Search Results (15,113)

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

In a time of extraordinary global volatility, the survival and competitiveness of the Architectural, Engineering, and Construction (AEC) industry rely less on technological supremacy and more on cultural agility to repurpose resources effectively. Although Industry 4.0’s digital transformation offered tools for operational efficiency, the new Industry 5.0 paradigm emphasizes a human-centric approach, with organizational culture serving as a crucial link between advanced technology and organizational resilience. This study explores how organizational culture mediates resource repurposing and the shift from Industry 4.0 to Industry 5.0 in the AEC sector. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a sample of 120 AEC professionals, it examines how cultural traits—viewed as strategic leadership influence, employee adaptability, and innovation— mediate operational results. The results indicate that employee technology use and innovation are key drivers of resource reconfiguration, directly improving productivity and lowering project costs. Importantly, the findings show that organizational culture is not merely a background factor but a strategic enabler that partly mediates the link between Industry 4.0 adoption and cost savings. Thus, this research offers a theoretical framework for AEC firms to harness cultural flexibility as a strategic resource, advancing beyond simple digital adoption to embed innovation for sustainable long-term growth. Full article

Amid escalating global crises, tourism sustainability is threatened by heightened industry vulnerability, yet the intrinsic coupling of tourism industry vulnerability (TIV) and resilience (TIR) remains underexplored via systemic theoretical frameworks. This study aimed to define TIV/TIR as industry-specific constructs and develop an integrated analytical model grounded in dissipative structure theory to characterize tourism systems’ crisis responses. We selected Southwest China’s ethnic minority regions (Guizhou, Guangxi, Yunnan) as cases, using 2015–2024 prefecture-level panel data to explores the spatio-temporal differentiation characteristics of TIV/TIR. Results revealed severe COVID-19-induced TIV surges in 2020–2021, followed by rapid TIR rebounds; TIV and TIR exhibited a significant negative correlation with regional heterogeneity. Most cities showed high TIV–low TIR, with Guizhou displaying negative TIV-TIR spatial autocorrelation and Guangxi–Yunnan showing TIR clustering; inter-city TIV disparities widened while TIR levels converged, leading to a low-vulnerability, balanced-resilience tourism system by 2024. This research introduces the novel sensitivity-adaptive capacity-recovery (SACR) framework, advancing understanding of TIV-TIR dynamics and providing targeted empirical insights for tourism resilience building and sustainable development in resource-dependent destinations. Full article

Red mud discharged during alumina production via the Bayer process is characterized by high contents of sodium carbonate, sodium sulfate, and other soluble salts, and it remains poorly utilized and accumulates in long-term stockpiles. Sodium percarbonate has found extensive industrial applications, and its synthesis via the salting-out method represents one of the dominant industrial routes. In this context, sodium sulfate was employed as a salting-out agent. On the basis of relevant ternary systems, the phase equilibrium of the quaternary system Na₂CO₃–Na₂SO₄–H₂O₂–H₂O at 5 °C was systematically investigated and calculated. The objective was to utilize red mud as a waste resource and develop a novel integrated process that favored the wet synthesis of sodium percarbonate while enabling the efficient separation of sodium salts. The solubility data for the ternary subsystems constituting the above quaternary system were correlated using the Pitzer model, yielding the corresponding ion interaction parameters and activity coefficients. The validated model was then applied to predict the phase equilibrium data of the quaternary system. Verification results indicate that the calculated values are in satisfactory agreement with the experimental data. On the basis of the phase equilibrium data of the Na₂CO₃–Na₂SO₄–H₂O₂–H₂O system at 5 °C, a phase diagram was constructed. Along with five solid-phase crystallization fields, three invariant points were identified: the co-saturation point of Na₂SO₄·10H₂O, Na₂CO₃·10H₂O, and Na₂CO₃·1.5H₂O₂·H₂O; the co-saturation point of Na₂SO₄·10H₂O, Na₂CO₃·1.5H₂O₂·H₂O, and Na₂SO₄·0.5H₂O₂·H₂O; and the co-saturation point of Na₂CO₃·1.5H₂O₂·H₂O, Na₂SO₄·0.5H₂O₂·H₂O, and Na₂CO₃·2H₂O₂·H₂O. From phase diagram analysis, a novel wet process route for sodium percarbonate production using waste red mud is proposed. The process involves chemical reaction, crystallization, separation, and drying to obtain the final product. A new process flow diagram for the value-added production of sodium percarbonate is also presented. Full article

