Hasil untuk "Transportation and communication"

Subul Malik, Marina Plesons, Monica Faraldo et al.

Abstract Background People who inject drugs (PWID) are at increased risk for viral hepatitis, yet hepatitis A virus (HAV) and hepatitis B virus (HBV) screening and immunization rates remain low. Although offering HAV and HBV services at syringe services programs (SSPs) is effective, few U.S. SSPs currently offer them. Limited qualitative research exists on the advantages and optimization of these services at SSPs. This study explored PWID and SSP staff perspectives regarding barriers to HAV and HBV prevention and care services in traditional healthcare, facilitators for SSP-based provision, and opportunities to improve service delivery. Methods This study was conducted at an SSP in Miami, Florida serving over 2500 PWID annually. Quantitative data on vaccine administration from August 2023 to May 2025 were abstracted from the SSP database. Prior to implementation, in May 2022, we conducted in-depth interviews with 15 PWID and 11 SSP staff. Transcripts were analyzed using codebook thematic analysis in Dedoose. Results From August 2023 to May 2025, the SSP administered 114 HAV and 176 HBV vaccine doses. Qualitative interviews from May 2022 revealed several key findings. Barriers included limited knowledge, stigma and discrimination, resource and transportation challenges, navigation difficulties, and limited prioritization. Facilitators for SSP-based services included the benefits of co-located, on-demand care, and non-stigmatizing and supportive environment. Opportunities for improvement included offering incentives, expanding outreach, and increasing communication. Conclusion PWID face significant barriers to HAV and HBV services in traditional healthcare, including stigma, logistical challenges, and limited awareness of viral hepatitis. Integrating these services into SSPs enhanced accessibility and uptake by leveraging trust, convenience, and harm reduction principles.

Dazhe Wang, Xiaolei Yang

Enhancing the subjective well-being of poor households is crucial for the world’s sustainable development. Using a comprehensive household-level dataset from the China Household Finance Survey (CHFS) spanning 2011 to 2019, this study employed a multi-period difference-in-differences (DID) approach to systematically identify the causal effect and underlying mechanisms of the Targeted Poverty Alleviation (TPA) program on the subjective well-being of poverty-stricken households. Then it explored the heterogeneous effects of different assistance measures on their subjective well-being. We found that the TPA program significantly improves the subjective well-being of rural poor households after a series of robustness checks. The analysis indicated that the TPA program improves the happiness of poor households by reducing their relative poverty and promoting their labor participation to eliminate poverty. We found that providing basic public services, means of agricultural production, and communication infrastructure all enhance the positive impact of TPA on happiness, while the housing relocation program, transportation infrastructure investment, and agricultural technical support do not. The conclusions of this study have important policy implications for ensuring equitable access to basic public services, consolidating the effective link between poverty alleviation achievements and rural revitalization in the post-poverty era, thereby promoting the common prosperity of rural households.

Mingqian Liu, Zhenju Zhang, Yunfei Chen et al.

Air transportation communication jamming recognition model based on deep learning (DL) can quickly and accurately identify and classify communication jamming, to improve the safety and reliability of air traffic. However, due to the vulnerability of deep learning, the jamming recognition model can be easily attacked by the attacker’s carefully designed adversarial examples. Although some defense methods have been proposed, they have strong pertinence to attacks. Thus, new attack methods are needed to improve the defense performance of the model. In this work, we improve the existing attack methods and propose a double level attack method. By constructing the dynamic iterative step size and analyzing the class characteristics of the signals, this method can use the adversarial losses of feature layer and decision layer to generate adversarial examples with stronger attack performance. In order to improve the robustness of the recognition model, we use adversarial examples to train the model, and transfer the knowledge learned from the model to the jamming recognition models in other wireless communication environments by transfer learning. Simulation results show that the proposed attack and defense methods have good performance.

Zhiqing Wei, Jinzhu Jia, Yangyang Niu et al.

Integrated sensing and communication (ISAC) is expected to play a crucial role in the sixth-generation (6G) mobile communication systems, offering potential applications in the scenarios of intelligent transportation, smart factories, etc. The performance of radar sensing in ISAC systems is closely related to the characteristics of radar sensing and communication channels. Therefore, ISAC channel modeling serves as a fundamental cornerstone for evaluating and optimizing ISAC systems. This article provides a comprehensive survey on the ISAC channel modeling methods. Furthermore, the methods of target radar cross section (RCS) modeling and clutter RCS modeling are summarized. Finally, we discuss the future research trends related to ISAC channel modeling in various scenarios.

Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ahmed Ali Ahmed et al.

