Search Results (17,775)

To better understand the impact that multi-scale unsteadiness has on industrial flows, we use Large Eddy Simulation (LES) data representative of a midspan compressor section operating in an idealized multi-stage environment. We collect a large number of three-dimensional flow snapshots and perform a large-scale flow decomposition using a parallel framework based on the Proper Orthogonal Decomposition (POD). Once the flow is split into orthogonal modes, we quantify kinetic energy budgets on a mode-by-mode basis. This enables us to characterize energy exchanges between these modes and analyze the flow in a multi-scale manner. As a result we are able to reconstruct an approximate energy cascade within the domain. The results provide insights into the role that various scales play in modulating the energy transfer within the flow. This work is a stepping stone towards utilizing all the information embedded in the 3D unsteady flowfield and its evolution for the purpose of informing turbulence modeling. Full article

In red tea production, fermentation is critical for flavor. However, manual determination of its stages is inaccurate and inefficient, often spoiling flavor and lowering product value. To solve this, this study combines CNN and knowledge distillation to build a lightweight classification model, AT-ShuffleNet, for accurate, efficient stage identification in real processing. It collected images of Fuding and Tieguanyin tea at different fermentation stages. ResNet (teacher model) and ShuffleNet v2-0.5 (student model) were used for distillation. Focal and Poly Losses optimized both models to tap distillation potential. STD, MGD, SPKD, ATD, and KD methods were tested at various ratios to find the optimal strategy, forming AT-ShuffleNet. The lightweight model performed well: P (89.11%), R (90.16%), Kappa (89.29%), ACC (91.2%), F1 (89.53%). It addresses manual limitations, enabling accurate classification and reducing deployment issues in unstructured environments. For industrial validation, it was deployed on edge devices and integrated into a self-developed WeChat mini-program. Full article

The construction of green mines is a core strategy for promoting ecological civilization in China’s mining sector, yet its long-term ecological effects require quantitative assessment. Using a cement-grade limestone mine operated by Linyi Zhonglian Cement Co., Ltd. in Shandong Province as an illustrative case, we employed Landsat 8 OLI/TIRS imagery acquired in 2015, 2020, and 2025 to develop a five-indicator framework for assessing ecological environment quality. The selected indicators comprised greenness (NDVI), wetness, dryness (NDBSI), land surface temperature (LST), and dust concentration (MECDI). These five indicators were subsequently integrated via principal component analysis to generate the Mine Ecological Quality Index (Mine-EQI). Using this index, we applied the Theil–Sen median slope estimator alongside zonal statistics to examine ecological change trajectories across the full study area and three functional zones—the industrial square, haul roads, and active mining area—over the 2015–2025 period. The ecological outcomes attributable to the green mine policy were then quantified. The results show that (1) the mean Mine-EQI of the study area decreased from 0.3713 in 2015 to 0.3460 in 2025, exhibiting a slight overall decline. However, the rate of decline decreased from −6.1% during 2015–2020 to −0.7% during 2020–2025, yielding a Temporal Change Intensity index (TCI) of +88.5%, indicating that the ecological degradation trend has been effectively curbed. (2) Significant spatial heterogeneity was observed. The industrial square showed substantial improvement (Theil–Sen slope = +0.0726), while the haul roads (slope = −0.0705) and mining area (slope = −0.0408) continued to exhibit degradation trends. The improved areas (9.7% of the study area) were spatially coincident with green mine engineering projects. (3) The dust indicator (MECDI) decreased by 24.7% during 2020–2025, and the vegetation index (NDVI) increased by 19.5% over the decade, representing the dominant contributors to ecological improvement. This study reveals that China’s green mine policy has yielded remarkable ecological improvements in relatively stable functional zones such as industrial squares. In contrast, ecological restoration within persistently disturbed areas, including haul roads and mining pits, demands long-term sustained investment and governance. By integrating remote sensing techniques with policy analysis, this research establishes a replicable framework for evaluating progress toward sustainable mining practices. The findings directly support the monitoring of SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), providing a quantitative pathway to balance mineral resource extraction with ecological protection—a core sustainability challenge for resource-dependent regions. Full article

