Research on an Obstacle Avoidance Method for UAV

With the continuous development of UAV technology, UAV has been widely used in various industries. In the flight process of UAV, UAV often changes the given path because of obstacles (including static nonliving body and moving living body). According to the properties of obstacles and the characteristics of UAV, standard Kalman filter is used for nonmaneuvering targets, and sigma point Kalman filter is used for maneuvering targets. In the aspect of obstacle avoidance, the minimum search method is used to get the initial population of local programming. Then, the improved genetic algorithm is run. Combined with the predicted obstacle features, the local planning path can be obtained. Finally, the local planning path and global planning path are combined to generate the planning path with new obstacles. At the end of the paper, the obstacle avoidance strategies of static and moving obstacles are simulated. The simulation results show that this method has fast convergence speed and good feasibility and can flexibly deal with the obstacle avoidance and local path planning of various new obstacles.

Search for Compatible Source Code

Third-party libraries always evolve and produce multiple versions. Lucene, for example, released ten new versions (from version 7.7.0 to 8.4.0) in 2019. These versions confuse the existing code search methods to retrieve the source code that is not compatible with local programming language. To solve this issue, we propose DCSE, a deep code search model based on evolving information (i.e. evolved code tokens and evolution description). DCSE first deeply excavates evolved code tokens and evolution description in the code evolution process; then it takes evolved code tokens and evolution description as one feature of source code and code description, respectively. With such fuller representation, DCSE embeds source code and its code description into a high-dimensional shared vector space, and makes the cosine distance of their vectors closer. For the ever-evolving third-party libraries like Lucene, the experimental results show that DCSE could retrieve the source code that is compatible with local programming language, it outperforms the state-of-the-art methods (e.g. CODEnn) by 56.9–60.9[Formula: see text] in RFVersion. For the rarely-evolving third-party libraries, DCSE outperforms the state-of-the-art methods (e.g. CODEnn) by 4–11[Formula: see text] in Precision.

Biosociolink: A Decision Support System for Analyzing Link Properties in Biological and Social Networks

Network based data representation has received widespread attention over the years. Data is oriented in graph format by aligning information as nodes and edges. Some of the predominant network cases include biological and social sciences. There is a growing need to extract knowledge patterns from network orientations. In such scenario, the current study focuses on extracting data patterns from network data. Schizophrenia gene data and TRAI wireless performance data is identified for performing biological and social network analysis. Biological network analysis is performed to identify relevant gene ties which act as hotspots for identifying disease causing genes. On similar lines, social network analysis is performed on wireless dataset to identify essential telecom operators responsible for customer retention. Based on these outcomes, a decision support system, BioSocioLink is designed in R programming language to perform biological and social network analysis. The support system accurately detects knowledge patterns from both the datasets. The study is concluded by deploying the support system in local programming environment.

College Student Food Insecurity and Awareness and Use of Supports: Recent Findings from a Survey of Students at a Mid-Sized State Comprehensive University in Kansas

Recognition of food insecurity among college students in the U.S. is growing. With college costs outpacing inflation for many years now, to what extent do students at “affordable” public-supported state universities experience difficulty affording food throughout the academic year? This article highlights the level of food insecurity among students enrolled in on-campus courses at Fort Hays State University. A two-wave, self-administered mail survey found that 34% of on-campus students experienced food insecurity in the previous year, and those who were food insecure were much more likely to use local food supports, including a campus food pantry. Current combined household income was the strongest correlate of food insecurity, and a number of other sociodemographic characteristics were not associated with being food insecure. In addition, the lower the reliance on savings among sources used to pay for college costs, the more likely a student was to be food insecure. The article concludes with a discussion of implications for local programming.

The Solutions of Localism

Chapter 5 chronicles regulatory attempts to bolster local television, particularly local television news, in the US, UK and Canada. It argues that each of these attempts ultimately fell victim to status quo thinking, a reliance on market factors, and a fallback to default localism. The American case study is an analysis of the FCC’s Broadcast Localism Inquiry. The Canadian case study is an evaluation of the Local Programming Improvement Fund, a subsidy to bolster local news amongst television stations. The UK case study is on the Local Digital Television Programme Service, which was an attempt to establish a local television system akin to those in North America in 2013.

