Search Results (15,097)

Special economic zones (SEZs) are widely used to stimulate investment, employment, and industrial growth. Yet their contribution to sustainable regional development remains poorly measured. This is especially true in Kazakhstan, where zone-level assessment is largely absent from regional planning frameworks. This study addresses that gap. We construct a Regional Sustainable Development Index (RSDI) that integrates economic, social, and environmental indicators across nine Kazakhstani regions hosting active SEZs. Economic performance alone gives an incomplete picture. Omitting social and environmental dimensions distorts policy conclusions and masks structural imbalances. Our results reveal sharp differentiation across regions. In the Atyrau region, high investment volumes correspond closely with sustainability gains. This suggests structural coherence between zone operations and broader regional outcomes. The Pavlodar region presents a contrasting case. There, the leading driver of sustainability performance is not investment volume but the reduction of environmental pollution. This finding underscores why disaggregating sustainability components matters—the composite index alone is not sufficient. A comparison against official target indicators identifies both achievements and systematic shortfalls. Investment and employment targets are frequently decoupled: capital attraction does not reliably generate proportional job creation. The social dimension remains the weakest across most zones. Environmental governance shows formal recognition but limited implementation. The RSDI framework offers a practical diagnostic tool for public authorities. It makes imbalances visible before they become entrenched. Beyond Kazakhstan, the index provides a transferable instrument for resource-dependent emerging economies seeking to embed sustainability criteria into SEZ governance and regional planning. Full article

Urbanization increases pluvial flood risk by expanding impermeable surfaces, which is a trend likely to intensify with climate change. Permeable pavement (PePav) made from industrial byproducts, in accordance with circular economy principles, may improve soil permeability. Public acceptance remains a critical barrier to its implementation. Existing measures of willingness to accept (WtA) new technologies are inconsistent, limiting interdisciplinary collaboration. Therefore, a concise WtA scale was adapted from the Bogardus Social Distance Scale to assess acceptance of PePav at varying levels of proximity in residential contexts, from public flood-prone roads to private yards. The scale was evaluated across three studies: Study 1 (N = 195) and Study 2 (N = 187) utilized mixed student samples, while Study 3 (N = 625) involved a non-student sample. The 5-item solution, identified through factor analysis in Study 1, consistently demonstrated a unidimensional and cumulative structure and satisfactory reliability, even after the proposed PePav ingredient modification in subsequent studies. The scale correlated with recycling experience and professional background, indicating convergent validity, but not with flooding or informal construction experience, across all samples. Study 3 provided evidence of external validity by incorporating empirically well-established Theory of Planned Behavior (TPB) constructs and showing that WtA predicted PePav use beyond TPB variables and demographics. The scale also showed measurement invariance across sample type (student vs. general population) and different levels of construction experience. The constructed WtA scale is suitable for efficiently assessing professional and public acceptance of circular building materials and may have broad cross-disciplinary relevance. This enables timely, targeted interventions and informed policy decisions to advance sustainable technologies in the built environment, with substantial implications for education, professional policy, and sustainable engineering. Nevertheless, further validation is required. Full article

Bingham fluids, also called Bingham plastics, are used in different industries including the production of food, pharmaceuticals, household products, construction and oil and gas drilling. The behavior of Bingham fluids is viscous above a critical shear stress and rigid-body below the threshold stress value. Knowledge of the size of the entrance region has several applications including hemodynamics and microfluidics. A model for steady Bingham fluid flow in the entrance region between parallel plates is developed using the inlet-filled region concept. A boundary layer model is used to solve the fluid flow dynamics in the inlet region up to the point where the critical shear stress is reached at the edge of the boundary layer. Beyond that point, the boundary layer does not grow, while the velocity profile keeps readjusting in the filled region to asymptotically reach the fully developed flow. The results include boundary layer thickness profiles, dimensionless pressure drop, centerline velocity, friction factor and inlet and entrance region sizes as functions of the Bingham number. The results are validated against the results for the Newtonian fluid case (Bingham fluid yield stress equal to zero) and CFD results, using the finite element method, for nonzero Bingham numbers. In addition, the results are found to asymptotically reach the fully developed flow values for the general Bingham fluid flow case. The effects of the Bingham number are addressed and compared with the literature. The present model is largely analytical, requiring minor numerical tasks. Full article

