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The most common variant of VRP of Iraqi big-scale enterprises is Capacitated Vehicle Routing Problems (CVRP) where such enterprises have several dispersed affiliates. This paper presents an application-database 3-stages customizable framework based on urban asymmetric road network by including Sweep clustering, PostgreSQL Dijkstra and saving heuristics for routing applied on actual dataset considering CVRP for an enterprise composed of a single depot and 91 nodes in Baghdad. The solution was built in a generalizable flexible architecture applicable for other organizations. It also demonstrates utilizing parallel-processing techniques for speed up. Improved versions of Clarke-Wright and Parker-Holmes algorithms were used and achieved improvement of (15.7 and 9.03) times in asymmetric clustering scenario, 1.57 times in case of asymmetric non-clustering scenario, and 1.678 in symmetric implementation of the cost minimization satisfied by their corresponding original bases. The study includes implementation details, results analysis and provides insightful information to operation-researches practitioners especially in Iraq. [ABSTRACT FROM AUTHOR]

Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to introduce a solution for network analysis based on pgRouting to resolve the bicycle accessibility of forested green spaces. The proposed application uses open-source software tools such as PostgreSQL with PostGIS extension. The solution includes a complete description of how to perform network analysis using a spatial database with SQL and pgRouting. The implemented functionalities consist of solutions for finding the equidistance or isochrone area for any selected point location. The method is tested on case study data drawn from a total of 9,500 km of roads suitable for cyclists in the Poznań Metropolitan Area, located in western Poland. The results of the analysis were isochrones determining the bicycle accessibility of forested areas. The accessibility analysis was performed considering an urbanised residential area. As a result of the analysis, locations with the best and limited access to forested green areas were identified. Moreover, the described methodology is ready to be used to solve various accessibility problems.

After the occurrence of mine water inrush, choosing the appropriate emergency evacuation path is very important to reduce the casualty and the mine accident level. The traditional Dijkstra algorithm can not be effectively applied to the dynamic environment of mine water inrush. It can only statically plan a single optimal escape path. In view of the limitations of this algorithm, the time equivalent length is introduced to optimize the traditional Dijkstra algorithm to realize the dynamic change of the optimal escape path with time and water level height. Moreover, the optimized Dijkstra algorithm is combined with the K optimal path algorithm to realize the dynamic planning of multiple optimal escape paths. Field application shows that the optimized dynamic D-K algorithm can be effectively applied to mine water inrush with characteristics of randomness, dynamics and complex, which can provide important decision support for affected personnel to choose emergency escape path.

In response to the issue of travel comfort in electric vehicle path planning, an electric vehicle path planning model considering traveler comfort was constructed. Based on the Dijkstra algorithm, path planning simulations were conducted considering various influencing factors such as time, distance, electric vehicle (EV) energy consumption, and traveler comfort. The effectiveness of the model and algorithm was verified through corresponding test cases, and an example calculation and analysis was conducted in surrounding area of Nanning Station in Nanning City. The results indicate that the optimal path model constructed has increased travel distance, travel time, and travel energy consumption by 3.1%, 13.3%, and 4.6% compared to conventional path planning models, but has improved comfort by 9.4% . Therefore, the study can provide reference for research on improving the quality of electric vehicle travel. [ABSTRACT FROM AUTHOR]

