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Name resolution system is an important infrastructure in Information Centric Networking (ICN) network architecture of identifier–locator separation mode. In the Local Name Resolution System (LNMRS), a hierarchical name resolution system for latency-sensitive scenarios; higher-level resolution nodes serve more users and suffer more storage pressure, which causes the problem of unbalanced storage load between layers, and requires inter-layer collaborative storage under the constraint of deterministic service latency characteristics. In this paper, we use the constraints required for inter-layer collaborative resolution to construct an index neighbor structure and perform collaborative storage based on this structure. This method relieves storage pressure on high-level resolution nodes. Experimental results show that the increase of total storage load brought by the proposed method is 57.1% of that by MGreedy algorithm, 8.1% of that by Greedy algorithm, and 0.8% of that by the K-Mediod algorithm when relieving the same storage load for high-level resolution nodes. Meanwhile, deterministic service latency feature is still sustained when our proposed method is used for collaborative resolution. [ABSTRACT FROM AUTHOR]

Based on the data from two field surveys in 2015 and 2022, this paper calculates the weight of values using the entropy weight method and the variation coefficient method, and evaluates risk using the information quantity method. The information quantities of four levels of criteria (hazards, exposure, vulnerability, emergency responses, and capability of recovery) were extracted and inputted into a random forest model. After optimizing the hyperparameters of the random forest using GridSearchCV, the risk assessment was performed again. Finally, the accuracy of the two evaluation results was verified using an ROC curve, and the model with the higher AUC value was selected to create a risk map. Compared with previous studies, this paper considers the factors of emergency responses and recovery capability, which makes the risk assessment more comprehensive. Our findings show that the evaluation results based on the coupling model are more accurate than the evaluation results of the information method, as the coupling model had an AUC value of 0.9329. After considering the indices of emergency responses and capability of recovery, the risk level of the highest-risk area in the study area decreased. [ABSTRACT FROM AUTHOR]

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations' load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations' operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs. [ABSTRACT FROM AUTHOR]

Background: The global concern for the environment and sustainable development goals has led to a growing focus on the increasing activities of city logistics. This research study aims to evaluate the current state of city logistics within the framework of sustainable development and analyze the key factors that impact the efficient management of city logistics in Jordan. Methods: To achieve these research objectives, a quantitative approach utilizing a survey questionnaire was employed. The findings emphasize a significant disparity between current practices in city logistics and desired outcomes, indicating an urgent need for improvement in cost reduction, enhancement of quality of life, and promotion of sustainability. Results: The results reveal that the performance of urban authorities in managing city logistics is influenced by various factors, including regulatory inefficiencies, suboptimal human resource performance, deficiencies in information systems, and challenges related to coordination amongst stakeholders. Conclusions: Consequently, this paper puts forth actionable recommendations for improving city logistics management, which involve amending existing regulations, implementing comprehensive training programs for employees, strengthening infrastructure for information systems, and fostering robust communication channels between urban authorities and stakeholders. [ABSTRACT FROM AUTHOR]

This study provides a viable path to save energy by means of e-commerce development. Taking the national e-commerce demonstration cities (NEDC) pilots policy implemented in China as a quasi-natural experiment, based on the city panel data from 2006 to 2019, this study applies the multi-period difference-in-difference (DID) method to evaluate the effect of NEDC on energy saving in pilot cities. The empirical results suggest that the NEDC policy obviously contributes to energy conservation. The treated cities reduced energy consumption by 14.2% as a result of the implementation of NEDC, relative to the untreated cities. The conclusions remain valid after conducting robustness tests such as placebo test, instrumental variables regression, propensity score matching-difference-in-difference (PSM-DID), and synthetic difference-in-difference (SDID). The NEDC achieves energy-saving effects through technological innovation, industrial restructuring, and economic agglomeration. Furthermore, the heterogeneity analysis indicates that, in cities with high levels of human capital, well-developed information infrastructure, non-resource-based cities, and favorable business environments, the impact of NEDC on energy saving is more significant. Analysis of spatial effects shows that the implementation of NEDC has negative externalities, increasing energy consumption in the surrounding area. In the context of the digital economy, this paper presents new insights on the relationship between e-commerce and energy consumption and provides policy direction for countries looking for energy-saving solutions. [ABSTRACT FROM AUTHOR]

Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field. They can help achieve fast and precise detection, classification, and localization of infrastructure along highways, which can significantly enhance efficiency and ease the burden on road management staff. As the infrastructure along the road is exposed to the outdoors for a long time, it is easily damaged and obscured by objects such as sand and rocks; on the other hand, based on the high resolution of the images taken by Unmanned Aerial Vehicles (UAVs), the variable shooting angles, complex backgrounds, and high percentage of small targets mean the direct use of existing target detection models cannot meet the requirements of practical applications in industry. In addition, there is a lack of large and comprehensive image datasets of infrastructure along highways from UAVs. Based on this, a multi-classification infrastructure detection model combining multi-scale feature fusion and an attention mechanism is proposed. In this paper, the backbone network of the CenterNet model is replaced with ResNet50, and the improved feature fusion part enables the model to generate fine-grained features to improve the detection of small targets; furthermore, the attention mechanism is added to make the network focus more on valuable regions with higher attention weights. As there is no publicly available dataset of infrastructure along highways captured by UAVs, we filter and manually annotate the laboratory-captured highway dataset to generate a highway infrastructure dataset. The experimental results show that the model has a mean Average Precision (mAP) of 86.7%, an improvement of 3.1 percentage points over the baseline model, and the new model performs significantly better than other detection models overall. [ABSTRACT FROM AUTHOR]

Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach. [ABSTRACT FROM AUTHOR]

