In modern Industry 4.0 environments, real-time scheduling presents a complex challenge requiring both formal correctness guarantees and optimal performance. <i>Background</i>: Traditional approaches fail to provide an optimal integration between formal correctness guaranteeing and optimization, and such failure either produces suboptimal results or a correct result lacking guarantee, and studies have indicated that poor scheduling decisions could cause productivity losses of up to 20–30% and increased operational costs of up to USD 2.5 million each year in medium-scale manufacturing facilities. <i>Methods</i>: This work proposes a new hybrid approach by integrating Extended Time Petri Nets (ETPNs) and Finite-State Automata (FSAs) with formal modeling, abstracting ETPNs by extending conventional Time Petri Nets to deterministic time and priority systems, accompanied by Genetic Algorithms (GAs) to optimize the solution to tackle a multi-objective optimization problem. Our solution tackles indeterministic problems by incorporating suitable priority resolution methods and GA to pursue optimal solutions to very complex scheduling problems and starting accurately from standard real-time scheduling-policy models such as DM, RM, and EDF-EDF. <i>Results</i>: Experimental evaluation has clearly verified performance gains up to 48% above conventional techniques, covering completely synthetic and practical case studies, including 31–48% improvement on synthetic benchmarks, 24% increase on resource allocation, and total elimination of constraint violations. <i>Conclusions</i>: The new proposed hybrid technique is, to a considerable extent, a dramatic advancement within real-time scheduling techniques and Industry 4.0, successfully and effectively integrating optimal correctness guaranteeing and favorable GA-aided optimization techniques, which particularly guarantee optimal correctness to safe-related applications and provide considerable improvements to support efficient and optimal performance, extremely helpful within Industry 4.0.
Transportation and communication, Management. Industrial management
To validate and expand upon a framework of diabetic retinopathy (DR) screening adherence by examining barriers and facilitators among individuals with severe DR and those under-adherent to screening. From March 2021 to February 2022, we conducted eight remote semi-structured interviews with adults with diabetes across two participant populations: (1) participants with severe DR who had undergone procedures (n = 4) and (2) participants under-adherent to screening, defined as no eye exam in >1 year (n = 4). We recruited participants from the Yale Eye Center and community referrals. During the interviews, we collected demographic data and presented participants with a DR screening adherence framework previously developed by our group. We transcribed all interviews and conducted analyses using a hybrid deductive-inductive analytic approach informed by grounded theory techniques to identify recurring themes. Themes across both participant populations aligned with the existing framework, including vision status, emotional context, competing concerns, resource availability, cues to action, knowledge-creating experiences, and in-clinic experiences. The patient-doctor relationship emerged as a sub-theme of in-clinic experiences, highlighting the role of trust and communication in supporting sustained eye care engagement. At the individual level, participants with stable vision often perceived no need for screening. At the institutional and structural level, participants identified lack of insurance and transportation as significant barriers. These findings support the robustness of the DR screening adherence framework across varying levels of disease severity and engagement. Interventions that improve patient education, address structural barriers, and strengthen patient-doctor relationships may enhance screening adherence among populations at high risk for vision loss.
The use of tire-derived aggregates (TDA) in railway infrastructure has gained significant attention in recent years due to its potential benefits, including vibration mitigation, ballast degradation reduction, increased damping, decreased stiffness, and improved dynamic performance of bridges. However, one major concern regarding the use of TDA-ballast mixes is the potential for increased settlement, primarily due to the softer nature of rubber particles. Hence, investigating the effect of TDA content and size on track settlement and identifying the optimal mix configuration is of critical importance. This study evaluates these effects through a series of laboratory ballast box tests conducted on different mixtures. The experimental program included pure ballast samples (as control) and ballast-TDA mixes with 5%, 10%, 15%, and 20% TDA by weight. Two TDA size ranges were considered: fine particles (10–20 mm diameter) and coarse particles (comparable to ballast size). The results revealed that, except for the mix with 10% coarse TDA—which exhibited a 1.7% reduction in settlement compared to the control—all other mixes (with 5%, 15%, and 20% coarse TDA) experienced increased settlement by 24%, 155%, and 217%, respectively. Furthermore, settlement increased significantly with rising TDA content in mixes using fine particles. Specifically, the increases in settlement for 5%, 10%, 15%, and 20% fine TDA mixes, compared to the pure ballast sample, were 22%, 88%, 234%, and 310%, respectively. Based on the results, the mix with 10% coarse TDA is recommended as the optimal configuration, offering improved long-term performance without compromising stability. These findings provide valuable insight for the design and optimization of ballast–TDA mixes, facilitating the effective integration of recycled materials in railway tracks while mitigating long-term settlement issues.
