Many Intelligent Transportation Systems (ITS) applications require strong privacy guarantees for both users and their data. Homomorphic encryption (HE) enables computation directly on encrypted messages and thus offers a compelling approach to privacy-preserving data processing in ITS. However, practical HE schemes incur substantial ciphertext expansion and communication overhead, which limits their suitability for time-critical transportation systems. Hybrid homomorphic encryption (HHE) addresses this challenge by combining a homomorphic encryption scheme with a symmetric cipher, enabling efficient encrypted computation while dramatically reducing communication cost. In this paper, we develop theoretical models of representative ITS applications that integrate HHE to protect sensitive vehicular data. We then perform a parameter-based evaluation of the HHE scheme Rubato to estimate ciphertext sizes and communication overhead under realistic ITS workloads. Our results show that HHE achieves orders-of-magnitude reductions in ciphertext size compared with conventional HE while maintaining cryptographic security, making it significantly more practical for latency-constrained ITS communication.
<i>Background</i>: Amid global sustainability imperatives, the logistics sector serves as a key economic enabler while remaining a major contributor to greenhouse gas emissions. This study investigates the causal relationships between green logistics practices and sustainable business performance in Vietnamese small- and medium-sized enterprises, mediated by competitiveness, and forecasts future trends to inform transitions aligned with net-zero goals. <i>Methods</i>: A mixed-methods design integrates structural equation modeling with the gray model. Primary data were collected via Likert-scale questionnaires administered to 350 managers to measure latent variables. Secondary financial metrics (revenue, costs, assets, profits) from 15 firms spanning 2021–2024 enabled forecasting. <i>Results</i>: SEM, employing bootstrapping for path estimation, revealed positive direct effects, with the strongest effects for green transportation and weaker effects for technology, packaging, and warehousing. Mediation via competitiveness yielded mixed indirect effects: positive for warehousing and transportation, but negative for technology. GM(1,1) projected moderate performance growth under conditions of data uncertainty. <i>Conclusions</i>: The hybrid framework advances the resource-based view in emerging market contexts, recommending prioritization of transportation and technology initiatives alongside policy incentives to align with sustainable development goals and enhance resilience in Vietnam’s logistics sector.
Transportation and communication, Management. Industrial management
The Internet of Vehicles (IoV) is an emerging technology that aims to connect vehicles, infrastructure, and other devices to enable intelligent transportation systems. One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities. This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV. The system leverages Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to collect real-time data about the environment and the vehicles. The data is collected to acknowledge the heterogeneity of vehicles and human behavior. The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions. The system takes into account the heterogeneity of vehicles, such as their size, speed, and maneuverability, to optimize collision avoidance strategies. The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems. The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5% using the SVM algorithm. The trial outcomes demonstrated that the new system, incorporating vehicular, weather, and human behavior factors, outperformed previous systems that only considered vehicular and weather aspects. This innovative approach is poised to lead transportation efforts, reducing accident rates and improving the quality of transportation systems in smart cities. By offering predictive capabilities, the proposed model not only helps control accident rates but also prevents them in advance, ensuring road safety.
The overall impact of working from home (WFH) on transportation emissions remains a complex issue, with significant implications for policymaking. This study matches socioeconomic information from American Community Survey (ACS) to the global carbon emissions dataset for selected Metropolitan Statistical Areas (MSAs) in the US. We analyze the impact of WFH on transportation emissions before and during the COVID-19 pandemic. Employing cross-sectional multiple regression models and Blinder-Oaxaca decomposition, we examine how WFH, commuting mode, and car ownership influence transportation emissions across 141 MSAs in the United States. We find that the prevalence of WFH in 2021 is associated with lower transportation emissions, whereas WFH in 2019 did not significantly impact transportation emissions. After controlling for public transportation usage and car ownership, we find that a 1% increase in WFH corresponds to a 0.17 kilogram or 1.8% reduction of daily average transportation emissions per capita. The Blinder-Oaxaca decomposition shows that WFH is the main driver in reducing transportation emissions per capita during the pandemic. Our results show that the reductive influence of public transportation on transportation emissions has declined, while the impact of car ownership on increasing transportation emissions has risen. Collectively, these results indicate a multifaceted impact of WFH on transportation emissions. This study underscores the need for a nuanced, data-driven approach in crafting WFH policies to mitigate transportation emissions effectively.