The rapid expansion of e-commerce has transformed market access in developing economies, yet its impact on the spatial structure of market participation remains insufficiently understood. While existing studies primarily examine welfare outcomes such as income growth and consumption smoothing, few investigate how digital platforms reshape the balance of market entry between urban and rural areas. Drawing on New Economic Geography and platform economics theory, this study proposes that e-commerce development rebalances urban–rural market vitality through three associative pathways: alleviating rural capital constraints, improving rural innovation environments, and promoting agricultural-industry agglomeration. Using county-level panel data covering 2725 Chinese counties from 2011 to 2022, we employ a Double Machine Learning (DML) framework to examine the association between designation as an “E-commerce into Rural Comprehensive Demonstration County” and changes in the urban–rural market vitality balance (URMAR). The results indicate that demonstration county designation is associated with a statistically significant reduction in urban–rural market disparity, as measured by both the Theil index and the absolute difference in new enterprise registrations. The directional URMAR indicator further reveals that this convergence is driven primarily by accelerated rural enterprise formation. Subsample analysis confirms that the rebalancing interpretation holds across counties with different baseline market structures. Mechanism analysis provides suggestive evidence consistent with all three proposed associative pathways. Heterogeneity analysis further reveals that these effects are stronger in economically developed eastern regions, in counties linked to higher-tier cities, and in secondary and tertiary industries. These findings advance a market-structure perspective on digital development that complements existing welfare-based approaches and offer policy insights for fostering balanced regional development through targeted digital and complementary investments. Full article

This study explores the use of prompt engineering for big data analysis with large language models (LLMs) in the context of smart maintenance within Industry 4.0 environments. Industrial systems generate large volumes of heterogeneous data, which are often underutilized due to the complexity of traditional analytical approaches. This work employs a systematic literature review based on PRISMA to identify the current state of the art and existing gaps in the integration of big data and LLMs. Based on this analysis, an approach is proposed that uses prompt engineering as a core mechanism to transform industrial data into actionable information through language models. The proposed approach is validated through a case study in an industrial company, where the traditional manual data analysis process is compared with the proposed approach. The results demonstrate a significant reduction in analytical effort and in the time required to generate relevant information, highlighting gains in operational efficiency for decision-making in smart maintenance. Full article

Quantifying the detection efficiency of buoy-based sonar and optimizing deployment strategies in complex marine environments remain significant challenges. This study proposes a transceiver depth optimization method based on the Bellhop ray model to enhance underwater remote sensing data quality. For the first time, we validated the applicability of acoustic reciprocity in deep-sea environments exceeding 5000 m, characterized by non-uniform sound speed profiles, horizontal inhomogeneity, and steep seamount terrain, with a maximum relative error of <1.2%. This extends the applicable boundaries of the acoustic reciprocity theorem from idealized simple waveguides to complex, realistic deep-sea environments. Building on this validation, we developed a novel, equivalent, superposition modeling framework for bidirectional transmission loss (TL), which converts the computationally intractable TL from target to receiver into the calculable TL from receiver to target, thus significantly reducing computational complexity. Systematic simulations uncovered a depth-layered dependency mechanism: shallow sources (23.14~69.42 m) and deep sources (≥347.10 m) show robustness to large depth differences exceeding 500 m, whereas mid-layer sources (161.98~231.40 m) exhibit a distinct critical threshold effect. Static simulations identify a performance degradation cliff with an onset at an approximate depth difference of 185 m, leading to a 50% reduction in detection range and fragmented near-field detection coverage. To accommodate environmental temporal variability (e.g., internal waves), a conservative safety margin was incorporated, establishing a robust engineering threshold of 150 m. Accordingly, we define 160~350 m as the optimal detection depth window and propose a layered deployment protocol that fills a critical industry gap in quantitative deployment design for deep-sea acoustic detection. Specifically, transceiver depth differences should be strictly constrained to <150 m for mid-layer operations, while more-flexible depth configurations are permissible for shallow and deep sources. These findings furnish quantitative engineering criteria for the design of reliable underwater remote sensing networks, while balancing long-range detection stability and near-field coverage integrity. Full article

