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Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time‐outs and its back‐off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light‐weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next‐generation IoT networks. [ABSTRACT FROM AUTHOR]

Enterprises need to innovate business strategies to deal with the negative effects of low‐carbon development, among which digital transformation is an important breakthrough technology. This paper discusses the impact of low‐carbon development on enterprises' digital transformation. Theoretical analysis shows that environmental regulations driven by low‐carbon development have the compensatory effect of stimulating enterprise digital transformation to mitigate misallocation of capital and labor, especially for enterprises with higher productivity. In addition, both the construction of network infrastructure and the development of digital finance can strengthen the incentives for enterprises' digital transformation. The empirical tests based on Chinese A‐share listed enterprises show that the degree of enterprises' digital transformation increases by 0.0122 standard deviation for every one standard deviation reduction in regional carbon emission intensity. These results deepen the understanding of the relationship between green development and microenterprise digitalization. [ABSTRACT FROM AUTHOR]

The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real‐time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self‐powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG‐enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG‐enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services. [ABSTRACT FROM AUTHOR]

Charging behaviours of electric vehicles (EVs) exhibit substantial randomness, making accurate prediction or modelling challenging. Furthermore, as the number of EVs continues to increase, charging stations are diversifying their offerings to accommodate distinct charging characteristics, addressing a wide spectrum of EV charging needs. Previous research mostly focused on the randomness of EVs while neglecting the heterogeneity in charging infrastructure. Therefore, this paper introduces a decentralized collaborative optimal method for EV charging stations, taking into account the varying facility types and the power limitations. First, a decentralized collaborative framework is proposed. The energy boundary model and the average laxity of EVs contribute to transforming the optimization problem into a Markov Decision Process (MDP) with uncertain transitions. Then, multi‐agent deep deterministic policy gradient multi‐individuals (MADDPG‐MI) algorithm is developed to train several heterogeneous agents presenting different types of charging facilities. Each agent makes decisions for multiple homogenous charging piles. Numerous simulation studies validate that the proposed method can effectively reduce charging costs and manages in scenarios involving either homogeneous or multiple heterogeneous charging facilities. Moreover, the MADDPG‐MI algorithm demonstrates performance consistency among multiple decision‐making units while consuming lower training resources offering enhanced scalability. [ABSTRACT FROM AUTHOR]

We thank the discusser's comments on our paper from the perspective of railway network and line planning. DIFFERENT RAILWAY PLANNING OR DESIGN STAGES Railway design is very complex and must be accomplished through a series of stages, including the "network and line planning", "major design standards determination", and "railway alignment design", as shown in Figure 1. These comments from the network and line planning perspective have led us to contemplate more comprehensive railway alignment-related studies that incorporate influencing factors of network and line planning. [Extracted from the article]

The efficient, reliable, and resilient supply of electricity has become essential for social and economic well‐being of the modern society. However, more frequent occurrence of extreme weather events has exposed inequity in the planning and operation practices of power distribution systems, evidenced in higher vulnerability and longer power interruptions for some parts of the grid as compared to others. This paper proposes an equity‐aware power distribution system restoration model in an effort to ensure a more equitable yet resilient power distribution operation after outages. To this end, the proposed equity‐aware distribution system restoration model balances the efficiency of the restoration operation and the equitable allocation of distributed energy resources among affected customers after an outage, while prioritizing the critical infrastructure (e.g. hospitals). The results demonstrate the effectiveness of the proposed framework to ensure a more equitable restoration process as measured by the proposed fairness and restoration performance indices. [ABSTRACT FROM AUTHOR]

The international journal of I Computer-Aided Civil and Infrastructure Engineering i is a rigorously peer-reviewed research journal, devoted to the publication of original research articles describing novel computational algorithms and innovative applications of computers in civil and infrastructure engineering. Seven papers from four different countries that met the high standards of the journal were finally approved for publication in the special issue. We thank the authors of all the papers from the many active research groups who have shared innovative ideas for this series of special issues on CAV. [Extracted from the article]

Copyright of Papers in Regional Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reserve power systems are widely used to provide power to critical infrastructure systems in the event of power outages. The reserve power system may be subject to regulation, typically focussing on a strict operational time commitment, but the energy involved in supplying reserve power may be highly variable. For example, if heating or cooling is involved, energy consumption may be strongly influenced by prevailing weather conditions and seasonality. Replacing legacy assets (often diesel generators) with modern technologies could offer potential benefits and services back to the wider electricity system when not in use, therefore supporting a transition to low‐carbon energy networks. Drawing on the Great Britain telecommunications systems as an example, this paper demonstrates that meteorological reanalyses can be used to evaluate capacity requirements to maintain the regulated target of 5‐days operational reserve. Across three case‐study regions with diverse weather sensitivities, infrastructure with cooling‐driven electricity demand is shown to increase energy consumption during summer, thus determining the overall capacity of the reserve required and the availability of 'surplus' capacity. Lower risk tolerance is shown to lead to a substantial cost increase in terms of capacity required but also enhanced opportunities for surplus capacity. The use of meteorological forecast information is shown to facilitate increased surplus capacity. Availability of surplus capacity is compared to a measure of supply–stress (demand‐net‐wind) on the wider energy network. For infrastructure with cooling‐driven demand (typical of most UK telecommunication assets), it is shown that surplus availability peaks during periods of supply–stress, offering the greatest potential benefit to the national electricity grid. [ABSTRACT FROM AUTHOR]