At present, there are almost no reports on vehicle scheduling methods for three-dimensional rail transit system, and the real-time performance of existing vehicle scheduling algorithms is poor. Aiming at the scheduling problem of three-dimensional rail transit vehicles, this paper studied an order allocation algorithm combining high and low frequency station judgment and a Dijkstra path planning algorithm combining time window, namely intelligent scheduling algorithms, to improve the operating efficiency of vehicles. Firstly, it used the order allocation algorithm to select the appropriate execution vehicle for the order to reduce the waiting time of passengers. Secondly, it added the judgment of high and low frequency stations on the basis of the order allocation algorithm, and scheduled vehicles to the high frequency stations in advance to ensure the balance of supply and demand. Then, it combined the ordinary Dijkstra algorithm and time window judgment to realize multi-vehicle conflict-free path planning. Finally, it redeveloped the OpenTCS software and simulated the scheduling algorithm with the software. The results show that the average waiting time of the passenger from calling the vehicles is 8.043 s only using the order allocation algorithm. After the advance scheduling of vehicles combined with high and low frequency stations, the average waiting time is reduced to 5.724 s, and the waiting time of each passenger is reduced by 2.319 s. During the path planning, both the ordinary Dijkstra algorithm and the Dijkstra algorithm combined with time window took less than 1 ms to plan. However, the Dijkstra algorithm combined with time window only increases the time about 0.1 ms, and solves the problems of the vehicle path conflicts. The studied intelligent scheduling algorithm can reduce the waiting time of the passengers and improve the running efficiency of vehicles. The algorithm has good real-time performance and can meet the scheduling requirements of three-dimensional rail transit vehicles. [ABSTRACT FROM AUTHOR]

Recently, the unmanned aerial vehicle (UAV)-assisted edge computing is proposed to improve the quality of service in some scenarios within sparse or unavailable base stations (BSs). Meanwhile, the caching technology is adopted to reduce the wireless traffic load and the data transmission delay. However, due to the limited storage capacity of edge nodes, the edge nodes cannot store all of the contents required by user equipment (UE). So, how to select the reasonable contents for caching on edge nodes to reduce the content delivery delay becomes a challenge in the UAV-assisted edge computing environment. In this paper, the Dijkstra algorithm based cooperative caching strategy for UAV-assisted edge computing system is proposed. Specially, the content transmission delay between two nodes is computed. Then, for each requested content, the weighted edge-undirected graph (WEUG), in which one vertex represents one node, is built. Furthermore, Dijkstra algorithm is adopted to achieve the minimal content transmission delay from the edge node caching the requested content to UE. Finally, the optimization problem of content caching is built, and the corresponding cache strategy is achieved by solving the optimization problem. The experimental results imply that the proposed cooperative caching algorithm can achieve better performance on the average content transmission delay, the average cache hit rate, and the total of hops, respectively, comparing with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

During the daily operations of emergency response, the decision maker is faced with the complex challenge of selecting a team and route in a short time period to respond and attend to the emergency. This study presents the combined use of the Analytic Hierarchy Process (AHP), the Élimination Et Choix Traduisant la Realité II (ELECTRE II), and the Dijkstra algorithm to deal with such situations. First, the AHP method is implemented to rank the aspects that are most relevant to a given emergency. Subsequently, this ranking is employed in ELECTRE-II to determine which emergency response team is best prepared to provide support. In the last stage of the proposed model, regarding the geographic coordinates of the team and the emergency, the Geographic Information System (GIS) utilizes the Dijkstra algorithm to regulate the most suitable route for assistance. [ABSTRACT FROM AUTHOR]

The Gran Sasso National Laboratory (LNGS) is, at present, the largest deep underground laboratory in operation for astroparticle physics and rare event research. The LNGS was created to carry out this research exploiting an overburden of 1,400 m of rock to reduce the flux of muons from cosmic rays. Operating an underground laboratory and its facilities implies a high level of risk. To mitigate risks at the LNGS, a crucial aspect is represented by the evacuation of people from an underground environment during emergencies. The connection between the underground facilities and the outside infrastructure is limited, and the intervention by rescue teams is complicated. This paper reports the study of an adaptive evacuation system to improve the evacuation performance in underground laboratories. The system proposed is composed of a combination of passive, dynamic, and adaptive signage that is able to adapt itself to lead the laboratory occupants to the safe location for evacuation (assembly point). The system collects information from all safety plants, and the data are processed using a customized path-finding algorithm. In the computational algorithm, the underground laboratory is represented as a grid, and the customized path-finding algorithm discovers all available paths to reach the identified evacuation assembly point. [ABSTRACT FROM AUTHOR]