Radio Frequency Identification (RFID) is an emerging technology in Industry 4.0 for low-cost logistics, yet direction and orientation estimation typically requires multiple antennas, and robustness under industrial multipath fading, operator variability, and signal fragmentation has not been evaluated. To address this gap, this study proposes a single-antenna RFID system that evaluated thirteen architectures spanning unsupervised methods (clustering algorithms) and supervised methods (classical machine learning, deep learning, and hybrid architectures) on Received Signal Strength Indicator (RSSI) and phase time-series reconstructed through a pipeline of Savitzky–Golay smoothing, phase unwrapping, and cubic spline resampling to $N=50--300$ samples, preserving signal morphology across variable-length RFID passes. The system further incorporates a physics-informed augmentation strategy that encodes multipath fading, distance variation, and fragmentation into synthetic training samples for cross-domain generalization without hardware modification. In controlled laboratory experiments, both direction and orientation tasks achieved $>99.5\%$ accuracy, while direction tracking was additionally validated on an industrial shop floor under varying distances, Non-Line-of-Sight (NLoS) occlusions, and signal fragmentation. Zero-shot transfer caused accuracy to degrade to near-chance levels for several configurations, confirming a pronounced domain gap. Domain adaptation with XGBoost recovered direction accuracy to $>97\%$ under severe fragmentation under NLoS conditions, with an inference latency of ≈150 $\mathsf{\mu }$s. Under domain-adapted shop floor conditions, direction accuracy exceeded the 75–92% reported in prior single-antenna laboratory studies, suggesting that physics-informed domain adaptation is a promising approach for single-antenna RFID tracking in Industrial Internet of Things (IIoT) logistics environments. Full article

Construction firms in emerging markets operate in highly volatile environments that threaten business continuity and sector-wide resilience. This study provides a novel, integrated framework that links multiple strategic survival models to construction business resilience and development in Ghana’s construction industry, with particular emphasis on the evolving role of digitalisation. Four survival models are conceptualised as strategic portfolios: Innovation and Digital Transformation, Diversification and Growth, Lean and Resilience, and Strategic Risk and Partnerships. A quantitative research design was employed, using structured questionnaires administered to 128 construction industry stakeholders. Data were analysed using Partial Least Squares Structural Equation Modelling to assess direct, indirect, and mediating effects among survival models, construction business resilience, and construction business development. All four survival models have significant positive effects on construction business resilience, with Diversification and Growth (β = 0.404) and Innovation and Digital Transformation (β = 0.377) exerting the strongest influence, followed by Strategic Risk and Partnerships (β = 0.265) and Lean and Resilience (β = 0.207). The structural model explains 55.7% of the variance in construction business resilience, while construction business resilience is positively and strongly related to construction business development (β = 0.439), accounting for 19.3% of its variance. The findings show, for the first time in this context, that construction business resilience systematically mediates the relationship between distinct strategic survival portfolios and business growth in an emerging-market construction sector. This study advances the resilience and construction management literature by empirically demonstrating the hierarchical effectiveness of different survival models and by positioning construction business resilience as both a defensive capability and a strategic engine of sustainable development for construction firms in volatile markets. This paper recommends that firms develop composite resilience portfolios that integrate these strategies, while policymakers foster enabling regulations, digitalisation incentives, and joint risk-sharing arrangements that amplify sector-wide resilience. It offers a portfolio-based perspective on how to combine diversification, digital transformation, lean management, and strategic partnerships to build resilient, growth-oriented construction businesses. Convenience sampling and a cross-sectional design in a single national context highlight the need for longitudinal and cross-country research to validate and extend the proposed framework. Full article

China’s steel decarbonization is a key sustainability challenge because cleaner production routes must be evaluated not only by their mitigation potential, but also by their implications for industrial continuity, cost affordability, resource security, and transition manageability. This study develops a national-scale soft-linked sustainability assessment framework that translates policy-conditioned macro signals into a multi-period, multi-objective optimization model of steelmaking-route transition from 2025 to 2050. Three policy environments are examined: carbon-control pressure, electricity-cost support for electrified routes, and their combined application. The model evaluates route portfolios by cumulative system cost, emissions, and transition adjustment intensity, linking mitigation with affordability and implementation feasibility. Results show that policy environments do not shift pathways uniformly; instead, they reshape the feasible trade-off frontier and alter which route combinations emerge as plausible compromise solutions. Across scenarios, scrap-based electric arc furnace steelmaking (Scrap-EAF) becomes the central medium-term route, while blast furnace–basic oxygen furnace steelmaking (BF-BOF) contracts but remains residual. Hydrogen-based direct reduced iron–electric arc furnace steelmaking (H₂-DRI-EAF) expands under favorable conditions, but does not become dominant by 2050 under the baseline national-scale parameterization. Overall, this study contributes to sustainability-oriented industrial transition analysis by showing how policy-conditioned environments reshape route feasibility, transition sequencing, affordability–mitigation trade-offs, and the practical manageability of China’s steel-sector decarbonization. Full article

