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Tourism consumption as a typical representative of service consumption, has strong comprehensiveness and driving force, continuously expanding new consumption upgrade space. In the context of the digital economy, the development of the mobile internet has opened up new opportunities for the tourism industry acting as an important factor influencing the consumer behaviour of the residents. Based on 9007 resident survey data from the China General Social Survey (CGSS) in 2017, this paper explores the relationship between Internet use, access to information and residents' travel consumption behavior by means of probit model and mediating effect model. First, according to the findings, Internet use has a positive impact on residents' travel consumption behavior, increasing residents' tourism expenditure. Second, the mechanism suggests that access to information plays a partially mediating role between Internet use and travel consumption behavior. Third, further analysis revealed that, for the retired population, the more frequently they use the Internet, the more likely they are to spend on travel. Compared to those with lower incomes, those with higher incomes will spend less on travel after retirement. With this in mind, in order to promote residents' tourism consumption, speed and fee reduction should be continuously promoted, urban and rural information infrastructure should be strengthened, and accessibility of tourism information for residents should be improved. At the same time, there is a need to innovate the way tourism products are promoted, improve the types of tourism products and launch diversified tourism products. [ABSTRACT FROM AUTHOR]

Environmental protection supervision is an important manifestation of ecological accountability system reform. Taking the implementation of China's central environmental protection inspection (CEPI) policy as a quasi-natural experiment, this paper applies the time-varying difference-in-difference (DID) model to identify the impact of the CEPI policy on the likelihood of enterprises' environmental violations. The main conclusions include: (1) the implementation of CEPI policy is conducive to inhibiting environmental violations. After a series of robustness tests, including the parallel trend test, the placebo test, the exclusion of policy interference, and the replacement of explained variables, the result remains robust. In terms of dynamic perspective, there is a lag in the impact of CEPI policy on enterprises' environmental violations. (2) The effect of CEPI policy is more prominent on samples of large enterprises, enterprises with highly concentrated equity, enterprises that polluted heavily, enterprises owned by executives with a political background, and enterprises that are situated in regions with higher level of marketization and more developed information infrastructure. (3) The CEPI policy can restrain the environmental violations of enterprises by a dual mechanism of releasing punishment and deterrence signals from the external governance, and forcing enterprises to enhance environmental management system from the internal governance. [ABSTRACT FROM AUTHOR]

This paper discusses three case studies of early science museum-related websites in the 1990s and early 2000s, when web technology was still relatively new and evolving. The Virtual Museum of Computing (VMoC) was a completely virtual museum, originally produced in 1995 as part of the Virtual Library museums pages (VLmp), an international online museum directory within the WWW Virtual Library, adopted by the International Council of Museums (ICOM). The Science Museum in London was one of the first museums in the United Kingdom to have its own web server. The museum hosted an early meeting on web service provision by and for museums, concurrently with an exhibition on the Information Superhighway at the museum in 1995. Exhiblets were launched online in 1998. Ingenious was a multi-site digital collections transformation project, launched as a website in 2003. Virtual Leonardo and Leonardo's Ideal City were two experiments conducted by the digital team of the Science and Technology Museum of Milan, between 1999 and 2001. The experiment consisted of the creation of a shared online 3D world, namely a reconstruction of the real museum in the first case and a completely imaginary world in the second case. This paper describes the above three case studies from the early World Wide Web and then draws some conclusions, from first-hand experience of developments at the time. We cover both the advantages and the challenges encountered by the various projects and illustrate why they did not necessarily become established, despite promising early results. [ABSTRACT FROM AUTHOR]

Currently, China has entered a stage of high-quality development due to innovations and improving capabilities of the cities. Being the most valuable urban brand, civilized cities are a symbol of the city's comprehensive ability. Their construction blueprint coincides with the realization of high-quality development. Therefore, exploring the relationship between civilized cities' construction and urban innovative development is a matter of great significance for improving national high-quality development policies and strengthening civilized cities' construction. This paper examined the data from 242 cities from 2003 to 2016, applying the multi-period difference-in-difference model to test the established hypothesis. The results indicate that the pilot policy for civilized cities has effectively improved the level of urban innovation. The results also validate that the title of the civilized city effectively promotes the urban innovation level by promoting the agglomeration of human capital, improving the level of informatization, and increasing investment in scientific as well as technological innovation. At the same time, there are significant differences in policy effectiveness for cities with different urban levels and geographical locations. The construction of future civilized cities should focus on attracting and cultivating high-quality talents, strengthening investment and support in information infrastructure construction, and scientific and technological innovation research and development to adapt to local conditions and resultantly achieve the improvement in urban innovation level. [ABSTRACT FROM AUTHOR]

Purpose: Information infrastructures can enable or constrain how companies pursue their visions of sustainability reporting and help address the urgent need to understand how corporate activity affects sustainability outcomes and how socio-ecological challenges affect corporate activity. The paper examines the relationship between sustainability reporting information infrastructures and sustainability reporting practice. Design/methodology/approach: The paper mobilises a socio-technical perspective and the conception of infrastructure, the socio-technical arrangement of technical artifacts and social routines, to engage with a qualitative dataset comprised of interview and documentary evidence on the development and construction of sustainability reporting information. Findings: The results detail how sustainability reporting information infrastructures are used by companies and depict the difficulties faced in generating reliable sustainability data. The findings illustrate the challenges and measures undertaken by entities to embed automation and integration, and to enhance sustainability data quality. The findings provide insight into how infrastructures constrain and support sustainability reporting practices. Originality/value: The paper explains how infrastructures shape sustainability reporting practices, and how infrastructures are shaped by regulatory demands and costs. Companies have developed "uneven" infrastructures supporting legislative requirements, whilst infrastructures supporting non-legislative sustainability reporting remain underdeveloped. Consequently, infrastructures supporting specific legislation have developed along unitary pathways and are often poorly integrated with infrastructures supporting other sustainability reporting areas. Infrastructures developed around legislative requirements are not necessarily constrained by financial reporting norms and do not preclude specific sustainability reporting visions. On the contrary, due to regulation, infrastructure supporting disclosures that offer an "inside out" perspective on sustainability reporting is often comparatively well developed. [ABSTRACT FROM AUTHOR]