The construction industry remains among the most hazardous, with workers in horizontal transportation infrastructure facing additional risks from dynamic work zones, live traffic exposure, and variable environmental conditions. Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) offer new approaches to accident analysis and prevention, yet their applications toward improving occupational safety in transportation construction have not been comprehensively reviewed. This paper presents a systematic review of 54 studies published between 2016 and 2025 collected from two online databases (Transportation Research International Documentation and Web of Science). This review synthesizes how immersive technologies contribute to occupational risk assessment, safety training, and real-time hazard monitoring in the construction of roads, bridges, tunnels, and work zones. Each study is classified across two dimensions: the immersive medium (VR, AR, etc.) and the operational context within the construction lifecycle (onsite tools, offsite monitoring and planning, simulation-based analysis, and workforce education). This dual classification is the first to systematically map immersive technology applications for occupational safety, specifically within horizontal transportation infrastructure. The findings of this review demonstrate the unique use cases of each immersive medium, revealing that VR is primarily used for controlled experimentation and full-immersion remote analysis, whereas AR and handheld devices are preferred for field-deployed applications. Despite these promising capabilities, widespread adoption remains limited by hardware constraints, challenging field conditions, and organizational resistance. This suggests that future work should focus on safety systems tested in real-world settings and rigorously evaluated by domain experts to enable their integration into standard workplace risk management practices. Full article

The low-carbon transition (LCT) of manufacturing clusters is a critical pathway to addressing bottlenecks in global climate governance and promoting sustainable economic development in developing countries. Accurately measuring the level of this transition and clarifying its dynamic trends are of great significance. Drawing on the economic rationale of a low-carbon economy, this study constructs a comprehensive evaluation indicator system and employs the entropy-weighted CRITIC-grey relational TOPSIS method to measure the LCT levels of China’s four major industrial bases from 2013 to 2023. Combined with convergence analysis, the Theil index, mechanism analysis, and policy scenario simulation, it systematically analyzes the characteristics of disparities and the underlying mechanisms. The study’s results show that low-carbon technology is the core driver of the LCT of the four major industrial bases. The LCT levels of the four major industrial bases have generally increased, with some bases exhibiting a catch-up effect internally. The overall disparity among the four major industrial bases has widened, primarily driven by intra-base differences. Specifically, the Beijing–Tianjin–Tangshan industrial base displays polarization characteristics, while the Central-Southern Liaoning industrial base shows a relatively low-level equilibrium. The transition of resource-based cities lags, mainly constrained by rigid industrial structures and insufficient investment in technology. Industrial structure optimization plays a certain role in improving resource-based regions, whereas technological innovation has a more pronounced effect in developed regions. This study constructs a comprehensive analytical framework of “measurement–evolution–mechanism–simulation,” which refines the quantitative evaluation system for the LCT of manufacturing clusters. The findings provide empirical support for formulating differentiated low-carbon policies for manufacturing clusters and optimizing coordinated emission reduction pathways, while also offering a reference paradigm for similar research in other developing countries. Full article

The depletion of natural river sand resources in the construction industry and the pollution caused by iron tailings storage in the steel industry are the two major challenges currently faced. The use of iron tailings in construction materials is widely regarded as one of the most sustainable and cost-effective approaches. Based on C30 concrete, 12 steel tube iron tailings sand (IOT) concrete columns with different IOT substitution rates were designed and fabricated in this paper, and axial compression test research was conducted on them; finite element simulations were conducted for comparison with the experimental results, focusing on the influences of IOT substitution rate (0–100%), steel pipe wall thickness (1–4 mm), and steel strength (Q235, Q355, Q390, Q420, Q460) on the bearing capacity of concreted steel tube columns were parametrically analyzed. By comparing the calculation methods of the bearing capacity of concrete-filled steel tube columns in five relevant standards, the calculation formula for the bearing capacity of IOT columns was corrected and obtained. The results show that the failure mode of the IOT column is similar to that of the ordinary column, and the steel tube wall has all undergone circumferential band shear buckling. As the replacement ratio of IOT increases, the load-bearing capacity of columns initially improves and then declines. The finite element analysis results show that the bearing capacity of the IOT column is directly proportional to the wall thickness of the steel pipe, and increasing the wall thickness of the steel pipe can effectively improve the bearing capacity of IOT columns. The discrepancy between the predicted and experimental bearing capacities of IOT columns obtained based on the revision of the “Technical Code for Concrete-filled Steel Tube Structures” (GB 50936-2014) is within 10%, which can effectively predict the load-bearing capacity of IOT columns within a certain range. Full article

