Search Results (15,120)

This study develops an Industry 5.0- and IoT-enabled roadmap for green transformation in manufacturing, with a particular focus on Turkish industry. The study combines a structured literature review, bibliometric keyword mapping based on Web of Science records, and interview-informed framework refinement drawing on the sustainability departments of five large-scale manufacturing firms operating in Türkiye. Rather than treating green transformation as a single initiative, the roadmap organizes it into five interrelated modules: emission reduction, clean and reliable energy, circular-economy mobilization, energy- and resource-efficient construction and renovation, and zero-pollution waste management. The main contribution is a five-level qualitative maturity model that shows how firms can move from compliance- and governance-based foundations to integrated, data-driven, and predictive sustainability practices. The framework clarifies which factors are foundational, enabling, or advanced at each level and is intended to be used as a practitioner checklist and strategic assessment tool rather than as a fixed quantitative scoring model. The interview insights were used to refine the sequencing of actions, identify implementation bottlenecks, and adapt the framework to the realities of Turkish manufacturing. By linking human-centric Industry 5.0 principles with operational sustainability priorities, this study offers both conceptual novelty and practical guidance for firms and policymakers seeking to align industrial upgrading with long-term environmental competitiveness. Full article

This study proposes a sensor-free approach for indoor fire detection that leverages existing Wi-Fi infrastructure as a passive sensing modality. By extracting Channel State Information (CSI) from prevalent 802.11n Wi-Fi signals and applying an unsupervised deep anomaly detection model, the approach conceptualizes the wireless environment as a sensorless detection field capable of identifying combustion-induced perturbations without requiring any physical sensors. CSI data were collected in both normal and flame-induced states under three combustion conditions (gasoline, wood, plastic), each introducing unique signal perturbations. These data, which exhibit diverse signal perturbations, were used as input to four unsupervised deep anomaly detection architectures: a variational autoencoder (VAE), a 1D convolutional autoencoder (CNN-AE), a long short-term memory autoencoder (LSTM-AE), and a hybrid CNN-LSTM autoencoder. Each architecture was trained exclusively on baseline data to learn compact latent representations of normal signal patterns. Among the evaluated architectures, CNN-AE achieved perfect detection across all scenarios, showing high responsiveness to signal disruptions. LSTM-AE tracks prolonged combustion but struggles with fast-onset anomalies. VAE maintains low error during baseline but misses sharp deviations. These findings validate that Wi-Fi CSI encodes latent combustion features. The method requires no additional sensors and operates on existing signals, enabling scalable smart building integration via lightweight software updates. Full article

In the context of intensified trade frictions and frequent financial market fluctuations, assessing the risk resilience of strategic emerging industries holds significant strategic value. Based on quarterly data from 2010 to 2025, this study empirically examines the time-varying and asymmetric shock effects of trade policy uncertainty and financial stress on the profitability of China’s strategic emerging industries using the TVP-VAR-SV model. The study finds that China’s strategic emerging industries exhibit significant asymmetric resilience differences when facing different external shocks, specifically demonstrating stronger trade resilience and weaker financial resilience. The shocks brought by trade uncertainty typically show short-term pain followed by rapid recovery, with the negative impact being largely eliminated within two quarters and subsequently turning into positive growth, reflecting outstanding recovery capability. In contrast, the impact of financial stress on corporate profitability has a profound long-tail effect, with negative disruptions often persisting for more than two years before gradually dissipating. This contrast indicates that trade policy uncertainty and financial stress affect industrial resilience through asymmetric response patterns in terms of impact intensity and persistence. Over time, as autonomy and controllability have improved, the industry’s defensive ability to cope with trade frictions has significantly strengthened, yet credit tightening and liquidity pressure in the financial sector remain the core threats to its profitability recovery. This study not only reveals the asymmetric resilience paths of strategic emerging industries under different external shocks but also provides empirical evidence and policy recommendations for the future improvement of the technology–finance system and the construction of a more resilient domestic industrial chain. Full article

