Understanding the influencing factors and the evolving trends of the Water-Sediment Regulation System (WSRS) is vital for the protection and management of the Yellow River. Past studies on WSRS have been limited in focus and have not fully addressed the complete engineering control system of the basin. This study takes a holistic view, treating sediment management in the Yellow River as a dynamic and ever-evolving complex system. It merges concepts from system science, information theory, and dissipative structure with practical efforts in sediment engineering control. The key findings of this study are as follows: between 1990 and 2019, the average Yellow River Sediment Regulation Index (YSRI) was 55.99, with the lowest being 50.26 in 1990 and the highest being 61.48 in 2019; the result indicates that the WSRS activity decreased, yet it fluctuated, gradually approaching the critical threshold of a dissipative structure.

Reliability-redundancy allocation, preventive maintenance, and spare parts logistics are crucial for achieving system reliability and availability goal. Existing methods often concentrate on specific scopes of the system’s lifetime. This paper proposes a joint redundancy-maintenance-inventory allocation model that simultaneously optimizes redundant component, replacement time, spares stocking, and repair capacity. Under reliability and availability criteria, our objective is to minimize the system’s lifetime cost, including design, manufacturing, and operational phases. We develop a unified system availability model based on ten performance drivers, serving as the foundation for the establishment of the lifetime-based resource allocation model. Superimposed renewal theory is employed to estimate spare part demand from proactive and corrective replacements. A bisection algorithm, enhanced by neighborhood exploration, solves the complex mixed-integer, nonlinear optimization problem. The numerical experiments show that component redundancy is preferred and necessary if one of the following situations occurs: extremely high system availability is required, the fleet size is small, the system reliability is immature, the inventory holding is too costly, or the hands-on replacement time is prolonged. The joint allocation model also reveals that there exists no monotonic relation between spares stocking level and system availability.

Automated driving has recently attracted significant attention. While considerable research has been conducted on the technologies and societal acceptance of autonomous vehicles, investigations into the control and scheduling of urban automated driving traffic are still nascent. As automated driving gains traction, urban traffic control logic is poised for substantial transformation. Presently, both manual and automated driving predominantly operate under a local decision-making traffic mode, where driving decisions are based on the vehicle’s status and immediate environment. This mode, however, does not fully exploit the potential benefits of automated driving, particularly in optimizing road network resources and traffic efficiency. In response to the increasing adoption of automated driving, it is essential for traffic bureaus to initiate proactive dialogs regarding urban traffic control from a global perspective. This paper introduces a novel global control mode for urban automated driving traffic. Its core concept involves the central scheduling of all autonomous vehicles within the road network through vehicle-infrastructure cooperation, thereby optimizing traffic flow. This paper elucidates the mechanism and process of the global control mode. Given the operational complexity of expansive road networks, the paper suggests segmenting these networks into multiple manageable regions. This mode is conceptualized as an autonomous vehicle global scheduling problem, for which a mathematical model is formulated and a modified A-star algorithm is developed. The experimental findings reveal that (i) the algorithm consistently delivers high-quality solutions promptly and (ii) the global scheduling mode significantly reduces traffic congestion and equitably distributes resources. In conclusion, this paper presents a viable and efficacious new control mode that could substantially enhance urban automated traffic efficiency.

This study explores urban air mobility (UAM) as a strategy for mitigating escalating traffic congestion in major urban areas as a consequence of a static transportation supply versus dynamic demand growth. It offers an in-depth overview of UAM development, highlighting its present state and the challenges of integration with established urban transport systems. Key areas of focus include the technological advancements and obstacles in electric vertical take-off and landing (eVTOL) aircrafts, which are essential for UAM operation in urban environments. Furthermore, it explores the infrastructure requirements for UAM, including vertiport deployment and the creation of adept air traffic control (ATC) systems. These developments must be integrated into the urban landscape without exacerbating land-use challenges. This paper also examines the regulatory framework for UAM, including existing aviation regulations and the necessity for novel policies specifically designed for urban aerial transport. This study presents a comprehensive perspective for various stakeholders, from policymakers to urban planners, highlighting the need for a thorough understanding of UAM’s potential and effective assimilation into urban mobility frameworks.

