This paper introduces a coordinated framework in which a base station (BS) and a Reconfigurable Intelligent Surfaces (RIS) jointly support their respective user groups while mitigating the impact of a Uncrewed Aerial Vehicle (UAV)-based jammer. Specifically, the RIS-assisted users rely on a reflective surface under attack from an UAV-jammer, and the BS simultaneously supports its own associated UEs alongside the RIS. We adopt an alternating optimization strategy that decomposes the original non-convex formulation into tractable subproblems using Block Coordinate Descent (BCD), thereby enabling multi-tier cooperation in complex interference environments. We frame our objective as the sum rate maximization, a metric for overall system performance, to maintain service quality and enhance robustness under severe jamming. The core of our methodology is the transformation of the resulting non-convex subproblems into tractable convex formulations via Semidefinite Programming (SDP). We employ a joint design of beamforming and phase shift optimization, whose combined effect leads to substantial performance improvements within the proposed framework. Extensive simulations and comparisons with existing algorithms demonstrate that this design achieves superior interference suppression and resource efficiency, significantly outperforming state-of-the-art baselines in sum rate, energy efficiency, UAV-jamming resilience and group-aware support for both BS- and RIS-served users.
Telecommunication, Transportation and communications
Stergios Mavromatis, Vassilios Matragos, Konstantinos Markos
et al.
Passing sight distance (PSD) is a vital design element that directly imposes economic, as well as safety and operational, considerations. The provision of PSD is highly prioritized, at least for rural road sections without additional passing lanes. The paper investigates areas with PSD inadequacy on rural roads with crest vertical curves. The research is based on the German rural roads design guidelines, where PSD is currently dependent on the homogeneousness of the proposed road design classes and no longer on speed. Therefore, the required PSD for all the examined design classes was set to 600 m. The interaction between the road surface and the line of sight between the passing and the opposing vehicles was assessed through six different cases, while every case was associated with the resulting formulas. The analysis revealed that, excluding one situation for the EKL4 design class, the boundaries of PSD inadequacy were concentrated in advance and inside the vertical curve, and do not depend on the grade difference of the vertical curve but only on the crest vertical curvature rate value. The paper delivers a ready-to-use tool for engineers to identify areas with inadequate PSD in the early stages of the design process and avoid implementing costly additional passing lanes.
Branislav Šarkan, Jacek Caban, Arkadiusz Małek
et al.
The article contains an analysis of power generation by a photovoltaic system with a peak power of 3 MWp and a wind turbine with a power of 3.45 MW. The acquired time series of generated power was analyzed using traditional and modern analytical methods. The power generated by these two Renewable Energy Sources was characterized separately and then by their mix. In this article, the power signature was defined as the power generated by the photovoltaic system and the wind turbine in the state space over a period of one month. The state space was extracted from the results of cluster analysis. The experiment with clustering was carried out into 10 classes. The K-Means clustering algorithm was used to determine the clusters in a variant without prior labeling of classes with the method of learning without the participation of the teacher. In this way, the trajectories of the power generation process from two Renewable Energy Sources were determined in the 10-state space. Knowing which class each data record belongs to, the frequencies of staying in each state were determined. The computational algorithm presented in the article may have great practical application in balancing the power grid powered by energy produced from renewable sources.
Saki Rezwana, Mohammad Razaur Rahman Shaon, Nicholas Lownes
et al.
he advent of autonomous technologies necessitates a deeper understanding of pedestrian behavior and safety in environments where pedestrians need to interact with driverless vehicles (DV). Our study explores how pedestrians perceive and react to DVs compared with Human-Driven Vehicles (HDV), focusing on objective measures such as gap acceptance (GA) and psychophysiological indicators like Electro-dermal Activity (EDA). Structured in three phases, the study comprises a preliminary questionnaire to gauge public perception, followed by immersive virtual reality (VR) simulations that mimic real-world traffic scenarios within a VR environment, and concludes with a post-experiment survey. The simulation experiment was designed to analyze pedestrian responses to varying traffic scenarios developed using DVs and HDVs, measuring EDA to assess emotional and stress responses leading to changes in the gap acceptance behavior. The study employed hypothesis testing to assess DV's impact on pedestrians' psychophysiological reactions that can lead to changes in pedestrian behavior. This study also explored the effect of education level and perception of pedestrians towards automation technology that may influence outcomes. The analysis of EDA showed higher stress levels in scenarios involving DVs measured using the Galvanic Skin Response component. This result heightened stress response may be attributed to the unpredictability and novelty of DVs. The analysis with gap acceptance (GA) time revealed significant differences in GA times across traffic scenarios. Pedestrians exhibited longer GA times with DVs than HDVs, suggesting cautious crossing behavior. Our results underscore the impact of traffic scenarios on pedestrian behavior and stress levels, highlighting the influence of driverless technology on pedestrian dynamics.
