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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association 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.)

Government infrastructure provision is much more important than its conception in mainstream economic theory as a response to 'market failure'. In practice, it draws on the state's capacity to fUnd [...]

A direct consequence of the rapid expansion of civilization and modernization trends is the escalation in global warming and the consequential climatic upheavals. The world has actively advocated the adoption of electric vehicles (EVs) as a response to the environmental challenges posed by vehicular emissions. It is evident that conventional fuel‐based charging infrastructures are economically impractical and lack organizational cohesion in light of the proliferation of EVs. An EV charging station powered by renewable energy presents a promising opportunity for enhancing flexibility and control. It is imperative that EV charging stations be equipped with solar power and standby batteries (SBBs). Consequently, this article presents and evaluates a system that utilizes a proportional‐integral‐derivative controller, a neural network‐equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller. To achieve optimal power management within the charging station, MATLAB/Simulink is used to implement and rigorously test the proposed system. It orchestrates the interaction between the solar panel, backup battery, grid and EVs. Compared to existing systems in the literature, the comprehensive system exhibits commendable efficiency. Due to the pivotal role played by grid integration and the SBB, the system can ensure a reliable power supply to the charging station under any weather conditions. [ABSTRACT FROM AUTHOR]

Vehicle‐mounted detection methods have been widely applied in the maintenance of high‐speed railways (HSRs), providing feasibility for diagnosing ballastless track arching. However, applying detection data faces several key limitations: (1) The threshold mostly requires manual setting, making recognition accuracy highly subjective; (2) the extensive workload of manual inspections makes it challenging to label detection data, hindering the application of supervised learning approaches. To address these problems, this paper utilizes the longitudinal level irregularity data obtained from vehicle‐mounted detection, employing the concept of unsupervised learning for dimensionality reduction, combined with clustering algorithms and minimal label fine‐tuning, to design two frameworks: the fully unsupervised framework (FUF) and the few‐shot fine‐tuned framework (FFF). Experiments on dynamic detection data from a Chinese HSR line were conducted, comparing the performance of data dimensionality reduction, clustering, and classification under different strategy combinations. The results show that the improved variational autoencoder significantly enhances the performance of the encoder in dimensionality reduction, facilitating better feature extraction; the FUF achieves effective clustering outcomes without any labeled samples and its adjusted rand index score exceeded 0.8, showcasing its robustness and applicability in scenarios with no prior annotations; the FFF requires only a small number of labeled samples (labeling ratio of 5%) and achieves excellent performance, with metrics such as accuracy exceeding 0.85, thus greatly reducing the reliance on labeled data. This study offers a novel method for solving engineering issues with limited labeled data, providing an efficient solution for identifying track arching defects and advancing railway infrastructure monitoring. [ABSTRACT FROM AUTHOR]

Rapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing (BG) algorithm, specifically designed for crack length estimation in asphalt pavements, along with an optimized OrthoBoundary algorithm tailored for crack width estimation. Leveraging four widely adopted deep learning models for asphalt pavement crack segmentation, four distinct sets of image segmentation results have been produced. Subsequently, a comprehensive evaluation has been conducted to assess the effectiveness of both crack dimensions estimation algorithms. The findings demonstrate that the integration of the BG algorithm, the optimized OrthoBoundary algorithm, and the fully convolutional network with the HRNet backbone achieve a prediction accuracy of 80.21% for crack length estimation and 84.32% for average width estimation. Moreover, the image processing speed, at a resolution of 3024 × 3024, can be maintained at approximately 5 s, with average width estimation observed to be up to 9.1‐fold faster than the unoptimized OrthoBoundary algorithm. These results signify advancements in automated crack quantification methodologies, with implications for enhancing civil infrastructure maintenance practices. [ABSTRACT FROM AUTHOR]

An increasing adoption of electric vehicles (EVs) is expected in the coming decades mainly due to the need to achieve carbon neutrality until 2050. However, predicting electric mobility's future is challenging due to three main factors: technological advancements, regulatory policies, and consumer behaviour. The projections presented in this study are based on several scenarios driven mainly from reports published by public entities and consultants. It considers the evolution of electric road mobility by defined targets in the electrification of the transport sector. Therefore, the gathered data addresses different horizon times regarding EV penetration in the World, Europe, Portugal, Denmark, Greece, and Slovenia. Thus, an extensive literature review and estimating approach for EV forecast was conducted concerning EV markets, charging infrastructure, and electricity demand. Also, the dataset aims to provide a demand projection by 2050 and serving as a critical input to further work on EV mass deployment in the context of the project Electric Vehicles Management for carbon neutrality in Europe (EV4EU) and other works related to this field. [ABSTRACT FROM AUTHOR]

Strain gauge plays vital roles in various fields as structural health monitoring, aerospace engineering, and civil infrastructure. However, traditional flexible strain gauge inevitably brings the pseudo-signal caused by the substrate temperature effect and determines its accuracy. Here, we present an anisotropic composite substrate designed to modify the thermal expansion performance via Micro-electro-mechanical System (MEMS) technology, which facilitates the development of strain gauges that are minimally affected by substrate temperature-induced effect. Compared to the isotropic flexible substrate, the simulated expansion displacement in the thermal insensitive direction is reduced by 53.6% via introducing an anisotropic thermal expansion structure. The developed strain gauge exhibits significantly reduced sensitivity to temperature-induced effect, with a temperature coefficient of resistance decreasing from 87.3% to 10%, along with a notable 47.1% improvement in TCR stability. In addition, the strain gauge displays a sensitivity of 1.99 and boasts a wide strain operational range of 0–6000 µε, while maintaining excellent linearity. Furthermore, stress response conducted on a model of an aircraft wing illustrates the rapid monitoring of the strain gauge, which can detect strain as low as 100 µε. This study strongly highlights the potential applicability of the developed strain gauge in the aircraft, ships, and bridges for monitoring stress. [ABSTRACT FROM AUTHOR]

