Breadth-first Research Articles

An Intelligent Optimazitation Method for Evacuation Route Planning in the Occurrence of Natural Disasters

This research aims to design and apply intelligent optimization methods using various algorithms to find disaster evacuation routes. The efficiency and effectiveness of evacuation routes are essential in disaster situations to ensure the safety of the affected residents. This research focuses on developing an intelligent optimization method utilizing the Multi Vertex Multi Goals (MVMG) scheme to find optimal evacuation routes. In this scheme, multiple starting points and evacuation destinations reflect the actual conditions on the ground. The Ant Colony Optimization (ACO) algorithm was chosen because of its superiority in finding optimal solutions in dynamic and complex conditions. This research also compares the performance of ACO with traditional algorithms, such as Dijkstra and Breadth-First Search (BFS). The test results show that ACO consistently achieves the lowest evacuation time and the highest efficiency compared to the other two algorithms. In addition, this research opens opportunities for further research by considering complex factors, including traffic congestion and disaster-prone areas, to improve the robustness and application of optimization algorithms in more realistic and dynamic scenarios.

A Biochemistry-Inspired Algorithm for Path Planning in Unmanned Ground Vehicles

Unmanned ground vehicles (UGVs) have gained significant attention due to their extensive applications in both military and civilian sectors. For effective UGV deployment, path planning algorithms must prioritize computational efficiency, solution reliability, and runtime performance while maintaining path quality. Autonomous path planning remains a critical challenge in UGV navigation, as conventional methods, while effective, often suffer from considerable computational overhead. To address this issue, we propose a novel biochemistry-inspired path planning algorithm designed specifically for static grid-based scenarios. MetaPath demonstrates remarkable computational efficiency while maintaining solution quality across different obstacle densities in benchmark environments. Specifically, the algorithm achieves path lengths within ±5% of all benchmark algorithms while dramatically reducing the exploration space, visiting up to 10% of the cells explored by conventional approaches such as A*. This superior efficiency translates into exceptional runtime performance, executing up to 3000 times faster than bio-inspired algorithms like Ant Colony Optimization (ACO) and the Genetic Algorithm (GA), performing nearly three times faster than the widely used A* algorithm, and maintaining competitive performance with efficient algorithms like Breadth-First Search (BFS) and Particle Swarm Optimization (PSO), thereby establishing the algorithm as a highly efficient pathfinding solution. Most notably, MetaPath introduces a novel approach as the first chemistry-inspired pathfinding algorithm, guaranteeing path discovery when one exists within reasonable computational time, a crucial advantage over some benchmark algorithms that may fail to converge or require excessive computational resources in complex scenarios.

Comparative analysis of AI-based search algorithms in solving 8 puzzle problems

BackgroundThe 8-puzzle problem is a well-known combinatorial search problem, often used to test the effectiveness of various artificial intelligence (AI) algorithms. This study aims to evaluate the performance of three AI-based search algorithms—Breadth-First Search (BFS), Depth-First Search (DFS), and A* Search—in solving the 8-puzzle problem. The algorithms were implemented in Java, and their performance was measured in terms of solution length, the number of expanded nodes, and execution time.ResultsExperiments were conducted on eight randomly generated instances of the 8-puzzle problem. The findings reveal that both BFS and A* Search consistently identify optimal solutions in all cases. However, BFS was found to use more memory and operate slower than A* Search. Conversely, DFS demonstrated faster execution times and lower memory usage compared to BFS but was less reliable in finding optimal or feasible solutions. The study also explored the impact of different heuristic functions on the performance of A* Search. Among the heuristics tested, the Manhattan distance heuristic was determined to be the most effective, offering the best balance between accuracy and efficiency.ConclusionsIn conclusion, both BFS and A* Search are effective in finding optimal solutions, with A* Search being more efficient in terms of speed and memory usage. DFS, while faster and more memory-efficient, is less consistent in producing optimal solutions. The Manhattan distance heuristic significantly enhances the performance of A* Search, making it the preferred heuristic for the 8-puzzle problem. These results suggest that A* Search, particularly with the Manhattan distance heuristic, is a highly efficient choice for solving the 8-puzzle problem, especially in contexts where computational resources are limited.