Bus bunching has been a persistent issue in urban bus system since it first appeared, and it remains a challenge not fully resolved. This phenomenon may reduce the operational efficiency of the urban bus system, which is detrimental to the operation of fast-paced public transport in cities. Fortunately, extensive research has been undertaken in the long development and optimization of the urban bus system, and many solutions have emerged so far. The purpose of this paper is to summarize the existing solutions and serve as a guide for subsequent research in this area. Upon careful examination of current findings, it is found that, based on the different optimization objects, existing solutions to the bus bunching problem can be divided into five directions, i.e., operational strategy improvement, traffic control improvement, driver driving rules improvement, passenger habit improvement, and others. While numerous solutions to bus bunching are available, there remains a gap in research exploring the integrated application of methods from diverse directions. Furthermore, with the development of autonomous driving, it is expected that the use of modular autonomous vehicles could be the most potential solution to the issue of bus bunching in the future.

Numerous maintenance strategies have been proposed in the literature related to reliability. This paper focuses on the utilization of reliability importance measures to optimize maintenance strategies. We analyze maintenance strategies based on importance measures and identify areas lacking sufficient research. The paper presents principles and formulas for advanced importance measures within the context of optimizing maintenance strategies. Additionally, it classifies methods of maintenance strategy optimization according to importance measures and outlines the roles of these measures in various maintenance strategies. Finally, it discusses potential challenges that optimization of maintenance strategies based on importance measures may encounter with future technologies.

There is notable variability in carbon emission reduction efforts across different provinces in China, underscoring the need for effective strategies to implement carbon emission allowance auctions. These auctions, as opposed to free allocations, could be more aligned with the principle of “polluter pays.” Focusing on three diverse regions — Ningxia, Beijing, and Zhejiang — this study employs a system dynamics simulation model to explore markets for carbon emissions and green certificates trading. The aim is to determine the optimal timing and appropriate policy intensities for auction introduction. Key findings include: (1) Optimal auction strategies differ among the provinces, recommending immediate implementation in Beijing, followed by Ningxia and Zhejiang. (2) In Ningxia, there’s a potential for a 6.20% increase in GDP alongside a 21.59% reduction in carbon emissions, suggesting a feasible harmony between environmental and economic objectives. (3) Market-related policy variables, such as total carbon allowances and Renewable Portfolio Standards, significantly influence the optimal auction strategies but have minimal effect on carbon auction prices.

In the steelmaking industry, enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities. Thus, effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain, reducing management costs and complexities. This paper proposes, for the first time, the integration of Vision-Language Model (VLM) and Large Language Model (LLM) technologies in the steel manufacturing domain, creating a novel steelmaking process management system. The system facilitates data collection, analysis, visualization, and intelligent dialogue for the steelmaking process. The VLM module provides textual descriptions for slab defect detection, while LLM technology supports the analysis of production data and intelligent question-answering. The feasibility, superiority, and effectiveness of the system are demonstrated through production data and comparative experiments. The system has significantly lowered costs and enhanced operational understanding, marking a critical step toward intelligent and cost-effective management in the steelmaking domain.

In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.

The joint optimization of production, maintenance, and quality control has shown effectiveness in reducing long-term operational costs in production systems. However, existing studies often assume that changes in the mean value of product quality characteristics in a deteriorating system follow a specific distribution while keeping variance constant. To address this limitation, we propose an innovative method based on the continuous ranking probability score (CRPS). This method enables the simultaneous detection of changes in mean and variance in nonconformities, thus removing the assumption of a specific distribution for quality characteristics. Our approach focuses on developing optimal strategies for production, maintenance, and quality control to minimize cost per unit of time. Additionally, we employ a stochastic model to optimize the production time allocated to the inventory buffer, resulting in significant cost reductions. The effectiveness of our proposed joint optimization method is demonstrated through comprehensive numerical experiments, sensitivity analysis, and a comparative study. The results show that our method can achieve cost reductions compared to several other related methods, highlighting its practical applicability for manufacturing companies aiming to reduce costs.