Some critical civil infrastructure projects, such as government offices, command buildings, airport stations, and transportation routes, are highly susceptible to missile attacks during conflicts. To investigate the influence of impact velocity ( v 0) and impact location on the dynamic responses of RC slabs and projectiles, finite element models were established to simulate the perforation of the 300 mm-diameter projectile into the 100 mm-thick RC slab. The results indicate that both v 0 and impact location have effects on the relative relationship between kinetic energy and internal energy of the RC slab. Stresses exceeding 48 MPa ( f c) and maximum displacement of the RC slab are primarily concentrated around the bullet hole, extending to a distance of approximately 450 mm (1.5 d) from the impact center. After perforation, the relationship between velocity loss ratio and v 0 , as well as the relationship between kinetic energy loss ratio and v 0 , conforms to the power function distribution. Considering the damage to the RC slab, the Chen model, Peng model, and Konyaew model of the residual velocity prove more suitable for this working condition, especially the Chen model. [ABSTRACT FROM AUTHOR]

Background: Reliable infrastructure is vital for Nordic societies, but they face escalating climate risks. Climate change is increasing magnitude and frequency of floods, storms, and landslides, making adaptive solutions crucial. Methods: This review explores Nature-Based Solutions (NbS) for mitigating natural hazards along Nordic linear infrastructure. The motivation of the review comes as result of a preliminary survey conducted among to the main infrastructure managers in the Fennoscandian peninsula. The objective was to pinpoint the natural hazards that pose greatest concern under future climate scenarios, as well as to understand which specific information is needed to adopt NbS Results: Floods, erosion, landslides and rockfalls emerged as primary hazards of concern for the infrastructure owners, hence the review process was focused only on NbS aimed at mitigating the effects of these specific hazards. A total of 78 documents were identified from the review process and were integrated with examples and case studies from other relevant on-going and past projects. Despite only a few of the NbS identified in these documents were directly implemented for linear infrastructure such as roads and railways, and none dealing with electric grids, several NbS were identified to have a potential for implementation for Nordic linear infrastructure. A list of NbS options, not all implemented along linear infrastructure but with potential for it, is provided. This list is meant to serve as "vade mecum" for a quick and easy access to NbS as mitigation options for linear infrastructure managers in the Nordic Countries. The NbS are classified in green, blue, green/blue and hybrid approaches, and supported by examples of case studies both in the Nordic Countries as well as countries having similar climates. Conclusions: This review underlines the challenges and opportunities of adopting NbS. Challenges such as the lack of expertise, space and climate constraints, and path dependency on adoption of traditional infrastructure must be addressed to mainstream NbS. The review highlights the importance of standardization, European guidelines, and technical manuals in promoting NbS adoption among infrastructure managers, as well as the necessity of accounting for the wider co-benefits of NbS, including carbon sequestration, biodiversity and ecosystem services. This paper contributes to the understanding of NbS as potential natural hazards mitigation options for Nordic infrastructure networks in the face of evolving climate risks, providing valuable insights for infrastructure managers and policymakers alike. [ABSTRACT FROM AUTHOR]

This study examines the influence of digital government initiatives on corporate total factor productivity (TFP). Employing a difference-in-differences (DID) methodology and analyzing data from publicly listed companies spanning the period 2010 to 2020, we investigate the impact of digital governance on corporate TFP. Our findings reveal a noteworthy positive effect, with an average TFP increase of 5%. Further exploration through heterogeneity analysis indicates that this impact is particularly pronounced in regions with robust network infrastructure, increased marketization, and decreased economic uncertainty, particularly among privately-owned enterprises. Moreover, we identify key mechanisms through which digital governance fosters this enhancement in TFP, including the facilitation of technological innovation, efficient allocation of high-skilled labor, and improved investment efficiency. Our research underscores the significant role of digital government initiatives in bolstering corporate TFP and contributes to a deeper understanding of the mechanisms underlying this relationship. [ABSTRACT FROM AUTHOR]

The China-Pakistan Economic Corridor (CPEC) is a cornerstone of China's Belt and Road Initiative (BRI). It aims to enhance regional trade and economic expansion in Pakistan. We adopted systematic review approach to investigate the agricultural trade narrative of the CPEC and identify future research avenues. Our study uses the Web of Sciences and Scopus database to extract the relevant literature. We executed the search query for 2013–2023. We followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement to identify the literature. The final analysis includes only 14 articles. We found that the literature mainly focuses on three topics, Gwadar port, road infrastructure, and agricultural complementarity and competitiveness. The review reveals the significant potential of CPEC on the agricultural trade of Pakistan. Based on the review, we point to avenues that could be considered in future research work to fully exploit the potential of CPEC in the agriculture sector of Pakistan. [ABSTRACT FROM AUTHOR]

Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory and the principle of economy of scale that uses little information in input to generate structures that are similar to those of public transportation networks. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure but rather extracts this directly from a continuous space using only a few origin and destination points, generating networks from scratch. Analyzing a set of urban train, tram and subway networks, we find a noteworthy degree of similarity in several of the studied cases between simulated and real infrastructures. By tuning one parameter, our method can simulate a range of different subway, tram and train networks that can be further used to suggest possible improvements in terms of relevant transportation properties. Outputs of our algorithm provide naturally a principled quantitative measure of similarity between two networks that can be used to automatize the selection of similar simulated networks. Planning effective urban network infrastructure often involves optimization principles that use a backbone network as a starting point. The authors propose an approach based on optimal transport theory to simulate real urban rail networks structure without need of initial backbone knowledge. [ABSTRACT FROM AUTHOR]