Visible Light Communication (VLC) has the potential to advance Intelligent Transportation Systems (ITS). This study explores the current advancements of VLC in ITS applications that may enhance traffic flow, road safety, and vehicular communication performance. The potential, benefits, and current research trends of VLC in ITS applications are discussed first. Then, the state-of-the-art VLC technologies including overall concept, IEEE communication protocols, hybrid VLC systems, and software-defined adaptive MIMO VLC systems, are discussed. We investigated different potential applications of VLC in ITS, such as signalized intersection and ramp metering control, collision warning and avoidance, vehicle localization and detection, and vehicle platooning using vehicle–vehicle (V2V), infrastructure–vehicle (I2V), and vehicle–everything (V2X) communications. Besides, VLC faces several challenges in ITS applications, and these concerns, e.g., environmental issues, communication range issues, standards and infrastructure integration issues, light conditions and integration issues are discussed. Finally, this paper discusses various advanced techniques to enhance VLC performance in ITS applications, such as machine learning-based channel estimation, adaptive beamforming, robust modulation schemes, and hybrid VLC integration. With this review, the authors aim to inform academics, engineers, and policymakers about the status and challenges of VLC in ITS. It is expected that, by applying VLC in ITS, mobility will be safer, more efficient, and sustainable.
Vehicular Ad-hoc Networks (VANETs) are integral to the fabric of Intelligent Transportation Systems (ITSs), facilitating essential vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, the rising prevalence of jamming attacks, characterized by the intentional disruption of communications through interference signals, presents a significant challenge to the security of VANETs and, consequently, public safety. This emerging threat highlights a critical research gap in the development of sophisticated, AI-driven security solutions for VANETs. In response to this challenge, our study introduces an innovative artificial intelligence (AI) model, meticulously engineered to detect jamming attacks in VANETs. This model represents a synergistic integration of an array of machine learning (ML) and deep learning (DL) classifiers, meticulously analyzing signal characteristics within VANET communication channels. Its primary aim is the effective identification of anomalous patterns signaling the presence of jamming attacks. Extensive simulations were conducted to rigorously test the model’s efficacy, which yielded encouraging results. Initially, we assessed the detection accuracy of 14 different ML classifiers and 4 DL classifiers. Subsequently, we proposed a voting-based ensemble AI classifier combining the most accurate ML and DL classifiers, namely Random Forest (RF), Extra Tree (ET), and fine-tuned Convolutional Neural Network (CNN). This ensemble classifier, RF+ET+CNN, achieved the highest detection accuracy, outperforming the individual classifiers. Specifically, the CNN algorithm demonstrated an exceptional detection accuracy of 99.133%, while the RF and ET classifiers were the most accurate among the ML algorithms tested, with accuracy rates of 97.4359% and 97.4357%, respectively. Notably, the proposed RF+ET+CNN ensemble classifier achieved an impressive detection accuracy of 99.8125%. These findings underscore the superiority of our proposed model over existing jamming detection models. The integration of this model into existing VANET security systems is anticipated to significantly enhance their capability to mitigate jamming attacks, thereby reinforcing the overall security and reliability of VANET communications. This advancement is particularly pertinent in the context of smart city infrastructures, where the safety and efficiency of transportation networks are paramount.