In recent decades, interest in project-based learning within organizational learning has grown significantly. This study synthesizes principles that facilitate learning at the project level. Through a cross-case analysis of the Gaasperdammer Tunnel project in the Netherlands and the Hong Kong-Zhuhai-Macao Bridge in China, and validation via focus group discussions, we have identified five key principles: Owner Commitment, Social Environment Approach, Collaboration Vision, Value Orientation, and Open Mindset. These principles highlight the mindsets that guide the behavior and thinking of project practitioners beyond prescriptive processes and routines. Our research enhances the understanding of how project participants can learn from their involvement in unique, complex projects and improve their capabilities for future endeavors. We emphasize the critical role of learning in the development of project capabilities and suggest it be a focal point in future research on infrastructure development projects.

This study investigates the use of autonomous vehicles in bus rapid transit lanes during the initial phases of autonomous driving development. The aim is to accelerate the advancement of autonomous driving technologies and enhance the efficiency of bus lane usage. We first develop a dynamic joint optimization model that adjusts autonomous vehicle speeds and bus timetables to minimize vehicle travel times while reducing bus passenger waiting times. We account for random variables such as stochastic passenger arrivals at bus stations and variable demand for autonomous vehicle travel by constructing a stochastic dynamic model. To address the computational challenges of large-scale scenarios, we implement a simulation-based heuristic algorithm framework. This framework is designed to efficiently produce high-quality solutions within feasible time limits. Our numerical studies on an actual bus line show that our approach significantly improves system throughput compared to existing benchmarks. Moreover, by strategically managing the entry of autonomous vehicles into the lane and modifying bus timetables, we further enhance the operational efficiency of the system.

Artificial intelligence (AI) has emerged as a promising technological solution for addressing critical infrastructure construction challenges, such as elevated accident rates, suboptimal productivity, and persistent labor shortages. This review aims to thoroughly analyze the contemporary landscape of AI applications in the infrastructure construction sector. We conducted both quantitative and qualitative analyses based on 594 and 91 selected papers, respectively. The results reveal that the primary focus of current AI research in this field centers on safety monitoring and control, as well as process management. Key technologies such as machine learning, computer vision, and natural language processing are prominent, with significant attention given to the development of smart construction sites. Our review also highlights several areas for future research, including broadening the scope of AI applications, exploring the potential of diverse AI technologies, and improving AI applications through standardized data sets and generative AI models. These directions are promising for further advancements in infrastructure construction, offering potential solutions to its significant challenges.

The widespread use of energy storage systems in electric bus transit centers presents new opportunities and challenges for bus charging and transit center energy management. A unified optimization model is proposed to jointly optimize the bus charging plan and energy storage system power profile. The model optimizes overall costs by considering battery aging, time-of-use tariffs, and capacity service charges. The model is linearized by a series of relaxations of the nonlinear constraints. This means that we can obtain the exact solution of the model quickly with a commercial solver that is fully adapted to the time scale of day-ahead scheduling. The numerical simulations demonstrate that the proposed method can optimize the bus charging time, charging power, and power profile of energy storage systems in seconds. Monte Carlo simulations reveal that the proposed method significantly reduces the cost and has sufficient robustness to uncertain fluctuations in photovoltaics and office loads.