<i>Background</i>: Governments face critical decisions regarding road remediation projects, requiring careful economic evaluation, especially in countries like Slovakia where road infrastructure is crucial for attracting foreign investment. These decisions are complex, involving short-term and long-term costs and revenues, along with inherent uncertainty about future outcomes. Traditional economic assessments often fail to capture the full scope of these factors, potentially leading to suboptimal choices. <i>Methods</i>: This study proposes four probability-based models: the Short-term Model (SM), Long-term-Short-term Model (LSM), Social Long-term-Short-term Model (SLSM), and Long-term-Short-term Model with a Time Aspect (TLSM). These models incorporate probabilistic functions to calculate expected costs and profits, considering various factors such as reparation costs, financial compensations, social costs, and time-related costs, as well as long-term benefits like increased investment and lives saved. <i>Results</i>: The proposed models were partially validated through an ex post analysis of a past road remediation project on road 1/18 (E50) under the Strecno castle cliff in Slovakia. The analysis demonstrated the models’ utility for multi-criteria decision-making in transportation problems, highlighting their ability to capture the complex interplay of economic and societal factors. <i>Conclusions</i>: The models enable governments to maximize societal benefit while mitigating potential risks, contributing to a more sustainable and efficient transportation sector. Future research could focus on refining the models and adapting them to other sectors beyond transportation.
Transportation and communication, Management. Industrial management
Abstract Numerous real-world networks, including those in communication, transportation, and social systems, inherently possess weighted structures. While traditional centrality metrics like degree, betweenness, and closeness centrality offer useful insights, they often overlook key aspects such as the contribution of the number and importance of shortest paths across extended neighborhoods, particularly in weighted networks. Moreover, existing methods frequently face scalability challenges and struggle to accurately rank nodes in large-scale networks. To address these gaps, this study proposes Semi-local centrality based on the importance of Shortest Paths in Weighted complex networks (SSPW). SSPW employs a distributed approach to extract semi-local subgraphs, leveraging an extended neighborhood concept that incorporates multi-hop connections. This enables a more comprehensive representation of the network structure and the propagation potential of nodes. By integrating the theory of average shortest paths, SSPW ensures an efficient and precise identification process, even in large-scale networks with significant complexity. Additionally, the metric is extended to unweighted networks, further enhancing its applicability. The effectiveness of SSPW through susceptible–infected–recovered (SIR) information diffusion model has been carried out on real-world datasets. These findings indicate that SSPW outperforms the best available centrality in identifying influential nodes by an average of 4.6% in terms of Kendall’s $$\:\tau\:$$ coefficient.
Computer engineering. Computer hardware, Information technology
There are long periods of winter construction in China’s eastern and western Alpine regions. The decreased construction temperature adversely affects the workability, mechanical properties, and durability of cement-based materials and alkali-activated materials. Under low-temperature curing conditions, the hydration reaction of these materials slows down, resulting in limited strength development and reduced durability. In response to this problem, researchers have summarized three measures to improve performance: the use of anti-freezing components, nanoparticles, and pre-curing. The effects of anti-freezing components on the mechanical properties and micro-mechanism changes of Portland cement, sulphoaluminate cement, magnesium phosphate cement-based materials, and alkali-activated cementitious materials are organized. Additionally, the improvement of macro-micro properties in cement-based materials through mineral admixtures, nanoparticles, and hydrated calcium silicate seeds is summarized. The influence of pre-curing on the mechanical properties of cement-based materials is analyzed, focusing on the relationship between pre-curing time and the critical strength of frost resistance. Finally, existing research challenges are summarized, and future research directions are proposed, providing valuable references for the further development of materials and engineering applications.
Víctor Hugo de la Cruz Madrigal, Liliana Avelar Sosa, Jose-Manuel Mejía-Muñoz
et al.
<i>Background:</i> The COVID-19 was a determining factor in the disruption of supply chains in the automotive industry, exacerbating material shortages. This led to increased supplier order cancelations, longer lead times, and reduced safety inventory levels. <i>Methods:</i> This study analyzes and models supply chain disruptions using system dynamics as a key tool, focusing on the disruptions caused by delays in scheduled orders and their impact on service levels within automotive supply chains in Mexico. This approach allowed us to capture the dynamic relationships and cascading effects associated with inventory shrinkage at Tier 2 suppliers, highlighting how these delays affect the chain’s overall performance. In addition to modeling using system dynamics, a deep-learning-based network was proposed to detect disruptions using the data generated by the dynamic model. The network architecture integrates convolutional layers for feature extraction and dense layers for classification, thereby enhancing its ability to identify disruption-related patterns. <i>Results:</i> The performance of the proposed model was evaluated using the AUC metric and compared with alternative methods. The proposed network achieved an AUC of 0.87, outperforming the multilayer perceptron model (AUC = 0.76) and a Neyman–Pearson-based model (AUC = 0.63). These results confirm the superior discriminatory ability of our approach, demonstrating higher accuracy and reliability in detecting disruptions. Furthermore, the dynamical models reveal that the domino effect increases delays in order reception due to the reduction in raw material inventories at Tier 2 suppliers. <i>Conclusions:</i> This paper effectively evaluates the impact of disruptions by demonstrating how reduced service levels propagate through the supply chain.
