Shortest Path Research Articles

UAV Path Planning: A Dual-Population Cooperative Honey Badger Algorithm for Staged Fusion of Multiple Differential Evolutionary Strategies

To address the challenges of low optimization efficiency and premature convergence in existing algorithms for unmanned aerial vehicle (UAV) 3D path planning under complex operational constraints, this study proposes an enhanced honey badger algorithm (LRMHBA). First, a three-dimensional terrain model incorporating threat sources and UAV constraints is constructed to reflect the actual operational environment. Second, LRMHBA improves global search efficiency by optimizing the initial population distribution through the integration of Latin hypercube sampling and an elite population strategy. Subsequently, a stochastic perturbation mechanism is introduced to facilitate the escape from local optima. Furthermore, to adapt to the evolving exploration requirements during the optimization process, LRMHBA employs a differential mutation strategy tailored to populations with different fitness values, utilizing elite individuals from the initialization stage to guide the mutation process. This design forms a two-population cooperative mechanism that enhances the balance between exploration and exploitation, thereby improving convergence accuracy. Experimental evaluations on the CEC2017 benchmark suite demonstrate the superiority of LRMHBA over 11 comparison algorithms. In the UAV 3D path planning task, LRMHBA consistently generated the shortest average path across three obstacle simulation scenarios of varying complexity, achieving the highest rank in the Friedman test.

Exploring the transmission of cognitive task information through optimal brain pathways.

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

Potential mechanism of impaired perceptual reasoning in children with obstructive sleep apnea syndrome: topological analysis of brain white matter network employing graph theory.

Childhood obstructive sleep apnea syndrome (OSAS) disrupts normal ventilation and sleep structure and affects cognitive functions. However, the neurophysiological mechanisms underlying cognitive impairment are unclear. This study investigates the topological connectivity of white matter networks in children with moderate to severe OSAS and explores the underlying mechanisms of cognitive impairment. We collected clinical data of patients with moderate to severe OSAS (n = 43) and non-OSAS (n = 30). Intelligence testing was conducted using the China Wechsler Intelligence Scale for Children-Fourth Edition (C-WISC IV), including Processing speed, Working memory, Verbal comprehension, Perceptual reasoning, and Full-scale intelligence quotient (FSIQ). DTI data were collected using 3.0T MRI scanner (Ingenia, Philips, Netherlands). White matter network topology connections were analyzed using FSL and DSI Studio and inter group differences were statistically assessed. The difference of clinical and intelligence test was calculated by two sample t-test. Pearson correlation analysis was employed to examine the correlation between the abnormal white matter network metrics and cognitive function in OSAS patients. Clustering coefficient (Cp) and global efficiency (Eg), nodal degree (Dc), and nodal efficiency (Ne) were lower in the OSAS group (p < 0.05). Correlations between white matter network metrics and cognitive function: The Cp and Eg were positively correlated with Perceptual reasoning, and the shortest path length (Lp) was negatively correlated with Perceptual reasoning. The results indicate that there was impairment of cognitive function and abnormality of topological structural connectivity in white matter networks for children with OSAS. The Cp, Eg, and Lp correlate with Perceptual reasoning, indicating that abnormal topological structural connectivity of the white matter network might be neurofunctional basis for impaired perceptual reasoning.

Design of Simulation Program for Analysis of Shortest Path Algorithms in Grid-Based Path Planning

This study examines the analysis of algorithms used to find the shortest path between cells with different terrain types and elevation levels on a map comprising hexagonal cells ranging from 91 to 7651. A simulation program is designed for the analysis. The simulation program is implemented using the Unity 3D game engine and the C# programming language. The Unity game engine is used to design a map comprising hexagonal grid-based cells and visualize the simulation results of the shortest path finding algorithms. Each cell has neighborhood connections that determine the movement cost between cells. A smart agent is included in the simulation within the scope of the study. The smart agent perceives its environment, evaluates terrain type and elevation factors, and attempts to find the shortest path between two points with the lowest transition cost according to the selected algorithm. The performance of the algorithms is compared in terms of computation time, number of cells visited, and transition cost. The results demonstrate that the heuristic algorithms achieve high performance in terms of computation time and number of cells visited. However, it is seen that they cannot achieve the same success in terms of transition costs.

Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery

Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterogeneity network, where Meta-paths contain all the shortest paths between drug targets and disease-related proteins in the network and drug efficacy is measured by a predictive score according to drug disease network proximity. Experiments show that Meta-DEP performs better than traditional network topology analysis on drug-disease interaction prediction task. Further investigations demonstrate that the key targets identified by Meta-DEP for drug efficacy are consistent with clinical pharmacological evidence. To prove that Meta-DEP can be used to discover active natural compounds, we apply it to predict the relationship between the monomeric components of traditional Chinese medicine included in the TCMSP database and diseases. Results indicate that Meta-DEP can accurately predict most of the drug-disease pairs included in the TCMSP database. In addition, biological experiments are directly used to demonstrate that Meta-DEP can mined active compound from traditional Chinese medicine with integrating disease transcriptomic data. Overall, the model developed in this study provides new impetus for driving the natural compound into innovative lead molecule. Code and data are available at https://github.com/t9lex/Meta-DEP.

The role of artificial intelligence in reducing carbon emissions: evidence from Chinese manufacturing firms

ABSTRACT Artificial intelligence (AI) plays a significant role in reducing carbon emissions. This study constructs a mathematical model encompassing elements such as artificial intelligence technology to explore the influence mechanism of AI on carbon emissions in the manufacturing industry and selects data at the manufacturing enterprise level from 2011 to 2022 for verification. This study reveals that AI can facilitate emissions reduction in manufacturing enterprises. Specifically, for every one-unit increase in AI, the emissions of enterprises decreased by an average of 0.408%. This influence is mainly realized through technological progress, enhanced human capital, and differences in CE attributes. In high-tech, non-pollution-intensive, low-energy-consuming, technology-intensive enterprises, the application of AI is particularly effective in reducing emissions. Additionally, AI can exert its emissions reduction effect in the network by shortening the shortest path distance among enterprises in the network and enhancing the ‘bridge’ role of enterprises within the network. These findings offer inspiration for formulating relevant policies, including encouraging technological innovation, strengthening talent cultivation, and optimizing industrial chain management, to clarify the intrinsic logic of intelligence-driven low carbonization and guide the formulation and implementation of practical policies.

A Graph-Based and Pattern Classification Approach for Kannada Handwritten Text Recognition Under Struck-Out Conditions

This research focuses on the processing and identification of handwritten Kannada text, particularly under struck-out conditions. The database considered in this study comprises handwritten data. When such a database is processed using optical character recognition (OCR)-based digital systems, the output may often be in an unrecognizable format. To address this issue, a model has been developed incorporating pattern classification and a graph-based method for text identification. For pattern classification, feature extraction is performed using two different classes with a support vector machines (SVMs) classifier. In the graph-based approach, struck-out strokes are analyzed using the shortest path algorithm. To handle zigzag or wavy struck-out Kannada text, all possible paths of the strike-out strokes are identified, and suitable features are extracted for further processing. The synthesized/recovered text is processed using an inpainting cleaning method to ensure text recovery. The proposed methodology has been tested on both trained and untrained datasets of Kannada script. Performance evaluation was conducted using three parameters: precision, F1 score, and accuracy.

Optimized Traffic Routing System for Urban Congestion Management

Traffic congestion is a major challenge in modern urban areas, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic control systems often rely on fixed signal timing, which lacks adaptability to dynamic traffic conditions. To overcome these limitations, the study proposes an Optimized Traffic Routing System for Urban Congestion Management that integrates multiple algorithms, including Fixed Cycle, Longest Queue First, Q-learning, and Search-Based Techniques which combines Genetic Algorithms and A Search* where Genetic Algorithm optimizes traffic signal timing through evolutionary methods, while A* search dynamically reroutes vehicles to minimize congestion by finding the shortest and least crowded paths. The approach utilizes reinforcement learning, heuristic optimization, and real-time simulations to dynamically optimize traffic signals and improve vehicle throughput while reducing the waiting time of vehicles. The approach was implemented using Python, and SUMO (Simulation of Urban Mobility), the system adapts to fluctuating traffic patterns and provides an efficient solution for urban traffic management.

