Search Results (770)

Industrial heat exchangers are widely used in industries such as petrochemicals, energy and power, and food processing, making them one of the most important pieces of heat and mass transfer equipment in industry. During operation, a layer of fouling often adheres to the heat transfer surfaces, which reduces the heat transfer coefficient of the equipment and increases the thermal resistance of the surfaces. Additionally, fouling can corrode the material of the heat transfer surfaces, compromise their integrity, and even lead to perforations and leaks, severely impacting equipment operation and safety while increasing energy consumption and costs for enterprises. The application of anti-fouling coatings on surfaces is a key technology to address fouling on heat transfer surfaces. This paper focuses on introducing major types of anti-fouling coatings, including polymer-based coatings, “metal material + X”-type coatings, “inorganic material + X”-type coatings, carbon-based material coatings, and other varieties. It analyzes and discusses the current research status and hotspots for these coatings, elaborates on their future development directions, and proposes ideas for developing new coating systems. On the other hand, this paper summarizes the current research on the main factors—surface roughness, surface free energy, surface wettability, and coating corrosion resistance—that affect the anti-fouling performance of coatings. It outlines the research hotspots and challenges in understanding the influence of these three factors and suggests that future research should consider the synergistic effects of multiple factors, providing valuable insights for further studies in the field of anti-fouling coatings. Full article

The current retired recycling system suffers from “systemic coordination failure”, primarily due to ambiguous responsibility boundaries hindering interenterprise collaboration, unequal profit distribution discouraging technological innovation investment, and low participation from both consumers and recycling enterprises undermining the efficiency of recycling channels. However, the simplified tripartite game models commonly adopted in existing research exhibit significant limitations in explaining and addressing the above practical challenges, as they fail to incorporate consumers and third-party recyclers as strategic decision-makers into the analytical framework. To address these issues, this study develops, for the first time, a five-party evolutionary game model involving governments, vehicle manufacturers, battery producers, third-party recyclers, and consumers within a reverse supply chain framework. We further employ system dynamics to simulate the dynamic evolution of stakeholder strategies. The results show that: (1) When tri-party synergistic benefits exceed 15, the system transitions from resource dissipation to circular regeneration. (2) Government subsidies reaching the threshold of 2 effectively promote low-carbon transformation across the industrial chain. (3) Bilateral synergistic benefits of 12 can stimulate green technological innovation and industrial upgrading. (4) Establishing a multi-stakeholder governance framework is key to enhancing resource circulation efficiency. This research provides quantitative evidence and policy implications for constructing an efficient and sustainable power battery recycling system. Full article

Insider threats remain one of the most challenging security risks in modern enterprises due to their subtle behavioral patterns and the difficulty of distinguishing malicious intent from legitimate activity. This paper presents a unified and adaptive generative framework for insider threat detection that integrates Variational Autoencoders (VAEs) and Transformer Autoencoder architectures to learn personalized behavioral baselines from sequential user event logs. Anomalies are identified as significant deviations from these learned baseline distributions. The proposed framework incorporates an adaptive learning mechanism to address both cold-start scenarios and concept drift, enabling continuous model refinement as user behavior evolves. In addition, we introduce a privacy-preserving latent-space design and evaluate the framework under formal privacy attacks, including membership inference and reconstruction attacks, demonstrating strong resilience against data leakage. Experiments performed on the CERT Insider Threat Dataset (v5.2) show that our approach outperforms traditional and deep learning baselines, with the Transformer Autoencoder achieving an F1-score of 0.66 and an AUPRC of 0.59. The results highlight the effectiveness of generative sequence models for privacy-conscious and adaptive insider threat detection in enterprise environments, providing a comparative analysis of two powerful architectures for practical implementation. Full article