While encrypting information with color images, most encryption schemes treat color images as three different grayscale planes and encrypt each plane individually. These algorithms produce more duplicated operations and are less efficient because they do not properly account for the link between the various planes of color images. In addressing the issue, we propose a scheme that thoroughly takes into account the relationship between pixels across different planes in color images. First, we introduce a new 1D chaotic system. The performance analysis shows the system has good chaotic randomness. Next, we employ a shortest-path cross-plane scrambling algorithm that utilizes an enhanced Dijkstra algorithm. This algorithm effectively shuffles pixels randomly within each channel of a color image. To accomplish cross-plane diffusion, our approach is then integrated into the adaptive diffusion algorithm. The security analysis and simulation results demonstrate that the approach can tackle the issue of picture loss in telemedicine by encrypting color images without any loss of quality. Furthermore, the images we utilize are suitable for both standard RGB and medical images. They incorporate more secure and highly sensitive keys, robustly withstanding various typical ciphertext analysis attacks. This ensures a reliable solution for encrypting original images.

During the daily operations of emergency response, the decision maker is faced with the complex challenge of selecting a team and route in a short time period to respond and attend to the emergency. This study presents the combined use of the Analytic Hierarchy Process (AHP), the Élimination Et Choix Traduisant la Realité II (ELECTRE II), and the Dijkstra algorithm to deal with such situations. First, the AHP method is implemented to rank the aspects that are most relevant to a given emergency. Subsequently, this ranking is employed in ELECTRE-II to determine which emergency response team is best prepared to provide support. In the last stage of the proposed model, regarding the geographic coordinates of the team and the emergency, the Geographic Information System (GIS) utilizes the Dijkstra algorithm to regulate the most suitable route for assistance.

The Sumba region, Indonesia, is known for its extraordinary natural beauty and unique cultural richness. There are 19 interesting tourist attractions spread throughout the area, but tourists often face difficulties in planning efficient visiting routes. From this case, it can be solved by applying graph theory in terms of searching for the shortest distance which is completed using the shortest path search algorithm. Then these 19 tourist objects are used to build a weighted graph, where the nodes represent the tourist objects and the edges of the graph describe the distance or travel time between these objects. Therefore, this research aims to compare the shortest path search algorithm with parameters to compare the shortest distance results, algorithm complexity and execution time for tourism in the Sumba area. The results of this research involve a comparison of several shortest path search algorithms, with the aim of finding the shortest distance results, algorithm complexity, and execution time for tourism in the Sumba area. Based on the test results of the five algorithms with the parameters that have been prepared, and the findings show that each algorithm has its own characteristics, the results are as follows: Dijkstra's algorithm can be used to calculate the shortest route for single-source and single-destination types. This resembles the Bellman-Ford algorithm, only the Bellman-Ford algorithm can be used simultaneously on graphs that have negative weight values. Meanwhile, the Floyd-Warshall algorithm is suitable for use on the all-pairs type. Then, the Johnson Algorithm can be used to determine the shortest path from all pairs of paths where the destination node is located in the graph. Finally, the Ant Colony algorithm to compute from a node to each pair of destination nodes.

Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.

With the development of satellite cluster technology, the earth observation capability of satellite clusters has been greatly enhanced, along with the improvement of satellite earth observation and inter-satellite data transmission ability. Nevertheless, it is difficult to coordinate satellite observation, inter-satellite data transmission, and satellite–ground data download to satisfy the constraints of satellite multi-subsystems. In this article, the multi-satellite observation-relay transmission-downloading coupling scheduling problem is described. Based on the conventional tabu search algorithm for multi-satellite earth observation, the data transmission path planning algorithm is integrated to carry out the entire coupling process of multi-satellite observation, inter-satellite data transmission, and satellite–ground data downloading. Referring to the idea of the artificial potential field method, the satellite cluster profit-state evaluation function is introduced to enhance the local search process within the tabu search framework. Moreover, in the data transmission planning algorithm, the rule-based Dijkstra data transmission path planning method is proposed based on two data transmission path planning strategies and the satellite cluster state-strategy selection rules. The simulation results show that the proposed method can realize the entire process of scheduling satellite cluster observation, relay transmission, and downloading and enhance the ability of the satellite cluster to obtain observation data. The improved Dijkstra method enhances the adaptability of the data transmission path planning method to the multi-subsystem coupled problem, and the improved local search in the tabu search method elevates the searching capability of the algorithm. [ABSTRACT FROM AUTHOR]