This paper explores the dynamics of peer support and companionship among older adults on a social networking site during the COVID-19 lockdown. Drawing from the authors' five-month experience as volunteer facilitators and a qualitative study involving users, social workers, and managers, the paper examines two modes of online peer support and companionship: one based on voice messages, the other on visual messages. Guided by critical media and data studies, and incorporating concepts from cultural studies of mobile media and information infrastructure studies, the analysis highlights the interplay of relational/emotional and infrastructural work and uncovers intricate gendered and age-related configurations. Our conclusion emphasises, first, the need to comprehend how socio-technical systems shape emotional, relational, and infrastructural work during emergency digital interventions, and second, the importance of examining how specific notions of support and older people's agency and response-ability are embedded in the socio-technical organisation of these digital interventions. [ABSTRACT FROM AUTHOR]

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Green technology innovation is an effective means to achieve high-quality economic development. The impact and mechanism of digital financial inclusion on regional green technology innovation are tested using a threshold regression model and the panel fixed effect model, based on China's provincial Panel data (provincial Panel data are regional annual report data) from 2011 to 2020. According to the study, there is a direct link between local green technology innovation and digital financial inclusion. This paper highlights the differences in their influence by location and usage depth and underscores the necessity of government engagement to improve these characteristics. Information infrastructure needs to be strengthened, especially in areas with gaps. Greater investment in research and development (R&D) indirectly supports regional green technology innovation since it is impacted by digital financial inclusion. Interestingly, a threshold effect becomes most noticeable when digital financial inclusion rises above a particular threshold. Promoting utilizing digital financial inclusion to lessen regional differences in green technology innovation is important. [ABSTRACT FROM AUTHOR]

Background: This paper explores the feasibility of establishing a large-scale population-based cohort and biobank in Switzerland by assessing potential participants' needs, expectations, and concerns about such an infrastructure providing information on health, lifestyle, and exposure trajectories, the development of disease, and risk factors over time. Methods: We utilized a scenario-based questionnaire in the Swiss Health Study pilot phase (2020–2021), involving 1349 adults aged 20–69 from the cantons Vaud and Bern. We conducted descriptive statistics supported by R and qualitative content analysis of n = 374 open responses related to attitudes towards research. Results: We highlight the benefits and challenges of the scenario-based approach, discuss the sample represented in the pilot phase, and present implications for building a full cohort. We also report on participants' attitudes towards and previous experience with health research. We analyze references regarding informed consent and feedback, attitudes towards the Swiss Health Study, and recommendations on improving its scope, design, and instruments. Results indicate a high interest (90%) in participating in a national health study, with 85% of a random population sample willing to join a long-term cohort. Only 43% were familiar with biobanks, and 44% preferred general consent. Trust was high for Swiss-based public research but lower for researchers from other countries or private sector. Over 95% expressed willingness to complete online questionnaires, undergo physical examination, and donate biosamples. Almost all participants wanted to know the outcomes of the medical tests (99.5%) and the exposure to environmental stressors (95%) from their study center visit. Preferred tools for monitoring sleep, physical activity, and diet were known smartphone apps with automatic data management. Conclusion: Overall, the study reveals a positive attitude towards personalized health research, with a strong willingness to share data and samples. Key insights focus the meaning of informed consent for participation, the relevance of sampling and representativeness, as well as the significance and challenges of personalized feedback, especially regarding environmental health concerns. Findings emphasize participants' supportive yet reflexive stances, underscoring the importance of aligning research values with individual values in personalized health research. These insights contribute valuable considerations for refining the scope, design, and instruments of future cohort studies. [ABSTRACT FROM AUTHOR]

This study specifically set to identify the status and extent of utilization of Blockchain technology in the delivery of construction projects in Imo State, Nigeria. The study was guided by the technology acceptance model (TAM) theory. The study used a descriptive research design method of investigation, and a simple random sampling technique was used to select a sample size of 123 from a population of 194 practitioners using the Krejcie and Morgan's method of sample size determination. The data collection and survey instrument included a well-structured questionnaire, discussions, personal observations and visits to elicit information from respondents/construction project locations. The collected data was presented in the form of frequency distribution, figures, and charts using descriptive statistical tools using IBM SPSS Statistics version 25.0. The study's findings indicate a general low level of utilization/application of Blockchain technology in the delivery of construction projects in Imo state. This is due to the fact that none (0.00%) of the respondents have heard of Blockchain technology as applicable to construction projects-related activities, 9 (10.47%) have used Blockchain technology sparingly, 35 (40.70%) have never heard of Blockchain technology and 42 (48.84%) have heard of Blockchain technology, but never applied it on any construction project-related activity out of the eighty-six (86) responses used for the study. This study recommends that, a united front is needed to make Blockchain technology awareness-raising and encourage practitioners to embrace it. Blockchain can provide a dependable infrastructure for information management throughout all stages of the construction project life cycle as well as secure storage of sensitive sensor data. [ABSTRACT FROM AUTHOR]

