Autonomous navigation with constrained consistency for C-Ranger

Autonomous underwater vehicles (AUVs) have become the most widely used tools for undertaking complex exploration tasks in marine environments. Their synthetic ability to carry out localization autonomously and build an environmental map concurrently, in other words, simultaneous localization and mapping (SLAM), are considered to be pivotal requirements for AUVs to have truly autonomous navigation. However, the consistency problem of the SLAM system has been greatly ignored during the past decades. In this paper, a consistency constrained extended Kalman filter (EKF) SLAM algorithm, applying the idea of local consistency, is proposed and applied to the autonomous navigation of the C-Ranger AUV, which is developed as our experimental platform. The concept of local consistency (LC) is introduced after an explicit theoretical derivation of the EKF-SLAM system. Then, we present a locally consistency-constrained EKF-SLAM design, LC-EKF, in which the landmark estimates used for linearization are fixed at the beginning of each local time period, rather than evaluated at the latest landmark estimates. Finally, our proposed LC-EKF algorithm is experimentally verified, both in simulations and sea trials. The experimental results show that the LC-EKF performs well with regard to consistency, accuracy and computational efficiency

Mechanism Analysis and Dynamics Simulation of Assist Manipulator

In order to reduce labour intensity and improve working efficiency, a kind of assist manipulator was designed which is an auxiliary tool used for the assembly line of the marine diesel engine that can conveniently realize the delivery of parts and field assembly. Motion and force analysis of the mechanism of assist manipulator was examined with the help of MATLAB software on the base of d\u27Alembert principle, the disciplinary of displacement, velocity, acceleration, and force rules in the process of mechanism movement was obtained by mechanical analysis. Based on the kinematical analysis, the parameters of mechanism size were optimized to improve the loading state. The Creo software, ANSYS software, and RecurDyn software were used to model and analyse the rigid-flexible coupling dynamics of the manipulator, and the motion law and stress distribution of the key components was obtained

Construction of Ideological and Political Mixed Teaching in Higher Education under the Digital Transformation

This article focuses on the current demands for reform in ideological and political education in higher education, within the context of the digital information and communication era. Specifically, it proposes a plan for integrating ideological and political education into higher education courses using the widely-used blended learning mode in a digitally transformed environment. This plan aims to leverage digital teaching methods, such as the development of multimedia courseware, to enrich classroom teaching content, increase student engagement and learning outcomes, deepen students\u27 understanding and awareness, and enable them to effectively absorb a wealth of information in a limited time. By subtly linking the process of learning professional knowledge with their personal, social, and national development, this plan seeks to foster a professional education philosophy that cultivates "socialist successors.

Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement

Deep learning (DL) package supply chains (SCs) are critical for DL frameworks to remain competitive. However, vital knowledge on the nature of DL package SCs is still lacking. In this paper, we explore the domains, clusters, and disengagement of packages in two representative PyPI DL package SCs to bridge this knowledge gap. We analyze the metadata of nearly six million PyPI package distributions and construct version-sensitive SCs for two popular DL frameworks: TensorFlow and PyTorch. We find that popular packages (measured by the number of monthly downloads) in the two SCs cover 34 domains belonging to eight categories. Applications, Infrastructure, and Sciences categories account for over 85% of popular packages in either SC and TensorFlow and PyTorch SC have developed specializations on Infrastructure and Applications packages respectively. We employ the Leiden community detection algorithm and detect 131 and 100 clusters in the two SCs. The clusters mainly exhibit four shapes: Arrow, Star, Tree, and Forest with increasing dependency complexity. Most clusters are Arrow or Star, but Tree and Forest clusters account for most packages (Tensorflow SC: 70%, PyTorch SC: 90%). We identify three groups of reasons why packages disengage from the SC (i.e., remove the DL framework and its dependents from their installation dependencies): dependency issues, functional improvements, and ease of installation. The most common disengagement reason in the two SCs are different. Our study provides rich implications on the maintenance and dependency management practices of PyPI DL SCs.Comment: Manuscript submitted to ACM Transactions on Software Engineering and Methodolog

A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARα Knockout by Gene Set Enrichment Analysis