GEMimp: An Accurate and Robust Imputation Method for Microbiome Data Using Graph Embedding Neural Network

Microbiome research has increasingly underscored the profound link between microbial compositions and human health, with numerous studies establishing a strong correlation between microbiome characteristics and various diseases. However, the analysis of microbiome data is frequently compromised by inherent sparsity issues, characterized by a substantial presence of observed zeros. These zeros not only skew the abundance distribution of microbial species but also undermine the reliability of scientific conclusions drawn from such data. Addressing this challenge, we introduce GEMimp, an innovative imputation method designed to infuse robustness into microbiome data analysis. GEMimp leverages the node2vec algorithm, which incorporates both Breadth-First Search (BFS) and Depth-First Search (DFS) strategies in its random walks sampling process. This approach enables GEMimp to learn nuanced, low-dimensional representations of each taxonomic unit, facilitating the reconstruction of their similarity networks with unprecedented accuracy.Our comparative analysis pits GEMimp against state-of-the-art imputation methods including SAVER, MAGIC and mbImpute. The results unequivocally demonstrate that GEMimp outperforms its counterparts by achieving the highest Pearson correlation coefficient when compared to the original raw dataset. Furthermore, GEMimp shows notable proficiency in identifying significant taxa, enhancing the detection of disease-related taxa and effectively mitigating the impact of sparsity on both simulated and real-world datasets, such as those pertaining to Type 2 Diabetes (T2D) and Colorectal Cancer (CRC). These findings collectively highlight the strong effectiveness of GEMimp, allowing for better analysis on microbial data. With alleviation of sparsity issues, it could be greatly facilitated in downstream analyses and even in the field of microbiology.

Breadth-first search algorithm on the finite element simulation of the electrical resistivity of the carbon black elastomeric pressurized sensor

Resistivity and piezoresistive sensitivity of Carbon Black (CB) elastomeric nanocomposites are studied using a finite element method with a conductive network model. CB spheres are placed into Representative Volume Elements (RVEs) in random positions to perform simulations and obtain the strained state and new position of particles. Numerical results are implemented into a breadth-first search algorithm tailored to find percolation pathways from one end of the RVE to another based on the shortest distance between CBs in the strained regime. Percolation pathways are used by the conductive network model to determine the critical distance for resistivity. Resistivity diminishes as the critical distance increases attributed to a greater number of electrons penetrating the barriers. Critical distance at which tunneling can occur expands with an increase in barrier potential. Smaller CBs that can more efficiently occupy the gaps lead to a reduction in the critical distance range necessary for percolation to happen.

Orthopedic disease classification based on breadth-first search algorithm

Orthopedic diseases are widespread worldwide, impacting the body’s musculoskeletal system, particularly those involving bones or hips. They have the potential to cause discomfort and impair functionality. This paper aims to address the lack of supplementary diagnostics in orthopedics and improve the method of diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO) for feature selections, and the BBFS makes an average error of 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, and ET. The dataset used contains 310 instances and six distinct features. Through experimentation, the RF model led to optimal outcomes during comparison to the remaining models, with an accuracy of 91.4%. The parameters of the RF model were optimized using four optimization algorithms: BFS, PSO, WAO, and GWO. To check how well the optimized RF works on the dataset, this paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that the BFS-RF can improve the performance of the original classifier compared with others with 99.41% accuracy.

Research on the application technology of expert knowledge graph based on genetic search algorithm

Abstract The present study investigates the application of a genetic search algorithm on an expert knowledge graph. By employing binary coding for candidate experts in the expert database, the matching evaluation function is utilized to define the environmental fitness index, thereby optimizing the selection of expert groups. Experimental results demonstrate that the GES algorithm exhibits a superior matching degree and reduced time consumption when addressing science and technology item-matching requirements in review work. In comparison with the traditional BFS algorithm and GIS algorithm, the GES algorithm showcases enhanced potential for application and stability when confronted with large datasets. This paper further analyzes the impact of factors such as the total number of experts, number of fields, and number of scientific and technological projects on algorithm performance, confirming that adjustment of algorithm parameters can effectively enhance matching degree while reducing time consumption, thus providing technical support for practical implementation.