As the power industry continues to advance rapidly, large-scale generators are evolving toward higher voltage levels and greater capacity. The heat accumulation associated with high voltage and large capacity accelerates the aging of the main insulation. It is necessary to enhance the thermal conductivity (λ) and dielectric properties of existing main insulation materials. This work focuses on investigating the effects of varying addition levels of two different-sized BN particles on the λ and dielectric properties of the mica tape composite dielectric. The experimental findings demonstrate a progressive enhancement in the λ of the mica tape corresponding to the incremental addition of h-BN concentration. When the doping concentration reaches 20 wt.%, the λ of the two h-BN-doped mica tape (h-BN/MT) reaches a maximum of 0.382 W/(m·K), 0.4 W/(m·K), respectively, which enhances the λ of the contrasting pure mica tape (0.199 W/(m·K)) by 91.95% and 101.01%, respectively. In terms of electrical insulation properties, both sizes of h-BN/MT perform well, with breakdown strength above 32 kV/mm. Furthermore, the second-order thermal conductivity model of mica tape doped with different sizes of h-BN was constructed by combining the Halpin–Tsai model with the Series model, which allows the calculation of λ of mica tape composites doped with different sizes of h-BN. This work provides a novel structural design approach for preparing mica tape composite dielectric that simultaneously exhibits high λ and high insulation properties. Full article

In the process of digital transformation, human capital and data capital are the most critical production factors. This paper innovatively introduces the learning effect, spillover effect, and shock effect, and studies the intrinsic mechanism of the interactive development of manufacturing enterprises and logistics enterprises under the background of digital transformation. It establishes an evolutionary game model and uses Matlab simulation software to verify the impact of different parameters on the digital interaction of the two industries. The research results indicate that the cost of digital construction has a negative impact on digital interaction, but there is a reasonable cost-sharing coefficient that increases the probability of interaction between the two parties. The initial willingness to interact, the level of digital technology, the coefficient of learning ability, and the absorption coefficient of data capital all increase the probability of enterprises choosing to participate in digital interaction. Moreover, the level of digital technology, learning ability, and data capital absorption capability accelerate the speed of collaborative evolution under constant returns to scale, there is a critical value for the income elasticity of human capital and data capital, affecting the equilibrium trend of enterprises choosing digital interaction. Digital interaction is advantageous in resisting the impact of external digital technological development. When facing significant external shocks, enterprises are more likely to choose to participate in digital interaction. These conclusions will provide decision-making basis for promoting the deep integration and interaction of the manufacturing and logistics industries. Full article

