The utilization of rooftop space offers various benefits to cities and their residents, such as urban heat island mitigation, energy saving, and water management. However, a comprehensive understanding of these benefits and their regional differences is still lacking. We reviewed 97 articles published between 2000 and 2022 to evaluate the efficiency of various rooftop engineering approaches, including green roofs, white roofs, solar roofs, blue roofs, and wind turbine roofs. The main findings are as follows: (I) As of 2020, there are ~245 billion m² of rooftop space worldwide, equivalent to the land area of the UK. About 29%–50% of these rooftops are suitable for utilization. (II) Effective use of rooftop space can cool cities by ~0.60°C, meet ~44% of city energy demand, reduce runoff by ~17%, and save ~23% of building water demand. (III) Climate and building types influence the efficiency of rooftop engineering, with mediterranean climates and low-rise buildings offering the most favorable conditions. This review provides a comprehensive evaluation of global rooftop resources and their potential benefits, offering valuable guidance for cities to adopt differentiated rooftop strategies.

Realizing the 2060 carbon neutrality target and air quality goals simultaneously is a common challenge for provinces in China aiming for sustainable development. This study examines the costs and benefits of achieving these dual targets in six southern Chinese provinces. Using a multi-model assessment approach—comprising the economic IMED|CGE model, the environmental GAINS model, and the IMED|HEL health risk assessment model—this study captures the uneven air quality and health burdens associated with inter-regional economic connections. We found that achieving carbon neutrality significantly improves air quality and provides health co-benefits through rapid energy system transformation. However, there is substantial provincial heterogeneity in the pathways chosen to reduce pollution and carbon emissions. As the potential of end-of-pipe control measures diminishes, stricter air quality standards will depend increasingly on profound transformations in energy and economic systems driven by carbon neutrality goals. Carbon reduction policies will reshape trade structures, altering the flow of embodied carbon dioxide (CO₂) and air pollutants. Additionally, provincial carbon quotas significantly influence the health-related net benefits of carbon reduction, as GDP losses are sensitive to the allocation of these quotas. Our study demonstrates the feasibility of simultaneously achieving CO₂ emission reduction and air quality improvement. Policymakers should integrate air quality targets with low-carbon development objectives when creating regional blueprints for green transformation, thereby aligning air pollution and carbon reduction management organically.

Consistently threatened by climate change, cities need to adapt to emerging hazards and risks. One such risk relates to extreme heat, which is a particular problem in urban areas and is also linked to air pollution. Together, these risks can have a substantial impact on human health. Our analysis of air quality, ambient temperatures, and climate change adaptation plans in 30 UK cities found strong evidence that London and Cambridge exhibit the highest risk of both extreme temperature and air pollution. Furthermore, although a heatwave in London led to lower levels of PM₁₀ and NO₂, it was highly correlated with increased levels of O₃, a low-level pollutant that exacerbates respiratory problems. We also found a lack of data availability (e.g., O₃, PM₁₀) in some local authorities and inconsistencies in their climate change adaptation strategies. We therefore identify a clear need for standardised assessment of hazards at the city level, and their incorporation into local adaptation plans. Further assessment of climate hazards and risks at the city level are required for effectively adapting to a changing climate in the UK and other cities worldwide.

Cloud systems, which are typical cyber–physical systems, consist of physical nodes and virtualized facilities that collaborate to fulfill cloud computing services. The advent of virtualization technology engenders resource sharing and service parallelism in cloud services, introducing novel challenges to system modeling. In this study, we construct a systematic model that concurrently evaluates system reliability, performance, and power consumption (PC) while delineating cloud service disruptions arising from random hardware and software failures. Initially, we depict system states using a birth–death process that accommodates resource sharing and service parallelism. Given the relatively concise service duration and regular failure distributions, we employ transient-state transition probabilities instead of steady-state analysis. The birth–death process effectively links system reliability, performance, and PC through service durations governed by service assignment decisions and failure/repair distributions. Subsequently, we devise a multistage sample path randomization method to estimate system metrics and other factors related to service availability. The findings highlight that the trade-off between performance and PC, under the umbrella of reliability guarantees, hinges on the equilibrium between service duration and unit power. To further delve into the subject, we formulate optimization models for service assignment and juxtapose optimal decisions under varying availability scenarios, workload levels, and service attributes. Numerical results indicate that service parallelism can improve performance and conserve energy when the workload remains moderate. However, as the workload escalates, the repercussions of resource sharing-induced performance loss become more pronounced due to resource capacity limitations. In cases where system availability is constrained, resource sharing should be approached cautiously to ensure adherence to deadline requirements. This study theoretically analyzes the interrelations among system reliability, performance, and PC, offering valuable insights for making informed decisions in cloud service assignments.