This study investigates whether Battery Electric Vehicles (BEVs) or Fuel Cell Electric Vehicles (FCEVs) represent the superior alternative to conventional vehicles for last-mile delivery, with a particular focus on large enterprises that prioritize both economic feasibility and environmental performance. Life Cycle Assessment and Life Cycle Cost methodologies are applied to evaluate both technologies across the full cradle-to-grave life cycle within a unified framework. The functional unit is defined as one kilometer traveled by a BEV or FCEV in last-mile transportation, and the system boundary includes vehicle manufacturing, operation, maintenance, and end-of-life treatment. The environmental impacts are assessed using the ReCiPe 2016 Midpoint (H) method implemented in OpenLCA 2.0.4, and normalization follows the standards provided by the official ReCiPe 2016 framework. The East China Power Grid serves as the baseline electricity mix for the operational stage. Regarding GHG emissions, FCEVs demonstrate a 12.36% reduction in carbon dioxide (CO₂) emissions compared to BEVs. This reduction is particularly significant during the operational phase, where FCEVs can lower CO₂ emissions by 53.51% per vehicle relative to BEVs, largely due to hydrogen energy’s higher efficiency and durability. In terms of economic costs, BEVs hold a slight advantage over FCEVs, costing approximately 0.8 RMB/km/car less. However, during the manufacturing phase, FCEVs present greater environmental challenges. It is recommended that companies fully consider which environmental issues they wish to make a greater contribution to when selecting vehicle types. This study provides insight and implications for large companies with financial viability concerns about environmental impact regarding selecting the two types of vehicles for last-mile transportation. The conclusions offer guidance for companies assessing which vehicle technology better aligns with their long-term operational and sustainability priorities. It can also help relevant practitioners and researchers to develop solutions to last-mile transportation from the perspective of different enterprise sizes. Full article

Public ecological products constitute the most fundamental public goods supporting human well-being. Enhancing the high-quality supply of public ecological products is critical for maintaining ecological safety, ensuring the ecological regulation function, and promoting the harmonious coexistence of humans and nature. To deeply investigate the supply process and behavioral mechanisms of public ecological products, this study constructs a simulation model based on Agent-Based Modeling (ABM) to simulate the behavior rules and dynamic processes of four main subjects involved in the supply of public ecological products: government, enterprises, the public, and environmental non-governmental organizations (ENGOs). After calibrating the model parameters with relevant data from the water production and supply industry in Beijing, the good fit of the model output results verifies the effectiveness of the model. This study reveals the operating mechanism of multi-subject collaborative supply of public ecological products, providing a basic model for investigating the mechanism and evolution process of ecological product supply under more complex conditions, and also providing a powerful tool for the ex-ante evaluation of the implementation effect of public ecological product supply policies. Full article

Modern cryptographic systems increasingly depend on certified hardware modules to guarantee trustworthy key management, tamper resistance, and secure execution across Internet of Things (IoT), embedded, and cloud infrastructures. Although numerous FIPS 140-certified platforms exist, prior studies typically evaluate these solutions in isolation, offering limited insight into their cross-domain suitability and practical deployment trade-offs. This work addresses this gap by proposing a unified, multi-criteria evaluation framework aligned with the FIPS 140 standard family (including both FIPS 140-2 and FIPS 140-3), replacing the earlier formulation that assumed an exclusive FIPS 140-3 evaluation model. The framework systematically compares secure elements (SEs), Trusted Platform Modules (TPMs), embedded Systems-on-Chip (SoCs) with dedicated security coprocessors, enterprise-grade Hardware Security Modules (HSMs), and cloud-based trusted execution environments. It integrates certification analysis, performance normalization, physical-security assessment, integration complexity, and total cost of ownership. Validation is performed using verified CMVP certification records and harmonized performance benchmarks derived from publicly available FIPS datasets. The results reveal pronounced architectural trade-offs: lightweight SEs offer cost-efficient protection for large-scale IoT deployments, while enterprise HSMs and cloud enclaves provide high throughput and Level 3 assurance at the expense of increased operational and integration complexity. Quantitative comparison further shows that secure elements reduce active power consumption by approximately 80–85% compared to TPM 2.0 modules (<20 mW vs. 100–150 mW) but typically require 2–3× higher firmware-integration effort due to middleware dependencies. Likewise, SE050-based architectures deliver roughly 5× higher cryptographic throughput than TPMs (∼500 ops/s vs. ∼100 ops/s), whereas enterprise HSMs outperform all embedded platforms by two orders of magnitude (>10 000 ops/s). Because the evaluated platforms span both FIPS 140-2 and FIPS 140-3 certifications, the comparative analysis interprets their security guarantees in terms of requirements shared across the FIPS 140 standard family, rather than attributing all properties to FIPS 140-3 alone. No single architecture emerges as universally optimal; rather, platform suitability depends on the desired balance between assurance level, scalability, performance, and deployment constraints. The findings offer actionable guidance for engineers and system architects selecting FIPS-validated hardware for secure and compliant digital infrastructures. Full article