Drought is a major global challenge causing significant socio-economic and environmental impacts. A paradigm shift from crisis to risk management is advocated to reduce the impacts of droughts, and to build the resilience of societies, and water and environmental systems against drought. A number of drought policy and planning guidelines are developed and used to support the transition from crisis to risk management and enhancing resilience. However, research is lacking on critical reflection, evaluation and update of the available drought guidelines. For example, there is no study on assessing the correspondence of the available guidelines to the contemporary disaster risk reduction agenda. Therefore, this study evaluates twelve drought policy and planning guidelines for their alignment with the four priority areas of the SENDAI framework for disaster risk reduction 2015–2030. A qualitative evaluation matrix was developed and used in the assessment. The examined priorities and associated thematic elements were scored in the range 0–100, and classified under Very Low (0–10), Low (11–30), Medium-Low (31–50), Medium-High (51–70), High (71–90), and Very High (91–100) categories. Most guidelines achieved (medium) high to very high scores on data and information, risk assessment, and communication and dissemination elements associated with priority 1 (understanding disaster risk). Whereas, mostly very low to low coverage was found for science-policy-practice dialogue, local knowledge and practices, and research and development. Strengthening disaster risk governance to manage disaster risk (priority 2) earned high scores on most elements, notably for strategies and plans, coordination mechanisms, community representation, and policy and governance. In contrast, most elements under priority 3 (investing in disaster risk reduction) were classified under low to medium categories, which include financial allocation, risk transfer, and mainstreaming drought risk reduction into land use and rural development planning, business resilience and protection of livelihoods, and health and safety. Most elements under priority 4 (enhancing disaster preparedness) scored under medium low to medium high ranges, as sufficient information was lacking on multi-hazard early warning systems, post-disaster recovery, rehabilitation, and reconstruction, and resilience of critical infrastructure. Furthermore, the study outlined several strengths, weaknesses, opportunities and threats pertaining to the examined guidelines. In general, the study reveals an urgent need to better align drought policy and planning guidelines with the contemporary disaster risk reduction agenda outlined in the SENDAI Framework. The findings of this study can be instructive in designing the next generation of drought guidelines in support of an accelerated transition towards drought risk management, and building resilient societies and ecosystems under a changing climate and increasing anthropogenic pressures. [ABSTRACT FROM AUTHOR]

The global transportation electrification commerce sector is now booming. Stakeholders are paying an increased attention to the integration of electric vehicles and electric buses into the transportation networks. As a result, there is an urgent need to invest in public charging infrastructure, particularly for fast charging facilities. Consequently, and to complete the portfolio of the green environment, these fast-charging stations (FCSs) are designed using 100% of renewable energy sources (RESs). Thus, this paper proposes an optimization model for the techno-economic assessment of FCSs comprising photovoltaic and wind turbines with various energy storage devices (ESDs). In this regard, the FCS performance is evaluated using flywheels and super capacitors due to their high-power density and charging/discharging cycles and rates. Then, optimal sizing of these distributed generators is attained considering diverse technical and economical key performance indicators. Afterwards, the problem gets more sophisticated by investigating the effect of RES's uncertainties on the selection criterion of the FCS's components, design and capacity. Eventually, as an effort dedicated to an online energy management approach, a deep learning methodology based on radial basis network (RBN) is implemented, validated, and carried out. In stark contrast to conventional optimization approaches, RBN demonstrates its superiority by obtaining the optimum solutions in a relatively short amount of time. [ABSTRACT FROM AUTHOR]

Microbiologically Influenced Corrosion (MIC) is one of the main threats for marine infrastructures, leading to severe safety and environmental risks associated with structural failures and/or leakages of dangerous fluids, together with potential huge economic losses and reputational damage for the involved parts. For a safe design and a proper installation of infrastructure systems in contact with the seabed, a deep knowledge of the site-specific microbial community of the sediments should be beneficial. Therefore, in addition to the simple detection or the sole quantification of Sulphate-Reducing Bacteria (SRB), the whole characterization of the microbial members involved in MIC phenomena is desirable. In this study, 16S rRNA-based comparison between bacterial communities thriving in offshore and nearshore marine sediments was performed, with a focus on the main bacterial groups putatively responsible for MIC. The nearshore sediments were significantly enriched in bacterial members associated with human and organic compounds contamination belonging to the Bacteroidota, Desulfobacterota, and Firmicutes phyla, while the offshore sediments hosted Alphaproteobacteria, Nitrospinota, and Nitrospirota members, representative of a low anthropogenic impact. Quantitative PCR targeting the dsrA gene and detailed community analyses revealed that the nearshore sediments were significantly enriched in SRB mainly affiliated to the Desulfobulbus and Desulfosarcina genera potentially involved in biocorrosion, compared to the offshore ones. These results suggest that the bacterial community associated with the high concentration of organic compounds derived by an elevated anthropogenic impact is likely to favour MIC. Such observations highlight the importance of microbiological investigations as prevention strategy against MIC processes, aiming both at characterizing sites for the establishment of new infrastructures and at monitoring those already installed. [ABSTRACT FROM AUTHOR]

Southern Thailand has experienced significant shifts in precipitation patterns in recent years, exerting substantial impacts on regional water resources and infrastructure systems. This study aims to elucidate these changes and underlying factors based on daily precipitation observations from Nakhon Si Thammarat Province spanning 1980 to 2022. Additionally, data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is utilized to investigate projected changes in precipitation for 2015–2100 relative to the historical period (1980–2014), employing a comprehensive analysis considering two emissions scenarios (SSP245 and SSP585) across six models. Various precipitation indices are selected to assess trends and statistical significance using the Mann-Kendall test. Both observed and climate model data indicate an increasing precipitation trend in Southern Thailand, with a reduced association with the El Niño-Southern Oscillation (ENSO) under warming conditions. Extreme precipitation indices also exhibit an increasing trend, with total precipitation and the 95th percentile of daily precipitation (R95p) revealing very wet conditions in recent years, projected to continue increasing. Contrastingly, the number of dry days is also mounting, suggesting that both dry and wet extremes will impact Southern Thailand under a warmer climate. The findings from this study provide an early indication of future precipitation and extreme event scenarios, which can inform the development of measures to mitigate climate change-related hazards in the region. [ABSTRACT FROM AUTHOR]

This study seeks to develop a conceptual model for increasing the productivity of a province in Indonesia. Based on trade facilities, it is hoped that it will become a key factor or strategy to increase trade that is oriented towards increasing the productivity of the provinces in Indonesia. Model In this study, in 34 provinces in Indonesia using secondary time series data and primary cross-sectional data. Self-data analysis using Amos 25 And from the results of data analysis all hypotheses proposed in this study is accepted, it shows that trade facilities are a key strategy for increasing trade and its orientation towards productivity growth of the gross regional domestic product. [ABSTRACT FROM AUTHOR]

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections. [ABSTRACT FROM AUTHOR]

In this article, the authors present the design of a compact multiband monopole antenna measuring 30 × 10 × 1.6 mm3, which is aimed at optimizing performance across various communication bands, with a particular focus on Wi-Fi and sub-6G bands. These bands include the 2.4 GHz band, the 3.5 GHz band, and the 5–6 GHz band, ensuring versatility in practical applications. Another important point is that this paper demonstrates effective methods for reducing mutual coupling through two meander slits on the common ground, resembling a defected ground structure (DGS) between two antenna elements. This approach achieves mutual coupling suppression from −6.5 dB and −9 dB to −26 dB and −13 dB at 2.46 GHz and 3.47 GHz, respectively. Simulated and measured results are in good agreement, demonstrating significant improvements in isolation and overall multiple-input multiple-output (MIMO) antenna system performance. This research proposes a compact multiband monopole antenna and demonstrates a method to suppress coupling in multiband antennas, making them suitable for internet of things (IoT) sensor devices and Wi-Fi infrastructure systems. [ABSTRACT FROM AUTHOR]

Advanced metering infrastructures (AMIs) aim to enhance the efficiency, reliability, and stability of electrical systems while offering advanced functionality. However, an AMI collects copious volumes of data and information, making the entire system sensitive and vulnerable to malicious attacks that may cause substantial damage, such as a deficit in national security, a disturbance of public order, or significant economic harm. As a result, it is critical to guarantee a steady and dependable supply of information and electricity. Furthermore, storing massive quantities of data in one central entity leads to compromised data privacy. As such, it is imperative to engineer decentralized, federated learning (FL) solutions. In this context, the performance of participating clients has a significant impact on global performance. Moreover, FL models have the potential for a Single Point of Failure (SPoF). These limitations contribute to system failure and performance degradation. This work aims to develop a performance-based hierarchical federated learning (HFL) anomaly detection system for an AMI through (1) developing a deep learning model that detects attacks against this critical infrastructure; (2) developing a novel aggregation strategy, FedAvg-P, to enhance global performance; and (3) proposing a peer-to-peer architecture guarding against a SPoF. The proposed system was employed in experiments on the CIC-IDS2017 dataset. The experimental results demonstrate that the proposed system can be used to develop a reliable anomaly detection system for AMI networks. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to determining the ampacity of DC and AC railway overhead contact lines, considering both operational and design factors. The ampacity of contact lines is influenced by several design parameters, including materials, cross section and the number of catenary and contact wires, etc. Additionally, the permissible long-term and short-term current values, which correspond to the permissible temperatures defined by EN 50119:2020, are affected by operational factors such as ambient temperature, solar radiation, heat exchange conditions, wind speed, wear of contact wires. Existing methods for determining the ampacity of overhead contact lines often fail to account adequately for these critical factors and lack the necessary procedures to calculate the 30 min ampacity required by EN 50119:2020. In response, the method proposed in this study adapts the procedures of IEEE 738-2012 to the specific needs of railway contact lines, taking into account the effect of contact wire wear on current distribution and overall ampacity. The proposed method offers a more realistic and practical approach to determining the ampacity of contact lines, thereby enhancing the reliability and efficiency of the traction power supply infrastructure. This method could be useful for railway infrastructure managers to ensure the safe and efficient operation under varying environmental and operational conditions. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are transforming the transportation sector, driven by the rapid expansion of charging infrastructure, including fast-charging stations (FCSs), significantly reducing charging time compared to standard charging stations. Despite the advantages of faster charging, the substantial power demand of EVs poses significant technical challenges for distribution networks. In particular, the existing literature has a research gap regarding how FCSs may impact or interact with step-voltage regulators' (SVRs) tap operations. In this study, we characterize and evaluate the effects of fast recharging at varying penetration levels (PLs) on SVRs' tap operations using probabilistic simulations and sensitivity analysis. To address these challenges, we propose a local and innovative application of the Volt/Var control on EV fast charging. The proposed application aims to inject reactive power into the network, depending on the FCS's nominal active power, when the bus voltage connected to the FCS exceeds a minimum value. Our research on an actual feeder in northern Brazil reveals that reducing the active power supplied to the vehicle or oversizing the charging station power converters is unnecessary. Furthermore, our strategy reduces the probability of undervoltage violations and minimizes SVR tap changes, mitigating EVs' impact on voltage quality. [ABSTRACT FROM AUTHOR]