Green Infrastructures as Best Management Practice (GI‐BMP) play important role in preserving cities from urban flood and excessive runoff. In the process of using GI‐BMP in cities for stormwater management, a number of steps are taken that normally include selection of suitable sites, formulating proper combination of infrastructures, and optimization of the place and design of GI‐BMPs to maximize their cost‐effectiveness. This paper presents a site‐scale GI‐BMP implementation in Tehran West Bus Terminal (TWBT), Iran. To achieve this goal, this study applies a three steps framework namely GI‐BMP suitability analysis, GI‐BMP combination planning, and GI‐BMP optimization. In the first step, using the BMP Siting Tool, the suitable places for allocating GI practices were identified. In the next step, suitable GI‐BMP practices, including permeable pavements, bioretention basin, infiltration trench, and rain barrel were planned and arranged for each subwatersheds of the study area. In the third step, with the use of System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) model and NSGA‐II algorithm, the sizes of the planned GI‐BMP types were optimized for each subwatershed. The results indicate that runoff problem caused by surface runoff in the study area was serious and needed to be controlled. The results also revealed that there were 104 near‐optimal solutions that help reduce runoff volume by up to 70%. According to the results, applying GI‐BMPs in TWBT will reduce 60% of flow volume in the site with the price of 353,568$. This research is of practical importance for stormwater management using nature‐based solutions in bus terminals. Recommendation For Resource Managers: Considering suitable places, selecting proper combination, and optimizing GI‐BMP location and design will result in an efficient green infrastructure planning in the context of cities.BMP Siting Tool and SUSTAIN model applied in this research provided a framework for planning and optimization of GI‐BMP in TWBT.This study suggested 104 near‐optimal solutions for GI‐BMP planning for TWBT reducing the cost of GI construction and reduce runoff volume.The proposed solutions for GI‐BMP development in TWBT may reduce up to 60% of annual volume of runoff with the cost of 353,568$. [ABSTRACT FROM AUTHOR]

In this modern era of cyber–physical–social systems, there is a need of dynamic coordination strategies for electric vehicles (EVs) to enhance the resilience of modern power distribution networks (MPDNs). This paper proposes a two‐stage EV coordination framework for MPDN smart restoration. The first stage is to introduce a novel proactive EV prepositioning model to optimize planning prior to a rare event, and thereby enhance the MPDN survivability in its immediate aftermath. The second stage involves creating an advanced spatial–temporal EV dispatch model to maximize the number of available EVs for discharging, thereby improving the MPDN recovery after a rare event. The proposed framework also includes an information system to further enhance MPDN resilience by effectively organizing data exchange among intelligent transportation system and smart charging system, and EV users. In addition, a novel bidirectional geographic graph is proposed to optimize travel plans, covering a large penetration of EVs and considering variations in traffic conditions. The effectiveness is assessed on a modified IEEE 123‐node test feeder with real‐world transportation and charging infrastructure. The results demonstrate a significant improvement in MPDN resilience with smart restoration strategies. The validation and sensitivity analyses evidence a significant superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

Abstract: Extreme events are of interest worldwide given their potential for substantial impacts on social, ecological, and technical systems. Many climate‐related extreme events are increasing in frequency and/or magnitude due to anthropogenic climate change, and there is increased potential for impacts due to the location of urbanization and the expansion of urban centers and infrastructures. Many disciplines are engaged in research and management of these events. However, a lack of coherence exists in what constitutes and defines an extreme event across these fields, which impedes our ability to holistically understand and manage these events. Here, we review 10 years of academic literature and use text analysis to elucidate how six major disciplines—climatology, earth sciences, ecology, engineering, hydrology, and social sciences—define and communicate extreme events. Our results highlight critical disciplinary differences in the language used to communicate extreme events. Additionally, we found a wide range in definitions and thresholds, with more than half of examined papers not providing an explicit definition, and disagreement over whether impacts are included in the definition. We urge distinction between extreme events and their impacts, so that we can better assess when responses to extreme events have actually enhanced resilience. Additionally, we suggest that all researchers and managers of extreme events be more explicit in their definition of such events as well as be more cognizant of how they are communicating extreme events. We believe clearer and more consistent definitions and communication can support transdisciplinary understanding and management of extreme events. [ABSTRACT FROM AUTHOR]

G.fast profile 212a technology is the perfect choice for an operator offering a broadband service, as it operates using the existing copper telecommunications infrastructure (cables) already installed in user premises. Unfortunately, such telecommunications infrastructure is not designed to transmit data at high frequencies used by G.fast technology, resulting in radiation during signal transmission. This radiation can have a direct impact on the performance and reliability of radio services operating in the same frequency range. In order to limit such radio interference, International Telecommunication Union proposed radiation limits for wired telecommunications networks. This paper provides a comparison between ITU‐T K.60 Recommendation with the measurements of the electric field radiation from the telecommunications network when the G.fast profile 212a signal is transmitted through different types of telecommunications cables. The aim of this comparison is to assess whether the radiation from the telecommunications network in this study meets the radiation limits defined in ITU‐T K.60 Recommendation and, therefore, whether this radiation can be a source of interference to radio services operating in the same frequency range. In addition, this paper provides an analysis of the impact of cable construction on the total irradiated field from the in‐house part of the telecommunications network. [ABSTRACT FROM AUTHOR]