Power grids play a crucial role in connecting electricity suppliers and consumers. They facilitate efficient power transmission and energy management, significantly contributing to the transition toward low-carbon practices across both upstream and downstream sectors. Effectively managing carbon reduction in the power industry is essential for enhancing carbon reduction efficiency and achieving dual-carbon goals. Recent studies have focused on the outcomes of carbon reduction efforts rather than the management process. However, when power grids prioritize the process of carbon reduction in their management, they are more likely to achieve better results. To address this gap, we propose an evaluation model for managing carbon reduction activities in power grids, comprising the carbon management efficiency (CME) module based on the maturity model and the carbon reduction efficiency (CRE) module based on the entropy method. The CME module provides a scorecard corresponding to a detailed and continuous evaluation model for carbon management processes to calculate its performance. Simultaneously, the CRE module relates carbon reduction results to the development direction of the government and power grid, allowing for effective adjustments and updates based on actual situations. The evaluation model was applied to provincial power grids within the China Central Power Grid. The results reveal that despite some fluctuations in carbon reduction performance, provincial power grids within the China Central Power Grid have made continuous progress in carbon reduction efforts. According to the synergy model, there is evidence suggesting that power grids are steadily improving their carbon reduction performance, and a more organized approach would lead to a greater degree of synergy. The evaluation model applies to power grids, and its framework can be extended to other industries, providing a theoretical reference for evaluating their carbon reduction efforts.

The integration of Blockchain Technology (BT) with Digital Twins (DTs) is becoming increasingly recognized as an effective strategy to enhance trust, interoperability, and data privacy in virtual spaces such as the metaverse. Although there is a significant body of research at the intersection of BT and DTs, a thorough review of the field has not yet been conducted. This study performs a systematic literature review on BT and DTs, using the CiteSpace analytic tool to evaluate the content and bibliometric information. The review covers 976 publications, identifying the significant effects of BT on DTs and the integration challenges. Key themes emerging from keyword analysis include augmented reality, smart cities, smart manufacturing, cybersecurity, lifecycle management, Ethereum, smart grids, additive manufacturing, blockchain technology, and digitalization. Based on this analysis, the study proposes a development framework for BT-enhanced DTs that includes supporting technologies and applications, main applications, advantages and functionalities, primary contexts of application, and overarching goals and principles. Additionally, an examination of bibliometric data reveals three developmental phases in cross-sectional research on BT and DTs: technology development, technology use, and technology deployment. These phases highlight the research field’s evolution and provide valuable direction for future studies on BT-enhanced DTs.

Predicting short-term traffic crashes is challenging due to an imbalanced data set characterized by excessive zeros in noncrash counts, random crash occurrences, spatiotemporal correlation in crash counts, and inherent heterogeneity. Existing models struggle to effectively address these distinct characteristics in crash data. This paper proposes a new joint model by combining the time-series generalized regression neural network (TGRNN) model and the binomially weighted convolutional neural network (BWCNN) model. The joint model aims to capture all these characteristics in short-term crash prediction. The model was trained and tested using real-world, highly disaggregated traffic data collected with inductive loop detectors on the M1 motorway in the UK in 2019, along with crash data extracted from the UK National Accident Database for the same year. The short-term is defined as a 30-min interval, providing sufficient time for a traffic control center to implement interventions and mitigate potential hazards. The year was segmented into 30-min intervals, resulting in a highly imbalanced data set with over 99.99% noncrash samples. The joint model was applied to predict the probability of a crash occurrence by updating both the crash and traffic data every 30 min. The findings revealed that 75.3% of crashes and 81.6% of noncrash events were correctly predicted in the southbound direction. In the northbound direction, 78.1% of crashes and 80.2% of noncrash events were accurately captured. Causal analysis and model-based interpretation were used to analyze the relative importance of explanatory variables regarding their contribution to crashes. The results reveal that speed variance and speed are the most influential factors contributing to crash occurrence.