Transportation and communication, Management. Industrial management
Sukaina Hasan Mousa, Ruslan Saad Abdulrahman, Shams Mhmood Abd Ali
The Vehicular Ad Hoc Network (VANET) is a novel and auspicious performance within the Intelligent Transportation Systems (ITS) field. Numerous scholars endeavoured to enhance the VANETs' Quality of Service (QoS) by creating routing procedures that were reliable, scalable, and efficient. One of the most significant issues of vehicular networks is building a routing protocol that guarantees a specific level of quality of service (QoS), as VANETs are distinguished by special properties, including constrained mobility, a very dynamic topology, and high node speed. For drivers to be able to make the right decisions, communication between vehicular nodes must be highly reliable. Link breakdown issues typically lower the quality of service (QoS) of a vehicular ad hoc network (VANET). This, in turn, lowers the following parameters that influence the QoS. This method depends on a for parameter Packet parameter of Delivery Ratio (PDR ), Packet Loss, Delay of End to End, and Throughput. The simulation results show that the proposal protocol has increased the longevity effectiveness of the network by around (PDR) 93%, Loss of packet 6%, (E2ED) 45.6 seconds, and Throughput 1500/ bps.
Vehicular ad hoc networks (VANETs) are an emerging type of mobile ad hoc networks (MANETs) with robust applications in intelligent traffic management systems. VANET has drawn significant attention from the wireless communication research community and has become one of the most prominent research fields in intelligent transportation system (ITS) because of the potential to provide road safety and precautionary measures for the drivers and passengers. In this survey, we discussed the basic overview of the VANET from the architecture, communication methods, standards, characteristics, and VANET security services. Second, we presented the threats and attacks and the recent state-of-the-art methods of the VANET security services. Then, we comprehensively reviewed the authentication schemes that can protect vehicular networks from malicious nodes and fake messages. Third, we discussed the latest simulation tools and the performance of the authentication schemes in terms of simulation tools, which was followed by the VANET applications. Lastly, we identified the open research challenges and gave future research directions. In sum, this survey fills the gap of existing surveys and summarizes the latest research development. All the security attacks in VANETs and their related countermeasures are discussed with respect to ensuring secure communication. The authentication schemes and comprehensive applications were introduced and analyzed in detail. In addition, open research challenges and future research directions were issued.
Abstract Vehicles equipped with significant computing, communication and sensing (also known as “smart” vehicles), are being focused by Intelligent Transportation Systems (ITS). The primitive target of Vehicular Ad-Hoc Networks (VANETs) is to deliver safer and efficient traffic conditions by providing real time traffic conditions to automobiles and involved trusted third parties. This paper reviews eminent safety solutions to address the security aspects for VANETs. Four ingredients of this paper are (a) attacks and security mechanisms in VANETs (b) comparative analysis of security schemes based on cryptography mechanism used (c) trust management schemes based upon discrete characteristics and intrusion detection systems (d) open issues which need a thorough consideration in the future. Here we discuss how the research reflects the evolutionary growth of security attacks with its future prophesy, based upon the past developments in the area of computer security.
Modern societies are increasingly dependent on the proper functioning of critical infrastructures (CIs). CIs produce and distribute essential goods or services, as for power transmission systems, water treatment and distribution infrastructures, transportation systems, communication networks, nuclear power plants, and information technologies. Being resilient becomes a key property for CIs, which are constantly exposed to threats that can undermine safety, security, and business continuity. Nowadays, a variety of approaches exist in the context of CIs’ resilience research. This paper provides a state-of-the-art review on the approaches that have a complete qualitative dimension, or that can be used as entry points for semi-quantitative analyses. The study aims to uncover the usage of qualitative research methods through a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The paper identifies four principal dimensions of resilience referred to CIs (i.e., techno-centric, organisational, community, and urban) and discusses the related qualitative methods. Besides many studies being focused on energy and transportation systems, the literature review allows to observe that interviews and questionnaires are most frequently used to gather qualitative data, besides a high percentage of mixed-method research. The article aims to provide a synthesis of literature on qualitative methods used for resilience research in the domain of CIs, detailing lessons learned from such approaches to shed lights on best practices and identify possible future research directions.
