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The formation of macroscopic deformation bands constitutes undesired stress fluctuations in the compression of porous metals. This research is aimed at revealing the predictability of the formation of deformation bands in porous metals with unidirectional through‐pores. Finite element analyses of compression tests of such porous metals are conducted with the commercial code Abaqus/Standard and Explicit 2018. Three cubic specimens with different L/d (specimen edge length L, pore diameter d) but with the almost same porosity are prepared, and quasi‐static compression is simulated. The results suggest that the formation of horizontally propagated deformation bands is suppressed as L/d increases. A path search is performed to predict the location of deformation bands on a graph that represents equivalent plastic strain (PEEQ) distribution in the specimens. It is revealed that horizontally propagated deformation bands at compressive strain e = 10.0 pct can be predicted as the path with the largest mean of PEEQ at e = 1.0 pct when the following two conditions are met; the minimum PEEQ in the path has to be larger than zero, and the standard deviation of the PEEQ mean in candidate paths has to be sufficiently large. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Prior to switching operations, the dispatch automation system is required to be able to search for closed-loop paths quickly in real time. In order to improve the efficiency of closed-loop path search, this paper proposes a multi-stage real-time fast search method for closed-loop paths based on the characteristics of power grid hierarchical partitioning. First, we divide the closed-loop judgement process into three phases: initialization, acceptance of the closed-loop judgement command, and post-operation. Then, we define three types of nodes, including root nodes, same-layer contact nodes, and common nodes, and construct path data models for each type of node in accordance with the node liaison relationship in grid hierarchical zoning. In the initialization phase, we design a method for the automatic generation of real-time nodal path data models and study a hierarchical partitioned closed-loop path search method based on a nodal path data model for the phase of accepting closed-loop judgement commands. Next, we design a fast local correction method for nodal path models after the switch closure and disconnection operations in the post-operation idle phase are performed. Finally, a real power grid model is used as an example to test the above closed-loop path search method, which improves the search efficiency by eight times compared with the conventional breadth-first search method. The method is applicable to real large and complex power grids. [ABSTRACT FROM AUTHOR]

Electromagnetic navigation bronchoscopy (ENB) uses electromagnetic positioning technology to guide the bronchoscope to accurately and quickly reach the lesion along the planned path. However, enormous data in high-resolution lung computed tomography (CT) and the complex structure of multilevel branching bronchial tree make fast path search challenging for path planning. We propose a coordinate-based fast lightweight path search (CPS) algorithm for ENB. First, the centerline is extracted from the bronchial tree by applying topological thinning. Then, Euclidean-distance-based coordinate search is applied. The centerline points are represented by their coordinates, and adjacent points along the navigation path are selected considering the shortest Euclidean distance to the target on the centerline nearest the lesion. From the top of the trachea centerline, search is repeated until reaching the target. In 50 high-resolution lung CT images acquired from five scanners, the CPS algorithm achieves accuracy, average search time, and average memory consumption of 100%, 88.5 ms, and 166.0 MB, respectively, reducing search time by 74.3% and 73.1% and memory consumption by 83.3% and 83.0% compared with Dijkstra and A* algorithms, respectively. CPS algorithm is suitable for path search in multilevel branching bronchial tree navigation based on high-resolution lung CT images. [ABSTRACT FROM AUTHOR]

Does main path analysis (MPA), in producing the main paths (MPs), invariably choose the longer paths over the shorter ones? This work examines the various combinations of the most popular path search algorithms, i.e., local and global searches (including the key-route variant), and the weight assignment algorithms, i.e., search path count (SPC), search path link count (SPLC), and search path node pair (SPNP), to investigate the path preference of MPA. Based on a simplified model, this work finds that, when there are multiple paths between a pair of nodes, MPA indeed goes for the longer paths under some, but not all, algorithm combinations, but it may retain both longer and shorter paths under other combinations. [ABSTRACT FROM AUTHOR]

The main task of path search methods is to generate a possible path through a section of the environment while bypassing obstacles and minimizing the path length. Existing methods focus on the shortest geometric path to the target and do not take into account various parameters, such as energy consumption or the complexity of sections of the path. This article proposes a method for finding a path and determining its optimality using Petri nets. In addition to generating solutions based on several parameters, this method allows you to expand the visual feedback. When calculating the optimality of the model using the geometric distance from the entry point of the robot to the goal along a certain route and the cost of the abstract amount of energy to move each route. The object of research is Petri nets and their use to model the process of creating routes and finding a way for autonomous robots. The subject of research is the mathematical apparatus of Petri nets and the feasibility of their use in modeling the process of creating routes and finding a way for autonomous robots. The research aims to demonstrate the benefits of Petri nets for visual modeling of the process of finding the path and motion of autonomous robots. Conclusions. The presented technique is suitable for use in modeling the process of finding a way and provides comprehensive statistics for further processing and analysis.

