Routing Resources Research Articles

Self‐supervised vessel trajectory segmentation via learning spatio‐temporal semantics

AbstractThe study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self‐supervised vessel trajectory segmentation method (SS‐VTS), which segments VTs based on their inherent spatio‐temporal semantics. SS‐VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi‐dimensional feature sequence of the VTs using self‐supervised learning. Finally, spatio‐temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS‐VTS achieves state‐of‐the‐art segmentation results compared to seven baseline methods.

Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics

We have proposed two algorithms in this paper for dynamic traffic in elastic optical networks (EON). Algorithm1 considers the spatial access blocking probability metric (SABPM) during dynamic operation and route traffic to a path with lower SABPM. Therefore, it considers a path with more contiguous blocks to support heterogeneous bandwidths (BW) and modulation schemes. Algorithm1 keeps a balance of fragmentation on available routes. Algorithm2 makes routing decision for dynamic traffic based on the maximum idle slots as well as minimum SABPM. Therefore, it selects a less fragmented path with more number of available contiguous blocks to support heterogeneous BW. Algorithm2 balances the path utilization and avoids over utilization of congested route. We have evaluated the performance of both algorithms in terms of spatial external fragmentation, contiguity ratio, contiguous aligned spectrum ratio, SABPM, and BW blocking probability (BwBP). Python with seaborn, pandas, and other libraries are used for network analytics. Results show that resource utilization improves with the proposed algorithms. It has been shown that algorithm2 and algorithm1 achieve 27% and 18% traffic gains respectively over the alternate routing.

Optimization for route selection under the integration of dispatching and control at the railway station: A 0‐1 programming model and a two‐stage solution algorithm

AbstractAt present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm.

Entropy Analysis of FPGA Interconnect and Switch Matrices for Physical Unclonable Functions

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.

Decentralized Routing and Radio Resource Allocation in Wireless Ad Hoc Networks via Graph Reinforcement Learning

Decentralized Routing and Radio Resource Allocation in Wireless Ad Hoc Networks via Graph Reinforcement Learning

Distributionally robust disaster relief planning under the Wasserstein set

We study a two-stage natural disaster management problem modeled as a stochastic program, where the first stage consists of a facility location problem, deciding where to open facilities and pre-allocate resources such as medical and food kits, and the second stage is a fixed-charge transportation problem, routing resources to affected areas after observing a disaster. Our model has binary variables present in both stages. Due to the lack of data, classical stochastic programming approaches may be ill-suited, and we propose a two-stage distributionally robust formulation with a Wasserstein ambiguity set, where we consider distributions consistent with historical data and a tunable parameter to control the level of risk aversion. We develop a tailored column-and-constraint generation (CCG) algorithm to solve an extensive reformulation, where scenarios are iteratively generated. We handle the presence of binary variables in the second stage by leveraging the structure of our support set and second-stage problem, and provide conditions under which the optimal value of the latter is concave with respect to the intensity of the disaster, leading to an efficient scenario generation procedure. We also show that our results extend to the case where the second stage is a fixed-charge network flow problem. We perform extensive computational experiments demonstrating the computational advantage of our method over classical CCG implementations on synthetic instances, and illustrate the benefits of our approach on a popular case study from the literature of hurricane threats on the Gulf of Mexico in the United States.

Asymptotically sharp estimates for the area of multiplexers in the cellular circuit model

Abstract A general cellular circuit of functional and switching elements (CCFSE) is a mathematical model of integral circuits (ICs), which takes into account peculiarities of their physical synthesis. A principal feature of this model distinguishing it from the well-known classes of circuits of gates (CGs) is the presence of additional requirements on the geometry of the circuit which ensure the accounting of the necessary routing resources for IC creation. The complexity of implementation of a multiplexer function of Boolean algebra (FBA) in different classes of circuits has been extensively studied. In the present paper, we give asymptotically sharp upper and lower estimates for the area of a CCFSE implementing a multiplexer FBA of order n. We construct a family of circuit multiplexers of order n of area equal to the halved upper estimate, and provide a method of delivering the corresponding lower estimate.

The use of quality tools to improve the efficiency of international transport corridors

In the context of the development of international trade and economic integration the opening of new international transport corridors (ITC) is becoming an urgent issue of ensuring the optimization of logistics processes, reducing time costs and improving the quality of service.This article discusses a comprehensive approach to improving international transport corridors, including the use of various quality and strategic planning tools. Each of these tools has its own characteristics and goals, but the overall objective is to improve processes, eliminate obstacles and improve the efficiency of the ITC. Some of the quality tools discussed in the article include: route optimization methods, resource and infrastructure management, as well as the use of information technology to monitor and manage processes within international transport corridors.Also, the use of quality tools will allow for cooperation and coordination between various participants of the ITC, such as the state, transport companies, freight forwarders and logistics services, which will ultimately contribute to the development of international trade and ensure a more sustainable and efficient logistics infrastructure.

