Search Results (5,995)

Industrial Internet of Things (IIoT) systems are increasingly exposed to sophisticated and rapidly evolving cyber threats. In response, this work proposes a proactive threat detection framework that leverages pretrained transformer-based language models to identify emerging attack patterns within IIoT ecosystems. This work introduces a transformer-based framework that fine-tunes domain-specific pretrained models (SecBERT, SecRoBERTa, CyBERT), derives potential attack-path patterns from vulnerability–tactic mappings, and incorporates a retrieval-based fallback mechanism. The fallback not only improves robustness under uncertainty, but also provides a practical solution to the absence of labeled datasets linking ICS-specific MITRE ATT&CK tactics with vulnerabilities, thereby filling a key research gap. Experiments show that the fine-tuned models substantially outperform traditional machine learning baselines; SecBERT achieves the best balance while maintaining high inference efficiency. Overall, the framework advances vulnerability-driven threat modeling in IIoT and offers a foundation for the proactive identification of attack patterns. Full article

This work investigates the use of LLMs to enhance automation in software testing, with a particular focus on generating high-quality, context-aware test scripts from natural language descriptions, while addressing both text-to-code and text+code-to-code generation tasks. The Codestral Mamba model was fine-tuned by proposing a way to integrate LoRA matrices into its architecture, enabling efficient domain-specific adaptation and positioning Mamba as a viable alternative to Transformer-based models. The model was trained and evaluated on two benchmark datasets: CONCODE/CodeXGLUE and the proprietary TestCase2Code dataset. Through structured prompt engineering, the system was optimized to generate syntactically valid and semantically meaningful code for test cases. Experimental results demonstrate that the proposed methodology successfully enables the automatic generation of code-based test cases using large language models. In addition, this work reports secondary benefits, including improvements in test coverage, automation efficiency, and defect detection when compared to traditional manual approaches. The integration of LLMs into the software testing pipeline also showed potential for reducing time and cost while enhancing developer productivity and software quality. The findings suggest that LLM-driven approaches can be effectively aligned with continuous integration and deployment workflows. This work contributes to the growing body of research on AI-assisted software engineering and offers practical insights into the capabilities and limitations of current LLM technologies for testing automation. Full article

In recent years, with the continuous development of information technology, network security issues have become increasingly prominent. Intrusion detection has garnered significant attention in the field of network security protection due to its ability to detect anomalies in a timely manner. However, existing intrusion detection methods often fail to effectively capture spatiotemporal correlations in traffic and struggle with imbalanced, high-dimensional feature spaces—problems that become even more pronounced under complex network environments—ultimately leading to low identification accuracy and high false-positive rates. To address these challenges, this paper proposes a spatiotemporal correlation-based intrusion detection method that utilizes feature space transformation and Euclidean distance. Specifically, the method first considers the relationship between the characteristics of different operating systems and attack behaviors through feature space transformation and integration. Then, it constructs a graph structure between samples using Euclidean distance and captures the spatiotemporal correlations between samples by combining graph convolutional networks with bidirectional gated recurrent unit networks. Through this design, the model can deeply mine the spatial and temporal features of network traffic, thereby improving classification accuracy and detection efficiency for network attacks. Experimental results show that the proposed model significantly outperforms existing intrusion detection approaches across multiple evaluation metrics, including accuracy, weighted precision, weighted recall, and weighted F1 score. Full article

