Distribution Of Terminals Research Articles

Topological Properties of Network Spring Models that Determine Terminal Distributions of Plastic Deformations

Topological Properties of Network Spring Models that Determine Terminal Distributions of Plastic Deformations

Optimization and Application Analysis of the UAV "Last Mile" Mobile Terminal Distribution Station in Rural Area under the Background of E-Commerce

With the booming demands of global consumers, the grand total of international e-commercial procurement in 2024 will be expected to exceed 6.3 trillion USD. Under the pressure of huge orders and shipping lag, figuring out a cost-effective last mile delivery achieving real door-to-door service, especially in rural areas, has become one of the focuses in the international e-commercial market. To better solve this problem, this paper starts with the analysis of international e-commercial status and the feasibility of UAV mobile terminal station launch, taking good account of drone delivery route setting, time schedule, and costs spent to optimize the mobile station moving. Based on a series of experiments to iterate the Unmanned Aerial Vehicle (UAV) mobile terminal station delivery mode, it will provide some insights to the market about how the last mile delivery will develop in the next decade. This paper will focus on applying the hybrid ant colony algorithm to improve truck-drone collaborative last mile delivery route settings and figure out the best delivery route combo for cost saving.

Data-driven state-of-charge estimation of a lithium-ion battery pack in electric vehicles based on real-world driving data

State-of-charge (SOC) is one of the most important indicators in the battery management system (BMS) of electric vehicles (EVs). Accurate online estimation of battery pack SOC is essential for ensuring safety by preventing overcharging and overdischarging, as well as for facilitating proper operation and efficient energy utilization. This paper proposes a deep learning model, CNN-BiLSTM-Attention, to drive the battery pack SOC estimation by datasets collected from actual operating EVs. Multivariate time series (MVTS) signals such as vehicle speed, battery voltage, current and temperature are carefully selected as inputs to the model. Additionally, a three-parameter Weibull probability model is used to model the terminal voltage distribution of all cells within the investigated battery pack, and the evolution of cell terminal voltage inconsistency concerning depth of discharge (DOD) is described from the perspective of dispersion and symmetry. By adding these distribution features to the input dimension of the CNN-BiLSTM-Attention model, the estimation accuracy of battery pack SOC has been further improved. Experimental results indicate that when the window length (WL) is set to 25 sampling time steps and the window overlap rate is >72 %, the SOC estimation error is <3 % and the root mean square error (RMSE) is <1.12 % within the maximum DOD variation range experienced by the battery pack. Especially when the battery pack is in the discharge platform period of 30 %–100 % SOC, the SOC estimation error is reduced to <2 %. After adding the Weibull distribution features that reflect the cell inconsistency evolution into the model's input dimensions, the overall SOC estimation RMSE is reduced by about 31 %, especially by about 51 % at lower SOC levels (below 31 %).

Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences.

Explainable artificial intelligence models for predicting pregnancy termination among reproductive-aged women in six east African countries: machine learning approach

Pregnancy termination remains a complex and sensitive issue with approximately 45% of abortions worldwide being unsafe, and 97% of abortions occurring in developing countries. Unsafe pregnancy terminations have implications for women’s reproductive health. This research aims to compare black box models in their prediction of pregnancy termination among reproductive-aged women and identify factors associated with pregnancy termination using explainable artificial intelligence (XAI) methods. We used comprehensive secondary data on reproductive-aged women’s demographic and socioeconomic data from the Demographic Health Survey (DHS) from six countries in East Africa in the analysis. This study implemented five black box ML models, Bagging classifier, Random Forest, Extreme Gradient Boosting (XGB) Classifier, CatBoost Classifier, and Extra Trees Classifier on a dataset with 338,904 instances and 18 features. Additionally, SHAP, Eli5, and LIME XAI techniques were used to determine features associated with pregnancy termination and Statistical analysis were employed to understand the distribution of pregnancy termination. The results demonstrated that machine learning algorithms were able to predict pregnancy termination on DHS data with an overall accuracy ranging from 79.4 to 85.6%. The ML classifier random forest achieved the highest result, with an accuracy of 85.6%. Based on the results of the XAI tool, the most contributing factors for pregnancy termination are wealth index, current working experience, and source of drinking water, sex of household, education level, and marital status. The outcomes of this study using random forest is expected to significantly contribute to the field of reproductive healthcare in East Africa and can assist healthcare providers in identifying individuals’ countries at greater risk of pregnancy termination, allowing for targeted interventions and support.