Copyright of Papers in Regional Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Developing a self‐sufficient renewable power (RP) road transport (SRPRT) system is an important future direction for transport–energy integration. More well‐developed studies must be conducted on the coordinated planning of transport, power supply, and power generation networks. This paper carries out the joint operation and planning of highway charging networks with the wind‐photovoltaic‐energy storage (HCN‐WPE) system. Under multi‐network integration and the interaction among multiple entities, a nested bi‐level optimization model is proposed to optimize the users' charging and travel behavior, charging network's deployment, and power generation system's (PGS) configuration. An H‐M‐L algorithm structure is developed, combining the heuristic algorithm, multi‐agent‐based simulation technology, and linear programming algorithm. Its convergence and applicability are verified on the Nguyen‐Dupius network. An empirical case in the Hu‐Bao‐Wu city agglomeration in China is employed to explore and discuss the managerial insights for the HCN‐WPE system. The study finds that multi‐network coordinated planning can improve the benefits of multiple entities, where the net present value, RP supply rate, and RP consumption rate increase by 12.0%, 3.2%, and 10.5%, compared to independent planning. Network‐level planning can play a management and induction role in balancing the station's load pressure. In addition, the PGS co‐configuration can leverage the complementary power supply of multiple RP generators and the peak cutting and valley filling of energy storage systems, which is essential for achieving the SRPRT goal. [ABSTRACT FROM AUTHOR]

Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality of connected and autonomous vehicles (CAVs) into drive‐by damage detection by introducing In‐Fleet SHM. To the authors' knowledge, this is the first study that proposes using CAVs for SHM application in civil engineering structures. Each In‐Fleet CAV could automatically collect the vehicle's persistent and temporal data by the embedded sensors and transmit them to edge computing systems for analysis. These persistent data include type and model and temporal parameters encompassing position, speed, heading, and vertical acceleration of CAVs. Knowing the persistent and temporal data of the passing vehicles over the transportation infrastructures enables the identification of the dynamic parameters of the bridge from the vehicles' vertical acceleration response using drive‐by techniques and, on the other hand, reconstruction of the finite element model of the passing vehicles over the supporting bridges in a near real‐time manner. In contrast to the drive‐by SHM, In‐Fleet monitoring has an expanded spatial and temporal coverage, enabling continuous near real‐time monitoring of highway bridges of the transportation network. The accuracy and resolution of the identified modal components in In‐Fleet SHM are enhanced due to the crowdsensing nature of the collected data. Furthermore, by offering a unique set of characteristics, this method fills the crucial gap in implementing Industry 4.0 technologies and digital twins for SHM of bridges. [ABSTRACT FROM AUTHOR]

Motivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)‐based approach to well handle multi‐source uncertain features and constraints at a fast speed. To begin with, UHCRP is mathematically described and reformulated as a dual interdependent deep reinforcement learning (diDRL) framework to reflect real‐world scenarios. Afterward, a novel policy network named the attention‐based deep neural network (A‐DNN) is introduced to learn the route planning decisions for the combinatorial optimization problem. In particular, A‐DNN is made up of an encoder and a dual decoder for UAV and human inspection, where the multi‐head attention mechanism is incorporated to generate richer representations for model performance improvement. Performance of the proposed dual multi‐head attention model (DAM) has been tested in simulations and a real‐world case study regarding wind farm inspection. Results indicate that DAM under the sampling decoding strategy can deliver a high‐quality path plan and show better generalizability for larger scale problem sizes compared to single‐head attention model (SAM), multi‐head attention model (AM), and two baseline models, namely OR‐Tools and genetic algorithm. Moreover, DAM trained by randomly generated data can be directly employed to solve the practical problem with standardization of inputs. Overall, DRL integrates decision‐making for inspection method selection and inspected infrastructure selection, providing adaptive and intelligent inspection path planning for UAV and human in complex and dynamic engineering environments. [ABSTRACT FROM AUTHOR]

The phenomenon of overweight vehicles severely threatens traffic safety and the service life of transportation infrastructure. Rapid and effective identification of overweight vehicles is of significant importance for maintaining the healthy operation of highways and bridges and ensuring the safety of people's lives and property. With the problems of high cost and low efficiency, the traditional vehicle weighing systems can only meet some of the requirements of different scenarios. The development of artificial intelligence technologies, especially deep learning, has greatly enhanced the accuracy and efficiency of computer vision. To this end, the paper proposes a method using computer vision and deep learning for the non‐contact identification of overweight vehicles. By constructing two deep learning models and combining them with the vehicle vibration model and relevant specifications, the weight and maximum allowable weight of the vehicle are obtained to make a comparison for determining overweight. Experimental verification was performed using a two‐axle vehicle as an illustrative example, and the results demonstrate that the proposed method exhibits excellent feasibility and effectiveness. It shows significant potential in real‐world scenarios, laying a research foundation for practical engineering applications. Additionally, it provides a reference for the governance and decision‐making of overweight issues for relevant authorities. [ABSTRACT FROM AUTHOR]