The mutual identification of spatial objects is a fundamental issue when updating geographic data with other data sets. Representations of spatial objects in different sources may not have the same identifiers, which would unambiguously assign them to each other. Intersections of spatial objects can be used for this purpose, but this does not allow for the detection of possible changes and their quantification. The aim of this paper is to propose a simple, applicable procedure for calculating the shape similarity measure, which should be able to efficiently identify different representations of spatial objects in two data sources, even though they may be changed or generalised. The main result is the aggregated index of shape similarity and instructions for its calculation and implementation. The shape similarity index is based on the calculation of the set similarity, the distance of the boundaries, and the differences in the area, perimeter, and number of the vertices of areal spatial objects. In the case study, the footprints of the building complexes in Dúbravka (part of the city of Bratislava, the capital of Slovakia) are compared using data from OpenStreetMap and INSPIRE (Infrastructure for Spatial Information in Europe) Buildings. A contribution to the quality check of the OpenStreetMap data is then a secondary result. The proposed method can be effectively used in the semi-automatic integration of heterogeneous data sources, updating the data source with other spatial data, or in their quality control. [ABSTRACT FROM AUTHOR]

Maintenance management is assuming an increasingly important role and garnering increased attention in Small and Medium Enterprises (SMEs). However, the difficulty of collecting data and processing information is evident in such contexts. In the current literature, few maintenance maturity models focus on the maintenance information management practices field. Moreover, though the existing models allow for assessing the maturity level, they do not indicate or assist in identifying and defining actions to reach the highest level. Furthermore, these models are not suitable for any type of organisation, as the assessment areas defined are quite generic (high level). For this reason, this paper proposes an innovative model for assessing the maturity level of maintenance management information practices in Small and Medium Enterprises (SMEs). The model provides the organisation with the strengths and weaknesses of their maintenance information management practices. The proposed model allows a clear measure of the maturity of the maintenance information management practices in smaller industrial contexts and provides a customised improvement programme. The model proposed supports small and medium companies to improve the effectiveness and efficiency of their maintenance management information infrastructure. The maturity model developed, in addition to being an assessment tool, provides and supports knowledge on the behaviours and practices for achieving world-class results. [ABSTRACT FROM AUTHOR]

As a typical and special type of urban setting, the university campus usually faces similar challenges as cities raised by high-density inhabitants. The smart campus has been introduced based on the smart city, as concepts, technologies, and solutions to improve livability and energy efficiency. Inhabitants' occupancy in buildings and open spaces on campus is critical to optimize campus management and services. Information about spatial occupancy of campus inhabitants can be produced based on various location-based solutions, such as global navigation satellite systems (GNSS), campus cameras, Bluetooth, and Wi-Fi. As an essential component in campus information infrastructure, Wi-Fi network covers almost the entire university campus and has advantages in collecting locations of campus inhabitants. In this paper, geo-visualization of spatial occupancy of campus inhabitants is designed and implemented using anonymized Wi-Fi network log data. First, 3-dimension building models are reconstructed based on LiDAR point clouds and construction drawings. Then, the Wi-Fi network log data are cleaned and preprocessed. Campus inhabitants' locations are extracted from structural Wi-Fi data. Geo-visualization at room, floor, and building levels is designed and implemented. On a temporal dimension, spatial occupancy can be visualized by each second, minute, hour, or day of the week in 3D buildings. The implementation of the geo-visualization is based on CesiumJS, which offers an interface for 3D-animated visualization and interaction. The research can be used to support university management and educators to implement the smart campus and optimize pedagogical research. [ABSTRACT FROM AUTHOR]

Data sharing and service reuse in the health sector pose significant privacy and security challenges. The European Commission recognizes health data as a unique and cost-effective resource for research, while the OECD emphasizes the need for privacy-protecting data governance systems. In this paper, we propose a novel approach to health data access in a hospital environment, leveraging homomorphic encryption to ensure privacy and secure sharing of medical data among healthcare entities. Our framework establishes a secure environment that enforces GDPR adoption. We present an Information Sharing Infrastructure (ISI) framework that seamlessly integrates artificial intelligence (AI) capabilities for data analysis. Through our implementation, we demonstrate the ease of applying AI algorithms to treated health data within the ISI environment. Evaluating machine learning models, we achieve high accuracies of 96.88% with logistic regression and 97.62% with random forest. To address privacy concerns, our framework incorporates Data Sharing Agreements (DSAs). Data producers and consumers (prosumers) have the flexibility to express their prefearences for sharing and analytics operations. Data-centric policy enforcement mechanisms ensure compliance and privacy preservation. In summary, our comprehensive framework combines homomorphic encryption, secure data sharing, and AI-driven analytics. By fostering collaboration and knowledge creation in a secure environment, our approach contributes to the advancement of medical research and improves healthcare outcomes. A real case application was implemented between Portuguese hospitals and universities for this data sharing. [ABSTRACT FROM AUTHOR]

This paper investigates the potential link between changes in NASA's Black Marble VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500 m Linear Lat Lon Grid (VNP46A2) nighttime lights product (NTL) and human dynamics, particularly population counts and changes at a variety of spatial and temporal scales. We conducted analyses in four case studies at varying resolutions to explore the relationship of NTL data for population studies, including demographic research, disaster mitigation and adaptation planning, and infrastructure development. The analyses were conducted using different administrative geographies, including a refugee camp, a subnational region, and a country. We compared changes in population counts, density, migration, and displacement against changes in daily, weekly, monthly, and annual NTL values. Our case study results demonstrate that out-migration does not always lead to a decrease in NTL. We found that rural population decline did not correspond to a decrease in NTL. Despite significant out-migration in many rural areas NTL remained largely unchanged. NTL provided essential information on infrastructure damage in the short-term aftermath of this disaster; however, NTL alone was not correlated to the location of displaced individuals. Through news reports, we were able to corroborate the NTL changes to downtimes of the electrical systems. Monthly NTL averages were highly correlated to population counts, but a pixel-level analysis showed that the changes in NTL were primarily attributed to economic diversification. In summary, NTL is the product of several factors including demographic, environmental, economic, and political forces that shape electricity infrastructure, and we suggest that NTL data must first be parameterized with ancillary ground-level information in order to be effectively applied to population models. [ABSTRACT FROM AUTHOR]