The analysis of influencing factors serves as the cornerstone for the research on smart community construction. Drawing upon both field research and extensive literature study, this paper explores the influencing factors of China's smart community construction and its effective paths by taking 52 national pilot zones for community governance and service innovation in China as examples. In the constructed analytical framework of influencing factors, elements such as economic development, capital investment, information infrastructure, community governance, public support system, and smart platform are included. By the use of the qualitative comparative analysis (QCA) method, the results of the study show that community governance, public support system, and smart platform are necessary conditions for smart community construction, while economic development, capital investment and information infrastructure play a leading role in the four combined paths. Finally, this study provides a new perspective for theoretical research, a reference forgovernmental departments to make decisions, and experience for the construction of smart communities in other developing countries. [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]

The road agencies collect and submit weigh-in-motion (WIM) data to the Federal Highway Administration as part of their traffic monitoring program. Therefore, the WIM data should be precise and accurate. One way to evaluate WIM measurement errors is by using the test truck data collected immediately before and after equipment calibration. The limitation of this approach is that the data represent a snapshot in time and may not represent a long-term WIM site performance. This paper presents an approach for estimating WIM system accuracy based on axle load spectra attributes (normalized axle load spectra (NALS) shape factors). This alternative approach allows for characterizing temporal changes in WIM data consistency. The WIM error data collected before and after calibration were related to Class 9 NALS shape factors in the proposed methodology. This paper aims to determine WIM system errors based on axle loading without physically performing WIM equipment performance validation using test trucks. The presented methodology can be used to estimate systematic errors (drift) in the WIM system at any point in time after the equipment calibration. This approach can help highway agencies select optimum timings for routine maintenance and calibration of WIM equipment without compromising its accuracy. The results show that the WIM accuracy for the single axle (SA) and tandem axle (TA) can be estimated with SA and TA NALS shape factors with an acceptable degree of error for bending plate to quartz piezo sensors. Examples are included to demonstrate the application and significance of the developed models. [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]

Due to the harsh environment of highway tunnels and frequent breakdowns of various detection sensors and surveillance devices, the operational management of highway tunnels lacks effective data support. This paper analyzes the characteristics of operational surveillance data in highway tunnels. It proposes a multimodal information fusion method based on CNN-LSTM-attention and designs and develops a digital twin for highway tunnel operations. The system addresses issues such as insufficient development and coordination of the technical architecture of operation control systems, weak information service capabilities, and insufficient data application capabilities. The system also lacks intelligent decision-making and control capabilities. The developed system achieves closed-loop management of "accurate perception-risk assessment-decision warning-emergency management" for highway tunnel operations based on data-driven approaches. The engineering demonstration application underscores the system's capacity to enhance tunnel traffic safety, diminish tunnel management costs, and elevate tunnel driving comfort. [ABSTRACT FROM AUTHOR]

This paper draws on information infrastructures (IIs) in science and technology studies (STS), as well as on feminist STS scholarship and contemporary critical accounts of digital technologies, to build an initial mapping of the infrastructural mechanisms and implications of large language models (LLMs). Through a comparison with discriminatory machine learning (ML) systems and a case study on gender bias, I present LLMs as contested artefacts with categorising and performative capabilities. This paper suggests that generative systems do not tangibly depart from traditional, discriminative counterparts in terms of their underlying probabilistic mechanisms, and therefore both technologies can be theorised as infrastructures of categorisation. However, LLMs additionally retain performative capabilities through their linguistic outputs. Here, I outline the intuition behind this phenomenon, which I refer to as "language as infrastructure". While traditional, discriminative systems "disappear" into larger IIs, the hype surrounding generative technologies presents an opportunity to scrutinise these artefacts, to alter their computational mechanisms and introduce governance measures [1]. I illustrate this thesis through Sharma's [2] formulation of "broken machine", and suggest dataset curation and participatory design as governance mechanisms that can partly address downstream harms in LLMs (Barocas, et al., 2023). [ABSTRACT FROM AUTHOR]

Driver characteristics have been linked to the frequency and severity of car crashes. Among these, age and gender have been shown to impact both the possibility and severity of a crash. Previous studies have used standard ordered probit (OP) models to analyze crash data, and some research has suggested heteroskedastic ordered probit (HETOP) could provide improved model fit. The objective of this paper is to evaluate potential improvements of the heteroskedastic ordered probit (HETOP) model compared to the standard ordered probit (OP) model in crash analysis, by examining the effect of gender across age on injury severity among drivers. This paper hypothesizes that the HETOP model can provide a better fit to crash data, by allowing heteroskedasticity in the distribution of injury severity across driver age and gender. Data for 20,222 crashes were analyzed for North Carolina from 2016 to 2018, which represents the state with the highest number of fatalities per 100 million vehicle miles traveled amongst available crash data from the Highway Safety Information System. Darker lighting conditions, severe road surface conditions, and less severe weather were associated with increased injury severity. For driver demographics, the probability of severe injuries increased with age and for male drivers. Moreover, the variance of severity increased with age disproportionately within and across genders, and the HETOP was able to account for this. The results of the two applied approaches revealed that HETOP model outperformed the standard OP model when measuring the effects of age and gender together in injury severity analysis, due to the heteroskedasticity in injury severity within gender and age. The HETOP statistical method presented in this paper can be more broadly applied across other contexts and combinations of independent variables for improved model prediction and accuracy of causal variables in traffic safety. [ABSTRACT FROM AUTHOR]

