Yunhao Yu¹, Fuhua Luo¹, Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074277 - 10 February 2026

Abstract This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control (LFC) systems integrated with Battery Energy Storage Systems (BESS). As renewable energy sources gain greater penetration, power grids are becoming increasingly vulnerable to cyber threats, potentially leading to frequency instability and widespread disruptions. We model two significant attack vectors: load-altering attacks (LAAs) and false data injection attacks (FDIAs) that corrupt frequency measurements. These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models, incorporating generation rate constraints and nonlinear loads. A coordinated attack strategy is… More >

Mehran Tarif¹, Mohammadhossein Homaei^2,*, Abbas Mirzaei³, Babak Nouri-Moghaddam³

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.071676 - 10 February 2026

Abstract The Routing Protocol for Low-power and Lossy Networks (RPL) is widely used in Internet of Things (IoT) systems, where devices usually have very limited resources. However, RPL still faces several problems, such as high energy usage, unstable links, and inefficient routing decisions, which reduce the overall network performance and lifetime. In this work, we introduce TABURPL, an improved routing method that applies Tabu Search (TS) to optimize the parent selection process. The method uses a combined cost function that considers Residual Energy, Transmission Energy, Distance to the Sink, Hop Count, Expected Transmission Count (ETX), and More >

Md. Al-Hasan¹, Mst. Rubina Aktar², Fahmid Al Farid^3,4,*, Md. Shamim Anower⁵, Abu Saleh Musa Miah^1,6, Md. Hezerul Abdul Karim^4,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.071280 - 10 February 2026

Abstract The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic, creating significant challenges for network energy consumption. Also, with the extraordinary growth of mobile communications, the data traffic has dramatically expanded, which has led to massive grid power consumption and incurred high operating expenditure (OPEX). However, the majority of current network designs struggle to efficiently manage a massive amount of data using little power, which degrades energy efficiency performance. Thereby, it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of… More >

Bo Su¹, Mwansa Chambalile¹, Shihao He¹, Wan Sun², Enyuan Zhang¹, Tong Guo³, Jianming Hao⁴, Md. Mahbub Alam^5,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.076681 - 06 February 2026

Abstract Predicting the precise impacts of climate change on extreme winds remains challenging, yet strong storms are widely expected to occur more frequently in a warming climate. Wind barriers are commonly used on bridges to reduce aerodynamic loads on trains through blocking effects. This study develops a novel wind barrier based on Tesla valves, which not only blocks incoming flow but also dissipates mechanical energy through fluid collision. To demonstrate this energy-dissipation capability, a Tesla plate is placed in a circular duct to examine its influence on pressure drop. Experimental tests and numerical simulations comparing a… More >

Wei Sun^1,2, Yanqing Feng^1,2,*, Yuan Wang^1,2, Zengping Zhao^1,2, Qian Wang², Xiangyun Li³, Dong Feng⁴

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.075630 - 06 February 2026

Abstract Significant differences exist between deep and medium-shallow coalbed methane (CBM) reservoirs. The unclear understanding of flowback and production behavior severely constrains the development of deep CBM resources. To address this challenge, guided by the gas-liquid two-phase flow theory in ultra-low permeability reservoirs, and integrating theoretical analysis, numerical simulation, and insights from production practices, this study classifies the flowback and production stages of deep CBM well considering the Daning-Jixian Block, Eastern Ordos Basin as a representative case. We summarize the flowback characteristics for each stage and establish a standard flowback production type curve, aiming to guide… More > Graphic Abstract

Jing Li¹, Bo Ning^1,*, Bo Li², Xuemei Jin¹, Dezhi Qiu¹, Fenlan Ou¹

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.074335 - 06 February 2026

Abstract As a controllable power generation method requiring no energy storage, Ocean Thermal Energy Conversion (OTEC) technology demonstrates characteristics of abundant reserves, low pollution, and round-the-clock stable operation. The free-standing cold-water pipe (CWP) in the system withstands various complex loads during operation, posing potential failure risks. To reveal the deformation and stress mechanisms of OTEC CWPs, this study first analyzes wave particle velocity and acceleration to determine wave loads at different water depths. Based on the Euler-Bernoulli beam model, a quasi-static load calculation model for OTEC CWPs was established. The governing equations were discretized using the… More >

Maddina Dinesh Kumar^1,#, S. U. Mamatha², Khalid Masood³, Nehad Ali Shah^4,#, Se-Jin Yook^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074680 - 29 January 2026

Abstract Fluid dynamic research on rectangular and trapezoidal fins is aimed at increasing heat transfer by means of large surfaces. The trapezoidal cavity form is compared with its thermal and flow performance, and it is revealed that trapezoidal fins tend to be more efficient, particularly when material optimization is critical. Motivated by the increasing need for sustainable energy management, this work analyses the thermal performance of inclined trapezoidal and rectangular porous fins utilising a unique hybrid nanofluid. The effectiveness of nanoparticles in a working fluid is primarily determined by their thermophysical properties; hence, optimising these properties… More >

Mohammad Shokouhifar^1,*, Fakhrosadat Fanian², Mehdi Hosseinzadeh^3,4,*, Aseel Smerat^5,6, Kamal M. Othman⁷, Abdulfattah Noorwali⁷, Esam Y. O. Zafar⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.073789 - 29 January 2026

Abstract Wireless Sensor Networks (WSNs) have become foundational in numerous real-world applications, ranging from environmental monitoring and industrial automation to healthcare systems and smart city development. As these networks continue to grow in scale and complexity, the need for energy-efficient, scalable, and robust communication protocols becomes more critical than ever. Metaheuristic algorithms have shown significant promise in addressing these challenges, offering flexible and effective solutions for optimizing WSN performance. Among them, the Grey Wolf Optimizer (GWO) algorithm has attracted growing attention due to its simplicity, fast convergence, and strong global search capabilities. Accordingly, this survey provides… More >

Amel Ksibi^*, Hatoon Albadah, Ghadah Aldehim, Manel Ayadi

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073533 - 29 January 2026

Abstract Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give… More >

Jihoon Moon^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071052 - 29 January 2026

Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >