Search Results (2,559)

Industrial heritage is increasingly becoming an important resource for sustainable urban renewal. With the acceleration of deindustrialization and urban transformation, Adaptive Reuse (AR) is regarded as the core path connecting heritage protection and functional renewal. Balancing the diverse value dimensions of AR has also become a key research focus. However, existing research mostly focuses on financial returns and investment efficiency, ignoring the long-term impact of community space and cultural dimensions on economic feasibility; at the same time, culture is often simplified into a tool for asset appreciation and urban branding, lacking a systematic model that reveals the structural role of culture in economic feasibility. Therefore, this study constructs a multi-attribute decision-making framework that integrates economic performance, community space, and cultural value. Using Guangzhou Guanggang New City as a representative case, the Fuzzy Delphi Method (FDM), Analytic Network Process (ANP), and Grey Relational Analysis (GRA) were employed to screen and rank the highest-priority reuse schemes. The results show that the economic dimension holds the highest overall weight, followed by the community and cultural dimensions. This suggests that economic feasibility remains a key prerequisite for industrial heritage renewal, while cultural and community factors play an important supporting role in achieving long-term sustainability. This study provides a quantifiable assessment path for the adaptive reuse of industrial heritage and offers a basis for decision making in other cities seeking a balance between economic rationality and cultural sustainability. Full article

Against the backdrop of policy-driven transformation in construction industrialization, the EPC general contracting model has emerged as a core pathway for the large-scale development of prefabricated buildings. However, the EPC mode integrates the links of design, procurement, production, and transportation, construction, resulting in a complex coupling correlation among the risk factors of prefabricated construction schedule, which is easy to induce the risk contagion effect and increase the difficulty of risk control of project schedule delay. To address this, this study constructs a hybrid model integrating the “Fuzzy Interpretive Structural Model (FISM)-Coupling Degree Model-Bayesian Network (BN)” to systematically analyze risk contagion mechanisms. Taking an EPC prefabricated building project as an example, FISM is used to reveal the hierarchical structure of risk factors, while the coupling degree model quantifies interaction strengths and maps them into the BN to optimize conditional probability parameters. Through comprehensive hazard analysis, seven key causal risk factors and two critical risk propagation paths are identified. Targeted control measures are designed for the key risk factors, and BN-based simulation is applied to locate critical risk nodes and implement break-chain interventions for the risk paths, resulting in a 23% reduction in the probability of schedule delay. Engineering applications demonstrate that this model can effectively achieve the dynamic identification and blocking of risk paths, providing valuable reference for similar projects and offering informed support for managers in formulating scientific response strategies. Full article

In the highly competitive restaurant industry, prioritising customer satisfaction is crucial for establishments pursuing differentiation and repeat business. Within this context, creating unique and memorable experiences has evolved from a marketing trend into a strategic imperative, compelling restaurants to deliver encounters that transcend mere functional service and quality. However, prior research has primarily examined quality factors and satisfaction in isolation, overlooking the mediating role of experiential realms in this relationship. This study offers a novel contribution by integrating service quality and experiential marketing within a single empirical model, addressing a gap in the hospitality literature. Specifically, few studies have empirically examined how tangible and intangible quality cues translate into the four experiential realms of the Experience Economy—aesthetic, escapist, entertainment, and educational—and how these, in turn, influence satisfaction and revisit intentions. Drawing on the Experience Economy framework, this study develops and tests a conceptual model linking quality antecedents—physical environment, food quality, and customer service—to the four experiential realms (aesthetic, escapist, entertainment, and educational) and subsequent satisfaction and revisit intentions. Using data collected from 312 restaurant customers, the hypotheses were tested through Structural Equation Modelling (SEM). The findings reveal that quality antecedents significantly influence experiential realms, which in turn enhance satisfaction and revisit intentions—offering a more nuanced mechanism than previously theorised. By being among the first to empirically test these relationships in the sit-down restaurant context, this study adds theoretical and practical insight into experience-based brand differentiation. Moreover, it provides actionable insights for restaurant managers seeking to transform quality delivery into memorable, loyalty-building experiences. Full article

