John Ojo Ajayi^*, Grace Egenti

Journal of Cyber Security, Vol.8, pp. 641-666, 2026, DOI:10.32604/jcs.2026.086419 - 14 September 2026

Abstract Artificial intelligence (AI)-based intrusion detection systems (IDS) have evolved to be fundamental in detecting cyberattacks in contemporary networks. Unfortunately, the significant class imbalance present in cybersecurity datasets may introduce unfair biases during model learning, producing unpredictable predictions and inflating false alarm alerts, which eventually hampers practical deployment. This research examines the influence of class imbalance mitigation on model performance, operational fairness, explainability, and the operational integrity of an AI-based IDS subject to extreme class imbalance conditions. Three learning strategies based on Random Forest (i.e., baseline, Synthetic Minority Over-sampling Technique (SMOTE), and cost-sensitive learning) are evaluated… More >

Chirag Devendrakumar Parikh^*

Journal on Big Data, Vol.8, pp. 11-26, 2026, DOI:10.32604/jbd.2026.077330 - 07 September 2026

Abstract Sensors and large-scale measurement systems generate continuous data streams used in industrial monitoring, IoT analytics, and decision-making. However, sensor drift, undocumented maintenance, environmental stress, and component variability often degrade the integrity and reliability of collected measurements. This study proposes a compliance-integrated data integrity framework that combines hardware qualification records, traceability documentation, lifecycle validation checkpoints, and automated anomaly detection methods. The framework introduces five integrity layers that link sensor characterization with statistical filtering and compliance-driven verification. To validate feasibility, a proof-of-concept simulation was conducted using drift-injected sensor datasets. Results show that integrating compliance metadata improve anomaly More >

Chuang Guan^1,2, Wei Fu³, Guodong Cai¹, Fengling Yang¹, Jiangtao Liu¹, Hangyu Wu¹, Chuantao Wu¹, Chunyu Zhang¹, Ming Gao^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.8, 2026, DOI:10.32604/fdmp.2026.084304 - 04 September 2026

Abstract Flow maldistribution and local short-circuiting within blast furnace gas fine desulfurization systems can substantially impair desulfurization performance while increasing hydraulic losses and energy consumption. To elucidate the underlying flow mechanisms, a three-dimensional computational fluid dynamics (CFD) model accounting for the pressure variation across the top gas recovery turbine (TRT) was developed to investigate the flow characteristics under both high- and low-pressure operating conditions. Guided by the numerical analysis, an integrated structural optimization strategy, combining inlet deflector plates with the sealing of perforated plates adjacent to partition regions, was proposed. The optimized configuration significantly enhanced flow… More >

Guofen Rui¹, Xiangyu Hu², Jingyu Cao^3,4,*, Yibo Zhang², Yangyang Zhu², Haifei Chen^1,2,*

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.086281 - 31 August 2026

Abstract Spectral beam splitting is a promising approach for thermally decoupling photovoltaic and photothermal processes in PV/T systems. However, existing liquid spectral splitters still suffer from insufficient short-wavelength absorption and/or the high cost of noble-metal nanoparticles. In this study, a water-based Cu@SiO₂ nanofluid was developed as a low-cost absorption/transmission spectral splitting filter for monocrystalline silicon PV/T systems. The full solar spectrum considered in this system covers both photovoltaic and thermal utilization, while 750–1000 nm was selected only as the target transmission window for the c-Si cell. This window was chosen because c-Si cells can effectively utilize this… More >

Jiaxi Shen, Zhiyun Wang^*

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.081961 - 31 August 2026

Abstract This study focuses on the modeling and performance prediction of the condensation heat transfer process within an ACC system of a 100 MW unit. A one-dimensional physical model was established, and solutions were obtained using an iterative numerical method based on the first law of thermodynamics. The core of this work involves precise modifications to the heat transfer coefficient. Firstly, the Shah correlation for horizontal tube condensation was improved by incorporating the air mass fraction and the Jakob number to quantify the impact of non-condensable gases. Secondly, to assess the influence of the pipe installation More >