Vascular calcification and vascular ageing are “silent” diseases but are highly prevalent in patients with end stage renal failure and type 2 diabetes, as well as in the ageing population. Melatonin (MT) has been shown to induce cardiovascular protection effects. However, the role of MT on vascular calcification and ageing has not been well-identified. In this study, the aortic transcriptional landscape revealed clues for MT related cell-to-cell communication between endothelial cells (ECs) and vascular smooth muscle cells (VSMCs) in vascular calcification and vascular ageing. Furthermore, we elucidated that it was exosomes that participate in the information transportation from ECs to VSMCs. The exosomes secreted from melatonin-treated ECs (MT-ECs-Exos) inhibited calcification and senescence of VSMCs. Mechanistically, miR-302d-5p was highly enriched in MT-ECs-Exos, while depletion of miR-302d-5p blocked the ability of MT-ECs-Exos to suppress VSMC calcification and senescence. Notably, Wnt3 was a bona fide target of miR-302d-5p and modulated VSMC calcification and senescence. Furthermore, we found that maturation of endothelial derived exosomal miR-302d-5p was promoted by WTAP in an N6-methyladenosine (m6A)-dependent manner. Interestingly, MT alleviated vascular calcification and ageing in 5/6-nephrectomy (5/6 NTP) mice, a chronic kidney disease (CKD) induced vascular calcification and vascular ageing mouse model. MT-ECs-Exos was absorbed by VSMCs in vivo and effectively prevented vascular calcification and ageing in 5/6 NTP mice. ECs-derived miR-302d-5p mediated MT induced anti-calcification and anti-ageing effects in 5/6 NTP mice. Our study suggests that MT-ECs-Exos alleviate vascular calcification and ageing through the miR-302d-5p/Wnt3 signaling pathway, dependent on m6A methylation.
Materials of engineering and construction. Mechanics of materials, Biology (General)
Mobility is facing a transformation in terms of connectivity, allowing vehicles to communicate with each other, to the road infrastructure, and to other road users. This enables coordination and cooperation, hence managing traffic and mobility at an entirely new level. Indeed, Cooperative, Connected and Automated Mobility enables and provides ITS services with better Quality of Service (QoS), compared to the same ITS services by only one of the ITS sub-systems (personal, vehicle, roadside, and central, infrastructures), thus improving the road management, reducing congestion, and contributing to sustainable and eco-mobility. By leveraging a network of Smart Infrastructures, it is possible to be continuously and promptly aware about the circulation and environment conditions, as well as the status of connected devices, along with the related technological services. Such knowledge, gained via the adoption of advanced sensing/communication technologies, has the potential to fundamentally shift the mobility paradigm towards mobility as a service. This contributes to more safe, efficient, and comfortable transportation systems. Along this line, information is continuously communicated/shared to vehicles and travellers thanks to dedicated communication services, thus enabling mobility automation and control. Different services - such as providing information about traffic light signal phases and their predicted changes or barriers on the route in realtime- support smooth and comfortable traveling by avoiding strong accelerations/decelerations, by reducing fuel/energy consumption of vehicles with favoured effects on lowering noise and emissions. In this perspective, the special section aims at exploring how to face Coordination and Cooperation challenges for autonomous vehicles in this new connected environment, also in the transition phase where connected human-driven vehicles are present.
Transportation engineering, Transportation and communications
This study investigates the transformation of e-commerce warehouse operations by integrating Lean Six Sigma tools to enhance efficiency and sustainability. Beginning with Value Stream Mapping (VSM) to identify inefficiencies, followed by a Hoshin Kanri plan to align improvement initiatives with strategic objectives, the study implemented measures such as pallet pooling, process standardization, automation in inspection and picking, layout optimization, and Kanban systems for continuous improvement. A case study of a local e-commerce warehouse specializing in medical devices and healthcare products identified 29 activities across receiving, inspection, storing, picking, packing, and shipping, highlighting inefficiencies addressed through Lean-driven initiatives. These efforts resulted in a 23% reduction in total lead time, doubled value-added time, and significant improvements in inspection, picking, packing, and automation, reducing delays, lowering costs, and enhancing workflow. The study fills a gap in the literature by integrating multiple Lean tools and utilizing the Critical to Quality (CTQ) matrix to ensure sustainable improvements in e-commerce warehousing, emphasizing the strategic value of Lean Six Sigma in creating efficient, customer-focused operations.
