In sixth-generation (6G) application scenarios like industry 5.0, augmented reality (AR), autonomous transportation, and eHealth, there is a growing demand for Human Activity Recognition (HAR). Meanwhile, with the deployment of millimeter-wave (mmWave) technologies in fifth-generation (5G) cellular communications, higher-resolution sensing becomes feasible. Utilizing mmWave for communication and HAR has garnered attention, necessitating accurate modeling of sensing channels. This paper proposes a mmWave scattering channel model for indoor HAR, which facilitates system design, optimization, and implementation. In the proposed model, we integrate primitive-based human body scattering where the human body is indicated by a set of primitives, and cluster-based environment scattering models, enabling detailed modeling of self-shadowing and double-bounce environment scattering. Additionally, we develop a simulation framework encompassing signal transmission, sensing channels, and processing, allowing adjustment of system parameters. Simulation results indicated by micro-Doppler signatures including multi-link effects show good agreements with measurements, validating the effectiveness of the proposed model. Meanwhile, the time consumption of the proposed simulation workflow for generating micro-Doppler signatures for most human activities is within 10 minutes.
Smart farms use advanced technologies to grow crops indoors without soil, offering environmental benefits such as reduced transportation distances and lower water consumption. However, consumers who prefer local food may resist these products. This study examines this ‘local food paradox’ among South Korean consumers. We surveyed 1,247 consumers and used latent class analysis to identify distinct consumer groups based on their technology acceptance, resistance to food technology, and local food values. Three groups emerged: Technology Enthusiasts (37.0%), who strongly support smart farming due to its perceived safety and efficiency benefits; Conflicted Moderates (25.0%), who hold mixed views balancing technological advantages against traditional values; and Tradition-Oriented Skeptics (38.0%), who prioritize terroir and authenticity and reject smart farm products despite their environmental advantages. Interestingly, consumers who frequently purchase organic food or shop at farmers’ markets were more likely to belong to the skeptical group, confirming the local food paradox: those most committed to sustainable food consumption may resist the most environmentally efficient production method. Prior experience with smart farm products, environmental concern, and health consciousness also significantly influenced group membership. These findings suggest that expanding the smart farm market requires tailored communication strategies. For technology-oriented consumers, emphasizing innovation and safety is effective. For tradition-oriented consumers, messaging should focus on freshness and community connections rather than technical features. This study contributes to understanding why sustainable food technology faces consumer resistance rooted not in fear of novelty, but in deeper conflicts over what constitutes authentic food.
Vehicle interactions in weaving sections are relatively frequent and complex, posing significant challenges to traffic congestion management and safety. Trajectory data-driven driving behavior analysis can effectively reveal differences in driving behaviors. Therefore, in accordance with the research requirements, this study selected the CitySim dataset as the foundation after comparison and utilized intelligent algorithms to extract 1349 lane-changing samples from a specific weaving section within the dataset for analyzing the lane-changing behavior characteristics of vehicles in weaving areas. After analyzing the sample data using traffic flow theory and statistical theory, the following results were obtained: the speeds increase upon entering and decrease upon exiting weaving zones, while headway distances consistently grow. Vehicles in the inner lanes exhibit smoother transitions and higher speeds, while lane-changing speeds range from 10 to 55 km/h (median: 29 km/h) and durations vary from 2 to 18 s (median: 8.5 s). Statistical analyses highlight significant behavioral differences based on lane and direction. Vehicles on entrance ramps demonstrate higher speeds, longer durations, and larger headways than those on exit ramps. Furthermore, right-lane changes are associated with lower speeds and shorter durations compared with left-lane changes. Based on these findings, the study proposes targeted traffic management strategies, including ramp flow control and optimized road markings, to enhance safety and efficiency in weaving areas. This research provides actionable insights for traffic management and road design in the expressway weaving areas.
Transportation engineering, Transportation and communications
Bell inequality violation has been widely tested by using the bipartite entangled pure states and properly encoding the local observables in various experimental platforms, and the detector-, local-, and random loopholes have already been closed. A natural question is, how to deliver the Bell inequality violation by properly encoding the local observables? Here, we show that the Bell inequality violation is directly related to the coherence degree, which is controllable by encoding the different local observables into the entangled state. With the usual space-like correlation detections, we show that the coherence degree can be measured and thus the Bell nonlocality can be tested. The feasibility of the proposal is demonstrated by a numerical experiment typically with the cavity quantum electrodynamic system, in which the coherence degrees of the locally encoded bipartite entangled state can be conveniently measured by the spectral detection of the driven cavity. The present work might provide a feasible approach to verify the Gisin theorem, i.e., Bell inequality can be violated for any bipartite entangled pure state, once the local observables are properly encoded into the entangled state for keeping the desirable coherence.