With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push–pull communication mode to obtain real-time traffic conditions and charging infrastructure information (i.e., charging stations and energy segments). We also analyze the selection of relay vehicles in multi-hop communication routing, considering factors such as link stability, vehicle distance, and reputation values. Furthermore, we formulate a dynamic optimization problem based on real-time information to minimize travel and charging costs. Our proposed algorithm enables AEVs to obtain charging services from charging stations and conduct dynamic wireless charging via energy segments. We present a dynamic real-time A* algorithm to solve the path-optimization problem and a dynamic real-time charging selection algorithm based on dynamic path optimization when the state of charge is lower than the charging threshold. Extensive simulations demonstrate that the proposed joint push-pull communication mode can provide vehicles the up-to-date information and the developed optimization algorithms effectively reduce travel and charging costs. [ABSTRACT FROM AUTHOR]

The advancements in communication speeds have enabled the centralized financial market to be faster and more complex than ever. The speed of the order execution has become exponentially faster when compared to the early days of electronic markets. Though the transaction speed has increased, the underlying architecture or models behind the markets have remained the same. These models come with their own disadvantages. The disadvantages are usually faced by non-institutional or small traders. The bigger players, such as financial institutions, have an advantage over smaller players because of factors such as information asymmetry and access to better infrastructure, which give them an advantage in terms of the speed of execution. This makes the centralized stock market an uneven playing field. This paper discusses the limitations of centralized financial markets, particularly the disadvantage faced by non-institutional or small traders due to information asymmetry and better infrastructure access by financial institutions. The authors propose the usage of blockchain technology and the data highway protocol to create a decentralized stock exchange that can potentially eliminate these disadvantages. The data highway protocol is used to generate new blocks with a flexible finality condition that allows for the consensus mechanism to configure security thresholds more freely. The proposed framework is compared with existing frameworks to confirm its effectiveness and identify areas that require improvement. The evaluation of the proposed approach showed that the improved highway protocol boosted the transaction rate compared to the other two mechanisms (PoS and PoW). Specifically, the transaction rate of the proposed model was found to be 2.2 times higher than that of PoS and 12 times higher than that of the PoW consensus model. [ABSTRACT FROM AUTHOR]

Vehicle trajectory prediction is an important decision-making and planning basis for autonomous driving systems that enables them to drive safely and efficiently. To accurately predict vehicle trajectories, the complex representations and dynamic interactions among the elements in a traffic scene are abstracted and modelled. This paper presents vehicle–road relationships net, a deep learning network that extracts features from vehicle–road relationships and models the effects of traffic environments on vehicles. The introduction of geographic highway information and the calculation of spatiotemporal distances with a reference not only unify heterogeneous vehicle–road relationship representations into a time series vector but also reduce the requirement for sensing transient changes in the surrounding area. A hierarchical long short-term memory network extracts environmental features from two perspectives—social interactions and road constraints—and predicts the future trajectories of vehicles by their manoeuvre categories. Accordingly, vehicle–road relationships net fully exploits the contributions of historical trajectories and integrates the effects of road constraints to achieve performance that is comparable to or better than that of state-of-the-art methods on the next-generation simulation dataset. [ABSTRACT FROM AUTHOR]

Preventive maintenance practices have been proven to reduce maintenance costs in many industries. In the mining industry, preventive maintenance is the main form of maintenance, especially for mobile equipment. With the increase of sensor data and the installation of wireless infrastructure within underground mines, predictive maintenance practices are beginning to be applied to the mining equipment maintenance process. However, for the transition from preventive to predictive maintenance to succeed, researchers must first understand the maintenance process implemented in mines. In this paper, we conducted interviews with 15 maintenance experts from 7 mining sites (6 gold, 1 diamond) across East-Canada to investigate the maintenance planning process currently implemented in Canadian mines. We documented experts' feedback on the process, their expectations regarding the introduction of predictive maintenance in mining, and the usability of existing computerized maintenance management software (CMMS). From our results, we compiled a summary of actual maintenance practices and showed how they differ from theoretical practices. Finally, we list the Key Performance Indicators (KPIs) relevant for maintenance planning and user requirements to improve the usability of CMMS. [ABSTRACT FROM AUTHOR]

This paper describes a new information system developed as part of the Portuguese R&D project DECIdE. The project aimed at the development of a platform for infrastructure asset management tailored to Portuguese water utilities. The platform allows the integration of different data from several information systems of the water utilities and includes several tools for the performance assessment of the water supply systems in terms of water losses, energy efficiency and quality of service (i.e., water and energy balances and key performance indicators). The developed platform was tested with data from five small to medium size Portuguese water utilities with different maturity levels in terms of technological and human resources. The obtained results are very promising because the platform allows for periodic system performance assessment which constitutes an important part of the infrastructure asset management for small and medium-sized water utilities. [ABSTRACT FROM AUTHOR]

North Korean cyber-attack groups such as Kimsuky, Lazarus, Andariel, and Venus 121 continue to attempt spear-phishing APT attacks that exploit social issues, including COVID-19. Thus, along with the worldwide pandemic of COVID-19, related threats also persist in cyberspace. In January 2022, a hacking attack, presumed to be Kimsuky, a North Korean cyber-attack group, intending to steal research data related to COVID-19. The problem is that the activities of cyber-attack groups are continuously increasing, and it is difficult to accurately identify cyber-attack groups and attack origins only with limited analysis information. To solve this problem, it is necessary to expand the scope of data analysis by using BGP archive data. It is necessary to combine infrastructure and network information to draw correlations and to be able to classify infrastructure by attack group very accurately. Network-based infrastructure analysis is required in the fragmentary host area, such as malware or system logs. This paper studied cyber ISR and BGP and a case study of cyber ISR visualization for situational awareness, hacking trends of North Korean cyber-attack groups, and cyber-attack tracking. Through related research, we estimated the origin of the attack by analyzing hacking cases through cyber intelligence-based profiling techniques and correlation analysis using BGP archive data. Based on the analysis results, we propose an implementation of the cyber ISR visualization method based on BGP archive data. Future research will include a connection with research on a cyber command-and-control system, a study on the cyber battlefield area, cyber ISR, and a traceback visualization model for the origin of the attack. The final R&D goal is to develop an AI-based cyber-attack group automatic identification and attack-origin tracking platform by analyzing cyber-attack behavior and infrastructure lifecycle. [ABSTRACT FROM AUTHOR]