The urgent global challenge of environmental sustainability has intensified interest in integrating Lean Management practices with environmental objectives, particularly within the automotive industry, a sector known for both innovation and high environmental impact. This study investigates the systemic relationships between 16 lean practices and three environmental performance metrics: energy consumption, CO₂ emissions, and waste generation. Using the Fuzzy Decision-Making Trial And Evaluation Laboratory (DEMATEL) methodology, data were collected from seven lean experts in the Italian automotive industry to model the cause–effect dynamics among the selected practices. The analysis revealed that certain practices, such as Total Productive Maintenance (TPM), just-in-time (JIT), and one-piece-flow, consistently act as influential drivers across all environmental objectives. Conversely, practices like Statistical Process Control (SPC) and Total Quality Management (TQM) were identified as highly dependent, delivering full benefits only when preceded by foundational practices. The results suggest a strategic three-step implementation roadmap tailored to each environmental goal, providing decision-makers with actionable guidance for sustainable transformation. This study contributes to the literature by offering a structured perspective on lean and environmental sustainability in the context of the automotive sector in Italy. The research is supported by a data-driven method to prioritize practices based on their systemic influence and contextual effectiveness. Full article

The high stability of chelated heavy metal complexes like Cu-EDTA renders their effective removal from industrial wastewater a persistent challenge for conventional treatment processes. This study developed a sustainable and high-performance CuO-modified biochar (CuO-BC) from corn straw waste for peroxydisulfate (PDS)-activated degradation of Cu-EDTA. Through systematic optimization, hydrothermal co-precipitation using copper acetate as the precursor followed by secondary pyrolysis at 350 °C was identified as the optimal synthesis strategy, yielding a dandelion-like structure with highly dispersed CuO on the BC surface. It achieved 93.8% decomplexation efficiency and 57.3% TOC removal within 120 min under optimized conditions, with an observed rate constant (K_obs) of 0.0220 min⁻¹—five times higher than BC. Comprehensive characterization revealed that CuO-BC possessed a specific surface area and pore volume of 4.36 and 15.5 times those of BC, along with abundant oxygen-containing functional groups and well-exposed Cu–O active sites. The enhanced performance is attributed to the synergistic effects of hierarchical porosity facilitating mass transfer, uniform dispersion of CuO preventing aggregation, and surface functional groups promoting PDS activation. This work presents a green and scalable approach to transform agricultural waste into an efficient metal oxide-BC composite catalyst, offering dual benefits of environmental remediation and resource valorization. Full article

Amid the rapid evolution of the digital economy reshaping global competitiveness, China has advanced regional coordination through the Digital China initiative and the “Data Elements ×” Three-Year Action Plan (2024–2026). To further integrate digital transformation with high-quality growth in the urban agglomerations of the middle and lower Yellow River, this study aims to strengthen regional competitiveness, expand digital industries, foster new productivity, refine the development pathway, and safeguard balanced economic, social, and ecological progress. Taking the Yellow River urban clusters as the research object, a comprehensive assessment framework encompassing seven subsystems is established. By employing a mixed-weighting approach, entropy-based TOPSIS, hotspot analysis, coupling coordination models, spatial gravity shift techniques, and grey relational methods, this study investigates the spatiotemporal dynamics between the digital economy and high-quality development. The findings reveal that: (1) temporally, the coupling–coordination process evolves through three distinct phases—initial fluctuation and divergence (1990–2005), synergy consolidation (2005–2015), and high-level stabilization (2015–2022)—with the average coordination index rising from 0.21 to 0.41; (2) spatially, a persistent “core–periphery” structure emerges, while subsystem coupling consistently surpasses coordination levels, reflecting a pattern of “high coupling but insufficient coordination”; (3) hot–cold spot analysis identifies sharp east–west contrasts, with the gravity center shift and ellipse trajectory showing weaker directional stability but greater dispersion; and (4) grey correlation results indicate that key drivers have transitioned from economic scale and infrastructure inputs to green innovation performance and data resource allocation. Overall, this study interprets the empirical results in both temporal and spatial dimensions, offering insights for policymakers seeking to narrow the digital divide and advance sustainable, high-quality development in the Yellow River region. Full article