Abstract WP83: Prioritize Rural Routing With Large Vessel Occlusion Stroke Severity Scale

Background: As a growing rural Primary Stroke Center (PSC), we found acute stroke patients bypassing a PSC via air care to receive care at a Comprehensive Stroke Center (CSC) without a Large Vessel Occlusion (LVO) severity scale assessment. In collaboration with our local Emergency Medicine Services (EMS), we aimed to initiate a LVO severity scale assessment with routing algorithm to enhance stroke care. Our goal was to improve pre-hospital acute stroke assessments with a standard LVO severity scale and routing algorithm. Further improving time sensitive care and utilizing resources appropriately during a time of hospital diversion and staff shortage. Method: Utilizing stroke guidelines, a standardized LVO scale and routing algorithm were developed and incorporated into Emergency Department (ED) and EMS education and communication. From March 2022 to March 2023, we reviewed all pre-hospital stroke alerts. We compared LVO assessments competed, appropriate routing, and air care activation; as well as, door-to-needle, and door-to transfer times. Results: There were 31 pre-hospital stroke alerts prior to the initiation of LVO assessment and routing interventions from March through June 2022 (Total LVO assessed 23%, bypass PSC with positive LVO assessed 33%, Door-to-needle median 30 minutes, Door-to-transfer median 64 minutes). After initiation of LVO assessment and routing algorithm from July 2022 to March 2023 there were 84 pre-hospital stroke alerts (Total LVO assessed 95%, bypass PSC with positive LVO assessed 100%, activation of air care to PSC with positive LVO assessed 100%, Door-to-needle median 27 minutes, Door-to-transfer 48 minutes). Conclusion: By identifying an assessment gap and providing education with assessment and routing resources, we experienced an increase in LVO assessments, improved routing of the acute stroke patient, along with improved door-to-needle and door-to-transfer times. Initiating and educating ED and EMS staff on a LVO assessment scale with routing algorithm was an effective intervention within our rural services area.

The Characterization of Residues Related to the Roasting– Leaching–Electrowinning Zinc Production Route for Further Metal Extraction

Super-hot acid leach residue is generated during zinc production in the roasting–leaching–electrowinning route, where both primary and secondary resources are used as feed material. This residue may contain valuable metals, such as lead, zinc, and iron, as well as precious metals, such as gold and silver. Four materials, namely super-hot acid leach residue, a residue formed when super-hot acid leach residue is selectively leached for lead with triethylenetetramine, as well as flotation concentrate, and flotation tailings formed in a selective silver flotation process with super-hot acid leach residue as the feed material were characterized to obtain a deeper understanding of possible further metal extraction. These four materials were characterized for chemical composition, mineralogy, and mineral distribution via chemical analyses, X-ray diffraction, and energy-dispersive scanning electron microscopy, respectively. The scanning electron microscope images showed that the materials have large variations in particle size distribution and composition. The results showed that the main lead phase in super-hot acid leach residue is lead sulfate, whereas it is mostly converted to lead sulfide during the selective lead leaching of the super-hot acid leach residue. The remaining lead sulfate is found in a solid solution with barium sulfate. Extracting lead from super-hot acid leach residue via triethylenetetramine leaching resulted in increased concentrations of gold and silver by 41% and 42%, respectively. The identified silver phases in super-hot acid leach residue may correspond to silver sulfide, silver chloride, and elementary silver, where silver sulfide was the most commonly occurring silver phase. After leaching this selectively for lead with triethylenetetramine, similar silver phases were identified, but silver sulfide and silver chloride occurred to a similar extent. Additionally, silver copper sulfide was detected. The presence of different silver phases might pose a challenge to reaching high silver recovery during leaching as the optimum leaching conditions differ somewhat. Furthermore, elemental sulfur, with a tendency to coat gold and silver particle surfaces, which is indicated to be present in all materials except the silver flotation tailings, may hinder metal extraction.

Computation Offloading and Resource Allocation for E2E Tasks in Satellite Edge Computing Networks

Satellite edge computing shows promise in delivering seamless, low-latency services in challenging contexts such as deserts, oceans, and during natural disasters. Previous studies focused on the selection of suitable offloading nodes for task processing and result transmission to the original node. However, for end-to-end tasks, exemplified by applications like satellite-based remote sensing and forestry monitoring, the disparity between source and destination nodes emphasizes the importance of routing selection in data transmission. This aspect substantially limits the effectiveness of traditional computation offloading methods. In this paper, we formulate a joint computation offloading, routing, and multiple resource allocation optimization problem for end-to-end tasks in satellite edge computing networks. Moreover, to reduce the complexity of this nondeterministic polynomial-time hard (NP-hard) problem, we decompose it into the multidimensional resource allocation in fixed offloading and routing condition and the computation offloading and routing selection problem. An algorithm based on binary particle swarm optimization for joint computation offloading, routing, and multiple resource allocation optimization problem is proposed. Simulation results are presented to demonstrate the performance of our scheme compared with other baseline schemes.

DEVELOPMENT OF PARAMETERIZED MODEL OF LOGIC ELEMENTS AT CLOCK TREE SYNTHESIS

Clock synthesis, routing optimization, placement and logic optimization are the three primary phases of physical design implementation. Since clock network synthesis uses at least 30% of the entire power budget, it is one of the crucial steps. Power consumption for high-performance blocks can reach 50% of the entire power. Not only would a high-quality clock tree will fix timing violations, but it will also minimize power usage and routing resource use. A new neural network based parameterized model is proposed in this paper, which can be used to obtain not only the list of logic elements, but it also can predict the circuits timing behaviour. Different ICs using SAED 14 and 32 nm technologies are designed using the proposed method.

ОПТИМІЗАЦІЯ ЛОГІСТИКИ ТА РЕАЛІЗАЦІЇ ПРОДУКЦІЇ В СІЛЬСЬКОГОСПОДАРСЬКИХ ПІДПРИЄМСТВАХ НА УМОВАХ КООПЕРАЦІЇ

This extensive article delves into the critical importance of optimizing transport logistics within the agricultural sector, providing a thorough examination of key algorithms and advanced techniques aimed at significantly improving industry efficiency. The primary focus is dedicated to the meticulous selection of optimal routes, involving a detailed review of state-of-the-art routing algorithms and dynamic programming techniques. The article systematically guides readers through concrete steps for determining optimal delivery routes, considering a myriad of factors such as distance, road conditions, transport volume, type, delivery deadlines, and transportation costs. The complexity of this task necessitates an in-depth analysis of various elements, and the article provides valuable insights into how these considerations collectively contribute to the overall efficiency of agricultural transport logistics. Emphasizing the pivotal role of effective resource management, the article underscores the need for strategic allocation, transportation, and utilization of diverse resources, including transport assets, human resources, equipment, time, and more. The integration of modern technologies is identified as a crucial component for achieving optimization goals, ensuring real-time data analysis, efficient communication, and adaptive decision-making. As a practical outcome, the article offers strategic recommendations for the implementation of optimization approaches within the agricultural logistics framework. These recommendations encompass innovative solutions for route planning, resource allocation, and technology integration. The overarching goal is to enhance the viability and productivity of agriculture, positioning logistical optimization as a key driver for sustainable growth in the industry. The synthesis of algorithmic strategies and cutting-edge technologies is positioned as instrumental in fostering advancements within the agricultural landscape. The article serves as a comprehensive guide, catering to various stakeholders, including farmers, logistics professionals, and policymakers, who aspire to leverage logistical optimization for transformative outcomes in agriculture. In conclusion, this in-depth exploration not only highlights the immediate benefits of optimizing transport logistics but also underscores the long-term sustainability and growth potential it brings to the agricultural sector. The integration of advanced algorithms and technologies is positioned as a cornerstone for addressing the evolving challenges and demands of modern agriculture.

Energy-aware Joint Route Selection and Resource Allocation in Heterogeneous Satellite Networks

Energy-aware Joint Route Selection and Resource Allocation in Heterogeneous Satellite Networks

Algorithm for operational planning of transportation by machine shipments in long-distance traffic

Efficient operational planning of transportation is crucial in longdistance traffic, where the coordination of shipments plays a pivotal role in optimizing logistics operations. This article presents an algorithm designed to streamline the planning process for machine shipments in long-distance transportation. The algorithm leverages advanced optimization techniques and machine learning to address the complexities associated with route selection, scheduling, and resource allocation.

Optimal routing and heterogeneous resource allocation for computing-aware networks

Computing-aware networking (CAN) is introduced to unite the computing resources distributed in different platforms. This paper proposes a joint routing and resource allocation model to minimize the total operational cost in CAN. We formulate the problem as an integer linear programming problem. We introduce a polynomial-time algorithm for larger-size problems. The numerical results reveal that the introduced algorithm reduces the computation time 9.18 times, with increasing the objective no more than 4% compared to the optimal solution; the proposed model can reduce 70% of the total cost compared to a baseline adopting a two-stage strategy in our examined cases.