Amid the coordinated advancement of the digital economy and rural revitalization, Digital Rural Construction (DRC) has increasingly emerged as a critical catalyst for agricultural modernization and sustainable development. Faced with dual challenges of land resource constraints and agricultural green transformation, improving the Agricultural Land Green Utilization Efficiency (ALGUE) has become essential for achieving high-quality agricultural development. Based on panel data from 29 Chinese provinces from 2012 to 2023, this study employs the super-efficiency SBM model to quantify ALGUE. A comprehensive four-dimensional evaluation system—encompassing digital infrastructure, service capacity, human capital quality, and practical application—is constructed, and the entropy method is used to measure the level of digital rural construction. By applying two-way fixed effects models, mediation analysis, and heterogeneity tests, this study systematically examines the impact of digital rural construction on ALGUE and its underlying transmission pathways. The results demonstrate that: (1) Digital rural construction significantly enhances ALGUE, and this finding remains robust under multiple sensitivity checks. (2) Pronounced heterogeneity exists in two dimensions: the promotion effect is stronger in economically developed regions and in regions with higher agricultural mechanization intensity, while it is weaker in less developed and low-mechanization regions. (3) Mechanism analysis reveals that digital rural construction promotes ALGUE through two channels. The first involves accelerating the transition of the primary industry toward intelligent and high-value-added models, thereby optimizing resource allocation and reducing environmental pressure. The second operates by fostering regional economic growth in an inverted U-shaped nonlinear pattern that supports agricultural green transformation. By integrating DRC and ALGUE into a unified framework, this study identifies two mediating channels and reveals heterogeneity across economic development levels and agricultural structures. These findings provide empirical support and policy implications for digitally driven green agricultural development. Full article

With the in-depth advancement of the “Digital China” initiative, policies and research discourses related to the digital economy have continued evolved, making it necessary to systematically examine their stage-specific characteristics and underlying logic from a long-term perspective. Accordingly, this study adopts information society theory as the analytical framework and selects the annual series of reports on China’s digital economy development published by the China Academy of Information and Communications Technology (CAICT) from 2015 to 2024 as the research corpus. Using text mining techniques and Latent Dirichlet Allocation (LDA) topic modeling, this paper conducts a longitudinal examination of the stage-wise systemic evolution of key topics in China’s digital economy development. The findings indicate that over the past decade, the topic structure of China’s digital economy has followed a clear evolutionary trajectory, progressing from “informatization-driven development” to “platform expansion,” and subsequently to “data factors and institutional governance.” In the early stage, the focus was on information infrastructure development and industrial integration; the middle stage shifted toward the platform economy and enterprise growth; more recently, the emphasis has increasingly been placed on the construction of data factor markets and the improvement of governance frameworks. This process of topic evolution not only reflects changes in the practical forms of the digital economy but also reveals the ongoing adjustment of the state’s cognitive framework and governance logic regarding digital economy development. These findings provide empirical evidence for understanding the systemic evolution of China’s digital economy over time. By identifying the stage-specific pathways of China’s digital economy, this study extends the application of information society theory within this context and provides new empirical evidence for understanding the evolutionary logic underlying high-quality digital economy development. Full article

As China’s urbanization transitions from incremental expansion to stock-based renewal, industrial land redevelopment has become a key pathway for promoting high-quality urban development. However, existing studies mostly assess redevelopment potential from a single dimension and lack a systematic framework integrating ecological function (E), spatial structure (S), economic conditions (C), and building foundations (B). Taking the built-up area of Xi’an as a case study, this study adopts a functional–structural optimization perspective and constructs a four-dimensional ESCB assessment framework based on 13 indicators covering ecological function, spatial structure, economic conditions, and building foundations. GIS-based spatial quantification, MiniBatchKMeans clustering, and the XGBoost algorithm were employed to identify the redevelopment potential of industrial land, while SHAP analysis was used to interpret indicator contributions and determine the core influencing factors. The results show that industrial land in the study area can be classified into four types: vitality–density dominant, transport–scale coordinated, scale–facility lagging, and topography–vegetation sensitive, with significant differences in spatial distribution and indicator characteristics. The interpretable machine learning model further identifies road network density, block-level economic vitality, and land-use suitability as the three principal drivers of redevelopment potential, among which road network density plays the most critical role. By integrating clustering analysis with interpretable machine learning, the ESCB framework effectively reveals the synergies and trade-offs among multidimensional indicators and provides differentiated and precise support for industrial land redevelopment strategies. Full article