Water quality improves with increased spatially surface hydrological connectivity in plain river network areas.

Water quality improves with increased spatially surface hydrological connectivity in plain river network areas.

Study of Cross-layer Design of the Shortest Path Routing and the STDMA in Wide-area Directed MANET

Background: To satisfy the battle need, the information transmission delay of Mobile Ad Hoc Networks (MANETs) which are the neural network and information transmission channel for the modern battlefield must be lower. Therefore, the design of low-delay routing algorithms is of great significance for improving the operational capabilities of wide-area directional MANETs. The traditional routing algorithm design is mostly based on OSI/RM model, not on the global optimization, resulting in the algorithm not having adaptability and performance being far from optimal. Objective: An algorithm named MNCR(MAC and Network Cross-layer Routing) based on crosslayer optimizing was proposed, which can finish the end-to-end data transmission with lower delay for some particular application like wide-area directed mobile ad hoc network(MANET) for a cooperative strike. Method: The proposed MNCR algorithm selected the shortest routing path based on the SDMA (Space Division Multiple Access) timetable applied and informed by MAC. When a packet arrived, the node computed all the possible paths and the corresponding delay according to the SDMA timetable and then selected the path with the lowest delay, which ensured that the packets were transmitted to the destination. Results: The theoretical analysis shows that the MNCR algorithm is less expensive than the classical shortest path algorithm. The simulation results show that the MNCR routing algorithm can save the end-to-end delay and ensure the real-time transmission of information. At the same time, the algorithm effectively balances the load of network nodes and overcomes the problem that the shortest path algorithm is prone to the "bottleneck effect", which can effectively improve the reliability of information transmission. Conclusion: The proposed MNCR algorithm can finish the end-to-end data transmission with lower delay. So it has certain particular applications in wide-area directed mobile ad hoc networks (MANET) for cooperative strikes.

Hypothesizing mechanistic links between microbes and disease using knowledge graphs

Knowledge graphs have been a useful tool for many biomedical applications because of their effective representation of biological concepts. Plentiful evidence exists linking the gut microbiome to disease in a correlative context, but uncovering the mechanistic explanation for those associations remains a challenge. Here we demonstrate the potential of knowledge graphs to hypothesize plausible mechanistic accounts of host-microbe interactions in disease. We have constructed a knowledge graph of linked microbes, genes and metabolites called MGMLink, and, using a shortest path or template-based search through the graph and a novel path-prioritization methodology based on the structure of the knowledge graph, we show that this knowledge supports inference of mechanistic hypotheses that explain observed relationships between microbes and disease phenotypes. We discuss specific applications of this methodology in inflammatory bowel disease and Parkinson’s disease. This approach enables mechanistic hypotheses surrounding the complex interactions between gut microbes and disease to be generated in a scalable and comprehensive manner.

A Dynamic Shortest Travel Time Path Planning Algorithm with an Overtaking Function Based on VANET