Gene expression profiling of PPARα has been used in several studies, but fewer studies went further to identify the tissue-specific pathways or genes involved in PPARα activation in genome-wide. Here, we employed and applied gene set enrichment analysis to two microarray datasets both PPARα related respectively in mouse liver and intestine. We suggested that the regulatory mechanism of PPARα activation by WY14643 in mouse small intestine is more complicated than in liver due to more involved pathways. Several pathways were cancer-related such as pancreatic cancer and small cell lung cancer, which indicated that PPARα may have an important role in prevention of cancer development. 12 PPARα dependent pathways and 4 PPARα independent pathways were identified highly common in both liver and intestine of mice. Most of them were metabolism related, such as fatty acid metabolism, tryptophan metabolism, pyruvate metabolism with regard to PPARα regulation but gluconeogenesis and propanoate metabolism independent of PPARα regulation. Keratan sulfate biosynthesis, the pathway of regulation of actin cytoskeleton, the pathways associated with prostate cancer and small cell lung cancer were not identified as hepatic PPARα independent but as WY14643 dependent ones in intestinal study. We also provided some novel hepatic tissue-specific marker genes

How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?

Although the open source model bears many advantages in software development, open source projects are always hard to sustain. Previous research on open source sustainability mainly focuses on projects that have already reached a certain level of maturity (e.g., with communities, releases, and downstream projects). However, limited attention is paid to the development of (sustainable) open source projects in their infancy, and we believe an understanding of early sustainability determinants is crucial for project initiators, incubators, newcomers, and users. In this paper, we aim to explore the relationship between early participation factors and long-term project sustainability. We leverage a novel methodology combining the Blumberg model of performance and machine learning to predict the sustainability of 290,255 GitHub projects. Specificially, we train an XGBoost model based on early participation (first three months of activity) in 290,255 GitHub projects and we interpret the model using LIME. We quantitatively show that early participants have a positive effect on project's future sustained activity if they have prior experience in OSS project incubation and demonstrate concentrated focus and steady commitment. Participation from non-code contributors and detailed contribution documentation also promote project's sustained activity. Compared with individual projects, building a community that consists of more experienced core developers and more active peripheral developers is important for organizational projects. This study provides unique insights into the incubation and recognition of sustainable open source projects, and our interpretable prediction approach can also offer guidance to open source project initiators and newcomers.Comment: The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023

Personalized First Issue Recommender for Newcomers in Open Source Projects

Many open source projects provide good first issues (GFIs) to attract and retain newcomers. Although several automated GFI recommenders have been proposed, existing recommenders are limited to recommending generic GFIs without considering differences between individual newcomers. However, we observe mismatches between generic GFIs and the diverse background of newcomers, resulting in failed attempts, discouraged onboarding, and delayed issue resolution. To address this problem, we assume that personalized first issues (PFIs) for newcomers could help reduce the mismatches. To justify the assumption, we empirically analyze 37 newcomers and their first issues resolved across multiple projects. We find that the first issues resolved by the same newcomer share similarities in task type, programming language, and project domain. These findings underscore the need for a PFI recommender to improve over state-of-the-art approaches. For that purpose, we identify features that influence newcomers' personalized selection of first issues by analyzing the relationship between possible features of the newcomers and the characteristics of the newcomers' chosen first issues. We find that the expertise preference, OSS experience, activeness, and sentiment of newcomers drive their personalized choice of the first issues. Based on these findings, we propose a Personalized First Issue Recommender (PFIRec), which employs LamdaMART to rank candidate issues for a given newcomer by leveraging the identified influential features. We evaluate PFIRec using a dataset of 68,858 issues from 100 GitHub projects. The evaluation results show that PFIRec outperforms existing first issue recommenders, potentially doubling the probability that the top recommended issue is suitable for a specific newcomer and reducing one-third of a newcomer's unsuccessful attempts to identify suitable first issues, in the median.Comment: The 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Congenital Defects in Actin Dynamics of Germinal Center B Cells

The germinal center (GC) is a transient anatomical structure formed during the adaptive immune response that leads to antibody affinity maturation and serological memory. Recent works using two-photon microscopy reveals that the GC is a highly dynamic structure and GC B cells are highly motile. An efficient selection of high affinity B cells clones within the GC crucially relies on the interplay of proliferation, genome editing, cell-cell interaction, and migration. All these processes require actin cytoskeleton rearrangement to be well-coordinated. Dysregulated actin dynamics may impede on multiple stages during B cell affinity maturation, which could lead to aberrant GC response and result in autoimmunity and B cell malignancy. This review mainly focuses on the recent works that investigate the role of actin regulators during the GC response