Cybersecurity risk is among the top risks that every organization must consider and manage, especially during this time wherein technology has become an integral part of our lives; however, there is no efficient and simplified measurement method that organizations or regulators could use, as frequently as they need, to evaluate and compare the outcome of cybersecurity efforts that have been put in place. Consequently, this has resulted in an absence of critical data for cybersecurity improvement. This research proposes a Cyber Trust Index (CTI), a novel and simplified framework for evaluating, benchmarking, and improving organizations' cybersecurity performance. Methods: The researchers analyzed prominent scientific research papers and widely used security standards to develop baseline security controls that serve as a measurement foundation. Then, they identified Control Enablers and Capability Tiers that were used as base measures and measurement methods. The CTI framework was evaluated by experts and tested with 35 organizations from the critical information infrastructure (CII) sector, as well as other generic sectors, in Thailand to confirm its validity and reliability in real organization settings and identify the priorities and factors that can contribute to better cybersecurity performance. Results: The CTI has two key elements: the baseline controls and rating methods. The baseline controls comprise 12 dimensions, 25 clusters, and 70 controls. The rating methods utilize five control enablers and five capability tiers to compute scores. A binary questionnaire is used to capture data for the rating process. Based on a statistical analysis of CTI results from 35 pilot organizations, 28.57% are in the beginner group with high-risk exposure, 31.43% are in the leader group with low-risk exposure, and 40% of organizations are in between (the intermediate and advanced groups). Two key factors distinguish between the beginner and leader groups: (1) an internal factor, which is the Control Enablers; and (2) an external factor, which is the influence of a cyber regulating body. Our study confirms that Control Enablers in higher Tiers will help organizations achieve better cybersecurity performance (R = 0.98021) and highlights the significance of cyber regulating bodies by showing a shear difference of 197.53% in cyber performance between highly regulated and low-regulated industries. Conclusions: This research reveals key insights into the importance of Control Enablers, which are the internal factors that organizations must leverage to drive better cybersecurity performance, and the positive return on enforcement, which emphasizes the need for cyber regulating bodies. The CTI framework has proven to be valid and efficient for measuring cybersecurity performance. At the very least, a step-wise roadmap is provided for organizations and regulators to adopt and adapt the CTI framework for their cybersecurity measurement and improvement mission. [ABSTRACT FROM AUTHOR]

Benefiting from the large-scale commercial use of 5G, smart campuses have attracted increasing research attention in recent years and are expected to revolutionize traditional campus activities. However, there are some obstacles that hinder the practical deployment of MEC (multi-access edge computing). First, traditional information infrastructures on campus cannot support latency-sensitive and computing-intensive smart applications, such as AR/VR, live interactive lectures and digital twin experiments. In addition, the mixture of old and new applications, isolated data islands and heterogeneous equipment management introduce more challenges. Moreover, the existing MEC framework proposed by ETSI and 3GPP cannot meet the specific deployment requirements of smart campuses, e.g., educational data security, real-time interactive applications, heterogeneous connections, and others. In this paper, we propose a 5G-based architecture for smart education information infrastructure; a new dedicated cloud architecture eMEC (educational multi-access edge computing) is defined. It consists of a UGW (universal access gateway) and an eMEP (educational multi-access edge computing platform), making it possible to satisfy education-specific requirements and long-term evolution. Furthermore, we implement the framework and conduct real-world field tests for eMEC in a university campus. Based on the framework and practical field tests, we also conduct a measurement study to unveil the spatial-temporal characteristics of mobile users in the smart campus and discuss exploiting them for better network performance. The experimental results show that the system achieves satisfactory performance in terms of both throughput and latency. [ABSTRACT FROM AUTHOR]

This paper proposes a tool for objective evaluation of organisations' information and communication technologies (ICT) infrastructure development level: Composite Indicator of the Organisational Information and Communication Infrastructure—CIOICTI. Based on separate research in 68 different companies, it aggregates hardware, software and humanware aspects of ICT development in organisations, especially valuing companies with synergetic aspects. It consists of six linear and non-linear indicator components, each designed to evaluate one specific aspect of ICT development, based on the weighted sum principle. The proposed CIOICTI examines ICT infrastructure as a necessary but not sufficient condition for successful ICT usage in business performance improvement. The obtained CIOICTI values are summarised in the distribution and tested for normality. Results are tested on the data from observed companies, averaged according to company size and industry and benchmarked. The proposed composite indicator can be used as an objective tool for researchers and practitioners to assess the level of ICT development for benchmarking with other companies, planning organisational changes or assessing infrastructural requirements for planning and implementation of ICT related projects in a business organisation. [ABSTRACT FROM AUTHOR]

The duplication of semantic data modelled in different disciplines poses interoperability challenges. Information models that benefit from the application of standards that enable data reusability and prevent redundancy and misuse should be compatible, particularly when different systems are used in the same domain-specific area, as in the case of public transportation. Transportation information models, leading and providing reliable and robust public transport services, can nowadays be enriched with information found in building information models devoted to transportation infrastructure. Therefore, in the present paper, a mapping between the standards and technical specifications that support each modelling approach is explored, analysed, and aligned to maximise the best out of the current common practices used. The information from the Industry Foundation Classes IFC specification-based models complements the conceptual and physical transportation models, where Transmodel and NeTEx are well-established, with accurate (as-built) detailed characteristics of the transportation infrastructure (stop places, quays, entrances, access spaces and equipment as ramps, staircases, and escalators). The extraction of topological information stored in IFC models allows for navigation-network extraction, information about spaces, and passages between spaces. A specific public transport case study focused on a train station is detailed to better corroborate and illustrate the advantages and uses of compatible and complementing modelling approaches. As building-information-modelling awareness and use is growing, the validated mapping approach can benefit from the future application of automation procedures. [ABSTRACT FROM AUTHOR]

One of the key elements to consider in business management is the management of IT (Information Technology) departments by implementing processes as described in the ITIL (Information Technology Infrastructure Library) standard. This is particularly important in industries that are not directly related to ICT (Information and Communication Technologies), such as the tourism industry. In this paper, we present a methodology to sequence the implementation of the ITIL processes in any company to support its development. The methodology is based on an optimization algorithm and the information extracted from a survey. The optimal sequence is built from this information and from the information of the restrictions imposed by the company that implements the ITIL processes. We applied the methodology to a company in the tourism industry. The sequence obtained shows the need to implement operative processes (initial positions in the sequence), and afterwards strategic processes (final positions in the sequence). A comparison with other authors' proposals shows differences in the order of processes proposed by this methodology. The main conclusion is that it is possible to use the information of companies to efficiently generate an optimal sequence of ITIL processes which enhances their management; this sequence is unique for every company that wants to implement ITIL in the tourism industry, and it is independent from the proposals of other authors who designed generic/non-optimal sequences. [ABSTRACT FROM AUTHOR]

