Navjeet Kaur¹, Ayush Mittal², Saad Alahmari^3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079991 - 08 May 2026

Abstract Mobile fog computing must support latency-sensitive applications under dynamic user mobility and time-varying network conditions. Existing mobility-aware scheduling approaches are largely reactive and often ignore prediction uncertainty, resulting in service disruptions and inefficient task migration. This paper proposes an uncertainty-aware digital twin-based orchestration framework for proactive mobility-aware fog computing. The framework maintains real-time synchronized digital twins of users and fog nodes and integrates a hybrid Gated Recurrent Unit-Exponentially Weighted Moving Average (GRU-EWMA) mobility prediction model with fog-load forecasting to enable joint mobility- and load-aware decision-making. An entropy-based confidence mechanism is introduced to regulate proactive handover More >

Duong Do The^1,2, Duc-Nghia Tran³, Hoang-Dieu Vu⁴, Manh-Tuyen Vi^4,*, Duc-Tan Tran^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079056 - 08 May 2026

Abstract Road-surface identification is important for transportation monitoring and maintenance. However, this task is challenging due to the complexity of vibration signals, feature overlap among different surface types, and variations in real-world operating conditions. These challenges become more significant in time-series classification, where models must achieve high accuracy while remaining computationally efficient and suitable for low-cost hardware. This study investigates the design and evaluation of an automatic road-surface classification system using motion data collected from inertial sensors mounted on a vehicle, including accelerometers and gyroscopes. The system segments synchronized IMU signals into fixed-length windows and assigns… More >

Reshma Siyal¹, Jun Long^1,*, Muhammad Asim^2,*, Mudasir Ahmad Wani³, Kashish Ara Shakil⁴, Sajid Shah²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.074282 - 08 May 2026

Abstract The rapid growth of the Internet of Things (IoT) has led to dense wireless sensor networks (WSNs) deployed in critical applications such as smart cities, industrial monitoring, and healthcare. However, energy constraints, unpredictable communication delays, and inefficient data aggregation remain significant challenges that limit network reliability and operational lifespan. Traditional approaches often fail to balance delay minimization with energy efficiency, especially in large-scale or dynamic networks. To address these issues, this study proposes CTSO-DRNN, a novel framework that integrates Chronological Tangent Search Optimization (CTSO) with a Deep Recurrent Neural Network (DRNN) for accurate delay prediction… More >

Wei Dong¹, Baoqi Guo², Weiwei Zhao², Hui Jian², Zhenzong He^2,*

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2025.075848 - 30 April 2026

Abstract This study explores the design of a tapered cathode flow channel in a proton exchange membrane fuel cell (PEMFC), leveraging artificial intelligence and multi-objective optimization techniques to attain an optimal configuration. First, the influence of the channel height ratio and mass flow rate on PEMFC performance was systematically examined. The results reveal that decreasing the height ratio and increasing the mass flow rate lead to reduction in the standard deviation of current density, accompanied by a monotonic rise in pressure drop. The average current density initially rises before exhibiting a slight decline. Subsequently, a surrogate… More >

Ratan Kumar Chanda¹, Rakesh Bhowmick², Giulio Lorenzini^3,*, Rabindra Nath Mondal^1,*

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2025.075311 - 30 April 2026

Abstract Due to ample engineering and industrial applications involving electrically conducting fluids, such as in magnetic flow control devices, thermal magnetic systems, magnetic filtration and separation, and fluid transport in curved rotating channels, the present study examines the impacts of pressure-induced instability characteristics and chaotic nature of Magneto-hydrodynamic fluid flow in a rotating curved square duct (CSD), incorporating Hall and ion-slip currents. The rotational speed (Ω_T) around the vertical axis of the duct is constant while a variable transverse magnetic field is applied perpendicular to the fluid. The numerical solutions are obtained through the spectral method as a… More >

Zhukui Tan¹, Xiaoyong Cao^2,*, Qihui Feng¹, Dong Liu², Xiayu Chen³, Fei Chen²

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.078440 - 27 April 2026

Abstract The rapid integration of photovoltaic (PV) generation and energy storage systems has significantly increased the operational complexity of low-voltage direct current (LVDC) distribution networks in zero-carbon parks. Under highly variable operating conditions, conventional DC protection schemes relying on fixed overcurrent thresholds often suffer from maloperation or failure to trip, particularly during fluctuations in PV power, load switching, and changes in network topology. To address these challenges, this paper proposes an adaptive DC protection strategy based on an artificial neural network (ANN)-driven dynamic threshold optimization mechanism. The proposed method replaces static protection settings with an adaptive… More > Graphic Abstract

Hongsheng Su, Dan Li^*, Yuwei Du

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2025.072397 - 27 April 2026

Abstract When the three-phase grid-connected inverter system is in operation, there are problems of significant switching losses and power losses. At the same time, if the switching frequency is not fixed, it will lead to problems such as a high content of low-order harmonics in the current on the grid side. This paper takes the three-phase grid-connected inverter as the research object and proposes a solution. Establish a mathematical model for the inverter system and analyze the transformation relationships of relevant electrical quantities across different coordinate systems. First, the paper proposes an improved three-vector model predictive… More >

Benjamin Heckelmann^#, Jannis Duhn^#, Rüdiger Braun^*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.075028 - 22 April 2026

Abstract Pancreatic ductal adenocarcinoma (PDAC) is currently the third leading cancer-related cause of death worldwide and is forecasted to become the second leading cause in the United States by 2030. Despite the development of multimodal treatment regimens, 5-year overall survival remained as low as 12%. Several efforts have been made to account for different aspects of heterogeneous tumor biology in PDAC, aiming to enable treatment stratification of defined subtypes. Besides targeting specific mutations, the definition of molecular (transcriptional) subtypes has gained substantial interest regarding response prediction and treatment stratification. Despite numerous advances in the field of… More >

Muhammad Mukhtar^1,2, Farizah Yunus¹, Ahmad Shukri Mohd Noor^1,*, Zulfiqar Ali³, Muhammad Junaid^4,*, Mehmood Ahmed⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076246 - 09 April 2026

Abstract The increasing adoption of 5G cellular networks has introduced significant challenges for network operators. The main challenge lies in the management of seamless handoff (HO), which occurs owing to the rapid expansion of equipment, data, and network complexity. To address this challenge, a model named optimal HO management deep learning neural network (OHMDLNN) is proposed. The model is trained on network activity data, and it uses KPIs (key performance indicators) and system-level parameters to make HO decisions. As demonstrated in the article, OHMDLNN is successful in analyzing the effect and interdependence of KPIs from both… More >

M. Mohsin Raza^1,#, Muhammad Umair^1,#, Imran Arshad Choudhry¹, Muhammad Qasim¹, Muhammad Tahir Naseem^2,*, Mamoona Naveed Asghar³, Daniel Gavilanes^4,5,6,7, Manuel Masias Vergara^4,8,9, Imran Ashraf^10,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.074164 - 30 March 2026

Abstract Over the past decade, the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks. Traditional detection techniques, while still in use, often fall short when confronted with modern threats that use advanced evasion strategies. This systematic review critically examines recent developments in malware detection, with a particular emphasis on the role of artificial intelligence (AI) and machine learning (ML) in enhancing detection capabilities. Drawing on literature published between 2019 and 2025, this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore, SpringerLink, ScienceDirect, and ACM… More >