Location Selection Methods for Urban Terminal Co-Distribution Centers with Air–Land Collaboration

Urban terminal logistics and distribution enterprises face the problems of duplicated network layouts, high costs, and inefficient urban environments. Recently, collaborative distribution using unmanned aerial vehicles (UAVs) and ground vehicles has been considered as a means of reducing costs and enhancing efficiency, thus overcoming the issues created by the high-density layout of enterprises’ relatively independent networks. This essentially involves constructing an air–ground collaborative common distribution network. To optimize the economic cost and distribution time of network operations, we established a site selection planning model for air–ground cooperative urban co-distribution centers and designed a solution method based on gray wolf optimization with K-means clustering. Taking the Wangsheren area of Jinan City, China, as an example, 15 UAV co-distribution centers and 13 vehicle co-distribution centers were identified. Although the average distribution cost of the network rose by 35–50% compared to traditional terminal distribution, the time saving was 80–85%, greatly improving the high-value-added service capacity. The end-distribution efficiency and customer satisfaction were also enhanced, which fully verifies the feasibility, validity, and applicability of the proposed model. Our approach can be applied to landing sites and the planning and optimization of large-scale commercial operations using logistics UAVs in urban areas.

A Two-Echelon Multi-Trip Capacitated Vehicle Routing Problem with Time Windows for Fresh E-Commerce Logistics under Front Warehouse Mode

Given the swift expansion of fresh e-commerce, the front warehouse mode can respond quickly and ensure the quality of fresh products. However, the complexity of the supply chain structure under front warehouse mode poses a challenge in reducing logistics costs and improving distribution efficiency while meeting consumers’ immediate delivery demands. Therefore, this paper studies the vehicle routing problem of two-echelon fresh e-commerce under front warehouse mode. Considering trans-shipment time constraints between the two echelons and the characteristics of terminal distribution, this paper initially models the vehicle routing problem for front warehouses as a two-echelon multi-trip capacitated vehicle routing problem with time windows. A mixed-integer linear programming model is subsequently established. To solve the model, a hybrid genetic algorithm integrated with neighborhood search is developed. Matrix coding is employed to merge vehicle selection and route assignment decisions. Simultaneously, neighborhood search is applied to enhance the search capability of algorithms, thereby improving the quality of solutions. Furthermore, the effectiveness and efficiency of the model and algorithm are verified through experiments of varying scales. Finally, comparative strategies and sensitivity analysis highlight the advantages of multi-trip strategies and provide insights into the optimal vehicle capacity limit.

On the breakup frequency of bubbles and droplets in turbulence: A compilation and evaluation of experimental data

The dispersed phase in liquid–liquid emulsions and air–liquid mixtures can often be fragmented into smaller sizes by the surrounding turbulent carrier phase. The critical parameter that controls this process is the breakup frequency, which is defined from the breakup kernel in the population balance equation. The breakup frequency controls how long it takes for the dispersed phase to reach the terminal size distribution for given turbulence. In this article, we try to summarize the key experimental results and compile the existing datasets under a consistent framework to find out what is the characteristic timescale of the problem and how to account for the inner density and viscosity of the dispersed phase. Furthermore, by pointing out the inconsistency of existing experimental data, the key important unsolved questions and related problems on the breakup frequency of bubbles and droplets are discussed.

Risk Assessment and Countermeasures of Logistics Terminal Distribution under Public Health Emergencies

In recent years, the frequent occurrence of public health emergencies has affected people’s daily lives. In response, this paper evaluates the risk of logistics terminal distribution and puts forward the corresponding countermeasures. Firstly, this paper summarizes relevant literature and analyzes the characteristics of public health emergencies and logistics terminal distribution. This paper constructs the risk index system of logistics terminal distribution under public health emergencies for the first time. Secondly, this paper conducts a questionnaire survey on a domestic e-commerce enterprise. In this paper, the intuitionistic fuzzy entropy weight method is applied to the obtained data to determine the weight of risk index. Finally, this paper adopts the grey fuzzy comprehensive evaluation method to evaluate the risk index of logistics terminal distribution under public health emergencies. This paper takes the impact of public health emergencies into account for the risk of logistics terminal distribution for the first time. Some suggestions are provided for preventing the logistics terminal distribution from stagnation and paralysis when public health emergencies occur. This paper supplements the existing research in this field.