Cyber-physical systems (CPSs) play a crucial role in modern transportation, particularly in transportation cyber-physical systems (TCPSs) for emergency supply logistics. By utilizing real-time data collection, analysis, and communication technologies, TCPS improves the efficiency, safety, and reliability of emergency supply transportation systems. However, existing research often overlooks the dynamic nature of the transportation environment and the complexities of joint emergency supply transportation amid uncertainty. The risk factors for both the physical and cyber layers are inadequately addressed. Consequently, assessing the risks associated with transporting emergency materials within CPS frameworks remains a significant challenge. This study proposes a risk assessment model based on TCPS to analyze the risks associated with transporting emergency supplies via various transportation modes. Initially, a comprehensive analysis of risk factors spanning both the cyber and physical layers within the TCPS is conducted. A risk assessment model is subsequently developed by considering transportation time costs, expenses, and delays. A risk area is then introduced to simulate the impact of recurrent emergency events on emergency supply transportation. Finally, we simulate emergency supply transportation scenarios to facilitate effective risk evaluation.

In recent years, improvements in energy storage technology, cost reduction, and the increasing imbalance between power grid supply and demand, along with new incentive policies, have highlighted the benefits of battery energy storage systems. These systems offer long life, low cost, and high energy conversion efficiency. While energy storage is gradually transitioning from demonstration projects to commercial operations, its technical and economic performance is still limited, and it lacks economies of scale. Research on the design and operational optimization of energy storage systems is crucial for advancing project demonstrations and commercial applications. Therefore, this paper aims to provide insights into system configuration and operational optimization. It first summarizes the optimal configuration of energy storage technology for the grid side, user side, and renewable energy generation. It then analyzes and reviews the economic optimization and cybersecurity challenges in power system operations. Finally, this paper discusses unresolved issues in energy storage applications and highlights important considerations for future implementation and expansion.

The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.

Surface quality monitoring of manufacturing products is critical for manufacturing industries to ensure product quality and production efficiency. With the rapid development of 3D scanning technology, high-density 3D point cloud data can be generated by 3D scanners in complex manufacturing systems. However, due to the challenges of complex surface modeling and various types, it lacks effective surface anomaly detection methods that can meet the practical requirements regarding detection accuracy and speed. This survey aims to review the surface anomaly detection methodology of manufacturing products based on 3D machine vision. Specifically, the machine learning methodologies will be systematically reviewed for 3D point cloud data modeling and anomaly detection. Related public data sets for this research are also summarized. Finally, the future research directions are pointed out.

Urban road networks play a crucial role in transport and urban planning and have the potential to contribute to more sustainable futures if their hierarchy is properly understood. However, the concept of the urban road network hierarchy, which refers to street classification and prioritization, is not well defined within the domain of transport engineering management, leaving many questions unanswered. Is it simply a planning tool, or does it extend to defining the essence of cities? Is it a qualitative or quantitative concept? Does it emerge organically or require proactive planning? Given the lack of comprehensive answers to these questions, this research aims to provide a contextual understanding of the urban road network hierarchy through the lens of sustainable transport futures. To this purpose, we conducted a systematic literature review, which is an effective method for consolidating knowledge on a specific topic. A total of 42 articles were analyzed using both quantitative bibliometric analysis and qualitative content analysis. Our work demonstrates that the road network hierarchy consists of 16 sub-concepts. Four main research trends were identified and discussed: a) road morphology and structure, b) advanced algorithms for street classification, c) integrated street classification planning, and d) the social dimension of street classification. Recent literature indicates a shift toward alternative road network hierarchy approaches that prioritize sustainable mobility over car-centric models. In conclusion, our analysis reveals that the urban road network hierarchy is a multifaceted yet under researched “vehicle for change,” which, if utilized effectively, offers opportunities to reimagine urban road environments.

