Hasil untuk "Transportation and communication"

Chenliang Tian, Zebo Yang, Raj Jain et al.

In quantum networks, one way to communicate is to distribute entanglements through swapping at intermediate nodes. Most existing work primarily aims to create efficient two-party end-to-end entanglement over long distances. However, some scenarios also require remote multipartite entanglement for applications such as quantum secret sharing and multi-party computation. Our previous study improved end-to-end entanglement rates using an asynchronous, tree-based routing scheme that relies solely on local knowledge of entanglement links, conserving unused entanglement and avoiding synchronous operations. This article extends this approach to multipartite entanglements, particularly the three-party Greenberger-Horne-Zeilinger (GHZ) states. It shows that our asynchronous protocol outperforms traditional synchronous methods in entanglement rates, especially as coherence times increase. This approach can also be extended to four-party and larger multipartite GHZ states, highlighting the effectiveness and adaptability of asynchronous routing for multipartite scenarios across various network topologies.

Tibor Petrov, Wissam Sleiman, Pedram Beigi et al.

Milan Tamis, Gerdien de Vries, Reint Jan Renes

Yusef Modami, Hamzeh Beyranvand, Mohammad Taghi Dabiri

Claudemir Leif Tramarico, Aneirson Francisco Da Silva, José Eduardo Holler Branco

<i>Background:</i> Effective decision-making in supply chain contexts requires understanding how criteria interact to shape rational and transparent decision structures. This study investigates how behavioral aspects influence the structuring of decision-making logic and the interdependencies between key criteria in supply chain contexts. <i>Methods:</i> Using Fuzzy DEMATEL, the research models the interactions between five core criteria —classification, definition, specification, decision, and action feedback—based on inputs from experienced professionals in a global chemical company. The approach enables mapping of causal influences while accounting for subjectivity and uncertainty in expert judgments. <i>Results:</i> The analysis identified specification, definition, and action feedback as causal criteria, with classification and decision being primarily influenced by them. The modeling process supported clearer prioritization and revealed how expert-based interactions can reduce decision biases. <i>Conclusions:</i> This study demonstrates how structuring decision-making logic through causal modeling enhances clarity and reduces subjectivity. The findings contribute to the development of decision support tools applicable across strategic supply chain contexts, offering practical implications for professionals seeking to improve decision transparency and effectiveness.

Supanida Nanthawong, Panuwat Wisutwattanasak, Chinnakrit Banyong et al.

<i>Background</i>: Truck drivers are a vital workforce sustaining Thailand’s freight transport, particularly in Northeastern Thailand (Isan), a major logistics hub connecting with Laos, Vietnam, and Cambodia via Highway No. 2 and the AEC network. However, these drivers face disproportionately high risks of severe road accidents due to occupational factors such as fatigue, time pressure, and long-distance driving. <i>Methods</i>: This study developed and validated a second-order confirmatory factor analysis (CFA) model to examine the multidimensional structure of risky driving behavior among Thai truck drivers. Grounded in the Driver Behavior Questionnaire (DBQ), the framework was extended to include seven dimensions: traffic violations, errors, lapses, aggressive behavior, substance use, technology-related distractions, and pedestrian-related risks. <i>Results</i>: Data were collected from 400 truck drivers in Isan using a structured questionnaire. CFA results confirmed the model’s structural validity, with satisfactory fit indices (X²/df = 2.122, CFI = 0.913, TLI = 0.897, RMSEA = 0.053, SRMR = 0.079). <i>Conclusions</i>: The findings reveal that risky driving behavior in this group extends beyond traditional DBQ categories, incorporating emerging risks specific to the commercial transport environment. This framework can be effectively utilized for risk assessment, behavioral screening, and the development of targeted safety interventions for this high-risk occupational group.