Urban rail transit (URT) plays a pivotal role in mitigating urban congestion and emissions, positioning it as a sustainable transportation alternative. Nevertheless, URT’s function in transporting substantial numbers of passengers within confined public spaces renders it vulnerable to the proliferation of infectious diseases during public health crises. This study proposes a decision support model that integrates operational control strategies pertaining to passenger flow and train capacity utilization, with an emphasis on energy efficiency within URT networks during such crises. The model anticipates a URT system where passengers adhere to prescribed routes, adhering to enhanced path flow regulations. Simultaneously, train capacity utilization is intentionally limited to support social distancing measures. The model’s efficacy was assessed using data from the COVID-19 outbreak in Xi’an, China, at the end of 2021. Findings indicate that focused management of passenger flows and specific risk areas is superior in promoting energy efficiency and enhancing passenger convenience, compared to broader management approaches.

Mega infrastructure projects (MIPs) play crucial roles in promoting social development, regional growth, and disaster and crisis resilience. These complex projects frequently face challenges in stakeholder management, which might be a risk for their sustainability. Hence, this paper proposes affordance theory as a new theoretical framework, particularly on the basis of understanding and managing MIPs. This paper aims to achieve three main objectives: 1) conceptualizing MIP affordance, 2) documenting the influence of MIP affordance on stakeholder management and project sustainability, and 3) developing strategies for managing MIP affordance. It applies critical realism to conceptualize MIP affordance and its mechanism, and employs the expectation-confirmation theory to identify critical determinants for managing MIP affordance. The paper thus provides new knowledge and insights into the management of MIP stakeholders concerning project sustainability.

Roadmapping is a well-established technique in the context of innovation and strategy, with the potential to support organizations address the complex transformative challenges facing humanity in the 21st century. This is enabled by its systems-based architecture and visual form of roadmaps, supporting communication and reduction of information asymmetries in complex sociotechnical systems. This paper focuses on an adaptation of the roadmapping method to support strategic planning for roadmapping systems in organizations, addressing implementation challenges. This represents a novel application of roadmapping to business processes and systems, demonstrating the flexibility of the roadmapping approach. A workshop template (‘R2’) and process for supporting the roadmapping of roadmapping systems is presented, developed and refined through a series of six industrial cases, and illustrated with an application example in the additive manufacturing sector.

The development of shale oil is of considerable strategic importance, particularly concerning national security implications. Effective management is vital to maximize both efficiency and socio-economic benefits. This process necessitates addressing four critical relationships: balancing local and global factors, reconciling universality with particularity, integrating inheritance with innovation, and resolving primary and secondary contradictions. These relationships pose several management challenges that must be overcome to develop a robust management model for shale oil extraction. This paper uses the Gulong shale oil in the Daqing oilfield as a case study to examine the implications and specific manifestations of these relationships. To address the limitations of traditional management models, which often overly emphasize local factors, particularity, innovation, and secondary contradictions, we have developed the “Integrated Dialectical Four-Domain Coupling Management Model.” This model incorporates systems engineering theory into management strategies. Key strategies include the global deployment of experimental zone construction, systematic geological and engineering integration, combining historical practices with innovative approaches, phase analysis, and contradiction coordination. These strategies have significantly advanced the development of Gulong shale oil, demonstrating positive on-site results. The innovative management processes detailed in this paper provide valuable insights applicable to similar reservoirs and other large-scale engineering management projects.

This paper investigates whether e-hailing performs better than on-street searching for taxi services. By adopting the Poission point process to model the temporal-spatial distributions of idle vehicles, passengers’ waiting time distributions of on-street searching and e-hailing are explicitly modeled, and closed-form results of their expected waiting time are given. It is proved that whether e-hailing performs better than on-street searching mainly depends on the density of idle vehicles within the matching area and the matching period. It is proved that given the advantage of e-hailing in rapidly pairing passengers and idle vehicles, the expected waiting time for on-street searching is always longer than that of e-hailing as long as the number of idle vehicles within a passenger’s dominant temporal-spatial area is lower than 4/π. Moreover, we extend our analysis to explore the market equilibria for both e-hailing and on-street searching, and present the equilibrium conditions for a taxi market operating under e-hailing versus on-street searching. With a special reciprocal passenger demand function, it is shown that the performance difference between e-hailing and on-street searching is mainly determined by the ratio of fleet size to maximum potential passenger demand. It suggests that e-hailing can achieve a higher capacity utilization rate of vehicles than on-street searching when vehicle density is relatively low. Furthermore, it is shown that an extended average trip duration improves the chance that e-hailing performs better than on-street searching. The optimal vehicle fleet sizes to maximize the total social welfare/profit are then analyzed, and the corresponding maximization problems are formulated.

