A STUDY OF THE RELATIONSHIP BETWEEN STUDENTS’ PERCEPTIONS TOWARDS SCHOOL CLIMATE AND THEIR SATISFACTION AT SHEHONG MIDDLE SCHOOL, SICHUAN, CHINA

This study aimed to investigate the relationship between students' perceptions of school climate and their satisfaction at SheHong Middle School, Sichuan, China. By applying the Framework of school climates of Emmons, Haynes, & Comer (2002) and Maslow's Hierarchy of Needs Theory (1987) theories, this study identified students' perceptions towards school climate of six dimensions: 1) Order and Discipline 2) Fairness 3) Parent Involvement 4) Sharing Resources 5) Student Interpersonal Relationship 6) Student-Teacher Relationship, compared their perceptions according to 3 pieces of demographics. The results of this study indicated a significant relationship between students' perceptions towards school climate and students' satisfaction. The findings for research objective one revealed that the total mean score of the level of students' perceptions of school climate was 3.43, which was interpreted as Moderate. The findings for research objective two showed that the total mean score of the level of students' satisfaction was 3.46, which was interpreted as Moderate as well. The correlation result showed a positive relationship between students' perceptions of school climate and students' satisfaction. The researcher discussed the research findings and provided corresponding suggestions to related principals, teachers, and administrators at the selected school in Sichuan, China

Self-Supervised Deep Visual Odometry with Online Adaptation

Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer from a notable decrease in performance when confronted with scenes different from the training data, which makes them unsuitable for practical applications. In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner. The proposed method utilizes convolutional long short-term memory (convLSTM) to aggregate rich spatial-temporal information in the past. The network is able to memorize and learn from its past experience for better estimation and fast adaptation to the current frame. When running VO in the open world, in order to deal with the changing environment, we propose an online feature alignment method by aligning feature distributions at different time. Our VO network is able to seamlessly adapt to different environments. Extensive experiments on unseen outdoor scenes, virtual to real world and outdoor to indoor environments demonstrate that our method consistently outperforms state-of-the-art self-supervised VO baselines considerably.Comment: Accepted by CVPR 2020 ora

Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge

Instance segmentation is applied widely in image editing, image analysis and autonomous driving, etc. However, insufficient data is a common problem in practical applications. The Visual Inductive Priors(VIPriors) Instance Segmentation Challenge has focused on this problem. VIPriors for Data-Efficient Computer Vision Challenges ask competitors to train models from scratch in a data-deficient setting, but there are some visual inductive priors that can be used. In order to address the VIPriors instance segmentation problem, we designed a Task-Specific Data Augmentation(TS-DA) strategy and Inference Processing(TS-IP) strategy. The main purpose of task-specific data augmentation strategy is to tackle the data-deficient problem. And in order to make the most of visual inductive priors, we designed a task-specific inference processing strategy. We demonstrate the applicability of proposed method on VIPriors Instance Segmentation Challenge. The segmentation model applied is Hybrid Task Cascade based detector on the Swin-Base based CBNetV2 backbone. Experimental results demonstrate that proposed method can achieve a competitive result on the test set of 2022 VIPriors Instance Segmentation Challenge, with 0.531 [email protected]:0.95.Comment: The first place solution for ECCV 2022 VIPriors Instance Segmentation Challenge. arXiv admin note: text overlap with arXiv:2209.1389

Inhibition of Ascorbic Acid on Lotus Rhizome Polyphenol Oxidase: Inhibition Kinetics and Computational Simulation

Polyphenol oxidase(PPO) is widely known to be involved in enzymatic browning reaction in many fruits and vegetables including lotus rhizome with different catalytic mechanisms. In this study, the inhibitory effect and mechanisms of action of ascorbic acid (AA) on the lotus rhizome PPO were investigated using inhibition kinetics and computational simulation. The lotus rhizome PPO was extracted with PBS (pH 7.0), fractionated with ammonium sulphate, concentrated, and purified with DEAE-52(2.6×30 cm) and Sephadex G-75(2.6×60 cm) chromatography. The active fractions were pooled and the PPO activity was determined to be 2627.36Units/mg. AA exhibited inhibition on lotus rhizome PPO with residual activity of 13.79% at concentration of 0.08mM and IC50 of 0.045mM. Kinetic analyses determined by Lineweaver-Burk plots showed that ascorbic acid was reversible and competitive inhibitor to the enzyme. The 3D structure of the lotus rhizome PPO was simulated by SWISS-MODEL program and molecular docking was performed between PPO and its ligands (catehol and AA) by SYBYL-X 2.0. Simulation results showed that AA and catechol compete with the binding site of the PPO active center for its stronger affinity with the enzyme. In conclusion, the AA was established as a competitive inhibitor of lotus rhizome PPO, which provides a theoretical basis for it as an anti-browning agent in storage and preservation of lotus rhizome. Keywords: Lotus rhizome, Polyphenol oxidase, Computational simulation, Inhibition mechanis

Restoration of soil quality of degraded grassland can be accelerated by reseeding in an arid area of Northwest China

Grassland restoration measures control soil degradation and improve soil quality (SQ) worldwide, but there is little knowledge about the effectiveness of restoration measures affecting SQ in arid areas, and the restoration rate of degraded grasslands to natural restoration grasslands and reseeded grasslands remains unclear. To establish a soil quality index (SQI) to evaluate the effects of different grassland restoration measures on SQ, continuous grazing grassland (CG) (as a reference), grazing exclusion grassland (EX), and reseeding grassland (RS) were selected and sampled in the arid desert steppe. Two soil indicator selection methods were conducted (total data set (TDS) and minimum data set (MDS)), followed by three SQ indices (additive soil quality index (SQIa), weighted additive soil quality index (SQIw), and Nemoro soil quality index (SQIn)). The results indicated that SQ was better assessed using the SQIw (R2 = 0.55) compared to SQIa and SQIn for indication differences among the treatments due to the larger coefficient of variance. The SQIw-MDS value in CG grassland was 46% and 68% lower than that of EX grassland and RS grassland, respectively. Our findings provided evidence that restoration practices of grazing exclusion and reseeding can significantly improve the SQ in the arid desert steppe, and native plant reseeded can accelerate soil quality restoration

Non-Destructive Assessment of Stone Heritage Weathering Types Based on Machine Learning Method Using Hyperspectral Data

Stone cultural heritage is exposed to various environments, resulting in a diverse range of weathering types. The identification of these weathering types is vital for targeted conservation efforts. In this paper, a weathering type classification method based on hyperspectral imaging technology is proposed. Firstly, fresh sandstones are collected from Yungang Grottoes to carry out the simulated weathering experiments, including freeze-thaw cycles and wet-dry cycles with acid, alkali and salt solutions. Subsequently, the hyperspectral imaging system was used to collect the visible-near-infrared (VNIR) and short-wave infrared (SWIR) images of the sandstone samples with different weathering types and degrees. The surface spectral reflectance of sandstone samples with different weathering types were used as training data, with weathering types serving as the labels. Support vector machine (SVM), K-nearest neighbour (KNN), linear discriminant analysis (LDA) and random forest (RF) were used to establish weathering type classification models. The results show that the SVM model and LDA model based on both VNIR and SWIR spectra exhibit outstanding performance, with a best accuracy of 0.994. The framework proposed in this paper facilitates rapid and non-contact assessment of the weathering types of the superficial layers of stone cultural heritage, thereby supporting more targeted conservation work