Search Results (44)

Last-mile logistics is a critical operational and environmental challenge in urban areas. This paper introduces an intelligent path planning system using the Resistive Grid Path Planning Methodology (RGPPM) to optimize distribution based on energy and environmental metrics. The foundational innovation is the integration of electrical-circuit analogies, modeling the distribution network as a resistive grid where optimal routes emerge naturally as current flows, offering a paradigm shift from conventional algorithms. Using a multi-connected grid with georeferenced resistances, RGPPM estimates minimum and maximum paths for various starting points and multi-agent scenarios. We introduce five key performance indicators (KPIs)—Percentage of Distance Savings (PDS), Coefficient of Savings (CS), Coefficient of Global Savings (CGS), Percentage of Load Imbalance (PLI), and Percentage of Deviation with Multi-Agent (PDM)—to evaluate system performance. Simulations for textbook delivery to 129 schools in the Veracruz–Boca del Río area show that RGPPM significantly reduces travel distances. This leads to substantial savings in energy consumption, CO₂ emissions, and operating costs, particularly with electric vehicles. Finally, the results validate RGPPM as a flexible and scalable strategy for sustainable urban logistics. Full article

Non-terrestrial networks (NTNs) have become increasingly crucial, particularly with the standardization of fifth-generation (5G) technology. In parallel, the rise of Internet of Things (IoT) technologies has amplified the need for human-centric solutions in 5G and beyond (5 GB) systems. To address diverse communication requirements from a human-centric perspective, leveraging the advantages of both terrestrial networks (TNs) and NTNs has emerged as a key focus for 5 GB communications. In this paper, a machine learning (ML)-based approach is proposed to facilitate decision making between TN and NTN networks within a multi-connectivity scenario, aiming to provide a human-centric solution. For this approach, a novel synthetic dataset is constructed using various sensing information, based on the assumption that numerous interconnected sensor systems will be available in smart city networks with sixth-generation (6G) technologies. The ML results are derived from this newly generated dataset. These simulation results demonstrate that the proposed approach, designed to meet the requirements of next-generation systems, can be effectively utilized with 6G. Full article

Palmprint recognition is becoming more and more common in the fields of security authentication, mobile payment, and crime detection. Aiming at the problem of small sample size and low recognition rate of palmprint, a small-sample palmprint recognition method based on image expansion and Dynamic Model-Agnostic Meta-Learning (DMAML) is proposed. In terms of data augmentation, a multi-connected conditional generative network is designed for generating palmprints; the network is trained using a gradient-penalized hybrid loss function and a dual time-scale update rule to help the model converge stably, and the trained network is used to generate an expanded dataset of palmprints. On this basis, the palmprint feature extraction network is designed considering the frequency domain and residual inspiration to extract the palmprint feature information. The DMAML training method of the network is investigated, which establishes a multistep loss list for query ensemble loss in the inner loop. It dynamically adjusts the learning rate of the outer loop by using a combination of gradient preheating and a cosine annealing strategy in the outer loop. The experimental results show that the palmprint dataset expansion method in this paper can effectively improve the training efficiency of the palmprint recognition model, evaluated on the Tongji dataset in an N-way K-shot setting, our proposed method achieves an accuracy of 94.62% ± 0.06% in the 5-way 1-shot task and 87.52% ± 0.29% in the 10-way 1-shot task, significantly outperforming ProtoNets (90.57% ± 0.65% and 81.15% ± 0.50%, respectively). Under the 5-way 1-shot condition, there was a 4.05% improvement, and under the 10-way 1-shot condition, there was a 6.37% improvement, demonstrating the effectiveness of our method. Full article

This paper uses the multi-regional input–output model to measure China’s interprovincial embodied carbon transfer and constructs an interprovincial network; then, the temporal exponential random graph model is applied to analyze the spatial correlation characteristics and endogenous evolutionary mechanism of the network. The results show that interprovincial embodied carbon transfer relationships are increasingly close in China, but the weak symmetric accessibility between the eastern and central regions leads to less reciprocity in the embodied carbon network, and carbon emission inequality still exists. Based on the identification of networks, it is shown that the global network structures are stable, with obvious small-world characteristics and a core–periphery structure. And a structure-dependent effect and time-dependent effect also exist in the formation and evolution of the interprovincial embodied carbon transfer network in China. The popularity, multi-connectivity, and path-dependent effects among the provinces are significant, but the imperfection of interprovincial communication and the cooperation mechanism leads to the failure to form stable structures of ternary closed loops. Interprovincial embodied carbon transfer relationships tend to occur between provinces, with large differences in energy consumption structures, while geographical distance can hinder the formation of embodied carbon transfer relationships. Consequently, considering the spatial network correlation and its endogenous dynamic evolutionary mechanism, it is important to implement policies to guide coordinated carbon reduction among the provinces and to improve the fairness in embodied carbon transferring, in order to promote the fine governance of all links in the transferring process of embodied carbon. Full article