Study on “three places and four fields” airline resource cooperative allocation based on decision tree algorithm

In view of the disharmony development status of Beijing Tianjin Hebei region, the paper puts forward a route optimization Principle of cooperative allocation of route resources of airport group from the angle of flight punctuality, airlines share and function orientation. An optimal model of airline resources cooperative allocation is established to maximize the overall operational benefit of regional airports and the modified decision tree C4.5 algorithm was applied to solve the problem. Since this algorithm combined with constraint condition of model as pruning standard, the efficiency of the algorithm was greatly improved. Finally, the problem find the optimal solution could be found by comparing the profit and loss value of the program. The analysis example showed that the overall benefit of multi-airport system increased by 7.03%. This model was useful to optimize airline network and conformed to the actual situation, and the research results can provide a reference for civil aviation authorities in planning the airline network of multi-airport system.

Operations Research Improves the Efficiency of United Parcel Service

With today's increasingly fierce commercial competition, logistics companies must optimize their own operating strategies if they want to win and get development in the cruel competition. And if they want to optimize their operating strategies, they naturally cannot do without the help of operations research. This report will rely on data obtained from the Internet and use data analysis methods to explore the impact of operations research on United Parcel Service, including: Route optimization, Resource allocation, Inventory management, and Customer service. Operations research enables United Parcel Service to increase revenue year by year in the fiercely competitive market while maintaining high customer satisfaction. Overall, this report will show how operations research can help with United Parcel Service by analyzing the data of United Parcel Service revenue and income from 2009 to 2021, Number of United Parcel Service employees from 2013 to 2021 and American Consumer Shipping Companies Customer Satisfaction Index scores from 2017 to 2023.

Degradation Measurement and Modelling under Ageing in a 16 nm FinFET FPGA.

Most of the latest generation of integrated circuits use FinFET transistors for their performance, but what about their reliability? Does the architectural evolution from planar MOSFET to FinFET transistor have any effect on the integrated circuit reliability? In this article, we present a test bench we have developed to age and measure the degradation of 5103 ring oscillators (ROs) implemented in nine FPGAs with 16nm FinFET under different temperature and voltage conditions (Vnom≤Vstress≤1.3Vnom and 25°C≤Tstress≤115°C) close to operational conditions in order to predict reliability regarding degradation mechanisms at the transistor scale (BTI, HCI and TDDB) as realistically as possible. By comparing our initial RO measurements and the data extracted from Vivado, we will show that the performance of the nine FPGAs is between 50% and 70% of the best performance expected by Vivado. After 8000 h of ageing, we will see that the relative degradations of the RO are a maximum of 1%, which is a first indicator proving the FPGAs' good reliability. By comparing our results with similar studies on 28 nm MOSFET FPGAs, we will reveal that 16 nm FinFET FPGAs are more reliable. To be implemented in an FPGA, an RO uses logic resources (LUT) and routing resources. We will show that degradation in the two types of resources is different. For this reason, we will present a method for separating degradations in logical and routing resources based on RO degradation measures. Finally, we will model rising and falling edge propagation time degradations in an FPGA as a function of time, temperature, voltage, signal duty cycle and resources used in the FPGA.

Кластерный анализ арктических поселений Якутии с позиции концепции резильентности

Shifting the focus from the anti-crisis agenda to the tasks of contributing to the achievement of national goals in the Arctic requires the development of new approaches to the implementation of state policy in these territories of the Russian Federation, taking into account modern trends: reconfiguring relations with the vector to the East and reorienting to the development of anchor settlements as geostrategic points of Russia. In modern realities, priorities have been set – the development of the Northern Sea Route and mineral resource centers, the im-plementation of economic and infrastructure projects, and the creation of a new Arctic economy. New ap-proaches are also required to “fine-tune” the tools to increase/ensure the sustainability of the economic sys-tems of strategically important Arctic regions. In this regard, an assessment of the socio-economic develop-ment of the municipal districts of the Arctic zone of the Republic of Sakha (Yakutia) using cluster analysis is given. In the article it is considered by the authors as an effective tool of “pre-planning” and one of the tools for selecting potential candidate settlements for inclusion in the list of strategic territories of the Arctic, which is es-pecially relevant in the light of the forthcoming development by the executive authorities of master plans for the development of reference settlements of the Arctic zone of Russia and comprehensive plans for socio-economic development until 2035.