Building energy conservation through the development of transparent thermal insulation materials that selectively block near-infrared radiation while maintaining visible light transmittance has emerged as a key strategy for global carbon neutrality. WO₃ is a semiconductor oxide with near-infrared absorption capabilities. However, the limited absorption efficiency and narrow spectral coverage of pure WO₃ significantly diminish its overall transparent thermal insulation performance, thereby restricting its practical application in energy-saving glass. Therefore, this study successfully prepared Sn-doped WO₃ materials using a one-step hydrothermal method, controlling the Sn:W molar ratio from 0.1:1 to 2.0:1. Through evaluation of transparent thermal insulation performance of a series of Sn-doped WO₃ samples, we found that Sn:W = 0.9:1 exhibited the most excellent performance, with NIR shielding efficiency reaching 93.9%, which was 1.84 times higher than pure WO₃. Moreover, this sample demonstrated a transparent thermal insulation index (THI) of 4.38, representing increases of 184% and 317%, respectively, compared to pure WO₃. These enhancements highlight the strong NIR absorption capability achieved by Sn-doped WO₃ through structural regulation. When Sn doping reaches a certain concentration, it triggers a structural transformation of WO₃ from monoclinic to tetragonal phase. After reaching the critical solubility threshold, phase separation occurs, forming a multiphase structure composed of a Sn-doped WO₃ matrix and secondary SnO₂ and WSn₀.₃₃O₃ phases, which synergistically enhance oxygen vacancy formation and W⁶⁺ to W⁵⁺ reduction, achieving excellent NIR absorption through small polaron hopping and localized surface plasmon resonance effects. This study provides important insights for developing high-performance transparent thermal insulation materials for energy-efficient buildings. Full article

Heavy metal contamination in water, predominantly from copper (Cu(II)) ions, poses substantial risks to human and environmental health. This study developed a novel, robust adsorbent known as a carboxylate cellulose nanocrystal–silica–graphene oxide hybrid composite porous monolith, which effectively removes Cu(II) from water in a rapid manner. Carboxylate cellulose nanocrystals with enhanced metal-binding properties were synthesized from cellulose extracted from sugarcane bagasse, a significant agricultural byproduct. The porous monolith was synthesized through the combination of carboxylate cellulose nanocrystals, tetraethyl orthosilicate (TEOS), and graphene oxide, utilizing a sol–gel method. The efficacy of the synthesis was confirmed using Fourier-Transform Infra-red (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscope (SEM), and Brunauer–Emmett–Teller (BET) analyses. The material exhibited a highly porous mesoporous structure with a surface area of 512 m²/g, signifying a significant enhancement. Batch adsorption experiments under optimal conditions (pH = 5.5, contact time = 240 min, initial concentration = 200 mg/L) demonstrated a high experimental adsorption capacity of 172 mg/g for Cu(II). The adsorption process was best described by the Langmuir isotherm model, which yielded a theoretical maximum capacity (q_m) of 172 mg/g, and the pseudo-second-order kinetic model, confirming monolayer coverage and chemisorption as the rate-limiting step. Thermodynamic analyses demonstrate that the process is both spontaneous and exothermic. The porous monolith demonstrates the capability for multiple uses, maintaining over 70% efficiency after five cycles. The findings indicate that the carboxylate cellulose nanocrystal–silica–graphene oxide hybrid composite porous monolith is an efficient and robust method for the remediation of copper-contaminated water. Full article

The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly scalable, or are ineffective computationally, because of the presence of redundant or irrelevant features, and they suffer from high false positive rates. Addressing these limitations, this study proposes a hybrid intelligent model that combines quantum computing, chaos theory, and deep learning to achieve efficient feature selection and effective intrusion classification. The proposed system offers four novel modules for feature optimization: chaotic swarm intelligence, quantum diffusion modeling, transformer-guided ranking, and multi-agent reinforcement learning, all of which work with a graph-based classifier enhanced with quantum attention mechanisms. This architecture allows as much as 75% feature reduction, while achieving 4% better classification accuracy and reducing computational overhead by 40% compared to the best-performing models. When evaluated on benchmark datasets (NSL-KDD, CICIDS2017, and UNSW-NB15), it shows superior performance in intrusion detection tasks, thereby marking it as a viable candidate for scalable and real-time IoT security analytics. Full article