Transportation engineering, Transportation and communications
[Objective] In order to strengthen the passive safety design ability of urban rail transit trains during operation, it is necessary to study the effects of train crash dynamics parameters on climbing behavior. [Method] Based on the vehicle-track coupling crash dynamics model, Hertz contact theory and rigid nonlinear connection model, a three-dimensional train crash dynamics simulation platform is built. Speed, acceleration and maximum wheelset lift are selected as comparison parameters, and the finite element model of train crash is used to verify the precision of the three-dimensional dynamic model of train crash. Based on the established simulation platform, some important parameters of the train structure are selected to study their effects on the wheelset lift. Five relatively more sensitive parameters, including the vertical distance from the installation points of the devices such as energy absorbing anti-climbing device, head car coupler, middle car coupler to the barycenter of the car body, the mass of the car body, the height of the barycenter of the car body, the vertical stiffness of the secondary suspension device and the vertical stiffness of the primary suspension device, are screened out to analyze the sensitivity of the above parameters to the train crash dynamics. [Result & Conclusion] Based on the finite element model of train crash, the precision of the three-dimensional dynamic model of train crash meeting the requirements is verified. The sensitivity analysis shows that when the parameter ratio varies within the range of 0.8~1.0, the five sensitive parameters of sensitivity is in the sequence of the vertical stiffness of the secondary suspension device, the mass of the car body, the height of the barycenter of the car body, the vertical distance from the installation point to the barycenter of the car body, and the vertical stiffness of the primary suspension device. When the parameter ratio varies within the range of 1.0 to 1.2, the above parameters of sensitivity is in the sequence of the vertical stiffness of the primary suspension device, the vertical distance from the installation point to the barycenter of the car body, the height of the barycenter of the car body, the mass of the car body, and the vertical stiffness of the secondary suspension device.
This paper adopts the fluid–structure coupling algorithm based on the acoustic fluid element, the fluid dynamic artificial boundary, and the consistent viscoelastic artificial boundary of solid media to establish a finite element model of the dynamic interaction of the reef-island–seawater system. Then, a numerical simulation of the seismic response of the reef-island site is carried out to study the seismic ground motion distribution patterns of the reef–seawater site and the reef-island–lagoon site. The innovation of this article is that the influence of reef–island topography and fluid–structure coupling is considered in the analysis when vertical ground motion is input. The results show that the slope angle of the bottom layer has a significant influence on the peak ground acceleration distribution and peak size on the island slope surface and the reef platform. For high-frequency input motion, a smaller reef platform width will induce a larger peak acceleration response on the reef platform. Seawater has a significant suppressive effect on vertical ground acceleration. The more high-frequency components of the input bedrock motion, the more obvious this suppression effect will be. The existence of the lagoon will amplify the maximum peak acceleration on the reef platform. According to the calculation results, lagoon terrain can amplify the maximum horizontal and vertical peak accelerations on the reef platform by about 19 and 6 times relative to the free-field results, respectively.
Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.
The crystalline blockage of tunnel drainage pipes poses a common challenge to tunnel engineering, particularly in regions with abundant groundwater and severe ion erosion. Based on the design of concrete mix proportion, this paper improves the calcium ion erosion resistance of shotcrete by adding permeable crystalline sodium methyl silicate waterproofing agent and silane waterproofing agent. Concurrently, the mechanical properties and microscopic characterization of waterproofing agent concrete are carried to determine the optimal dosage for practical engineering applications. The experimental results reveal that the sodium methyl silicate waterproofing agent has minimal impact on the calcium ion dissolution resistance of shotcrete. In contrast, the silane waterproofing agent significantly enhances the calcium dissolution performance of shotcrete, and the optimum dosage is identified as 0.4%. During long-term maintenance, the silane waterproofing agent proves more effective in improving the compressive strength of shotcrete compared to the sodium methyl silicate waterproofing agent, showing an increase rate exceeding 20%. Both waterproofing agents contribute to the enhancement of the tensile strength of shotcrete, and the enhancement effect is consistent with the compressive strength. The permeable crystalline waterproofing agent expedites the cement hydration process, resulting in the formation of higher-density calcium silicate hydrates (C-S-H) gel and ettringite (AFt). These substances fill the pores of shotcrete leading to a more compact internal structure. The calcium ion dissolution and mechanical strength test results collectively validate the superior effectiveness crystallization prevention of aterproofing agents . This research provides valuable insights for addressing the tunnel crystallization phenomenon caused by the calcium dissolution.