The 2D-IR spectrum of hydrogen-bonded silanol groups in pyrogenic silica.

Pyrogenic silica is a form of amorphous silica with a high surface area and a heterogeneous distribution of silanol hydroxyl terminations and defects. In this work, the interesting and unusual form of the hydroxyl-stretch 2D-IR spectrum of pyrogenic silica is presented and explored in the deuterated (deuteroxyl) form. Transition dipole couplings between hydrogen-bonded and non-hydrogen-bonded silanol groups give a distinct cross-peak in the 2D-IR spectrum, displaying interstate coherence oscillations during the 2D-IR experimental waiting time. The strong asymmetry about the diagonal is proposed to be the result of both the relatively small transition dipole coupling strength and the extreme differences in the width of the hydrogen-bonded and non-hydrogen-bonded silanol bands. The resulting interference of negative and positive cross-peaks has minimal intensity in the below-diagonal ω3 < ω1 region of the spectrum. An additional strong positive cross-peak is observed at a position in the 2D-IR spectrum inconsistent with transition dipole coupling. An assignment as a fifth order effect is proposed.

Research on cooperative optimization of terminal distribution of Chongqing underground logistics system based on metro network

This thesis presents a novel solution for the distribution problem in the urban underground logistics system, specifically addressing the challenge of the “last kilometer” distribution during the non-operating hours of the Chongqing metro system. The study commences by examining the present condition and significance of subterranean logistics systems. It thoroughly analyzes the distinctive topography and traffic obstacles specific to Chongqing’s underground logistics system, and highlights the practicality and advantages of utilizing the metro system for logistics and distribution. The thesis proposes a comprehensive metro logistics and distribution system, which includes the selection of transfer locations, efficient connection between distribution centers and metro stations, and the implementation of appropriate management and scheduling strategies for distribution trucks. This study presents a unique approach by effectively combining the metro system with urban logistics requirements. It offers a fresh perspective on addressing the main challenges in urban logistics and distribution. Additionally, it provides a comprehensive theoretical and practical framework that can be applied to other regions with similar urban characteristics.

Leveraging an all-fixed transfer framework to predict the interpretable formation energy of MXenes with hybrid terminals.

MXenes have attracted substantial attention for their various applications in energy storage, sensors, and catalysts. Experimental exploration of MXenes with hybrid terminal surfaces offers a unique means of property tailoring that is crucial for expanding the performance space of MXenes, wherein the formation energy of an MXene with mixed surface terminals plays a key role in determining its relative stability and practical applications. However, the challenge of identifying energetically stable MXenes with multifunctional surfaces persists, primarily due to the absence of precise surface modification during experiments and the vast structural space for DFT calculations. Herein, we use an all-fixed transfer (AFT) framework combined with first-principles calculations to predict the formation energies of MXenes terminated by binary elements from groups VIA and VIIA. The trained model exhibits a high average R2 of 0.99, maintaining transferability and accuracy in predicting larger supercells from smaller-sized MXenes and datasets despite the structural imbalance between the training and prediction sets. The underlying interpretation of the high accuracy is revealed through the capture of main attributes and comparison of node features. Additionally, it is important to mention that the factors influencing the average formation energy include the types of element pairs, the ratio of terminal groups, and the distribution of terminations on two surfaces, with the first one being dominant. Finally, we successfully streamline the diverse structural cardinality of a large hybrid terminated MXene space of over 700 million, thereby facilitating the rapid screening of the top 5 stable MXene classes with binary terminal elements (FO, FCl, FBr, FS, and FSe). Besides, in the scenarios of lithium storage, the TL-predicted MXene can enhance its relative stability by increasing the fluorine ratio where the capacity can be optimized by different surface group combinations. All results indicate that the AFT framework has the advantages of screening functional MXenes with a huge structure space from smaller and imbalanced data sets.