High-throughput and compact volume phase holographic (VPH) grating transmission spectrometers are widely employed in scientific research, agriculture, and industrial applications. Conventional transmission spectrometers generally adopt a fixed configuration and therefore have limitations in simultaneously achieving high spectral resolution and broad wavelength coverage. To address the limited tunability of transmission spectrometers, this work presents the theoretical analysis and experimental validation of a transmission spectrometer incorporating a novel catadioptric grating assembly, which consists of a transmitting VPH and a planar reflector. A catadioptric system is a combination of reflective (catoptric) and refractive (dioptric) elements. In the proposed configuration, a VPH grating and a plane mirror arranged at a fixed 90° angle form the catadioptric dispersion module. Synchronous rotation of this assembly enables wavelength scanning. The structure ensures that the diffracted ray along the optical axis of the imaging lens maintains the Bragg condition across the scanning range, thereby preserving maximum diffraction efficiency. The optical configuration and structural parameters of the spectrometer were theoretically derived, and a prototype spectrometer with an f-number of 1.8 employing a 2400 g/mm grating was constructed. Measurements demonstrate that, when the rotation angle is tuned from 30.5° to 50.5°, the accessible spectral range covers from 410 nm to 650 nm. Spectral response measurements using a tungsten–halogen light source confirm that the spectrometer maintains an acceptable diffraction efficiency across the entire tuning range. The measured spectral resolution is 0.1 nm at 626 nm with a 2400 g/mm grating and 0.18 nm with a 1500 g/mm grating. The spectrometer was further applied to fiber-enhanced gas Raman spectroscopy, where it successfully resolved the closely spaced Raman peaks of CH₄ and C₂H₆ that are difficult to distinguish using conventional compact spectrometers. These results demonstrate that the proposed tunable catadioptric spectrometer simultaneously provides excellent wavelength tunability and high spectral resolution. Full article

With the expansion of seismic exploration targets to deeper and more complex geological structures, traditional fault interpretation methods face significant challenges in terms of efficiency and accuracy. The extensive application of artificial intelligence (AI) technologies is driving the evolution of fault recognition techniques toward automation and intelligence. This paper systematically reviews the development of AI technologies in fault recognition, from traditional machine learning-based seismic attribute fusion analysis to deep learning-based end-to-end recognition and semantic segmentation. It provides a detailed discussion of key technological advancements, such as sample set construction, weak signal enhancement, and noise suppression. To address the current challenges, including the insufficient authenticity of synthetic data, poor model interpretability, and weak quantitative representation capabilities, this study proposes three future research directions: the development of benchmark datasets based on real geological evolution, the construction of interpretable model architectures that incorporate geological prior information, and the realization of multi-parameter collaborative intelligent fault system analysis. These directions aim to provide theoretical support for advancing the practical and industrial applications of intelligent fault recognition technology. Full article

Understanding how policy mixes influence systemic digital transformation is a key concern in socio-technical transition research. This study explores the configurational effects of policy tools in construction digitalization to address this issue. Based on an analysis of policies issued across 31 Chinese provinces (2020–2025), key policy tools were identified using a Multi-Level Perspective (MLP), followed by fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine their configurational effects. The results show that: (1) policies can be grouped into three categories—technology application, management norms, and industrial ecology—comprising six thematic tools; (2) no single condition is essential for high or low digital development; (3) high-level outcomes are associated with three distinct configurations: “technology-talent driven”, “foundation-governance linkage”, and “system-ecology leading”, while “factor-deficient” and “system-mismatched” types are frequently linked to low-level outcomes. By identifying equifinal pathways and cross-level coordination mechanisms, the study offers configurational insights for designing digital policy mixes. While the theoretical implications are broadly applicable, the specific configurations require validation across different contexts because they depend on China’s provincial policy data. Full article

Based on green growth theory and the theory of spatial externalities, this study systematically examines the mechanisms through which digital finance influences inclusive green growth, as well as the spatial characteristics and nonlinear patterns of urban green total factor productivity (GTFP). Using a sample of 285 prefecture-level cities in China, we constructed fixed effects and threshold effects models for empirical analysis. The results indicate that digital finance has a significant positive impact on inclusive green growth; there exists a dual threshold effect based on urban green total factor productivity (GTFP) and a significant positive spatial spillover effect between the two. Institutional environment, industrial upgrading, and digital infrastructure moderate these relationships, and heterogeneous differences exist across different regions. By integrating digital finance, inclusive green growth, and green total factor productivity into a unified analytical framework, this study provides empirical insights and policy recommendations for advancing urban sustainable development and regional collaborative governance. Full article