With the rapid development of the economy, urban road congestion has become more serious. The travel times for vehicles are becoming more uncontrollable, making it challenging to reach destinations on time. In order to find an optimal route and arrive at the destination with the shortest travel time, this paper proposes a dynamic shortest travel time path planning algorithm with an overtaking function (DSTTPP-OF) based on a vehicular ad hoc network (VANET) environment. Considering the uncertainty of driving vehicles, the target vehicle (vehicle for special tasks) is influenced by surrounding vehicles, leading to possible deadlock or congestion situations that extend travel time. Therefore, overtaking planning should be conducted through V2V communication, enabling surrounding vehicles to coordinate with the target vehicle to avoid deadlock and congestion through lane changing and overtaking. In the proposed DSTTPP-OF, vehicles may queue up at intersections, so we take into account the impact of traffic signals. We classify road segments into congested and non-congested sections, calculating travel times for each section separately. Subsequently, in front of each intersection, the improved Dijkstra algorithm is employed to find the shortest travel time path to the destination, and the overtaking function is used to prevent the target vehicle from entering a deadlocked state. The real-time traffic data essential for dynamic path planning were collected through a VANET of symmetrically deployed roadside units (RSUs) along the roadway. Finally, simulations were conducted using the SUMO simulator. Under different traffic flows, the proposed DSTTPP-OF demonstrates good performance; the target vehicle can travel smoothly without significant interruptions and experiences the fewest stops, thanks to the proposed algorithm. Compared to the shortest distance path planning (SDPP) algorithm, the travel time is reduced by approximately 36.9%, and the waiting time is reduced by about 83.2%. Compared to the dynamic minimum time path planning (DMTPP) algorithm, the travel time is reduced by around 18.2%, and the waiting time is reduced by approximately 65.6%.

Developing IoT Performance in Healthcare Through the Integration of Machine Learning and Software-Defined Networking (SDN)

In this era of rapid growth of the Internet of Things (IoT) in healthcare, the networking solution must be robust and efficient for smooth data transfer and the network. It is very well established that Deep Learning (DL) is a subset of Machine Learning (ML) and has shown great promise in enhancing network performance through intelligent data analysis and making smart decisions. The wide deployment of Wireless sensor networks and Mobile ad hoc networks (MANET) is why they are used in healthcare applications, as they offer low-cost deployment and real-time data exchange. Nevertheless, WSNs suffer limitations in energy availability due to their inherent constraints. However, the energy consumption in a WSN routing is a significant problem because of resource limitations. Integrating ML in a Software Defined Networking (SDN) framework shows the possibility of network resource optimization and a sustainable network architecture. Typically, conventional routing annoys most about shortest path algorithms, where nodes are used unevenly, and critical nodes die early from battery depletion. This work proposes a new DRL-based algorithm for maximizing traffic distribution and maintaining energy consumption equilibrium across the nodes in WSNs to improve their lifetime. The model proposes dynamically changing routing paths to preserve the nodes’ loading with a limited power supply system to persist the network lifespan and thus improve efficiency. However, the approach adds some hops to the average packet transmissions per hop. Still, it delays the first node’s energy depletion long enough to provide sustained network operability in IoT-driven healthcare environments. The experimental results validate the effectiveness of the proposed method in furthering the network functionality and reliability for data transmission, thereby making it a viable solution for the next generation of IoT healthcare networks.

Comparison of data-driven thresholding methods using directed functional brain networks.

Over the past two centuries, intensive empirical research has been conducted on the human brain. As an electroencephalogram (EEG) records millisecond-to-millisecond changes in the electrical potentials of the brain, it has enormous potential for identifying useful information about neuronal transactions. The EEG data can be modelled as graphs by considering the electrode sites as nodes andthe linear and nonlinear statistical dependencies among them as edges (with weights). The graph theoretical modelling of EEG data results in functional brain networks (FBNs), which are fully connected (complete) weighted undirected/directed networks. Since various brain regions are interconnected via sparse anatomical connections, the weak links can be filtered out from the fullyconnected networks using a process called thresholding. Multiple researchers in the past decades proposed many thresholding methods to gather more insights about the influential neuronal connections of FBNs. This paper reviews various thresholding methods used in the literature for FBN analysis. The analysis showed that data-driven methods are unbiased since no arbitrary user-specified threshold is required. The efficacy of four data-driven thresholding methods, namely minimum spanning tree (MST), minimum connected component (MCC), union of shortest path trees (USPT), and orthogonal minimum spanning tree (OMST), in characterizing cognitive behavior of the normal human brain is analysed using directed FBNs constructed from EEG data of different cognitive load states. The experimental results indicate that both MCC and OMST thresholding methods can detect cognitive load-induced changes in the directed functional brain networks.