Deep learning for mean field optimal transport

Mean field control (MFC) problems have been introduced to study social optima in very large populations of strategic agents. The main idea is to consider an infinite population and to simplify the analysis by using a mean field approximation. These problems can also be viewed as optimal control problems for McKean-Vlasov dynamics. They have found applications in a wide range of fields, from economics and finance to social sciences and engineering. Usually, the goal for the agents is to minimize a total cost which consists in the integral of a running cost plus a terminal cost. In this work, we consider MFC problems in which there is no terminal cost but, instead, the terminal distribution is prescribed as in optimal transport problem. By analogy with MFC, we call such problems mean field optimal transport problems (or MFOT for short) since they can be viewed as a generalization of classical optimal transport problems when mean field interactions occur in the dynamics or the running cost function. We propose three numerical methods based on neural networks. The first one is based on directly learning an optimal control. The second one amounts to solve a forward-backward PDE system characterizing the solution. The third one relies on a primal-dual approach. We illustrate these methods with numerical experiments conducted on two families of examples.

Development of ocular dominance columns across rodents and other species: revisiting the concept of critical period plasticity.

The existence of cortical columns, regarded as computational units underlying both lower and higher-order information processing, has long been associated with highly evolved brains, and previous studies suggested their absence in rodents. However, recent discoveries have unveiled the presence of ocular dominance columns (ODCs) in the primary visual cortex (V1) of Long-Evans rats. These domains exhibit continuity from layer 2 through layer 6, confirming their identity as genuine ODCs. Notably, ODCs are also observed in Brown Norway rats, a strain closely related to wild rats, suggesting the physiological relevance of ODCs in natural survival contexts, although they are lacking in albino rats. This discovery has enabled researchers to explore the development and plasticity of cortical columns using a multidisciplinary approach, leveraging studies involving hundreds of individuals-an endeavor challenging in carnivore and primate species. Notably, developmental trajectories differ depending on the aspect under examination: while the distribution of geniculo-cortical afferent terminals indicates matured ODCs even before eye-opening, consistent with prevailing theories in carnivore/primate studies, examination of cortical neuron spiking activities reveals immature ODCs until postnatal day 35, suggesting delayed maturation of functional synapses which is dependent on visual experience. This developmental gap might be recognized as 'critical period' for ocular dominance plasticity in previous studies. In this article, I summarize cross-species differences in ODCs and geniculo-cortical network, followed by a discussion on the development, plasticity, and evolutionary significance of rat ODCs. I discuss classical and recent studies on critical period plasticity in the venue where critical period plasticity might be a component of experience-dependent development. Consequently, this series of studies prompts a paradigm shift in our understanding of species conservation of cortical columns and the nature of plasticity during the classical critical period.

Capacity matching relative to derived demand

This research investigates the synchronisation of freight momentum from Port to terminal distribution in Australian supply chains. This research builds upon earlier studies that examine the impact of critical asset capacity, freight accumulation, and atomised processing on supply chains. It also uncovers the underlying reasons for supply chain misalignment. A qualitative case study was undertaken across the Port Botany to Moorebank rail corridor to assess critical asset alignment. The key finding from the mini-case study identified that critical asset investment (massification) in the Metro Freight Line and Moorebank IMEX terminal will not achieve its full potential in 2030 due to a capacity-constrained Port rail yard and the Southern Sydney Freight line. The study concluded that investment in critical assets should be orchestrated and harmonised across multiple actors' infrastructure. Matching capacity within a feasible throughput range is essential to maintain the required draw-down rate for freight momentum. This study makes theoretical and practical contributions to this field. In the service industry, the theory of constraints is a viable process for solving supply chain problems. The study suggests that investing in capacity alignment, particularly interfaces and timing, is crucial for reaching supply chain objectives.