Considering that one of the goals of the future network generations is to provide ubiquitous communication in the most diverse scenarios to achieve high connection coverage, it is foreseen that the use of unmanned aerial vehicles as flying base stations (UAV-BSs) can potentially extend the network and communication range. UAVs as flying base station can bring the potential to assist user devices and vehicles by carrying communication resources that can accommodate clients that were not previously planned by the ground infrastructure design due to flash crowd events, sudden natural disasters, or any other event that let to an overloaded environment. Allocating UAVs as flying base station still poses significant challenges in their deployment and the effectiveness of information transmission through UAVs as flying base station in the context of wireless communication since it is necessary to deal with both wireless communication capability and interference in the presence of terrestrial infrastructures already present. Besides, it is essential to understand how communication resources affect network performance. This paper studies the feasibility of using UAVs as flying base station in the assistance of wireless communication in a scenario where there is a sudden demand for data transmission due to possible congestion of local infrastructure. We show how the number of communication resources provided by the UAV-BS, the interference caused by the presence of multiple next generation node Bs (gNBs), and the UAV as flying base station positioning affect the network performance. We also highlight the need for a better next generation node B (gNB) and UAVs placement criteria since the received signal power prevents the user equipments (UEs) from using most of the available resources. [ABSTRACT FROM AUTHOR]

Over the past 20 years, drive-by monitoring, using data from sensors in passing vehicles, has become increasingly popular due to its low cost and high efficiency at a network level. Despite significant advances, the dynamics of vehicles remains one of the main challenges to overcome before accurately identifying critical information about the infrastructure being crossed, bridges or road pavements. This paper introduces a novel approach to estimating bridge profiles (i.e. road surface profiles on the bridges) and vehicle properties using a fleet of passing vehicles. In this method, bridge profiles are first calculated using the innovative Inverse Newmark-beta integration method, and the cross-entropy optimization algorithm is employed to solve the problem. Numerical results demonstrate that the proposed approach is highly effective in extracting bridge profiles and predicting vehicle properties, even in scenarios with significant levels of measurement noise. [ABSTRACT FROM AUTHOR]

In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the ALBERT (A Lite BERT) model and the improved ResNet model are constructed for feature extraction of text and image, respectively, and high-dimensional feature vectors are obtained. Secondly, to solve the problem of insufficient semantic information expression in cross-scene, a prior knowledge enhancement model is proposed to enrich the data characteristics of each modality. Finally, to solve the problem of poor cross-modal fusion effect, a composite hierarchical fusion model is proposed, which combines the temporal convolutional network and the attention mechanism to fuse the sequence features of each modality information and realizes the information interaction between different modalities. Experiments on MVSA-Single and MVSA-Multi datasets show that the proposed model is superior to a series of comparison models and has good adaptability in new scenarios. [ABSTRACT FROM AUTHOR]

The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks , i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks , i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies. [ABSTRACT FROM AUTHOR]

Information technology infrastructure (ITI) has gone through an exponential change due to the rapid evolution of digital technologies during the past few years. It is therefore relevant to identify the factors that push organizations towards the adoption of new technologies. This paper considers a specific case study of the higher education institutions (HEI) to present (i) the motivational factors that push HEIs toward the adoption of new technologies, and (ii) the key challenges HEIs face in the adoption of these technologies. The data was gathered after conducting 17 open-ended interviews with practitioners working at HEIs in Finland and Pakistan. Data analysis identified 10 motivational factors and 41 challenges faced by HEI in the adoption of the latest technologies. Furthermore, the data was further classified to identify broad themes and categories representing the challenges and motivation factors. [ABSTRACT FROM AUTHOR]

Oil and gas (O&G) organizations are progressively being digitalized in order to facilitate substantial information flow to remain competitive in the information age. This critical sector is spearheading the establishment of technical security measures to mitigate information security risks, yet employee behavioral influence remains an ongoing challenge in assuring information security. Existing studies of this domain primarily focus on employee behavior reshaping through multiple psychological theories. However, these studies ignore how these critical infrastructures implement information security. Most such infrastructures follow the International Society of Automation (ISA)-95 levels of automation and implement information security controls in line with these levels. This research paper proposed a theoretical framework to enhance information security policy compliance (ISPC) at level 4 to level 2 automation level in O&G organizations. To support the hypotheses, data were collected from 13 Malaysian O&G organizations. A total of 254 O&G employees participated in the survey and the structural equation modeling technique was used for data analysis. The study confirmed that ISA-95-based organizational governance factors and social bonding could enhance ISPC in O&G organizations. However, risk assessment and involvement factors have shown less support to the notion. For information systems practitioners, this study has shown how to enhance ISPC in O&G organizations through ISA-95-based organizational governance and social bonding. [ABSTRACT FROM AUTHOR]

Analyzing traffic accident data is crucial for pinpointing contributing factors, forecasting accident patterns, and informing effective safety measures. This insight leads to enhanced road safety, decreased fatalities, and better resource allocation in transportation planning. Severe weather conditions, including rain, fog, and sandstorms, can significantly reduce the visibility of highways and increase the risk of traffic accidents. The paper proposes a deep mining algorithm for road traffic accident data under adverse weather conditions. Firstly, collect traffic accident data under adverse weather conditions and complete attribute reduction, denoising, and normalization processing. Then, by improving the decision tree algorithm, the attribute selection criteria of the decision tree algorithm were enhanced; Finally, the improved decision tree algorithm is applied to achieve deep mining of traffic accident data. The experimental results show that proposed algorithm has an accuracy rate of over 95.3%, an accuracy rate of over 94.3%, a recall rate of over 92.7%, and a stable F1 value of around 0.8, demonstrating excellent performance. The provided deep mining algorithm for traffic accident data can provide more scientific and accurate decision support for traffic management departments. [ABSTRACT FROM AUTHOR]