Devising a method for solving a multi-criteria shortest path problem with fuzzy initial data

The object of this study is the optimization of road freight transportation routes under conditions of martial law or emergencies. The paper addresses the task of building a model and devising a method for solving the multi-criteria shortest path problem, taking into account the uncertainty of input data and the multiplicity of optimization criteria. The input data consists of communication lengths, their safety level, and road surface quality, which are represented by elements of fuzzy sets with corresponding membership functions, as well as a road network graph. The introduction of a system of rules, according to which the communication optimal by three criteria is chosen, has made it possible to formulate a generalized fuzzy optimization criterion for the edges of the graph, represented by the membership function of the fuzzy goal. This criterion is used as the weight of the edges in the devised method for solving the problem and makes it possible to simultaneously take into account the uncertainty of the input data and several optimization criteria. The method for solving the problem is based on a modified Dijkstra’s algorithm. For fuzzy data processing, fuzzy logical inference is used to form a generalized optimization criterion, and the Bellman-Zadeh approach is used for the optimization problem. The results of solving the problem are the optimal route, its length, safety level, and road surface quality. For the considered road network, the length of the optimal route (41 km) is not the shortest, compared to other methods (ranging from 19 km to 50 km), but the safety level of the route is high (0.75). This is due to the values of the weight coefficients of the optimization criteria. The application of this method for optimizing freight transportation routes under conditions of martial law could improve the efficiency and reliability of transport systems under conditions of uncertainty

Multi-Area OSPF Analysis Using Virtual Link and GRE Tunnel

This study discusses the implementation and analysis of network performance using multi-area OSPF (Open Shortest Path First) with the application of Virtual Link and GRE Tunnel. OSPF is a dynamic routing protocol that is often used in large networks due to its ability to find the shortest path quickly and efficiently. However, on large networks, the use of OSPF in a single area can increase routing overhead and slow down convergence times. Therefore, multi-area OSPF is a solution by limiting the spread of routing information only to related areas. This study uses an experimental method with the PNETLab simulator running five Cisco routers. The test was carried out by measuring QoS parameters such as throughput, packet loss, jitter according to TIPHON standards and OSPF convergence time using iPerf3 and Wireshark software. The results show that multi-area OSPF with Virtual Link has a more stable performance than GRE Tunnel in terms of jitter and convergence time, namely the average convergence time of Virtual Link is 24.1166 seconds while GRE Tunnel is 28.6144 seconds. Nonetheless, GRE Tunnel shows lower packet loss at large data sizes. This study provides practical guidance for network professionals in optimizing multi-area OSPF configurations.

Common and disease-specific patterns of functional connectivity and topology alterations across unipolar and bipolar disorder during depressive episodes: a transdiagnostic study

Bipolar disorder (BD) and unipolar depression (UD) are defined as distinct diagnostic categories. However, due to some common clinical and pathophysiological features, it is a clinical challenge to distinguish them, especially in the early stages of BD. This study aimed to explore the common and disease-specific connectivity patterns in BD and UD. This study was constructed over 181 BD, 265 UD and 204 healthy controls. In addition, an independent group of 90 patients initially diagnosed with major depressive disorder at the baseline and then transferred to BD with the episodes of mania/hypomania during follow-up, was identified as initial depressive episode BD (IDE-BD). All participants completed resting-state functional magnetic resonance imaging (R-fMRI) at recruitment. Both network-based analysis and graph theory analysis were applied. Both BD and UD showed decreased functional connectivity (FC) in the whole brain network. The shared aberrant network across groups of patients with depressive episode (BD, IDE-BD and UD) mainly involves the visual network (VN), somatomotor networks (SMN) and default mode network (DMN). Analysis of the topological properties over the three networks showed that decreased clustering coefficient was found in BD, IDE-BD and UD, however, decreased shortest path length and increased global efficiency were only found in BD and IDE-BD but not in UD. The study indicate that VN, SMN, and DMN, which involve stimuli reception and abstraction, emotion processing, and guiding external movements, are common abnormalities in affective disorders. The network separation dysfunction in these networks is shared by BD and UD, however, the network integration dysfunction is specific to BD. The aberrant network integration functions in BD and IDE-BD might be valuable diagnostic biomarkers.

