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An impetus towards the electrification of mobility is crucial for India. However, the adoption rates of electric two-wheelers remain low, and hence, for fruitful penetration of electric two-wheelers in India, this study analyses the factors affecting the adoption behaviour of electric two-wheelers using discrete choice models. This study uses data collected from 1375 potential electric two-wheeler buyers of Bengaluru, India, to study the influence of various vehicle attributes such as purchase price, operating cost, charging time, driving range, charging infrastructure and exhaust emissions on the intention to adopt electric two-wheelers. In addition, the impact of important policies such as free and reserved parking, access to priority lanes and toll tax exemption for electric two-wheeler owners is also analysed and discussed in this study. Apart from the vehicle attributes, this study also studies the effect of socio-demographic variables, travel variables and certain attitudinal latent variables such as environmental enthusiasm, technological enthusiasm, and perceived social image on the electric two-wheeler adoption behaviour. The results reveal purchase price, operating cost and charging time to have a significant negative impact and driving range and density of the charging infrastructure network to have a significant positive impact on the intention to adopt electric two-wheelers. • Explores the influence of vehicle attributes, policies and travel attitudinal variables on electric two-wheeler adoption intention. • Purchase price and charging time are major hindrances to widespread adoption of electric two-wheelers. • Improvement in driving range and charging infrastructure will aid in the adoption of electric two-wheelers. • Policy initiatives and incentives for electric vehicles can further induce acceptance of electric two-wheelers. [ABSTRACT FROM AUTHOR]

Private sector engagement in infrastructure procurement and ownership can have benefits for both investors and society. However, a significant challenge for authorities is determining when private sector participation provides value for money. Private investors may demand high returns on their investments even in projects with seemingly low risks. Governments with good credit ratings and access to lower cost capital may view private investors overprice in comparison to the level of risk they take. This paper examines whether the financing premiums (in addition to the cost of state financing) of four public-private partnership road projects in Finland are reasonable relative to the risks borne by private partners, using long time series of actual data. To the best of our knowledge, this is the first study to mostly use actual ex-post data on project costs over an extended period to evaluate whether the government overpays private investors. Our analysis indicates that the financing cost of the four projects is on average 201 basis points higher than the financing cost the government pays on its debt. We conclude that this premium is reasonable compensation for the risk the private investors bear in the projects. This finding has implications for selecting the most effective procurement policy for road projects. • We use long time series of actual financial data from PPPs to calculate the financing cost of PPPs. • We conclude that the extra financing cost of the PPPs is a justified compensation for the risk transfer. [ABSTRACT FROM AUTHOR]

Vehicle electrification has shown the potential to reduce environmental impacts and greenhouse gas emissions from the transport sector. As electric vehicles (EVs) become increasingly prominent, the efficient placement of charging infrastructure poses a complex challenge that demands careful consideration. This paper delves into the investigation of how travel and parking patterns, derived from empirical data on freight vehicles, influence the optimal distribution of charging infrastructure across the freight network. This paper presents a node-based approach to optimize the allocation of charging infrastructure tailored explicitly for freight transport. The study identifies optimal locations for operator-owned charging infrastructure by leveraging GPS-based data collected from a fleet of freight vehicles operating in the greater Gothenburg metropolitan area. This research aims to enhance our understanding of the charging infrastructure requirements inherent in the freight transport system and provide decision support to logistics companies contemplating the shift from conventional fossil fuel vehicles to electric freight vehicles. The proposed model holds the potential for seamless adaptation to diverse freight transport systems, offering valuable insights to expedite the transition toward fossil-free freight transport on a broader scale. • Complexity in charging infrastructure placement for freight transport. • Empirical data from a fleet of freight vehicles in Gothenburg metropolitan area. • Investigation of travel and parking patterns of freight vehicles. • Node-based optimization approach to allocate charging infrastructure. [ABSTRACT FROM AUTHOR]

A MANET is a decentralized type of wireless network of mobile devices, and it can also be defined as an autonomous system of nodes. All the nodes in the network are connected by wireless links and are mobile. They can come together and form a network without any support from any existing network infrastructure. MANET is a new field of study based on blockchain in a wireless ad hoc environment. However, the main challenge for blockchain applications in ad hoc networks is how to adapt to the extreme computational complexity of block validation while preserving the characteristics of the blockchain and including nodes in the validation process. This article proposes a blockchain-based mobile network (MANET) with an ensemble algorithm. The proposed scheme provides a distributed environment for MANETS routing using a blockchain based on the Byzantine fault tolerance (BFT) protocol. Taking advantage of the better approach of mobile ad hoc networking (BATMAN) to incorporate the concept of blockchain into the MANET as a representative protocol. The proposed method named Extended-BATMAN (E-BATMAN) incorporates the concept of blockchain into the BATMAN protocol using MANET. As a secure, distributed, and reliable platform, Blockchain solves most BFT security issues, with each node performing repeated security operations individually. The experimental analysis of the proposed ensemble algorithm is based on four parameters such as packet delivery rate, average end-to-end latency, network throughput, and energy. All of these parameters show better results with the proposed ensemble protocol than with existing state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