As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizing satellite remote sensing for lock extraction can significantly reduce manual workload and costs, assist in the daily dynamic maintenance of lock hubs, and provide more comprehensive data support for the construction and management of water transport infrastructure. In this context, this paper proposes a new method for ship lock object extraction. Leveraging fuzzy theory and prior knowledge of locks, the extraction of lock objects is achieved from Gaofen-1 (GF-1) high-resolution remote sensing images. The experimental results demonstrate that the proposed algorithm can effectively extract small lock objects in remote sensing images, achieving an average extraction accuracy of 80.9% in the study area. [ABSTRACT FROM AUTHOR]

Unsaturated polyester resin (UPR) systems are extensively used in composite materials for applications in the transportation, marine, and infrastructure sectors. There are continually evolving formulations of UPRs that need to be evaluated and optimized for processing. Differential Scanning Calorimetry (DSC) provides valuable insight into the non-isothermal and isothermal behavior of UPRs within a prescribed temperature range. In the present work, non-isothermal DSC tests were carried out between temperatures of 0.0 °C and 250 °C, through different heating and cooling ramp rates. The isothermal DSC tests were carried out between 0.0 and 170 °C. The instantaneous rate of cure of the tested temperatures were measured. The application of an autocatalytic model in a calculator was used to simulate curing behaviors under different processing conditions. As the temperature increased from 10 °C up to 170 °C, the rate of cure reduced, and the heat of reaction increased. The simulated cure behavior from the DSC data showed that the degree of cure (α) maximum value of 71.25% was achieved at the highest heating temperature of 85 °C. For the low heating temperature, i.e., 5 °C, the maximum degree of cure (α) did not exceed 12% because there was not enough heat to activate the catalyst to crosslink further. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) represents a pivotal shift towards sustainable mobility, yet the challenge of efficient charging station recommendations persists, influencing user convenience and EV uptake. This study introduces a novel approach utilizing Q-learning for simulating EV charging station recommendations, aiming to optimize the matching process between EVs and charging infrastructure. By integrating Markov decision processes with Q-learning algorithms, we dynamically adapt recommendations to user behaviours and preferences, significantly enhancing recommendation accuracy and personalization. The methodology involves constructing a simulation environment to model EV charging behaviour, evaluating the performance of the Q-learning based recommendation system under various scenarios. Results demonstrate the effectiveness of this approach in identifying optimal charging strategies, thus contributing to improved user satisfaction and charging station utilization. The findings underscore the importance of innovative technological integration for addressing the complexities of sustainable urban mobility. [ABSTRACT FROM AUTHOR]

This study proposes a novel self‐training framework for unsupervised domain adaptation in the segmentation of concrete wall cracks using accumulated crack data. The proposed method incorporates Bayesian neural networks for uncertainty estimation of pseudo‐labels, and spatial priors of cracks for screening noisy labels. Experiments demonstrate that the proposed approach achieves significant improvements in F1 score. Comparing the F1 scores, Bayesian DeepLabv3+ and Bayesian U‐Net showed performance improvements of 0.0588 and 0.1501, respectively, after domain adaptation. Furthermore, the integration of Stable Diffusion for few‐shot image generation enhances domain adaptation performance by 0.0332. The proposed framework enables high‐precision crack segmentation with as few as 100 target images, which can be easily obtained at the site, reducing the cost of model deployment in infrastructure maintenance. The study also investigates the optimal number of iterations for domain adaptation based on the uncertainty score, providing insights for practical implementation. The proposed method contributes to the development of efficient and automated structural health monitoring using AI. [ABSTRACT FROM AUTHOR]

The utilization of low-cost video data is becoming more prevalent in pavement surveys to meet the increasing demand for timely distress detection and repair. Semantic segmentation algorithms can effectively segment pavement features and distresses simultaneously. Previous studies on pavement distress segmentation have primarily focused on cracks, and most multiobjective segmentation algorithms are not accurate or efficient. This paper presents a new method for pavement segmentation using a lightweight network segmentation model that employs DeepLabV3+ with MobileNetV2 as the backbone and a convolution block attention module to extract effective information in the encoder. The authors constructed a self-created data set called ChongQing University Pavement management (CQUPM), which includes five pavement features and six types of distress. Based on the CQUPM data set and a publicly available data set, RTK, the proposed model demonstrates superior accuracy and complexity compared to DeepLabv3+, U-Net, and Segformer-b3. Its lightweight nature is particularly noteworthy, with a parameter size of only about 1/10 to 1/4 that of other models based on the same data set. The case analysis highlights the exceptional performance of the proposed model, especially in scenarios where multiple types of pavement distress overlap. Furthermore, the model excels in edge segmentation and shows good generalization performance, indicating strong potential for practical applications. Practical Applications: Maintenance management organizations at the grassroots level, in certain regions or serving specific projects, often face significant daily workloads. Routine survey work is primarily reliant on manual labor due to the high acquisition and operating costs of detection equipment. The segmentation model, trained on a small data set constructed from front-view images, can complete the survey of 2–3 lanes at a time. This model enables the detection of pavement type, pavement marking, and distress information. The model's excellent generalization capabilities and the small data set lower the technical threshold of the application. This approach can be applied to other transportation infrastructures to address similar management problems. By using low-cost video recording devices to capture video data and quickly construct small data sets, training, and applications based on semantic segmentation techniques, problems can be identified in a timely manner without relying on human labor. This method has strong potential for replication. [ABSTRACT FROM AUTHOR]

