Search Results (15,483)

Industrial control systems (ICSs) are increasingly interconnected with enterprise IT networks and remote services, which expands the attack surface of operational technology (OT) environments. However, collecting sufficient attack traffic from real OT/ICS networks is difficult, and the resulting scarcity and class imbalance of malicious data hinder the development of intrusion detection systems (IDSs). At the same time, large language models (LLMs) have shown promise for security analytics when system events are expressed in natural language. This study investigates an LLM-based network IDS for a smart-factory OT/ICS environment and proposes a synthetic data generation method that injects domain knowledge into attack samples. Using the ICSSIM simulator, we construct a bottle-filling smart factory, implement six MITRE ATT&CK for ICS-based attack scenarios, capture Modbus/TCP traffic, and convert each request–response pair into a natural-language description of network behavior. We then generate synthetic attack descriptions with GPT by combining (1) statistical properties of normal traffic, (2) MITRE ATT&CK for ICS tactics and techniques, and (3) expert knowledge obtained from executing the attacks in ICSSIM. The Llama 3.1 8B Instruct model is fine-tuned with QLoRA on a seven-class classification task (Benign vs. six attack types) and evaluated on a test set composed exclusively of real ICSSIM traffic. Experimental results show that synthetic data generated only from statistical information, or from statistics plus MITRE descriptions, yield limited performance, whereas incorporating environment-specific expert knowledge is associated with substantially higher performance on our ICSSIM-based expanded test set (100% accuracy in binary detection and 96.49% accuracy with a macro F1-score of 0.958 in attack-type classification). Overall, these findings suggest that domain-knowledge-infused synthetic data and natural-language traffic representations can support LLM-based IDSs in OT/ICS smart-factory settings; however, further validation on larger and more diverse datasets is needed to confirm generality. Full article

To align with disaster monitoring and sustainable risk assessment, the low-carbon transition of fisheries necessitates comprehensive carbon emission management throughout the supply chain. As China advances supply-side structural reform, transitioning from traditional to low-carbon fisheries is vital for the green development of the industry and its associated sectors. This study employs input–output models and LMDI decomposition to examine the trends and drivers of embodied carbon emissions within China’s fishery production system from 2010 to 2019. By constructing a cross-sectoral full-emission accounting system, the research calculates total direct and indirect emissions, exploring how accounting scopes influence regional responsibility and reduction strategies. Empirical results indicate that while China’s aquatic trade and processing have steadily developed, the sector remains dominated by low-value-added primary products. This structure highlights vast potential for deep processing development amidst shifting global dietary habits. Factor decomposition reveals that economic and technological development are the primary drivers of carbon emissions. Notably, technological progress within fisheries emerges as the most significant factor, playing a pivotal role in both driving and potentially mitigating emissions. Consequently, to effectively lower carbon intensity, the study concludes that restructuring the fishery industry is crucial. Promoting low-carbon development and enhancing the R&D of green technologies are essential strategies to navigate the dual challenges of industrial upgrading and environmental protection. Full article

The article examines the implementation of 3D printing in civil protection and crisis management with a focus on the educational process, while 3D printing technology enables the creation of various teaching aids that streamline teaching and enrich theoretical knowledge. The empirical part of the study is based on a quantitative questionnaire survey among students of the Faculty of Safety Engineering of the University of Žilina in Žilina, with hypotheses set in advance and forming the basis for the construction of the questionnaire. The questionnaire collected data on the subjective evaluation of 3D printing through continuous, nominal, and ordinal responses and was completed by 277 students. Statistical methods of simple and group classification, as well as t-test, ANOVA, Kruskal–Wallis and Pearson’s correlation analysis were used to evaluate the data. Statistical significance was used to determine whether observed differences and relationships were unlikely to have arisen by chance. In addition, effect size measures were used in correlation and regression analyses to assess the strength and practical relevance of statistically significant relationships. The results of the study show that 3D printing significantly contributes to improving education and preparedness in civil protection, as it allows for more material-efficient and flexible production of educational aids compared to traditional custom production. Thus, it supports the development of more resilient communities and contributes to long-term sustainability. The findings confirmed that 3D printing is a suitable tool for improving public preparedness for emergencies. Full article

