Search Results (33,101)

Grounded in the United Nations Sustainable Development Goal 4 (SDG4), specifically addressing the urgent need to increase relevant skills for decent work (Target 4.4) while ensuring inclusive access and quality (Targets 4.3, 4.5, 4.c), this study develops a province-level indicator system for the “talent chain” and “industry chain” and integrates entropy-weighted composite evaluation, a coupling coordination model, correlation tests, and mismatch typology classification to systematically assess the alignment between higher education talent formation and industrial demand across 31 Chinese provinces during 2000–2022. The analysis aims to characterize China’s phase-specific progress in SDG4-consistent development at the education–industry interface and to provide a theoretical and empirical basis for improving graduate job matching. The results show that (1) overall talent–industry matching improved steadily from 2000 to 2022, yet pronounced regional disparities persist, with eastern provinces generally outperforming central and western regions; (2) educational quality and structural inputs—such as faculty capacity, per-student expenditure, and the composition of human capital—are the primary drivers of talent-chain performance, whereas expansion-oriented indicators exhibit limited marginal contributions, implying that sustainable graduate job matching hinges more on quality upgrading and supply-structure optimization than on quantitative expansion alone; (3) industry-chain advancement is jointly driven by industrial scale, structural upgrading, and employment absorptive capacity, with the tertiary sector playing a particularly prominent role in shaping demand for higher-skilled labor; and (4) a divergence in driving mechanisms—quality- and structure-oriented on the education side versus scale- and structure-oriented on the industry side—combined with regional heterogeneity produces stage-specific mismatch typologies, suggesting remaining scope for structural alignment between higher education systems and industrial upgrading. Overall, strengthening regional coordination, integration, quality, and upgrading drives synergistic development, advancing SDG 4 targets by validating that quality-driven education reform is the key lever for sustainable employment in China. Full article

Real-time monitoring of hazardous gas emissions in open environments remains a critical challenge. Conventional spectrometers and filter wheel systems acquire spectral and spatial information sequentially, which limits their ability to capture multiple gas species and dynamic dispersion patterns rapidly. A High-Resolution Snapshot Multispectral Imaging System (HRSMIS) is proposed to integrate high spatial fidelity with multispectral capability for near real-time plume visualization, gas species identification, and concentration retrieval. Operating across the 7–14 $\mathsf{\mu }$m spectral range, the system employs a dual-path optical configuration in which a high-resolution imaging path and a multispectral snapshot path share a common telescope, allowing for the simultaneous acquisition of fine two-dimensional spatial morphology and comprehensive spectral fingerprint information. Within the multispectral path, two $5\times 5$ microlens arrays (MLAs) combined with a corresponding narrowband filter array generate 25 distinct spectral channels, allowing concurrent detection of up to 25 gas species in a single snapshot. The high-resolution imaging path provides detailed spatial information, facilitating spatio-spectral super-resolution fusion for multispectral data without complex image registration. The HRSMIS demonstrates modulation transfer function (MTF) values of at least 0.40 in the high-resolution channel and 0.29 in the multispectral channel. Monte Carlo tolerance analysis confirms imaging stability, enabling the real-time visualization of gas plumes and the accurate quantification of dispersion dynamics and temporal concentration variations. Full article

In the context of accelerated digital transformation, organizations increasingly operate as complex systems in which strategic decision-making is challenged by uncertainty, data heterogeneity, and bounded rationality. The integration of artificial intelligence (AI) into organizational processes is therefore redefining how decisions are supported and enacted. This study develops and validates an integrated conceptual model that explains how trust in AI-based decision support systems (AI-DSSs), data transparency and quality, perceived usefulness, and ease of use influence decision-making efficiency and the intention to adopt AI-DSS in complex organizational contexts. The empirical analysis is based on a questionnaire survey administered to 324 respondents from Romanian organizations operating in IT, services, industry, and public administration. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) implemented in SmartPLS 4. The results show that data transparency and quality strongly enhance trust in AI-DSS (β = 0.784, p < 0.001). Trust positively influences both perceived usefulness (β = 0.229, p < 0.01) and perceived ease of use (β = 0.482, p < 0.001), confirming its role as a key psychological enabler of favorable technology perceptions. Furthermore, perceived ease of use significantly affects perceived usefulness (β = 0.597, p < 0.001). Regarding adoption-related attitudes, perceived usefulness (β = 0.352, p < 0.001), trust (β = 0.311, p < 0.001), and perceived ease of use (β = 0.135, p < 0.05) exert significant positive effects on the intention to adopt AI-DSS, which in turn demonstrates a strong association with decision-making efficiency (β = 0.544, p < 0.001). By extending traditional technology acceptance models (TAM) with AI-specific dimensions—namely transparency, data quality, and trust—this study contributes to the literature on decision-making in complex systems and offers practical insights for organizations seeking to improve decision effectiveness through AI-based support. Full article