Least-cost path analysis (LCPA) is a widely recognized and practical algorithm for determining cost-effective paths between two points. This research presents an innovative LCPA designed to identify paths for infrastructure projects with slope threshold conditions (an interval of acceptable slopes). The algorithm incorporates potential DEM modifications through civil engineering structures, including cut-and-fill, tunnelling, and bridging, to include paths that otherwise exceed the maximum slope threshold. We conducted experiments of the developed LCPA on an area with complex topography, the Zagros Mountains in Iran, using three slope thresholds (5°, 8°, and 10°), with and without tunnelling and bridging. The results demonstrated the algorithm's ability to identify paths with lower cost across all slope thresholds, with the inclusion of tunnelling and bridging leading to further cost reductions. A comparison with an LCPA based on an extended moving window approach highlighted the identification of paths with lower costs, even in the absence of tunnelling and bridging, across all slope thresholds and window sizes. The implementation of the developed LCPA in real-world scenarios is expected to significantly enhance transportation planning and infrastructure design processes. [ABSTRACT FROM AUTHOR]

This paper investigates the infrastructure projects undertaken for the event initially known as the Semaine des sports d'hiver, which took place in Chamonix, France, from 25 January to 4 February 1924 and was later recognized as the first Winter Olympics. Although the already famous resort town was able to use its existing hotels to accommodate visitors and athletes, it also made a considerable investment in the construction of new sports infrastructure. Following an agreement signed just 9 months before the Games, these facilities included a large ice rink, a bobsleigh run and a ski jump. The project was entrusted to the Ponts et Chaussées engineers, who encouraged local firms to help with the construction. Archival analysis will be used to examine the relationship between the project and the changing environment. Our aim is to show how the work in Chamonix modified the environment by exploiting certain natural elements (e.g. water, soil and forests) and, conversely, how the natural (in particular, the geological and climatic) and historical (notably land ownership) components of the environment affected the execution of these works. [ABSTRACT FROM AUTHOR]

As part of its ongoing review of the processes surrounding the hosting of the Olympic Games (OGs), the International Olympic Committee (IOC) has set out the Olympic Agenda 2020 and the related New Norm (NN). These reforms, respectively approved in 2014 and 2018 to deal with the growing withdrawal of the bids, are in line with recent management studies highlighting the importance of standardization and replicability in the delivery of physical and social infrastructure. Indeed, they aim to convert development projects, historically-embedded in places, into programmable and transferable spaces of action, in which rules of project management and organization can be applied. The example of the Milano-Cortina Winter Games (MCWG) 2026 is used to assess the first effects and impacts of such IOC's new approach. At the backdrop of the historical evolution of mega-event planning in post-war Italy, the rolling-out of the MCWG is examined at the multiple scales of the Olympic macro-region and of the Milan Olympic Village. The analysis shows that, despite the objectives of the NN to overcome existing tensions and conflicts in the involved places, the Games has only succeeded in amplifying them. Such contradiction demands for a further reflection on this model, that remains under-discussed and under-researched. [ABSTRACT FROM AUTHOR]

Building and other construction assets constitute a significant part of a country's physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries' specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980-2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country's construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development. [ABSTRACT FROM AUTHOR]

In the Xi Jinping era, China is actively implementing the Belt and Road Initiative (BRI) around the globe. Under the BRI, infrastructure development has become the main agenda for China to create connectivity. One of the infrastructure projects in the energy sector is the Nam Ou Cascade Hydropower Project. The Nam Ou River is a tributary of the Mekong River located in Laos and borders China. For Laos, this project is part of their vision to be "The battery of Southeast Asia." Therefore, this article attempts to explain that Nam Ou Hydropower has implications for China's dominance in the Mekong region. The circle hydro-hegemony is used as the conceptual framework for analyzing the phenomenon. This article also applies a qualitative method with secondary data. The article shows that the Nam Ou Cascade Hydropower Project in Laos elevates China's influence in the Mekong region. By actively pulling and pushing its neighbors into cooperation, China exercises its power as an upstream country. [ABSTRACT FROM AUTHOR]

In 2018, the G20 endorsed the 'Roadmap to Infrastructure as an Asset Class'. This document crystallizes a roaring policy agenda focused on transforming 'infrastructure' into a homogeneous, tradeable investment opportunity, attractive to global financial markets. Although the Roadmap is formally a mere policy blueprint, this article studies it as a legal document—one whose purpose is to code infrastructures into capital. Specifically, the article makes three claims, two analytical and one normative. First, that the Roadmap's way of attracting investors ultimately consists in shielding the decision-making processes related to the design and maintenance of infrastructures from democratic interference. Second, that these restrictions are enforced through global governance mechanisms that push the relevant decision-making authority from the national to the global. And third, that the Roadmap's agenda is problematic from a democratic standpoint, because it disempowers those who should have a say over the development and maintenance of these infrastructures. [ABSTRACT FROM AUTHOR]