Hydroxypropyl methylcellulose (HPMC) is a cellulose derivative characterized by physicochemical properties (gel formation, water solubility, biodegradability, and biocompatibility). These properties explain their wide use in industries such as pharmaceuticals, food, and construction. This review evaluates the classification, production processes, and analytical characterization of HPMC, with particular attention to its versatility and sustainability life cycle. The environmental impact of HPMC is analyzed through its energy-intensive production, waste generation, emissions, and end-of-life biodegradability. In comparison with many petroleum-based polymers, HPMC is often considered a greener option and its use as an additive in modern chemical industry is extended. Therefore, the adoption of more sustainable production practices is essential to minimize its ecological footprint. In this sense, greener raw material sourcing, improved production process efficiency, lower emission etherification and purification routes, and broader implementation of life-cycle-based optimization strategies were identified as key priorities to be addressed. Full article

Tree bark, an abundant by-product of the timber industry, represents a promising feedstock for sustainable construction. This study investigates the thickness swelling, water absorption, hygroscopicity and mechanical (compressive strength) properties of insulation panels produced from hardwood bark (Tilia spp. and Robinia pseudoacacia) via hydromechanical treatment and a wet-forming process. The panels were produced without added adhesives, relying on the formation of hydrogen bonds during the drying phase to ensure structural integrity. Both bark-based insulation boards (thermal conductivity coefficient 0.055–0.057 W/m·K) showed similar hygroscopic behavior, reaching equilibrium moisture contents of max. 25% at 93.9% RH. Water absorption after 24 h immersion was highly material-dependent; Tilia-based panels showed 57.11 ± 5.81%, and Robinia-based panels 320.61 ± 11.34%. Thickness swelling remained low (max. 6% for Robinia), showing significant orthotropic anisotropy. At 10% compressive strain, the Tilia and Robinia bark-based panels showed compressive strengths of 188 ± 14.6 kPa and 298 ± 18.1 kPa, accordingly. These findings demonstrate that hardwood bark can be successfully valorized into high-performance, binderless insulation, supporting circular economic strategies. Full article

The global poultry hatching industry faces severe challenges of resource waste and animal ethics issues due to the routine culling of day-old male chicks. Meanwhile, early sex identification of 4-day-incubated chicken embryos is limited by low accuracy, as embryos at this stage have weak, low-contrast blood vessels that are highly susceptible to interference from the eggshell’s texture. To address these issues, this paper proposes a non-destructive early sex identification method for chicken embryos based on an improved MobileViT-V3 model. Taking the lightweight hybrid architecture MobileViT-V3 as the backbone, we embedded a Micro Feature Enhancement module (MFE-Module) in Stage 3 to strengthen the extraction of fine vascular details, and a Multi-Scale Adaptive Attention Fusion module (MSAAF-Module) in Stage 4 to realize adaptive weighted screening of multi-source features. Experiments on the self-constructed dataset of 4-day-incubated embryos show that the improved model achieves a test set classification accuracy of 92.26%, with an F1-score of 92.15%, a recall rate of 92.12%, and a Kappa coefficient of 0.845. It outperforms mainstream models such as YOLOv12, ShuffleNetV2, ConvNeXt-T, ResNet, and Swin-ViT, with only 2.98 M parameters and an inference speed of 97.6 FPS, well exceeding the 30 FPS real-time requirement of industrial sorting lines and showing high potential for practical industrial deployment. This method provides a new scheme for non-destructive, high-precision, and high-efficiency early sex identification in poultry hatching. Full article

In the highly competitive e-commerce landscape, platforms must strategically balance complex operational and marketing parameters. These real-world systems inherently involve high-dimensional nonlinear interactions and strongly coupled variables, leading to complex consumer response behaviors and highly non-convex optimization landscapes. Traditional optimization approaches usually suffer from high computational costs in business environments, while conventional surrogate models are prone to premature convergence during hyperparameter estimation. To address these management and operational challenges, this study proposes a Chaos-initialized Quantum-behaved Particle Swarm Optimization Kriging (CQPSO–Kriging) framework. Chaotic mapping is introduced to enhance population diversity, while quantum-behaved particle dynamics improve global exploration capability. Utilizing large-scale real-world transaction data from the Brazilian e-commerce industry, high-fidelity surrogate response surfaces are constructed for three core business indicators: profitability, customer loyalty, and value density. Experimental results show that the proposed CQPSO–Kriging model significantly outperforms conventional approaches, such as support vector regression and radial basis function networks, achieving an exceptional coefficient of determination of R² = 0.9586 in profit prediction. Furthermore, Sobol variance-based global sensitivity analysis is employed to extract critical managerial insights, revealing that financial variables act as interaction-driven utility multipliers in consumer decision-making. Multi-objective Pareto analysis further demonstrates that profit maximization naturally converges toward a balanced operational configuration, providing a robust quantitative tool for e-commerce precision marketing. Full article