While India's low female labor force participation in urban areas is often attributed to its demographic and labor market characteristics, education, and cultural aspects, attention is not paid to the labor market opportunity structure limiting women's labor market participation. We examine the role of transport infrastructure in gendergap in labor force participation and its variations by gender-norms across communities. Using India Human Development Survey and city-level data on transport infrastructure, the causal effects of differential employment status of women and men are related to size and quality of transport in twelve Indian cities. Interaction effects are explored to understand varying impacts of transport on employment by gender-context in communities. We find that an improvement in the size and quality of transportation infrastructure improves women's labor market participation more than that of men. In gender egalitarian communities, stronger positive effect of transport on female labor and reduction of gender gap in employment. Along with generation of new job opportunities suitable for women, it is important to encourage a gender-friendly institutional and social fabric to allow women to connect to new jobs. Using a novel data, the paper highlights the importance of ostensibly gender-neutral development policies for shaping gender inequalities in outcomes. [ABSTRACT FROM AUTHOR]

Local integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms. [ABSTRACT FROM AUTHOR]

A list of books received in the June 1997 issue of the "British Journal of Industrial Relations" is presented. They include "Antisocial Behaviour in Organizations," by Robert A. Giacalone and Jerald Greenberg, "Build, Operate, Transfer: Paving the Way for Tomorrow's Infrastructure," by Sidney M. Levy, and "Bureaucracy," 2nd edition, by David Beetham.

Uncertainty arising from climate change poses a central challenge to the long‐term performance of many engineered water systems. Water supply infrastructure projects can leverage different types of flexibility, in planning, design, or operations, to adapt infrastructure systems in response to climate change over time. Both flexible planning and design enable future capacity expansion if‐and‐when needed, with flexible design proactively incorporating physical design changes that enable retrofits. All three forms of flexibility have not previously been analyzed together to explicitly assess their relative value in mitigating cost and water supply reliability risk. In this paper, we propose a new framework to evaluate combinations of flexible planning, design, and operations. We develop a nested stochastic dynamic optimization approach that jointly optimizes dam development and operating policies under dynamic climate uncertainty. We demonstrate this approach on a reservoir project near Mombasa, Kenya. Our results find that flexible operations have the greatest potential to reduce costs. Flexible design and flexible planning can amplify the value of flexible operations under higher discounting scenarios and when initial infrastructure capacities are undersized. This approach provides insight on the climate change and techno‐economic conditions under which flexible planning, design, and operations can be best leveraged individually or in combination to reduce climate change uncertainty risks in water supply infrastructure projects. Key Points: We develop a framework to compare the value of combinations of flexibility in water supply infrastructure planning, design, and operationsFlexible operations are more effective than flexible planning or design in addressing climate uncertainty in water‐limited environmentsFlexible planning and design are more useful when initial reservoir capacity is low, discounting is high, or the cost of shortages is high [ABSTRACT FROM AUTHOR]

This paper investigates how a group of firms collaborate horizontally to find innovative solutions to be more environmentally friendly by sharing charging infrastructure among themselves. Such collaborations enable a faster transition to electrifying road freight transport. We conduct an embedded case study of firms operating in Stockholm that are exploring the possibility of sharing charging infrastructure for their electric freight vehicles (EFVs). The study reveals four business models for sharing: sharing existing privately owned infrastructure, jointly building new infrastructure, jointly building new infrastructure at a shared customer's site and organizing sharing through a third party. The results show that these sharing models have both economic and environmental benefits, providing new revenue streams, enabling cost sharing, reducing investment costs, increasing utilization and reducing material usage. From a collaborative point of view, the results point to the importance of having multiple actors that form a network to provide large‐scale benefits. Our study reveals that there needs to be both environmental and economic incentives for firms to join horizontal networks characterized by coopetition. Trust between actors is also important in the forming of networks and during collaboration, especially since actors need to continuously share data within the network. [ABSTRACT FROM AUTHOR]

Extreme weather events can cause significant damage to power distribution network infrastructure, often resulting in power outages. Distribution Network Operators (DNOs) are faced with the challenging task of responding to these outages in real time while maintaining a resilient grid. Our paper presents an innovative approach to alert operators about the potential risk associated with upcoming extreme weather through a normalized fragility curve. The uniqueness of the curve is the ability to capture regional differences across a DNO's territory while presenting operators with a means of setting unified risk thresholds. This can support a proactive response and allow the staging of necessary resources to minimize the threat posed by such events. Our approach captures the changes in failure probability associated with differing wind regimes and demonstrates the benefit of sub‐regional meteorological information. The proposed approach is demonstrated for wind events using 20 years of historical fault records from a DNO in the United Kingdom (UK). While its efficacy is demonstrated for windstorms in the UK, the approach could be applied globally to develop normalized fragility curves for other types of seasonal extreme weather events such as snowstorms, hurricanes, or linked hazards such as wildfires. The approach can also facilitate an understanding of how infrastructure may operate under future climate conditions, supporting proactive adaptation. [ABSTRACT FROM AUTHOR]

Energy hubs (EHs), with interconnecting different energy carriers, technologies, and sectors, improve energy efficiency and enhance the flexibility of energy management. EHs can be an alternative to the upgradation of the transmission system infrastructures. Then, it is necessary to evaluate accurately the impact of integrating EHs across the power system on the expansion planning of the transmission system. This paper proposes an expansion planning of the transmission system and integrated EHs across the power system in deterministic and stochastic environments. In addition, the correlation among the uncertain variables may affect the expansion planning and scheduling decisions as well as the resulting costs. Therefore, the stochastic modelling of the proposed planning approach is developed considering the correlation among the uncertain variables. The uncertainties related to the generation of the renewable energy sources (RESs), energy demands, and electrical/thermal/cooling/gas price are addressed with a stochastic scenario‐based scheme. For this purpose, numerous scenarios are generated by implementing the well‐known Monte Carlo simulation (MCS) technique. Then the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. Next, the k‐means method as an efficient scheme of data clustering is used to reduce the initial scenarios to some limited ones. [ABSTRACT FROM AUTHOR]