This study investigates the feasibility of incorporating high-volume municipal solid waste incineration (MSWI) fly ash into geopolymers, with a focus on its effects on mechanical performance and fragmentation behavior. A systematic experimental program was conducted in three stages. Geopolymer mixtures were first prepared with MSWI fly ash substitution rates ranging from 50% to 100% at seven distinct levels. Uniaxial compression tests were then performed to evaluate mechanical properties, followed by sieve analysis to examine fragment size distribution. The fractal dimension (D) was adopted to quantitatively characterize the degree of fragmentation. The results indicate that dry density, compressive strength, and elastic modulus all decrease progressively with increasing MSWI fly ash content. Specifically, as the fly ash content increased from 50% to 100% the compressive strength decreased from 9.57 MPa to 3.18 MPa. Notably, even at a 100% substitution rate, the compressive strength reached 3.18 MPa, which is 59% higher than the 2.0 MPa minimum requirement specified in the JTG/T F20-2015 standard. These findings demonstrate that MSWI fly ash can be effectively utilized at high replacement levels to produce sustainable geopolymers with satisfactory mechanical properties. This approach presents a viable strategy for recycling industrial solid waste into value-added construction materials. Full article

We estimate Oregon’s mass timber-related market value and economic contribution using two complementary valuation strategies and two IMPLAN implementations. Although mass timber includes CLT, glulam, nail-laminated timber, dowel-laminated timber, mass plywood panels, and structural composite lumber products, the empirical market-value estimates are centered primarily on CLT- and MPP-related evidence because these products have the most consistently available Oregon-specific data. Market value is inferred from production-based approaches, including facility capacity, Oregon’s share of U.S. output, and tracer-product scaling, and from demand-based approaches, including harvest routing, construction floor area, and U.S. demand allocation. These direct values are then entered into industry contribution analysis (ICA) for Oregon’s Engineered Wood Member and Truss Manufacturing sector and into analysis-by-parts (ABP) using a custom mass timber spending pattern. During 2018–2023, production-based estimates were larger and more variable than demand-based estimates, bracketing a plausible scenario range rather than providing a single point estimate. In 2022 price scenarios, all price-exposed cases scale proportionally with assumed panel prices. When identical direct values are used, ABP produces larger total employment and output effects than ICA because it routes more activity through upstream supplier industries. Output-per-worker sensitivity affects only direct employment in ABP. Forward scenarios for 2030 and 2035 indicate substantially larger total effects under ABP than ICA, but these estimates are conditional scenarios rather than forecasts. The framework provides a transparent basis for policy, investment, supplier-development, and workforce-planning discussions in an emerging industry with incomplete product-level data. Full article

With the formal inclusion of the shipping industry in the European Union Emissions Trading System (EU ETS), the speed optimization of ocean-going container ships must simultaneously balance operating costs, incorporating carbon emission costs and shipper satisfaction with transportation timeliness. Taking ocean-going container liner routes as the research object, this paper establishes a ship navigation resistance model based on meteorological and hydrological conditions, and constructs a route segmentation mechanism and a ship fuel consumption model on this basis. The spatially differentiated carbon accounting rules of the EU ETS are introduced, a fuzzy membership function is adopted to quantify shipper satisfaction, and a Q-learning-based solution algorithm for ship speed optimization that balances operating costs and shipper satisfaction is designed. Numerical experiments on a 20,150 Twenty-foot Equivalent Unit (TEU) container ship demonstrate that the proposed framework reduces total operating costs by 5.56%, EU ETS carbon compliance costs by 18.72%, and total voyage carbon emissions by 11.01% compared with the conventional constant-speed strategy. Meanwhile, the algorithm can spontaneously form an optimal speed strategy adapted to meteorological conditions and policy rules. Through parameter sensitivity analysis, this paper further extracts management implications for liner-operating companies. Full article