In tunnel construction with tunnel boring machines (TBMs), accurate prediction of the remaining useful life (RUL) of disc cutters is critical for timely maintenance and replacement to avoid delays and cost overruns. This paper introduces a novel hybrid model, integrating fundamental and data-driven approaches, to enhance wear prediction of TBM disc cutters and enable accurate RUL estimation. The fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques, showcasing superior prediction accuracy compared to single-mechanism models. Additionally, the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model, leading to a high-performance wear prediction model. Using actual field data from a highway tunnel project in Shenzhen, the performance of the hybrid model is rigorously tested and compared with pure fundamental and data-driven models. The hybrid model outperforms the other models, achieving the highest accuracy in predicting TBM disc cutter wear (mean absolute error (MAE) = 0.53, root mean square error (RMSE) = 0.64). Furthermore, this study thoroughly analyzes the hybrid model's generalization capability, revealing significant impacts of geological conditions on prediction accuracy. The model's generalization capability is also improved by expanding and updating the data sets. The RUL estimation results provided by the hybrid model are straightforward and effective, making it a valuable tool by which construction staff can monitor TBM disc cutters. [ABSTRACT FROM AUTHOR]

• Robust infrastructure is essential to enhance urban productivity and support urban development. • Transportation infrastructure enhances urban productivity by promoting economic agglomeration. • Information infrastructure promotes urban productivity through labor mobility and agglomeration effects. • The promotion effect on urban productivity in eastern regions and megacities is more obvious. • Transportation and information infrastructure have positive spatial spillover effects on urban productivity. Improving total factor productivity is an important driving force for achieving high-quality economic development. Based on the panel data of 256 cities in China from 2009 to 2019, this paper investigates the effect of transportation and information infrastructure on urban productivity. The results show that transportation and information infrastructure promote urban productivity, and the effect in eastern regions and megacities is more obvious. The mechanism analysis shows that transportation infrastructure enhances urban productivity by promoting economic agglomeration, and information infrastructure enhances urban productivity by promoting labor mobility, economic agglomeration, financial agglomeration and industrial agglomeration. The spatial spillover effect shows positive spatial autocorrelation of urban productivity. Transportation and information infrastructure have significant positive spatial spillover effects on urban productivity. China should continue to promote the construction of transportation infrastructure, strengthen investment in information infrastructure, and promote the joint construction, sharing, coordination and integration of transportation and information infrastructure. [ABSTRACT FROM AUTHOR]

In the realm of healthcare digitalization, the advancement of patient-centred care models hinges crucially on the establishment of inter-organizational information infrastructures (IIs): intricate "systems of systems" designed to facilitate efficient data sharing, consolidation, and utilization. However, closed and siloed health information systems often fragment patient data across multiple repositories, resulting in inefficiencies and limitations in inter-organizational access and decision-making at the population level. Bridging these divides demands the integration of loosely coupled information systems within and across organizations. The ongoing evolution of these IIs is intricately shaped by the complex sociotechnical installed base governing their development. It can be difficult to engage potential participants in building and using an II that spans organisational boundaries particularly in contexts marked by diversity of mission, high organizational autonomy and lacking a single authoritative actor. Through a case study in England's National Health Service, this paper examines the intricate process of mobilizing autonomous organizations to collaborate in developing a shared II for integrated care. In non-hierarchical settings, lacking a clear organizing entity such mobilization requires strategic partnerships and a mix of skills. I introduce the term "collective mobilizer" to represent the collaborative coalition of entities with the capacity to garner sufficient interest among potential constituencies and align visions and resources. By unpicking the role of collective mobilizers, this paper offers insights into the challenges of bootstrapping II development in non-hierarchical contexts and how they may be overcome. [ABSTRACT FROM AUTHOR]

Introduction/Background: Safe and quality surgery is crucial for child health. In Rwanda, district hospitals serve as primary entry points for pediatric patients needing surgical care. This paper reports on the organizational readiness and facility capacity to provide pediatric surgery in three district hospitals in rural Rwanda. Methods: We administered the Children's Surgical Assessment Tool, adapted for a Rwandan district hospital, to assess facility readiness across 5 domains (infrastructure, workforce, service delivery, financing, and training) at three Partners in Health supported district hospitals (Kirehe, Rwinkwavu, and Butaro District Hospitals). We used the Safe Surgery Organizational Readiness Tool (SSORT) to measure perceived individual and team readiness to implement surgical quality improvement interventions across 14 domains. Results: None of the facilities had a dedicated pediatric surgeon, and the most common barriers to pediatric surgery were lack of surgeon (68%), lack of physician anesthesiologists (19%), and inadequate infrastructure (17%). There were gaps in operating and recovery room infrastructure and information management for pediatric outpatients and referrals. In SSORT interviews (n = 47), the highest barriers to increasing pediatric surgery capacity were facility capacity (mean score = 2.6 out of 5), psychological safety (median score = 3.0 out of 5), and resistance to change (mean score = 1.5 out of 5 with 5 = no resistance). Conclusions: This study highlights challenges in providing safe and high‐quality surgical care to pediatric patients in three rural district hospitals in Rwanda. It underscores the need for targeted interventions to address facility and organizational barriers prior to implementing interventions to expand pediatric surgical capacity. [ABSTRACT FROM AUTHOR]