Traveling wave network location method based on adaptive waveform similarity

High-voltage transmission technology can effectively address the issue of uneven spatial and temporal energy distribution, leading to its rapid development in recent years. To address the challenge of accurately identifying the source of the second traveling wave head in complex transmission network scenarios with existing single-ended fault location methods, a traveling wave network fault location method based on adaptive waveform similarity is proposed. The paper analyzes the propagation process of traveling waves in transmission lines and quantitatively derives the time-domain expression of the traveling wave waveform. The BFS algorithm is enhanced by incorporating the propagation characteristics of traveling waves, allowing for the determination of all paths from any location in the topological network to the measurement points. Based on the path information and the derived expression, the traveling wave waveform at the measurement points for the fault location is calculated. An optimization algorithm is used to iteratively solve for unknown parameters such as fault location, traveling wave speed, and fault point information, with the objective of maximizing the similarity between the adaptive waveform and the real waveform by adaptively adjusting the waveform shape. When the similarity between the adaptive waveform and the real waveform is maximized, the adaptive fault location is identified as the actual fault location. Verified through the PSCAD simulation platform, this method can achieve accurate location under different fault conditions.

Grapher: A Reconfigurable Graph Computing Accelerator with Optimized Processing Elements

In recent years, various graph computing architectures have been proposed to process graph data that represent complex dependencies between different objects in the world. The designs of the processing element (PE) in traditional graph computing accelerators are often optimized for specific graph algorithms or tasks, which limits their flexibility in processing different types of graph algorithms, or the parallel configuration that can be supported by their PE arrays is inefficient. To achieve both flexibility and efficiency, this paper proposes Grapher, a reconfigurable graph computing accelerator based on an optimized PE array, efficiently supporting multiple graph algorithms, enhancing parallel computation, and improving adaptability and system performance through dynamic hardware resource configuration. To verify the performance of Grapher, this paper selected six datasets from the Stanford Network Analysis Project (SNAP) database for testing. Compared with the existing typical graph frameworks Ligra, Gemini, and GraphBIG, the processing time for the six datasets using the BFS, CC, and PR algorithms was reduced by up to 39.31%, 35.43%, and 27.67%, respectively. The energy efficiency has also been improved by 1.8× compared to Hitgraph and 4.7× compared to ThunderGP.

An Effective LiDAR-Inertial SLAM-Based Map Construction Method for Outdoor Environments

SLAM (simultaneous localization and mapping) is essential for accurate positioning and reasonable path planning in outdoor mobile robots. LiDAR SLAM is currently the dominant method for creating outdoor environment maps. However, the mainstream LiDAR SLAM algorithms have a single point cloud feature extraction process at the front end, and most of the loop closure detection at the back end is based on RNN (radius nearest neighbor). This results in low mapping accuracy and poor real-time performance. To solve this problem, we integrated the functions of point cloud segmentation and Scan Context loop closure detection based on the advanced LiDAR-inertial SLAM algorithm (LIO-SAM). First, we employed range images to extract ground points from raw LiDAR data, followed by the BFS (breadth-first search) algorithm to cluster non-ground points and downsample outliers. Then, we calculated the curvature to extract planar points from ground points and corner points from clustered segmented non-ground points. Finally, we used the Scan Context method for loop closure detection to improve back-end mapping speed and reduce odometry drift. Experimental validation with the KITTI dataset verified the advantages of the proposed method, and combined with Walking, Park, and other datasets comprehensively verified that the proposed method had good accuracy and real-time performance.

Effectiveness of regional risk mitigation policies in equitably improving connectivity to essential service during hurricane-induced floods

This study aimed to evaluate the effectiveness of regional disaster mitigation policies in providing equitable access to essential services during hurricane-induced floods in Southeast Louisiana. Studies have been conducted on access to essential service facilities during and after disasters. However, there is limited research on whether current regional disaster mitigation plans ensure equitable access to emergency facilities. To this end, a methodology was developed to assess the effect of the 2017 Louisiana Coastal Master Plan developed by the Coastal Protection and Restoration Agency (CPRA) on equity in connectivity to essential service facilities (hospitals and fire stations) in a case study region in southeastern Louisiana. First, a graph-based model was used to represent the road network and determine the connectivity of five demographic groups during hurricane-induced flood scenarios. Herein, a hurricane-induced flood scenario with a low return period (50 years) was chosen for future years up to 2065, considering sea level rise and subsidence. Roads with flood depths exceeding 30 cm were considered impassable and were removed from the network. Connectivity was then determined using breadth-first search. Monte Carlo simulations were performed to propagate uncertainties in flood depths. It was found that minority groups were at a higher risk of losing access to emergency services during the selected hazard scenario. Native American and Asian populations were found to be at the highest risk of disconnection, followed by the Hispanic and African-American populations. Overall, the coastal master plan did not affect the equity (or lack thereof) in connectivity to essential service facilities.