This article presents a unique chapter in the history of Romanian-Dutch relations. During the 19th century, the hinterland at the mouth of the Danube was an important centre for the supply of grain to European warehouses, and from the 1880s onwards, international trading houses became increasingly interested in placing the main commodity of Moldo-Wallachian production structures on the markets of Amsterdam and Rotterdam. Due to its entrepreneurial and infrastructural connections with Germany, the port of Rotterdam became a transit and temporary storage point for grain dedicated to the Rhine-Ruhr industrial region. This study is based on unpublished documents from archives, databases, and statistical yearbooks in the Netherlands and Romania. [ABSTRACT FROM AUTHOR]

Battery-electric buses (BEBs) are considered suitable technology for transit to tackle climate change and promote environmentally friendly mobility solutions. However, the systemic configuration of BEBs in transit requires sophisticated planning efforts due to contradictory objectives and decisions. The optimal design of a BEB transit system is often approached from various perspectives, leading to different system configurations and distinct impacts on the electricity grid. Towards that end, this study develops three BEB system configuration optimization models, including minimizing capital costs, electricity costs, and greenhouse gas (GHG) emissions. All three models inform the optimal charging system configuration, BEBs battery capacity, and BEBs charging schedule for a general hub-and-spoke transit network. The proposed models are applied to a case study of the Belleville City, Ontario, Canada, bus transit network. The results demonstrate that BEB system configuration and GHG emissions vary significantly according to the optimization perspective. Moreover, the findings emphasize the importance of using the energy storage system to reduce electricity costs and GHG emissions. • Assess the impacts of optimization approaches on the BEB system configuration. • Investigate the benefits of implementing ESS in BEB system configuration. • Propose an integrated BEB system planning and operation optimization model. [ABSTRACT FROM AUTHOR]

Ex-post benefit-cost analyses (BCAs) can improve the accuracy of future ex-ante BCAs and strengthen accountability for policy implementation. However, benefits and costs are usually analyzed prior to policymaking, not after. The rare ex-post BCAs available consider road and rail projects and often face methodological challenges such as difficulties describing what would have occurred in the absence of a project (the counterfactual situation). Earlier studies also mention the problem of projects being evaluated too soon after implementation. Our case study evaluates benefits and costs of a Dutch waterway project more than 10 years after its implementation, including the counterfactual situation, and compare results of the ex-post BCA with those of the ex-ante BCA. The outcome of the ex-post BCA is negative. This is in contrast with the positive outcome of the ex-ante BCA. The difference between the ex-post BCA and the ex-ante BCA is decomposed by changing assumptions step-by-step. This enables us to isolate forecasting errors of the ex-ante BCA from other potential sources of differences, such as methodological differences resulting from changes in guidelines occurring in the period between the ex-ante and ex-post BCA. As far as we know, this approach has not been attempted before in ex-ante/ex-post comparisons of BCA's for transport projects. Ex-post BCAs that explicitly define the counterfactual situation, use up-to-date insights into economic developments and check changes in the methodologies applied should accordingly be carried out more often. Ex-ante / ex-post comparisons are useful tools to improve the accuracy of BCAs, especially when they decompose of sources of difference. • Benefit-cost analyses (BCAs) are key instruments for policymaking on infrastructure projects in many countries. • Ex-post BCAs can improve ex-ante BCAs but have only rarely been undertaken and often face methodological challenges. • Our study addresses the methodological challenges and decomposes sources of difference between ex-ante and ex-post BCAs. • For the Burgemeester Delen Canal case, we have isolated assessment errors from other sources of differences. [ABSTRACT FROM AUTHOR]

Mass rapid transit (MRT) systems are the backbone of transportation infrastructure in many cities. During emergencies, indoor subway stations become particularly susceptible to panic and overcrowding, making evacuation difficult. Therefore, simulating the evacuation and sheltering processes of a large number of individuals during emergencies is key in urban disaster response. In this study, a spatial evacuation risk model was used to enhance the safety of evacuation strategies in an MRT system. Data were collected on spatial architectural floor plans, and a database for communication and pedestrian flow was established. The behavioral patterns and distribution of individuals within stations and the characteristics of refuge spaces were also investigated. To simulate the processes of evacuation and sheltering, the Pathfinder evacuation simulation tool was used to construct a three-dimensional spatial model of a subway station. Specific scenarios were then constructed to simulate the procedures of evacuation and sheltering. Subsequently, the factors contributing to overcrowding-related hazards were analyzed to examine the spatial risks associated with crowd aggregation and evacuation and provide recommendations for improvement. Given its numerous advantages, this approach can serve as a valuable reference for conducting risk assessments in other large-scale crowd aggregation spaces. [ABSTRACT FROM AUTHOR]

