Towards Large-Scale Small Object Detection: Survey and Benchmarks

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field. Datasets and codes are available at: \url{https://shaunyuan22.github.io/SODA}

Genome-wide QTL mapping for stripe rust resistance in spring wheat line PI 660122 using the Wheat 15K SNP array

IntroductionStripe rust is a global disease of wheat. Identification of new resistance genes is key to developing and growing resistant varieties for control of the disease. Wheat line PI 660122 has exhibited a high level of stripe rust resistance for over a decade. However, the genetics of stripe rust resistance in this line has not been studied. A set of 239 recombinant inbred lines (RILs) was developed from a cross between PI 660122 and an elite Chinese cultivar Zhengmai 9023.MethodsThe RIL population was phenotyped for stripe rust response in three field environments and genotyped with the Wheat 15K single-nucleotide polymorphism (SNP) array.ResultsA total of nine quantitative trait loci (QTLs) for stripe rust resistance were mapped to chromosomes 1B (one QTL), 2B (one QTL), 4B (two QTLs), 4D (two QTLs), 6A (one QTL), 6D (one QTL), and 7D (one QTL), of which seven QTLs were stable and designated as QYrPI660122.swust-4BS, QYrPI660122.swust-4BL, QYrPI660122.swust-4DS, QYrPI660122.swust-4DL, QYrZM9023.swust-6AS, QYrZM9023.swust-6DS, and QYrPI660122.swust-7DS. QYrPI660122.swust-4DS was a major all-stage resistance QTL explaining the highest percentage (10.67%–20.97%) of the total phenotypic variation and was mapped to a 12.15-cM interval flanked by SNP markers AX-110046962 and AX-111093894 on chromosome 4DS.DiscussionThe QTL and their linked SNP markers in this study can be used in wheat breeding to improve resistance to stripe rust. In addition, 26 lines were selected based on stripe rust resistance and agronomic traits in the field for further selection and release of new cultivars

Influence of Different Tool Electrode Materials on Electrochemical Discharge Machining Performances

Electrochemical discharge machining (ECDM) is an emerging method for developing micro-channels in conductive or non-conductive materials. In order to machine the materials, it uses a combination of chemical and thermal energy. The tool electrode’s arrangement is crucial for channeling these energies from the tool electrode to the work material. As a consequence, tool electrode optimization and analysis are crucial for efficiently utilizing energies during ECDM and ensuring machining accuracy. The main motive of this study is to experimentally investigate the influence of different electrode materials, namely titanium alloy (TC4), stainless steel (SS304), brass, and copper–tungsten (CuW) alloys (W70Cu30, W80Cu20, W90Cu10), on electrodes’ electrical properties, and to select an appropriate electrode in the ECDM process. The material removal rate (MRR), electrode wear ratio (EWR), overcut (OC), and surface defects are the measurements considered. The electrical conductivity and thermal conductivity of electrodes have been identified as analytical issues for optimal machining efficiency. Moreover, electrical conductivity has been shown to influence the MRR, whereas thermal conductivity has a greater impact on the EWR, as characterized by TC4, SS304, brass, and W80Cu20 electrodes. After that, comparison experiments with three CuW electrodes (W70Cu30, W80Cu20, and W90Cu10) are carried out, with the W70Cu30 electrode appearing to be the best in terms of the ECDM process. After reviewing the research outcomes, it was determined that the W70Cu30 electrode fits best in the ECDM process, with a 70 μg/s MRR, 8.1% EWR, and 0.05 mm OC. Therefore, the W70Cu30 electrode is discovered to have the best operational efficiency and productivity with performance measures in ECDM out of the six electrodes

Effects of BOPPPS combined with TBL in surgical nursing for nursing undergraduates: a mixed-method study

Abstract Background Surgical Nursing is a core subject for nursing undergraduates that requires active and effective learning strategies to cultivate students’ autonomous learning competencies and critical thinking. The effects of BOPPPS (Bridge-in, Objectives, Pretest, Participatory Learning, Post-test and Summary) model combined with team-based learning (TBL) have rarely been explored in Surgical Nursing courses. Objective To explore the effects of BOPPPS combined with TBL in Surgical Nursing for nursing undergraduates. Methods A mixed research method of quasi-experimental study design and descriptive qualitative research was used. The control group included 27 nursing undergraduates who had finished the Surgical Nursing course using traditional learning. The experimental group included 36 nursing undergraduates were enrolled in to receive the Surgical Nursing course in the teaching mode of BOPPPS combined with TBL. The quantitative data of students’ Surgical Nursing final scores, autonomous learning competencies and critical thinking ability of the two groups were collected and compared by t-test. Qualitative results were obtained through semi-structured interviews and data were analyzed by thematic analysis method. Results Compared with the traditional learning mode, BOPPPS combined with TBL significantly improved nursing students’ final examination scores, autonomous learning competencies and critical thinking ability (p < 0.05). Qualitative results from 14 undergraduate nursing students’ interviews were summarized into five themes: (1) stimulating learning interest; (2) improving autonomous learning ability; (3) improving the sense of teamwork; (4) exercising critical thinking; and (5) suggestions for improvement. Conclusions The combination of BOPPPS and TBL positively impacted nursing students by improving their autonomous learning competencies and critical thinking ability. The study suggests BOPPPS combined with TBL learning as an effective, alternative learning mode