Transformer-based models have accelerated EEG motor imagery (MI) decoding by using self-attention to capture long-range temporal structures while complementing spatial inductive biases. This systematic survey of Scopus-indexed works from 2020 to 2025 indicates that reported advances are concentrated in offline, protocol-heterogeneous settings; inconsistent preprocessing, non-standard data splits, and sparse efficiency frequently reporting cloud claims of generalization and real-time suitability. Under session- and subject-aware evaluation on the BCIC IV 2a/2b dataset, typical performance clusters are in the high-80% range for binary MI and the mid-70% range for multi-class tasks with gains of roughly 5–10 percentage points achieved by strong hybrids (CNN/TCN–Transformer; hierarchical attention) rather than by extreme figures often driven by leakage-prone protocols. In parallel, transformer-driven denoising—particularly diffusion–transformer hybrids—yields strong signal-level metrics but remains weakly linked to task benefit; denoise → decode validation is rarely standardized despite being the most relevant proxy when artifact-free ground truth is unavailable. Three priorities emerge for translation: protocol discipline (fixed train/test partitions, transparent preprocessing, mandatory reporting of parameters, FLOPs, per-trial latency, and acquisition-to-feedback delay); task relevance (shared denoise → decode benchmarks for MI and related paradigms); and adaptivity at scale (self-supervised pretraining on heterogeneous EEG corpora and resource-aware co-optimization of preprocessing and hybrid transformer topologies). Evidence from subject-adjusting evolutionary pipelines that jointly tune preprocessing, attention depth, and CNN–Transformer fusion demonstrates reproducible inter-subject gains over established baselines under controlled protocols. Implementing these practices positions transformer-driven BCIs to move beyond inflated offline estimates toward reliable, real-time neurointerfaces with concrete clinical and assistive relevance. Full article

MXenes, an emerging class of two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides, have demonstrated exceptional potential in tribology: the study of friction, wear, and lubrication. Their remarkable mechanical strength, thermal stability, and tunable surface chemistry make them ideal candidates for solid lubricants, lubricant additives, and protective coatings in mechanical systems. This review comprehensively examines the tribological performance of MXenes under diverse environmental conditions, including high temperatures, vacuum, humid atmospheres, and liquid lubricants. A particular emphasis is placed on the influence of surface terminations (-OH, -O, -F) on friction reduction and wear resistance. Additionally, we discuss strategies for enhancing MXene performance through hybridization with polymers, nanoparticles, and ionic liquids, enabling superior durability in applications ranging from micro/nano-electromechanical systems (MEMS/NEMS) to aerospace and biomedical devices. We also highlight recent advances in experimental characterization techniques and computational modeling, which provide deeper insights into MXene tribomechanics. Despite their promise, key challenges such as oxidation susceptibility, high synthesis costs, and performance variability hinder large-scale commercialization. Emerging solutions, including eco-friendly synthesis methods and optimized composite designs, are explored as pathways to overcome these limitations. Overall, MXenes represent a transformative avenue for developing next-generation tribological materials that combine high efficiency, sustainability, and multifunctionality. Continued research and innovation in this field could unlock groundbreaking advancements across industrial and engineering applications. Full article

The use of saponins with biosurfactant, antioxidant, anti-inflammatory, and anti-cancer properties is limited by their toxicity and bioavailability. This study focused on the fabrication, characterization, and bioactivity of crude tea saponin (TS) and TS-incorporated silica nanoparticles (TSNPs). Our results showed that TS contained seven saponins and that TSNPs had an average diameter of 200–300 nm, a negative surface charge, and high polydispersity. Fourier Transform Infrared Spectroscopy (FTIR) revealed an incorporation bond of Si-O- and -OH controlling releasing behavior with t₅₀ = 24 h. Using HaCaT cells, it was demonstrated that TSNPs reduced cytotoxicity. Reactive oxygen species (ROS) production was lowered in both TS and TSNP treatments, with significantly greater efficacy at higher concentrations. Additionally, TSNPs significantly accelerated cell migration in the wound closure model as efficiently as TGFβ. Together, these findings offer promising TSNPs for biomedical applications and therapeutic agents due to their antioxidant properties, cytotoxicity protection, and wound closure acceleration. Full article