Hybrid hierarchical aggregation with semi dynamic optical line terminal distribution algorithms optimization for ethernet passive optical networks

The goal of hybrid fiber-wireless (Fi-Wi) access networks is to ubiquitously integrate the accessible vast amounts of optical network bandwidth with wireless network mobility at minimal cost and simple network installation. Fi-Wi networks can employ both Radio over Fiber (RoF) and Radio and Fiber (R&F) technologies. The difference between them is that R&F uses two MAC controls: one for optical fiber and one for wireless networks. One can reduce interference within the wireless subnetwork while avoiding the negative effects of fiber optic propagation delays on the network. RoF, on the other hand, uses MAC control for both optical and wireless networks, resulting in network latency and congestion. This paper demonstrates (i) the use of semi-dynamic bandwidth allocation and (ii) the use of hierarchical frame aggregation for access in next-level integrated Ethernet passive optical network EPON/WLAN-based generations and presents the ongoing research to improve the Quality of Service in wireless and fiber access networks. The study includes an offered load up to 10 Gbps, reaching the up-to-date levels. The obtained results reveal that the data, voice, and video latency are reduced, respectively, by 35%, 25%, and 30%. At the same time, the corresponding throughput is increased, respectively, for data, voice, and video by 35%, 25%, and 30%.

Formulation and heuristic method for urban cold-chain logistics systems with path flexibility – The case of China

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations.

Near-Atomic-Scale Perspective on the Oxidation of Ti3 C2 Tx MXenes: Insights from Atom Probe Tomography.

MXenes are a family of 2D transition metal carbides and nitrides with remarkable properties, bearing great potential for energy storage and catalysis applications. However, their oxidation behavior is not yet fully understood, and there are still open questions regarding the spatial distribution and precise quantification of surface terminations, intercalated ions, and possible uncontrolled impurities incorporated during synthesis and processing. Here, atom probe tomography (APT) analysis of as-synthesized Ti3 C2 Tx MXenes reveals the presence of alkali (Li, Na) and halogen (Cl, F) elements as well as unetched Al. Following oxidation of the colloidal solution of MXenes, it is observed that the alkalis are enriched in TiO2 nanowires. Although these elements are tolerated through the incorporation by wet chemical synthesis, they are often overlooked when the activity of these materials is considered, particularly during catalytic testing. This work demonstrates how the capability of APT to image these elements in 3D at the near-atomic scale can help to better understand the activity and degradation of MXenes, in order to guide their synthesis for superior functionalproperties.

Monge–Kantorovich optimal transport through constrictions and flow-rate constraints

We consider the problem to transport resources/mass while abiding by constraints on the flow through constrictions along their path between specified terminal distributions. Constrictions, conceptualized as toll stations at specified points, limit the flow-rate across. We quantify flow-rate constraints via a bound on a sought probability density of the times that mass-elements cross toll stations and cast the transportation scheduling in a Kantorovich-type of formalism. Recent work by our team focused on the existence of Monge maps for similarly constrained transport minimizing average kinetic energy. The present formulation in this paper, besides being substantially more general, is cast as a (generalized) multi-marginal transport problem — a problem of considerable interest in modern-day machine learning literature and motivated extensive computational analyses. An enabling feature of our formalism is the representation of an average quadratic cost on the speed of transport as a convex constraint that involves crossing times.

Installation and Activation of Fiber To The Home (FTTH) Networks and Macrobending Problems in the Feeder Cable Segment

Macrobending is a form of disturbance in Fiber To The Home (FTTH) networks that occurs due to macro-level bending of the cable caused by damage to the fiber optic. Macrobending frequently occurs in FTTH networks within the feeder cables. Feeder cables serve as connectors between the Optical Line Terminal (OLT) and Optical Distribution Cabinet (ODC) in the FTTH system. The occurrence of macrobending in feeder cables affects the quality of the FTTH network. In this study, the impact of macrobending is analyzed based on curvature diameters of 50 cm, 25 cm, and 5 cm on feeder cables before and after FTTH network activation. Before FTTH network activation, the High Super Luminescent Diode (HSL) is used as the input power source, whereas after activation, the input power source comes from the OLT using Small Form-factor Pluggable (SFP) modules. The attenuation (loss) before activation due to macrobending, with curvature diameters of 50 cm, 25 cm, and 5 cm, is found to be 0.02 dB, 0.05 dB, and 0.26 dB, respectively. After activation, the attenuation with the same curvature diameters is measured as 0.01 dB, 0.02 dB, and 0.20 dB, respectively. It is observed that as the curvature diameter decreases, the attenuation increases. The comparison of attenuation before and after network activation doesn't show a significant difference because the input power doesn't affect macrobending, rather it is influenced by the curvature diameter.