This paper evaluates the driving forces of urban shrinkage, focusing on transportation infrastructure using a conceptual framework. Employing a difference‐in‐difference approach, this study explores the impact of the high‐speed railway on local shrinkage by focusing on implementing the bullet train in the Kyushu Shinkansen region of Japan. Our results show that after the introduction of the bullet train line, remote peripheral regions suffered population loss while suburban areas near the metropolitan area experienced population growth. Further analysis verifies that the bullet train had a particularly significant negative impact on local employment and establishments in the service sector in remote regions. [ABSTRACT FROM AUTHOR]

Rapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV‐dominated transportation systems are intertwined with power grid operations both economically and technically. High penetration of EVs in the future can increase the charging loads and cause a wide range of operational issues in power distribution networks (PDNs). This paper aims to design an EV charging network with an embedded PDN layout to account for energy dispatch and underlying traffic flows in urban transportation networks supporting electric mobility in the near future. A mixed‐integer bilevel model is proposed with the EV charging facility location and PDN energy decisions in the upper level and user equilibrium traffic assignment in the lower level considering an uncertain charging demand. The objective is to minimize the cost of PDN operations, charging facility deployments, and transportation. The proposed problem is solved using a column and constraint generation (C&CG) algorithm, while a macroscopic fundamental diagram concept is implemented to estimate the arc travel times. The methodology is applied to a hypothetical and two real‐world case study networks, and the solutions are compared to a Benders decomposition benchmark. The east‐coast analysis results indicate a 77.3% reduction in the computational time. Additionally, the benchmark technique obtains an optimality gap of 1.15%, while the C&CG algorithm yields a 0.61% gap. The numerical experiments show the robustness of the proposed methodology. Besides, a series of sensitivity analyses has been conducted to study the impact of input parameters on the proposed methodology and draw managerial insights. [ABSTRACT FROM AUTHOR]

In the power system operational/planning studies, it is a crucial task to provide the load consumption information in the look‐ahead times. The huge variation of the power system infrastructure in recent years has led to significant changes in the consumers' consumption pattern. Therefore, short‐term load forecasting (STLF) is transformed to a more complicated problem in recent years. To address this issue, this paper proposes a graph‐based deep neural network to capture full spatial‐temporal features and be able to oversee high volatility time series including load sequence. The proposed spatial deep learning structure benefits from learning the spatial feature using Gabor filter‐oriented layers and full understanding the temporal behaviour based on bidirectional networks. The designed learning‐based system is developed as a graph‐based learning system to improve the accuracy considering the meteorological information behaviour. To verify the performance of the designed deep graph network, the actual load data of Shiraz, Iran, is used. Besides, to demonstrate the superiority and effectiveness of the proposed, the designed deep graph network is compared with three well‐known shallow and deep networks in different cases including yearly performance, seasonal performance, and sensitivity analysis on the meteorological data. [ABSTRACT FROM AUTHOR]

Value capture means capturing in some way the value of an investment in public infrastructure. Value capture methods include five forms of property‐related taxes: a tax on land value uplift, a broad‐based land tax, selective land tax, property taxes and transaction taxes. They may also include three forms of user charges: developer charges, consumer charges and sale of development rights. The paper assesses these options against three standard policy objectives of efficiency, equity and practicality. The paper finds that all three main forms of user charges meet these criteria. Special area land and property taxes may be appropriate in limited cases. Pure betterment taxes on land value uplift are rarely practical. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are critical infrastructure used to convey water from sources to consumers. The mathematical framework governing the distribution of flows and heads in extended period simulations of WDSs lends itself to application in a wide range of optimization problems. Applying the classical mixed integer linear programming (MILP) approach to model WDSs hydraulics within an optimization framework can contribute to higher solution accuracy with lower computational effort. However, adapting WDSs models to conform to a MILP formulation has proven challenging because of the intrinsic non‐linearity of system hydraulics and the complexity associated with modeling hydraulic devices that influence the state of the WDS. This paper introduces MILPNet, an adjustable framework for WDSs that can be used to build and solve an extensive array of MILP optimization problems. MILPNet includes constraints that represent the mass balance and energy conservation equations, hydraulic devices, control rules, and status checks. To conform to MILP structure, MILPNet employs piece‐wise linear approximation and integer programming. MILPNet was implemented and tested using Gurobi Python API. Modeling accuracy was shown to be comparable to EPANET, a public domain software for hydraulic modeling, and sensitivity analyses were conducted to examine the impacts of the modeling assumptions on the performance of MILPNet. Additionally, application of the framework was demonstrated using pump scheduling optimization examples in single and rolling horizon scenarios. Our results show that MILPNet can facilitate the construction and solution of optimization problems for a range of applications in WDSs operations. Key Points: A mixed‐integer linear programming framework (MILPNet) for formulating and solving water distribution system optimization problems is presentedMILPNet models system dynamics, hydraulic devices, control rules, and status checks and is flexible to adding more devices and conditionsThe optimization model can be generated from a.INP file and case‐specific objectives and constraints can be specified via Python interface [ABSTRACT FROM AUTHOR]