This paper presents a process to develop a CityGML-based 3D city model that, together with results from a flood simulation, can be used to investigate direct and indirect effects of floods on a city, its inhabitants and its critical infrastructure, and to quantify such effects by means of a Flood Resilience Score. In addition, the model can be used as a spatial planning support tool for urban planners to prioritise the redevelopment of certain areas and to test new spatial design decisions. First, a semantic 3D city model is prepared and enriched with additional building and infrastructure information. Then a Flood Resilience Score (FReSco) is defined and computed by quantifying the direct and indirect impacts of flooding on buildings, households, and critical infrastructure points using information from both the 3D city model and the flood simulation results. Lastly, a prototype of a spatial planning support tool is proposed to evaluate the flood resilience of a new environmental plan. As a case study, the neighbourhood of "Nieuw Kralingen" in Rotterdam was chosen. Overall, the outcomes of this work are meant to help cities better understand the impacts of flooding and adjust their urban planning activities accordingly. At the same time, the developed methodology also tests the strengths and limits of CityGML-based 3D city models in combination with openly available data and software. [ABSTRACT FROM AUTHOR]

Constructing geometric digital twins of highways at present still demands substantial human effort. Unlike most previous work that uses deep learning models to segment point clouds of highways into class level or object instance level, this paper further segments pavements into a more detailed level (lanes, hard shoulders, central reserves). The central curves of each lane marking are fitted in a two-step method, approximated by a polynomial and then converted into the Frenet coordinated system. The fitted curves with smoothly changing curvature are used to separate points of road surfaces into lanes, hard shoulders, and central reserves, resulting in a mean Intersection over Union (mIoU) at around 90%. This automatic approach extracts geometric and object category information from point clouds and stores the information in a graph, showing the hierarchical relationships among various components and offering the potential for expansion into more comprehensive digital twins encompassing the entire highway network. • Segment point clouds of pavements into a more detailed level. • Fit curves to lane markings in point clouds and use them to segment pavement points. • Generate graph representation of highway geometric digital twins from point clouds. [ABSTRACT FROM AUTHOR]

This paper suggests a synthetization of three classic approaches to analyze infrastructures as interconnected compositions that subtly distribute power and governance. We draw upon conceptualizations within large technological systems (LTS), Internet governance, and information infrastructures (II) and connect them to our own extensive research in energy infrastructure and systems spanning over a decade. This combination serves as the bedrock for our methodological work, which we establish here as pivotal signposts for the study of governance by infrastructures. Rooted empirically in energy research, we advocate for scholars to expand these methodologies to other infrastructures, such as the Internet and II, to illustrate how governance operates across diverse infrastructural networks and their intersections. Our empirical examples draw from studies in smart grids and smart energy technologies, electricity supply management and markets, and energy computer modeling. These diverse cases are utilized to inform the proposed methodological approach. [ABSTRACT FROM AUTHOR]

The term "infrastructure" has evolved to span a wide range of disciplines, reflecting its diverse applications and conceptualizations. This paper argues that information infrastructures (II) represent a distinct and increasingly significant socio-material form, crucial for understanding governance by infrastructure. We examine the origins and evoluIon of II, emphasizing the socio-material differences between II and other technological forms such as systems and online platforms. We highlight particularities of II innovation and implementation process and consider their implications for the politics of technology and governance by infrastructures. Our analysis is grounded on examination of several II in our increasingly digitised information societies, spanning the Internet, social security, platforms, financial infrastructures, energy systems and infrastructures, and health information systems. [ABSTRACT FROM AUTHOR]

Purpose: This study explores whether media coverage of Chinese food safety guide consumer behaviour and determines its impact. Design/methodology/approach: Using data from the China Family Panel Studies, this study implements unsupervised machine learning methods to quantitatively identify themes in news media coverage of food safety across various provinces and regions. Based on these findings, this study examines the impact of coverage of food safety on consumer behaviour related to FAFH. Findings: We find that media coverage of food safety in the restaurant sector significantly decreases household expenditure on FAFH relative to total expenditure. While negative coverage substantially decreases expenditure on FAFH, non-negative coverage significantly increases it. Reports of food safety incidents outside consumers' province are negatively correlated with expenditure on FAFH, whereas reports within province significantly increases such spending. Further, the negative impact of media coverage on FAFH spending is less pronounced among higher-income families, households headed by individuals with high educational levels and those with low sensitivity to newspaper information. A robust government information infrastructure also mitigates this negative impact. Research limitations/implications: The findings have important policy reference value for promoting the healthy development of catering and other life services by improving news reporting and the regulatory system. Originality/value: This article employs machine learning methods to identify news reports related to food safety in the catering industry quantitatively and incorporates them into the study of household consumption in China. Consequently, this not only fills a gap in the existing literature but also provides a new perspective for interdisciplinary research in economics, sociology and computer science. [ABSTRACT FROM AUTHOR]

Countries have focused on agile regulatory approaches to strengthen integration, manage risks, and ensure comprehensive digital transformations within the digital economy ecosystem. It is essential to identify the areas and requirements of agile regulation in developing the digital economy. This qualitative research aims to formulate the areas and requirements for agile regulation by focusing on upstream documents related to the development of the digital economy. In the first step of the research, an initial list of 40 upstream documents was prepared using the documentary method, and 16 effective documents were selected by using the theoretical selection approach. The content of these documents was analyzed using the thematic analysis method, resulting in the identification of 14 areas and 26 requirements for agile regulation, which were then prioritized using the AHP method. The results show that in the core layer, communication and information infrastructure, in the digital layer, public digital services and in the digitized layer, data and content have more priority. [ABSTRACT FROM AUTHOR]