The utilization of sustainable feedstocks offers significant opportunities for innovation in sustainable and efficient processing technologies, targeting a vacuum residue upgrade industry projected to be valued at around USD 26 billion in 2024. This review examines advances in catalytic strategies for upgrading waste-derived products (plastics, tires) and biomass, in addition to heavy oil feedstocks. Particular emphasis is placed on hydrogen addition pathways, specifically, residue hydroconversion facilitated by dispersed nanocatalysts and waste co-processing methodologies. Beyond nanoscale catalyst design and reaction performance, this work also addresses refinery-level sustainability impacts. The advanced catalytic conversion of heavy oil residue demonstrates superior conversion efficiency, significant coke suppression, and improved carbon utilization, while life cycle and illustrative techno-economic comparisons indicate greenhouse gas reductions and a net economic gain of approximately USD 2–3 per barrel relative to conventional refining under scenarios assuming decarbonized hydrogen production. Co-processing of plastics, tires, and biomass with heavy oil feedstocks is highlighted as a practical and effective approach. Together, these findings outline a rational catalytic pathway toward optimized refining systems. Within the framework of the circular carbon economy, these catalytic processes enable enhanced feedstock utilization, integration of low-carbon hydrogen, and coupling with carbon-capture technologies. Full article

The pharmaceutical industry faces a broken drug development pipeline, characterized by high costs, slow timelines and is prone to high failure rates. The convergence of Artificial Intelligence (AI) and quantum technologies is poised to fundamentally transform this landscape. AI excels in interpreting complex data, optimizing processes and designing drug candidates, while quantum systems enable unprecedented molecular simulation, ultra-sensitive sensing and precise physical control. This convergence establishes an integrated, self-learning ecosystem for the discovery, development, and delivery of therapeutics. This framework co-designs strategies from molecular targeting to formulation stability, compressing timelines and enhancing precision, which may enable safer, faster, and more adaptive medicines. Full article

Elemental arsenic (As) is essential for diverse industrial applications. Most elemental As in China is produced by reducing gaseous arsenic trioxide (As₂O₃) with carbonaceous materials in steel reactors. This study aimed to extend the reactor lifespan through corrosion experiments and analysis. In this study, corroded regions of steel reactors were inspected after each production batch, and the corrosion process was examined. X-ray diffraction (XRD) was used to identify the major corrosion products, X-ray fluorescence (XRF) was used to measure the composition of corroded area, scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used to inspect the features and elemental distributions of the corroded steel-plate cross-sections. The results revealed that the steel wall near the charcoal zone exhibited the highest corrosion rate. Tin (Sn), selenium (Se), and antimony (Sb) did not promote the corrosion process, whereas carbon (C) accelerated it by forming an Fe–As–C system at the grain boundaries of the steel matrix, characterized by a low melting temperature. The important source of C responsible for initiating corrosion was solid-state C particles originating from reused materials from previous batches. Additionally, owing to the high processing temperature, oxygen (O) was transferred to the inner side of the steel wall before the dramatical corrosion of the matrix by elemental As and C. Results of this study provide references to increase the lifespan of steel reactors for elemental As production. Full article

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

The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more well-known and are being quickly used worldwide. However, the exponential rise in EV sales has also raised a number of issues, which are becoming important and demanding. These challenges include the need of driving security, the battery degradation, the inadequate infrastructure for charging EVs, and the uneven energy distribution. In order for EVs to reach their full potential, intelligent systems and innovative technologies need to be introduced in the field of EVs. This is where business intelligence (BI) can be employed, along with artificial intelligence (AI), data analytics, and machine learning. In this paper, we provide a comprehensive survey on the use of BI strategies in the EV transportation sector. We first introduce the EVs and charging station technologies. Then, research works on the application of BI and data analysis techniques in EV technology are reviewed to further understand the challenges and open issues for the research and industry community. Moreover, related works on accident analysis, battery health prediction, charging station analysis, intelligent infrastructure, locating charging stations analysis, and autonomous driving are investigated. This survey systematically reviews 75 peer-reviewed studies published between 2020 and 2025. Finally, we discuss the fundamental limitations and the future open challenges in the aforementioned topics. 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