This study presents the development of a route planner, called Mission Planner, for an agricultural weeding robot that generates efficient and safe routes both in the field and on the farm using a graph-based approach. This planner optimizes the robot's motion throughout the farm and performs weed management tasks tailored for high-power laser devices in narrow-row crops (wheat, barley, etc.) and wide-row crops (sugar beet, maize, etc.). Three main algorithms were integrated: Dijkstra's algorithm to find the most optimal route on the farm, the VRMP (Visibility Road-Map Planner) method to select the route within cultivated fields when roads are not visible, and an improved version of the Hamiltonian path to find the best route between the crop lines. The results support the effectiveness of the strategies implemented, demonstrating that a robot can safely and efficiently navigate through the entire farm and perform an agricultural treatment, in this case study, in laser-based weed management. In addition, it was found that the route planner reduced the robot's operation time, thus improving the overall efficiency of precision agriculture. [ABSTRACT FROM AUTHOR]

This study explored human ability to extract closed boundary of a target shape in the presence of noise using spatially global operations. Specifically, we investigated the contributions of contour-based processing using line edges and region-based processing using color, as well as their interaction. Performance of the subjects was reliable when the fixation was inside the shape, and it was much less reliable when the fixation was outside. With fixation inside the shape, performance was higher when both contour and color information were present compared to when only one of them was present. We propose a biologically-inspired model to emulate human boundary extraction. The model solves the shortest (least-cost) path in the log-polar representation, a representation which is a good approximation to the mapping from the retina to the visual cortex. Boundary extraction was framed as a global optimization problem with the costs of connections calculated using four features: distance of interpolation, turning angle, color similarity and color contrast. This model was tested on some of the conditions that were used in the psychophysical experiment and its performance was similar to the performance of subjects. [ABSTRACT FROM AUTHOR]

From the view of comprehensive consideration, this considers the uncertainty of transportation time and cost due to seasonality and human factors, and a multi-objective chance constrained programming model with minimum transportation time and cost was established. Due to the complexity of the problem, traditional algorithms are not enough, and more efficient optimization algorithms are still needed. According to the characteristics of the problem, the beetle antennae search (BAS) with one beetle is changed into multiple beetles, and Dijkstra algorithm is embedded, a hybrid beetle swarm optimization algorithm (HBSO) is designed to solve the problem, and the case analysis and algorithm analysis are made for three different examples; the datasets are referenced in literature (Lu et al. in Computer Integrated Manufacturing Systems 5:10–14, 2020). Under the three examples, the influence of the customer on the time and cost is different. The confidence level and weight of cost and time have great influence on the decision results. 4PL provider will not affect the choice of transportation route, but will change the choice of 3PL supplier and transportation mode. HBSO is an effective method to solve this problem. It is proved on efficiency, effectiveness and reliability in experiments by comparing with GA, PSO and BAS. [ABSTRACT FROM AUTHOR]

In 5-axis machining, the existing tool's axis vector optimization methods are limited since they only consider the global collision between the tool and the workpiece while aiming at the ball-nosed cutter. A multi-factor vector optimization method for the face milling cutter shaft is proposed to solve this problem. This method comprehensively considers machining global collision, cutting force, the angular displacement of a rotating shaft, and angular speed. An improved global collision detection method of cutter axis vector based on the NURBS surface principle is developed, and a global collision detection algorithm is employed to determine the cutter machining global collision. The relationship model between the end-milling cutter axis vector and cutting force variation is established to optimize the cutting force. In addition, an optimization model of angular displacement and velocity of the machine tool's rotating axis is proposed based on Dijkstra optimal path algorithm. The CAM software simulation and experimental validation are conducted using a large propeller with a complex surface. The tool's axis vector optimization algorithm is applied to the propeller results. Comparing the tool's axis vector optimization results to those obtained without optimization, it is discovered that the surface workpiece's machining quality has significantly increased. [ABSTRACT FROM AUTHOR]