There is an urgent need for the development of cutting-edge port infrastructure monitoring solutions that exploit different multimodal data towards timely optimal port management strategies and decision-making. Advanced monitoring applications allow for optimising maintenance, rehabilitation, and upgrade actions by assessing the structural integrity of port structures that is affected by a vast variety of stressors such as aging, imposed loads, inadequate maintenance treatments, human-induced factors, natural hazards, and the ever-changing climate. The purpose of this research is to enhance Remote Sensing (RS) port monitoring practices by investigating the potential of combining different types of RS methods to record and assess infrastructure condition. Two RS types of data, a) satellite imagery and b) aerial imagery from Unmanned Aerial Vehicle (UAV), were considered for structural monitoring at Lavrio port, located in northeastern Attica, Greece. In particular, the applied monitoring program was focused on its windward rubble mound structure where temporal changes in the armour layer were detected. Significant parameters regarding spatial resolution and UAV flight characteristics were further investigated aiming at ensuring high-quality data. The overall research indicated that RS synergies proved to be a promising practice for acquiring advanced spatial and temporal information on port infrastructure condition. [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]

A growing strand of research revolving around the concept of legibility emphasizes the central function that information plays for state capacity accumulation. The bulk of these contributions remain focused on the existence of infrastructure supporting such information gathering rather than its actual content and quality. Further, attribution of agency in these narratives tends to remain at the level of the central state. Instead, we focus on local actors, specifically municipalities in Sweden. We use a publicly provided database of Swedish municipalities, containing around 5,000 miscellaneous indicators on various topics, to operationalize the propensity for producing missing data in a given municipality-year as a measure of (low) local legibility. The resulting “missingness”-index is devised through item-response theory modeling and covers all 290 Swedish municipalities between 1995 and 2022. Insofar, we make a methodological contribution by showing how public data repositories—often riddled by data issues endogenous to state capacity—can be leveraged as a valid empirical indicator of the same concept. Further, missingness is positively related to municipal capacity, measured by the administration size. Conversely, it is negatively related to local political autonomy, measured using voting patterns. These findings illustrate the importance of considering local government for the maintenance of state capacity in general. [ABSTRACT FROM AUTHOR]

Despite the needs of assessing the time-varying queue length for incident traffic management, such imperative information has not yet been available in practice, due partly to the lack of reliable sensor data on many highways plagued by nonrecurrent congestion. Hence, this study first presents an incident queue impact assessment system that allows highway agencies to perform reliable estimate of the queue distance during the incident clearance period with available detector data, and then discusses two alternative models for supporting approximating such impacts on roadway networks without traffic sensors. The proposed system’s major model, grounded in the classical shockwave theory, has augmented the formulations for queue propagation dynamics with the impacts from incoming drivers’ perceptions and responses to the progressively constrained traffic conditions. To cope with the lack of real-time traffic flow information, the first alternative model integrates the increasing available speed data from probe vehicles or other sources with lane blockage patterns to approximate the discharge flow rate of the incident. The second alternative model centers on the methodology and calibration methods for constructing a set of speed– flow rate relationships for different times of day with archived traffic data to approximate the arriving flow rate. With such information, responsible agencies can then proceed the approximation of incident queue variation length and take necessary actions. Performance evaluation of the proposed system with both field incident data and simulated scenarios has confirmed its promising properties, especially for highway networks without reliable traffic surveillance systems. [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]

Objectives: Health care organizations are taking a greater role in providing social care. Federally qualified health centers (FQHCs) lead these efforts, leveraging investments in technology to manage patient care and information. This study describes the challenges of meeting patient social needs and presents new opportunities for engaging in communitywide activities to address care and quality. Study Design: Sociotechnical systems theory was applied in qualitative research. Methods: Eleven of the 40 Michigan FQHCs were recruited into the study. Twenty-five semistructured interviews were conducted with practice leadership, clinicians, and staff to explore information technology and other resources used for managing social care. Analyses of the interviews were used to organize infrastructural and technological factors that emerged into key themes. Results: Interviews revealed that Michigan FQHCs participated in community-wide activities to develop value-based care practices and quality initiatives using shared data and technology. Study participants revealed that their community-level involvement facilitated improved data management, digital information sharing, and quality improvement efforts. Ultimately, however, limited housing services and inadequate behavioral health care created challenges in connecting patients with essential treatments and social supports. Conclusions: Developing information infrastructure and new technological capabilities to manage patient care and population health is necessary but insufficient. Major investments in both technology and human capital are critical to effectively provide social care and improve quality. Building on the evidence gained from FQHC partnerships and data activities offers public officials and health care leaders insight into the local resources and policy changes that are needed. [ABSTRACT FROM AUTHOR]

In an increasingly digitalized world, cybersecurity has emerged as a critical component of safeguarding sensitive information and infrastructure from malicious threats. The threat actors are often in line or even one step ahead of the defense, causing the increasing reliance of security teams on artificial intelligence while trying to detect zero-day attacks. However, most of the cybersecurity solutions based on artificial intelligence that can be found in the literature are trained and tested on reference datasets that are at least five or more years old, which gives a vague insight into their security performances. Moreover, they often tend to be designed as isolated, self-focused components. The aim of this paper is to design and implement a modular network intrusion detection architecture capable of simulating cyberattacks based on real-world scenarios while evaluating its defense capabilities. The architecture is designed as a full pipeline from real-time network data collection and transformation to threat-information presentation and visualization, with a pre-trained artificial intelligence module at its core. Well-known components like CICFlowMeter, Prometheus, and Grafana are used and modified to fit our data preparation and core modules to form the proposed architecture for real-world network traffic security monitoring. For the sake of cyberattack simulation, the proposed architecture is situated within a virtual environment, surrounded by the Kali Linux-based penetration simulation agent on one side and a vulnerable agent on the other. The intrusion detection artificial intelligence module is trained on the CICIDS-2017 dataset, and it is demonstrated using the proposed architecture that, despite being trained on an outdated dataset, the trained module is still effective in detecting sophisticated modern attacks. Two case studies are given to illustrate how modular architectures and virtual environments can be valuable tools to assess the security properties of artificial intelligence-based solutions through simulation in real-world scenarios. [ABSTRACT FROM AUTHOR]