Abstract Vehicular Ad-Hoc Networks (VANETs) have facilitated the massive exchange of real-time traffic and weather conditions, which have helped prevent collisions, reduce accidents, and road congestions. This can effectively enhance driving safety and efficiency in technology-driven transportation systems. However, the transmission of massive and sensitive information across public wireless communication channels exposes the transmitted data to a myriad of privacy as well as security threats. Although past researches has developed many vehicular ad-hoc networks security preservation schemes, several of them are inefficient or susceptible to attacks. This work, introduces an approach that leverages reverse fuzzy extraction, bilinear pairing, and Physically Unclonable Function (PUF) to design an efficient and anonymity-preserving authentication scheme. We conduct an elaborate formal security analysis to demonstrate that the derived session key is secure. The semantic security analyses also demonstrate its resilience against typical VANET attacks such as impersonations, denial of service, and de-synchronization, instilling confidence in its effectiveness. Moreover, our approach incurs the lowest computational overheads at relatively low communication costs. Specifically, our protocol attains a 66.696% reduction in computation costs, and a 70% increment in the supported security functionalities.

Mateusz Zajac, Tomislav Rožić, Justyna Swieboda-Kutera et al.

<i>Background</i>: The increasing complexity of global supply chains and environmental expectations has highlighted the strategic importance of digital transformation in the transport, forwarding, and logistics (TFL) sector. Despite a growing portfolio of available tools, adoption rates—particularly among small and medium-sized enterprises (SMEs) in Central and Eastern Europe—remain low. This study investigates the barriers and motivations related to the implementation of digital planning tools supporting intermodal transport planning. <i>Methods</i>: A structured online survey was conducted among 80 Polish TFL enterprises, targeting decision-makers responsible for operational and digital strategies. The questionnaire included 17 closed and semi-open questions grouped into three thematic sections: tool usage, implementation barriers, and digital readiness. <i>Results</i>: The findings indicate that only 20% of respondents use dedicated route planning tools, and merely 10% report satisfaction with their performance. Key barriers include lack of awareness, organizational inertia, and the prioritization of other initiatives, with financial cost cited less frequently. While environmental sustainability is declared as a priority by most enterprises, digital support for emission tracking is limited. The results highlight the need for targeted education, integration support, and differentiated platform functionalities for SMEs and larger firms. <i>Conclusions</i>: This study offers evidence-based recommendations for developers, policymakers, and logistics managers aiming to accelerate digital adoption in the intermodal logistics landscape.

H. A. Elhamy, A. B. Eltawil

<i>Background</i>: Defining the optimal fleet portfolio is a crucial process in airline planning. The published efforts in literature provide ways to anticipate the disruption effects on the passenger demand; however, the proposed solution in this paper provides visibility on the impact of sustainable disruption and the way an airline can resist it. <i>Methods</i>: This paper proposes a two-stage methodology to find the best portfolio for airline operational requirements under the impact of disruption. The first stage considers optimization for normal airline operations under a specific fleet portfolio using an Integer Linear Programming (ILP) model. The second stage of the analysis is a mapping for the scenario-based methodology to find a way out for an airline subjected to some given disruption in operations. <i>Results</i>: The result of the two-stage analysis shall define the best fleet portfolio to withstand sustained disruptions by mapping the results in a disruption funnel and showing the impact of the supply and demand gap on the airline’s sustainable profitability. <i>Conclusions</i>: This paper provides a novel, practical way of evaluating strategic decisions to choose the best fleet portfolio and make airlines rely on the mapping of the disruption funnel to modify their network while increasing supply chain resilience.

Tan Yigitcanlar, Adam T. Downie, Shane Mathews et al.

Azizulrahman Shafiqurrahman, V. Khadkikar, A. Rathore

The concept of energy transfer between two electric vehicles and communication between them is a promising one for the future of the electrified transportation sector. In response to the growing research and interest in vehicle-to-vehicle (V2V) technology, this article provides an in-depth review of the actual energy transfer between two vehicles and their communication aspects. The literature is addressed to analyze power electronics topologies for successful V2V power transfer and compare V2V charging optimization techniques. Communication protocols and standards relevant to V2V technology are also discussed with a focus on their potential applications for improving transportation safety and efficiency. Furthermore, challenges faced by existing V2V power transfer solutions and the commercial products available for implementing V2V charging are described. In contrast to other literature surveys, this article provides a comprehensive overview of V2V power transfer and communication technologies with implications for the future of sustainable electrified transportation. The study and discussion of over 300 papers on the topic are encompassed in this article.

Y. Rajkumar, Sripathi Venkata Naga Santhosh Kumar

Mingan Luan, Bo Wang, Zheng Chang et al.

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

Habibullah Safi, Ali Imran Jehangiri, Zulfiqar Ahmad et al.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.

Ligang Cheng, Jie Cao, Wenxian Wang et al.

It is a challenge for the particle swarm optimization algorithm to effectively control population diversity and select and design efficient learning models. To aid in this process, in this paper, we propose multiple learning strategies and a modified dynamic multiswarm particle swarm optimization with a master slave structure (MLDMS-PSO). First, a dynamic multiswarm strategy with a master–slave structure and a swarm reduction strategy was introduced to dynamically update the subswarm so that the population could maintain better diversity and more exploration abilities in the early stage and achieve better exploitation abilities in the later stage of the evolution. Second, three different particle updating strategies including a modified comprehensive learning (MCL) strategy, a united learning (UL) strategy, and a local dimension learning (LDL) strategy were introduced. The different learning strategies captured different swarm information and the three learning strategies cooperated with each other to obtain more abundant population information to help the particles effectively evolve. Finally, a multiple learning model selection mechanism with reward and punishment factors was designed to manage the three learning strategies so that the particles could select more advantageous evolutionary strategies for different fitness landscapes and improve their evolutionary efficiency. In addition, the results of the comparison between MLDMS-PSO and the other nine excellent PSOs on the CEC2017 test suite showed that MLDMS-PSO achieved an excellent performance on different types of functions, contributing to a higher accuracy and a better performance.

Yajie He, Menglei Kong, Chunshan Du et al.

The transportation system has entered the era of 5G intelligent Internet of things (IoT), which can realize the comprehensive monitoring, perception, and intelligent decision-making of people, vehicles, roads, and the environment. The purpose is to solve the problems in the communication security of intelligent transportation system (ITS) and improve the vulnerability of traditional distributed architecture. The security issues of the Internet of vehicles (IoV) in 5G environment are analyzed from the perspective of big data. An access control mechanism based on risk prediction is proposed aiming at the problems existing in the node access control process. A Wasserstein Distance-based Combined Generative Adversarial Network (WCGAN) is proposed. It modifies the loss function to solve the gradient disappearance problem, and a combination of multiple generators is designed to solve the pattern collapse. The simulation experiment is carried out on the dataset of the intrusion detection evaluation project. The WCGAN model has the smallest prediction error than the other models regarding the node packet transmission rate. Its loss value is close to 0 after 10 iterations, while the loss value of the BP neural network (BPNN) is about 0.28. The prediction accuracy of the WCGAN model can reach 86.3% when the training set is 5000, which is much higher than that of BPNN (77.8%). The reason is that the WCGAN model increases the number of generators according to GAN, which improves the low accuracy caused by pattern collapse. The IoT-based ITS can implement corresponding strategies according to the prediction results and control the access rights of nodes, thus ensuring the security of information resources effectively. The research content reduces the communication delay under ensuring the integrity and confidentiality of information in the process of data transmission, and provides a reference for ensuring the safe communication of IoV.

Yue Han, Haowei Wang, Hui Chen et al.

Chemotherapy is one of the most commonly treatments of advanced colorectal cancer (CRC). However, the drug resistant following chemotherapeutic treatment is a significant challenge in the clinical management of CRC. Therefore, understanding the resistance mechanisms and developing new strategies for enhancing the sensitivity are urgently needed to improve CRC outcome. Connexins contribute to the formation of gap junctions among neighboring cells and then advance gap junctional intercellular communication (GJIC) for transportation of ions and small molecules. Although the drug resistance resulted from GJIC dysfunctional by aberrant expression of connexins is relatively well understood, the underlying mechanisms of mechanical stiffness mediated by connexin responsible for chemoresistance are largely unknown in CRC. Here, we demonstrated that connexin 43 (CX43) expression was downregulated in CRC and that loss of CX43 expression was positively correlated with metastasis and poor prognosis of CRC patients. The CX43 overexpressing suppressed CRC progression and increased the sensitivity to 5-fluorouracil (5-FU) via enhanced GJIC in vitro and in vivo. Moreover, we also highlight that the downregulation of CX43 in CRC increases the stemness of cells via reducing the cell stiffness, thus promoting the drug resistance. Our results further suggest that both effects, that is changes in the mechanical stiffness of the cell and GJIC mediated by CX43 deregulated, are closely related to drug resistance in CRC, which indicating CX43 as a target against cancer growth and chemoresistance in CRC.