Although organisational climate is increasingly examined, explicit modelling of demographic interaction effects remains comparatively underrepresented. A search strategy was conducted (25 September 2025), and 358 records were identified and filtered in the Scopus and Covidence databases; subsequently, 60 peer-reviewed articles met the inclusion criteria following PRISMA-guided screening. R-project, reference to VOSviewer, and Biblioshiny were used to perform the bibliometric mapping to demonstrate three (3) large thematic clusters: (1) conceptual models with a focus on the Job Demands–Resources (JD–R) framework; (2) growing cross-sector and post-COVID literature; and (3) small but growing incorporation of interactive demographic variables (age, gender, tenure) other than control-variable treatment. The results show that organisational climate is always placed at the forefront as an important predictor of satisfaction, but intersectional demographic modelling is underdeveloped and geographically biased to Western and Asian factors. Yet improvements have been made in theoretical integration; however, a lack of constructs, methodological conservatism, and geographic skewness limit theoretical cumulation and practical translation. The proposed multi-factor model is conceptually derived from bibliometric patterns and requires empirical validation using CFA, SEM, and multilevel modelling. However, organisations should integrate satisfaction policies that reflect diverse demographic and contextual realities, rather than adopting a general approach. The study advances the model of employee satisfaction research by offering practical evidence and a theoretical framework to support the sustainability of industrial and organisational psychology. Full article

To accurately predict the fishing intensity of the Northwest Pacific squid fishing grounds and address the limitations of traditional models in capturing long-term temporal and spatial correlations and neglecting the coupling relationships of deep environmental factors, this study constructs a 3DCNN model and three fusion models incorporating residual, attention, and Transformer mechanisms. Using the 2017–2024 AIS fishing data and ocean environmental variables from the North Pacific squid fishing industry, the models’ performance is compared at 12 different temporal and spatial scales, and key core environmental variables are identified. The results show that the ResNet3D model exhibits the best overall performance, achieving an F1 score of 0.7909 at the 1.0°-7 days temporal–spatial scale. The residual connections effectively mitigate the gradient vanishing problem, balancing prediction accuracy and stability. The optimal spatial resolution is 1.0°, and the key environmental variables include S₁₀₀, Chl-a₁₀₀, PP₁₀₀, and DO₁₀₀. S₁₀₀ is the core driving variable, consistently exhibiting the highest feature importance value at all time scales. It should be noted that Chl-a is considered an indirect indicator of primary productivity, which may influence squid distribution through trophic transfer processes rather than direct biological effects. This study demonstrates the prediction accuracy and applicability of the multi-mechanism fusion 3DCNN model, reveals the temporal and spatial distribution patterns of fishing intensity in the Northwest Pacific squid fishing grounds, and provides scientific methods and technical support for dynamic monitoring, intelligent management, and sustainable utilization of squid resources. Full article

Laser surface hardening is a technique that improves various mechanical characteristics of different materials. The methods are being extensively used in the automobile, aerospace, tool manufacturing, and construction industries for various components. The present review highlights the hardness and hardened surface depth improvement of different steels and non-ferrous alloys in as-bought and pre-heat treatment conditions. Diode and fibre lasers have rendered higher surface hardness and hardened depth, while consuming higher power. Nd:YAG lasers have resulted in a precise increase in hardness and a very minimal 0.8 in ferrous and 2 mm in surface-hardened depth of non-ferrous alloys, proving a better efficiency. The pre-heat treatments are selected to enhance mechanical properties and reduce the deformations and defects. An increase of 300.43 and 282.38% of surface hardness due to laser hardening as compared to the core material of AISI 420 was observed using a high-power diode laser. A huge 281.41% of increase in surface hardness was observed for ICD-5 tool steel using Nd:YAG lasers. The annealing pre-heat treatment has also affected the hardenability, resulting in high hardness. Non-ferrous alloys such as titanium and A356 alloys have recorded 200 and 125% increase in surface hardness compared to their core using Nd:YAG lasers. Full article