Prior to switching operations, the dispatch automation system is required to be able to search for closed-loop paths quickly in real time. In order to improve the efficiency of closed-loop path search, this paper proposes a multi-stage real-time fast search method for closed-loop paths based on the characteristics of power grid hierarchical partitioning. First, we divide the closed-loop judgement process into three phases: initialization, acceptance of the closed-loop judgement command, and post-operation. Then, we define three types of nodes, including root nodes, same-layer contact nodes, and common nodes, and construct path data models for each type of node in accordance with the node liaison relationship in grid hierarchical zoning. In the initialization phase, we design a method for the automatic generation of real-time nodal path data models and study a hierarchical partitioned closed-loop path search method based on a nodal path data model for the phase of accepting closed-loop judgement commands. Next, we design a fast local correction method for nodal path models after the switch closure and disconnection operations in the post-operation idle phase are performed. Finally, a real power grid model is used as an example to test the above closed-loop path search method, which improves the search efficiency by eight times compared with the conventional breadth-first search method. The method is applicable to real large and complex power grids.

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users. [ABSTRACT FROM AUTHOR]

Currently, a large number of different mathematical models and methods aimed at solving problems of multidimensional optimization and modeling of complex behavioral systems have been developed. One of the areas of search for solutions is the search for solutions in conditions of incomplete information and the need to take into account changing external factors. Often such problems are solved by the method of complete search. In some conditions, the method of complete search can be significantly improved through the implementation and use of behavioral models of natural formations. Examples of such formations can be group behavior of insects, birds, fish, various flocks, etc. The idea of copying group activity of a shoal of fishes at the decision of problems of joint activity on extraction of food is used in work. The reasoning based on the simulation of the behavior of such a natural object allowed to justify the choice as a mathematical model - cellular automata. The paper examines the key features of such a model. Modeling of his work is carried out, strategies of behavior of group of mobile objects at search of the purposes are developed, key characteristics are investigated and the method of adaptive choice of strategy and change of rules of behavior taking into account features of the solved problem is developed. The search strategy is implemented in the work, which takes into account the need to solve the optimization problem on two parameters. The obtained results testify to the high descriptive possibility of such an approach, the possibility of finding the optimal strategy for the behavior of the cellular automaton and the formalization of the process of selecting the parameters of its operation. A further improvement of this approach can be the implementation of simulation to study the properties of the developed model, the formation of the optimal set of rules and parameters of the machine for the whole set of tasks.

This paper proposes a path search formulation and its solution method to a finite optimal control problem on self-triggered control systems. Previous methods to the problem have high computational complexity. This paper focuses on the formulation and its data structure for reduction of calculation time and reformulates the optimal control problem to a path search problem. We also consider a data structure of a graph for the path search problem. The key point is the sharing of vertices. The sharing leads to the reduction of calculation time. We compare the calculation time of the path search algorithm and the mixed-logical dynamical method.

An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research.

To improve patient safety in ambulance transport service, this paper proposes a new method for finding an optimal route of an ambulance depending on the disease or injury of a patient. Three transport cases are considered in this paper: Heart disease (HD), hemorrhagic cerebrovascular accident (HCVA) and fracture/dislocation (Fx/dx). The route optimality is measured with three evaluation functions. The first function evaluates an elapsed time from the scene of an emergency to a hospital. The second evaluates a blood pressure variation induced by the acceleration/deceleration motion of an ambulance. The third evaluates a compression load acting on the back of a patient due to the centrifugal force. They are calculated by applying the traveling model of an ambulance to the three-dimensional road network. The optimal route is selected in two stages. First, the candidate routes are found by solving the multiobjective optimization problem of minimizing the three evaluation functions. If the completely optimal solution (route), which minimizes the three functions simultaneously, is found, the selection process is finished. Otherwise, obtain the Pareto solutions to go to the second stage, in which a preference solution is selected from the Pareto solutions depending on the disease or injury of a patient. In the numerical example, the route selection is shown for HD, HCVA and Fx/dx. For the reference sake, the real routes of the ambulance recorded in Hiroshima City and the shortest routes obtained by Google Maps Navigation are also presented and compared to each other.

With the increasing development of marine science and technology, autonomous underwater robots have become a hot spot in the field of underwater research and applications, and have been widely used in deep-sea scientific research, marine resources research, and deep-sea security. This paper focuses on analyzing the current research and development status and application prospects of autonomous underwater robots and predicts their future development trend. Aiming at the difficulties in engineering practice of path search in underwater unknown space, the traditional space path exploration method is analyzed, and a hybrid search algorithm is proposed to plan a reasonable and efficient dynamic path for target search in unknown underwater space.

