from Scania trucks, this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification. First, the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness, allowing LightGBM to leverage its inherent split rules without ad-hoc imputation. Then, a two-stage LightGBM framework is developed… More >

Ateeqa Jalal^1,*, Umar Farooq^1,4,5, Ihsan Rabbi^1,4, Afzal Badshah², Aurangzeb Khan^1,4, Muhammad Mansoor Alam^3,4, Mazliham Mohd Su’ud^4,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068289 - 09 December 2025

Abstract The exponential growth of Internet of Things (IoT) devices, autonomous systems, and digital services is generating massive volumes of big data, projected to exceed 291 zettabytes by 2027. Conventional cloud computing, despite its high processing and storage capacity, suffers from increased network latency, network congestion, and high operational costs, making it unsuitable for latency-sensitive applications. Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership (TCO). Hybrid solutions, such as fog computing, cloudlets, and Mobile Edge Computing (MEC), attempt to balance cost and performance;… More >

Mohammed Majid Abed^1,2,*, Murat Aydin¹, Esma Yiğider¹, Melek Ekinci³, Ertan Yildirim³

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 3845-3860, 2025, DOI:10.32604/phyton.2025.073163 - 29 December 2025

Abstract Plants are continuously exposed to abiotic and biotic stresses that threaten their growth, reproduction, and survival. Adaptation to these stresses requires complex regulatory networks that coordinate physiological, molecular, and ecological responses. However, such adaptation often incurs significant costs, including reduced growth, yield penalties, and altered ecological interactions. This review systematically synthesizes recent advances published between 2018 and 2025, following PRISMA criteria, on plant responses to abiotic and biotic stressors, with an emphasis on the trade-offs between adaptation and productivity. It also highlights major discrepancies in the literature and discusses strategies for enhancing plant stress tolerance More >

Mukesh Kumar Verma^1,2,*, Manohar Yadav¹

Revue Internationale de Géomatique, Vol.34, pp. 855-879, 2025, DOI:10.32604/rig.2025.069914 - 17 November 2025

Abstract Precision Agriculture (PA) is revolutionizing modern farming by leveraging remote sensing (RS) technologies for continuous, non-destructive crop monitoring. This review comprehensively explores RS systems categorized by platform—terrestrial, airborne, and space-borne—and evaluates the role of multi-sensor fusion in addressing the spatial and temporal complexity of agricultural environments. Emphasis is placed on data from LiDAR, GNSS, cameras, and radar, alongside derived metrics such as plant height, projected leaf area, and biomass. The study also highlights the significance of data processing methods, particularly machine learning (ML) and deep learning (DL), in extracting actionable insights from large datasets. By More >

Hongsheng Su¹, Wenyao Su¹, Yulong Che^1,*, Xiping Ma², Tian Zhao¹, Limiao Ren¹

Energy Engineering, Vol.122, No.11, pp. 4777-4797, 2025, DOI:10.32604/ee.2025.068134 - 27 October 2025

Abstract Aiming at the issues of insufficient carrying capacity, limited flexibility, and weak source-network-load-storage coordination capability in distribution networks under the background of high-proportion new energy integration. This study proposes a bi-level optimization model for ADN integrating hybrid wind-solar-hydrogen-storage systems. First, an electro-hydrogen coupling system framework is constructed, including models for electrolytic hydrogen production, hydrogen storage, and fuel cells. Meanwhile, typical scenarios of wind-solar joint output are developed using Copula functions to characterize the variability of renewable energy generation. Second, a bi-level optimization model for ADN with electrolytic hydrogen production and storage systems is established: the… More >

Reza Nadimi^1,*, Maryam Hashemi², Koji Tokimatsu³

Journal on Internet of Things, Vol.7, pp. 49-69, 2025, DOI:10.32604/jiot.2025.070822 - 30 September 2025

Abstract Existing Internet of Things (IoT) systems that rely on Amazon Web Services (AWS) often encounter inefficiencies in data retrieval and high operational costs, especially when using DynamoDB for large-scale sensor data. These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems. This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services. The proposed method employs AWS Lambda functions with Amazon Relational Database Service (RDS) to facilitate the transmission of data collected from temperature and humidity sensors to the… More >

Ziming Hu¹, Jingfa Li^1,*, Chaoyang Fan¹, Jiale Xiao¹, Huijie Huang², Bo Yu¹, Baocheng Shi¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010823

Abstract Hydrogen production from offshore wind power is an effective means to address the challenges of wind power grid integration and has emerged as a focal point in the development and research of offshore wind energy in recent years. However, the current state of hydrogen storage and transportation technologies for offshore applications lacks comprehensive economic analysis. This study aims to provide a thorough economic evaluation of these technologies by considering both fixed investment costs and operational and maintenance costs. A levelized cost model is employed to analyze four offshore hydrogen storage and transportation schemes: gas hydrogen… More >