Empirical Study on Public High School System in Vietnam: Post Doi Moi

The system of education in Vietnam is administered by the Ministry of Education and Training (MOET), and it is a broad system of state-run schools for students from about four years of age to high school age. The educational system comprises of five classes: kindergarten, primary, secondary, upper-optional (additionally alluded to as secondary school), and college level, with broadly managed exit and selection tests between each. The principal motivation behind this study is to analyze the connection between pre-secondary school factors, school condition, school structure, collective duty, scholarly optimism with the scholastic performance of the public high school students in Vietnam. SPSS analysis shows that only two variables can be a significant indicator of academic performance, that are school environment (B= -1.369, t=51.356, p<0.01) and pre-high school factor (B=-.384, t= -13.947, p<0.01) while school structure, collective responsibility, and academic optimism have found to be insignificant indicator of academic performance as compared to the other two variables in a multivariate context although, during the bivariate analysis, academic optimism had been found to be significantly related to academic performance. School environment was also found to have higher ‘B’ value compared to pre-high school factor. Hence, this study suggests that among all the independent variables studied, school environment gave the most effective towards the academic performance of students in the public high school of Vietnam

Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation

We consider a stochastic individual-based model in continuous time to describe a size-structured population for cell divisions. This model is motivated by the detection of cellular aging in biology. We address here the problem of nonparametric estimation of the kernel ruling the divisions based on the eigenvalue problem related to the asymptotic behavior in large population. This inverse problem involves a multiplicative deconvolution operator. Using Fourier technics we derive a nonparametric estimator whose consistency is studied. The main difficulty comes from the non-standard equations connecting the Fourier transforms of the kernel and the parameters of the model. A numerical study is carried out and we pay special attention to the derivation of bandwidths by using resampling

A general formula for the index of depth stability of edge ideals

By a classical result of Brodmann, the function $\operatorname{depth} R/I^t$ is asymptotically a constant, i.e. there is a number $s$ such that $\operatorname{depth} R/I^t = \operatorname{depth} R/I^s$ for $t > s$. One calls the smallest number $s$ with this property the index of depth stability of $I$ and denotes it by $\operatorname{dstab}(I)$. This invariant remains mysterious til now. The main result of this paper gives an explicit formula for $\operatorname{dstab}(I)$ when $I$ is an arbitrary ideal generated by squarefree monomials of degree 2. That is the first general case where one can characterize $\operatorname{dstab}(I)$ explicitly. The formula expresses $\operatorname{dstab}(I)$ in terms of the associated graph. The proof involves new techniques which relate different topics such as simplicial complexes, systems of linear inequalities, graph parallelizations, and ear decompositions. It provides an effective method for the study of powers of edge ideals.Comment: 23 pages, 4 figure

Exploiting Context-Aware Event Data for Fault Analysis

Fault analysis in communication networks and distributed systems is a difficult process that heavily depends on system administrator’s experience and supporting tools. This process usually requires analytic techniques and several types of event data including log events, debug messages, trace obtained from these systems to investigate the root cause of faults. This paper introduces an approach of exploiting context-aware data and classification technique for improving this process. This approach uses both event data and context-aware data including CPU load, memory, processes, temperature, status to train a decision tree, and then applies the tree to assess suspected events. We have implemented and experimented the approach on the OpenStack cloud computing system with the Hadoop computing service and MELA event collection system. The experimental results reveal that the accuracy score of the approach reaches 85% on average. The paper also includes detailed analysis for the results

Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review

Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard

An Extended Occlusion Detection Approach for Video Processing

Occlusions become conspicuous as failure regions in video processing when unified over time because the contraventions of the restriction of brightness have accumulated and evolved in occluded regions. The accuracy at the boundaries of the moving objects is one of the challenging areas that required further exploration and research. This paper presents the work in process approach that can detect occlusion regions by using pixel-wise coherence, segment-wise confidence and interpolation technique. Our method can get the same result as usual methods by solving only one Partial Differential Equations (PDE) problem; it is superior to existing methods because it is faster and provides better coverage rates for occlusion regions than variation techniques when tested against a varied number of benchmark datasets. With these improved results, we can apply and extend our approach to a wider range of applications in computer vision, such as background subtraction, tracking, 3D reconstruction, video surveillance, video compression