Carbon fiber reinforced polymer (CFRP) is prone to delamination damage during drilling, which seriously affects the processing quality. This study focuses on the use of variable parameter drilling technology. Firstly, an anisotropic constitutive model and a Hashin failure model for CFRP were constructed. Then, based on ABAQUS and VUMAT user subroutines, the influence laws of cutting parameters (spindle speed and feed rate) on delamination damage were explored. For the two methods of conventional fixed parameter drilling and variable parameter drilling (dynamic adjustment of feed rate when the drill reaches the exit plane), comparative simulation experiments were conducted. Subsequently, the genetic algorithm was introduced to optimize the spindle speed and feed rate under the variable parameter mode, and the results were verified through hole-making experiments. The results show that: under a constant spindle speed, the delamination damage factor increases monotonically with the increase in feed rate; under a constant feed rate, the delamination damage factor decreases first and then increases with the increase in spindle speed, presenting a nonlinear change characteristic. Among them, the variable parameter strategy of “first high feed, then low feed” can significantly reduce the delamination damage; the obtained optimal parameters can effectively balance the drilling quality and processing efficiency. This research provides theoretical and experimental support for optimizing CFRP hole-making parameters, addressing delamination control challenges in traditional drilling, and facilitating CFRP applications in aerospace and intelligent manufacturing. Full article

The construction industry relies on the Internet of Things (IoT) and wearable technologies to enhance workplace safety. This research investigates the use of IoT and wearable technology among Saudi Arabian construction sector employees, analyzing their implementation difficulties and the factors contributing to successful implementation. A structured questionnaire was distributed to 567 construction professionals across different roles and projects. Frequency analysis was used to study adoption patterns, chi-square tests to study demographic factors, and principal component analysis for exploratory factor analysis to discover hidden adoption factors. The findings show that smart safety vests and helmets receive the highest level of recognition. On the other hand, advanced monitoring systems, including fatigue and environmental sensors, are not used enough. Group differences in device adoption were investigated in terms of years of experience, academic qualification, job role, and project budget. The findings from factor analysis show that three main factors determine adoption rates, which include (1) safety and operational effectiveness, (2) worker acceptance and support structures, and (3) technical and adoption barriers. A data-driven system is created to help policymakers and industry leaders accelerate construction safety digitalization efforts. Full article

The Qulong–Jiama polymetallic ore concentration area, located in the eastern segment of the Gangdese metallogenic belt, is one of China’s most significant copper resource production zones. With the growing demand for copper resources, this area has become a key target for mineral exploration. The current study aims to explore the application potential of multispectral and hyperspectral remote sensing technologies in porphyry copper deposit prospecting, establish a hyperspectral remote sensing prospecting model tailored to this region, and provide technical support for prospecting prediction and resource exploration of similar deposits. Sentinel-2 and Landsat 8 data were used to outline major alteration anomalies at the regional scale, while GF-5 hyperspectral data enabled precision mineral mapping. Results show clear porphyry-style alteration zoning. Hyperspectral mineral identification reveals 33 mineralization- and alteration-related minerals, including muscovite, biotite, pyrophyllite, dickite, chlorite, epidote, and limonite. The ore concentration area exhibits a well-developed inner–middle–outer alteration sequence: (1) an inner potassic–silicic zone locally accompanied by skarn; (2) a middle phyllic and argillic zone dominated by quartz–sericite–pyrite assemblages; and (3) an outer propylitic zone of chlorite–epidote–carbonate with supergene iron oxides. These alteration patterns spatially coincide with known deposits and metallogenic structures such as faults, annular features, and intrusive contacts. Based on these spatial relationships, a hyperspectral remote sensing prospecting model was constructed. The model defines diagnostic mineral assemblages for each zone, highlights structurally altered overlapping areas as priority targets, and effectively predicts the distribution of ore-related alteration belts. The strong correspondence between remote sensing-derived anomalies and existing deposits demonstrates that hyperspectral alteration information is a reliable indicator of ore-forming systems. The proposed model not only provides a scientific basis for further prospecting and exploration in the Qulong–Jiama area but also serves as a reference for copper exploration in the Gangdese metallogenic belt and other similar porphyry–epithermal metallogenic systems. Full article

Against the backdrop of rapid urbanization, megacities have become crucial drivers of development. As the country with the largest number of megacities (seven in total), China is confronted with significant challenges such as population–resource–environment conflicts, which render high-quality development an imperative pursuit. This study employs a system dynamics approach to assess high-quality development in China’s megacities. It analyzes interactions among economic growth, technological innovation, environmental quality, and livelihood security under policy regulation, clarifying their evolutionary mechanisms and constructing a model to project the high-quality development index (HQDI) and coupling coordination degree (CCD) among subsystems. Findings reveal an upward trend in both HQDI and CCD across the seven megacities, with notable stratification. Beijing, Shanghai, and Shenzhen form the top echelon, leveraging financial and technological resources, driven by science and green development. Guangzhou and Chongqing constitute the second tier, supported by regional integration and industrial clusters, while Chengdu and Tianjin form the third echelon via regional strategic transformations. In coordinated development, Shanghai, Beijing, Shenzhen, and Guangzhou lead with multi-link synergy, whereas Chengdu, Chongqing, and Tianjin advance industry–ecology–livelihood coordination through regional strategies. This study offers insights for overcoming development bottlenecks, optimizing policies, and enhancing urban governance to foster a coordinated, high-quality development pattern. Full article