In recent years, interest in carbon dioxide (CO₂) hydrogenation technologies has intensified. Driven by the continuous rise in greenhouse gas emissions and the unprecedented negative impacts of global warming, these technologies offer a viable pathway toward sustainability and support the development of low-carbon industrial processes. In addition to methanol and methane, other possible hydrogenation products (i.e., hydrocarbons, formic acid, acetic acid, dimethyl ether, and dimethyl carbonate) are of industrial relevance due to their wide range of applications. Therefore, this review aims to provide a comprehensive overview of the various aspects associated with thermocatalytic CO₂ hydrogenation processes, from thermodynamic and kinetic studies to upscaled reactor modeling and process synthesis and optimization. The review proceeds to examine different integration strategies and optimization approaches for multi-product systems, with the objective of evaluating how distinct technologies may be combined in an integrated flowsheet. It then concludes by outlining future research opportunities in this field, particularly those related to developing comprehensive kinetic rate expressions and reactor modeling studies for routes with low technology readiness levels, the exploration of prospective reaction pathways, strategies to mitigate the dependence on green hydrogen (which, today, exhibits high costs), and the consideration of market price or product demand fluctuations in optimization studies. Overall, this review provides a solid base to support other decarbonization studies focused on hydrogenation technologies. 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

Current saw- and pin-type lint-cleaning systems used by the ginning industry have challenges retaining lint quality. The objective of the research was to test a novel pneumatic fractionator for the lint cleaning of an Upland cotton variety that was both saw- and roller-ginned. The process variables tested were initial lint moisture content in the range of 5.5–15% w.b., line pressure in the range of 276–552 kPa, and residence time in the range of 15–45 s. Experiments were conducted based on a central composite design. Models based on response surface methodology (RSM) were developed for final lint moisture, total trash extracted during lint cleaning, and High-Volume Instrument (HVI) fiber quality. The RSM models adequately described the pneumatic fractionation process, as indicated by the coefficient of determination, predicted vs. observed plots, and residual values. The results indicated that the interactions among initial lint moisture content, residence time, and line pressure significantly affected lint quality. At the optimized pneumatic fractionator process conditions, the predicted lint quality attributes were better for both roller- and saw-ginned lint compared to lint cleaned with saw- and pin-type lint cleaners. The upper half mean length increased by 1 mm, the uniformity index was higher by 0.5–1 percentage points, the strength was 1–2 g/tex higher, and the short fiber content was reduced by more than one percentage point. Color grades were better for pneumatic fractionated lint compared to saw- and pin-type lint cleaning methods. Lint value was approximately 4 cents/kg higher for both saw- and roller-ginned pneumatic fractionated lint, compared to lint cleaned using saw- and pin-type lint cleaners. The novel pneumatic fractionator, when compared to industry-standard saw- and pin-type lint cleaners, effectively cleaned lint while retaining fiber quality and removing most of the motes and trash. 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

Industrial deployment of photocatalysis for recalcitrant wastewater treatment remains constrained by economic uncertainty and scale-up limitations. This study first reviews the current technological routes and application status of photocatalytic processes and then addresses the key obstacles through a quantitative techno-economic assessment (TEA) of a full-scale (10,000 m³/d) photocatalytic wastewater treatment plant. A process-level model integrating mass- and energy-balance calculations with equipment sizing was developed for a 280 kW UVA-LED reactor using Pt/TiO₂ as the benchmark catalyst. The framework quantifies capital (CAPEX) and operating (OPEX) expenditures and evaluates the overall economic performance of the photocatalytic treatment system. Sensitivity analysis reveals that the catalyst replacement interval and electricity tariffs are the principal economic bottlenecks, whereas improvements in catalyst performance alone provide limited cost leverage. Furthermore, the analysis indicates that supportive policy mechanisms such as carbon-credit incentives and electricity subsidies could substantially enhance economic feasibility. Overall, this work establishes a comprehensive integrated TEA framework for industrial-scale photocatalytic wastewater treatment, offering actionable design parameters and cost benchmarks to guide future commercialization. Full article

Most aircraft skin components are typical sheet metal components, and stretch forming serves as the primary forming process. The die face is the core foundation for both the finite element simulation and mold trial. Due to the intricate geometric characteristics of aircraft skin components and iterative revisions caused by stretch forming process adjustments and product design changes, the die face design of aircraft skin components is inherently time-intensive, highly complex, and prone to instability. To address these issues, a Hierarchical and Hybrid Association Method (HHAM) based on a robust updating mechanism and hybrid associations is proposed for the intelligent design system. HHAM can significantly enhance the stability and efficiency of die face design. Specifically, the hierarchical and automatic updating process of HHAM, incorporating robust error handling mechanisms, is the core methodology that guarantees the stability of complex and iterative die face design for aircraft skin. Moreover, the inter-module hybrid association, which integrates parametric modeling and automatic connection techniques, eliminates the instability in die face design updating caused by feature and topology variations. Additionally, robust geometric algorithms for wireframe modeling effectively improve the surface quality and generation success rate of the die face. The intelligent design system developed based on the CATIA platform has been successfully applied in two professional aircraft skin component manufacturing enterprises. Case studies and industrial application practices verify the effectiveness of the proposed system, achieving a 72.7% improvement in design efficiency and a 70.27% reduction in the risk of die face update errors. Full article