This study develops a ternary Fe-based geopolymer system composed of metakaolin (MK), red mud (RM), and fly ash (FA) for the preparation of sustainable water-retaining subgrade materials for sponge-city roadbed applications. Unlike conventional formulations primarily designed for structural strength or rapid permeability, the proposed MK–FA–RM system was designed to improve water-storage capacity while maintaining adequate mechanical support and environmental compatibility. In this ternary system, MK provides highly reactive aluminosilicate species for geopolymer network formation, RM introduces Fe-bearing phases and enhances industrial solid-waste utilization, and FA contributes to particle packing, workability, and resource efficiency. A constrained ternary mixture design implemented using Design-Expert software was adopted to optimize precursor proportions. Within the investigated compositional range, the fitted first-order mixture model showed acceptable statistical adequacy for preliminary composition screening (R² = 0.86). The optimal blend (60% MK, 30% RM, and 10% FA) achieved a 7-day compressive strength of 8.37 MPa and a water retention rate of 35.3% under ambient curing conditions, satisfying the strength requirement considered for the target subgrade/base-layer application. Microstructural and phase analyses suggest that the synergistic interaction of the three precursors promoted Fe-modified aluminosilicate gel formation together with conventional geopolymer gel products, while improving matrix continuity and preserving interconnected pore space for water storage. This multiscale structural effect helps explain how the material achieved a balance between water retention capacity and mechanical support. Under the tested conditions, the material maintained acceptable residual strength after short-term exposure to water, acid, and sulfate-containing solutions. Life-cycle assessment indicated a 70% reduction in CO₂ emissions compared with ordinary Portland cement, while pilot-scale cost analysis showed a 39% lower production cost than MetaMax-based geopolymer materials. Pilot-scale application further demonstrated the constructability and water-regulation potential of the material in practical environments. Overall, the proposed ternary Fe-based geopolymer demonstrates that Fe-rich industrial wastes can be engineered into low-carbon and economically viable water-retaining subgrade materials that balance hydraulic regulation, structural adequacy, and sustainability. Nevertheless, long-term durability, cyclic loading performance, and direct nanoscale characterization of Fe-bearing gel evolution still require further investigation. Full article

Industry 4.0 (I4.0) is transforming industrial systems through interconnected, data-driven technologies, raising questions about how these developments affect Occupational Health and Safety (OHS). This study investigates research trends, thematic structures, and knowledge gaps at the intersection of I4.0 and OHS using a multilevel bibliometric framework applied to Scopus records published from 2011 to 2025. The analysis moves from a broad overview of the I4.0 landscape to more focused examinations of specific I4.0–OHS publications, prevention-oriented studies, and emerging-risk research. The results show that OHS has limited visibility in the general I4.0 literature and is more prominent mainly in targeted subsets, where digital sensing, artificial intelligence, machine learning, and immersive technologies drive prevention-focused research. Conversely, emerging risks such as cognitive load, psychosocial stressors, and human–autonomy interaction appear in smaller, more dispersed clusters. Overall, the findings suggest that the relationship between I4.0 and OHS is unevenly developed, with established prevention mechanisms and early-stage conceptualization of new risks. Strengthening this field will require integrating human factors with digital indicators, better characterizing emerging risks, and ensuring that digital transformation supports SDG 8 by fostering safe and healthy working environments. Full article