Project bundling is an innovative project-delivery approach that bundles and groups different infrastructure projects into a single contract. Project bundling provides numerous opportunities and benefits for agencies and states while maintaining, rehabilitating, and repairing their infrastructure assets. Although previous research provided insights on some aspects of project bundling, none have identified the success factors needed to enhance the performance of bundled projects after the project bundling decision has been made. This study aims to fill this gap through identifying, studying, and prioritizing the success factors needed to efficiently bundle several infrastructure projects together. First, a survey was developed and distributed to numerous experts to examine the likelihood of occurrence and relative impact of 25 success factors. Second, the reliability of the survey and its results were assessed and validated using internal and external reliability assessment measures. Third, various statistical analysis tests were performed to examine the alignment between the perspectives of different project stakeholders in relation to the various project-bundling success factors. Fourth, the criticality of the different project-bundling success factors was quantified. Fifth, the success factors were ranked and prioritized to identify the key and most critical factors. The results showed that the most critical project-bundling success factors are (1) having well-defined design features; (2) having a complete project design prior to construction; (3) requiring prior knowledge or experience with similar project size and scope (by the contractor and the design agency); (4) having clear design control requirements; and (5) maintaining constant coordination and communication between all project stakeholders. This study adds to the body of knowledge by quantifying and studying the criticality of different success factors needed to enhance the performance of bundled projects. This study also provides decision-makers with the required knowledge and guidance on the key success factors that influence project-bundling performance. [ABSTRACT FROM AUTHOR]

Digital storytelling combines traditional storytelling with digital technologies. Although recognised as a powerful creative method across many domains, its application in the context of widening participation remains underexplored. This study from Australia sought to investigate teachers' perceptions of the benefits and limitations of using digital storytelling in a widening participation programme. Five partner schools engaged in a programme that involved creating a digital narrative about students' post-school futures. A total of 36 teachers delivering the 10-week programme to students of 13–14 years of age participated in focus groups, with 12 completing post-delivery surveys. Data were analysed qualitatively. The analysis suggested that, according to the teacher participants, digital narrative can be an effective tool for maximising student engagement in widening participation activities. However, emphasis was placed on the impact of the digital divide, which disproportionately affects students from low socioeconomic backgrounds in under-resourced school environments. The study highlights the potential of using digital storytelling in a widening participation context. It also underscores how support for developing teacher and student digital literacies, as well as reliable access to technology and infrastructure, needs to be in place if the digital narrative is to be strongly embedded into future widening participation outreach activities. [ABSTRACT FROM AUTHOR]

The warming climate in high-latitude permafrost regions is leading to permafrost degradation. Estimating seismic wave velocities in permafrost could help predict the geomechanical properties of permafrost and provide information to plan and design resilient civil infrastructure in cold regions. This paper evaluates the performance of seven models when predicting the seismic wave velocities of permafrost statistically; these models are the time-average, Zimmerman and King, Minshull et al., weighted equation, three-phase, Biot–Gassmann theory modified by Lee (BGTL), and Dou et al. models. The data used in the evaluation are from published laboratory and in situ data, which includes 369 data points for joint P and S wave velocities from nine publications and 943 unfrozen water content data points from 12 publications. The unfrozen water content that is used in these models is determined from a modified Dall'Amico's model that is proposed, which is evaluated against six existing unfrozen water content models based on soil temperature. This paper finds that saturated nonsaline permafrost generally shares similar linear trends between the P and S wave velocities, regardless of soil type, porosity, grain size, and temperature. Fitting all existing data, an empirical linear relationship is derived between the P and S wave velocities. Among the seven models evaluated, the Minshull et al. and BGTL models are the most accurate when predicting the seismic velocities of permafrost. Practical Applications: Unfrozen water content and seismic wave velocity models are valuable tools for quantitatively predicting permafrost dynamics and degradation, with practical applications in various engineering areas with permafrost environments. As permafrost thaws due to rising temperatures, these models could be used to guide the quantitative interpretation of geophysical changes in subsurface conditions, assess the potential for ground instability, and predict future permafrost degradation. Unfrozen water content models are used to predict the percentage of unfrozen water within permafrost, which links the changes with permafrost temperature. Unfrozen water content models of permafrost are essential when assessing permafrost thaw, thermal performance, heat transfer processes in permafrost, and the effect of civil infrastructure on permafrost (Chen et al.,). The seismic wave velocity models could help engineers assess the subsurface conditions in permafrost areas; this assessment is crucial for environmental and seismic monitoring, land use planning, infrastructure design and construction, and natural resources exploration. [ABSTRACT FROM AUTHOR]