Stanislav Trofimov, Leonid Voskov, Mikhail Komarov
The development of intelligent transportation systems (ITSs) is penetrating many economies around the globe. This paper presents three key innovations in the field of intelligent transportation systems, as follows: (1) a novel tokenization approach where each vehicle is represented as a macro-token subdivided into 500,000 micro-tokens for precise condition monitoring, (2) a comprehensive mathematical model for vehicle state assessment incorporating multiple operational factors, and (3) the GDEPZ method for optimizing data transmission via satellite communication. These innovations enable the autonomous control of technical conditions, transparent fleet management, and efficient data processing in hard-to-reach areas. Various researchers in both industry and academia are looking into more efficient management methods for both vehicles and related data processing aspects. A vast trend related to the latter is the distributed data processing of transmitted data. This article discusses approaches to the use of blockchain technology in ITSs. It explores the use of blockchains in modern transport industries. In particular, the paper proposes a novel approach to the maintenance of public transportation vehicles and buses. The specificity of the proposed approach is the autonomous control of technical conditions using information systems. When using blockchain technology, building a transparent vehicle fleet management system is possible. The specificity of the proposed approach lies in data processing. Within the organization, confidence in data increases, the possibility of manipulating transportation is eliminated, and the decision-making chain is reduced. As a result, the system can manage itself. This also helps to increase the service life of vehicles, makes it possible to predict their malfunctions, and improves the quality of data on their technical conditions.
<i>Background</i>: The rapid rise of e-commerce has intensified last-mile logistics challenges, fueling the need for sustainable, efficient solutions. Parcel locker crowdshipping systems, integrated with public transport networks, show promise in reducing congestion, emissions, and delivery costs. However, operational and physical constraints (e.g., crowded stations) and liability complexities remain significant barriers to broad adoption. This study investigates the demographic and operational factors that influence the adoption and scalability of these systems. <i>Methods</i>: A mixed-methods design was employed, incorporating survey data from 368 participants alongside insights from 20 semi-structured interviews. Quantitative analysis identified demographic trends and operational preferences, while thematic analysis offered in-depth contextual understanding. <i>Results</i>: Younger adults (18–34), particularly gig-experienced males, emerged as the most engaged demographic. Females and older individuals showed meaningful potential if safety and flexibility concerns were addressed. System efficiency depended on locating parcel lockers within 1 km of major origins and destinations, focusing on moderate parcel weights (3–5 kg), and offering incentives for minor route deviations. Interviews emphasized ensuring that lockers avoid station congestion, clearly defining insurance/liability protocols, and allowing task refusals during peak passenger hours. <i>Conclusions</i>: By leveraging public transport infrastructure, parcel locker crowdshipping requires robust policy frameworks, strategic station-space allocation, and transparent incentives to enhance feasibility.
Transportation and communication, Management. Industrial management
The optimal transportation problem, first suggested by Gaspard Monge in the 18th century and later revived in the 1940s by Leonid Kantorovich, deals with the question of transporting a certain measure to another, using transport maps or transport plans that minimize the total cost of transportation. This problem is very popular since it has a variety of applications in economics, physics, computer science and more. One of the main tools in this theory is the duality theorem, which states that the optimal total cost equals the value of a different optimization problem called the dual problem. In this work, I show how the problem and duality theorem can be generalized to an abstract formulation, in which I omit the use of measures. I show how this generalization implies a wide range of different optimal transport problems, and even other problems from game theory, linear programming and functional analysis. In particular, I show how the optimal transport problem can be generalized to deal with vector measures as a result of the abstract theorem and discuss the properties of this problem: I present its corresponding duality theorem and formulate conditions for the existence of transport plans, transport maps and solutions to the dual problem.