Despite significant research interest, the understanding of how to systematically implement Lean practices in supply chains remains limited. Therefore, this study analyzes the impact of blockchain technology on implementing Lean principles within supply chain networks. A theoretical model was developed based on a comprehensive literature review, utilizing innovation diffusion theory, agency theory, and transaction cost economics. The LDA topic modeling, based on big data from the past decade, was employed to explore key areas and essential industry practices related to blockchain technology. By cross-validating big data analysis and survey results, we also developed reliable metrics that can be used to study blockchain utilization in SCM. The hypotheses were empirically tested using survey data from 219 US enterprises that have adopted blockchain technology. The empirical results revealed that blockchain adoption significantly improved Lean management practices within supply chain networks. Furthermore, research has shown that blockchain can significantly enhance operational performance, including cost reduction, quality improvement, delivery capacity, and greater flexibility. These compelling results suggest that blockchain has the potential to serve as a powerful platform for systematically integrating and orchestrating Lean management practices across the entire supply chain network, thereby achieving operational excellence. An in-depth discussion of the study’s practical implications and theoretical contributions is presented. Full article

This study addresses the challenge of electric vehicle power battery recycling by proposing a dynamic deposit-refund system (DRS) under the Extended Producer Responsibility (EPR) framework, as an alternative to the conventional static DRS. An evolutionary game model is developed to capture the strategic interactions between local governments and responsible enterprises, incorporating a feedback mechanism where the deposit level is dynamically adjusted based on corporate EPR fulfillment rates. Using system dynamics simulation, the evolutionary paths under both static and dynamic DRS regimes are compared. The results demonstrate that the dynamic DRS effectively eliminates persistent oscillations and guides the system toward a stable equilibrium. Furthermore, by defining an ideal scenario, key factors are identified and prioritized to assist the government in steering the system toward this desired state. These findings offer actionable insights for designing adaptive regulatory mechanisms and fostering a self-sustaining battery recycling ecosystem. Full article

This article examines the growing relevance of photovoltaic (PV) energy amid rising electricity demand, sustainability goals, and the need for flexible energy management in households and enterprises. It analyzes six PV business models, ownership, leasing, Power Purchase Agreement (PPA), energy communities/peer-to-peer (P2P), crowdfunding, and subscription-based Solar-as-a-Service, using the Business Model Canvas (BMC) framework. A systematic literature review was combined with a unified BMC for each model, enabling structured comparison of value propositions, customer segments, cost structures, revenue streams, and risk allocation. The results show that no single universal model exists; each addresses different financial capacities, risk preferences, and strategic needs of households, SMEs, large enterprises, and energy communities. Significant differences were found in investment requirements, operational involvement, scalability, and potential for energy independence. The study’s novelty lies in providing a coherent, cross-model comparison using a standardized BMC approach, offering insights not systematically explored in previous research. These findings support informed decision-making for organizations considering PV adoption and provide a basis for further research on innovative energy management strategies. The topic is highly relevant in the context of the accelerating global energy transition, technological advances, regulatory changes, and increasingly diverse customer profiles, highlighting the need for comprehensive comparative analyses to guide flexible photovoltaic deployment. Full article