Abdulaziz Zaid A. Aljarwan, Md Asri Bin Ngadi

Vehicular networks play an integral role in Intelligent Transportation Systems (ITS) by facilitating real-time communication between vehicles, roadside infrastructure, and cloud-based services. But this inherent characteristics on mobility, decentralization, and open wireless broadcast nature makes security threats a big target to them with impersonation attacks, Sybil attacks, data breaches and unauthorized access. PKI-based authentication methods, on the other hand, do not fit our objective, given the high computational overhead, certificate management issues, and centralization risk involved in V2V authentication protocols. Certificateless authentication has been proposed to avoid the challenges and costs associated with certificate-based security infrastructures by providing a lightweight and efficient alternative solution. Based on authentication models, cryptographic techniques, privacy-preserving mechanisms, emerging technologies and security properties, this survey presents a systematic classification of certificateless authentication schemes. It encompasses an investigation of emerging cryptographic solutions like lattice-based encryption, blockchain-supported authentication/credential management, AI-empowered security, and considers their impact on VANET security, scalability, and real-time constraints. Moreover, this study considers the application of 5G networks, edge computing, and post-quantum cryptography in VANET authentication. To ensure privacy-preserving and secure vehicular communication systems that could be scaled for infrastructural networks, the results highlight the shortcomings of existing vehicular authentication methods, and the need for hybrid authentication models, standardization, and interoperability.

Manuel PINTO GRUNFELD

In a context of pervasive connectivity and multimedia environments, brand communication increasingly relies on discursive forms that transcend literal description to produce cultural meanings. These representational structures combine image and text across diverse formats and media, strategically engaging audiences and reconfiguring real referents into widely circulating symbolic narratives. This article advances conceptual foundations for a cultural–semiotic reading of brand communication, conceived as an intertextual and intermedial myth. The focus is on audiovisual and digital campaigns that adapt and appropriate authentic attributes, benefits, and spaces, transforming them into narratives of identity, belonging, and consumption. For exemplification, three main areas are considered: products such as food, clothing, or automobiles; services such as mobile telephony, entertainment venues, or urban transportation; and places such as tourist countries, heritage regions, or developed cities. Recent Portuguese campaigns illustrate these dynamics, demonstrating how cultural references are reworked into intertextual and intermedial myths within the online world. Without constituting an in–depth case study or a closed methodology, the article proposes an exploratory reading framework that can be applied to different communicational fields and sociocultural contexts. Ultimately, reflecting on brands as cultural semiotic myths clarifies their role in shaping collective imaginaries, generating symbolic value, and constructing shared social realities in the digital age.

Kai Xiong, Zhen Chen, Juefei Xie et al.

Electric Vertical Take-off and Landing vehicles (eVTOLs) are driving Advanced Air Mobility (AAM) toward transforming urban transportation by extending travel from congested ground networks to low-altitude airspace. This transition promises to reduce traffic congestion and significantly shorten commute times. To ensure aviation safety, eVTOLs must fly within prescribed flight corridors. These corridors are managed by ground-based Air Traffic Control (ATCo) stations, which oversee air-ground communication and flight scheduling. However, one critical challenge remains: the lack of high rate air-ground communication and safe flight planning within these corridors. The introduction of 6G-oriented Stacked Intelligent Metasurface (SIM) technology presents a high rate communication solution. With advanced phase-shifting capabilities, SIM enables precise wireless signal control and supports beam-tracking communication with eVTOLs. Leveraging this technology, we propose a Composite Potential Field (CPF) approach. This method dynamically integrates target, separation, and communication fields to optimize both SIM communication efficiency and flight safety. Simulation results validate the effectiveness of this DT-based approach. Compared to the potential field flight control benchmark, it improves the transmission rate by 8.3\%. Additionally, it reduces flight distance deviation from the prescribed corridor by 10\% compared to predetermined optimization methods.

Demayla Jenkins, Janeroza Matyenyi, Thobias Sando et al.

