Breadth-first Search Strategy Research Articles

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.

Research on Shortest Path BFS Strategy in Multi-AGV Scheduling System

With the increasing maturity of automated guided vehicles (AGV) technology and the widespread application of flexible manufacturing systems, enhancing the efficiency of AGVs in complex environments has become crucial. This paper analyzes the challenges of path planning and scheduling in multi-AGV systems, introduces a map-based path search algorithm, and proposes the BFS algorithm for shortest path planning. Through optimization using the breadth-first search (BFS) algorithm, efficient scheduling of multiple AGVs in complex environments is achieved. In addition, this paper validated the effectiveness of the proposed method in a production workshop experiment. The experimental results show that the BFS algorithm can quickly search for the shortest path, reduce the running time of AGVs, and significantly improve the performance of multi-AGV scheduling systems.

Deep Reinforcement Learning Algorithm for Latency-Oriented IIoT Resource Orchestration

Due to geographical factors and resource constraints, the traditional Internet architecture cannot meet the needs of the space–air–ground-integrated network (SAGIN) resource layout in the Industrial Internet of Things (IIoT) service. How to arrange network resources in SAGIN quickly and efficiently to meet the quality of service requirements of users has become a hot research topic in the industry. Based on the characteristics of SAGIN with multiple network segments, we convert the resource scheduling problem of SAGIN into a multidomain virtual network embedding (VNE) problem. This article proposes a latency-sensitive VNE algorithm based on deep reinforcement learning (DDRL-VNE) in the SAGIN environment. Unlike traditional latency optimization algorithms, we consider the effect of traffic size and hop count on latency when evaluating latency. We constructed a learning agent composed of a five-layer policy network and extracted a feature matrix as its training environment based on the network attributes of SAGIN. The node embedding is completed according to the probability that each node is embedded in the training, and then the breadth-first search strategy is used to complete the link embedding. The experimental results effectively illustrate the effectiveness of the algorithm in the SAGIN resource allocation problem.

Multi-Querying: A Subsequence Matching Approach to Support Multiple Queries

The widespread use of sensors has resulted in an unprecedented amount of time series data. Time series mining has experienced a particular surge of interest, among which, subsequence matching is one of the most primary problem that serves as a foundation for many time series data mining techniques, such as anomaly detection and classification. In literature there exist many works to study this problem. However, in many real applications, it is uneasy for users to accurately and clearly elaborate the query intuition with a single query sequence. Consequently, in this paper, we address this issue by allowing users to submit a small query set, instead of a single query. The multiple queries can embody the query intuition better. In particular, we first propose a novel probability-based representation of the query set. A common segmentation is generated which can approximate the queries well, in which each segment is described by some features. For each feature, the corresponding values of multiple queries are represented as a Gaussian distribution. Then, based on the representation, we design a novel distance function to measure the similarity of one subsequence to the multiple queries. Also, we propose a breadth-first search strategy to find out similar subsequences. We have conducted extensive experiments on both synthetic and real datasets, and the results verify the superiority of our approach.

Robot-assisted ultrasonic impact strengthening strategy for aero-engine blades

• Robot-assisted UIT system is developed for aero-engine blades. • A novel non-interference trajectory planning approach is proposed. • Strengthening system can operate without collision even in restricted space. • The proposed strategy can improve fatigue performance of TA19 blades. As a vital component of aero-engine, blade is susceptible to damage from long-term service under cyclic loads. Ultrasonic impact treatment (UIT) can improve the mechanical properties of the material surface. However, applying UIT onto aero-engine blade remains a challenge due to the complex structure. This study investigates robot-assisted ultrasonic impact strengthening strategy for the complex surface in the restricted space. On the one hand, a novel robot-assisted UIT system is developed for a wide range of complex surface features. On the other hand, a non-interference trajectory planning method is introduced for complex surfaces in the restricted space of the blade. The breadth-first search (BFS) strategy based on the criterion of the induced ray is proposed for interference inspecting and optimizing the pose of the UIT tool. Experiments are conducted on real aero-engine blades. The results of numerical and experimental case studies have demonstrated that the proposed strategy can effectively cover the fatigue vulnerable areas without collision. Moreover, compressive residual stresses are generated both on the pressure and suction surface, and the fatigue limit of the UIT blades is improved by 26% as compared with the as-received blades.