Wensheng Li¹, Zeyang Zhang¹, Xinjie Chai², Facheng Qiu^1,*

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.080472 - 31 August 2026

Abstract Hydrodynamic jet technologies have emerged as a promising approach for advanced wastewater treatment, offering engineering advantages through operational efficiency and system simplicity. As a critical review, this work examines three major hydrodynamic approaches—atomized jets, cavitation jets, and pulsed-jet systems—and analyzes their degradation mechanisms and practical applications in pollutant abatement. By systematically evaluating current research trends, this review elucidates the synergistic interplay between hydrodynamic effects, including turbulent shear, microbubble implosion, and reactive radical generation, and contaminant-decomposition pathways. Quantitative evidence from the reviewed literature further reveals significant performance enhancements achieved by these technologies. Optimized cavitating jets (e.g.,… More > Graphic Abstract

C. A. Bindyashree¹, Chitra G.², Syed Muzamil Basha³, Hamed Taherdoost^{4,5,6,7,8,9,*}

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.084451 - 30 August 2026

Abstract In the present times, Transformation finance has become a prominent approach for a systematic financial channel to facilitate the step-by-step decarbonization of carbon-intensive sectors. Such mechanisms rely on the accuracy of carbon emissions data to measure environmental performance and to inform capital decisions. The current carbon accounting methods are limited by inadequate data-collection provisions, slow verification processes, and low auditability, which undermine the reliability of emission-reduction claims and constrain the effectiveness of carbon asset markets. In the present research work, a blockchain-based framework is proposed that will create reliable carbon data accounting and facilitate structured… More >

Yi Pan, Mingshen Wang, Ye Xue, Huiyu Miao, Kemin Dai, Xiaodong Yuan^*, Fei Zeng

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2025.074882 - 30 August 2026

Abstract Electric vehicles (EVs), characterized by their large-scale deployment and flexible charging–discharging scheduling, represent a growing form of transportation. However, their widespread adoption poses considerable challenges to the security and stability of the power grid during peak charging periods, highlighting the need for effective management of the coupled traffic–grid system. To address this issue, this paper proposes a blockchain-driven optimization model for charging scheduling in dynamic traffic networks. Blockchain technology is introduced to ensure data transparency and security in decentralized decision-making. First, a queuing model integrating the Bureau of Public Roads (BPR) function with the M/M/c/K… More > Graphic Abstract

Yongzhi Liu, Qiang Li, Qianpeng Hao^*, Yaowen Liu, Wenze Li, Zijun Zhao, Zhenyu Chen, Yuxiang Liu, Jiaxing Ren, Wei Han

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.074322 - 30 August 2026

Abstract Aiming at the prominent problems of insufficient coordination of multi-type flexible resources and insufficient utilization of demand response potential in high-proportion renewable energy power systems, this paper proposes a two-stage optimal scheduling model for flexible resource aggregation that integrates price-based and segmented incentive-based demand response. In the first stage, the price-based demand response is adopted to optimize the time distribution of elastic loads with the goal of minimizing the net load fluctuation, so as to smooth the net load curve. In the second stage, based on the optimized net load, the segmented incentive-based demand response… More >

Ahmed Murtaza¹, Abdullah Memon², Sana Hafeez³, Muzammil Ali², Ghulam E Mustafa Abro^4,*

Intelligent Automation & Soft Computing, Vol.41, pp. 49-72, 2026, DOI:10.32604/iasc.2026.082765 - 28 August 2026

Abstract Civilian Unmanned Aerial Systems (UAS) are increasingly deployed in smart-city monitoring, infrastructure inspection, logistics, and emergency response applications. However, their integration with wireless networks, cloud services, and AI-driven analytics significantly expands cybersecurity and privacy risks. Existing studies mainly focus on isolated technical vulnerabilities such as GNSS spoofing, jamming, and communication attacks, while lacking a unified framework that systematically connects cyber threats with quantitative privacy risk assessment. To address this research gap, this study proposes a layered threat-modeling framework for collaborative civilian UAS based on multidimensional attack-surface analysis and STRIDE-oriented threat mapping. In addition, a quantitative More >