Wenwen Hu¹, Xinzhan Li², Tao Luo^2,*, Li Li²

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.077120 - 18 May 2026

Abstract This study experimentally investigates the Hybrid Effect of Steel Fiber (SF) and Recycled Rubber Powder (RRP) on Freeze-Thaw (F-T) Resistance and Pore Structure of Concrete. With respect to the mechanical properties of Steel Reinforced Concrete (SRC) before and after F-T cycles, the mixture incorporating 1.5% SF and 10% RRP achieves the optimal performance, exhibiting a distinct positive hybrid effect with the γ of the tensile-to-compressive strength ratio of 1.427. The synergistic interaction between SF and RRP preserves the compressive strength and significantly enhances the tensile performance of SRC. Meanwhile, it alleviates the degradation of mechanical More >

Akash Garg^*

Journal on Artificial Intelligence, Vol.8, pp. 273-298, 2026, DOI:10.32604/jai.2026.079401 - 12 May 2026

Abstract As cyber threats continue to evolve in scale and sophistication, the need for intelligent and adaptive security mechanisms has become increasingly urgent. Intrusion Detection Systems (IDS) are critical components in safeguarding computer networks from malicious activities. This review paper presents a comprehensive analysis of recent advancements in deep learning-based IDS, examining various architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). The study compares traditional intrusion detection techniques with modern deep learning approaches, highlighting their strengths, limitations, and suitability for real-world deployment. Special attention is given to… More >

Mohsin Sayeed^1,*, O. P. Singh¹, Vishal Singh Chandel², Azam Raza³, Kamal Batcha Mohamed Ismail⁴, Mayur Khan⁵, Navshad Alam^6,7, Mohammad Shariq⁸

Chalcogenide Letters, Vol.23, No.4, 2026, DOI:10.32604/cl.2026.079721 - 09 May 2026

Abstract An eco-friendly one-pot hydrothermal method was developed to synthesize molybdenum disulfide/graphene oxide (MoS₂/GO) nanocomposites for high-performance supercapacitor applications. X-ray diffraction (XRD) analysis confirmed the presence of the MoS₂ crystalline phase, with reduced peak intensities upon GO incorporation, indicating suppressed crystallite growth. Scanning electron microscopy (SEM) revealed rod-like MoS₂ structures uniformly distributed across layered GO sheets, and energy-dispersive spectroscopy (EDS) confirmed the presence of Mo, S, C, and O elements. Raman and FTIR analyses verified strong interfacial interactions between MoS₂ and GO. Brunauer–Emmett–Teller (BET) measurements revealed a mesoporous structure with a specific surface area of ~31.7 m² g⁻¹ and… More >

Yingying Yu^1,2,#,*, Jun Yuan^3,#, Tong Liu^1,2

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080767 - 08 May 2026

Abstract Accurate grasp detection is fundamental to successful robotic manipulation. Existing methods achieve reliable performance under good light conditions. However, their performance in low-light environments suffers from severe degradation due to the diminishing discriminative ability of visual features. In this paper, a novel low-light aware hybrid network LAH-Net is proposed. It comprises an alternating transformer-CNN module (ATCM) between the encoder and decoder, and a knowledge distillation-guided low-light enhancement module (KDLEM) before the encoder, which is activated by an illumination gate under low-light conditions. To generate highly robust and synergistic features, the ATCM module facilitates the iterative… More >

Marwan Ali Albahar^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079941 - 08 May 2026

Abstract Advanced Persistent Threats (APTs) are stealthy cyberattacks that can evade detection in system-level audit logs. Provenance graphs encode these logs as interacting entities and events, exposing a causal and dependency structure that is often obscured in linear representations. Prior provenance-based detectors typically apply anomaly detection over such graphs, yet they frequently incur high false-positive rates and produce coarse grained alerts; moreover, approaches that heavily depend on node-specific identifiers (e.g., file paths) can learn spurious correlations, reducing robustness and limiting reliability across heterogeneous workloads. In this paper, we present Self-Training Adaptive Graph Encoder (stage), a lightweight, self-supervised… More >