In recent years, cold regions have seen dramatic changes to the once-stable permafrost layers as a result of global warming. Changes in mean annual temperature directly impact the interaction between frozen ground and infrastructure systems such as piles and pipelines. Adfreeze, the controlling mechanism in a frozen soil–structure interface, is temperature dependent and weakens with rising temperature, while its cohesive nature may eventually change to a frictional one as pore ice turns into water. This paper studies the effect of temperature change on the behavior of steel piles in ice-poor soils subjected to pullout loading, especially during the transient melting state. A series of pullout pile load tests were carried out on a model steel pipe pile embedded in frozen sand and exposed to varying temperatures between −10∘C and 0°C. Strain-controlled pile load tests were performed to observe the pullout load transfer and failure mechanism of the pile, while constant-load tests were carried out to study the creep behavior of the pile exposed to different temperatures. Interface behavior was monitored by strain gauges and thermocouples mounted on the pile surface. Pullout capacities were found to vary linearly with temperature until entering the transient state near pore ice's melting temperatures. Creep rates under constant loads increased with the rise in temperature which eventually resulted in accelerated tertiary creep in the near-melting temperatures. Stress profiles showed severe variation close to the soil surface while changing to an almost average constant value toward the tip of the pile. Investigating the behavioral change during the step-wise temperature rise revealed the characteristics of the transition from a frozen to unfrozen soil–structure interface. [ABSTRACT FROM AUTHOR]

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Penetration Testing (PT), which mimics actual cyber attacks, has become an essential procedure for assessing the security posture of network infrastructures in recent years. Automated PT reduces human labor, increases scalability, and allows for more frequent evaluations. Real-world exploitation still challenges RL-based penetration testing because the agent's many possible actions make it hard for the algorithm to converge. To resolve these shortcomings, a deep learning- model named Adaptive Hunt Tuner algorithm optimized Q-learning based deep Convolutional neural Network (AHT-QCN) is developed for efficient PT. Specifically, the Q-learning employed in this model improves its efficiency by enabling optimal policy learning for decision-making. In addition, the Adaptive Hunt Tuner (AHT) algorithm enhances the model's performance by tuning its parameters with reduced computational time. The experimental outcomes demonstrate that the developed model attains 95.25% accuracy, 97.66% precision, and 93.81% F1 score. [ABSTRACT FROM AUTHOR]

Nowadays, within the built environment, railway infrastructures play a key role to sustain national policies oriented toward promoting sustainable mobility. For this reason, national institutions and infrastructure managers need to increase their awareness in relation to the current and future climate risks on their representative systems. Among climate change impacts, preventing the effects of sea-level rise (SLR) on coastal railway infrastructures is a priority. The first step in the climate change adaptation policy cycle is the development of an ad hoc climate risk assessment. In this view, this research develops a vulnerability and a risk assessment metric to identify the hotspots within a national coastal railway due to the SLR impacts. The proposed methodology required different steps to quantify the SLR projections and the vulnerability characteristics of the assets, in terms of sensitivity and adaptive capacity. The investigated case study is the coastal railway infrastructure in Italy, thanks to an initial approach of co-design participative processes with the national Infrastructure Manager: Rete Ferroviaria Italiana (RFI). The results of this application, although not included in the paper due to confidential reasons imposed by the infrastructure manager — led to a clear identification of the areas and the coastal railway sections which are exposed to high levels of risks and of the places which require priority actions for urgent adaptation in a view of climate proof infrastructures. [ABSTRACT FROM AUTHOR]

Urban communities have long been pivotal in wealth creation and technological innovation. In the contemporary context, their modus operandi is intricately tied to a diverse array of critical infrastructure systems (CISs). These systems—encompassing utilities, transportation, communication, and more—are indispensable for daily life; however, historical lessons underscore that the ever-growing interdependence among modern CISs has sapped their robustness. Furthermore, this vulnerability is compounded by the intensifying natural hazards catalysed by climate change, leaving urban communities with eroded resilience. Against this backdrop, pilot studies have harnessed breakthroughs in artificial intelligence (AI) to chart a new course toward resilient urban communities. This paper illuminates AI-driven resilience by reviewing the latest research in key aspects including (1) the limitation of state-of-the-art resilience assessment frameworks; (2) emergency response as a novel blueprint featuring swift response following catastrophes; (3) efficient loss assessment of CISs using AI algorithms; and (4) machine-learning-enabled autonomous emergency response planning. The remaining challenges and hardships faced on the journey toward resilient urban communities are also discussed. The findings could contribute to the ongoing discourse on enhancing urban resilience in the face of increasingly frequent and destructive climate hazards. [ABSTRACT FROM AUTHOR]

Two dominant driving forces for evolving communication technologies in the current society have been the proliferation of wireless access networks to the Internet and the broadbandization of access and infrastructure networks. Through these evolutions of communication technologies, high-resolution contents are instantly delivered to wireless devices such as mobile phones, wireless tablets, and headsets. Recently, wireless sensor networks, where up to 1000 low-power sensors are wirelessly connected to each other, have been created and connected to the Internet, which presents a new challenge of efficiently coordinating the transmissions of many wireless sensors with minimal transmission overheads. Developing an efficient Medium Access Control (MAC) protocol governing the transmissions of wireless sensor networks is crucial for the success of wireless sensor networks for the realization of the Internet of Things (IoT). In 2023, the node insertion algorithm was proposed to efficiently derive the minimal number of serially connected multipolling sequences of many wireless sensors, by which Access Points (APs) can poll wireless sensors with minimal polling overheads. In this paper, the combined sweeping and jumping method is presented to dramatically enhance the searching performance of the node insertion algorithm. To validate the performance of the combined sweeping and jumping method, simulation results are presented for wireless sensor networks where wireless sensors with varying transmission ranges exist. [ABSTRACT FROM AUTHOR]