While the technological development of vehicles and fuels is not adequate to meet current climate mitigation targets, infrastructure development also plays an important role in transforming the transport system. Previous studies have argued that conventional infrastructure planning is incapable of implementing climate mitigation. The aim of the paper is to provide insights into power means and mechanisms that counteract integration of climate mitigation targets in infrastructure planning. This is done by an in-depth case study of current Swedish national transport planning. This case provides a rich illustration of a situation with high political ambitions regarding climate mitigation on the one hand, and power mechanisms and resistance with regard to climate goals during the planning process on the other. The case is analysed using the perspective of power circuits and shows how forecasting works as an obligatory passage point, sorting in and out which analyses will be part of the decision-making material. Analyses which do not fit the forecasting model are dismissed from planning. The conclusion is that as long as the transport infrastructure planning practice is dependent on forecasting as the only central analysis there will be difficulties in changing the scope of infrastructure planning and making climate goals central for transport planning. [ABSTRACT FROM AUTHOR]

Subsea infrastructure is typically out of sight, but in recent times, seldom out of mind. The sabotage of the Nord Stream pipelines in October 2022 has magnified the vulnerability and critical importance of subsea infrastructure. It also exposed a lack of understanding on how subsea networks operate, how they are regulated, who controls them and how they are protected. Ireland matters in this context. Despite its official policy of neutrality, Ireland occupies an important strategic position in terms of transatlantic telecommunications cables between the United States, Britain and continental Europe. The conflict in Ukraine has amplified tensions in this context in terms of the increased threat of grey-zone/hybrid warfare activity. This article will consider for the first time how a global connectivity hub on the western periphery of Europe governs critical underwater infrastructure. It will discuss the context and agencies involved in subsea cable governance in Ireland and identify the gaps in this protection before formulating suggestions for the long-term improvement of Ireland's maritime security capacity. The Irish case is important as it can help inform defence policy and security practice in other island states with large maritime jurisdictions and in particular states with small navies. [ABSTRACT FROM AUTHOR]

The construction industry consists of various sectors that have different needs and functions. Those sector-specific differences create different risks that construction professionals empirically know. However, there is no documented and unbiased assessment to validate the empirical knowledge. Identifying the varying spectrum of risks present in different construction sectors in a systematic manner can facilitate more effective and efficient sharing, learning, and adoption of proven construction project management methods across these sectors. Ultimately, this can lead to significant improvements in construction productivity as a whole. This research analyzed risks in different construction sectors, including the commercial building, industrial, and civil infrastructure sectors in a comparative and quantitative manner. Project risk registers from the three construction sectors were analyzed using fuzzy set qualitative comparative analysis and coincidence analysis. The study identified various risk configurations in each sector, as well as their similarities and dissimilarities. Based on the findings, an ideal path to the utilization and dissemination of advanced construction management strategies and methods across the construction sectors was developed. These results are expected to assist project participants and stakeholders in adopting and implementing advanced project planning, delivery, and management strategies derived from other construction sectors while taking into account different risks. The construction industry possesses a wealth of effective and proven construction project management knowledge and know-how that holds the potential to enhance productivity. Despite these resources, the industry still faces productivity challenges. One contributing factor is the industry's isolated nature, which makes it difficult to share and disseminate knowledge and know-how across construction sectors and the industry as a whole. Identifying similarities and differences across the construction sectors is one prerequisite for promoting seamless knowledge sharing and dissemination. These factors influence the level of modifications and adjustments for successful implementation in different construction sectors. In this background, this study identified similarities and dissimilarities in project risk spectrums across construction sectors in a comparative manner. Ideal pathways for sharing and disseminating knowledge and know-how were proposed based on the observed risk spectrums. The findings aim to streamline the sharing and dissemination of construction management expertise by offering direction for evaluating and selecting appropriate project planning, delivery, and management strategies. Such insights, which take into account different project risk spectrums in different construction sectors, will contribute significantly to improving productivity throughout the construction industry. [ABSTRACT FROM AUTHOR]