Fast parallel algorithms for finding elementary circuits of a directed graph: a GPU-based approach

Circuits in a graph are interesting structures and identifying them is of an important relevance for many applications. However, enumerating circuits is known to be a difficult problem, since their number can grow exponentially. In this paper, we propose fast parallel approaches for enumerating elementary circuits of directed graphs based on graphics processing unit (GPU). Our algorithms are based on a massive exploration of the graph in a breadth-first search strategy. Algorithm V-FEC explores the graph starting from different vertices simultaneously. To further reduce the search space, we present T-FEC, another algorithm that uses triplets as an initial set to start exploring. To the best of our knowledge, those are the first parallel GPU-based algorithms for finding all circuits of a given graph. In addition, they find circuits of a given length and circuits with a specific vertex or edge. The evaluation results show that the proposed approaches achieve up to 190x speed-up over Johnson’s algorithm, one of the most efficient sequential algorithms for finding circuits.

Optimal Emergency Evacuation Route Planning Model Based on Fire Prediction Data

For the emergency evacuation of cruise ships in case of sudden fire, this research proposes a dynamic route optimization method based on the improved A∗ algorithm for real-time information, in order to obtain the real-time optimal evacuation route. Initially, a basic network topology diagram is established according to the internal structure of the cruise ship. Before the occurrence of the accident, the A∗ algorithm can be applied to obtain an a priori evacuation network consisting of all the optimal routes from each node to the exit. At the time of the accident, the dynamic diffusion of fire can be simulated using Fire Dynamics Simulator (FDS) based on the preliminary information of the fire, so as to estimate the impact of the fire domain on each node of the network. Then, according to the fire dynamic diffusion data, the evacuation route planning is carried out by the improved A∗ algorithm applying the breadth-first search strategy, so as to determine the optimal route from the current node to the safety exit and to reduce the possibility of casualties due to the uncertainty of the fire during the evacuation. This model allows for both people’s safety and evacuation time to dynamically avoid fire-affected nodes and helps people to reach the safe area as soon as possible. Finally, the evacuation model is established according to the open-source cruise ship structure, and the evacuation process of people under the dynamic spread of cruise ship fire is simulated. The results show that the route planning method proposed in this research works out well in evacuating mass people, which can effectively reduce the evacuation time and improve the safety of the evacuation process.

Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.

Jamu is an Indonesian traditional herbal medicine that has been practiced for generations. Jamu is made from various medicinal plants. Each plant has several compounds directly related to the target protein that are directly associated with a disease. A pharmacological graph can form relationships between plants, compounds, and target proteins. Research related to the prediction of Jamu formulas for some diseases has been carried out, but there are problems in finding combinations or compositions of Jamu formulas because of the increase in search space size. Some studies adopted the drug–target interaction (DTI) implemented using machine learning or deep learning to predict the DTI for discovering the Jamu formula. However, this approach raises important issues, such as imbalanced and high-dimensional dataset, overfitting, and the need for more procedures to trace compounds to their plants. This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. The branch and bound technique is implemented using the search strategy of breadth first search (BrFS), Depth First Search, and Best First Search. To show the performance of the proposed method, we compared our method with a complete search algorithm, searching all nodes in the tree without pruning. In this study, we specialize in applying the proposed method to search for the Jamu formula for type II diabetes mellitus (T2DM). The result shows that the bipartite graph search with the branch and bound algorithm reduces computation time up to 40 times faster than the complete search strategy to search for a composition of plants. The binary branching strategy is the best choice, whereas the BrFS strategy is the best option in this research. In addition, the the proposed method can suggest the composition of one to four plants for the T2DM Jamu formula. For a combination of four plants, we obtain Angelica Sinensis, Citrus aurantium, Glycyrrhiza uralensis, and Mangifera indica. This approach is expected to be an alternative way to discover the Jamu formula more accurately.