The widespread presence of bisphenol A (BPA), a persistent endocrine-disrupting pollutant, in aquatic environments poses significant ecological and health risks, necessitating its effective removal. However, conventional remediation technologies are often hampered by catalysts with narrow pH adaptability and poor stability. In this study, a novel catalyst, Zeolite-supported Cu₃Mn-layered double hydroxide (LDH), was fabricated using the co-precipitation method. The synthesized catalyst was applied to activate peroxymonosulfate (PMS), effectively enabling decomposition of BPA by advanced oxidation processes. The composite material was characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and transmission electron microscopy (TEM), which confirmed the successful synthesis of the zeolite-supported Cu₃Mn-LDH. The catalyst exhibited high activity in both neutral and strongly alkaline environments, achieving complete degradation of 10 mg⋅L⁻¹ bisphenol A (BPA) within 40 min and a 98% total organic carbon (TOC) removal rate when both the PMS and catalyst were dosed at 0.15 g⋅L⁻¹. Singlet oxygen was detected as the primary reactive species responsible for BPA degradation, as verified by quenching experiments and EPR analysis, which also identified the presence of sulfate (SO₄^•−), hydroxyl (•OH), and superoxide (•O₂⁻) radicals. The catalyst exhibited excellent reusability, maintaining high catalytic efficiency over two consecutive cycles with minimal performance loss. Gas chromatography-mass spectrometry (GC-MS) analysis revealed five intermediate products, enabling the proposal of potential BPA degradation pathways. This work not only presents a novel synthetic approach for zeolite-supported LDH composites, but also offers a promising strategy for the efficient removal of BPA from aqueous systems through AOPs. Full article

With the development of infrared imaging technology and the integration of intelligent algorithms, the realization of non-contact, dynamic and real-time detection of SF6 gas leakage based on infrared video has been a significant research direction. However, the existing real-time detection algorithms exhibit low accuracy in detecting SF6 leakage and are susceptible to noise, which makes it difficult to meet the actual needs of engineering. To address this problem, this paper proposes a real-time SF6 leakage detection method, VGEC-Net, based on multi-channel fusion and spatiotemporal feature extraction. The proposed method first employs the ViBe-GMM algorithm to extract foreground masks, which are then fused with infrared images to construct a dual-channel input. In the backbone network, a CE-Net structure—integrating CBAM and ECA-Net—is combined with the P3D network to achieve efficient spatiotemporal feature extraction. A Feature Pyramid Network (FPN) and a temporal Transformer module are further integrated to enhance multi-scale feature representation and temporal modeling, thereby significantly improving the detection performance for small-scale targets. Experimental results demonstrate that VGEC-Net achieves a mean average precision (mAP) of 61.7% on the dataset used in this study, with a mAP@50 of 87.3%, which represents a significant improvement over existing methods. These results validate the effectiveness and advancement of the proposed method for infrared video-based gas leakage detection. Furthermore, the model achieves 78.2 frames per second (FPS) during inference, demonstrating good real-time processing capability while maintaining high detection accuracy, exhibiting strong application potential. Full article

Adamantane is a unique tricyclic hydrocarbon with a rigid, diamond-like structure. It is widely distributed in natural hydrocarbons and has significant potential applications in medicine, materials science, and pharmaceuticals. Despite its high chemical stability, biocatalytic methods using cytochrome P450 enzymes and microorganisms, such as Pseudomonas strains and actinobacteria, demonstrate high regional specificity in the hydroxylation and modification of adamantane, primarily at tertiary C–H bonds. This review summarises the latest data on the mechanisms and pathways of the microbial transformation of adamantane and its derivatives, including the key metabolites and enzymatic systems involved. The advantages of biocatalysis—namely, high selectivity, environmental friendliness and mild reaction conditions—are highlighted as a promising approach to the synthesis of functional adamantane derivatives for use in the development of innovative drugs and materials. The limitations of current methods and prospects for development are also discussed, including searching for new microorganisms and regulating enzymatic activity to increase the efficiency of biotransformation. Full article

