Taotao Zhu, Pai Pang, Yang Wang^*

Energy Engineering, DOI:10.32604/ee.2025.073516

Abstract Frequent extreme weather events and the increasing popularity of renewable energy have exacerbated the frequency spatiotemporal imbalance in the new power system. To address these issues, this paper proposes a collaborative optimization strategy for virtual inertia spatiotemporal distribution replenishment, aiming to enhance nodal frequency stability through targeted virtual inertia allocation. This strategy integrates the nodal inertia characteristics with frequency response dynamics to establish a spatiotemporal quantitative model for evaluating the equivalent inertia distribution across nodes, thereby overcoming the limitations of conventional global inertia assessments. Furthermore, by implementing differentiated virtual inertia supplementation from renewable energy power More >

K. Naga Venkata Siva¹, Damodhar Reddy², P. Krishna Murthy³, Kiran Kumar Pulamolu⁴, M. Dharani⁵, T. Venkatakrishnamoorthy^6,*

Energy Engineering, DOI:10.32604/ee.2025.072982

Abstract Conventional multilevel inverters often suffer from high harmonic distortion and increased design complexity due to the need for numerous power semiconductor components, particularly at elevated voltage levels. Addressing these shortcomings, this work presents a robust 15-level Packed U Cell (PUC) inverter topology designed for renewable energy and grid-connected applications. The proposed system integrates a sensor less proportional-resonant (PR) controller with an advanced carrier-based pulse width modulation scheme. This approach efficiently balances capacitor voltage, minimizes steady-state error, and strongly suppresses both zero and third-order harmonics resulting in reduced total harmonic distortion and enhanced voltage regulation. Additionally, More >

Jian Wang¹, Gongqiang Yang^1,*, Yufeng Sun², Gangui Yan¹, Jie Long³

Energy Engineering, DOI:10.32604/ee.2025.072828

Abstract Given that the power grid partitioning method relying mainly on line reactive power flow information sees frequent changes in partitioning results with reactive power flow fluctuations under high-proportion fixed-power-factor PV-connected distribution networks, and traditional distributed PV collaborative optimization fails to adapt due to such changes, a stable partitioning and distributed PV collaborative optimization method for this scenario is proposed. Firstly, the Gaussian mixture model (GMM) is used to characterize the characteristics of PV reactive power output, obtaining the typical curve of PV reactive power output. Secondly, the Monte Carlo Simulation (MCS) probabilistic power flow calculation… More >

Mohammed Benamara^1,2, Boumediene Touati³, Said Bennaceur⁴, Bendjillali Ridha Ilyas^5,*

Energy Engineering, DOI:10.32604/ee.2025.072641

Abstract This study explores the thin-layer convective solar drying of Marrubium vulgare L. leaves under conditions typical of sun-rich semi-arid climates. Drying experiments were conducted at three inlet-air temperatures (40°C, 50°C, 60°C) and two air velocities (1.5 and 2.5 m·s⁻¹) using an indirect solar dryer with auxiliary temperature control. Moisture-ratio data were fitted with eight widely used thin-layer models and evaluated using correlation coefficient (r), root-mean-square error (RMSE), and Akaike information criterion (AIC). A complementary heat-transfer analysis based on Reynolds and Prandtl numbers with appropriate Nusselt correlations was used to relate flow regime to drying performance, and an… More > Graphic Abstract

Guo-Feng Fan^1,2, Jia-Jing Qian¹, Li-Ling Peng¹, Xin-Hang Jia¹, Ling-Han Zuo¹, Jia-Can Yan¹, Jiang-Yan Chen¹, Anantkumar J. Umbarkar³, Wei-Chiang Hong^4,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073615

Abstract Accurate prediction of perovskite photovoltaic materials’ optoelectronic properties is crucial for developing efficient and stable materials, advancing solar technology. To address poor interpretability, high computational complexity, and inaccurate predictions in relevant machine learning models, this paper proposes a novel methodology. The technical route of this paper mainly centers on the random forest-knowledge distillation-bidirectional gated recurrent unit with attention technology (namely RF-KD-BIGRUA), which is applied in perovskite photovoltaic materials. Primarily, it combines random forest to quantitatively assess feature importance, selecting variables with significant impacts on photoelectric conversion efficiency. Subsequently, statistical techniques analyze the weight distribution of More >

Ahmad Zia¹, Nazia Azim², Bekarystankyzy Akbayan³, Khalid J. Alzahrani⁴, Ateeq Ur Rehman^5,*, Faheem Ullah Khan⁶, Nouf Al-Kahtani⁷, Hend Khalid Alkahtani^8,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073818

Abstract The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks. Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay. In this network, the task processed at fog nodes reduces transmission delay. Still, it increases energy consumption, while routing tasks to the cloud server saves energy at the cost of higher communication delay. Moreover, the… More >

Huihui Zhu¹, Fei Tang^2,*, Chunhua Jin³, Ping Wang¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073550

Abstract The ubiquitous adoption of mobile devices as essential platforms for sensitive data transmission has heightened the demand for secure client-server communication. Although various authentication and key agreement protocols have been developed, current approaches are constrained by homogeneous cryptosystem frameworks, namely public key infrastructure (PKI), identity-based cryptography (IBC), or certificateless cryptography (CLC), each presenting limitations in client-server architectures. Specifically, PKI incurs certificate management overhead, IBC introduces key escrow risks, and CLC encounters cross-system interoperability challenges. To overcome these shortcomings, this study introduces a heterogeneous signcryption-based authentication and key agreement protocol that synergistically integrates IBC for client More >

Tong Wu, Dawei Zhou, Qingyu Ou^*, Fang Luo

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073482

Abstract The increasing interconnection of modern industrial control systems (ICSs) with the Internet has enhanced operational efficiency, but also made these systems more vulnerable to cyberattacks. This heightened exposure has driven a growing need for robust ICS security measures. Among the key defences, intrusion detection technology is critical in identifying threats to ICS networks. This paper provides an overview of the distinctive characteristics of ICS network security, highlighting standard attack methods. It then examines various intrusion detection methods, including those based on misuse detection, anomaly detection, machine learning, and specialised requirements. This paper concludes by exploring More >

Fei Tang^1,*, Ping Wang¹, Jiang Yu¹, Huihui Zhu¹, Mengxue Qin¹, Ling Yang²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073423

Abstract Attribute-Based Encryption (ABE) has emerged as a fundamental access control mechanism in data sharing, enabling data owners to define flexible access policies. A critical aspect of ABE is key revocation, which plays a pivotal role in maintaining security. However, existing key revocation mechanisms face two major challenges: (1) High overhead due to ciphertext and key updates, primarily stemming from the reliance on revocation lists during attribute revocation, which increases computation and communication costs. (2) Limited universality, as many attribute revocation mechanisms are tailored to specific ABE constructions, restricting their broader applicability. To address these challenges,… More >

Anis Zarrad¹, Thomas Armstrong², Jaber Jemai^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072768

Abstract Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability. While prioritization selects the most relevant test cases for optimal coverage, ranking further refines their execution order to detect critical faults earlier. This study investigates machine learning techniques to enhance both prioritization and ranking, contributing to more effective and efficient testing processes. We first employ advanced feature engineering alongside ensemble models, including Gradient Boosted, Support Vector Machines, Random Forests, and Naive Bayes classifiers to optimize test case prioritization, achieving an accuracy score of and… More >