Detailed Routing is one of the most time-consuming steps of physical synthesis of ICs. Also, it is very challenging due to the complexity of the design rules that the router must obey. For some of these rules, if they are handled in a postprocessing step after routing, it may be impossible to avoid design rule violations. This requires that these rules are handled by the path search algorithm used in routing. Thus, we propose DRAPS, a design rule aware path search algorithm. DRAPS is an A*-interval-based fast path search algorithm which is aware of the via library, the minimal area rule and the cut spacing rule of vias belonging to the same path. Our experiments show that DRAPS reduces the design rule violation count by 96% on average, in comparison with a non-design-rule aware path search. Besides, we have compared our detailed routing system to two state-of-the-art academic routers (Dr. CU and TritonRoute) that were tested using ISPD18 benchmarks. Our router has presented, on average, 22% less runtime and 81% less design rule violations w.r.t. Dr. CU, which was the better of the compared routers. [ABSTRACT FROM AUTHOR]

In preparation for optimal routing of patients to a hospital, this paper presents a traveling model of the ambulance that moves from the scene of an emergency to a hospital. The model is given in the form of a speed diagram along the route and constructed based on some rules: The ambulance moves with either a constant speed, a constant acceleration speed or a constant deceleration speed. The maximum speed changes depending on the legal speed limit for private vehicles. The ambulance decreases its speed to a specified level when it passes through an intersection with traffic lights or turns left or right. The model has eight adjustable parameters. They were optimized in the framework of the multi-objective optimization problem involving three objective functions, which are defined as modeling errors relevant to the traveling time, the fluctuation of blood pressure and the pressing force acting on the back of a patient. The genetic algorithm was used to obtain Pareto solutions to the problem. An optimal set of parameters was selected from among them. The validity of the model was confirmed by checking the objective functions for the validation data set. However, there was a limitation: The model does not match to the actual speed of the ambulance in such a case that it moves on the road where many traffic lights are placed close to each other and controlled synchronously.

In this paper, we propose a hybrid approach of combining A* and Genetic algorithm in the path search problem. In A*, the cost from a start node to the intermediate node is optimized in principle but the path from that intermediate node to the goal node is generated and tested based on the cumulated cost and the next node in a priority queue is chosen to be tested. In that process, we adopt the genetic algorithm principle in that the group of nodes to generate the next node from an intermediate node is tested by its fitness function. Top two nodes are selected to use crossover or mutation operation to generate the next generation. If generated nodes are qualified, those nodes are inserted to the priority queue. The proposed method is compared with the original sequential selection and the random selection of the next searching path in A* algorithm and the result verifies the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

With the proliferation of positioning technologies and GPS-enabled mobile devices, it has become very important to search for optimal paths to cover required points of interest (POIs, e.g., banks and restaurants) over road networks in many location-based services. In practice, however, traffic conditions are inherently uncertain and dynamically changing over time, which makes it rather challenging to provide accurate results for path queries based on travelling time. Inspired by this, we consider the practical settings of road networks and model them by uncertain road networks (URNs) on which the travelling time of each road is uncertain and captured by a set of travelling time samples. Then, we formalize the probabilistic time-constrained path ( PTP) query over uncertain road networks to retrieve those paths that not only cover required POIs with constrained service time but also have the minimum travelling times in high confidence. We prove that PTP query problem is NP-hard. In order to answer PTP queries efficiently, we propose an efficient PTP query approach with effective pruning strategies regarding the time constraints on POIs and the probabilistic/rank requirements of queries. Extensive experiments on real road networks validate the efficiency and effectiveness of our PTP query approach. [ABSTRACT FROM PUBLISHER]

With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems.

This paper presents the bio-inspired algorithm SoSACO-v2 that is explained as an extension of the Ant Colony Optimization in which the ants are empowered with the sense of smell, directing them straightly to privileged nodes when they are near enough of them. This algorithm is an evolution of a former version which main feature is efficiency through path search task in huge graphs of high connectivity. New requirements regarding this task in most applications include processing vast graphs, immediate comeback, and dealing with dynamicity. The here proposed algorithm gives response to new needs the former approaches cannot fulfill: fast finding of paths between two nodes through vast dynamic graphs. SoSACO-v2 does not provide the optimum path, but it is the quicker algorithm in providing a response. It stands for domains where optimality is not required, and often the path search takes more time than covering the path. The approach is evaluated, both in a generic huge graph and in a small-world type graph from a real social network, showing satisfactory results. [ABSTRACT FROM AUTHOR]

Untargetedmetabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resultingmetabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformaticsmethods make it possible to interpret results in light of the whole metabolism. This knowledge ismodelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents themain graph approaches used to interpretmetabolomic data usingmetabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications formostmethods. [ABSTRACT FROM AUTHOR]

Telecommunication networks often have structures that can be leveraged for efficient modeling, yet often are not; and these networks require analyses considering measures of performance beyond what most research enables. In this paper, we take advantage of that structure to show how we can make any path search algorithm more efficient, and apply those results in an extended measure of service and network performance which includes probabilistic measures including reliability, availability, and normalized performability. We further extend to a measure for comparison of networks which relates the correlation of connection points on the network, and show why the measure and comparison is widely useful. The work presented in this paper is easy to understand and apply to a broad range of Information and Communications Technology network design problems and approaches in use. [ABSTRACT FROM PUBLISHER]

Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.