The optimal placement of robotic manipulators within industrial workstations is a critical problem that directly affects task feasibility, accessibility, and operational efficiency. Improper base positioning can lead to joint saturation, reduced manipulability, and limited workspace utilization. This work presents an optimization framework for determining the optimal base placement of robotic manipulators by maximizing a joint-centering performance index based on the κ-index, which quantifies the proximity of joint variables to their allowable limits. The proposed methodology integrates geometric accessibility constraints with a constrained optimization formulation to ensure feasible robot configurations within the workspace. Three optimization strategies—constrained nonlinear programming, gradient projection methods, and genetic algorithms—are evaluated and compared in terms of solution quality and computational performance. Numerical simulations are conducted using a planar 2-DOF manipulator to illustrate the proposed framework and to analyze the influence of workspace geometry on optimal base placement. Additionally, an industrial case study involving the ABB IRB 120 robotic manipulator is presented to assess the practical applicability of the proposed approach. The results demonstrate that the optimization framework improves joint distribution within the allowable limits and enhances robot accessibility across the task workspace. The proposed method provides a practical tool for intelligent workstation design and robotic cell layout optimization in modern industrial environments. Full article

Carbon capture and storage (CCS) is widely recognized as a key technology for reducing carbon dioxide (CO2) emissions from large industrial sources. Among the stages of the CCS chain, CO2 transportation plays a decisive role in determining overall system safety, reliability, and economic viability. CO2 transportation through pipelines is generally preferred for large-scale, long-distance applications and is commonly operated under dense or supercritical conditions to maximize efficiency. However, internal corrosion of pipeline steels remains a major integrity concern, with corrosion accounting for approximately 45% of reported CO2 pipeline failures. This review provides a comprehensive assessment of internal uniform and localized corrosion phenomena in CO2 pipelines operating under supercritical CO2 environments. The influence of key CO2 stream impurities, including H2O, O2, H2S, SOx, and NO2, is examined, considering their individual and synergistic effects on corrosion mechanisms, corrosion morphology, corrosion products, and severity ranking. In addition, an in-depth analysis of operating parameters such as temperature, pressure, flow conditions, and exposure time is presented alongside material-related factors, including steel grade, internal surface roughness, and welded regions. Corrosion mitigation approaches are also reviewed, with particular emphasis on organic, inorganic, and composite corrosion inhibitors. The review concludes by identifying key knowledge gaps and outlining future perspectives for improving corrosion control in CO2 transport systems supporting large-scale CCS deployment. Full article

Article discusses Digital Twins (DTs) as a solution for artificial intelligence (AI)-based modeling and metrology in Industry 5.0 and Industry 6.0 manufacturing systems. DTs enable the creation of real-time virtual replicas of physical assets, processes, and systems, increasing transparency, prediction, and optimization in manufacturing environments. By integrating AI, machine learning (ML), and advanced sensor data, DT support adaptive, self-learning production models capable of responding to dynamic operating conditions. In metrology, DTs improve measurement accuracy, traceability, and quality assurance by continuously synchronizing data between the physical and virtual domains. This technology improves process simulation, predictive maintenance, and fault detection, reducing downtime and operating costs. Furthermore, DTs facilitate human-centric production by enabling collaborative decision-making between intelligent systems and skilled workers. Their role in sustainable production is significant, supporting energy optimization, waste reduction, and lifecycle performance analysis. In Industry 6.0, DTs go beyond cyber-physical integration to encompass cognitive intelligence, ethical automation, and autonomous optimization. However, challenges remain in data interoperability, cybersecurity, model scalability, and real-time computational performance. DTs represent a revolutionary framework for the development of intelligent, resilient, and precise manufacturing ecosystems in next-generation industrial systems. Full article

The automotive industry in Mexico faces significant logistical challenges in optimizing distribution routes, particularly in border regions, where traffic variability directly affects operational performance. This study proposes a multiperiod route optimization approach for a Tier 1 automotive supplier by applying the Floyd–Warshall algorithm to a cross-border transportation network. Distance matrices are constructed for multiple time windows to capture traffic-related variations in route efficiency. The algorithm is applied independently to each scenario, enabling the identification of time-dependent optimal routes and the development of alternative routing strategies. The results show that optimal routes vary across different periods of the day, leading to measurable improvements in routing efficiency and enhanced decision-making flexibility. The proposed approach supports more realistic logistics planning in congested urban environments and improves operational performance in cross-border automotive supply chains. Full article