Nonlinear difference subspace method of motor imagery EEG classification in brain-computer interface

Noninvasive Motor Imagery (MI) electroencephalography (EEG) based brain-computer interface (BCI) systems are in high demand in both the medical and consumer sectors. However, the low signal-to-noise ratio of EEG and its nonlinear dynamics present challenges in processing. The effectiveness of information retrieval is directly related to the complexity of the algorithms, making BCI impractical for real-time applications. To enhance performance, this study introduced classification frameworks using nonlinear subspace and difference subspace methods, employing manifold learning techniques such as Kernel principal component analysis (KPCA) and Isometric feature mapping (Isomap). The optimal dimensions for the subspaces were selected using overlapping criteria in conjunction with the breadth-first-search (BFS) and depth-first-search (DFS) graph search methods. The proposed methods were evaluated on the BCI competition III and IV datasets using metrics including classification accuracy, Cohen's kappa coefficient, and F1-score. By exploiting the nonlinear characteristics of EEG and employing the geodesic distance between all pairs of trials, the modified Isomap called supervised discriminant Isomap projection (SD-ISoP) outperformed the KPCA-based methods and other recently proposed state-of-the-art methods. The results demonstrssate that the SD-ISoP-based difference subspace method achieved an average classification accuracy of 86.25% and 85.44% for BCI competition III and IV datasets, respectively.

A Multi-Area Task Path-Planning Algorithm for Agricultural Drones Based on Improved Double Deep Q-Learning Net

With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA.

Transcriptome analysis provides new insights into the berry size in ‘Summer Black’ grapes under a two-crop-a-year cultivation system

Southern China has high temperatures and receives concentrated rainfall; therefore, the two-crop-a-year cultivation system has been applied to grape production so as to resolve the problem of relative seasonal surplus of grape yield. However, a common issue associated with this technique is the tendency of the second season fruits to be smaller than the first season fruits. We here used the first and second season fruits of ‘Summer Black’ at different ripening stages as research materials. Phenotypic and histological analyses revealed fewer cell number occurring between 7 and 14 days post anthesis (DPA) in the second season fruits, which ultimately resulted in a smaller fruit size compared with the first season fruits. To unravel the mechanism underlying this phenomenon, first and second season fruits of four time periods (7, 14, 21, and 28 DPA) were selected for RNA-seq analysis. This analysis identified 10 431 differentially expressed genes (DEGs). These DEGs were classified into 9 clusters through GO and KEGG enrichment analyses. Then the time-ordered gene co-expression network (TO-GCN) analysis with the breadth-first search algorithm showed that DEGs in the GCN were divided into 8 levels. The DEGs of early berry development (L1–L3) were enriched in heat stress- and cell division-related pathways. The field investigation of effective accumulated temperature confirmed that the growth and development of the second season fruits were subjected to high temperature stress during 7–14 DPA. Moreover, based on the results of interactive analysis of TO-GCN and transcriptional regulation prediction of L1–L3 genes, we constructed a unique hierarchical regulatory network for the heat stress regulation of berry size. The expression level of 5 candidate genes was verified through qRT-PCR. Vitvi10g00469 (HSFB2A), Vitvi16g00982 (HSFB2A), Vitvi02g00387 (HSFB2B), Vitvi15g01542 (NTL9), and Vitvi06g00592 (DIVARICATA) were upregulated in 7–14 DPA, whereas Vitvi18g00777 (HSFB4) was downregulated in 7 DPA. These results suggest that during intense cell division, heat stress might act as a major factor causing a reduction in cell number, thereby ultimately resulting in the smaller size of the second season fruits.