Feng Lv^*, Xin Xu, Cheng Yang, Yixuan Tang

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079917 - 08 May 2026

Abstract To ensure an effective disturbance response and maintain continuous production in hybrid flow shops, this paper focuses on the design of a rescheduling method. A rescheduling model is constructed that minimizes the makespan, total tardiness, and scheme deviation degree. A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling. The Whale Optimization Algorithm (WOA) is improved by integrating the good point set theory, nonlinear control parameter strategy, and Differential Evolution (DE) algorithm. Moreover, non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to More >

Samer R. Sabbah¹, Mohammad Rasmi Al-Mousa¹, Ala’a Al-Shaikh², Ahmad Al Smadi^3,*, Suhaila Abuowaida⁴, Amina Salhi^5,*, Arij Alfaidi⁶

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079241 - 08 May 2026

Abstract Web 4.0 platforms introduce intelligent, decentralized agents and real-time interactions that increase both utility and attack surface. This paper presents a comprehensive, reproducible AI blockchain hybrid designed to (1) detect SQL injection attacks at scale using a textual TFIDF + machine-learning pipeline, (2) incorporate reputation signals from a real-world Bitcoin OTC trust dataset to compute a TrustAlert Score (TAS) that prioritizes alerts and guides logging policy, and (3) record privacy-preserving audit digests on blockchain, optionally attested via a zero-knowledge proof (ZKP) pipeline. We evaluate the system on a 148 k SQL corpus and Soc-SignBitcoinOTC reputation More >

Liping Wang¹, Jiang Wu^1,2,*, Liang Wang³

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078591 - 08 May 2026

Abstract The rapid growth of networked systems and the increasing diversity of cyberattack behaviors have posed significant challenges to intrusion detection, particularly in scenarios characterized by high-dimensional features and severe class imbalance. Conventional detection approaches based on handcrafted rules or shallow representations often exhibit limited robustness under such conditions. To address these issues, this paper presents a hybrid deep learning framework for network intrusion detection that integrates complementary feature learning mechanisms within a dual-branch architecture. Specifically, a Transformer branch is employed to model long-range temporal dependencies in network traffic, while a convolutional neural network branch (CNN)… More >

Sarmad Dheyaa Azeez¹, Saadaldeen Rashid Ahmed^2,3, Muhammad Ilyas^4,*, Abu Saleh Musa Miah⁵, Fahmid Al Farid^6,7,*, Md. Hezerul Abdul Karim^6,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076283 - 08 May 2026

Abstract Traditional Intrusion Detection Systems (IDSs) that rely on fixed signatures or basic machine learning often struggle with sophisticated, multi-stage cyberattacks and previously unknown threats. To fix these problems, this paper introduces IntrusionNet, a mixed deep learning system that combines Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders in a two-part design. Differing from typical stacked models, IntrusionNet works on two levels at the same time. First, a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns. Second, an unsupervised Autoencoder process spots new anomalies by looking at reconstruction… More >

Maryem Lachgar^1,*, Mansour Lmkaiti¹, Ibtissam Larhlimi¹, Imad Aattouri², Hicham Ouchitachen¹, Hicham Mouncif¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.075234 - 08 May 2026

Abstract This study introduces a hybrid routing protocol, Low Energy Adaptive Clustering Hierarchy—Ant Colony Optimization—Genetic Algorithm (LEACH-ACO-GA), for wireless sensor networks. It combines regional ant colony optimization for cluster head selection with inter-cluster routing based on a genetic algorithm. The proposed method reduces energy consumption from 6.9 J (LEACH Classic) to 5.6 J (LEACH-ACO-GA) and decreases latency from 460 to 390 ms, while maintaining a packet delivery ratio of 0.97. These values are averaged over 70 rounds based on 30 independent simulation runs conducted on networks with 50 and 200 nodes. The hybrid method extends network More >