This paper proposes a novel simulation‐based hybrid approach coupled with time‐dependent Bayesian network analysis to model multi‐infrastructure vulnerability over time under physical, spatial, and informational uncertainties while considering cascading failures within and across infrastructure networks. Unlike existing studies that unrealistically assume that infrastructure managers have full knowledge of all the infrastructure systems, the proposed approach considers a realistic scenario where complete information about the infrastructure network topology or the supply–demand flow characteristics is not available while estimating multi‐infrastructure vulnerability. A novel heuristic algorithm is proposed to construct a dynamic fault tree to abstract the network topology of any infrastructure. In addition, to account for the unavailability of exact supply–demand flow characteristics, the proposed approach constructs the interdependence links across infrastructure network systems using different simulated parameters considering the physical, logical, and geographical dependencies. Finally, using parameters for geographical proximity, infrastructure managers' risk perception, and the relative importance of one infrastructure on another, the multi‐infrastructure vulnerability over time is estimated. Results from the numerical experiment show that for an opportunistic risk perception, the interdependencies attribute to redundancies, and with an increase in redundancy, the vulnerability decreases. On the other hand, from a conservative risk perspective, the interdependencies attribute to deficiencies/liabilities, and the vulnerability increases with an increase in the number of such interdependencies. [ABSTRACT FROM AUTHOR]

Water distribution networks (WDNs) are critical infrastructure for communities. The dramatic expansion of the WDNs associated with urbanization makes them more vulnerable to high‐consequence hazards such as earthquakes, which requires strategies to ensure their resilience. The resilience of a WDN is related to its ability to recover its service after disastrous events. Sound decisions on the repair sequence play a crucial role to ensure a resilient WDN recovery. This paper introduces the development of a graph convolutional neural network‐integrated deep reinforcement learning (GCN‐DRL) model to support optimal repair decisions to improve WDN resilience after earthquakes. A WDN resilience evaluation framework is first developed, which integrates the dynamic evolution of WDN performance indicators during the post‐earthquake recovery process. The WDN performance indicator considers the relative importance of the service nodes and the extent of post‐earthquake water needs that are satisfied. In this GCN‐DRL model framework, the GCN encodes the information of the WDN. The topology and performance of service nodes (i.e., the degree of water that needs satisfaction) are inputs to the GCN; the outputs of GCN are the reward values (Q‐values) corresponding to each repair action, which are fed into the DRL process to select the optimal repair sequence from a large action space to achieve highest system resilience. The GCN‐DRL model is demonstrated on a testbed WDN subjected to three earthquake damage scenarios. The performance of the repair decisions by the GCN‐DRL model is compared with those by four conventional decision methods. The results show that the recovery sequence by the GCN‐DRL model achieved the highest system resilience index values and the fastest recovery of system performance. Besides, by using transfer learning based on a pre‐trained model, the GCN‐DRL model achieved high computational efficiency in determining the optimal repair sequences under new damage scenarios. This novel GCN‐DRL model features robustness and universality to support optimal repair decisions to ensure resilient WDN recovery from earthquake damages. [ABSTRACT FROM AUTHOR]

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This paper explores how vulnerable consumers within systematic constraints of economic inequality, institutional barriers, and social segregation in an urban environment cope with their vulnerabilities to achieve their well‐being. Taking China's internal migrant workers as a research context, our study examines their vulnerable experiences and reveals the impact of systematic constraints on migrant workers' self‐perception, interpretation, and actions. It discovers a staged process through which migrant workers acquire resilience to optimize life satisfaction by fulfilling a sense of control over their migration life. Through a situated approach to capture the contextual impact of systematic constraints on vulnerability experiences and the construction of resilient pathways to achieve well‐being, this paper puts forward critical welfare issues such as inclusive marketplace, social capital, and community empowerment which are important to migrants' social integration and capability building. This calls for more coordinated efforts to promote effective resilience building and sustained well‐being among resource‐constrained consumers. [ABSTRACT FROM AUTHOR]

The deflection of railroad bridges under in‐service loads is an important indicator of the structure's health. Over the past decade, an increasing number of studies have demonstrated the efficacy of using vision‐based approaches for displacement tracking of civil infrastructure. These studies have relied primarily on external processing of manually recorded videos of a structure's motion to estimate displacements. To date, vision‐based techniques applied to long‐term structural health monitoring have yet to be proven effective as an alternative to the traditional displacement measurement methods, such as linear variable differential transformers. This paper proposes a wireless SmartVision system (WSVS) that uses edge computing to directly output bridge displacements that can be sent to the end user. The system estimates displacements using both target‐free and target‐based approaches. A synchronized sensing framework is developed for multipoint displacement estimation using several wireless vision‐based nodes for full‐scale displacement‐based modal analysis of structures. Pose estimation using an AprilTag, a fiducial marker, is employed with a modified algorithm for improved displacement tracking of targets installed on a bridge, yielding subpixel accuracy. The robustness of the results in field conditions is enhanced by linking a tracking quality factor to each timestamp to handle vision‐related uncertainties. To meet the need for precise error metrics evaluation, an inexpensive cyber‐physical setup using a synthetic testing environment is also developed in this study. Following laboratory validation, field tests on a cable‐stayed pedestrian bridge were performed to demonstrate the efficacy of the proposed WSVS. [ABSTRACT FROM AUTHOR]