The rapid increase in global urbanization, along with the growth of the construction industry, highlights the urgent need for effective management of construction and demolition (C&D) waste. Intelligent technologies offer a viable solution to this critical challenge. However, there remains a significant challenge in integrating these technologies into a cohesive framework. This study conducts a quantitative analysis of 214 papers from 2000 to 2023, highlighting the extensive use of artificial intelligence (AI) and building information modeling (BIM), along with geographic information systems (GIS) and big data (BD). A further qualitative analysis of 73 selected papers investigates the use of seven different intelligent technologies in the context of C&D waste management (CDWM). To overcome current limitations in knowledge, future research should concentrate on (1) the comprehensive integration of technology, (2) inclusive studies throughout all lifecycle phases of CDWM, and (3) the continued examination of new technologies, such as blockchain. Based on these insights, this study suggests a strategic framework for the effective implementation of intelligent technologies in CDWM. This framework aims to assist professionals in merging various technologies, undertaking lifecycle-wide research, and narrowing the divide between existing and new technologies. It also lays a solid foundation for future academic work to examine specific intelligent technologies, conduct comparative studies, and refine strategic decisions. Regular updates on technological developments are essential for stakeholders to consistently enhance CDWM standards.

In healthcare facilities, including hospitals, pathogen transmission can lead to infectious disease outbreaks, highlighting the need for effective disinfection protocols. Although disinfection robots offer a promising solution, their deployment is often hindered by their inability to accurately recognize human activities within these environments. Although numerous studies have addressed Human Activity Recognition (HAR), few have utilized scene graph features that capture the relationships between objects in a scene. To address this gap, our study proposes a novel hybrid multi-classifier information fusion method that combines scene graph analysis with visual feature extraction for enhanced HAR in healthcare settings. We first extract scene graphs, complete with node and edge attributes, from images and use a graph classification network with a graph attention mechanism for activity recognition. Concurrently, we employ Swin Transformer and convolutional neural network models to extract visual features from the same images. The outputs from these three models are then integrated using a hybrid information fusion approach based on Dempster-Shafer theory and a weighted majority vote. Our method is evaluated on a newly compiled hospital activity data set, consisting of 5,770 images across 25 activity categories. The results demonstrate an accuracy of 90.59%, a recall of 90.16%, and a precision of 90.31%, outperforming existing HAR methods and showing its potential for practical applications in healthcare environments.

human–robot collaboration (HRC) is set to transform the manufacturing paradigm by leveraging the strengths of human flexibility and robot precision. The recent breakthrough of Large Language Models (LLMs) and Vision-Language Models (VLMs) has motivated the preliminary explorations and adoptions of these models in the smart manufacturing field. However, despite the considerable amount of effort, existing research mainly focused on individual components without a comprehensive perspective to address the full potential of VLMs, especially for HRC in smart manufacturing scenarios. To fill the gap, this work offers a systematic review of the latest advancements and applications of VLMs in HRC for smart manufacturing, which covers the fundamental architectures and pretraining methodologies of LLMs and VLMs, their applications in robotic task planning, navigation, and manipulation, and role in enhancing human–robot skill transfer through multimodal data integration. Lastly, the paper discusses current limitations and future research directions in VLM-based HRC, highlighting the trend in fully realizing the potential of these technologies for smart manufacturing.