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Abstract High altitude is characterized by low oxygen, low pressure, and high radiation. When migrates from low to high altitudes, the body’s tissues and organs experience hypoxic stress and will present acoustic adaptation as the protective response. However, the mechanisms of acoustic adaptation at high altitudes remain unclear. In this study, cochlear tissues from Wistar rats were collected at 15, 30, 60, 120, and 180 days after high-altitude migration. Transcriptome sequencing was conducted and DESeq algorithm revealed expression patterns of Differentially Expressed Genes(DEGs) after high altitude migration. Day 60 is a critical stage for cochlear tissue “damage” and “repair” in high-altitude conditions. Transmission Electron Microscopy (TEM) observations of structures also support the findings. A time-series gene co-expression network algorithm was used to investigate gene regulatory patterns and key genes after migration. Immunofluorescence, immunohistochemistry, and qPCR were per-formed for key gene validation and localization. At Day 60, the peak DEG count occurs in rats migrating to high altitude, aligning with the critical phase for cochlear tissue damage and repair at high altitudes. Repair hinges on synaptic plasticity and myelination-linked processes, influencing modules M4 to M6. Module M4’s activation gradually diminishes from its peak. However, the ‘damage’ effect is orchestrated by inflammation-related processes in modules M3 to M5, with module M3’s activation also waning. Key gene module M4, pivotal for repair during this pivotal phase, encompasses Sptbn5, Cldn1, Gfra2, and Lims2 as its core genes. Immunohistochemistry reveals Sptbn5’s presence in cochlear neurons, hair cells, Schwann cells and stria vascularis tissue. Cldn1 and Gfra2 predominantly localize within the cochlear neuron region. These results may suggest new directions for future research on acoustic acclimatization to high altitude.

High altitude is characterized by low oxygen, low pressure, and high radiation. When migrates from low to high altitudes, the body's tissues and organs experience hypoxic stress and will present acoustic adaptation as the protective response. However, the mechanisms of acoustic adaptation at high altitudes remain unclear. In this study, cochlear tissues from Wistar rats were collected at 15, 30, 60, 120, and 180 days after high-altitude migration. Transcriptome sequencing was conducted and DESeq algorithm revealed expression patterns of Differentially Expressed Genes(DEGs) after high altitude migration. Day 60 is a critical stage for cochlear tissue "damage" and "repair" in high-altitude conditions. Transmission Electron Microscopy (TEM) observations of structures also support the findings. A time-series gene co-expression network algorithm was used to investigate gene regulatory patterns and key genes after migration. Immunofluorescence, immunohistochemistry, and qPCR were per-formed for key gene validation and localization. At Day 60, the peak DEG count occurs in rats migrating to high altitude, aligning with the critical phase for cochlear tissue damage and repair at high altitudes. Repair hinges on synaptic plasticity and myelination-linked processes, influencing modules M4 to M6. Module M4's activation gradually diminishes from its peak. However, the 'damage' effect is orchestrated by inflammation-related processes in modules M3 to M5, with module M3's activation also waning. Key gene module M4, pivotal for repair during this pivotal phase, encompasses Sptbn5, Cldn1, Gfra2, and Lims2 as its core genes. Immunohistochemistry reveals Sptbn5's presence in cochlear neurons, hair cells, Schwann cells and stria vascularis tissue. Cldn1 and Gfra2 predominantly localize within the cochlear neuron region. These results may suggest new directions for future research on acoustic acclimatization to high altitude. [ABSTRACT FROM AUTHOR]

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

Ensuring the accuracy and reliability of ventilation parameter monitoring is pivotal for the development of intelligent ventilation systems. To attain a visual representation of airflow, solving the challenge of airflow reconstruction necessitates the strategic use of a limited number of sensors. In addressing these concerns, this article introduces an optimization approach for ventilation airflow leveraging the Breadth-First Search (BFS) algorithm. Additionally, it proposes an optimization distribution method for mine ventilation sensors, grounded in the Independent Cut Set algorithm. Research has found that compared to the traditional PSO algorithm, the BFS algorithm produces a higher optimal air volume solution when optimizing the air volume; Comparatively, the proposed algorithm exhibits significantly shorter average running times than the Particle Swarm Optimization (PSO) algorithm. It boasts the highest average convergence rate, ensuring superior accuracy, and possesses a notable capability to escape local minima, facilitating the acquisition of optimal solutions. Leveraging the independent cut set algorithm optimizes the calculation process through matrix operations. Exploiting the properties of matrices allows for a more rapid and intuitive resolution of sensor localization problems.

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.