Transportation provides access to employment opportunities and essential services such as healthcare services; while urban areas have various transportation options, the situation differs in rural areas. Rural residents often have longer commute distances, limited access to public transit, and extended waiting times for public transportation if they exist, which can significantly impact their access to vital services and job opportunities. This study used data from the 2017 NHTS survey to examine the commuting patterns in rural areas by utilizing multinomial logistic regression to determine how various factors impact the choice of mode of transport in rural areas. Findings from this study revealed a higher dependency, 92.1%, on using personal vehicles when making trips in rural areas. Multinomial logistic regression results showed that socio-demographics, household, and trip characteristics affect the mode of transport used in a trip. Older adults, females, and individuals with higher education levels than high school graduates are less likely to use public transit when making trips. For household characteristics, the availability of vehicles in a household and households with higher income levels have lower probabilities of making a trip using public transit. Longer trip distances reduce the likelihood of a trip using active commuting modes such as walking and biking. These findings provide insights into understanding the transportation behaviors in rural areas and provide knowledge to be used in the planning and developing transportation projects that promote equitable and accessible transportation in rural areas

Akifumi Kira, Nobuo Terajima

In this study, we focus on a form of joint transportation called mixed transportation and enumerate the combinations with high cooperation effects from among a number of transport lanes registered in a database (logistics big data). As a measure of the efficiency of mixed transportation, we consider the reduction rate that represents how much the total distance of loading trips is shortened by cooperation. The proposed algorithm instantly presents the set of all mixed transports with a reduction rate of a specified value or less. This algorithm is more than 7,000 times faster than simple brute force.

Fan Liu, Hui-Yu Yang, Shuai-Li Wang et al.

Quantum battery exploits the principle of quantum mechanics to transport and store energy. We study the energy transportation of the central-spin quantum battery, which is composed of $N_b$ spins serving as the battery cells, and surrounded by $N_c$ spins serving as the charger cells. We apply the invariant subspace method to solve the dynamics of the central-spin battery with a large number of spins. We establish a universal inverse relationship between the battery capacity and the battery-charger entanglement, which persists in any size of the battery and charger cells. Moreover, we find that when $N_b=N_c$, the central-spin battery has the optimal energy transportation, corresponding to the minimal battery-charger entanglement. Surprisingly, the central-spin battery has a uniform energy transportation behaviors in certain battery-charger scales. Our results reveal a nonmonotonic relationship between the battery-charger size and the energy transportation efficiency, which may provide more insights on designing other types of quantum batteries.

Nazifa Mustari, Muhammet Ali Karabulut, A.F.M. Shahen Shah et al.

Laura Vaccari, Antonio Maria Coruzzolo, Francesco Lolli et al.

<i>Background:</i> Indoor Positioning Systems (IPS) have gained increasing relevance in logistics, offering solutions for safety enhancement, intralogistics management, and material flow control across various environments such as industrial facilities, offices, hospitals, and supermarkets. This study aims to evaluate IPS technologies’ performance and applicability to guide practitioners in selecting systems suited to specific contexts. <i>Methods:</i> The study systematically reviews key IPS technologies, positioning methods, data types, filtering methods, and hybrid technologies, alongside real-world examples of IPS applications in various testing environments. <i>Results:</i> Our findings reveal that radio-based technologies, such as Radio Frequency Identification (RFID), Ultra-wideband (UWB), Wi-Fi, and Bluetooth (BLE), are the most commonly used, with UWB offering the highest accuracy in industrial settings. Geometric methods, particularly multilateration, proved to be the most effective for positioning and are supported by advanced filtering techniques like the Extended Kalman Filter and machine learning models such as Convolutional Neural Networks. Overall, hybrid approaches that integrate multiple technologies demonstrated enhanced accuracy and reliability, effectively mitigating environmental interferences and signal attenuation. <i>Conclusions:</i> The study provides valuable insights for logistics practitioners, emphasizing the importance of selecting IPS technologies suited to specific operational contexts, where precision and reliability are critical to operational success.

Dexter Le, Aybars Yunusoglu, Karn Tiwari et al.

