Search Results (2,964)

Digital twins are increasingly being used to visualize, analyze, or control physical processes and systems. Implementation currently poses challenges for users due to the cross-domain complexity of digital twins. In this study, the authors utilize a self-developed method to support the implementation of a digital twin (DT) for a powder bed fusion additive manufacturing system (PBF-LB/M) for copper components, utilizing a green laser. The study highlights the support offered by the developed approach and the implications of using DTs for PBF of copper. The DT focuses in particular on monitoring maintenance requirements, assisting in the selection of correct process parameters, and alerting plant operators in the event of problems. In addition, a process model focused on lack of fusion is implemented, based on earlier studies. In the human–machine system, DTs thus represent a further building block towards an improved process stability in PBF-LB/M of copper, and the method used lowers the barrier to entry for widespread use of DTs. Full article

This article presents a database focused on measuring the experimental performance of a pilot parabolic trough collector (PTC) combined with the meteorological conditions corresponding to the installation site. Water was chosen as the fluid to recirculate through the PTC circuit. The data were recorded between August and September, assuming that global radiation was adequate for use in the concentration process. The database comprises seven experimental tests, which contain variables such as time, inlet temperature, outlet temperature, ambient temperature, global radiation, diffuse radiation, wind direction, wind speed, and volumetric flow rate. Based on the data obtained from this pilot PTC system, it is possible to provide relevant information for the installation and construction of large-scale solar collectors. Furthermore, the climatic conditions considered allow key factors in the design of multiple collectors to be determined, such as the type of arrangement (series or parallel) and manufacturing materials. In addition, the data collected in this study are key to validating future theoretical models of the PTC. Finally, considering the real operating conditions of a PTC in conjunction with meteorological variables could also be useful for predicting the system’s thermal performance using artificial intelligence-based models. Full article

While battery electric vehicles (BEVs) are pivotal for transport decarbonization, existing life cycle assessments (LCAs) often confound vehicle design effects with inter-brand manufacturing variations. In this study, a comparative cradle-to-grave LCA was conducted for three distinct BEV segments—a sedan, an SUV, and an MPV, produced by a single manufacturer on a shared platform. Leveraging detailed bills of materials, plant-level energy data, and region-specific emission factors for a functional unit of 150,000 km, we quantify greenhouse gas emissions across the full life cycle. Results show the total emissions scale with vehicle size from 25 to 31 t CO₂-eq. However, the MPV exhibits the highest functional carbon efficiency, with the lowest emissions per unit of interior volume. Material production and operational electricity use dominate the emission profile, with end-of-life metal recycling providing a 15–20% mitigation credit. Scenario modeling reveals that grid decarbonization can slash life cycle emissions by around 30%, while advanced battery recycling offers a further 15–18% reduction. These findings highlight that the climate benefits of BEVs are closely linked to progress in power system decarbonization, and provide references for future optimization of low-carbon vehicle production and reuse. Full article

This study examines how Green Human Resource Management (GHRM) is linked to sustainable organizational performance, encompassing environmental, economic, and social outcomes through the capability-building mechanisms of green innovation (GI) and green organizational culture (GOCL) in emerging manufacturing systems. Drawing on the Resource-Based View and capability-based sustainability perspectives, GHRM is conceptualized as a strategic organizational capability that enables firms in developing economies to beyond short-term regulatory compliance toward measurable and integrated sustainability performance outcomes. Survey data were collected from 446 managerial and technical respondents in Ethiopia’s garment and textile industrial parks, one of Africa’s fastest-growing industrial sectors facing significant sustainability challenges. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping-based mediation analysis, the results show that GHRM is positively associated with sustainable organizational performance, with GI and GOCL operating as key mediating mechanisms that translate HR-related practices into measurable sustainability outcomes. The findings highlight the role of GHRM in strengthening firms’ adaptive and developmental sustainability capabilities by fostering pro-sustainability mindsets and innovation-oriented behaviors, which are particularly critical in resource-constrained and weak-institutional contexts. The study contributes to sustainability and management literature by explicitly linking Green HRM to triple-bottom-line performance through a capability-building framework and by providing rare firm-level empirical evidence from a low-income emerging economy. Practically, the results provide guidance for managers and policy makers to design, monitor, and evaluate HRM systems that intentionally cultivate human, cultural, and innovative capabilities to support long-term organizational sustainability transitions. Full article

In modern Industry 4.0 environments, real-time scheduling presents a complex challenge requiring both formal correctness guarantees and optimal performance. Background: Traditional approaches fail to provide an optimal integration between formal correctness guaranteeing and optimization, and such failure either produces suboptimal results or a correct result lacking guarantee, and studies have indicated that poor scheduling decisions could cause productivity losses of up to 20–30% and increased operational costs of up to USD 2.5 million each year in medium-scale manufacturing facilities. Methods: This work proposes a new hybrid approach by integrating Extended Time Petri Nets (ETPNs) and Finite-State Automata (FSAs) with formal modeling, abstracting ETPNs by extending conventional Time Petri Nets to deterministic time and priority systems, accompanied by Genetic Algorithms (GAs) to optimize the solution to tackle a multi-objective optimization problem. Our solution tackles indeterministic problems by incorporating suitable priority resolution methods and GA to pursue optimal solutions to very complex scheduling problems and starting accurately from standard real-time scheduling-policy models such as DM, RM, and EDF-EDF. Results: Experimental evaluation has clearly verified performance gains up to 48% above conventional techniques, covering completely synthetic and practical case studies, including 31–48% improvement on synthetic benchmarks, 24% increase on resource allocation, and total elimination of constraint violations. Conclusions: The new proposed hybrid technique is, to a considerable extent, a dramatic advancement within real-time scheduling techniques and Industry 4.0, successfully and effectively integrating optimal correctness guaranteeing and favorable GA-aided optimization techniques, which particularly guarantee optimal correctness to safe-related applications and provide considerable improvements to support efficient and optimal performance, extremely helpful within Industry 4.0. Full article

The global shift toward decarbonization and the rise of the digital economy are compelling manufacturing firms to undergo a complex twin transformation across their structures, operations, and value chains. Business model innovation (BMI), especially in digital servitization (DSBMI), emerges as a crucial catalyst in facilitating this change. However, there is a lack of systematic exploration of how DSBMI influences corporate decarbonization (CD). To fill this knowledge gap, a comprehensive qualitative meta-analysis of 27 case studies was conducted, identifying multiple DSBMI practices for CD employed by industrial firms. These practices can be summarized into three main types: efficiency DSBMI, novelty DSBMI, and convergent DSBMI. A system has at least two of these, while all three may coexist. Based on dynamic capabilities theory, this study also introduces six roles for the three types of DSBMI practices, which interact to help firms sense opportunities, seize them through BMI, and transform their operations and ecosystems—collectively enabling decarbonization through internal optimization (efficiency DSBMI), downstream innovation (novelty DSBMI), and value chain-wide cooperation (convergent DSBMI). The findings offer a comprehensive theoretical framework that guides companies to achieve economic benefits while advancing their CD goals through multi-level BMI strategies. Finally, the study discusses its limitations and proposes directions for future research. Full article

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction. Full article

The expansion of green hydrogen requires technologies that are both manufacturable at a GW-to-TW power scale and adaptable for decentralized, renewable-driven energy systems. Recent advances in proton exchange membrane, alkaline, and solid oxide electrolysis reveal persistent bottlenecks in catalysts, membranes, porous transport layers, bipolar plates, sealing, and high-temperature ceramics. Emerging fabrication strategies, including roll-to-roll coating, spatial atomic layer deposition, digital-twin-based quality assurance, automated stack assembly, and circular material recovery, enable high-yield, low-variance production compatible with multi-GW power plants. At the same time, these developments support decentralized hydrogen systems that demand compact, dynamically operated, and material-efficient electrolyzers integrated with local renewable generation. The analysis underscores the need to jointly optimize material durability, manufacturing precision, and system-level controllability to ensure reliable and cost-effective hydrogen supply. This paper outlines a convergent approach that connects critical-material reduction, high-throughput manufacturing, a digitalized balance of plant, and circularity with distributed energy architectures and large-scale industrial deployment. Full article