K-Best Sphere Decoding Algorithm for Long Prediction Horizon FCS-MPC

Finite control set model predictive control (FCS-MPC) strategies for power conversion devices benefit from extending the prediction horizon length. Solving this problem relies on the definition of the underlying integer least-squares problem. Sphere decoding algorithm (SDA) has been extensively used in previous works as an approach to solve this problem. In this article, a parallel and fully scalable K-best SDA hardware design is proposed as an alternative. The K-best SDA establishes a different breadth-first search strategy, which addresses some of the main drawbacks of the SDA. Through experimental tests based on an uninterruptible power supply, the K-best SDA performance for long prediction horizon FCS-MPC is assessed and verified. Results demonstrate key beneficial aspects through which the K-best SDA is capable of rendering an improved control performance when compared to the conventional SDA.

Optimization of Breadth-First Search Algorithm for Path Solutions in Mazyin Games

A game containing elements of artificial intelligence, of course, requires an algorithm in its application. One example of a game that includes elements of artificial intelligence is the Labyrinth game. Maze is a simple educational game. This game is known as finding a way out of the maze to arrive at a predetermined goal. The labyrinth encounters numerous obstacles along the way, such as dead ends and parapets, to reach the target location. In this game, players are required to think logically about how to find the right maze path. The obstacle faced in this game is that sometimes players have difficulty finding a way out, especially if the game level has reached a high level in the process of finding a way out. To solve this problem, a graph tracing technique is needed. The Breadth-First Search (BFS) strategy can be used in conjunction with various graph search algorithms. An example of a broad search method is the Breadth-First Search Algorithm, which works by visiting nodes at level n first before moving on to nodes at level n+1. The advantage of the Breadth-First Search algorithm is that it can find a solution as the shortest path and find the minimum solution if there is more than one solution. This study will discuss how to find a path for the Labyrinth using the BFS algorithm. The result of applying this BFS algorithm is the shortest route solution raised so that the Labyrinth can arrive at the destination point through the route provided.

Dynamic Path Optimization with Real-Time Information for Emergency Evacuation

In order to find the optimal path for emergency evacuation, this paper proposes a dynamic path optimization algorithm based on real-time information to search the optimal path and it takes fire accident as an example to introduce the algorithm principle. Before the accidents, it uses the Dijkstra algorithm to get the prior evacuation network which includes evacuation paths from each node to the exit port. When the accidents occur, the evacuees are unable to pass through the passage where the accident point and the blocking point are located, then the proposed method uses the breadth-first search strategy to solve the path optimization problem based on the prior evacuation network, and it dynamically updates the evacuation path according to the real-time information. Because the prior evacuation network includes global optimal evacuation paths from each node to the exit port, the breadth-first search algorithm only searches local optimal paths to avoid the blockage node or dangerous area. Because the online optimization solves a local pathfinding problem and the entire topology optimization is an offline calculation, the proposed method can find the optimal path in a short time when the accident situation changes. The simulation tests the performances of the proposed algorithm with different situations based on the topology of a building, and the results show that the proposed algorithm is effective to get the optimal path in a short time when it faces changes caused by the factors such as evacuee size, people distribution, blockage location, and accident points.

User-Centered Clinical Display Design Issues for Inpatient Providers.

Suboptimal information display in electronic health records (EHRs) is a notorious pain point for users. Designing an effective display is difficult, due in part to the complex and varied nature of clinical practice. This article aims to understand the goals, constraints, frustrations, and mental models of inpatient medical providers when accessing EHR data, to better inform the display of clinical information. A multidisciplinary ethnographic study of inpatient medical providers. Our participants' primary goal was usually to assemble a clinical picture around a given question, under the constraints of time pressure and incomplete information. To do so, they tend to use a mental model of multiple layers of abstraction when thinking of patients and disease; they prefer immediate pattern recognition strategies for answering clinical questions, with breadth-first or depth-first search strategies used subsequently if needed; and they are sensitive to data relevance, completeness, and reliability when reading a record. These results conflict with the ubiquitous display design practice of separating data by type (test results, medications, notes, etc.), a mismatch that is known to encumber efficient mental processing by increasing both navigation burden and memory demands on users. A popular and obvious solution is to select or filter the data to display exactly what is presumed to be relevant to the clinical question, but this solution is both brittle and mistrusted by users. A less brittle approach that is more aligned with our users' mental model could use abstraction to summarize details instead of filtering to hide data. An abstraction-based approach could allow clinicians to more easily assemble a clinical picture, to use immediate pattern recognition strategies, and to adjust the level of displayed detail to their particular needs. It could also help the user notice unanticipated patterns and to fluidly shift attention as understanding evolves.