In the evolving landscape of transportation systems, integrating Large Language Models (LLMs) offers a promising frontier for advancing intelligent decision-making across various applications. This paper introduces a novel 3-dimensional framework that encapsulates the intersection of applications, machine learning methodologies, and hardware devices, particularly emphasizing the role of LLMs. Instead of using multiple machine learning algorithms, our framework uses a single, data-centric LLM architecture that can analyze time series, images, and videos. We explore how LLMs can enhance data interpretation and decision-making in transportation. We apply this LLM framework to different sensor datasets, including time-series data and visual data from sources like Oxford Radar RobotCar, D-Behavior (D-Set), nuScenes by Motional, and Comma2k19. The goal is to streamline data processing workflows, reduce the complexity of deploying multiple models, and make intelligent transportation systems more efficient and accurate. The study was conducted using state-of-the-art hardware, leveraging the computational power of AMD RTX 3060 GPUs and Intel i9-12900 processors. The experimental results demonstrate that our framework achieves an average accuracy of 91.33\% across these datasets, with the highest accuracy observed in time-series data (92.7\%), showcasing the model's proficiency in handling sequential information essential for tasks such as motion planning and predictive maintenance. Through our exploration, we demonstrate the versatility and efficacy of LLMs in handling multimodal data within the transportation sector, ultimately providing insights into their application in real-world scenarios. Our findings align with the broader conference themes, highlighting the transformative potential of LLMs in advancing transportation technologies.

Eleonora Grassucci, Jihong Park, Sergio Barbarossa et al.

While deep generative models are showing exciting abilities in computer vision and natural language processing, their adoption in communication frameworks is still far underestimated. These methods are demonstrated to evolve solutions to classic communication problems such as denoising, restoration, or compression. Nevertheless, generative models can unveil their real potential in semantic communication frameworks, in which the receiver is not asked to recover the sequence of bits used to encode the transmitted (semantic) message, but only to regenerate content that is semantically consistent with the transmitted message. Disclosing generative models capabilities in semantic communication paves the way for a paradigm shift with respect to conventional communication systems, which has great potential to reduce the amount of data traffic and offers a revolutionary versatility to novel tasks and applications that were not even conceivable a few years ago. In this paper, we present a unified perspective of deep generative models in semantic communication and we unveil their revolutionary role in future communication frameworks, enabling emerging applications and tasks. Finally, we analyze the challenges and opportunities to face to develop generative models specifically tailored for communication systems.

Huiqiang Xie, Zhijin Qin, Xiaoming Tao et al.

Deep learning enabled semantic communications have shown great potential to significantly improve transmission efficiency and alleviate spectrum scarcity, by effectively exchanging the semantics behind the data. Recently, the emergence of large models, boasting billions of parameters, has unveiled remarkable human-like intelligence, offering a promising avenue for advancing semantic communication by enhancing semantic understanding and contextual understanding. This article systematically investigates the large model-empowered semantic communication systems from potential applications to system design. First, we propose a new semantic communication architecture that seamlessly integrates large models into semantic communication through the introduction of a memory module. Then, the typical applications are illustrated to show the benefits of the new architecture. Besides, we discuss the key designs in implementing the new semantic communication systems from module design to system training. Finally, the potential research directions are identified to boost the large model-empowered semantic communications.

Walker Connor

Abubakar Ahmad Musa, Adamu Hussaini, Weixian Liao et al.

Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research.

Edi Nyoto Setyo Marsusiadi, Yuwono Wiarco, Lisma Meilia Wijayanti et al.

This study aims to describe the use of standard language in realizing effective communication between train facilities and infrastructure crews in carrying out their duties and functions for the creation of professional rail transportation mode services. Qualitative descriptive research methods that describe sociolinguistic conditions in the field of railway transport operators, as far as the observed knowledge that communication is used by the crew of facilities and infrastructure in carrying out their duties Observation, interview, and documentation techniques are carried out to determine the ability to use communication standards in the form of standard language and the implementation of effective communication of facilities and infrastructure crews in serving train travel Engineering Data analysis through the stages of reduction, presentation of data and drawing conclusions. The results of this study illustrate that good and correct mastery of Indonesian still needs to be improved and the use of standard language in train facilities and infrastructure crews still has limitations on the ability to use standard and non-standard types of words, both functionally the variety of standard language and standard sentence requirements. The goal of effective communication can be realized if between the communicator / sender of the message and the communicant / receiver of the message can carry out the message conveyed correctly without having to follow a standardized conversation pattern, although sometimes it does not follow the conversation pattern or recommended communication procedures following the technical guidance guidelines contained in the Standard Operating Procedure (SOP).