In recent decades, climate change and a shortage of resources have brought about the need for technology in agriculture. Farmers have been forced to use information and innovation in communication in order to enhance production efficiency and crop resilience. Systems engineering and information infrastructure based on the Internet of Things (IoT) are the main novel approaches that have generated growing interest. In agriculture, IoT solutions according to the challenges for Industry 4.0 can be applied to greenhouses. Greenhouses are protected environments in which best plant growth can be achieved. IoT for smart greenhouses relates to sensors, devices, and information and communication infrastructure for real-time monitoring and data collection and processing, in order to efficiently control indoor parameters such as exposure to light, ventilation, humidity, temperature, and carbon dioxide level. This paper presents the current state of the art in the IoT-based applications to smart greenhouses, underlining benefits and opportunities of this technology in the agriculture environment. [ABSTRACT FROM AUTHOR]

The Internet has rapidly grown into a distributed and collaborative network with over one billion users, e.g., the Internet of Things (IoT). The future Internet will become the core of the next information infrastructure in regard to computation and communication, being capable of extensibility, survivability, mobility, and adaptability. However, with the increasing complexity of the future Internet and boost in information sharing, there is a threat to such infrastructure in the aspects of security, trust, and privacy. This editorial discusses the state-of-the-art advancements in information and the future internet. [ABSTRACT FROM AUTHOR]

The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of edge/fog computing applications. This paper proposes a novel edge-computing application called explicit caching, which stores selective contents at BSs and exposes such contents to local users for interactive browsing and download. We formulate the explicit caching problem as a joint content recommendation, caching, and delivery problem, which aims to maximize the expected user quality-of-experience (QoE) with varying degrees of cross-layer sensing capability. Optimal and effective heuristic algorithms are presented to solve the problem. The theoretical performance bounds of the explicit caching system are derived in simplified scenarios. The impacts of cache storage space, BS backhaul capacity, cross-layer information, and user mobility on the system performance are simulated and discussed in realistic scenarios. Results suggest that, compared with conventional implicit caching schemes, explicit caching can better exploit the mobile edge network infrastructure for personalized content dissemination. [ABSTRACT FROM AUTHOR]

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%. [ABSTRACT FROM AUTHOR]

This paper proposes a performance-based resistance deterioration model that reflects the site environment and inspection data for highway bridges. Traffic characteristics and corrosive environment are considered as the site environment. The corrosive environments and traffic characteristics are classified into three categories, namely mild, normal, and severe for the former and light, normal, and heavy for the latter. The deterioration of the resistance capacity due to corrosive environments is evaluated considering both the service period and the concrete crack widths in the pre-stressed concrete (PSC) girder and the reinforced concrete (RC) slab. The deterioration model of the resistance capacity is calibrated by combining the performance degradation model. The performance degradation model is also calibrated using previous bridge inspection results from the standard performance degradation model, which has been developed based on the large amount of data available on many pre-stressed concrete-I (PSC-I) type girder bridges. The developed performance-based resistance deterioration model is used to evaluate the reliability of a bridge in the future. The results show that the performance inspection outcomes, either based on the current status or lifetime inspection history, are critical in estimating the future degradation of the reliability level, inherent to the bridge. [ABSTRACT FROM AUTHOR]

This study assessed the applicability of geolocation data provided by public Wi-Fi infrastructures as information sources that can contribute to urban planning and management. We focused particularly on modeling and monitoring real-time mobility and congestion using geolocation capabilities of Wi-Fi public networks in Smart cities. The proposed methodology combines a detailed geographic analysis of the space with high-frequency indicators generated from network data. This study emphasizes the importance of Wi-Fi infrastructures as noninvasive monitoring systems, and describes how network data can be applied to generate useful indicators for urban planning and management. The methodology was empirically implemented in the city of Palma (Balearic Islands, Spain), where the social distance level was measured to identify conflicting areas. We demonstrate how the proposed solution can estimate pedestrians' density efficiently and precisely through high-frequency monitoring (5 min or less) and the construction of comprehensive indicators. In this context, we suggest several public policies that can be implemented by using this methodological approach to monitor dynamic patterns of pedestrian mobility, especially during health crises or during high tourist seasons. [ABSTRACT FROM AUTHOR]

Recently, research interest in the field of infrastructure attack and defense scenarios has increased. Numerous methods have been proposed for studying strategy interactions that combine complex network theory and game theory. However, the unavoidable effect of constrained strategies in complex situations has not been considered in previous studies. This study introduces a novel approach to analyzing these interactions by including the effects of constrained strategies, a factor often neglected in traditional analyses. First, we introduce the rule of constraints on strategies, which depends on the average distance between selected nodes. As the average distance increases, the probability of choosing the corresponding strategy decreases. Second, we establish an attacker–defender game model with constrained strategies based on the above rule and using information theory to evaluate the uncertainty of these strategies. Finally, we present a method for solving this problem and conduct experiments based on a target network. The results highlight the unique characteristics of the Nash equilibrium when setting constraints, as these constraints influence decision makers' Nash equilibria. When considering the constrained strategies, both the attacker and the defender tend to select strategies with lower average distances. The effect of the constraints on their strategies becomes less apparent as the number of attackable or defendable nodes increases. This research advances the field by introducing a novel framework for examining strategic interactions in infrastructure defense and attack scenarios. By incorporating strategy constraints, our work offers a new perspective on the critical area of infrastructure security. [ABSTRACT FROM AUTHOR]