In the process of the integrated scheduling of multi-variety and small-batch complex products, the process structure and attribute characteristics are often ignored, which affects the overall scheduling effect. Aiming at solving this problem, a heuristic integrated scheduling algorithm (HIS-IDA) based on the improved Dijkstra algorithm is proposed. The algorithm takes the processing time of the process itself as the path value of the preceding and the following adjacent processes. Firstly, the improved Dijkstra algorithm prioritized the scheduling of the process sequence with long longitudinal paths and realized the "longitudinal optimization" of the integrated scheduling. Secondly, the layer priority strategy is used to shorten the interval time of process processing and realize the "horizontal optimization" of integrated scheduling. On the basis of "vertical and horizontal optimization", the idle time of the equipment is further reduced by using the process priority strategy of the leaf node, and the "idle optimization" of the integrated scheduling is realized, so as to optimize the overall effect of the integrated scheduling. The effectiveness and superiority of the algorithm are proved using comparison analysis. [ABSTRACT FROM AUTHOR]

The Gran Sasso National Laboratory (LNGS) is, at present, the largest deep underground laboratory in operation for astroparticle physics and rare event research. The LNGS was created to carry out this research exploiting an overburden of 1,400 m of rock to reduce the flux of muons from cosmic rays. Operating an underground laboratory and its facilities implies a high level of risk. To mitigate risks at the LNGS, a crucial aspect is represented by the evacuation of people from an underground environment during emergencies. The connection between the underground facilities and the outside infrastructure is limited, and the intervention by rescue teams is complicated. This paper reports the study of an adaptive evacuation system to improve the evacuation performance in underground laboratories. The system proposed is composed of a combination of passive, dynamic, and adaptive signage that is able to adapt itself to lead the laboratory occupants to the safe location for evacuation (assembly point). The system collects information from all safety plants, and the data are processed using a customized path-finding algorithm. In the computational algorithm, the underground laboratory is represented as a grid, and the customized path-finding algorithm discovers all available paths to reach the identified evacuation assembly point.

The determination of the shortest route can be done using various methods, one of which is the Dijkstra algorithm. This algorithm is often used in routing problems with minimum weight in computer networks, communication networks and transportation networks. There are several applications of the Djikstra algorithm, namely solving the problem of the shortest path of neutrosophy, mapping tourism potential, and developing geographic information systems in the health sector. In this research, Dijkstra algorithm will be applied to optimize the mileage of the delivery of goods packages. Data and information namely the address, weight and number of customer packages carried by a courier in one delivery trip are obtained from the drop point of PT. J&T Express in Sarijadi area of Bandung City. Meanwhile, data about mileage is obtained from the Google Maps application. All this data is used to construct an initial model graph that is a connected weighted graph, where the location of a drop point or a customer is a vertex and a road connecting two locations is an edge on the graph. The weight in this graph is the mileage from the drop point to the customers or from one customer to another. Then, the Djikstra algorithm is run on this graph where the drop point is the starting point of the route, so that the courier visits all customers and returns again to the drop point. The resulting route is a cycle in the graph which is the shortest closed route at 1890 meters.

Quantitative analysis of intracranial vessel segments typically requires the identification of the vessels’ centerlines, and a path-finding algorithm can be used to automatically detect vessel segments’ centerlines. This study compared the performance of path-finding algorithms for vessel labeling. Three-dimensional (3D) time-of-flight magnetic resonance angiography (MRA) images from the publicly available dataset were considered for this study. After manual annotations of the endpoints of each vessel segment, three path-finding methods were compared: (Method 1) depth-first search algorithm, (Method 2) Dijkstra’s algorithm, and (Method 3) A* algorithm. The rate of correctly found paths was quantified and compared among the three methods in each segment of the circle of Willis arteries. In the analysis of 840 vessel segments, Method 2 showed the highest accuracy (97.1%) of correctly found paths, while Method 1 and 3 showed an accuracy of 83.5% and 96.1%, respectively. The AComm artery was highly inaccurately identified in Method 1, with an accuracy of 43.2%. Incorrect paths by Method 2 were noted in the R-ICA, L-ICA, and R-PCA-P1 segments. The Dijkstra and A* algorithms showed similar accuracy in path-finding, and they were comparable in the speed of path-finding in the circle of Willis arterial segments.