DeepBlueR: Large-scale epigenomic analysis in R

Epigenetic research focuses on understanding non-inheritable factors influencing gene regulation and covers various cellular mechanisms such as DNA methylation, histone modification, miRNA function and transcription factor binding sites. Recent advances in high-throughput profiling technologies allow for systematically collecting data on each of these mechanisms in large-scale experiments. These efforts are fostered and concerted by international collaborations, such as the International Human Epigenome Consortium (IHEC) and its members. As a result of these collaborations, researchers can exploit massive amounts of publicly available epigenomic data on dozens of cell types, cell lines and tissues. Access to these data is streamlined by existing data portals and, in principle, allows for answering important biomedical questions. However, working with such data requires a suitable computational infrastructure not accessible ubiquitously. This creates a serious bottleneck in research and, as a result, data from these costly experiments are currently underused. To address this issue, we developed a new web resource, the DeepBlue Epigenomic Data Server to provide access to more than 40,000 experimental files from four major epigenome projects: ENCODE, ROADMAP, BLUEPRINT, the German Epigenome Program DEEP, the Canadian CEEHRC, and the Japanese CREST. A common challenge with this resources is that researchers are typically interested in a small fraction of the available epigenomic data to answer specific biomedical questions. Using a typical data repository to solve this task would require the user to download several files amounting to gigabytes of data that subsequently need to be filtered locally. In addition, it is often important to perform memory- and cpu-intensive operations to transform or aggregate these data, while the necessary computational resources are not accessible to every user. Therefore, the DeepBlue Data Server offers features beyond those of a centralized epigenomic data repository. It has a comprehensive programmatic interface (API) to enable users to perform complex data operations, such as searching, selecting, filtering, summarizing, and downloading of epigenomic data of interest. These operations can be combined into custom workflows, thus offering nearly the same degree of flexibility as a local programming environment. Here, we present DeepBlueR, a new R/Bioconductor package that enables users to engage with the DeepBlue server in a seamless fashion from within the R environment. DeepBlueR mirrors all DeepBlue data operations as R commands and provides additional features for compressing, downloading and transforming aggregated epigenomic data into suitable R data structures. A mechanism for local caching guarantees that complex scripts can be executed without the need to download previously requested data from the server.

DeepBlueR: Large-scale epigenomic analysis in R

Epigenetic research focuses on understanding non-inheritable factors influencing gene regulation and covers various cellular mechanisms such as DNA methylation, histone modification, miRNA function and transcription factor binding sites. Recent advances in high-throughput profiling technologies allow for systematically collecting data on each of these mechanisms in large-scale experiments. These efforts are fostered and concerted by international collaborations, such as the International Human Epigenome Consortium (IHEC) and its members. As a result of these collaborations, researchers can exploit massive amounts of publicly available epigenomic data on dozens of cell types, cell lines and tissues. Access to these data is streamlined by existing data portals and, in principle, allows for answering important biomedical questions. However, working with such data requires a suitable computational infrastructure not accessible ubiquitously. This creates a serious bottleneck in research and, as a result, data from these costly experiments are currently underused. To address this issue, we developed a new web resource, the DeepBlue Epigenomic Data Server to provide access to more than 40,000 experimental files from four major epigenome projects: ENCODE, ROADMAP, BLUEPRINT, the German Epigenome Program DEEP, the Canadian CEEHRC, and the Japanese CREST. A common challenge with this resources is that researchers are typically interested in a small fraction of the available epigenomic data to answer specific biomedical questions. Using a typical data repository to solve this task would require the user to download several files amounting to gigabytes of data that subsequently need to be filtered locally. In addition, it is often important to perform memory- and cpu-intensive operations to transform or aggregate these data, while the necessary computational resources are not accessible to every user. Therefore, the DeepBlue Data Server offers features beyond those of a centralized epigenomic data repository. It has a comprehensive programmatic interface (API) to enable users to perform complex data operations, such as searching, selecting, filtering, summarizing, and downloading of epigenomic data of interest. These operations can be combined into custom workflows, thus offering nearly the same degree of flexibility as a local programming environment. Here, we present DeepBlueR, a new R/Bioconductor package that enables users to engage with the DeepBlue server in a seamless fashion from within the R environment. DeepBlueR mirrors all DeepBlue data operations as R commands and provides additional features for compressing, downloading and transforming aggregated epigenomic data into suitable R data structures. A mechanism for local caching guarantees that complex scripts can be executed without the need to download previously requested data from the server.