Concrete bridge decks are critical transportation infrastructure components where deterioration can compromise structural integrity and public safety. This study develops machine learning (ML) models using the National Bridge Inventory (NBI) to classify deck conditions and predict deterioration trajectories. Models were tested and trained on inspection records from over 28,786 bridges in Michigan over 23 years, from 1992 to 2015. Eleven approaches were evaluated after hyperparameter optimization, based on 10-fold cross-validation, including logistic regression, gradient boosting, AdaBoost, random forest, extra trees, K-nearest neighbors, naive Bayes, decision tree, LightGBM, CatBoost, and bagging. Model effectiveness was assessed using accuracy, recall, F1-score, and area under the curve. Results indicate the optimized CatBoost classifier achieved 96.66% testing accuracy in rating deck conditions. The incorporation of hyperparameter optimization has significantly enhanced the overall predictive performance of the models, ensuring robust and reliable deterioration forecasting. The research sheds light on crucial factors such as deck age, area, and average daily traffic, contributing to a more comprehensive understanding of the factors influencing bridge deck condition ratings. These insights inform preventative maintenance planning to extend service life. This work pioneers a data-driven framework to forecast concrete deterioration, empowering officials with precise predictions to optimize infrastructure management under budget constraints. The approach provides a promising decision-support tool for sustainable infrastructure. This paper explores the use of machine learning techniques for the deterioration prediction of concrete bridge decks to estimate the remaining service life of bridges. These models will contribute to the safety, efficiency, and sustainability of bridge infrastructure by providing timely information and evidence-based decision making for bridge maintenance and management. Such prediction models have several practical applications such as (1) predicting when maintenance or repairs are likely to be needed; (2) assessing the risk of failure or deterioration of different components of a bridge; (3) effectively managing the bridge life cycle by providing insights into the aging process and helping authorities plan for rehabilitation or replacement strategies; (4) enabling ongoing monitoring of the performance of a bridge under various conditions such as heavy traffic loads, environmental factors, and seismic events; and (5) assisting in effective asset management by allowing for the prioritization of investments, the efficient allocation of budgets, and the planning for the long-term sustainability of the bridge infrastructure. [ABSTRACT FROM AUTHOR]

With the rapid development of high-speed railways (HSR) in China, the level of travel accessibility for residents has improved. While previous research has predominantly concentrated on the accessibility of HSR networks, it is imperative to recognize that the accessibility of HSR services is intrinsically intertwined with the spatial arrangement of HSR stations. The unreasonable location of HSR stations can lead to inequitable problems in service facilities within metropolitan areas. When investigating the accessibility of HSR stations, the road network emerges as the primary conduit for residents' mobility. The spatial variance of the road network manifests not only in divergent spatial densities but also in the traffic conditions across distinct temporal and spatial intervals. Based on the road network and dynamic road conditions, this study proposed a modified two-step floating catchment area method (M2SFCA) by using high-speed railway station location data, the subdistrict office (village and town) data, road network data, and floating car data (FCD). Taking the XZQ Metropolitan Core Area (the metropolitan core area of Xiamen, Zhangzhou, and Quanzhou, China) as an example, this paper analyzed the impact of a newly planned HSR station on the spatial distribution of accessibility in the metropolitan core area. The findings revealed that the newly planned HSR station has led to a substantial 68.18% elevation in overall regional accessibility. Notably, approximately 35% of the population has transitioned from locales with diminished accessibility to regions with heightened accessibility. Furthermore, the HSR station has ameliorated the hitherto uneven spatial distribution of accessibility advantages across the region. Under the limitation of travel time in metropolitan areas, this study explored an accessibility evaluation model based on dynamic traffic data, providing a method for an impartial and equitable evaluation of transportation infrastructure placement within the metropolitan core area. This study shows that the new high-speed railway station will not only improve regional accessibility but also affect the equilibrium degree of the spatial distribution of regional accessibility benefits. It is suggested that the metropolitan area should further build high-grade roads, strengthen transportation network links between urban areas, and improve regional accessibility. At the same time, for areas where the degree of accessibility fairness has declined, it is suggested to increase the energy level and scale of their high-speed railway stations to reduce the dependence of local residents on external high-speed railway stations and improve the convenience of local residents' travel. In addition, this paper improves the accessibility measurement method—2SFCA method. By improving the search carrier of the spatial action domain, the calculation method of road traffic state, and the selection of time threshold, the improved 2SFCA method is more in line with the actual situation, which expands the dynamic space–time analysis ability, improves the accuracy of accessibility calculation, and can more accurately judge the fairness of high-speed railway station construction. [ABSTRACT FROM AUTHOR]

With the widespread adoption of electric vehicles (EVs), the demand for public charging services is steadily increasing. Consequently, the development of effective charging scheduling strategies, aimed at optimizing the utilization of limited charging infrastructure, has become a key problem. Considering the diversity of user demands, we propose a Cost-Aware Charging Scheduling Architecture (CASA). This architecture considers both urgent and nonurgent charging customers by designing two charging modes with different power levels and associated costs. However, optimizing multiple objectives simultaneously while ensuring the interests of all parties involved in the charging demand response presents a challenge. Moreover, the uncertainty in customer charging demands and Time-of-Use (TOU) tariff further complicates the establishment of the model. To address the aforementioned challenges, this study formulates EV charging scheduling as a Markov Decision Process (MDP) based on deep reinforcement learning (DRL), employing the Deep Q-Network (DQN) algorithm for solution derivation. The objective is to minimize the operational costs of charging stations while ensuring the quality of service (QoS) requirements for customers. The simulation results demonstrate that CASA exhibits superior performance in optimizing both the average response time and service success rate, compared to commonly used baselines for charging scheduling. Furthermore, the CASA approach achieves a significant reduction in operating costs of EV charging station. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements. [ABSTRACT FROM AUTHOR]