Transportation and communication, Management. Industrial management
Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results. Achieving both accuracy and explainability in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models. To tackle these challenges, we propose a traffic flow prediction model based on large language models (LLMs) to generate explainable traffic predictions, named xTP-LLM. By transferring multi-modal traffic data into natural language descriptions, xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data. The LLM framework is fine-tuned using language-based instructions to align with spatial-temporal traffic flow data. Empirically, xTP-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions. This study contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation.
<p>This study aims to reveal conditions that have been neglected so far regarding the development of communication technology in online transportation. With a large number of partners, the company provides features to service users to provide an assessment of online motorcycle taxi drivers. Companies are no longer present to discipline their workers with strict regulations, but by using a panopticon monitoring system that is delegated to service users. This study uses a qualitative approach by conducting surveys and interviews with online motorcycle taxi drivers and users. The results of the participant interviews were then analyzed and supported by documentation studies originating from books, journals, and some data obtained from online media, or also known as data triangulation. The results of the study show that the panopticon system implemented by the company provides certainty of supervision so that it forms the discipline of those who are supervised by forming homogeneous behavior. The power relations that occur between companies and their partners place online motorcycle taxi drivers in a vulnerable position to symbolic violence through supervision. </p>
No existing automated vehicle can operate in all conditions and environments. In order to allow unmanned operation of automated vehicles in all conditions, many developers have the capability for human drivers to operate the vehicle from a remote location using wireless communication. This practice, referred to as remote operation or teleoperation, is prevalent among industry, yet has received little attention in the legal and transportation literature. This paper describes the legal environment for remote operation of vehicles, both in terms of existing motor vehicle codes and model legislation. The operational performance of remote operation is explored, and a model is developed to estimate the number of remote operators needed to manage large automated vehicle fleets using reasonable assumptions.
SummaryTwo‐dimensional discrete wavelet transform is an important tool for digital image analysis. It is widely used in the field of image editing, such as image coding and compression and digital image processing. The realization of effective two‐dimensional discrete wavelet transform has important realities in data or image processing. This paper mainly introduces the research of intelligent transportation system data denoising and compression based on two‐dimensional discrete wavelet transform and intends to provide solutions to the problem of data overload in the process of intelligent transportation system data collection, transmission, and storage. This paper proposes the construction of two‐dimensional discrete wavelet transform, including separation calculation method and non‐separation calculation method, and proposes the construction of time–space data model of intelligent transportation system, including support vector machine (SVM), K nearest neighbor algorithm, and deep neural network algorithm. It is proposed to construct two‐dimensional wavelet transform to denoise and compress data of intelligent transportation systems. The experimental results in this paper show that the signal after denoising by two‐dimensional discrete wavelet transform is smoother, with a maximum difference of 0.57, and the denoising effect is better.
Isakova Elena, Kryukova Natalia, Aleksandrova Elena
Modern transportation and communication technology, the Internet, have put people closer together, made the world a tiny place indeed, thus making interaction and cooperation between countries faster and ever more consistent. Growing numbers of interpersonal connections on the global scale often level traditions and cultures. Development of mass culture is making people, to some extent, similar. The urge to stabilise one’s internal structures of personality had become a natural reaction to these processes, that is why creating positive cultural image is now a pressing need for many. Tourism as a type of cross-cultural communication is one of the powerful stimuli for that need as well as the source of positive perception of one’s environment. In this article we will address the following issues: text rhetoric of educational and tourist brochures as well as their discourse, connected by genre of educational tourism brochure. We argue that it is possible to build and enhance the positive image of a region by means of linguistics, i.e., using a particular method of discourse – language of tourism – and various techniques that we point out when we describe specific features of discourse of education and discourse of tourism.
Nerea Fernández-Berrueta, Jon Goya, Jaione Arrizabalaga
et al.