The concept of the “smart city” has emerged with the advancement of technology, but some facilities are not sufficiently intelligent, such as parking lots. Hence, this paper proposes an inexpensive and plug-to-play camera-based smart parking system for airports. The system utilizes inverse perspective mapping (IPM) to provide an aerial view image of the parking lot, which is then processed to extract parking space information. The system also includes a guidance system to assist drivers in finding available parking spaces. The system is simulated on a 3D scene based on the parking lot of Macao International Airport. In the experiment, our system achieved an accuracy rate of 97.03% and a mean distance error of 8.59 pixels. This research study shows the potential of enhancing parking lots using only cameras as data collectors, and the results show that the system is capable of providing accurate and useful information. It performs well in parking lots with open space, in particular. Moreover, it is an economical solution for implementing a smart parking lot.

Digital maps have revolutionized our ability to navigate to desired locations by providing the shortest routes, primarily in open spaces. However, this functionality is limited to outdoor environments. This study aims to extend this capability by enabling the determination of the shortest routes within indoor spaces. The research employs the Haversine method for distance measurement and integrates the Dijkstra algorithm for route determination. The findings demonstrate the feasibility of implementing the Haversine method and the Dijkstra algorithm for route determination within enclosed spaces. The research results show that searching for the shortest route indoors be done and the route can be displayed on a digital map. It was observed that the routing machine between route nodes did not perform optimally, prompting its replacement with a polyline.

This study explored human ability to extract closed boundary of a target shape in the presence of noise using spatially global operations. Specifically, we investigated the contributions of contour-based processing using line edges and region-based processing using color, as well as their interaction. Performance of the subjects was reliable when the fixation was inside the shape, and it was much less reliable when the fixation was outside. With fixation inside the shape, performance was higher when both contour and color information were present compared to when only one of them was present. We propose a biologically-inspired model to emulate human boundary extraction. The model solves the shortest (least-cost) path in the log-polar representation, a representation which is a good approximation to the mapping from the retina to the visual cortex. Boundary extraction was framed as a global optimization problem with the costs of connections calculated using four features: distance of interpolation, turning angle, color similarity and color contrast. This model was tested on some of the conditions that were used in the psychophysical experiment and its performance was similar to the performance of subjects.

Due to the poor representation of NEDC emission test, the European Commission put into the force a new procedure, which contains both a laboratory test and a real word driving test. In this new test procedure, even though WLTC replaces NEDC, it is required to test the vehicle under real traffic conditions, which contains urban, rural and highway segments. Due to the new emission regulation, considerable number of academic work focus on the creation of RDE (Real Driving Emission) routes and RDE tests. In this study, it is introduced a new methodology for creating potential RDE routes. The routes, created in MATLAB code by using Istanbul road data containing a half an hour average trip records were used to predict a probable RDE test road. This model creates a number of RDE routes starting from defined coordinates and then analyses all alternative routes with respect to traffic flow rate and RDE boundary conditions identified by the European Commission. The routes obtained using the methodology developed are tested in real life conditions and evaluated according to actual existing traffic conditions. The agreement between the results of the method and the results of the actual driving test is limited in urban road section. However, for rural and highway road sections, there is a significant agreement between the predictions and actual test results. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)