The rising Greenhouse Gas (GHG) emissions stemming from the extensive use of automobiles across the globe represent a critical environmental challenge, contributing significantly to phenomena such as global warming and the deterioration of air quality. To address these challenges, there is a critical need for research and development in electric vehicles (EVs) and their associated charging infrastructure, including off-board and on-board chargers (OBCs). This paper aims to bridge the gaps in existing review literature by offering a comprehensive review of both integrated and non-integrated OBCs for EVs, based on the authors' knowledge at the time of writing. The paper begins by outlining trends in the EV market, including voltage levels, power ratings, and relevant standards. It then provides a detailed analysis of two-level and multi-level power converter topologies, covering AC-DC power factor correction (PFC) and isolated DC-DC topologies. Subsequently, it discusses single-stage and two-stage non-integrated OBC solutions. Additionally, various categories of integrated OBCs (iOBCs) are explored, accompanied by relevant examples. The paper also includes comparison tables containing technical specifications and key characteristics for reference and analysis. [ABSTRACT FROM AUTHOR]

Many challenges have arisen as a result of the rapid growth of the electric vehicles (EVs) market, due to the lack of charging infrastructure capable of handling such a large number of EVs. To alleviate power grid system overloads and reduce the cost of corresponding infrastructure deployments, a direct vehicle-to-vehicle (V2V) energy exchange strategy has become an emerging research topic. In this paper, we formulate the problem of V2V energy charging on a time–space network and develop a dynamic-programming solution methodology for efficiently finding the solution. The algorithm can pair and route the energy supplier (ES) and the requester (ER) in such a way that maximizes the supplier's profit. Specifically, the ES is incentivized to rendezvous ERs at any encounter nodes in order to dispense the requested energy amount through platooning. Unlike existing V2V charging solutions, our approach involves charging while vehicles are in motion. We validate the effectiveness of our approach in maximizing the profit of the ES and reducing the incurred overhead on the ER in terms of increased trip time, distance, and energy consumption. [ABSTRACT FROM AUTHOR]

The ductility and exhibition of the multiple, fine, self-controlled cracking of strain-hardening cementitious composites (SHCCs) under tension has made them attractive for enhancing the durability of civil infrastructure. These fine cracks are key to preventing the ingress of water and harmful chemicals into the structure and thereby achieving steel reinforcement. However, several studies have suggested that the short-term fine cracks shown in the laboratory may end up exceeding the acceptable crack widths that are specified in design codes when SHCC members are subjected to sustained constant loads. In real structures, however, the load is also shared by the steel reinforcement in the member, so the SHCC within may not be under a constant load; therefore, the crack widening will not be as severe. This study focuses on the creep behaviour of SHCCs when they are applied as an external layer on reinforced concrete to enhance durability. A novel approach to simulate various stress–strain regimes in such systems is developed by using a fixture to share a sustained moment exclusively between a reinforcement member and SHCC. The developed load-sharing system allows stresses within the reinforcement and SHCC to be monitored against time during the imposed loading, while ensuring access to the SHCC layer for instrumentation and monitoring of strain/cracking. The time-dependent widening of cracks in the SHCC layer is found to be much less significant than that under constant loading, so resistance to water/chemical penetration can still be ensured in the long term. The obtained information on the variation in stress, strain, and crack opening with time will be useful for the development of a general model for the creep behaviour of SHCC members. [ABSTRACT FROM AUTHOR]

Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency response. However, near-real prediction of ETLs has always been a great challenge with relatively low accuracy. This work proposes an ensemble prediction model of EnPr by integrating machine learning tree models and a deep learning convolutional neural network. EnPr exhibits relatively strong prediction and generalization performance and achieves relatively accurate prediction of ETLs. Six great seismic events occurring from 2008 to 2022 on the southeastern margin of the Tibetan Plateau are selected to conduct ETL prediction. In a chronological order, the 2008 Ms 8.0 Wenchuan, 2010 Ms 7.1 Yushu, 2013 Ms 7.0 Lushan, and 2014 Ms 6.5 Ludian earthquakes are employed for model training and learning. The 2017 Ms 7.0 Jiuzhaigou and 2022 Ms 6.1 Lushan earthquakes are adopted for ETL prediction. The prediction accuracy merits of ACC and AUC attain 91.28% and 0.85, respectively, for the Jiuzhaigou earthquake. The values of ACC and AUC achieve 93.78% and 0.88, respectively, for the Lushan earthquake. The proposed EnPr algorithm outperforms the algorithms of XGBoost, random forest (RF), extremely randomized trees (ET), convolutional neural network (CNN), and Transformer. Moreover, this work reveals that seismic intensity, high and steep relief, pre-seismic fault tectonics, and pre-earthquake road construction have played significant roles in coseismic landslide occurrence and distribution. The EnPr model uses globally accessible open datasets and can therefore be used worldwide for new large seismic events in the future. [ABSTRACT FROM AUTHOR]