Efficient Breadth-First Reduct Search

This paper formulates the problem of determining all reducts of an information system as a graph search problem. The search space is represented in the form of a rooted graph. The proposed algorithm uses a breadth-first search strategy to search for all reducts starting from the graph root. It expands nodes in breadth-first order and uses a pruning rule to decrease the search space. It is mathematically shown that the proposed algorithm is both time and space efficient.

A fast object registration method for augmented reality assembly with simultaneous determination of multiple 2D-3D correspondences

Abstract A fast and robust object registration is one of the fundamental steps of an augmented reality (AR) assembly guidance system. To manage the registration of texture-less objects from monocular images, this paper presents a three-dimensional (3D) object registration solution in the view-based framework, which incorporates a new image feature named as chain-of-lines feature (COLF). The COLF, constructed by several directed line segments with geometric constraints, enhances the robustness of the registration by establishing multiple correspondences between the two-dimensional (2D) image and 3D model simultaneously. In order to save on computational cost and improve the efficiency of the COLF-based registration, a compression algorithm based on the local breadth-first search (LBFS) strategy is also proposed; this adaptively reduces the number of keyframes according to the target model. In the experimental evaluation, the combination of COLF and LBFS-based keyframe compression shows higher registration success rate and faster speed in comparison with conventional registration methods. Moreover, an AR assembly guidance prototype system for reductors is introduced to illustrate the application of the proposed registration method.

Improvements of Directed Automated Random Testing in Test Data Generation for C++ Projects

This paper improves the breadth-first search strategy in directed automated random testing (DART) to generate a fewer number of test data while gaining higher branch coverage, namely Static DART or SDART for short. In addition, the paper extends the test data compilation mechanism in DART, which currently only supports the projects written in C, to generate test data for C++ projects. The main idea of SDART is when it is less likely to increase code coverage with the current path selection strategies, the static test data generation will be applied with the expectation that more branches are covered earlier. Furthermore, in order to extend the test data compilation of DART for C++ context, the paper suggests a general test driver technique for C++ which supports various types of parameters including basic types, arrays, pointers, and derived types. Currently, an experimental tool has been implemented based on the proposal in order to demonstrate its efficacy in practice. The results have shown that SDART achieves higher branch coverage with a fewer number of test data in comparison with that of DART in practice.

An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy

Inspired by the natural phenomenon that honey bees follow the elite group in the foraging process, we propose a novel artificial bee colony algorithm named ECABC based on elite group guidance and the combined breadth-depth search strategy in this paper. Firstly, by simulating the behavior that bees follow the elite group, a novel neighborhood search equation is proposed. According to the equation, with the center of the elite group as the starting point of the search, under the guidance of the global optimum, neighborhood search is performed. Secondly, a combined search strategy is designed as follows: the stochastic breadth-first search strategy for employed bees and the stochastic depth-first search strategy for onlooker bees. Thirdly, the random selection method of elite bees is adopted to replace the probability selection method of onlooker bees. Besides, the influencing parameters of the optimization results are studied and the optimum parameters allowing the best comprehensive performance are obtained. In addition, the proposed algorithm is experimentally verified with 22 benchmark functions and then compared with other improved artificial bee colony algorithms. The comparison results show that the ECABC can effectively improve the convergence speed, convergence precision, and robustness.