Additive Manufacturing (AM) has emerged as a transformative technology for rapid prototyping and fabrication of geometrically complex structures. However, the inherent thermal cycling and rapid solidification in processes such as Selective Laser Sintering (SLS) frequently induce deformation and residual stresses, leading to dimensional deviations and potential premature failure. This paper presents OpenAM-SimCCX, an open-source workflow for finite element-based thermo-mechanical simulation of AM processes using CalculiX 2.21. The framework employs a time-dependent thermo-mechanical model with layer-by-layer element activation to capture key aspects of SLS, including laser–material interaction and scanning strategy effects. Systematic comparisons of different scanning strategies demonstrate clear correlations between path planning, residual stress distributions, and distortion, while computational time analyses confirm the framework’s efficiency. By providing comprehensive documentation, implementation guides, and open repositories, OpenAM-SimCCX offers an accessible and economically viable alternative to commercial software, particularly for academic institutions and small- to medium-sized enterprises. This framework advances open-source simulation tools for AM and promotes broader adoption in both research and industry. Full article

Novel high-energy, binder-free, and solvent-free carbon–silicon layered composite anodes were manufactured using an industrially scalable Virtual Cathode Deposition (VCD) technique. The deposition process transforms commercial graphite target material into carbon polymorph (CALIB) layers, interposed with silicon layers deposited in situ from a silicon source, thereby forming high-capacity anodes for Li ion batteries. Composite CALIB-C/Si₄ anodes with a layered architecture exhibited a first-cycle specific capacity of over 1550 mAh g⁻¹ at 0.1 A g⁻¹ and retained a capacity of ~1080 mAh g⁻¹ at a 1 A g⁻¹ rate after 200 cycles. Detailed structural characterisation revealed a disordered carbon matrix encompassing nanosized sp²-bonded carbon clusters (average size ~20 nm), cross-linked by a network of sp³-bonded atomic sites, with predominant mesoporosity and high surface area. The silicon layers were found to consist of an amorphous Si matrix with embedded nanocrystalline components, emulating the growth mode of the CALIB buffer. The presence of the mesoporous carbon matrix accommodated the stress caused by the alloying/de-alloying of silicon nanolayers, thereby alleviating the pulverisation effect and preserving the structural integrity of the composite. The initial performance and capacity decay of the anodes were found to depend on the thickness of the CALIB-C buffer interlayers. Full article

At present, the construction of China’s shared manufacturing platform is developing rapidly. However, it is still in the stage of practical exploration, facing numerous challenges, such as difficulties in resource integration, immature business models, and a weak digital foundation. This paper takes Changzhou Zhiyun Tiangong’s “Super Virtual Factory” as an example, utilizing the grounded theory to conduct a case study on this shared manufacturing platform. Using a ‘condition-action-result’ framework, this paper explores the value co-creation (VCC) mechanism in a shared manufacturing ecosystem. We analyze how digital intelligence convergence (DIC) and supply chain collaboration (SCC) facilitate the digital intelligence transformation of consumption, production capacity, and products. The study finds that consumer insight, technological drive, government support, enterprise challenges, and the Changzhou home appliance industry cluster are the internal driving forces for the shared manufacturing ecosystem to carry out industrial ecological VCC; DIC and SCC are the two key elements for digital intelligence technology empowerment. Digital intelligence technology is empowered from three aspects—technology, resources, and structure—enabling organizational members with capability and authority while achieving “decentralization” of industrial chains. Finally, digital intelligence empowerment enables the shared manufacturing ecosystem to achieve VCC of the industrial ecosystem, thereby establishing a VCC model for the digital intelligence empowerment shared manufacturing ecosystem. The results of the study not only help enrich the theory of VCC in shared manufacturing platforms but also provide practical insights for the digital intelligence transformation of traditional manufacturing enterprises. Full article