To enhance the utilization of industrial coal gangue, response surface methodology was used to optimize the concrete mix proportions based on three key factors: the mass ratio of fly ash (FA) to metakaolin (MK) (A), the combined dosage of FA and MK (B), and the water-to-binder ratio (C). A quadratic regression model was established, and the optimal mixture was characterized using FT-IR, XRD, and SEM. The model exhibited high statistical significance (p < 0.001) and an excellent fit (R² > 0.95), confirming its predictive reliability. Single-factor analysis revealed that the order of influence on 28 d compressive strength was C > A > B, indicating that the water-to-binder ratio had the most significant effect on later-age strength. The optimal mix proportions were determined as follows: fly ash-to-MK ratio of 0.65, admixture dosage of 20% by mass of total binder, and C of 0.475. Under these conditions, the measured 28 d compressive strength reached 35.9 MPa, which was within 5% of the model-predicted value, thereby validating the model’s accuracy. Microstructural analysis demonstrated that the appropriate incorporation of FA and MK promoted the formation of C-S-H gel, refined the pore structure, and improved the quality of the interfacial transition zone, which collectively enhanced the mechanical performance. A systematic understanding of the strength and microstructural mechanisms of concrete incorporating coal gangue, fly ash, and metakaolin is currently lacking, which hinders the design of more robust and durable structures. This study addresses this gap by systematically clarifying the individual and combined effects of the key variables on the strength of coal gangue concrete. The findings reveal the underlying mechanisms, providing a scientific basis for the sustainable, large-scale application of coal gangue concrete in construction. Full article

Wireless sensor networks (WSNs) are the backbone of IoT-enabled smart manufacturing, environmental monitoring, and industrial automation. However, their broadcast nature makes communication links vulnerable to eavesdropping, routing manipulation, and denial-of-service attacks. Strategically placing monitor nodes to check each link is an effective approach to protect against attacks, but energy, connectivity, and capacity constraints should be considered while picking monitor nodes. In this paper, we tackle the Minimum-Weighted Connected Capacitated Vertex Cover (MWCCVC) problem, which minimizes monitoring costs, ensures backbone connectivity, and adheres to per-node capacity constraints. Unlike prior works that consider weighted vertex cover, connectivity constraints, or capacitated variants separately, the proposed MWCCVC model jointly integrates all three dimensions within a single vertex cover-based monitoring framework. We first provide a Branch-and-Bound (B&B) solver with linear programming relaxation bounds and constraint-based pruning strategies that produces optimum solutions. Three constructive greedy heuristics (GD, GR, GW) and two hybrid genetic algorithms (HGA, HGA-v2) that combine parameterized greedy decoders with evolutionary search are proposed; all methods guarantee full edge coverage, induced-subgraph connectivity, and max-flow-validated capacity feasibility. Tests on 130 small, 160 medium, and 19 large benchmark instances show that HGA matches B&B optima on every small instance, beats the time-limited B&B by 6.6% on medium instances, where the percentage is computed based on the relative difference in average total weight with respect to B&B, and stays the best on large graphs with up to 1000 nodes. The HGA-v2 tries to balance the quality and speed, with only a 3.1% difference at $10\times$ faster execution. Full article

Noise in industrial buildings affects workers’ productivity and can seriously impair their physical and mental health, yet existing studies often overlook workers’ subjective perceptions and rely on a single method. Therefore, this study recruited 263 workers from four industrial buildings in Beijing and adopted a mixed-methods approach. First, 30 semi-structured interviews were analyzed using grounded theory’s three-level coding procedure to construct a conceptual framework of a healthy acoustic environment and its influencing factors. Next, a 30-item subjective questionnaire was developed, and structural equation modeling was conducted on 256 valid responses. Finally, Spearman correlation analysis and multidimensional scaling were used to examine relationships between subjective evaluations and eight physical and psychoacoustic indicators. The results identified nine major dimensions, including Sound Source Localization, Physiological Effects at Work, and Regulatory Control, as well as 15 relational pathways. Compared with existing frameworks, Communication Barrier emerged as a more prominent dimension in industrial building contexts. Structural equation modeling confirmed that 12 pathways were statistically significant. Correlation analysis further showed that only a few objective–subjective associations were significant, indicating that objective acoustic indicators alone cannot explain workers’ multidimensional perceptions. In conclusion, this study developed an evaluation model for healthy acoustic environments in industrial buildings, highlighting the need to emphasize controllability, communication support, and integrated subjective–objective evaluation in acoustic design to better enhance workers’ well-being. Full article