Background: Paroxysmal nocturnal hemoglobinuria (PNH) is a clonal hematopoietic stem cell disease characterized primarily by intravascular hemolysis, thrombosis, and bone marrow failure. Complement inhibitors are commonly used in clinical treatment and show limited efficacy, highlighting the urgent need to identify new therapeutic targets and explore alternative treatment strategies to provide theoretical guidance for clinical practice. Methods: We established a PNH cell model and constructed an miRNA–mRNA regulatory network to identify key miRNAs and core target genes. Single-cell sequencing data were analyzed to further clarify the critical genes. Finally, integrated drug database analysis identified potential therapeutic agents for PNH, which were validated by molecular docking and molecular dynamics simulations. Results: Using CRISPR/RNP technology, we successfully constructed a PIGA-knockout (PIGA-KO) THP-1 cell model. Differential expression analysis identified 1979 differentially expressed mRNAs (DEmRNAs) and 97 differentially expressed miRNAs (DEmiRNAs). The multiMiR package in R was used to predict the target genes of DEmiRNAs, from which those experimentally validated through dual-luciferase reporter assays were selected. After integration with the DEmRNAs, an miRNA–mRNA regulatory network was constructed, comprising 26 miRNAs and 38 mRNAs. Subsequent miRNA pathway enrichment analysis identified hsa-miR-23a-3p as a key miRNA, with CXCL12, CXCL8, HES1, and TRAF5 serving as core target genes. The integration of single-cell sequencing datasets (PRJNA1061334 and GSE157344) was performed, followed by cell communication and enrichment analysis. This approach, combined with clinical relevance, identified the neutrophil cluster as the key cluster. Intersection analysis of neutrophil cluster differential analysis results with key modules from hdWGCNA further clarified the critical genes. Drug prediction using EpiMed, CMap, and DGIdb identified Leflunomide, Dipyridamole, and Pentoxifylline as potential therapeutic agents. Molecular docking and molecular dynamics simulations showed stable binding of these potential drugs to the critical molecules, indicating a viable molecular interaction foundation. Conclusions: Leflunomide, Dipyridamole, and Pentoxifylline may serve as promising therapeutic agents for PNH, and the hsa-miR-23a-3p/CXCL8 regulatory axis could play a pivotal role in the pathogenesis and progression of PNH. Full article

Expanding the chemical diversity of DNA–encoded libraries (DELs) is crucial for identifying binders to emerging drug targets using DEL technology. In the present study, as part of our ongoing efforts to develop on–DNA diazide platforms (D–DAPs)—platform molecules possessing both aromatic and aliphatic azide groups on a single core reactive scaffold—we designed and synthesized a new compact diazide platform, designated as a compact D–DAP (C–D–DAP). This molecule is based on a low–molecular–weight reactive scaffold, 3–azido–5–(azidomethyl)benzoic acid, to facilitate small–molecule drug discovery targeting enzymes and G protein–coupled receptors (GPCRs). Furthermore, we established two distinct stepwise warhead construction strategies that exploit the chemoselective transformations of the azide groups in the C–D–DAP, which exhibit different reactivities. In addition, four virtual DELs were generated based on stepwise warhead elaboration from the C–D–DAP scaffold. Comparative chemical diversity analysis against bioactive compounds from ChEMBL revealed that these virtual libraries populate structural regions that are sparsely represented among known molecules. Each virtual library also occupies a distinct region of structural space relative to the others and displays intermediate quantitative estimate of drug–likeness (QED) values. Full article