Prior solutions on session-based recommendation (SBR) are mainly limited by two major issues: (1) the sequence and transition relationships of items need further integration; (2) the context clues from the neighboring sessions remain largely under-explored. To overcome these issues, this paper proposes a novel Context-aware Graph Embedding Network (CGENet) with gate and attention mechanisms, that not only can effectively exploit the collaborative relationship between the sequence and transition patterns in each session, but also benefit greatly from topological context patterns among different sessions. Specifically, the proposed CGENet consists of three different parts, i.e., Transition Pattern Learning (TPL) module, Sequential Pattern Learning (SPL) module, and Context Pattern Learning (CPL) module. The TPL module is built on Graph Isomorphic Network (GIN) with multiple information highways to capture the transition relationships between items. To maximize the value of sequence-position information, a Gated Multilayer Perceptron (gMLP) is introduced into the SPL module to model the long-term dependencies between sequence tokens. Under the standardized guidance of the graph attention layer, the CPL module can further explore the topological contexts from neighboring sessions, thereby enhancing its ability to predict user preferences more effectively. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed CGENet compared to the state-of-the-art baselines. [Display omitted] [ABSTRACT FROM AUTHOR]

Today, it can be stated that ensuring cybersecurity in the field of critical information infrastructure and digital financial services is one of the key tasks of the legislator, given the possible catastrophic consequences of cyberattacks both at the state level and in the private sector of the economy. Therefore, there is a tendency to qualitatively update the legal norms in the field of critical information infrastructure security. Despite the fully developed national legislation in the field of critical information infrastructure security, the issue of ensuring cybersecurity of the state's critical information infrastructure against cyber risks remains extremely relevant. The legal regulation of relations in the field of protection of critical infrastructure and digital financial services from cyber threats is not sufficiently addressed in publications, which mainly analyse certain areas of cybersecurity. The above leads to a comprehensive study of this issue, the relevance of which is determined by the specifics of the economic sectors in which cyber risks (cyber incidents) lead to devastating consequences and financial losses. The purpose of the study is to determine the criteria for classification of critical information infrastructure facilities, to identify the necessary elements in ensuring the protection of critical information infrastructure, and to outline the areas of ensuring the reliability and security of digital Based on the analysis of current trends in the legal regulation of cybersecurity of critical information infrastructure and digital financial services, the author formulated a scientific and theoretical conclusion that critical information infrastructure facilities can be classified according to the criterion of the area of functioning and the subject criterion. This division, in addition to the general outlines of this category, will provide a thorough understanding of the research issue in further scientific developments, delve deeper into the understanding of its essence and provide additional features and clearly defined boundaries of the category "critical information infrastructure objects". In addition, it was determined that professional development, cyber awareness of society and scientific and technical support for cybersecurity are important elements in ensuring the protection of critical information infrastructure. In turn, the author describes the directions and ways to ensure the reliability and security of digital services throughout their entire life cycle in order to achieve a balance between the needs of society, the domestic market, the economy of the State and the necessary cybersecurity measures. [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]

The rapid advancement of technology is closely linked to the development of the modern era. Technology is always expected to provide convenience to each user according to their individual needs, especially when it comes to sending messages. In the present day, sending messages is a fundamental necessity and security plays a crucial role. Data transmission can be easily intercepted by third parties, even if the data are private or confidential. Therefore, it is crucial to have a security approach in place. However, some methods introduced earlier have not been suitable in terms of the capacity and quality produced. This study addresses this issue by using audio as a medium to insert a secret message. This research focuses on optimizing the quality of the stegoaudio produced without reducing the insertion capacity. The experimental results show an increase in quality based on the Peak Signal-to-Noise Ratio (PSNR) evaluation results. [ABSTRACT FROM AUTHOR]

A significant portion of rural crashes in Iran occur in the marginal areas around cities. Exclusive crash frequency models should be developed to identify the factors affecting the occurrence of crashes in these areas. In this study, the rural crash data of highways leading to the city of Isfahan were employed to develop a negative binomial (NB) model as a constant-parameter model, along with random effects negative binomial (RENB) and random parameters negative binomial (RPNB) models as the models based on the random parameter approach. The modeling results indicated the superiority of the RPNB model over the two other models. According to the findings of this study, the traffic volume of roads and the proportion of local and non-local vehicles, the proportion of longitudinal distance-limit violation, the way of controlling access points, and the access to rest areas affected the crash frequency in the marginal areas around cities. [ABSTRACT FROM AUTHOR]

This study explored how school librarian practices are resources that can support teaching and learning at schools in Sweden. The empirical material was produced through 22 semi-structured interviews with 'best practice' awarded librarians at 14 Swedish secondary and upper-secondary schools. The theoretical framework consisted of a practice theory approach coupled with analytical concepts from information infrastructure studies. The findings highlighted how teachers and librarians collaborate closely in schools with designated best practice libraries. Members of both professions collaborated in teaching and interdisciplinary projects and were supported by management teams at the schools under study. However, the librarians expressed a disconnect between themselves and the teachers and leaders of the school. This disconnect was evidenced by a lack of planning practices and classroom teaching, impelling the librarians to advocate continuously for better awareness and visibility of their practices. The article offers insights into school librarian practices at sites that function simultaneously as both workplaces for professionals with multiple competencies and educational settings for students. [ABSTRACT FROM AUTHOR]