Definition: In the context of open science, universities, research-performing and funding organizations and authorities worldwide are moving towards more responsible research assessment (RRA). In 2022, the European Coalition for Advancing Research Assessment (CoARA) published an agreement with ten commitments, including the recognition of the "diversity of contributions to, and careers in, research", the "focus on qualitative evaluation for which peer review is central, supported by responsible use of quantitative indicators", and the "abandon (of) inappropriate uses in research assessment of journal- and publication-based metrics". Research assessment (RA) is essential for research of the highest quality. The transformation of assessment indicators and procedures directly affects the underlying research information management infrastructures (also called current research information systems) which collect and store metadata on research activities and outputs. This entry investigates the impact of RRA on these systems, on their development and implementation, their data model and governance, including digital ethics. [ABSTRACT FROM AUTHOR]

Recently, the rapid growth of technology and the increased teleworking due to the COVID-19 outbreak have motivated cyber attackers to advance their skills and develop new sophisticated methods, e.g., Advanced Persistent Threat (APT) attacks, to leverage their cybercriminal capabilities. They compromise interconnected Critical Information Infrastructures (CIIs) (e.g., Supervisory Control and Data Acquisition (SCADA) systems) by exploiting a series of vulnerabilities and launching multiple attacks. In this context, industry players need to increase their knowledge on the security of the CIs they operate and further explore the technical aspects of cyber-attacks, e.g., attack's course, vulnerabilities exploitability, attacker's behavior, and location. Several research papers address vulnerability chain discovery techniques. Nevertheless, most of them do not focus on developing attack graphs based on incident analysis. This paper proposes an attack simulation and evidence chains generation model which computes all possible attack paths associated with specific, confirmed security events. The model considers various attack patterns through simulation experiments to estimate how an attacker has moved inside an organization to perform an intrusion. It analyzes artifacts, e.g., Indicators of Compomise (IoCs), and any other incident-related information from various sources, e.g., log files, which are evidence of cyber-attacks on a system or network. [ABSTRACT FROM AUTHOR]

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners' physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners' smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner. [ABSTRACT FROM AUTHOR]

Public water services are still failing rural Tanzanians. Emboldened by advances in information communication technologies, the Ministry of Water has been developing computing, financial and administrative technologies to update and visualise the status of rural water points. This amalgam of technologies marks the emergence of an information infrastructure for rural water governance. The information infrastructure will enable the ministry to "see" the functionality status of all rural water points and to plan and budget for their repair and maintenance. In this paper, we examine three administrative technologies, which aim to standardise the functionality status of water points, and to prescribe how the information flows within the government hierarchy, and who is a legitimate recipient of this information. We analyze qualitative data, collected over a period of four years, in the framework of an interdisciplinary research program, funded by the Netherlands Organisation for Scientific Research--Science for Global Development (NWO-Wotro). In contrast to other researchers who study how information infrastructure evolves over time, we study what infrastructure evolution reveals about water governance. Our analysis of the practices of participants in rural water governance reveals tensions between formal and informal processes, which affect rural water services negatively. [ABSTRACT FROM AUTHOR]

This review paper presents a framework to evaluate the artificial intelligence (AI) readiness for the healthcare sector in developing countries: a combination of adequate technical or technological expertise, financial sustainability, and socio-political commitment embedded in a healthy psycho-cultural context could bring about the smooth transitioning toward an AI-powered healthcare sector. Taking the Vietnamese healthcare sector as a case study, this paper attempts to clarify the negative and positive influencers. With only about 1500 publications about AI from 1998 to 2017 according to the latest Elsevier AI report, Vietnamese physicians are still capable of applying the state-of-the-art AI techniques in their research. However, a deeper look at the funding sources suggests a lack of socio-political commitment, hence the financial sustainability, to advance the field. The AI readiness in Vietnam's healthcare also suffers from the unprepared information infrastructure—using text mining for the official annual reports from 2012 to 2016 of the Ministry of Health, the paper found that the frequency of the word "database" actually decreases from 2012 to 2016, and the word has a high probability to accompany words such as "lacking", "standardizing", "inefficient", and "inaccurate." Finally, manifestations of psycho-cultural elements such as the public's mistaken views on AI or the non-transparent, inflexible and redundant of Vietnamese organizational structures can impede the transition to an AI-powered healthcare sector. [ABSTRACT FROM AUTHOR]

Geographical Information Systems (GISs) in architecture were initially limited to regional and urban development applications. Over recent years its potential has been recognized and its use has evolved to address urban planning and architectural heritage management subjects. Nevertheless, evidence shows that its use in architecture teaching is scarce and uneven. Directive 2007/2/EC, establishing the infrastructure for spatial information in the European Community (EU), is, in this way, an opportunity to develop a greater knowledge and application of GIS in the framework of higher education. In architecture, this can be achieved by problem solving based on real case scenarios, which can benefit from GIS-based techniques and analysis capabilities. In this paper, the authors aim to present a review of the use of GIS in teaching and research in architecture to assess its level in different European programmes. Experiences from three European universities (University of Seville, Spain, University of Beira Interior, Portugal, and Technical University of Crete, Greece), which are among the few in their respective countries that promote the inclusion of GIS in architectural education, particularly in the fields of urban and regional planning and architectural heritage, are compared and framed within the European scenario. The paper concludes with a reflection on the three universities' practice compared to the leading European architecture programmes listed in the main international university rankings. Main trends of future evolution on the use of GIS in architecture teaching are also presented. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms. [ABSTRACT FROM AUTHOR]

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ± 0.016 m−1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle's position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs. [ABSTRACT FROM AUTHOR]