In 5th generation mobile communication networks, the frequency band of the millimeter band of 30–100 GHz is supported to provide a transmission speed of 20 Gbps or higher. Furthermore, a huge transmission capacity, i.e., up to 1 Tbps, is one of the main requirements for the 6th generation mobile communication networks. In order to meet this requirement, the terahertz band is considered a new service band. Hence, we consider multi-band network environments, serving the sub-6Hz band, mmWave band, and additionally sub-terahertz band. Furthermore, we introduce the transmission point selection criteria with multiple connections for efficient multi-connectivity operation in a multi-band network environment, serving and receiving multiple connections from those bands at the same time. We also propose a point selection algorithm based on the selection criteria, e.g., achievable data rate. The proposed point selection algorithm attains lower computational complexity with a 2-approximation of the optimal solution. Our simulation results, applying the channel environment and beamforming in practical environments defined by 3GPP, show that selecting and serving multiple transmission points regardless of frequency band performs better than services through single-connectivity operation. Full article

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi —complementary WATs in the 5G-CLARITY project—in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms. Full article

In this paper, we study the properties of trajectories of systems of ordinary differential equations generated by the velocity field of a moving incompressible viscoelastic fluid with memory along the trajectories in a domain with multiple boundary components. The case of a velocity field from a Sobolev space with inhomogeneous boundary conditions is considered. The properties of the maximal intervals of existence of solutions to the Cauchy problem corresponding to a given velocity field are investigated. The study assumes the approximation of a velocity field by a sequence of smooth fields followed by a passage to the limit. The theory of regular Lagrangian flows is used. Full article

The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about $60\%$ of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of $(1-1/e)$. Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming. Full article

Different oils can be homogeneously dispersed in the network junctions of the separated bicontinuous micellar phases. Upon dilution, these dispersions spontaneously form nanoemulsions. The possibility of a micellar sponge phase formation in the case of mixtures with three anionic and two zwitterionic surfactants in the presence of divalent and monovalent salts is studied. The best results are obtained using sodium lauryl ether sulfate with 1 ethylene oxide group (SLES-1EO) and both cocamidopropyl betaine (CAPB) or N,N-dimethyldodecylamine N-oxide (DDAO) in the presence of an appropriate small amount of MgCl₂ and CaCl₂. Bicontinuous micellar phases can be produced also in high-salinity NaCl solutions. The bulk properties of these phases are independent of the concentration of the initial solutions from which they are separated, and their Newtonian viscosities are in the range from 0.3 Pa·s to 0.8 Pa·s. Both 8 wt% CAPB- and DDAO-containing sponge phases engulf up to 10 wt% limonene and spontaneously form nanoemulsion upon dilution with droplet sizes of 110–120 nm. Vitamin E can be homogeneously dispersed only in CAPB-containing saturated micellar network, and upon dilution, these dispersions spontaneously form nanoemulsions with smaller droplet sizes of 66 nm for both 8 diastereomers and 2 diastereomers mixtures of vitamin E. Full article

A digital twin is a virtual model of a real-world structure (such as a device or equipment) which supports various problems or operations that occur throughout the life cycle of the structure through linkage with the actual structure. Digital twins have limitations as a general simulation method because the characteristic changes (motion, stress, vibration, etc.) that occur in the actual structure must be acquired through installed sensors. Additionally, it takes a huge computing cost to output changes in the structure’s characteristics in real time. In particular, in the case of ships and offshore structures, simulation requires a lot of time and resources due to the size of the analysis model and environmental conditions where the wave load acts irregularly, so the application of a different simulation methodology from existing ones is required. The order reduction method, which accurately represents the system’s characteristics and expresses them in a smaller model, can significantly reduce analysis time and is an effective option. In this study, to analyze the applicability of the order reduction method to the development of digital twins for offshore structures, the structural responses of a multi-connected floating offshore structure were estimated by applying the order reduction method based on distortion base mode. The order reduction method based on the distortion base mode predicts the responses by constructing an order-reduced conversion matrix consisting of the selected distortion base mode, based on the mode vector’s orthogonality and autocorrelation coefficients. The predicted structural responses with the reduced order model (ROM) were compared with numerical analysis results derived using the higher order boundary element method and finite element method with in-house code owned by the Korea Research Institute of Ship & Ocean Engineering and measured responses with a model test. When compared with the numerical analysis results, the structural responses were predicted with high accuracy in the wave direction and wave frequency band of the selected distortion base mode, but there are differences due to changed characteristics of the structure when compared with the results of the model test. In addition, differences were also seen in reduced order model evaluation with different sensor locations, and it was confirmed that the more similar the extracted distortion base modes of input sensor location set is to the distortion base modes of predicted location set, the higher accuracy is in predicting the structural responses. As a result, the performance of the reduced order model is determined by the distortion base mode selection method, the locations of the sensor, and the prediction for the structural response. Full article