Abstract This study examines the relative impacts of transport and information and communications technology (ICT) components of trade-related infrastructure on bilateral trade flows between Nigeria and its major trading partners. An augmented standard gravity model that featured variables for the transport infrastructure component and the ICT component was estimated using bilateral trade data on 22 major trading partners of Nigeria for the period 2005–2021. The panel instrumental variables technique, precisely pooled two-stage least squares technique leveraged on fixed and random effects models, was used for the analysis. The findings show that the two components of trade-related infrastructure, transportation and information and communication technology (ICT) have a significant impact on trade flows between Nigeria and its trading partners. In the exports model, the differential impact of the transport infrastructure component is higher than the ICT component, but the differential impact of the ICT component is greater in the imports model. This suggests that the efficient provision of both transport and ICT infrastructure facilitates trade, while the inefficient provision of either or both hinders it. Therefore, greater attention must be placed on improving both components.
Economic growth, development, planning, Economics as a science
In-depth analysis of asteroid samples will provide a key scientific basis for understanding its evolution history, resource utilization, and life origin. Obtaining rock samples from asteroids is getting more and more attention. However, successful sampling in asteroid is difficult due to extremely complex environmental conditions. In this paper, the dynamic processes of rock failure by sampling device are investigated by a continuum-discontinuum element method (CDEM) to instruct the rock sampling in asteroid. The deformation and failure of rock samples under different conditions (impact speeds, impact positions, rock cut-slot numbers, cut-slot spacings, cut-slot depths and cut-slot widths, etc.) are analyzed, and the relationships between the rock failure and hammer impact method are obtained. The results show that the cut-slot formed by grinding wheel increases the free surface of rock sample, which is beneficial to impact fracture and asteroid sampling. The relative distance between cut-slot free surface and impact position significantly affects the rock fracture under impact loading. The fracture degree of rock with single cut-slot is obviously smaller than that of rock with double cut-slots, the cut-slot scheme of double grinding wheels is more suitable for asteroid sampling. Under the impact loading, the rock fracture is negatively correlated with the cut-slot spacing formed by double grinding wheel cutting, the effective spacing of double cut-slots is 12 mm. The fracture unit number of rock varies nonlinearly with an increase of the depth from impact position, there is an optimal cut-slot depth corresponding to a certain impact velocity, and the rock crushing efficiency of asteroid sampling can be improved as the cut-slot depth matches the crack propagation depth.
Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such as transportation, pharmaceuticals, and industrial manufacturing,achieving unprecedented milestones. In modern society, wireless communication stands as an indispensable infrastructure, with its essence lying in optimization. Although artificial intelligence (AI) algorithms such as Reinforcement Learning (RL) and mathematical optimization have greatly enhanced the performance of wireless communication, the rapid attainment of optimal solutions for wireless communication problems remains an unresolved challenge. QC, however, presents a new alternative. This paper aims to elucidate the fundamentals of QC and explore its applications in wireless communications and networking. First, we will provide a tutorial on QC, covering its basics, characteristics, and popular QC algorithms. Next, we will introduce the applications of QC in communication and networking, followed by its applications in miscellaneous areas such as security and privacy, localization and tracking, and video streaming. Finally,we will discuss remaining open issues before concluding.
Ślęzak Marcin, Szczepański Tomasz, Stasiak-Cieślak Beata
et al.
The article concerns the problem of lateral stabilization of a tricycle with variable front wheel track. The vehicle can operate in two modes: with the front wheels folded out and connected. A bicycle designed for people with special needs. The element that requires elaboration is the stabilization while driving with the front wheels unfolded. In this mode, a metastable state is created. When the lateral angle of the slope exceeds the limit value, the lateral force causes the lateral tilt. This phenomenon should be treated as a disadvantage as it makes it difficult to smoothly tilt the bicycle, which can lead to disorientation of the rider and difficulty in maintaining balance. The article presents mathematical simulations that allow for the analysis of factors influencing the discussed phenomenon.
As “Industry 4.0” progresses, construction machinery is evolving toward large-scale, automation, and integration, resulting in the equipment becoming increasingly sophisticated, and the designs more difficult. Labor costs, transportation, and time will be huge challenges for construction machinery, and mixed reality technology is one of several possible ways to solve this challenge. The research presented in this paper develops a holographic visual verification platform for a digital prototype of construction machinery based on virtual terminal equipment, through investigating the synchronous remote collaboration of multiple terminal devices in a mixed reality scenario. These included semi-physical virtual-real fusion assembly, multi-person real-time voice communication, dynamic loading of MR model based on a cloud server, virtual imitation control, interface design, and human-computer interaction. The effectiveness of this paper’s method is demonstrated through remote collaborative design cases. These included a double drum roller, loader, and milling planer welding production line, as well as tractor modeling review and virtual simulation manipulation of an aerial work platform. The experimental results show that this visual verification platform is a feasible, low-cost and scalable solution, which brings a qualitative breakthrough to the design, research and development, production and other stages in the field of construction machinery.