The study examined how transglutaminase (TG)-induced cross-linking affects the structural, functional, and in vitro digestibility characteristics of rice dreg protein (RDP). Analysis using SDS-PAGE showed that low-molecular-weight fragments vanished, while high-molecular-weight polymers formed. Additionally, Fourier transform infrared (FTIR) spectroscopy demonstrated a reduction in β-sheet content alongside an elevation in β-turn structures as the cross-linking process became more pronounced, which was associated with a reduction in both total and free sulfhydryl groups. The hydrophobic nature of the surface and the emulsifying properties of cross-linked RDP initially rose but began to decrease when TG concentrations surpassed 10 U/g of protein. Conversely, emulsion stability and water-binding capacity decreased, while oil-binding capacity improved compared to native RDP. Solubility and in vitro digestibility decreased with cross-linking, whereas rheological properties significantly improved with higher TG levels. These findings suggest that controlled TG-mediated cross-linking (e.g., 10 U/g) effectively enhances the functional properties of RDP, making it a promising ingredient for applications in plant-based meats, baked goods, and fortified beverages within the food industry. Full article

Digital marketing has become a game-changer by combining cutting-edge technologies, insights into how customers behave, and applicability across industries to change how businesses plan and how they interact with customers. Digital marketing is a key part of being competitive, sustainable, and innovative in a world where more and more people are using the internet and social media. Even though this subject is important, the study of it is still scattered, which shows that there is a need to systematically map out its intellectual structure. This research utilizes a bibliometric and topic modeling methodology, analyzing 4722 publications sourced from the Scopus database, including the string “Digital Marketing”. The authors employed Latent Dirichlet Allocation (LDA), a method from Natural Language Processing, to discern latent study themes and Vosviewer 1.6.20 for bibliometric analysis. The results explore ten main thematic clusters, such as digital marketing and blockchain, applications in the health and food industries, higher education and skill enhancement, machine learning and analytics, small and medium-sized enterprises (SMEs) and sustainability, emerging trends and ethics, sales transformation, tourism and hospitality, digital media and audience perception, and consumer satisfaction through service quality. These clusters show that digital marketing is becoming more interdisciplinary and is becoming more connected to ethical and technological issues. The report finds that digital marketing research is changing quickly because of artificial intelligence (AI), blockchain, immersive technology, and reflect it with a digital business environment. Future directions encompass the expansion of analyses to new economies, the implementation of advanced semantic models, and the navigation of ethical difficulties, thereby guaranteeing that digital marketing fosters both business progress and public welfare. Full article

Currently, many industrial cities in Northeast China are grappling with a surge in brownfields, population loss, and environmental degradation, largely driven by industrial decline. Consequently, brownfield regeneration has emerged as a critical pathway for exploring sustainable development in shrinking cities. This study investigates the regeneration potential of urban brownfields against the backdrop of population shrinkage and aging. Under the framework of Ecosystem Service Theory, this study adopts the Fulaerji District of Qiqihar City as a representative case, integrating its four key categories of ecosystem services into a three-dimensional “economic–social–environmental” evaluation framework. This approach facilitates the transformation of Ecosystem Service Theory from a conventional post-regeneration evaluation tool into a pre-regeneration framework for assessing potential and determining site prioritization. A tripartite evaluation system was constructed, integrating economic vitality (e.g., the population shrinkage index, the proportion of the elderly population, and transportation accessibility), social culture (e.g., the industrial heritage proximity index), and ecological regulation functions (e.g., proximity of green spaces and importance of ecosystem services). Leveraging multi-source geospatial data, land surveys, and field inspections, 12 candidate brownfield sites were identified. GIS spatial analysis and the Analytic Hierarchy Process (AHP) were employed to decipher their spatial distribution patterns and comprehensive potential grades. The findings reveal the following: (1) The evaluation results exhibit distinct spatial characteristics: high-potential sites, clustered near transport hubs and policy-supported zones, demonstrate multi-dimensional advantages, while low-potential sites, constrained by aging demographics, poor accessibility, and ecological vulnerability, are predominantly located on the urban periphery. (2) Correlation analysis between potential grades and various evaluation indicators, combined with a cross-comparison of population shrinkage levels and average values of other evaluation indicators across different potential grades, reveals that neither the population shrinkage index nor the elderly population proportion exerts a systematic negative impact on industrial brownfield regeneration potential. This finding indicates that within specific urban development contexts, brownfield regeneration potential is influenced more by structural factors such as locational conditions, policy support, and ecological service functions than by singular demographic trends. This research provides both theoretical foundation and decision-making support for differentiated brownfield regeneration and spatial governance in population-shrinking cities. Full article