The development of electric quarry transport puts a significant strain on local power grids, leading to sharp peaks in consumption and degradation of power quality. Existing methods of peak smoothing, such as generation control, virtual power plants, or intelligent load management, have limited efficiency under the conditions of stochastic and high-power load profiles of industrial charging stations. A new strategy for direct charge and discharge management of a system for integrated battery energy storage (IBES) is based on dynamic iterative adjustment of load boundaries. The mathematical apparatus of the method includes the formalization of an optimization problem with constraints, which is solved using a nonlinear iterative filter with feedback. The key elements are adaptive algorithms that minimize the network power dispersion functionality (i.e., the variance of $P_{grid}\left(t\right)$ over the considered time interval) while respecting the constraints on the state of charge (SOC) and battery power. Numerical simulations and experimental studies demonstrate a 15 to 30% reduction in power dispersion compared to traditional constant power control methods. The results confirm the effectiveness of the proposed approach for optimizing energy consumption and increasing the stability of local power grids of quarry enterprises. Full article

The rapid advancement of artificial intelligence has driven a surge in demand for computing power. As the core computing infrastructure, data centers have expanded in scale, escalating electricity consumption and magnifying a regional mismatch between computing capacity and energy resources: facilities are concentrated in the energy-constrained East, while the renewable-rich West possesses vast, untapped hosting capacity. Focusing on cross-regional data-center migration under the “Eastern Data, Western Computing” initiative, this study constructs a tripartite evolutionary game model comprising the Eastern Local Government, the Western Local Government, and data-center enterprises. The central government is modeled as an external regulator that indirectly shapes players’ strategies through policies such as energy-efficiency constraints and carbon-quota mechanisms. First, we introduce key parameters—including energy efficiency, carbon costs, green revenues, coordination subsidies, and migration losses—and analyze the system’s evolutionary stability using replicator-dynamics equations. Second, we conduct numerical simulations in MATLAB 2024a and perform sensitivity analyses with respect to energy and green constraints, central rewards and penalties, regional coordination incentives, and migration losses. The results show the following: (1) Multiple equilibria can arise, including coordinated optima, policy-failure states, and coordination-impeded outcomes. These coordinated optima do not emerge spontaneously but rather depend on a precise alignment of payoff structures across central government, local governments, and enterprises. (2) The eastern regulatory push—centered on energy efficiency and carbon emissions—is generally more effective than western fiscal subsidies or stand-alone energy advantages at reshaping firm payoffs and inducing relocation. Central penalties and coordination subsidies serve complementary and constraining roles. (3) Commercial risks associated with full migration, such as service interruption and customer attrition, remain among the key barriers to shifting from partial to full migration. These risks are closely linked to practical relocation and connectivity constraints—such as logistics and commissioning effort, and cross-regional network latency/bandwidth—thereby potentially trapping firms in a suboptimal partial-migration equilibrium. This study provides theoretical support for refining the “Eastern Data, Western Computing” policy mix and offers generalized insights for other economies facing similar spatial energy–demand asymmetries. Full article

Against the backdrop of China’s vigorous pursuit of its “carbon peaking and carbon neutrality” goals, transition finance has emerged as a critical instrument to tackle the financing constraints faced by high-carbon industries. However, the lack of a standardized evaluation system significantly impedes its effective implementation and sustainable development. This paper constructs an evaluation system for transition finance in China’s power sector, incorporating 15 indicators across three logical dimensions: external driving force, internal state, and management response. Using objective weighting and comprehensive ranking methods, the study assesses the transition finance performance of 64 Chinese power enterprises. Furthermore, a variance decomposition index is employed to analyze disparities and imbalances in transition finance development level. Results indicate: (1) the key to enhancing the level of power enterprises’ transition finance development lies in strengthening external policy support intensity and improving capital allocation efficiency; (2) China’s power sector exhibits slow growth in transition finance development, with pronounced internal divergence and uneven progress; and (3) the primary constraint on the development of transition finance in China’s power sector stems from the internal imbalances between two distinct types of enterprises: those heavily dependent on thermal power and those focused on renewable energy. This study proposes a quantifiable methodological framework to facilitate the development of transition finance in the power sector, while also constructing a reference evaluation paradigm to assist high-carbon industries in planning transition pathways and allocating transition capital. Full article