Quantitative analysis of intracranial vessel segments typically requires the identification of the vessels' centerlines, and a path-finding algorithm can be used to automatically detect vessel segments' centerlines. This study compared the performance of path-finding algorithms for vessel labeling. Three-dimensional (3D) time-of-flight magnetic resonance angiography (MRA) images from the publicly available dataset were considered for this study. After manual annotations of the endpoints of each vessel segment, three path-finding methods were compared: (Method 1) depth-first search algorithm, (Method 2) Dijkstra's algorithm, and (Method 3) A* algorithm. The rate of correctly found paths was quantified and compared among the three methods in each segment of the circle of Willis arteries. In the analysis of 840 vessel segments, Method 2 showed the highest accuracy (97.1%) of correctly found paths, while Method 1 and 3 showed an accuracy of 83.5% and 96.1%, respectively. The AComm artery was highly inaccurately identified in Method 1, with an accuracy of 43.2%. Incorrect paths by Method 2 were noted in the R-ICA, L-ICA, and R-PCA-P1 segments. The Dijkstra and A* algorithms showed similar accuracy in path-finding, and they were comparable in the speed of path-finding in the circle of Willis arterial segments. [ABSTRACT FROM AUTHOR]

An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect. [ABSTRACT FROM AUTHOR]

This paper introduces an optimal method entitled Dhouib-Matrix-SPP (DM-SPP) in order to solve the Shortest Path Problem with a complexity time of On+mwhere n and m are respectively the number of vertices and edges. DM-SPP is a rapid method, it can reduce reasonably the research time and consequently the running time as well as the energy consumption. It is composed of five simple steps repeated in (n-1) iterations and for more clarification a guided step-by-step application of the novel method DM-SPP is presented. Moreover, several examples are solved wherein a fully complete and random distribute graphs are analyzed with the variation of the number of vertices from 20 to 6000 and the number of edges from 49 to 17097565. DM-SPP is coded under Python programming language and all experimental results demonstrate that DM-SPP can rapidly generate the optimal short path. Furthermore, by comparing the complexity time required by DM-SPP to the time prerequisite by Dijkstra algorithm, it can be concluded that DM-SPP and Dijkstra are concurrent for small instances and for larger instances DM-SPP can easily outperform Dijkstra and especially for the case of incomplete undirected graphs which are more realistic in the real-world viewing that all graphs are really not complete. The performance of DM-SPP is statistically confirmed with the Mann–Whitney U nonparametric test Further research trends will focus on the test of DM-SPP for the uncertain Shortest Path problem and the resolution of the autonomous mobile robot path planning problem.

Aiming at the construction sites of construction projects with frequent crossover operations of multiple types of work, frequent changes in the environment, narrow sites, and many open fire operations, the evacuation time was used as the objective function to establish its personnel evacuation time model. Path optimization model, the modified Dijkstra (Dijkstra) algorithm was used to solve the model, and at the same time, combining with BIM( building information model) 4D and Anylogic software and a building under construction, was simulated and analied of the model. It can be seen from the simulation results that the evacuation time calculated by the evacuation time model is in good agreement with the simulated evacuation time. Using the optimized evacuation path to evacuate can greatly save labor on the construction site in emergency situations to a certain extent. The evacuation time can be shortened to improve the safety management efficiency during the construction stage, and within a certain range of evacuated people, the optimization degree increases with the increase of evacuated people. [ABSTRACT FROM AUTHOR]

To increase data availability and accuracy in the coastal zone, especially in the last 5 km to the coast, we present a SCMR (Seamless Combination of Multiple Retrackers) processing strategy to combine sea surface height (SSH) estimates from waveform retrackers of SGDR MLE4, ALES, WLS3 and MB4 for Jason-3 and Saral missions, and of SAMOSA and SAMOSA+ for Sentinel-3A mission in the Australian coastal zone. The SCMR does not require the waveform classification result. It includes two steps: (1) estimating and removing the SSH bias due mainly to the significant wave height (SWH) difference-dependent height differences, and (2) determining the optimal along-track SSH profile by using the Dijkstra algorithm. In the study region, the results show that the SCMR increases the data availability by up to 15% in the last 5 km to the coast and reduces the noise level by 28–34% at the spatial scales < 2.5 km. The validation results against tide gauges show that SCMR-derived SSH estimates achieve a better accuracy than that from any single retracker, with the improvement percentage of 6.26% and 4.94% over 0–10 km and 20–100 km distance bands, respectively. [ABSTRACT FROM AUTHOR]

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.)