Like any disaster, COVID-19 laid waste to infrastructure and the ability for a community to do community. But, unlike a tornado or nuclear meltdown, COVID-19 laid waste to social infrastructure in unique ways that only a disease can do. On the one hand, a pandemic brings biological dangers that, in turn, make all individuals—loved ones, too—into potential threats of biological contamination. On the other hand, the efforts to contain disease present social dangers, as isolation and distancing threatens mundane and spectacular ritualized encounters and mask-wearing heighten our awareness of the biological risk. By exploring the link between disasters and disease, this paper leverages the lens of contamination, beginning first with the barriers it presents to making and remaking the self in everyday life. Constraints on ritualized encounters, both in terms of delimiting face-to-face interaction and in determining that some spaces have contaminative risks, reduces collective life to imagined communities or shifts to digitally mediated spaces. The former intensifies the sense of anomie people feel as their social world appears as though it were disintegrating while the latter presents severe neurobiological challenges to reproducing what face-to-face interaction habitually generates. Finally, these micro/meso-level processes are contextualized by considering how institutions, particularly polity but also science, manage collective risk and how their efficacy may either contribute to the erosion of solidarity or provide a sense of support in the face of anomic terror. Using the US to illustrate these processes, we are able to show how an inefficacious state response weakens the already tenuous connective tissue that holds a diffuse and diverse population together, while also exposing and intensifying existing political, economic, and cultural fissures, thereby further eroding existing solidarity and the capacity to rebuild post-pandemic cohesion. [ABSTRACT FROM AUTHOR]

The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents' income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers' entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior. [ABSTRACT FROM AUTHOR]

In 2008, Vietnam's Prime Minister approved the construction of the 'Hanoi Urban Railway System', a major infrastructure project for the country's capital city. The construction of Line 2A, the first line of this 8‐line railway, took ten years to complete, and was finally inaugurated in November 2021. Spanning 13 km across the city centre, Line 2A encountered more than just construction setbacks, with its reputation tarnished by contractor choice, accidents, and public scepticism over safety and accessibility. Sowing further seeds of doubt in the minds of many Hanoi residents is the fact that two‐thirds of the original financing came from preferential loans from Vietnam's large northern neighbour, conditional on the contractor and key materials being sourced from Vietnam's large, northern neighbour. Moreover, the project is informally categorized as part of Vietnam's large northern neighbour's Belt and Road Initiative. Drawing from conceptual literature regarding infrastructural politics and mobility (in)justice, we analyse how Hanoi residents have experienced and negotiated the construction of this Chinese‐Vietnamese infrastructure project. In particular, we consider how the livelihoods of those directly affected by the railway's operations, namely motorbike taxi‐drivers, have been impacted to date. [ABSTRACT FROM AUTHOR]

Inspection robots for early detection of potential risks in severe environments require a high accuracy like human experts. A fine mechanism is crucial for extracting target components from noisy signals. We have proposed a detection system for submillimeter-width cracks in concrete surfaces of social infrastructures, such as bridges, by using morphological component analysis (MCA), for aerial image-inspection robots. Traditional schemes like PCA have relied on linear decomposition for the separation of target signal and noise components. Recent advancement in signal decomposition focuses on the enhancement of linearity in the separation by introducing a set of nonlinear basis functions to represent the raw signal even when multiple factors are mixed in a nonlinear manner. In this sense, MCA is a core technique to be able to isolate target components to represent submillimeter-width cracks from others. We proposed a proper pre- and post-processing operations to attach MCA, which demonstrated a high accuracy yet coarse and fine image components have to be integrated redundantly. In the present study, we successfully found a simpler mechanism to set the single basis function to extract the target by introducing a new thresholding mechanism. It suggests a high potential of MCA for inspection robots for various purposes. [ABSTRACT FROM AUTHOR]

Extensive usage of pervious concrete (PC) in pavement applications to mitigate the shortcomings in conventional impervious paving is on the rise. The primary performance parameters of PC are strength and porosity which greatly depend on aggregate grading and shape of the constituents, aggregate to cement (A/C) ratio, water to cement (W/C) ratio, and compaction effort. This paper discusses the development of a model to predict compressive strength of PC using compaction effort and A/C ratio and. For compaction effort of 0–90 blows and A/C ratios of 2.5–4.0, the proposed model perfectly predicted the compressive strengths of PCs in the range of 3.0–30.0 MPa. The mean deviation and mean relative error of the estimated compressive strengths were 1 MPa and 10%, respectively. Findings of this study could contribute in choosing the right A/C ratio and compaction effort for mix design of PC. Noticeably, strength requirements of typical pavement applications exist in the same range as the developed model predictions lie in, which guarantee the application of proposed model for transport infrastructure development but not limited to. Further extension of the proposed model is feasible by incorporating the effect of aggregate grading and shape on compressive strength. [ABSTRACT FROM AUTHOR]