The advantages and disadvantages of five commonly used mining monorail cranes, namely explosion-proof diesel engine monorail crane, explosion-proof battery monorail crane, pneumatic monorail crane, rope traction monorail crane, and cable dragging monorail crane, are analyzed. It is pointed out that explosion-proof diesel engine monorail crane and explosion-proof battery monorail crane have been widely used for underground equipment and material transportation in coal mines due to their strong transportation capacity. The explosion-proof battery monorail crane has green and clean features. It has gradually become a key research and development equipment for underground auxiliary transportation in coal mines. This paper analyzes the research status of the mining monorail crane system from four aspects of battery drive, positioning, measurement and control, and information transmission. It is pointed out that the current mining monorail crane system has low automation and intelligence levels. To achieve intelligent transportation of the monorail crane, it is necessary to study the key technologies such as lithium battery explosion-proof and battery management of the monorail crane, global and local fusion precise positioning, multi-sensor information fusion intelligent measurement and control, edge computing information transmission, etc. For the explosion-proof and battery management technology of lithium battery, a topology optimization design method of explosion-proof large capacity lithium battery structure based on variable density method, and a balanced management scheme based on improved sparrow search algorithm/genetic algorithm are proposed. For precise positioning technology, a global positioning method based on inertial navigation and odometer fusion, and a local positioning method based on visual+UWB combination are proposed. For intelligent measurement and control technology, an intelligent detection method based on recursive least squares algorithm/second-order approximate extended Kalman filter , the real-time construction method for follow-up electronic fences, and vector control method based on fuzzy rules are proposed. For the information transmission technology, the 'local-short range-ground' communication system architecture based on the fusion of 5G network and the distributed data calculation and transmission method based on edge computing are proposed. The proposal of key technical issues and solutions for intelligent monorail cranes in coal mines provides a new approach to accelerate the development of intelligent monorail cranes in coal mines.
Irina V. Filimonova , Medea V. Ivanova , Ekaterina A. Kuznetsova
et al.
In the current conditions of economic instability, the problem of regional space development in the Arctic zone, Eastern Siberia and the Far East, the regions where the main hydrocarbon and other mineral reserves are concentrated, is becoming more and more relevant. The main reserves of hydrocarbons and other minerals are concentrated in these regions. The integrated development of the mineral resource base on the principles of rational nature management, including the stages of exploration, production, processing, transportation, is the driver of the Russian regional economy development. The organization of raw materials centers (RMC) with regard to the development of energy resources and regional transport infrastructure, determines the effective development of the spatial economy. The aim of the study is to develop an integrated approach to the spatial development of the region by forming an RMC for achieving the national priority of efficient use of natural and economic resources. The achievement of the goal requires the solution of interrelated tasks, namely, the study of domestic experience in the spatial organization of regional economy; the analysis of the regulatory framework governing the functioning of specific industries in the Arctic zone, Eastern Siberia and the Far East in order to further socio-economic development of undeveloped regions, as well as the analysis of data on the current state of energy reserves in the Arctic. As a result, the study of the RMC organization indicates that their effectiveness is associated with the interaction of national and corporate interests and the development of a communication system. The interrelation of all components allows us to assess the multiplicative effect of the RMC organization on the economy of the region and the country as a whole, which determines the novelty of the work.
Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety.
Federated Learning (FL) is a communication-efficient and privacy-preserving distributed machine learning framework that has gained a significant amount of research attention recently. Despite the different forms of FL algorithms (e.g., synchronous FL, asynchronous FL) and the underlying optimization methods, nearly all existing works implicitly assumed the existence of a communication infrastructure that facilitates the direct communication between the server and the clients for the model data exchange. This assumption, however, does not hold in many real-world applications that can benefit from distributed learning but lack a proper communication infrastructure (e.g., smart sensing in remote areas). In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e.g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients. Two algorithms, called FedEx-Sync and FedEx-Async, are developed depending on whether the transporters adopt a synchronized or an asynchronized schedule. Even though the indirect communications introduce heterogeneous delays to clients for both the global model dissemination and the local model collection, we prove the convergence of both versions of FedEx. The convergence analysis subsequently sheds lights on how to assign clients to different transporters and design the routes among the clients. The performance of FedEx is evaluated through experiments in a simulated network on two public datasets.