On Frankl and Furedi's conjecture for 3-uniform hypergraphs

The Lagrangian of a hypergraph has been a useful tool in hypergraph extremal problems. In most applications, we need an upper bound for the Lagrangian of a hypergraph. Frankl and Furedi in \cite{FF} conjectured that the $r$-graph with $m$ edges formed by taking the first $m$ sets in the colex ordering of ${\mathbb N}^{(r)}$ has the largest Lagrangian of all $r$-graphs with $m$ edges. In this paper, we give some partial results for this conjecture.Comment: 19 pages, 1 figure. arXiv admin note: substantial text overlap with arXiv:1211.650

The Comparison of Composite Aircraft Field Repair Method (CAFRM) with Traditional Aircraft Repair Technologies

In the aviation industry, manufacturers made the transition from aluminum to composite materials for the majority of their primary structures over the last few decades. While the design and manufacturing techniques have consistently evolved, field repair methods were consistently overlooked. In this study, specimens fabricated using some of the common repair methods such as the autoclave repair method, and Double Vacuum Debulk (DVD) repair method were tested against the Composite Aircraft Field Repair Method (CAFRM) proposed by the researcher. Specimens were tested with microscopy, acid digestion, short beam shear, and mode I fracture tests. The researcher was able to determine the specimen\u27s void content, fiber volume fraction, shear strength, and opening mode interlaminar fracture toughness for the specimens fabricated using the different repair methods. The specimens fabricated using the autoclave repair method, DVD repair method, and CAFRM showed significant differences in void content, shear strength, and opening mode interlaminar fracture toughness. However, there were no significant differences between the specimens for fiber volume fraction

The Effects of Adding Attachments in Conventional Composite Hybrid Joints on Tensile Strength

Mechanical fasteners are commonly used in today’s composite aircraft adhesive joints. The primary purpose for using mechanical fasteners was to provide redundancy to the adhesive joints because of the uncertainties associated with an adhesive only joint. Therefore, the use of a fastener in a conventional composite hybrid joint was mainly a part of a fail-safe design. Acting as a safety backup, the fastener did not contribute tensile strength to the joint until the adhesive joint failed. When only being used for redundancy purposes in case of an adhesive joint failure, the existing fastener became simply an added weight to the aircraft structure. In this research, a proposed new design was incorporated into the composite hybrid joint where two different types of attachments were used in order to provide alternate load paths to redirect load to the fastener and utilize the fastener to provide strength to the joint once the joint was loaded. Two types of attachments were used: a stepped attachment and a curved attachment. The attachments would reduce the amount of load induced in the adhesive joint and increase the overall strength of the hybrid joint. Experiments were conducted with both the conventional hybrid joint and the new hybrid joint design with two different attachments to assess effectiveness of the new design, the more efficient attachment type, and whether the added weight of the attachments could be justified by the improvements in the overall strength of the joint. Each type of hybrid joints consisted of 15 specimens. A total of 45 specimens were prepared for this study. The specimens were tested with a MTS Systems Corporation testing apparatus with a 22 kip (±100kN) load cell. The ultimate tensile load data gathered were used to compare the strength of the different hybrid joints. From the tensile testing data collected of the three different types of specimens, the hybrid joints with attachments showed a significant improvement in ultimate tensile strength compared to the conventional hybrid joints. Conventional hybrid joints had an average ultimate tensile strength of 5859.99 lbf. Hybrid joints with curved attachments had an average ultimate tensile strength of 10617.62 lbf, which was 81.19% higher than the conventional hybrid joints. Hybrid joints with stepped attachments had an average ultimate tensile strength of 10342.14 lbf which was 76.49% higher than conventional hybrid joints. Hybrid joints with curved attachments were also found to be more efficient compared to hybrid joints with stepped attachments and improved the ultimate tensile strength 2.66%

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

International aviation collaboration in a study abroad program

Aviation is a global industry. As we continue to push the boundaries of technical advancements and operational efficiency in the aviation industry, the reliance on the global workforce will continue to increase. In the United States of America, the majority of educational establishments focus mainly in the education and training of students for the US aviation industry. The narrow focus significantly limits the amount of exposure and opportunities available to the students of these educational establishments. In order to improve global exposure and provide students with opportunities outside of the US aviation industry, the authors of the paper successfully established a study abroad program, allowing students to explore and understand European culture, history, and its\u27 aviation industry. Furthermore, students enrolled in the program had the opportunity to work with fellow aviation students from two different European universities on a joint research project. Students were required to complete homework assignments during the program designed to increase global awareness and cultural understanding. Feedback from students was positive in both written and scale questions

New insights into the mechanism of low high-density lipoprotein cholesterol in obesity

Obesity, a significant risk factor for various chronic diseases, is universally related to dyslipidemia mainly represented by decreasing high-density lipoprotein cholesterol (HDL-C), which plays an indispensible role in development of cardiovascular disease (CVD). However, the mechanisms underlying obesity and low HDL-C have not been fully elucidated. Previous studies have focused on the alteration of HDL catabolism in circulation following elevated triglyceride (TG). But recent findings suggested that liver and fat tissue played pivotal role in obesity related low HDL-C. Some new molecular pathways like microRNA have also been proposed in the regulation of HDL metabolism in obesity. This article will review recent advances in understanding of the potential mechanism of low HDL-C in obesity

Modeling relation paths for knowledge base completion via joint adversarial training

Knowledge Base Completion (KBC), which aims at determining the missing relations between entity pairs, has received increasing attention in recent years. Most existing KBC methods focus on either embedding the Knowledge Base (KB) into a specific semantic space or leveraging the joint probability of Random Walks (RWs) on multi-hop paths. Only a few unified models take both semantic and path-related features into consideration with adequacy. In this paper, we propose a novel method to explore the intrinsic relationship between the single relation (i.e. 1-hop path) and multi-hop paths between paired entities. We use Hierarchical Attention Networks (HANs) to select important relations in multi-hop paths and encode them into low-dimensional vectors. By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i.e. relation classifier and source discriminator), to capture shared/similar information between them. By joint adversarial training, we encourage our model to extract features from the multi-hop paths which are representative for relation completion. We apply the trained model (except for the source discriminator) to several large-scale KBs for relation completion. Experimental results show that our method outperforms existing path information-based approaches. Since each sub-module of our model can be well interpreted, our model can be applied to a large number of relation learning tasks.Comment: Accepted by Knowledge-Based System

Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset