Search Results (350)

Industry 5.0 transforms industrial ecosystems via artificial intelligence (AI), human–machine collaboration, and sustainability-focused innovations. This systematic literature review examines Industry 5.0′s role in energy transition through digital transformation, sustainable supply chains, and energy efficiency strategies. Key findings highlight AI-driven smart grids, blockchain-enabled energy transactions, and digital twin simulations as enablers of low-carbon, adaptive industrial operations. This review uniquely integrates technological, managerial, and policy perspectives, providing actionable insights for policymakers and industry leaders. Industry 5.0 enhances innovative energy management, renewable energy integration, and flexible energy distribution, strengthening resilience and sustainability. It fosters environmental responsibility, social impact, and circular economy principles, laying the foundation for a low-carbon economy and accelerating the global energy transition. Full article

Background: The COVID-19 pandemic significantly disrupted forensic science, exposing vulnerabilities and introducing unprecedented challenges. Five years later, its impact persists, necessitating ongoing adaptations in forensic practice. This study examines key transformations, persistent issues, and emerging challenges in forensic science post-pandemic. Methods: A critical analysis of forensic science’s response to the pandemic was conducted, focusing on operational disruptions, methodological advancements, educational shifts, and technological integration. Results: Forensic operations faced delays due to case backlogs, restricted in-person work, and postponed court proceedings. Forensic pathology evolved with increased reliance on molecular autopsy techniques to clarify COVID-19-related deaths. Educational methods shifted toward virtual learning, prompting discussions on standardized digital training. Additionally, artificial intelligence and automation gained prominence in forensic investigations, enhancing crime scene analysis and predictive modeling. Discussion: While forensic science demonstrated adaptability, challenges remain in international collaboration, resource distribution, and professional training. The pandemic accelerated technological integration but also raised ethical and procedural concerns, particularly regarding AI applications in legal contexts. Virtual learning innovations necessitate further development to ensure competency in forensic training. Conclusions: Forensic science continues to evolve in response to post-pandemic realities. Addressing gaps in cooperation, technology implementation, and training will be crucial to strengthening the field. By assessing these changes, this study underscores forensic science’s resilience and adaptability, offering insights into its future trajectory amid ongoing challenges. Full article

With the increasing penetration of renewable energy in the power system, how to ensure the normal operation of urban microgrids is gradually receiving attention. It is necessary to evaluate the overall active support capability and provide optimal operation strategies for urban microgrids. The paper proposes an active–reactive power coordinated optimization method for urban microgrids with a high proportion of renewable energy. Firstly, a quantification model of the active support capability is established to evaluate the active support capacity and reactive support capacity of urban microgrids, respectively. Then, an active–reactive power collaborative optimization model, which considers multiple types of distributed resources, is established to provide optimal scheduling strategies for urban microgrids. Consequently, a platform integrating evaluation and regulation functions is constructed to enable the evaluation of the active support capability for distributed resources in urban microgrids and the scheduling of distributed resource operations. This paper aims to solve the key technical challenges of the safe operation of new urban microgrids. The simulation results demonstrate that the proposed optimal scheduling method can reduce the comprehensive operating costs of urban microgrids with high renewable energy penetration by up to 19.86% and decrease the voltage deviation rate by up to 7.25%, simultaneously improving both economic efficiency and operational security. Full article

With the development of urbanization, building maintenance units (BMUs) have been widely used in super high-rise buildings. As aerial work machinery, condition monitoring plays a vital role in the safety maintenance and management of BMUs. However, BMUs have multi-source heterogeneous data relationships that are difficult for systems to understand. Moreover, at this stage, there is a lack of sufficient samples to support fault diagnosis data. Therefore, this paper proposes a real-time monitoring and fault diagnosis system for BMU operating conditions. This system, based on the Internet of Things (IoT) architecture, acquires and stores data from distributed BMU systems, improving the data collection and sharing rate throughout the entire process. A collaborative fault reasoning chain diagnosis model was established based on heterogeneous knowledge sources and real-time process signals, which increased the accuracy of fault identification to 97%. Finally, through simulation testing and evaluations, the system can stably transmit data within 6–7 days and accurately analyze the operational and fault status of BMU, with an error rate within 5%. It effectively improves the efficiency and accuracy of BMU condition monitoring and fault diagnosis and also provides a new method for the practical application of intelligent BMU operation and maintenance. Full article

Cyber-physical systems (CPSs) are fundamental components of Industry 4.0 production environments. Their interconnection is crucial for the successful implementation of distributed and autonomous production plans. A particularly relevant challenge is the optimal scheduling of tasks that require the collaboration of multiple CPSs. To ensure the feasibility of optimal schedules, two primary issues must be addressed: (1) The design of global systems emerging from the interconnection of CPSs; (2) The development of a scheduling formalism tailored to interconnected Industry 4.0 settings. Our approach is based on a Category Theory formalization of interconnections as compositions. This framework aims to guarantee that the emergent behaviors align with the intended outcomes. Building upon this foundation, we introduce a formalism that captures the assignment of operations to cyber-physical systems. Full article

This paper presents a novel deep reinforcement learning (DRL)-based method for the adaptive control of transient voltage in power systems. First, we propose a neural network-based nonlinear controller (TVCON) designed to modulate each voltage-source converter (VSC), such as photovoltaic systems or energy storage systems, that actively contributes to transient voltage control. Subsequently, all distributed TVCONs can collaborate to rapidly restore system voltage during fault transients by centrally optimizing their parameters (weight coefficients). Specifically, the optimization is conducted periodically using incremental DRL to efficiently update the TVCONs’ parameters in accordance with the practical operating conditions of the system and VSCs. Consequently, the provision of transient reactive current by VSCs, which have operational constraints related to current saturation, can be feasibly and adaptively controlled by the TVCONs while considering their steady-state active current outputs. Additionally, the inappropriate sacrifice of VSCs’ active current and the resulting adverse impacts can be effectively mitigated. Finally, simulations conducted on a modified IEEE 14-Bus system validate the proposed method. Full article

With the gradual application of blockchain technology in the domain of Social Internet of Vehicles (SIoV), the increasing volume of blockchain data has imposed significant storage pressure on roadside units (RSUs). Collaborative storage schemes, which organize RSUs into clusters to jointly store content for vehicles, have been explored. However, existing collaborative storage solutions in IoV primarily focus on caching content and are not well-suited to the deployment constraints of blockchain networks. Building on blockchain’s decentralized characteristics and data integrity mechanisms, this paper proposes a collaborative storage scheme that reduces RSU storage loads while sustaining distributed ledger operations in SIoV. Specifically, the RSU Access Preference-based Spectral Clustering Algorithm (RAPSCA) is proposed to address RSU clustering by analyzing both the RSUs’ access preferences for blockchain data and their resource availability. Subsequently, the Vehicle Service Priority-based Greedy Block Allocation Algorithm (VSPGBAA) is devised for intra-cluster storage allocation, which considers vehicles’ dwell times and block access probabilities to reduce overall access costs. Experimental results indicate that, compared to baseline algorithms, the proposed method achieves a 27.7% reduction in cost and a 3.5-fold decrease in execution time, thereby demonstrating the feasibility of collaborative storage optimization in blockchain-enabled SIoV. Full article

In the context of rapid advancement of smart cities, a distribution network (DN) serving as the backbone of urban operations is a way to confront multifaceted challenges that demand innovative solutions. Central among these, it is imperative to optimize resource allocation and enhance the efficient utilization of diverse energy sources, with particular emphasis on seamless integration of renewable energy systems into existing infrastructure. At the same time, considering that the traditional power system’s “rigid”, instantaneous, dynamic, and balanced law of electricity, “source-load”, is difficult to adapt to the grid-connection of a high proportion of distributed generations (DGs), the collaborative interaction of multiple flexible controllable resources, like flexible loads, are able to supplement the power system with sufficient “flexibility” to effectively alleviate the uncertainty caused by intermittent fluctuations in new energy. Therefore, an active distribution network (ADN) intraday, reactive, power optimization-scheduling model is designed. The dynamic reactive power collaborative interaction model, considering the integration of DG, energy storage (ES), flexible loads, as well as reactive power compensators into the IEEE 33-node system, is constructed with the goals of reducing intraday network losses, keeping voltage deviations to a minimum throughout the day, and optimizing static voltage stability in an active distribution network. Simulation outcomes for an enhanced IEEE 33-node system show that coordinated operation of source–network–load–storage effectively reduces intraday active power loss, improves voltage regulation capability, and achieves secure and reliable operation under ADN. Therefore, it will contribute to the construction of future smart city power systems to a certain extent. Full article

Customer relationship management (CRM) is a key factor driving innovation and organizational growth. The present study investigated the relationship between data-driven CRM (DDCRM) and innovation in Taiwan. We developed a research model involving CRM theory, innovation theory, and the technology adoption model (TAM) theory to account for the cultural and organizational contexts of Taiwan and investigate this relationship. The study distributed questionnaires to employees and stakeholders within Taiwanese firms to understand their firms’ innovation and CRM practices. The results indicate that technology adoption and organizational culture have mediating effects and industry dynamics and organizational size have moderating effects on the relationship between DDCRM and innovation. That is, adopting new technology and having an organizational culture that supports innovation and company-wide collaboration can enhance the effects of implementing DDCRM practices. In addition, certain industries (e.g., the technology industry) are more likely to effectively leverage DDCRM practices to drive innovation, and although large organizations have more resources and can therefore more easily implement CRM systems, small and medium-sized enterprises (SMEs) can more quickly adapt and innovate on the basis of CRM insights. These findings highlight the importance of DDCRM in driving innovation and reveal key factors influencing the effectiveness of CRM in doing so. The study features comprehensive suggestions of operable strategies and measures for Taiwanese SMEs, hopefully assisting them in gaining a market advantage and elevating their innovation capabilities by leveraging DDCRM practices. Full article

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article

This research explores the interaction between inter-organizational trust, marketing channels, and market and financial performance (FP) in establishing sustainable global marketing channels using Industry 4.0 technologies. It is conducted within the relational exchange theory (RET) framework and transaction cost economics (TCE). The sample (N = 131) was collected through the marketing research firm Centiment. PLS-SEM and Necessary Condition Analysis (NCA) were utilized as statistical methods. All hypotheses except the relationship between marketing channel operational performance and FP were accepted. This research highlights the vital role of inter-organizational trust in enhancing operational efficiency, profitability, and sustainability. It finds that trust fosters collaboration in global distribution channels, improving performance across multiple dimensions. Specifically, trust positively impacts marketing channel operations, boosting market performance. Nevertheless, all exogenous constructs were essential—“must-have” conditions for the endogenous FP construct. Applying the novel NCA is distinctive, primarily as it demonstrates that the relationship between marketing channel operational performance and FP is a necessary “must-have” condition, despite the insignificant path coefficient between the constructs. This is a crucial finding, as further investment in marketing channel operational performance and other antecedents of FP may be futile if the necessary conditions have not been met. Full article

In response to the challenges of imbalanced economic efficiency of charging stations caused by disorderly charging of large-scale electric vehicles (EVs), rising electricity expenditure of users, and increased risk of stable operation of the power grid, this study designs a user-side vehicle pile resource interaction strategy considering source load clustering to enhance the economy and safety of electric vehicle energy management. Firstly, by constructing a dynamic traffic flow distribution network coupling architecture, a bidirectional interaction model between charging facilities and transportation/power systems is established to analyze the dynamic correlation between charging demand and road network status. Next, an EV charging and discharging electricity price response model is established to quantify the load regulation potential under different scenarios. Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. Then, a multidimensional optimization objective function covering operator revenue, user economy, and grid power quality is constructed, and a collaborative decision-making model is established. Finally, the IEEE69 node system is validated through joint simulation with actual urban areas, and the non-dominated sorting genetic algorithm II (NSGA-II) based on reference points is used for the solution. The results show that the optimization strategy proposed by NSGA-II can increase the operating revenue of charging stations by 33.43% while reducing user energy costs and grid voltage deviations by 18.9% and 68.89%, respectively. Full article