Materials of engineering and construction. Mechanics of materials
The global collaboration and integration of online and offline channels have brought new challenges to the logistics industry. Thus, smart logistics has become a promising solution for handling the increasing complexity and volume of logistics operations. Technologies, such as the Internet of Things, information communication technology, and artificial intelligence, enable more efficient functions into logistics operations. However, they also change the narrative of logistics management. Scholars in the areas of engineering, logistics, transportation, and management are attracted by this revolution. Operations management research on smart logistics mainly concerns the application of underlying technologies, business logic, operation framework, related management system, and optimization problems under specific scenarios. To explore these studies, the related literature has been systematically reviewed in this work. On the basis of the research gaps and the needs of industrial practices, future research directions in this field are also proposed.
Foam concrete (FC) has the potential of being an alternative to ordinary concrete, as it reduces dead loads on the structure and foundation, contributes to energy conservation, and lowers the cost of production and labor cost during the construction and transportation. The paper reports a state-of-the-art review of foam concrete in terms of its components, manufacturing and material properties like drying shrinkage, compressive strength, stability and pore structure, etc. In view of the significance of the FC in engineering construction, it also includes a state-of-the-practice review of foam concrete in tunnel and underground engineering. Some shortcomings and technical limitations as well as emerging direction for performance enhancement of FC are also discussed. Current review concludes that the long-term performance and enhancement-associated properties need to be deeply investigated. This study can help alleviate consumer concerns and further encourage the wider application of FC in civil engineering.
David Holmes, Maria Papathanasaki, L. Maglaras
et al.
Digital twin technology today is diverse and emerging and its full potential is not yet widely understood. The concept of a digital twin allows for the analysis, design, optimisation and evolution of systems to take place fully digital, or in conjunction with a cyber-physical system to improve speed, accuracy and efficiency when compared to traditional engineering approaches. Digital twins continue to be a technology that enables new paradigms, such as Industry 4.0 and Factories of the Future as well as generating improved efficiencies within existing systems. The development of digital twin technology in traditional industries such as manufacturing, construction, the automotive industry, agriculture and transportation has highlighted its potential, but often insufficiently explored the risks associated with their integration. In this paper we explore risks relating to the cyber-security of systems employing digital twin technology and also consider the opportunities for digital twins themselves to mitigate cyber-security risks and become an integral part of a security in-depth defence.
Since the electrical vehicles (EVs) are infrastructure-dependent technology, their penetration faces the problem of lacking recharging infrastructure. Thus, there is a dilemma of chicken-egg in the penetration of EVs and their charging infrastructure for the decision-makers. The article examines the e-mobility scenario of 3 Indian cities to understand issues and challenges in implementing EVs. The study suggests a co-diffusion strategy for the EVs and charging infrastructure. Firstly, the priority EV segment has been decided based on the transport mode preference. Then, suitability of charging facilities according to the segment of the EVs has been presented by analyzing the turnover rate and time spent at several places. The study recommends policies on the upfront cost of EVs, charging infrastructure, awareness generation and others, while leveraging the existing government schemes like Atmanirbhar Bharat and FAME-II.
Deep Neural Networks (DNNs) are used in various domains and industry fields with great success due to their ability to learn complex tasks from high-dimensional data. However, the data-driven approach within deep learning results in various DNN-specific insufficiencies (e.g., robustness limitations, overconfidence, lack of interpretability), which makes the usage in safety-critical applications, like automated driving, challenging. An important safety strategy to address these limitations is the detection of DNN errors (e.g., false positives) during runtime. In this work, we present a general error detection approach for DNNs, which combines diverse monitoring methods to address different safety-related DNN insufficiencies simultaneously. To ensure consistency with the automotive safety domain, we take into account established concepts of the automotive safety standard ISO 21448 (SOTIF). We apply our error detection method on the safety-related use case of traffic sign recognition by using self-created 3D driving scenarios. In doing so, we consider different types of DNN errors related to in distribution, out of distribution, and adversarial data. We demonstrate that our approach is able to handle all these error types. Furthermore, we show the performance benefit of our method compared to a baseline DNN and to state of the art DNN monitoring methods.
Transportation engineering, Transportation and communications