Landslides recurrently cause severe damage and, in some cases, the full disruption of many highways in mountainous areas, which can last from a few days to even months. Thus, there is a high demand for monitoring tools and precipitation data to support highway alignment selections before construction. In this study, we proposed a new system highway alignment selection method based on coherent scatter InSAR (CSI) and ~1 km high-spatial-resolution precipitation (HSRP) analysis. Prior to the CSI, we calculated and analyzed the feasibility of Sentinel-1A ascending and descending data. To illustrate the performance of the CSI, CSI and SBAS–InSAR were both utilized to monitor 80 slow-moving landslides, which were identified by optical remote-sensing interpretation and field investigation, along the Barkam–Kangting Highway Corridor (BKHC) in southwestern China, relying on 56 Sentinel-1A descending images from September 2019 to September 2021. The results reveal that CSI has clearer deformation signals and more measurement points (MPs) than SBAS-InSAR. And the maximum cumulative displacements and rates of the landslides reach −75 mm and −64 mm/year within the monitoring period (CSI results), respectively. Furthermore, the rates of the landslides near the Jinchuan River are higher than those of the landslides far from the river. Subsequently, to optimize the highway alignment selection, we analyzed the spatiotemporal evolution characteristics of feature points on a typical landslide by combining the −1 km HSRP, which was calculated from the 30′ Climatic Research Unit (CRU) time-series datasets, with the climatology datasets of WorldClim using delta spatial downscaling. The analysis shows that the sliding rates of landslides augment from the back edge to the tongue because of fluvial erosion and that accelerated sliding is highly related to the intense precipitation between April and September each year (ASP). Consequently, three solution types were established in our method by setting thresholds for the deformation rates and ASPs of every landslide. Afterward, the risk-optimal alignment selection of the BKHC was finalized according to the solution types and consideration of the construction's possible impacts. Ultimately, the major problems and challenges for our method were discussed, and conclusions were given. [ABSTRACT FROM AUTHOR]

Building information modelling (BIM) is increasingly appropriate for infrastructure projects, and in particular for transport infrastructure. It is a digital solution that integrates the practices of the construction industry in facility management during the whole life cycle. This integration is possible through a single tool, which is the 3D digital model. Nevertheless, BIM standards, such as industry foundation classes, are still in the pipeline for infrastructure management. These standards do not fully meet the requirements of operation and maintenance of transport infrastructure. This paper shows how BIM could be implemented to address issues related to the operation and maintenance phase for transport infrastructure management. For this purpose, a new ontological approach, called Industry Foundation Classes for Operation and Maintenance of Infrastructures (IFCInfra4OM), is detailed. This ontology aims to standardise the use of building information modelling for operation and maintenance in road infrastructures. To highlight the interest of the proposed ontological approach, a building information model of a section on the A7 Agadir–Marrakech Highway in Morocco is produced according to IFCInfra4OM. The methodology is presented. The results obtained, including the IFCInfra4OM data model, are submitted. In the last section, an overview of the IFC extension approach is submitted. [ABSTRACT FROM AUTHOR]

The most important standard in technology services management is the Information Technology Infrastructure Library (ITIL). The literature review developed shows that one of the most important questions to answer is finding the sequence of processes to be implemented, mainly in small companies with few resources. The purpose of this paper is to show a methodology that defines an optimal specific sequence of processes for each small company depending on internal and external parameters. The main contribution of this paper is a proven methodology to obtain a particular sequence of ITIL processes specifically adapted to each company, based on a mathematical and statistical model that uses data from a web survey. Its application generates an optimal sequence of ITIL processes. The methodology has been applied with successful results in a real case, and it shows specific benefits over the previous approaches. The main learning objective of this research is a proven method to obtain an optimal sequence of processes for the implementation of ITIL in small companies. Finally, some future works are presented. [ABSTRACT FROM AUTHOR]

In the last two decades, the concept of ecological citizenship has become a recurrent theme in both popular and academic discussions. Discussions around the prospects of, and limitations to, ecological citizenship have mostly focused on the idea of political agency and the civic responsibility of individuals in relation to their environments, with an emphasis on environmental justice and sustainability. However, the current scholarship has yet to adequately characterize its conceptual bases and empirical applications from an information perspective. Therefore, this paper provides an overview of citizenship studies and infrastructure studies for developing more nuanced understanding(s) of epistemological models for ecological citizenship in our networked world. Drawing on the literature on information infrastructure, this paper then proposes a conceptual framework to understand ecological citizenship as constituted both discursively and techno-materially through neoliberal, anthropocentric informational infrastructures. [ABSTRACT FROM AUTHOR]

Prefabricated building holds promise for quality, efficiency, and sustainability when compared to traditional techniques. However, realizing prefabricated building work hinges on strengthening supply chain resilience. This research assesses interdependent risks undermining prefab network continuity during disruption. Questionnaire data from industry experts informed a structural equation model quantifying pathways between component production, construction, information, and other uncertainties. Findings confirm that project delays can be traced to manufacturing and on-site risks, with information gaps broadly propagating impacts. Meanwhile, organizational risks have an insignificant influence, suggesting partnership networks readily reconfigure around operational contingencies. Robust information infrastructures and coordination, therefore, offer crucial leverage. Accordingly, a multidimensional resilience enhancement strategy is formulated, prioritizing supply chain transparency, digital integration, inventory buffering, contingencies planning, and transportation flexibility. Our mixed-methods approach advances the construction literature by demonstrating the applicability of structural equation modeling for diagnostic resilience analytics. Industry leaders also gain actionable, evidence-based guidance on strategic investments to stabilize project flows. This dual theoretical and practical contribution underscores the versatility of tailored statistical assessments in furthering construction innovation objectives within complex, uncertain environments. [ABSTRACT FROM AUTHOR]

Regulators and actors in international trade are facing a difficult challenge of increasing control and security while at the same time lowering the administrative burden for traders. As a tentative response, the European Commission has introduced the concept of "trusted traders": certified traders that are in control of their business. Trusted traders are entitled to trade facilitations, faster border crossing, and fewer physical inspections. To enable the use of trusted traders, changes are required to the information infrastructure (II) of international trade. This article complements existing works on e-Government interoperability by a theoretically driven approach with theoretical development of the II concept and how II can be modified as additional focus. Following the principles of IS design research, this paper presents a design proposition for the II of international trade. Using theories of II development and change as kernel theory, our proposition presents a redesign of IT, organizational, human, and change and collaboration elements. The design proposition was evaluated and verified with proof of concept installations and a stakeholder value assessment. The paper contributes to the domain of II and how they may be changed to meet new requirements. [ABSTRACT FROM AUTHOR]