A Stack-Centric Processing Model For Iterative Processing

Complex data mining algorithms are processed in multiple iterations, where output of one iteration is used as input for the subsequent iterations. Existing parallel programming frameworks, e.g., MapReduce, Pregel and Spark, adopt the breadth first search (BFS) strategy to process those iterative jobs. They invoke the user-defined functions for every key-value pair or vertex to produce all possible intermediate results for the next iteration. Such BFS strategy incurs high I/O overheads, because normally, the size of intermediate search results of BFS is exponential to the size of original data, making it impossible to maintain those intermediate results in memory. In this paper, we present a new type of parallel programming model, the stack-centric model, where all computations are defined for a stack maintained in the distributed shared memory. The stack can be adaptively split into multiple stacks and disseminated to different compute nodes for parallel processing. The most distinguished feature of the stack-centric model is its support for the depth first search (DFS) algorithm which incurs much less memory overhead than its BFS counterpart. The maximal memory usage of DFS algorithm is determined by the height of its search tree, and hence, it is possible to conduct the computation of DFS algorithm mostly in memory. Our stack-centric model is not a pure DFS framework. It supports the hybrid BFS and DFS algorithms by tuning the trade-off between memory usage and parallelism. To show the advantages of stack-centric model, we implement two algorithms, frequent pattern mining algorithm and DNA sequence matching algorithm, on both stack-centric model and Spark. The memory usage of stack-centric model is 10 times less than the Spark, resulting in a significant performance improvement.

Package consolidation approach to the split-order fulfillment problem of online supermarkets

The split-order fulfillment problem has been a new key issue for online supermarkets. Multi-item orders to be fulfilled by multiple warehouses are split into several suborders and delivered with multiple shipments, increasing transportation cost. This paper presents a powerful order fulfillment method—package consolidation—in which two or more stock-keeping units (SKUs) in the suborders of one order will be packed into fewer packages through lateral transshipment. We propose a consolidation model and a packing model to determine which SKUs in suborders should be transshipped for package consolidation. In addition, we develop a modified first fit decreasing (FFD) algorithm to solve the packing model and a heuristic algorithm based on the breadth-first search strategy with cutting, long-sight and suborder rules to generate near optimal order fulfillment schemes in a reasonable time. The computation results verify the superiority of the package consolidation approach in solving split-order fulfillment problem. Moreover, our sensitivity analyses provide managerial insights for online supermarkets.

Mining frequent subgraphs from tremendous amount of small graphs using MapReduce

Frequent subgraph mining from a tremendous amount of small graphs is a primitive operation for many data mining applications. Existing approaches mainly focus on centralized systems and suffer from the scalability issue. Consider the increasing volume of graph data and mining frequent subgraphs is a memory-intensive task, it is difficult to tackle this problem on a centralized machine efficiently. In this paper, we therefore propose an efficient and scalable solution, called MRFSE, using MapReduce. MRFSE adopts the breadth-first search strategy to iteratively extract frequent subgraphs, i.e., all frequent subgraphs with $$i+1$$ edges are generated based on frequent subgraphs with i edges at the ith iteration. In our design, existing frequent subgraph mining techniques in centralized systems can be easily extended and integrated. More importantly, new frequent subgraphs are generated without performing any isomorphism test which is costly and imperative in existing frequent subgraph mining techniques. Besides, various optimization techniques are proposed to further reduce the communication and I/O cost. Extensive experiments conducted on our in-house clusters demonstrate the superiority of our proposed solution in terms of both scalability and efficiency.

Efficacy of a causal value function in game tree search

Classical search methods on game trees are based on a static evaluation function (that enable quantitative valuation of game states) and a decision strategy (such as the minimax method). These search methods are not always effective in some games such as the game of Go, as construction of the evaluation function is very hard and the search space is extremely huge. Recently, Monte Carlo tree search methods (especially the UCT algorithms) that enable efficient sampling of actions have been shown to be very effective. Here, we propose the loosely symmetric (LS) model applied to trees (LST), which utilises an action value function (LS model) that implements causal intuition of humans. By tuning a single intuitive parameter, LST enables fast search of the optimal action with its efficient satisficing behaviour. The satisficing search realised by LST enables pruning and exhibits intermediate properties between those of breadth-first and depth-first search strategies.