This study evaluated the properties of sugarcane bagasse ash (SCBA) obtained from different production processes for use as partial replacement of cement: (1) minimally post-processed (MP) SCBA, obtained by sieving and grinding SCBA from Louisiana's sugarcane field production; (2) fully post-processed (FP) SCBA, obtained by sieving, further burning under controlled conditions at temperature T, and grinding field-produced SCBA, denoted as FP-T; and (3) laboratory-produced (LP) SCBA, obtained by controlled burning at temperature T of bagasse fibers and grinding of the obtained ashes, denoted as LP-T. The temperatures of burning, T, for FP-T and LP-T SCBA were varied between 450 and 650 °C at 50 °C intervals. The significance of this study is the valorization of agricultural waste for the production of low-carbon concrete for transportation applications in Louisiana, and other locations where SCBA are produced from a low-efficiency boiler in a sugar mill. A comprehensive characterization of the different SCBA materials was conducted, including scanning electron microscopy (SEM)-energy dispersive X-ray spectroscopy (EDS), X-ray powder diffraction (XRD), laser diffraction particle size analysis, Chapelle's test, strength activity index (SAI), loss on ignition (LOI), and moisture content. Experimental results showed that all LP-T and FP-T SCBAs met ASTM C618 chemical requirements and SAI requirements for Class N pozzolans. By contrast, MP SCBA did not meet these requirements, thus resulting in unsuitable SCM application. LP calcinated at 650 °C and FP calcinated at 450 °C exhibited the greatest SAI and SiO2 + Al2O3 + Fe2O3 among LP-T and FP-T SCBAs, respectively. Based on a combination of properties and production practicality considerations, FP calcinated at 450 °C was identified as the most promising SCM-grade SCBA for large-scale production in Louisiana, and was used to further evaluate the fresh and hardened properties of concrete incorporating SCBA at 10%, 20%, 30%, and 40% cement replacement by mass. A decrease in workability as well as 28- and 90-day compressive strength with the increase in SCBA content was observed. However, the relative strength gain from 28 to 90 days and the 90-day surface resistivity increased with increments in SCBA dosage. Characterization of cement-SCBA paste through XRD and SEM revealed portlandite consumption and densification of the cementitious matrix from 28 to 90 days, thus suggesting the pozzolanic activity of SCBA. [ABSTRACT FROM AUTHOR]

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons. [ABSTRACT FROM AUTHOR]

The attributes of diversity and concealment pose formidable challenges in the accurate detection and efficacious management of distresses within subgrade structures. The onset of subgrade distresses may precipitate structural degradation, thereby amplifying the frequency of traffic incidents and instigating economic ramifications. Accurate and timely detection of subgrade distresses is essential for maintaining and repairing road sections with existing distresses. This helps to prolong the service life of road infrastructure and reduce financial burden. In recent years, the advent of numerous novel technologies and methodologies has propelled significant advancements in subgrade distress detection. Therefore, this review delineates a concentrated examination of subgrade distress detection, methodically consolidating and presenting various techniques while dissecting their respective merits and constraints. By furnishing comprehensive guidance on subgrade distress detection, this review facilitates the expedient identification and targeted treatment of subgrade distresses, thereby fortifying safety and enhancing durability. The pivotal role of this review in bolstering the construction and operational facets of transportation infrastructure is underscored. [ABSTRACT FROM AUTHOR]

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS. [ABSTRACT FROM AUTHOR]

Cloud computing has become a major component of the modern IT ecosystem. A key contributor to this has been the development of Infrastructure as a Service (IaaS) architecture, in which users' virtual machines (VMs) are run on the service provider's physical infrastructure, making it possible to become independent of the need to purchase one's own physical machines (PMs). One of the main aspects to consider when designing such systems is achieving the optimal utilization of individual resources, such as processor, RAM, disk, and available bandwidth. In response to these challenges, the authors developed an analytical model (the ARU method) to determine the average utilization levels of the aforementioned resources. The effectiveness of the proposed analytical model was evaluated by comparing the results obtained by utilizing the model with those obtained by conducting a digital simulation of the operation of a cloud system according to the IaaS paradigm. The results show the effectiveness of the model regardless of the structure of the emerging requests, the variability of the capacity of individual resources, and the number of physical machines in the system. This translates into the applicability of the model in the design process of cloud systems. [ABSTRACT FROM AUTHOR]

Because of the excessive prices and volatility in the energy derivatives markets over the period 2021–2023, margins increased considerably, leading major European energy companies to experience liquidity stress in meeting those. As a consequence, several local governments needed to provide guarantees to avoid their default. This article includes several legislative proposals to ensure that energy firms are prudentially safer and that there exists a level playing field among financial actors active in the same market segment. Specifically, this article proposes to (1) decrease the clearing threshold for commodity derivatives under the European Market Infrastructure Regulation (EMIR), (2) narrow the definition of hedging relevant to the calculation of the clearing threshold, (3) remove the intragroup exemption possibility under EMIR, and (4) make sure that energy firms can be categorised more easily as investment firms. [ABSTRACT FROM AUTHOR]