Microgrids facilitate the complementary and collaborative operation of various distributed energy resources. Implementing effective day-ahead scheduling strategies can significantly enhance the economic efficiency and operational stability of microgrid systems. In this study, the long short-term memory (LSTM) neural network is first employed to forecast photovoltaic (PV) power generation and load demand, using operational data from a full-scale microgrid system. Subsequently, an optimization model for a full-scale PV–energy storage microgrid is developed, integrating a PV power generation system, a battery energy storage system, and a specific industrial load. The model aims to minimize the total daily operating cost of the system while satisfying a set of system operational constraints, with particular emphasis on the safety requirements for grid exchange power. The formulated optimization problem is then transformed into a mixed-integer linear programming (MILP) model, which is solved using a computational solver to derive the day-ahead economic scheduling scheme. Finally, the proposed scheduling scheme is validated through field experiments conducted on the full-scale PV–energy storage microgrid system across various operational scenarios. By comparing the simulation results with the experimental outcomes, the effectiveness and practicality of the proposed day-ahead economic scheduling scheme for the microgrid are demonstrated. Full article

Numerous universities and national laboratories in the United States have collaboratively established a Common Component Architecture (CCA) forum to conduct research on parallel component technology. Given the overhead associated with component connection and management, performance optimization is of utmost importance. Current research often employs static load-balancing strategies or centralized dynamic approaches for load-balancing in parallel component applications. By analyzing the operational mechanism of CCA parallel components, this paper introduces a dynamic and distributed load-balancing method for such applications. We have developed a class library of computing nodes utilizing an object-oriented approach. The resource-management node deploys component applications onto sub-clusters generated by an aggregation algorithm. Dependency among different component calls is determined through data flow analysis. We maintain the load information of computing nodes within the sub-cluster using a distributed table update algorithm. By capturing the dynamic load information of computing nodes at runtime, we implement a load-balancing strategy in a distributed manner. Our dynamic and distributed load-balancing algorithm is capable of balancing component instance tasks across different nodes in a heterogeneous cluster platform, thereby enhancing resource utilization efficiency. Compared to existing static or centralized load-balancing methods, the proposed method demonstrates superior performance and scalability. Full article

This manuscript introduces a comprehensive framework for augmenting classical statistical methodologies through the targeted integration of core quantum mechanical principles—specifically superposition, entanglement, measurement, wavefunctions, and density matrices. By concentrating on these foundational concepts instead of the whole expanse of quantum theory, we propose “quantum-inspired” models that address persistent shortcomings in conventional statistical approaches. In particular, five pivotal distributions (normal, binomial, Poisson, Student’s t, and chi-square) are reformulated to incorporate interference terms, phase factors, and operator-based transformations, thereby facilitating the representation of multimodal data, phase-sensitive dependencies, and correlated event patterns—characteristics that are frequently underrepresented in purely real-valued, classical frameworks. Furthermore, ten quantum-inspired statistical principles are delineated to guide practitioners in systematically adapting quantum mechanics for traditional inferential tasks. These principles are illustrated through domain-specific applications in finance, cryptography (distinct from direct quantum cryptography applications), healthcare, and climate modeling, demonstrating how amplitude-based confidence measures, density matrices, and measurement analogies can enrich standard statistical models by capturing more nuanced correlation structures and enhancing predictive performance. By unifying quantum constructs with established statistical theory, this work underscores the potential for interdisciplinary collaboration and paves the way for advanced data analysis tools capable of addressing high-dimensional, complex, and dynamically evolving datasets. Complete R code ensures reproducibility and further exploration. Full article