PRO-MTL : Parameterized Route Optimization using Multi-Task Learning

In the current ridesharing scenario, finding a compatible passenger is highly challenging and largely dependent on chance. Existing algorithms prioritize the shortest route without considering future requests or traffic conditions, which reduces the likelihood of matching with another compatible passenger. This uncertainty leads to increased congestion along shortest routes and fewer ridesharing trips overall. This paper proposes a route recommendation strategy that goes beyond the shortest route, aiming to address these issues. The proposed strategy results in higher demand, reduced congestion, broader coverage of points of interests, and an increased probability of finding compatible passengers during a trip. To achieve this, we introduce a time-series forecasting method leveraging a multi-task long short-term memory model to predict demand and traffic patterns in city-zone neighborhoods. These predictions are then used to recommend optimized routes. To evaluate our approach, we tested it on three datasets containing trip and traffic details from New York City, Los Angeles, and Shenzhen. Our model demonstrated 96% accuracy and a 2% RMSE loss in predicting the expected number of passengers. Furthermore, during route recommendations, we observed a 23% increase in passenger count for 97% of trips and a reduction in travel time for the shortest path in 60% of trips. In light of the above experimentation, we believe that while our approach recommends a longer route than the shortest one (for 40% cases), it helps taxi drivers to find compatible passengers on most trips which increases the profit of ridesharing services, and reduces the waiting time for passengers.

Multi-Source Spatio-Temporal Data Fusion Path Estimation Method

To address the problem of overlooking target movement characteristics and historical activity patterns in conventional path estimation methods, we propose a method based on the principle of multi-source spatio-temporal data fusion. It integrates optical image data with navigation and positioning data and improves the A* algorithm. While seeking the shortest path, the algorithm prioritizes points within hotspot areas to achieve accurate target path estimation. The algorithm extracts hotspot areas using spatial analysis methods such as kernel density analysis and uses them as the basis for path estimation. Through many simulation experiments, it is verified that the proposed improved the A* algorithm is more consistent with the actual path than the traditional A* algorithm.

Graph-based Algorithms for Battery Management in Sensor Networks: Theoretical Insights and Practical Applications

Battery level management is crucial for the reliable operation of sensor networks. In this paper, we present a novel framework that leverages graph theory and computational geometry to enhance battery management, optimize communication, and maintain connectivity. Our approach builds weighted Delaunay Graphs as combinatorial models for sensor networks and investigates theoretical aspects of their hamiltonicity. We introduce six algorithmic methods addressing two main objectives. The first set focuses on battery management by identifying low-battery sensors, computing minimum spanning trees and shortest paths and performing edge contraction to simplify network topology while preserving connectivity. The second set examines the hamiltonicity of Delaunay Graphs using graph labeling and combinatorial techniques to analyze the existence of Hamiltonian paths. Our methodology integrates key graph procedures such as minimum spanning trees, shortest path algorithms, edge contraction, and graph labeling with computational geometry tools like Voronoi diagrams and Delaunay triangulation. This combination allows for an accurate modeling of spatial relationships within the network and dynamic adaptation to battery constraints. A case study on monitoring a sugar cane field demonstrates the practical application of our methods. The computational study reveals the efficiency and robustness of the proposed algorithms in optimizing battery usage and improving network performance in real-world scenarios. In conclusion, our work not only addresses critical battery management challenges in sensor networks but also deepens the understanding of the interplay between graph theory and network optimization. This integrated approach paves the way for further research in both theoretical and applied domains, offering promising solutions for enhancing the sustainability and reliability of sensor networks