Railway applications are in continuous evolution with the aim of offering a more efficient, sustainable, and safer transportation system for the users. Generally, these applications are constantly exchanging information between the systems onboard the train and the trackside through a wireless communication. Nowadays, Global System for Mobile communications-Railway (GSM-R) is the technology used by European Train Control System (ETCS), but it is becoming obsolete. Therefore, alternatives for this technology have to be found for the different railway applications. Its natural evolution is to move forward with the latest technology deployed: Long-Term Evolution (LTE), which the Public Land Mobile Networks (PLMN) have already deployed. Therefore, testing the performance of this communication technology in the railway environment could be useful to assess its suitability and reduce the cost of railway network dedicated deployment. In order to do that, a methodology to characterize the communication environment is proposed. The main goal is to measure geolocated impairments of any communication channel in a railway environment being able to determine its behavior of the different communication technologies and find out possible coverage issues. Moreover, it could help in the selection of suitable communication technology for railway. This paper presents a brief description of the communication for railways and its QoS parameters for performance measuring. Afterward, the testing methodology is described, and then, the communication channel measurement campaign on a real track in Spain where the railway environment is variable is presented (tunnels, rural/urban area…). Finally, the measurements and results on this real track in Spain are shown. The results provide suitability of the 4G technologies based on the delay requirements for the implementation of ETCS over it.
This study aims to provide valuable insights into the information logistics process and to ensure the effectiveness of its systems in the business environment by discussing the banking sector. In this regard, this study investigates the information logistics system of a bank’s IT service center. For this purpose, the instruments for transfer of the bank Z information logistics system were empirically tested in order to highlight the assessment factors; the various factors related to the bank Z information logistics management methodologies and the methods were examined. The key findings and contributions were achieved, i.e., we developed a systematic model for assessing information logistics systems in a bank. The model explains the adaptation of various methodologies and techniques of information logistics systems in the banking sector and justifies the platform of interaction of information logistics processes. The first part of the article introduces the concept of information logistics systems and unpacks its development and management methodologies and processes. The second part introduces the research methodology for assessing information logistics systems. The results highlight the system of management processes applied to the information logistics and discuss the areas and tools for improving this system. The main outcome of this research is presented in the third part, where the systematic model for assessing information logistics systems is developed. This model distinguishes the stages of information logistics cycles and the levels of the information logistics environments. It indicates the information logistics methodologies and techniques linking to the six types of management processes in the IT service center of a bank. The developed model helps to make the assessment of the information cycle more efficient and demonstrates a customizable result which can be adapted by other companies within their IT service centers.
Transportation and communication, Management. Industrial management
Vehicular congestion is directly impacting the efficiency of the transport sector. A wireless sensor network for vehicular clients is used in Internet of Vehicles based solutions for traffic management applications. It was found that vehicular congestion detection by using Internet of Vehicles based connected vehicles technology are practically feasible for congestion handling. It was found that by using Fog Computing based principles in the vehicular wireless sensor network, communication in the system can be improved to support larger number of nodes without impacting performance. In this paper, connected vehicles technology based vehicular congestion identification techniques are studied. Computing paradigms that can be used for the vehicular network are studied to develop a practically feasible vehicular congestion detection system that performs accurately for a large coverage area and multiple scenarios. The designed system is expected to detect congestion to meet traffic management goals that are of primary importance in intelligent transportation systems.
Abstract Identification of critical components in transportation networks is an essential part of designing robust and resilient systems. Topological criticality measures are based on graph theory and are applicable in multiple domains including communication and social networks. However, the non-linearity of link performance functions in transportation systems does not allow a perfect domain transfer of topological measures. Hence, transportation researchers take traffic flow characteristics into account while developing criticality measures. In such approaches, typically, a network performance measure is selected, then links are removed one-by-one, and traffic demand is reassigned to the updated network to calculate the impacts of each link failure. This consecutive link removal procedure requires multiple assignments which create a computational burden, especially for large networks. Overall objectives of this paper are (1) to compare and contrast selected criticality measures, and (2) to develop a new measure to identify critical components of transportation network, considering both traffic characteristics and network topology. For this purpose, the user equilibrium traffic assignment formulation is utilized, and the convex combinations solution algorithm is exploited for identification of link criticality ranking within a single traffic assignment. The developed measure is named Link Criticality Index (LCI). The LCI is compared with the existing measures in the literature through three numerical examples. Pros and cons of the LCI and selected measures are discussed in detail. The results indicate the proposed link criticality measure provides a balanced ranking with respect to connectivity/redundancy as well as the traffic conditions in the network.