Slimani Ibtissam, Zaarane Abdelmoghit, Atouf Issam

In this work, a day and night time vehicle detection system for traffic surveillance is proposed. Our system is composed of two main processes, day time and night time processes. In the night time, the vehicles are detected based on their taillights and headlights. First of all, the 2D-DWT (Two Dimensional Discrete Wavelet Transform) and the background subtraction are applied to the input image. Then, the connected component technique is used to extract the region of interest. If it is the daytime, the connected component candidates are taken as potential vehicles after applying a pre-processing algorithm to improve the result. If it is the night-time, a filtering operation is used to keep only the bright white and red connected component candidates (which represent potential headlights and taillights, respectively). Finally, potential lamp sets are formed by grouping the extracted components on the basis of their positions, sizes, and colours. The potential extracted vehicles are classified as a vehicle or non-vehicle by using a pre-trained CNN (Convolutional Neural Network) classifier. The proposed system was tested and evaluated using different works from the literature. The experiments show that our proposed system has reached a high accuracy in terms of vehicle detection process whether in day or night time. The experiments were performed using four different videos and were implemented using the C++ language, which facilitates mathematical computation, and its OpenCV library, which is used to run the image processing algorithms used, as well as the TensorFlow library, which facilitates transfer learning of pre-trained CNN models.

Restu Harywibowo, Agustinus Hariadi

This study aims: To determine the direction and strength of the effect of preventive maintenance on the performance of K3 transportation at Mining Service Company PT. To determine the direction and strength of the influence of traffic management on the performance of K3 transportation at the PT. To determine the direction and strength of the influence of technology and communication on the performance of K3 transportation at the PT XYZ Mining Service Company. This research is descriptive quantitative. The population in this study were 2145 workers and staff working in the construction service company PT. The sampling technique in this study is the saturated sampling technique, the samples taken are 145 workers and staff who work in the construction service company PT. The results of this study indicate that: There is an effect of preventive maintenance on the performance of K3 transportation. There is an effect of traffic management on the performance of K3 transportation. There is an effect of technology and communication support on the performance of K3 transportation. There is an effect of operator concern on the performance of K3 transportation

V. Vittal, Nirmal Nair, F. Rahmatian

This article examines critical smart city infrastructure components, like electricity supply, transportation, and telecommunication, in the face of an emergency like COVID-19. The electricity infrastructure is a critical component of any smart city and significantly impacts other systems, like transportation, communication, and water delivery and treatment.

Anita Mohanty, Subrat Kumar Mohanty, Bhagyalaxmi Jena et al.

Abstract The delay in transportation of necessary items is due to traffic congestion throughout the world. This is a serious phenomenon which results in waste of time and fuel. The detection of road conditions and dissemination of traffic information efficiently and effectively is a big challenge to authorities. Recently, the technologies of vehicular ad hoc networks (VANETs) have been utilized and become an important part of the intelligent transportation system (ITS). For this existing problem, vehicle‐to‐vehicle (V2V) communication provides a means for cooperation and route management in transport networks. This paper proposed a novel congestion detection system based on the combination of k‐means clustering and analytical hierarchy process. In the simulation of urban mobility (SUMO) simulator, a transport network is created and parameters of vehicles facing congestion are taken to extract the key parameter by using the k‐means clustering technique and mathematical mean algorithm. This parameter is utilized in analytical hierarchy process to detect the highest priorities parameter and based on that the congestion is detected in particular lane. The result can be a better technique for congestion detection as it requires low installation cost and can be incorporate in vehicles for congestion avoidance which will alternatively improve the traffic flow.

Binoy Debnath, Md Shihab Shakur, Fahmida Tanjum et al.

<i>Background:</i> Additive manufacturing (AM) applications in producing spare parts are increasing day by day. AM is bridging the digital and physical world as a 3D computer-aided manufacturing (CAM) method. The usage of AM has made the supply chain of the aviation spare parts industry simpler, more effective, and efficient. <i>Methods:</i> This paper demonstrates the impacts of AM on the supply chain of the aircraft spare parts industry following a systematic literature review. Hence, centralized and decentralized structures of AM supply chains have been evaluated. Additionally, the attention has been oriented towards the supply chain with AM technologies and industry 4.0, which can support maintenance tasks and the production of spare parts in the aerospace industry. <i>Results:</i> This review article summarizes the interconnection of the industry findings on spare parts. It evaluates the potentiality and capability of AM in conceptualizing the overall supply chain. Moreover, MROs can adopt the proposed framework technologies to assist decision-makers in deciding whether the logistics hub with AM facilities is centralized or decentralized. <i>Conclusions:</i> Finally, this review provides an overall view to make critical decisions on the supply chain design of spare parts driven by new and disruptive technologies of industry 4.0. The next-generation supply chain may replace the logistics barriers by reducing waste and improving capability and sustainability by implementing AM technologies.