The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO2 emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), promoting mobility while reducing dependence on fossil fuels. However, this trend is accompanied by multiple challenges related to EVs' traction systems, storage capacity, chemistry, charging infrastructure, and techniques. Additionally, the requisite energy management technologies and the standards and regulations needed to facilitate the expansion of the EV market present further complexities. This paper provides a comprehensive and up-to-date review of the state of the art concerning EV-related components, including energy storage systems, electric motors, charging topologies, and control techniques. Furthermore, the paper explores each sector's commonly used standards and codes. Through this extensive review, the paper aims to advance knowledge in the field and support the ongoing development and implementation of EV technologies. [ABSTRACT FROM AUTHOR]

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts. [ABSTRACT FROM AUTHOR]

The water supply system (WSS) is an anthropotechnical system whose reliability depends on proper human activity. Research indicates that 75% of WSS failures are due to human errors. The water treatment plant (WTP) is a key element of the WSS. The water treatment process requires human control as the operator. His task is to maintain an appropriate level of reliability and safety for the system by controlling the technological objects. The aim of the work was to assess the reliability of the WTP operator. The paper presents a Human Reliability Assessment (HRA) of the operator of the WTP using the Fuzzy-Bayes CREAM method. The values of the Human Error Probability (HEP) for operators were determined, which are key to carrying out further analyses of the human impact on the reliability and operational safety of anthropotechnical critical infrastructure systems. The HEP value of the water treatment plant operator varies in the range of 0.0005–0.0746 (depending on the technological process). Identification of new threats related to the impact of the human factor on the WSS's functioning and taking them into account in reliability calculations will allow for a better representation of actual operating conditions. [ABSTRACT FROM AUTHOR]

This article aims to study Latvian municipal budget expenditures on transport infrastructure and production in the context of improving the local economy. The authors hypothesize that the state of the local economy determines the comparative priority of municipal budget expenditures on two items. In municipalities with a more developed economy, it is 'transport' rather than 'production' budget expenses that are more likely to improve the local economy, and in municipalities with a less developed economy -- vice versa. The authors tested the hypothesis based on data for 2021 and 2022 (the time after the reform of the territorial-administrative structure of Latvia) for 43 Latvian municipalities using various methods of statistical analysis. The results show that the comparative priorities in budget expenditures of Latvian municipalities are determined not by the state of the local economy but rather by the geographical (or geopolitical/geoeconomic) location of the municipality. As a result, Latvian municipalities are grouped into territorial clusters using the agglomeration effect from the concentration of transport infrastructure or production. Over the past year, there has been a tendency towards 'transport-production' economic restructuring of the territory of Latvia, the reasons for which may be the geopolitical situation in Eastern Europe. [ABSTRACT FROM AUTHOR]

The article underscores the role of the localized socio-economic hierarchies and power asymmetries in mediating advantages and disadvantages associated with the provision of new road infrastructure development. The localized socio-institutional structures, such as the control over the land-use and informality of the postcolonial state, are central in mediating socioeconomic impacts of the road infrastructure. Therefore, the article argues that wider societal impact of infrastructure development can be best analyzed through the localized socio-institutional lens of original institutional economics. The article analyzes the localized socio-economic impact of a mega road infrastructure project on land-use in Sheikhupura district of Punjab, Pakistan. It illustrates that the provision of new roads has incentivized large landowners to extract super rents by transforming erstwhile farmland into commercial real estate housing projects. In this process, landless sharecroppers and small peasants have been evicted/dispossessed from the farmland. In other words, the existing socio-institutional structures have allowed large landholders to use the provision of infrastructure development in their own private interests at the expense of local agriculture and livelihoods of historically marginalized groups. [ABSTRACT FROM AUTHOR]

With the growth in services supplied over the internet, network infrastructure has become more exposed to cyber-attacks, particularly Distributed Denial of Service (DDoS) attacks, which can easily cause the disruption of services. The key factor for fighting against these attacks is the earlier separation and detection of the traffic in networks. In this paper, a novel approach, named Half Autoencoder-Stacked DNNs (HAE-SDNN) model, is proposed. We suggest using a Stacked Deep Neural Networks (SDNN) model. as a deep learning model, in order to detect DDoS attacks. Our approach allows feature selection from a preprocessed dataset using a Half AutoEncoder (HAE), resulting in a final set of important features. These features are subsequently used to train the DNNs that are stacked together by applying Softmax layer to combine their outputs. Experiments were performed on a benchmark cybersecurity dataset, named CICDDoS2017, containing various DDoS attack types. The experimental results demonstrate that the introduced model attained an overall accuracy rate of 99.95%. Moreover, the HAE-SDNN model outperformed existing models, highlighting its superiority in accurately classifying attacks. [ABSTRACT FROM AUTHOR]

Developing and maintaining infrastructure (e.g., roads, airports, water supply, communication networks, power plants, or hospitals) is a priority in a rapidly changing world. However, the gap between infrastructure needs and investments will continue to increase in the coming years, mainly impacting mid- and low-income countries. This problem is aggravated by the fact that traditional long-term planning approaches often lead to under- or over-designing infrastructure with the corresponding investment risks and environmental impacts. This article introduces the "Dynamic Infrastructure Systems" (DIS) concept as a new way to understand infrastructure design and management to support sustainable continuous growth, maintenance, and adaptation. In scenarios of deep uncertainty, infrastructure can best be designed and managed by creating a strategic vision of the future, committing to short-term actions, and establishing a flexible management policy to guide future decisions. This article is motivated by the urgent need to re-think how a key sector is managed and how to make it a positive contributor to sustainability. After the factual and conceptual discussion of the main principles behind DIS, we present a framework for its implementation in practice and discuss barriers and challenges to this vision. [ABSTRACT FROM AUTHOR]