Edge detection is a key building block in real-time image-processing applications such as drone-based infrastructure inspection, autonomous navigation, and remote sensing. However, its computational cost remains a challenge for resource-constrained embedded systems. This work presents a hardware-accelerated edge detection architecture based on a homogeneous 2D-mesh Network-on-Chip (NoC) integrating systolic arrays to efficiently perform the convolution operations required by the Sobel filter. The proposed architecture was first developed and validated as a 3 × 3 mesh prototype on FPGA (Xilinx Zynq-7000, Zynq-7010, XC7Z010-CLG400A, Zybo board, utilizing 26,112 LUTs, 24,851 flip-flops, and 162 DSP blocks), achieving a throughput of 8.8 Gb/s with a power consumption of 0.79 W at 100 MHz. Building upon this validated prototype, a reduced 2 × 2 node cluster with 14-bit word width was subsequently synthesized at the physical level as a proof-of-concept using the OpenLane RTL-to-GDSII open-source flow targeting the SkyWater 130 nm PDK (sky130A). Post-layout analysis confirms the manufacturability of the design, with a total power consumption of 378 mW and compliance with timing constraints, demonstrating the feasibility of mapping the proposed architecture to silicon and its suitability for drone-based infrastructure monitoring applications. Full article

Integrating emerging Industry 4.0 technologies into smart factories has been widely discussed, particularly challenges regarding the practical use of a blockchain; one remaining challenge is the role of a blockchain beyond logistics and traceability, as well as its ability to support explicit trust measurement in real industrial environments. Existing studies often treat trust as a conceptual or cloud-oriented construction, without linking it to measurable production events. This study proposes a blockchain service-level agreement (SLA) to measure trust at an open-source frugal smart factory (SF). Trust is defined as a dynamic quantitative score derived from measurable process events, including estimated and response times, assembly correctness, and transaction outcomes; all of this is calculated through a smart contract implemented on a blockchain network. The approach is implemented in a tangram puzzle assembly process that integrates cyber-physical systems, edge computing, artificial intelligence, cloud computing, data analytics, cybersecurity, and the blockchain within a unified SF architecture. The framework was experimentally validated across four representative assembly scenarios: (i) the SF delivered the puzzle in time and was correctly assembled (${\lambda }_s$ = 0.1734), (ii) the puzzle was completed within tolerance time (${\lambda }_s$ = 0.0649), (iii) the puzzle was delivered on time and was incorrectly assembled (${\lambda }_s$ = 0.0005), and (iv) the puzzle was completed outside the tolerance time and was correctly assembled (${\lambda }_s$ = 4.91 × ${10}^{-5}$); demonstrating that the model accurately estimates expected assembly times and updates trust without manual intervention during a physical manufacturing task, addressing the limitations of prior conceptual and cloud-based approaches. The main research contributions include an operational SLA-based trust model, the demonstration of the feasibility of applying blockchain-based SLAs in a physical SF environment, and evidence that a blockchain can be justified as a mechanism for managing and measuring trust in SF, rather than solely for traceability or logistics. Full article

Micro-combustion for micro-thermophotovoltaic (MTPV) and micro-thermoelectric (MTE) systems is gaining renewed interest as a pathway toward compact power generation with high energy density. This review examines how emerging artificial intelligence (AI) methodologies can accelerate the development of such systems by addressing longstanding modeling, optimization, and design challenges. We analyze four major research areas: artificial neural network (ANN)-based design optimization, AI-driven prediction of micro-scale flow variables, Physics-Informed Neural Networks for combustion modeling, and surrogate models that approximate high-fidelity computational fluid dynamics (CFD) and detailed chemistry solvers. These approaches enable faster exploration of geometric and operating spaces, improved prediction of nonlinear flow and reaction dynamics, and efficient reconstructions of thermal and chemical fields. The review outlines a wide range of future research directions motivated by advances in high-fidelity modeling, AI-based optimization, and hybrid data-physics learning approaches, while also highlighting key challenges related to data availability, model robustness, validation, and manufacturability. Overall, the synthesis shows that overcoming these limitations will enable the development of micro-combustors with higher energy efficiency, lower emissions, more stable and controllable flames, and the practical realization of commercially viable MTPV and MTE systems. Full article