Optimal sensor placement (OSP) is essential for effective structural health monitoring (SHM). More recently, deep learning algorithms have shown great potential in sensor‐based SHM. However, existing optimization frameworks, such as population‐based algorithms, are often not suited for data‐driven SHM. Evaluating a number of sensor layouts includes training on large datasets, which is computationally expensive. This paper proposes deep generative Bayesian optimization (DGBO) as a solution for a parallel optimization of black‐box/expensive OSP objective functions. Conditional variational autoencoders are leveraged as generative models that transform the OSP problem into a lower‐dimensional latent space. Additionally, DGBO utilizes a surrogate neural network to capture the probability distribution of the objective function space. The proposed method is validated on two case studies on a nine‐story reinforced concrete moment frame. The first one serves as a proof of concept to show that DGBO can find the global optimum configuration. The second case study aims to maximize the semantic damage segmentation (SDS) accuracy using a fully convolutional neural network. Transfer learning is proposed in training the vibration‐based SDS model, which reduces the evaluation times by more than 50%. Without compromising the performance, the number of accelerometers can be reduced by 52% and 43%, respectively, for damage location and severity predictions. It is also shown that DGBO can outperform genetic algorithm with the same number of function evaluations. DGBO can serve as a scalable solution to address the high‐dimensionality challenge in OSP for large‐scale civil infrastructure. [ABSTRACT FROM AUTHOR]

This paper examines the determinants of economic growth in developing countries within the standard growth regression framework, with special attention being paid to the experience of landlocked developing countries ( LLDCs). The results confirm that the landlockedness hampers economic growth, but the magnitude of negative impact is sensitive to alternative estimation methods. However, the analysis suggests that good governance, trade openness and coordinating infrastructure development with neighbours explain the significant aspect of the inter-country differences in growth rates among LLDCs. The results also suggest that African landlocked countries are not different from the other LLDCs. Contrary to the 'resource curse' hypothesis, natural resources seem to contribute to economic growth of LLDCs. [ABSTRACT FROM AUTHOR]

This document is a response to a discussion on a research paper titled "A Knowledge Transfer Enhanced Ensemble Approach to Predict the Shear Capacity of Reinforced Concrete Deep Beams without Stirrups." The authors address the issues raised by the discussant regarding the scope and significance of the study, the black box nature of machine learning models, and the engineering applicability of the proposed approach. The primary objective of the study is to propose a new learning algorithm that can mitigate the data scarcity problem and reduce expenses for gathering additional samples. The authors demonstrate the performance of their model, the TENN model, in predicting the shear capacity of reinforced concrete beams with limited training data. They emphasize that while the model may be difficult to interpret, it can aid in furthering our understanding of shear failure mechanisms. The authors also clarify that their proposed approach does not replace design principles or code requirements, but rather provides a novel methodology for estimating the structural capacity of RC beams when sufficient training samples are unavailable. The paper emphasizes the importance of safe design and references the latest design standards. [Extracted from the article]

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This paper presents a novel, sequentially executed supervised machine learning‐based electric theft detection framework using a Jaya‐optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre‐processing phase. An oversampling algorithm based on the Robust‐SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into "Honest" and "Fraudster," the ensemble machine learning‐based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re‐trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research. [ABSTRACT FROM AUTHOR]

Copyright of Papers in Regional Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the 5G worldwide deployment, the scale of vertical applications is innovated benefit from 5G technologies including MEC (Multi‐access Edge Computing), network slicing, etc. Especially for healthcare, 5G had been used for COVID‐19 protection and intelligent medical processing. However, limited by the hospital's traditional information infrastructures, those 5G‐based healthcare applications are hard to be deployed and most only for demonstration, also isolated from the existing medical systems. So what is the next generation of smart healthcare information infrastructures is the key issue for the long‐term development of 5G healthcare applications. Even though the standardized 5G MEC framework has been widely used in many vertical scenarios, it is also hard to satisfy hospital‐specific requirements such as hospital‐dedicated deployment, medical data security, and various network connections, etc. This paper proposes a 5G‐based architecture for smart healthcare information infrastructure, a new network element iGW (industry gateway) is defined, and the smart healthcare dedicated cloud platform iMEP (industry multi‐access edge platform) is also introduced here, making it possible to satisfy both the hospital‐specific requirements and the long‐term evolution. Meanwhile, the implementation methodology and the corresponding field test results are presented, which show the significant network performance gain achieved by the proposed new system structure. [ABSTRACT FROM AUTHOR]