This paper investigates the hydroelastic responses of offshore floating solar photovoltaic farms (OFPVs). OFPVs usually occupy a large sea space in the order of hectares and structural deformation under wave action has to be taken into consideration due to their huge structural length-to-thickness ratio. The flexible deformation of the structure under hydrodynamic loading is termed the hydroelastic response. In the hydroelastic analysis of an OFPV, the diffraction and radiation of waves have to be taken into account to accurately represent the hydrodynamic loadings on the floating platform. In this study, the numerical model is first validated by comparing the eigenvalues and eigenvectors of an OFPV, obtained from the proposed numerical scheme, with their counterparts obtained from an established finite element software. This is followed by an investigation of the hydroelastic responses of various OFPVs designed in varying layout configurations. The various layout configurations are obtained by altering the floating modular units’ dimensions as well as the spacing of the OFPVs when deployed adjacent to each other. The optimal configuration that gives the best performance in terms of the overall smallest response, known as compliance, is then suggested. The results suggest that a long-ish OFPV layout has a lower hydroelastic response and that the motion could be further reduced by rearranging the layout arrangement to increase the global flexural stiffness. Full article

With the progress of the eras and the development of science and technology, the requirements of device-to-device (D2D) connectivity increased rapidly. As one important service in future systems, ultra-reliable low-latency communication (URLLC) has attracted attention in many applications, especially in the Internet of Things (IoT), smart cities, and other scenarios due to its characteristics of ultra-low latency and ultra-high reliability. However, in order to achieve the requirement of ultra-low latency, energy consumption often increases significantly. The optimization of energy consumption and the latency of the system in the communication field are often in conflict with each other. In this paper, in order to optimize the energy consumption and the latency jointly under different scenarios, and since the detailed requirements for latency and reliability are diverse in different services, we propose an adaptive UE aggregation (AUA)-based transmission scheme that explores the diversity gain of multiple simultaneous paths to reduce the overall latency of data transmission, wherein multiple paths correspond to multiple coordination nodes. Furthermore, it could provide the feasibility of link adaptation by adjusting the path number according to the real transmission environment. Then, unnecessary energy waste could be avoided. To evaluate the performance, the energy-delay product (EDP) is proposed for the latency and energy comparison. The provided simulation results align with the numerical data. Through the analysis, it can be proven that the proposed scheme can achieve a joint optimization of latency and energy consumption to meet different types of URLLC services. Full article

Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes. Full article

Effective mobility management is crucial for efficient operation of next-generation cellular systems in the millimeter wave (mmWave) band. Massive multiple-input–multiple-output (MIMO) systems are seen as necessary to overcome the significant path losses in this band, but the highly directional beam makes the channels more susceptible to radio link failures due to blockages. To meet stringent capacity and reliability requirements, multi-connectivity has attracted significant attention. This paper proposes a multiagent distributed Q learning-based mobility management scheme for multi-connectivity in mmWave cellular systems. A hierarchical structure is adopted to address the model complexity and speed up the learning process. The performance is assessed using a realistic measurement data set collected from Wireless Insite in an urban area and compared with independent Q learning and a heuristic scheme in terms of handover probability and spectral efficiency. Full article

Innovation is the engine for the sustainable development of cities and regions, and an innovation perspective is used to study the collaborative innovation relationship between regional cities and the evolution mechanism of regional innovation network formation. Based on the social network analysis method and spatial analysis method, we explore the characteristics of the regional research cooperation network and its spatial pattern, as well as analyze the formation mechanism of the network using the exponential random graph model. The study finds that the scale of the regional innovation network is expanding, the overall network density is gradually increasing, but the nodes are loosely linked, and the regional innovation network is in the stage of deepening development. There is spatial heterogeneity in the regional innovation network, with a dumbbell-shaped distribution of “double-core dominance and central collapse”. During the formation of the regional innovation network, the endogenous structure of the innovation network is multi-connected under the path dependence, and the network development tends to be complicated; the economic strength and scientific research capability of cities and multi-dimensional proximity have a positive influence on the formation of the innovation network, and geographical proximity, social proximity and cognitive proximity can promote intra-regional innovation cooperation. Full article