This paper presents an IT infrastructure based on an event driven architecture with the objective to decrease the turnaround time for urban planning. Most urban planning takes a long time, not only to get all stakeholders involved, but also to assess various scenarios on several aspects according to (inter)national laws and regulations. By supporting urban planning with IT, the turnaround time is expected to decrease dramatically and supports the possibility to explore more scenarios without an extra burden on duration and personnel. The IT infrastructure developed integrates various data resources and calculation models for analyzing different scenarios. An event service bus and a data store for sharing data between computational models is the basis of the infrastructure. Various computational models are able to read and write to the data store and publish and subscribe to events. Based on a case study, the paper illustrates that indeed decision making is improved leading to better and possibly cheaper urban plans for all relevant stakeholders. The paper will also show that a closed environment, as currently available, is one of the major thresholds for acceptance by end-users. Endusers would like to apply their computational models, which can not yet be plugged into the infrastructure. Further extensions are foreseen in applying open standards and open link data for data collection and sharing. Thus, interoperability is a prerequisite for our IT infrastructure. [ABSTRACT FROM AUTHOR]

Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high complexity of mountainous road scene modeling and low visualisation efficiency. It is challenging to construct the digital twin scenarios efficiently for mountain highways. To solve this problem, this article proposes a knowledge-guided fusion expression method for digital twin scenes of mountain highways. First, we explore the expression features and interrelationships of mountain highway scenes to establish the knowledge graph of mountain highway scenes. Second, by utilizing scene knowledge to construct spatial semantic constraint rules, we achieve efficient fusion modeling of basic geographic scenes and dynamic and static ancillary facilities, thereby reducing the complexity of scene modeling. Finally, a multi-level visualisation publishing scheme is established to improve the efficiency of scene visualisation. On this basis, a prototype system is developed, and case experimental analysis is conducted to validate the research. The results of the experiment indicate that the suggested method can accomplish the fusion modelling of mountain highway scenes through knowledge guidance and semantic constraints. Moreover, the construction time for the model fusion is less than 5.7 ms; meanwhile, the dynamic drawing efficiency of the scene is maintained above 60 FPS. Thus, the construction of twinned scenes can be achieved quickly and efficiently, the effect of replicating reality with virtuality is accomplished, and the informatisation management capacity of mountain highways is enhanced. [ABSTRACT FROM AUTHOR]

In the rural and geographically remote regions of Taiwan, the high cost of establishing information infrastructure has resulted in significantly lower internet penetration and usage rates compared with urban areas. To address the network demands in such remote mountainous areas, the deployment of multiple mobile base stations has become essential. However, the wireless implementation of base stations can lead to signal interference issues. This research aims to enhance the signal reception capabilities of end-user devices by utilizing intelligent directional antennas. This study employs five directional smart antennas, each of which can be independently adjusted to be active or inactive. Unlike traditional omnidirectional antennas that cause interference in overlapping coverage areas for end-user devices, our proposed adaptive directional antenna algorithm optimizes energy consumption by selectively activating directional antennas and concurrently reduces signal interference problems for end-user devices. The results of this research offer valuable insights for improving network connectivity and efficiency in remote and underserved areas. Through experimental simulations conducted in an environment with 10 base stations per square kilometer, the utilization of smart antennas, as opposed to omnidirectional antennas, results in a significant improvement of 33.8% in signal coverage. [ABSTRACT FROM AUTHOR]

Due to higher penetration of renewable energy sources, grid reinforcements, and the utilization of local voltage control strategies, a significant change in the reactive power behavior as well as an increased demand for additional reactive power flexibility in the German power system can be predicted. In this paper, an application-oriented reactive power management concept is proposed, which allows distribution system operators (DSO) to enable a certain amount of reactive power flexibility at the grid interfaces while supporting voltage imitations in the grid. To evaluate its feasibility, the proposed concept is applied for real medium voltage grids in the south of Germany and is investigated comprehensively in different case studies. The results prove the feasibility and reliability of the proposed concept, which allows the DSO to control the reactive power exchange at grid interfaces without causing undesired local voltage problems. In addition, it can be simply adjusted and widely applied in real distribution grids without requiring high investment costs for complex information and communication infrastructures. As a significant contribution, this study provides an ideal bridging solution for DSOs who are facing reactive power issues but have no detailed and advanced monitoring system for their grid. Moreover, the comprehensive investigations in this study are performed in close cooperation with a German DSO, based on a detailed grid model and real measurement data. [ABSTRACT FROM AUTHOR]

Urban street networks impact urban space usage and movement across a city. Circuity, the ratio of network distances to straight-line distances, is considered a critical measurement in urban network morphology and transportation efficiency as it can measure the attractiveness of routes in terms of distance traveled. Here, we compare circuity measures for drivable, cyclable, and walkable networks to analyze how they evolved and understand whether urban changes have produced meaningful circuity changes. Our analyses rely on Lisbon data from OpenStreetMaps to explore circuity for the period 2013–2020, which we used to simulate 4.8 million routes using OpenRouteService to compute the different modes' circuity measures. Our findings suggest that it is crucial to analyze each transport network type separately when planning or modeling urban street networks. Their composition and design differ significantly from mode to mode, such as their attractiveness to users. We identify significant changes in modes' circuity over time, especially in cycling, following Lisbon's cycling infrastructure expansion. Our paper demonstrates that the circuity indicator is useful when planning and modeling street networks, in particular, to optimize the location choice for interventions required to increase the attractiveness of active modes and promote sustainable mobility. At the same time, we emphasize the lack of information on walking infrastructures required for more detailed analyses. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study differences in the adoption of e-commerce associated with the level of managers/owners' innovativeness. The background utilized to estimate the respondents' favorable reception of e-commerce is a model based on the Theory of Planned Behavior, which has been extended to include perceived drivers and obstacles as antecedents of attitude toward e-commerce acceptance. Small and medium enterprises in Chile participated in the study. A Partial Least Squares multi-group test was employed to note differences between groups. The main conclusions of the study are: (1) the level of managers/owners' innovativeness differentiates between adopters and non-adopters of e-commerce of small and medium enterprises in Chile; (2) the Theory of Planned Behavior has been successfully used to explain the process of adoption of e-commerce among Chilean managers/owners; (3) there are significant relationships between both drivers and obstacles and attitude toward using e-commerce; (4) a strong and significant relationship exists between attitude and intention to use e-commerce. Results can be used by small and medium enterprises in developing countries to boost e-commerce adoption. [ABSTRACT FROM AUTHOR]