Fostering and steering New-Quality Productivity (NQP) to underwrite the sustainable development of the Economic–Social–Environmental System (ESES) in ethnic-minority regions is both an intrinsic requirement and a strategic fulcrum for advancing modernization at the sub-national level. Despite growing policy attention, county-level evidence on how NQP translates into sustainability outcomes—and through which mechanisms—remains insufficient. Embedding NQP within a region-specific sustainability framework, this study first articulates the theoretical channels through which NQP can transform and sustain ethnic areas. It then exploits panel data covering 67 ethnic counties in Sichuan Province from 2005 to 2024 and applies benchmark regressions, multiple-mediator models, and spatial Durbin specifications to identify the mechanisms and impact footprints of NQP. Three core findings emerge: (1) NQP exerts a robust, positive effect on ESE sustainability that varies across geography, development stages, and sectoral structures. (2) Technological innovation, industrial upgrading, and optimized resource allocation all transmit NQP’s influence, with industrial upgrading displaying the strongest mediating power. (3) NQP generates positive spatial spillovers that extend its sustainability dividends to neighboring ethnic counties. These results sharpen the academic understanding of the NQP–sustainability nexus in ethnic contexts, expand NQP assessment frameworks, and furnish county-level policymakers with evidence to design differentiated strategies that align NQP cultivation with broader goals of regionally inclusive and sustainable development. Full article

As an important driving force for intelligent transformation, the development and application of industrial robots have promoted the transformation of traditional production modes and the upgrading of energy utilization methods, playing a significant role in improving energy efficiency. Based on the panel data of 283 prefectural-level cities in China from 2008 to 2019, this study used a two-way fixed-effects model to examine the impact of industrial robots on urban energy efficiency. The study found that industrial robots significantly improve energy efficiency, with the mechanisms including scale effects, structural effects, and green technology effects. Heterogeneity analysis shows that this effect is more prominent in innovative cities, central and western regions, and areas with high human capital. The research provides a basis for understanding the pathways through which industrial robots promote the improvement of energy efficiency and offers policy insights for China to advance intelligent manufacturing and green development. Full article

The fusion of infrared and visible images provides critical value in computer vision by integrating their complementary information, especially in the field of industrial detection, which provides a more reliable data basis for subsequent defect recognition. This paper presents STGAN, a novel Generative Adversarial Network framework based on a Swin Transformer for high-quality infrared and visible image fusion. Firstly, the generator employs a Swin Transformer as its backbone for feature extraction, which adopts a U-Net architecture, and the improved W-MSA is introduced into the bottleneck layer to enhance local attention and improve the expression ability of cross-modal features. Secondly, the discriminator uses a Markov discriminator to distinguish the difference. Then, the core GAN framework is leveraged to guarantee the retention of both infrared thermal radiation and visible-light texture details in the generated image so as to improve the clarity and contrast of the fused image. Finally, simulation verification showed that six out of seven indicators ranked in the top two, especially in key indicators such as PSNR, VIF, MI, and EN, which achieved optimal or suboptimal values. The experimental results on the general dataset show that this method is superior to the advanced method in terms of subjective vision and objective indicators, and it can effectively enhance the fine structure and thermal anomaly information in the image, which gives it great potential in the application of industrial surface defect detection. Full article