Complex networks are made up of vertices and edges. The edges, which may be directed or undirected, are equipped with positive weights. Modeling complex systems that consist of different types of objects leads to multilayer networks, in which vertices in distinct layers represent different kinds of objects. Multiplex networks are special vertex-aligned multilayer networks, in which vertices in distinct layers are identified with each other and inter-layer edges connect each vertex with its copy in other layers and have a fixed weight $γ>0$ associated with the ease of communication between layers. This paper discusses two different approaches to analyze communication in a multiplex. One approach focuses on the multiplex global efficiency by using the multiplex path length matrix, the other approach considers the multiplex total communicability. The sensitivity of both the multiplex global efficiency and the multiplex total communicability to structural perturbations in the network is investigated to help to identify intra-layer edges that should be strengthened to enhance communicability.
<i>Background</i>: Business continuity entails the potential negative consequences of uncertainty on a firm’s ability to achieve strategic objectives. The COVID-19 pandemic significantly impacted business continuity due to lockdowns, travel restrictions, and social distancing measures. Consequently, firms adopted specific supply chain (SC) practices to effectively navigate this global crisis. <i>Methods</i>: This research adopted a stochastic approach based on Bayesian Networks to evaluate the implications of business continuity on firms’ decisions to embrace SC practices, focusing on omnichannel strategies, SC coordination, and technologies such as artificial intelligence systems, big data and machine learning, and mobile applications. <i>Results</i>: Our findings revealed that firms facing disruption in a single performance area can apply specific strategies to maintain resilience. However, multiple areas of underperformance necessitate a varied approach. <i>Conclusions</i>: According to our empirical analysis, omnichannel strategies are critical when disruptions simultaneously impact quality, inventory, sales, and ROI, particularly during major disruptions such as the COVID-19 pandemic. AI and big data become vital when multiple risks coalesce, enhancing areas such as customer service and supply chain visibility. Moreover, supply chain coordination and mobile app adoption are effective against individual performance risks, proving crucial in mitigating disruption impacts across various business aspects. These findings help policy-makers and business owners to have a better understanding of how business continuity based on performance resistance to disruptions pushes companies to adopt different practices including new technologies and supply chain coordination. Accordingly, they can use the outputs of this study to devise strategies for improving resilience considering their supply chain vulnerabilities.
Transportation and communication, Management. Industrial management
In recent years, there has been a notable surge in research interest in vehicular ad-hoc networks (VANETs) due to advancements in wireless communication technology and the vehicle sector. Vehicles to vehicles (V2V) and vehicles to infrastructure comprise a vehicular network. The potential machine learning (ML) method can offer practical solutions for various application fields. Machine learning is a technique where a system uses data that has already been processed to learn from and improve itself automatically. Vehicular networks are a significant application domain where ML-based techniques are highly helpful in solving various issues. Vehicular nodes and infrastructure communicating wirelessly are susceptible to many kinds of assaults. Intelligent transportation systems (ITS) rely heavily on vehicle ad hoc networks (VANETs). These methods enable effective supervised and unsupervised learning of the acquired data, hence accomplishing the goal of VANETs. Because of identifying security concerns in-vehicle networks from source to destination, this evaluation attempts to apply it. We outlined the problems with traffic, safety, and communication in VANET systems, discussed whether or not they could be implemented, and investigated the potential solutions provided by machine learning techniques.
ABSTRACT Connected autonomous vehicles are considered as mitigators of issues such as traffic congestion, road safety, inefficient fuel consumption and pollutant emissions that current road transportation system suffers from. Connected autonomous vehicles utilise communication systems to enhance the performance of autonomous vehicles and consequently improve transportation by enabling cooperative functionalities, namely, cooperative sensing and cooperative manoeuvring. The former refers to the ability to share and fuse information gathered from vehicle sensors and road infrastructures to create a better understanding of the surrounding environment while the latter enables groups of vehicles to drive in a co-ordinated way which ultimately results in a safer and more efficient driving environment. However, there is a gap in understanding how and to what extent connectivity can contribute to improving the efficiency, safety and performance of autonomous vehicles. Therefore, the aim of this paper is to investigate the potential benefits that can be achieved from connected autonomous vehicles through analysing five use-cases: (i) vehicle platooning, (ii) lane changing, (iii) intersection management, (iv) energy management and (v) road friction estimation. The current paper highlights that although connectivity can enhance the performance of autonomous vehicles and contribute to the improvement of current transportation system performance, the level of achievable benefits depends on factors such as the penetration rate of connected vehicles, traffic scenarios and the way of augmenting off-board information into vehicle control systems.