Minimum wage is an important tool for reducing income inequality and supporting social welfare. Consequently, governments around the world have established minimum wage systems. As such, minimum wage policies connect distributive justice with the economy’s capacity to sustain broad-based welfare over time, placing the equity–efficiency trade-off at the center of societal sustainability. However, the micro-level impact of the minimum wage system on firms has always been an important topic for scholars. This study uses panel data from listed Chinese manufacturing firms over a period from 2005 to 2021 to construct an indicator of the minimum wage standards implemented in the firm locations. Employing the multiple linear regression model, this paper empirically examines the effects of minimum wage on labor productivity. The empirical findings demonstrate that minimum wage significantly reduced the sample firms’ labor productivity. Moreover, the negative impact of the minimum wage was primarily concentrated among non-state-owned firms, labor-intensive firms, firms operating in industries characterized by intense product market competition, firms situated in regions with strong legal protections, firms with comparatively low average employee wages, and export-oriented firms. Subsequently, this study delves into the mechanism through which minimum wage negatively affects labor productivity. We find that implementation of minimum wage leads to a reduction in corporate investment, indicating that there is no significant substitution relationship between capital and labor. These adjustment margins provide microfoundations through which statutory wage floors can influence the resilience and inclusiveness of development, indicating that the pace and design of wage increases should balance income protection with the preservation of productive capacity to support sustainable human development—grounded in steady productivity growth, equitable income distribution, and stable firm investment. Our findings contribute to a better understanding of the mechanism through which minimum wage affects labor productivity in theory, while concurrently furnishing policy insights for the optimization of the minimum wage system and maintaining sustainable societal development in practice. Full article

With the rapid development of artificial-intelligence technologies in the field of automated driving, many jurisdictions have successively adopted legislation and policy guidance to regulate the safe use of such technologies and to promote their orderly development. This article takes as its objects of study a set of jurisdictions that are particularly representative in terms of legislation and practice across different legal systems. The study finds that liability regimes for traffic accidents involving automated driving fall mainly into four types: the driver liability regime, the system liability regime, the manufacturer or operator liability regime, and the composite liability regime. In application, each of these regimes reveals different types of institutional dilemmas, including blurred boundaries of liability, underdeveloped mechanisms for evidence production and fact-finding, imbalanced allocation of liability, and fragmentation of the rules governing liability determination. In response to these dilemmas, this article proposes corresponding optimisation pathways, including clarifying the boundaries of driver liability and improving supplementary liability mechanisms; specifying in greater detail the obligations of system providers and strengthening data-related fact-finding rules; developing a reasonable allocation of liability between manufacturers and operators together with supporting insurance arrangements; and enhancing institutional coordination under the composite liability regime. These optimisation pathways not only provide institutional reference for jurisdictions seeking to maintain risk controllability while fostering innovation amid rapid technological evolution, but also lay the groundwork for the systematic improvement of future governance of automated driving. Full article

The use of vibrational transport for granular materials has significantly increased in the technological industry due to its reliability, operational efficiency, cost-effectiveness, and relatively uncomplicated technological setup. These transportation methods typically utilize various forms of asymmetry, such as kinematic, temporal (time), wave, and power asymmetry, to induce controlled motion on oscillating surfaces. This study analyses the motion of the granular materials on an inclined plane, where the central innovation lies in the creation of an additional system asymmetry of frictional conditions that enables the granular materials to move upward. This asymmetry is created by introducing dry friction dynamic manipulations. A mathematical model has been developed to describe the motion of particles under these conditions. The modelling results proved that in an inclined transportation system, the asymmetry of frictional conditions during the oscillation cycle—created through dynamic dry friction manipulations—generates a net frictional force exceeding the gravitational force, thereby enabling the upward movement of granular particles. Additionally, the findings highlighted the key control parameters governing the motion of granular particles. λ, which represents the segment of the sinusoidal period over which the friction is dynamically louvered, serves as a parameter that controls the velocity of a moving particle on an inclined surface. The phase shift ϕ serves as a parameter that controls the direction of the particle’s motion at various inclination angles. Experimental investigations were conducted to assess the practicality of this method. The experimental results confirmed that the granular particles can be transported upward along the inclined surface with an inclination angle of up to 6 degrees, as well as provided both qualitative and quantitative validation of the model by illustrating that motion parameters exhibit comparable responses to the control parameters, and strongly agree with the theoretical findings. The primary advantage of the proposed vibrational transport method is the capacity for precise control of both the direction and velocity of granular particle transport using relatively simple mechanical setups. This method offers mechanical simplicity, low cost, and high reliability. It is well-suited to assembly line and manufacturing environments, as well as to industries involved in the processing and handling of granular materials, where controlled transport, repositioning, or recirculation of granular materials or small discrete components is required. Full article

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems. Full article