Decentralized Multi-Area Economic Dispatch in Power Systems Using the Consensus Algorithm

A multi-area power system requires coordination to enhance reliability and reduce operating costs. Economic dispatch in such systems is crucial because of the uncertainties associated with variable loads and the increasing penetration of renewable energy sources. This paper presents a hierarchical consensus algorithm designed to determine the economic dispatch in a multi-area power system, accounting for the uncertainties in load and renewable generation. The proposed algorithm, which utilizes distributed agents, operates across three levels. Level 1 coordinates all areas, while levels 2 and 3 form a leader–follower consensus algorithm for overall economic dispatch. Breadth-first search is employed to identify the leader agent within each area. To address the uncertainties in loads and renewable generation, Monte Carlo simulations are performed. The efficacy of the proposed method is validated using the IEEE 39-bus and 118-bus systems, as well as a realistic 1968-bus power system in Taiwan. The traditional equal lambda method is employed to verify that the proposed approach is suitable for multi-area power systems using distributed computation.

Research on Parameter Correction of Distribution Network Model Based on CIM Model and Measurement Data

The construction of an energy distribution network can improve the system’s ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network needs accurate distribution network model parameters as support. At present, the installation of PMU equipment in China’s distribution network synchronous phase angle measurement unit is limited, which brings challenges to the parameter correction of the distribution network. In this paper, an automatic correction algorithm of distribution network parameters based on the CIM model and measurement data is proposed for the distribution network system without PMU. Firstly, the distribution network topology construction technology based on XML files and key fields of the distribution network is proposed, and the non/small impedance devices (such as switches) are merged and reduced. The breadth-first traversal algorithm is used to verify the connectivity of the constructed topology. Then, based on the topology construction and the least squares method, an iterative parameter correction technique is constructed. Finally, the accuracy and effectiveness of the proposed algorithm are verified in a standard IEEE 33-bus system distribution network and an example of the China Southern Power Grid. The topology connections constructed based on the CIM model have significantly enhanced the efficiency of parameter correction.

Path Planning Algorithms for Smart Parking: Review and Prospects

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.

The influence of Distributed BFS algorithms in reducing the computational complexity

Breadth-first search (BFS) is a cornerstone of algorithmic algorithms for complex graphs. In the worst case, BFS's linear complexity is determined by the number of edges and vertices, yet a recently discovered bottom-up method can reduce this complexity to the number of vertices in the best-case scenario, which is usually much less than the number of edges. The conventional top-down approach, however, always takes the same amount of time as the lowest case. To determine the direction, directional algorithms combine top-down and bottom-up methods, adapting between them as the boundary expands. Although bottom-up methods are not always the best option, their use in combination with top-down methods produces efficient algorithms. The main focus of this research is to improve search efficiency and reduce complexity by implementing a version of top-down algorithm optimisation. A scalable distributed-memory parallelisation technique is presented in this study, which has achieved speedups that are ten times faster than the previous purely toplevel approach. In previous studies, it has been demonstrated that this technique is superior to a 1D decomposition method.

Improve Parallel Resistance of Hashcash Tree

Denial of Service (DoS) attacks remain a persistent threat to online systems, necessitating continual innovation in defense mechanisms. In this work, we present an improved algorithm for mitigating DoS attacks through the augmentation of client puzzle protocols. Building upon the foundation of hashcash trees, a recently proposed data structure combining hashcash and Merkle trees, we introduce a new version of the data structure that enhances resistance against parallel computation (a common tactic employed by attackers). By incorporating the labels of children and the next node in a breadth-first traversal into the hash function, we establish a sequential processing order that inhibits parallel node evaluation. The added dependency on the next node significantly elevates the complexity of constructing hashcash trees, introducing a linear number of synchronization points and fortifying resilience against potential attacks. Empirical evaluation demonstrates the efficacy of our approach, showcasing its ability to accurately control puzzle difficulty while bolstering system security against DoS threats.

Optimizing the Implementation of the BFS and DFS algorithms using the web crawler method on the kumparan site

Efficient access to timely information is critical in today's digital era. Web crawlers, automated programs that navigate the Internet, play an important role in collecting data from websites such as Kumparan, a leading news site in Indonesia. This research shows the effectiveness of the Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms in indexing Kumparan content. The results of the research show that BFS consistently indexes more files comprehensively but with longer execution times compared to DFS, which provides faster initial results but with fewer files. For example, at depth 4 BFS indexed 949 files in 886.94 seconds, while DFS indexed 470 files in 233.02 seconds. These findings highlight the balance between precision and speed when selecting a crawling algorithm tailored to the needs of a particular website. This research provides insights into optimizing web crawler technology for complex websites such as Coil and suggests avenues for further research to improve permission efficiency and adaptability across a variety of crawling scenarios.