With the rapid expansion of data centers and the growing demand for cloud computing, their share in total electricity consumption has surged, making them a major high-power load in power systems. Consequently, accurately modeling their energy consumption and quantifying the feasible region have become critical research challenge. Existing studies have focused on energy consumption models for single data centers and single time periods, while limited attention has been given to multi-data centers energy optimization that considers spatiotemporal workload migration. This paper presents an energy consumption model for multi-data centers that accounts for the spatiotemporal transfer flexibility of delay-tolerant workloads. By enabling task migration across data centers (spatial dimension) and workload deferral within each center (temporal dimension), the model dynamically adjusts the operational states of IT equipment to minimize overall system operating costs while satisfying computational demands. To address the computational challenges caused by the large number of integer variables, the sliding window method and equipment aggregation method are employed to ensure the model can be efficiently solved. To further capture the flexibility of data center energy consumption, a method for computing the energy consumption elasticity space is proposed based on multi-parametric programming. This elasticity space characterizes the feasible range of energy consumption under operational constraints and provides boundary conditions for power system dispatch optimization. Simulation studies using real operational data from a large-scale Internet enterprise show that the proposed model reduces the total operational cost by approximately 3.4% compared to the baseline model without flexibility, decreases the frequency of IT equipment state transitions, and enhances the flexibility of data centers in supporting power system supply-demand balance and renewable energy integration. Full article

Migrant construction workers make up a significant portion of the workforce in many countries and play a crucial role in alleviating the skilled labor shortage. Although vocational education and training (VET) is essential for equipping these workers with the skills needed to enhance workforce quality and bridge the skills gap, their intentions to attend VET (IAVET) remain relatively low. Drawing on the theory of planned behavior (TPB), this study investigates the antecedents of IAVET among migrant construction workers and explores the moderating role of work pressure. A questionnaire survey was conducted among 547 construction workers in China, followed by exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. The results show that social support has a significant positive correlation with IAVET. Moreover, the three planned behavioral factors mediate the relationship between social support and IAVET, with the mediating effects varying depending on the level of work pressure experienced by workers. Notably, subjective norms (SN) emerge as the strongest mediator, while work pressure (WP) significantly moderates both the direct and indirect pathways, highlighting their critical roles in shaping VET participation intentions. These findings provide valuable insights into the mechanisms through which social support influences migrant construction workers’ IAVET, offering practical implications for improving workforce skills and addressing the skilled labor shortage in the construction sector and similar industries worldwide. Overall, the study strengthens the theoretical explanatory power of the extended TPB framework and offers actionable guidance for policymakers and construction enterprises to enhance migrant workers’ engagement in VET. Full article

This research examines the drivers of lean implementation in sustainable energy enterprises (SEEs) to balance efficiency, sustainability, and competitiveness. This research investigates the interdependence among lean drivers and classifies them by driving power and dependence. This study followed a novel mixed-method approach combining a systematic literature review for driver identification, interviews with entrepreneurs for expert consensus, and analysis using total interpretive structural modelling (TISM), cross-impact matrix multiplication applied to classification (MICMAC), and a graph-theoretic approach (GTA). The result indicated that leadership commitment, teamwork and collaboration, and time management are high drivers; cost reduction, resource optimization, and continuous improvement are linkage drivers; and customer focus and flexibility are found as dependent drivers, revealing the sustainable outcome. This provides a structured pathway for the SEEs for the lean implementation drivers, where prioritization is required. The exploration adds to the Resource-Based View, dynamic capability theory, system theory, etc. The study calls for policymakers’ interventions in designing capacity-building programmes, leadership training, and collaborations. This research incorporated the antecedents–decisions–outcomes (ADO) framework for highlighting the antecedents, leading to decisions, and the outcomes of the choices, with future research questions connecting with multiple sustainable development goals (SDGs), such as SDG7, SDG9, SDG12, and SDG13. Full article