To enhance the overall performance of asphalt pavements and promote the efficient utilization of solid waste resources, this study innovatively incorporates recycled polyethylene (PE) particles and recycled ethylene-vinyl acetate copolymer (EVA) particles, each compounded with waste cooking oil (WCO), to modify base asphalt. Systematic tests were conducted to evaluate the physical and rheological properties of the composite modified asphalt. Additionally, Fourier transform infrared spectroscopy (FTIR) and thin-layer chromatography with flame ionization detection (TLC-FID) were used to analyze the microstructures and internal components of the modified asphalt. The results indicate that the optimal mixing ratio for the WPA is 5% WCO, 5% EVA, and 5% PE. With the incorporation of these modified materials, the asphalt’s high-temperature and low-temperature properties, as well as its rutting and fatigue resistance, are enhanced to some extent. Furthermore, the modification significantly improves the rheological properties of the asphalt across the full temperature range. Additionally, the modified materials lead to changes in the internal composition of the asphalt: the content of lighter components decreases, while the content of heavier components increases. These changes in the internal composition are the primary cause of the observed improvements in the rheological properties of the asphalt. Full article

This article focuses on the utilization of the supramolecular self-assembly of renewable materials derivatives to obtain functional compounds. Novel bio-based amphiphile molecules (CALAH and PALAH) were synthesized through a tailored process, involving Williamson ether synthesis and amidation reactions, employing renewable amino acid and cashew nut shell liquid (CNSL) derivatives as essential reactants. Their molecular structures were confirmed by nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HRMS), and Fourier-transform infrared spectroscopy (FT-IR). Notably, these compounds self-assemble into nanofibers that organize into a fibrous network, unexpectedly exhibiting two distinct morphologies: curved and rigid nanostructures. These structures were characterized by scanning electron microscopy (SEM), and their formation mechanisms were elucidated through temperature-dependent NMR studies and density functional theory (DFT) calculations. The sodium salts of the compounds (PALA and CALA) exhibited fundamental surfactant properties, exhibiting a hydrophilic lipophilic balance (HLB) value of 13.7 and critical micelle concentration (CMC) values of 1.05 × 10⁻⁵ M and 4.10 × 10⁻⁶ M. They also demonstrated low cytotoxicity, suggesting potential suitability in consumer applications. Furthermore, the compounds exhibited multi-functional performance as effective inhibitors of Staphylococcus aureus and efficient adsorbents for gaseous pollutants. Full article

The isomerization of geraniol using natural, acid-modified minerals such as vermiculite presents a promising approach aligned with the principles of green chemistry. Vermiculite, a naturally abundant layered silicate mineral, was subjected to the acid activation and thoroughly characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). These methods allowed the evaluation of crystallinity, structural stability, and surface morphology, which are critical parameters in the heterogeneous catalysis. The catalytic performance of the modified vermiculite was examined in the transformation of geraniol under mild conditions. The study systematically investigated the influence of key process parameters—temperature, reaction time, and catalyst content—on the conversion of geraniol and products selectivities. Optimization using the response surface methodology (RSM), enabled the identification of conditions leading to high conversion of geraniol (up to 85%) and allowing us to obtain favorable selectivities toward linalool, thunbergol, and 6,11-dimethyl-2,6,10-dodecatrien-1-ol. The results indicate that the acid-treated vermiculite exhibits sufficient surface acidity to effectively catalyze isomerization and cyclization reactions, without requiring additional promoters or metal-based systems. Moreover, the use of RSM provided the efficient framework for optimization reaction conditions, reducing experimental workload, and enhancing process efficiency. This study demonstrates the viability of natural, low-cost minerals as environmentally friendly catalysts and supports their integration into sustainable and “green” chemical technologies. Full article