With the rapid intensification of industrial and agricultural activities, water contamination by heavy metal ions has emerged as a critical global challenge, gravely imperiling ecosystem stability and public health. Among the various remediation technologies, adsorption has been widely adopted due to its high efficiency, low-cost water treatment, and simplicity of operation. However, conventional inorganic or synthetic adsorbents often exhibit poor degradability and pose a risk of secondary contamination, substantially limiting their sustainable application. Consequently, the development of environmentally benign and renewable adsorbent materials has become a central research focus in this field. Recently, cellulose-based composite hydrogels, derived from renewable resources and characterized by excellent eco-friendliness and highly tunable three-dimensional porous structures, have attracted considerable attention as promising green adsorption materials. These hydrogels demonstrate outstanding performance in the efficient sequestration of heavy metal contaminants from aqueous environments. This review systematically summarizes recent advances in cellulose-based composite hydrogels for heavy metal removal, to elucidate the structure–performance relationships linking material fabrication strategies, structural modulation, and adsorption efficiency. First, we outline the principal construction approaches, including physical crosslinking, chemical modification, and supramolecular self-assembly, and comprehensively analyze how different synthesis routes regulate pore architecture, mechanical properties, and the distribution of surface functional groups. Second, the underlying adsorption mechanisms, primarily coordination complexation, electrostatic interactions, and ion exchange, are discussed in detail. Finally, recent studies on the adsorption of cationic heavy metals (e.g., Pb(II), Cu(II), and Cd(II)) and anionic oxyanions (e.g., As(III) and Cr(VI)) are critically reviewed, with particular emphasis on the relationships between selective adsorption performance, material design principles, and specific recognition mechanisms. Overall, this review provides a theoretical foundation and practical guidance for the design and development of next-generation water treatment materials with high adsorption capacity, excellent selectivity, non-toxicity, and strong environmental compatibility, followed by future research recommendations. Full article

Maintaining safe and consistent performance in industrial energy networks necessitates the dependable detection of asynchronous motor failures. However, in practical scenarios, diagnostic models often suffer from poor generalization and high false alarm rates when faced with incomplete datasets and limited high-quality samples. Aiming to overcome the aforementioned constraints, a PCA-KPLS integrated multi-fidelity scheme is presented in this work. The method utilizes low-fidelity data to construct a Principal Component Analysis (PCA) model for extracting basic features, and then integrates a small amount of high-fidelity target data via Kernel Partial Least Squares (KPLS) to establish a cross-domain feature mapping, enabling knowledge transfer between data of different fidelities. Validation through mathematical simulation and an engineering case study on a primary air fan demonstrates that the proposed method achieves higher prediction accuracy and lower root-mean-square error compared to models using only low-fidelity or high-fidelity data, significantly reduces false alarms, and enhances the accuracy of fault diagnosis and model generalization capability when training samples are insufficient. Full article

Graph-based entity deduplication has proven effective for resolving duplicate records when identifying information is sparse and heterogeneous, yet in continuously evolving industrial data systems, it remains insufficient on its own. As new records and relationships are added incrementally, previously separate entity components can merge, causing instability and inconsistency in entity identifiers that undermine downstream analytics, auditing, and system integration, requiring persistent, interpretable identifiers over time. This work introduces StableID, an iterative graph-based framework for persistent entity identification in dynamic environments. StableID treats identifiers as long-lived system assets rather than transient outputs, incorporating historical grouping results into subsequent graph constructions through a feedback mechanism to ensure previously resolved entities retain consistent identifiers. When components merge, a deterministic dominance rule assigns the identifier from the largest prior component to the unified entity, minimizing churn, while time-scoped identifier generation with execution-level prefixes prevents collisions and guarantees global uniqueness during incremental updates. Implemented with distributed graph processing, StableID was evaluated through iterative executions on large-scale, multi-state voter registration data lacking global identifiers and featuring heterogeneous schemas. Results demonstrate strong identifier stability, progressive convergence in the number of entity identifiers, and a clear trend toward a stable identity state as relational connectivity increases. Overall, StableID transforms graph-based deduplication into a production-ready identity management solution suitable for continuously updating industrial data. Full article