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

In the construction of grouting holes in high-mud-content layers, high-pressure jet cleaning technology effectively cuts and removes soil and sediments from the strata. This research designs the structure of a high-pressure jet cleaning device and establishes a numerical simulation model for the high-pressure jet cleaning nozzle, conducting orthogonal simulation tests. Based on the data from these tests, a Backpropagation (BP) Neural Network-based numerical prediction model for the high-pressure jet cleaning flow field is developed, enabling the prediction of cleaning flow rates and pressures for different nozzle channel structure parameters. Targeting jet fluid velocity and cleaning pressure, parametric shape optimization is performed on the nozzle channel structure: key parameters are identified via Analysis of Variance (ANOVA) and sensitivity analysis; an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to establish a multi-objective optimization model, which exhibits superior convergence speed and solution diversity compared to the traditional algorithm. The optimal jet fluid velocity, cleaning pressure, and fluid structure parameter solution space for the high-pressure jet cleaning nozzle are obtained. Through simulation and experimental verification, it is found that with the same number of nozzles, the optimized design significantly enhances both the average cleaning flow rate and the cleaning pressure. Finally, a high-pressure jet cleaning nozzle and device are prototyped based on the simulation and optimization results and tested in the grouting test area A2W-2-III-6 of the South-to-North Water Diversion Project Xiong’an Storage Reservoir Project. This study provides a scientific basis and technical support for the application of high-pressure jet cleaning technology in complex geological formations. Full article

This study presents a systematic literature review exploring the challenges and solutions for scaling 3D printing in affordable residential construction in Kuwait. This review explores the urgent need to alleviate housing shortages through faster, cost-effective, and sustainable building approaches, highlighting the potential of additive manufacturing. Guided by the PRISMA framework, this review synthesizes findings from 20 key sources selected from an initial pool of 141 studies. The analysis identifies major scalability challenges—high material costs, limited supply chain readiness, complex regulatory frameworks, environmental constraints, and technical limitations—and evaluates proposed solutions such as geopolymer concrete, advanced printing technologies, and policy reforms. While this study does not include empirical data, it offers a comprehensive synthesis of the existing literature to inform policymakers and industry leaders about the potential of 3D printing to address Kuwait’s housing crisis. Full article

Background: Mutations in the KRAS gene play a pivotal role in lung cancer development and progression and are becoming increasingly important in therapeutic decision-making. The detection of these mutations in circulating tumor DNA (ctDNA) has attracted attention as a minimally invasive diagnostic approach. However, the accuracy reported in different studies varies widely. Methods: We conducted a systematic review and meta-analysis in accordance with the PRISMA-DTA guidelines. Eligible studies evaluated the detection of KRAS mutations in ctDNA in plasma or serum for lung cancer diagnosis and reported sufficient data to construct 2 × 2 contingency tables. Primary pooled estimates of sensitivity, specificity and likelihood ratios were calculated using aggregated 2 × 2 contingency tables. Additionally, a bivariate random-effects model was applied in a secondary analysis to investigate between-study heterogeneity. Results: Nine diagnostic study arms comprising 691 patients met the inclusion criteria. Across all datasets, there were 255 true positives, 19 false positives, 136 false negatives, and 281 true negatives. The pooled sensitivity was 65.2%, while the pooled specificity was 93.7%. The positive likelihood ratio was 10.35, and the negative likelihood ratio was 0.37, resulting in a diagnostic odds ratio of 28.0, which indicates strong rule-in capability. Sensitivity showed moderate heterogeneity across studies. In contrast, specificity demonstrated minimal heterogeneity. Conclusions: ctDNA-based detection of KRAS mutations demonstrates high specificity but moderate sensitivity for diagnosing lung cancer. These findings suggest that a KRAS liquid biopsy could be a valuable complementary diagnostic tool, particularly when a tissue biopsy is not possible or is inadequate, and it could support more personalized decision-making as analytical technologies continue to advance. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

The construction sector is central to achieving the objectives of the European Green Deal (EGD). While existing research on transition risks predominantly focuses on project- or firm-level challenges, less is known about the transition risks implied by high-level EU policy documents. This study addresses this gap by systematically analysing 101 EGD-related policy and guidance documents published between 2019 and February 2025. A mixed human–AI content analysis approach was applied, combining human expert manual coding with automated validation using large language models (Kimi K2 and GLM 4.6). The final dataset contains 2752 coded risk references organised into eight main categories and twenty-six subcategories. Results show that transition risks are most frequently associated with environmental, economic, and legislative domains, with Climate Change Impact, Cost of Transition, Pollution, Investment Risks, and Implementation Variability emerging as the most prominent risks across the corpus. Technological and social risks appear less frequently but highlight important systemic and contextual vulnerabilities. Overall, analysis of the EGD policy texts reveals the green transition as being constrained not only by environmental pressures but also by financial feasibility and execution capacity. The study provides a structured, policy-level risk profile of the EGD and demonstrates the value of hybrid human–LLM analysis for large-scale policy content analysis and interpretation. These insights support policymakers and industry stakeholders to anticipate structural uncertainties that may affect the construction sector’s transition toward a low-carbon, circular economy. Full article