Escalating motorcycle crashes present a significant challenge due to the increase in motorcycle registrations and the corresponding increase in mortality rates. This issue is particularly acute in Cambodia, where motorcycles are the primary mode of transportation. In the analysis of motorcycle crashes, two key measures of severity are injury severity and crash size, notably the number of injuries. Typically, these indicators are analyzed independently to understand the impact and consequences of motorcycle accidents. Nevertheless, it is critical to recognize that both observed and unobserved factors may concurrently affect these crash indicators, indicating a possible interrelationship between injury severity and motorcycle crash size. Neglecting the joint occurrence of these variables can result in biased and incorrect parameter estimation. This research contributes to the existing body of knowledge by simultaneously analyzing the factors influencing both injury severity and motorcycle crash size. This approach further distinguishes itself by considering the interdependence between these two results utilizing a copula-based approach. Six models based on copulas were developed using the ordered logit model, which was designed to capture the ordinal nature of injury severity and crash size. By analyzing motorcycle crash data from 2016 in Cambodia, the Frank copula framework was identified as the most effective among the five approaches. The findings revealed that factors such as motorcycle-to-pedestrian collisions, head-on collisions, X junctions, and national roads significantly increase both motorcyclist injury severity and crash size. These insights are valuable for policymakers in formulating targeted strategies to improve motorcycle safety within transportation systems.

Since the implementation of the transportation power strategy, China’s transportation industry has developed rapidly, yet the number of road traffic accidents has remained high in recent years. Many scholars have investigated the factors influencing traffic accidents to find the underlying mechanisms, thereby enhancing road traffic safety. Compared to general accidents, the factors influencing major road traffic accidents are more complex. This study focuses on examining the relationships between factors affecting major road traffic accidents. Data on 968 major road traffic accidents from 2012 to 2018 in China were collected and organized. The accident information fields were analyzed to identify seven attributes: accident province, accident region, accident quarter, accident time, accident form, accident vehicle, and weather condition. The Apriori association rule algorithm was employed to mine and solve the strong association rules between accident attribute values. The associations between different influencing factors and the form of accident results were analyzed, with a deeper exploration of three-factor and four-factor rules. The results indicate that certain causal factors jointly contribute to major accidents, particularly in the western region, represented by Guangxi. These accidents mainly involved trucks and occurred in rainy and snowy weather during the first quarter. The conclusions of this research can provide the transportation management department with measures to improve urban road traffic safety and reduce the occurrence of traffic accidents.

The increasing complexity of global supply chains has presented critical challenges for businesses in coordinating resources, forecasting demand, and dynamically optimizing processes. Traditional supply chain management (SCM) methods are often inflexible, reactive, and prone to inefficiencies, which can result in missed opportunities and lost revenue. Technological advancements have played a pivotal role in addressing these challenges, with Generative Artificial Intelligence (GAI) emerging as a transformative force that offers numerous advantages for SCM. Despite the abundance of literature on the role of GAI in enhancing supply chain performance, it remains insufficient in providing a comprehensive theoretical framework for the construction of GAI applications and their empowerment mechanisms within SCM. This study first outlines the core GAI capabilities necessary for constructing the SCM framework. We then examine the empowerment mechanisms and challenges of GAI in SCM and propose corresponding solutions. Afterward, we discuss notable gaps and propose a comprehensive research agenda, focusing on the SCM framework empowered by GAI.

The emergence of systems-of-systems (SoS) brings new challenges for reliability research within the field of system-of-systems engineering (SoSE). In response, this study examines the requirements for measurement, evaluation, and design/testing of SoS reliability. Drawing on research practice in aerospace engineering, it outlines the concept of equipment SoS and proposes a corresponding research framework for equipment SoS reliability. By analyzing both the reliability demands of SoSE and the current research practice in aerospace equipment SoS, this study identifies several key challenges: Modeling of SoS and its reliability, network reliability in SoS, propagation and recovery of disturbances in SoS, reliability design and simulation methods, and reliability management in SoSE.