This paper introduces a novel technique for optimal distribution system (DS) planning with distributed generation (DG) systems. It is being done to see how active and reactive power injections affect the system’s voltage profile and energy losses. DG penetration in the power systems is one approach that has several advantages such as peak savings, loss lessening, voltage profile amelioration. It also intends to increase system reliability, stability, and security. The main goal of optimal distributed generation (ODG) is a guarantee to achieve the benefits mentioned previously to increase the overall system efficiency. For extremely vast and complicated systems, analytical approaches are not suitable and insufficient. Therefore, several meta-heuristic techniques are favored to obtain better performance from were convergence and accuracy for large systems. In this paper, an Improved Wild Horse Optimization algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems. IWHO is devised with inspirations from the social life behavior of wild horses. The suggested method is based on the horse’s decency. To assess the efficacy of the IWHO, it is implemented on the 23 benchmark functions Reliability amelioration is the most things superb as a result of DGs incorporation. Thus, in this research, a customer-side reliability appraisal in the DS that having a DG unit was carried out by a Monte Carlo Simulation (MCS) approach to construct an artificial history for each ingredient across simulation duration. For load flow calculations, the backward Forward Sweep (BFS) technique has been employed as a simulation tool to assess the network performance considering the power handling restrictions. The proposed IWHO method has been measured on IEEE 33 69 and 119 buses to ascertain the network performing in the presence of the optimal DG and the potential benefits of the suggested technique for enhancing the tools used by operators and planners to maintain the system reliability and efficiency. The results proved that IWHO is an optimization method with lofty performance regarding the exploration–exploitation balance and convergence speed, as it successfully handles complicated problems.

Due to the importance of the signature vector in studying the reliability of networks, some methods have been proposed by researchers to obtain the signature. The notion of signature is used when at most one link may fail at each time instant. It is more realistic to consider the case where none of the components, one component, or more than one component of the network may be destroyed at each time. Motivated by this, the concept of t-signature has been recently defined to get the reliability of such a network. The t-signature is a probability vector and depends only on the network structure. In this article, we propose an algorithm to compute the t-signature. The performance of the proposed algorithm is evaluated for some networks. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein–protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body’s stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC, RHOA, CD40LG, CSF1, TNFRSF1A, FCER1G, ICAM1, LAT, LCN2, PLAU, CXCL10, ICAM1, CD40, IRF7, and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19.

Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein–protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body's stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC , RHOA , CD40LG , CSF1 , TNFRSF1A , FCER1G , ICAM1 , LAT , LCN2 , PLAU , CXCL10 , ICAM1 , CD40 , IRF7 , and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19. [ABSTRACT FROM AUTHOR]

Tensor network and tensor computation are widely applied in scientific and engineering domains like quantum physics, electronic design automation, and machine learning. As one of the most fundamental operations for tensor networks, a tensor contraction eliminates the sharing orders among tensors and produces a compact sub-network. Different contraction sequence usually yields distinct storage and compute costs, and searching the optimal sequence is known as a hard problem. Prior work have designed heuristic and fast algorithms to solve this problem, however, several issues still remain unsolved. For example, the data format and data structure are not efficient, the constraints during modeling are impractical, the search of the optimal solution might fail, and the search cost is very high. In this paper, we first introduce a logk order representation and design an adjacency matrix-based data structure to efficiently accelerate the search of the optimal contraction sequence. Then, we propose an outer product pruning method with acceptable overhead to reduce the search space. Finally, we use a multithread optimization in our implementation to further improve the execution performance. We also present in-depth analysis of factors that influence the search time. This work provides a full-stack solution for optimal contraction sequence search from both high-level data structure and search algorithm to low-level execution parallelism, and it will benefit a broad range of tensor-related applications. [ABSTRACT FROM AUTHOR]

At the age of big data, the information changes quickly. How to extract the key information timely seems to be quite important. Therefore, improving the execution speed of BFS algorithm means a lot to the processing of big data. This paper firstly introduces the implementation flow, features and performance evaluation criteria of the breadth-first search algorithm, and secondly introduce the research status of BFS algorithm based on current CPU platform both at home and abroad. Thirdly, this paper optimizes the algorithm by using the local principle of program, load balancing method and so on. Finally, the comparison of the algorithm performance is shown in this paper: the program optimized in this paper gets good performance and could be popularized further in practice. [ABSTRACT FROM AUTHOR]

In this paper, a new viable bandwidth reduction algorithm for reducing the bandwidth of sparse symmetric matrices, is described. The proposed algorithm provides a reliable procedure to reduce the bandwidth and can easily be applied to the sparse symmetric matrices of any size. This algorithm is tested on structured graphs and the reduced bandwidth results obtained are compared with the GPS algorithm. The bandwidth obtained by the present method is smaller than or equal to the one obtained by the GPS and standard examples are included to illustrate in detail the proposed algorithm.

This study presents an efficient power flow method for analysing distribution systems. The proposed method utilises efficient quadratic‐based (QB) models for various components of distribution systems. The power flow problem is formulated and solved by a backward/forward sweep (BFS) algorithm. The proposed QBBFS method accommodates multi‐phase laterals, different load types, capacitors, distribution transformers, and distributed generation. The advantageous feature of the proposed method is robust convergence characteristics against ill conditions, guaranteeing lower iteration numbers than the existing BFS methods. The proposed method is tested and validated on several distribution test systems. The accuracy is verified using OpenDSS. Comparisons are made with other commonly used BFS methods. The results confirm the effectiveness and robustness of the proposed QBBFS at different conditions. [ABSTRACT FROM AUTHOR]