Transport infrastructure has represented one of the cornerstones of development and cohesion strategies in the European Union (EU) and elsewhere in the world. However, despite the considerable funds devoted to it, its impact remains controversial. This paper revisits the question of to what extent transport infrastructure endowment - proxied by regional motorways - has contributed to regional growth in the EU between 1990 and 2004. It analyses infrastructure in relationship to other factors which may condition economic growth, such as innovation, migration, and the local 'social filter', taking also into account the geographical component of intervention in transport infrastructure and innovation. The results of the two-way fixed-effect (static) and difference GMM (dynamic) panel data regressions indicate that infrastructure endowment is a relatively poor predictor of economic growth and that regional growth in the EU results from a combination of an adequate 'social filter', good innovation capacity, both in the region and in neighbouring areas, and a region's capacity to attract migrants. The meagre returns of infrastructure endowment on economic growth raise interesting questions about the opportunity costs of further infrastructure investments across most of Western Europe. Resumen La infraestructura de transporte ha venido siendo una de las piedras angulares de las estrategias de desarrollo y cohesión en la Unión Europea ( UE) y el resto del mundo. Sin embargo, y a pesar de los considerables recursos que se le ha dedicado, su impacto es un tema controvertido. Este artículo revisa el interrogante de hasta que punto ha contribuido la dotación de infraestructura de transporte - representada por las autopistas regionales - al crecimiento regional en la UE entre 1990 y 2004. Se analiza la infraestructura en relación a otros factores que podrían condicionar el crecimiento económico, como la innovación, la migración, y el 'filtro social' local, teniendo en cuenta asimismo el componente geográfico de la intervención en la infraestructura de transporte y la innovación. Los resultados de las regresiones de datos de panel de efectos fijos (método estático) de doble vía y MGM por diferencias (método dinámico) indican que la dotación de infraestructura es un pobre indicador del crecimiento económico y que el crecimiento regional en la UE tiene su origen en una combinación de un 'filtro social' adecuado, en una buena capacidad innovadora tanto en la región como en áreas vecinas, y en la capacidad de la región de atraer migración. Los escasos retornos para el crecimiento económico de la dotación de infraestructura suscitan cuestiones interesantes sobre los costos de oportunidad de futuras inversiones en infraestructura para la mayoría de Europa Occidental. [ABSTRACT FROM AUTHOR]

This paper attempts to shed new empirical light on infrastructure's role in economic growth using a semiparametric smooth coefficient model to avoid specification problems in some existing studies and admit infrastructure‐induced nonlinearity and parameter heterogeneity. Estimated by a three‐step procedure that controls for endogeneity in both the regressors and the environmental variable (infrastructure), the model is applied to the empirical context of telecommunications infrastructure in a fast‐growing economy, China. The results reveal that telecommunications contribute to output through various sources, namely its neutral and non‐neutral impacts. The total/net effect is positive but largely decreases with telecommunications stocks. [ABSTRACT FROM AUTHOR]

The extent and background of establishing international knowledge relations among young academic spin-off firms are explored in this paper. Drawing on survey data of 100 of such firms, the influence of human capital and social networks of these firms is examined, alongside their innovation level. International learning is measured in two ways, adoption of the strategy and spatial reach related to this adoption namely, from Europe to worldwide. The paper fits into a stream of research in which it is recognized that new technology-based firms interact both in local knowledge networks and knowledge networks abroad to remain competitive. A majority of the spin-off firms were found to be engaged in international networks and the most powerful influences tended to be the presence of PhD experience and size of the starting team. Social capital released through social networks is a relatively strong influence only in the spatial reach of knowledge relations, supporting the idea that strong social networks form a solid base from which global learning can be undertaken. The implications of the results of this work and future research steps are discussed. [ABSTRACT FROM AUTHOR]

This paper examines the extent to which PFI is a product of a modernisation agenda and its role in furthering it. We define modernisation using an analysis of the UK Government's legislation and pronouncements framing it in the context of New Public Financial Management(). The paper uses the example of healthcare to consider recent changes in the procurement and accounting for infrastructure investment. Finally we evaluate the use of PFI in the context of NPFM and the modernisation agenda and consider whether the public sector should provide funding for the infrastructure of the public services. [ABSTRACT FROM AUTHOR]

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traffic information plays an important role in the design and management of civil transportation infrastructure. Bridge weigh‐in‐motion (BWIM) provides an effective tool for traffic information gathering by estimating vehicle parameters including its weight through bridge responses. Most existing BWIM algorithms rarely consider the epistemic uncertainty of vehicle weight in terms of the probabilistic distribution of estimated axle weights (AWs) of the vehicle. This paper proposes a novel methodology for probabilistic vehicle weight estimation using a physics‐constrained generative adversarial network (GAN). Generative models are introduced to describe the probabilistic distributions of estimated AWs and bridge responses. Physics constraints on the generative models are formulated and enforced by minimizing a physics‐based loss function. The generative models are then learned by training a physics‐constrained GAN using the observed bridge responses. Numerical study and field testing are conducted to demonstrate the proposed method using representative highway bridges and vehicles. The results show that the proposed method can successfully capture the uncertainty in the vehicle weight estimation and provide the probabilistic distributions of the estimated AWs for different vehicle types and loading conditions considered, which can enhance the application of BWIM for relevant tasks such as traffic data collection and truck overloading enforcement. Based on the results obtained from the numerical study and field testing, the maximum coefficient of variation obtained for the AWs and gross vehicle weight of the presented cases are 0.55 and 0.11, respectively. [ABSTRACT FROM AUTHOR]

In times of steadily increasing traffic loads and extreme weather phenomena, the safe maintenance of infrastructure poses a difficult challenge to operators, especially when a vast number of aged structures exists and fundamental data is missing. This paper addresses the demand for cost‐efficient deformation monitoring of anchored retaining structures along public roads. The principal idea is to process laser scans of a motor‐vehicle‐based mobile mapping system with a high degree of automation. Starting with scene interpretation, our processing pipeline extracts the retaining wall from the rest of the point cloud, segments the anchored elements, and computes their deformations. This method requires, however, correcting for positioning errors to obtain accurate results. We exploit the high data redundancy of road patches and line markings for alignment. Due to the high degree of automation, computations scale to large numbers of point clouds and run in a repeatable manner. Even when traveling along highways with up to 100 km/h, we achieve repeatable accuracies for tilting and lateral displacements that compare to traditional, labor‐intense surveying methods. [ABSTRACT FROM AUTHOR]