A Study of Secondary School Students’ and Teachers’ Perceptions of and Satisfaction with Service Learning Activities at Ruamrudee International School in Thailand

This study was conducted mainly to examine a service-learning program at RIS by determining students’ and teachers’ perception of the linkage and satisfaction with service learning activities and classroom learning experiences done at RIS. The sample included 254 secondary students and 52 teachers from Ruamrudee International School. This research found differences in students’ and teachers’ perceptions of service learning activities and satisfaction of learning experience statistically significant. Teachers gave a high rating to perceptions and satisfaction than students. A number of suggestions were given by students showing how important that service learning has been in strengthening and enhancing their education. Service Learning has served to enhance the process of making connections with learning in classroom and bringing to life through the experiences of serving and learning. In conclusion, the researcher suggests when constructing service learning activities, consider the needs of students, and elicit the support of teachers and the community partners. It is crucial to have the whole community join force and working together to bring out what is best for all

R*-Grove: Balanced Spatial Partitioning for Large-scale Datasets

The rapid growth of big spatial data urged the research community to develop several big spatial data systems. Regardless of their architecture, one of the fundamental requirements of all these systems is to spatially partition the data efficiently across machines. The core challenges of big spatial partitioning are building high spatial quality partitions while simultaneously taking advantages of distributed processing models by providing load balanced partitions. Previous works on big spatial partitioning are to reuse existing index search trees as-is, e.g., the R-tree family, STR, Kd-tree, and Quad-tree, by building a temporary tree for a sample of the input and use its leaf nodes as partition boundaries. However, we show in this paper that none of those techniques has addressed the mentioned challenges completely. This paper proposes a novel partitioning method, termed R*-Grove, which can partition very large spatial datasets into high quality partitions with excellent load balance and block utilization. This appealing property allows R*-Grove to outperform existing techniques in spatial query processing. R*-Grove can be easily integrated into any big data platforms such as Apache Spark or Apache Hadoop. Our experiments show that R*-Grove outperforms the existing partitioning techniques for big spatial data systems. With all the proposed work publicly available as open source, we envision that R*-Grove will be adopted by the community to better serve big spatial data research.Comment: 29 pages, to be published in Frontiers in Big Dat

Using Deep Learning for Big Spatial Data Partitioning

This article explores the use of deep learning to choose an appropriate spatial partitioning technique for big data. The exponential increase in the volumes of spatial datasets resulted in the development of big spatial data frameworks. These systems need to partition the data across machines to be able to scale out the computation. Unfortunately, there is no current method to automatically choose an appropriate partitioning technique based on the input data distribution. This article addresses this problem by using deep learning to train a model that captures the relationship between the data distribution and the quality of the partitioning techniques.We propose a solution that runs in two phases, training and application. The offline training phase generates synthetic data based on diverse distributions, partitions them using six different partitioning techniques, and measures their quality using four quality metrics. At the same time, it summarizes the datasets using a histogram and well-designed skewness measures. The data summaries and the quality metrics are then use to train a deep learning model. The second phase uses this model to predict the best partitioning technique given a new dataset that needs to be partitioned.We run an extensive experimental evaluation on big spatial data, andwe experimentally showthe applicability of the proposed technique.We showthat the proposed model outperforms the baseline method in terms of accuracy for choosing the best partitioning technique by only analyzing the summary of the datasets

Towards a Learned Cost Model for Distributed Spatial Join: Data, Code & Models

Geospatial data comprise around 60% of all the publicly available data. One of the essential and most complex operations that brings together multiple geospatial datasets is the spatial join operation. Due to its complexity, there is a lot of partitioning techniques and parallel algorithms for the spatial join problem. This leads to a complex query optimization problem: which algorithm to use for a given pair of input datasets that we want to join? With the rise of machine learning, there is a promise in addressing this problem with the use of various learned models. However, one of the concerns is the lack of a standard and publicly available data to train and test on, as well as the lack of accessible baseline models. This resource paper helps the research community to solve this problem by providing synthetic and real datasets for spatial join, source code for constructing more datasets, and several baseline solutions that researchers can further extend and compare to

Lower and upper bound form of outage probability in one-way AF full-duplex relaying network under impact of direct link

This paper proposed and investigated the one-way amplify-and-forward (AF) full-duplex relaying network under impact of direct link. For the system performance analysis, the exact and lower and upper bound form of the system outage probability (OP) are investigated and derived. In this system model, authors assume that the E uses the MRC (maximal ratio combining) technique. Finally, we can see that the analytical and the simulation values overlap to verify the analytical section using the Monte Carlo simulation. Also, we investigate the influence of the system primary parameters on the proposed system OP

Performance analysis for three cases of outage probability in one-way DF full-duplex relaying network with presence of direct link

In this paper, the one-way decode-and-forward (DF) full-duplex relaying network system with presence of direct link is investigated. In the analysis section, we derived the exact, lower, and upper bound for outage probability (OP) with maximal ratio combining (MRC) at the receiver. Furthermore, the system performance's analytical expressions are verified by using the Monte Carlo simulation. In addition, we investigated the effect of the main parameters on the OP of the proposed system. Finally, we can sate that the simulation curves overlap the analytical curves to convince the analysis section. This research can provide a novel recommendation for the communication network

Antibacterial and Antioxidant Activity of Rhodomyrtus Tomentosa and Cinnamomum Zeylanicum Crude Extracts

The aim of this study was to investigate the extraction method for R. tomentosa and C. zeylanicum leaves and the evaluation of antibacterial and antioxidant activities of crude extracts. The results of the study showed that the active ingredients of crude extracts were clearly separated by Thin-layer chromatography and the presence of rhodomyrtone in R. tomentosa crude extract and cinnamaldehyde in C. zeylanicum crude extract. R. tomentosa crude extract was antibacterial activity against Staphylococcus aureus with 13.1 mm of inhibition zone, but is not effective against Salmonella Typhimurium. C. zeylanicum leaf extract did not show antibacterial activity on both S. aureus and S. Typhimurium. At a dilution of 1/2 of the R. tomentosa crude extract can completely inhibit S. aureus growth. This study also indicated the presence of antioxidant compounds such as flavonoids, tannins, phenols and terpenoids in C. zeylanicum and R. tomentosa crude extracts. The results showed that R. tomentosa and C. zeylanicum crude extracts should be used as a biotherapy alternative to antibiotic therapy. However, further study would be needed to investigate the antibacterial activity of crude extracts in vivo

Energy cost savings based on the UPS

Energy-saving, improving energy efficiency, and finding a new efficient way to use energy are considered as an urgent problem in over the world. In this paper, we consider the economics of energy use in combination with energy storage units where two forms of electricity exist in the power system. Then the problem of optimizing the installation capacity (to optimize the investment costs for energy storage) is presented and investigated in connection with the conversion systems. The topic opens a very significant result, including the introduction of a mathematical model to calculate the simulation in optimizing the installation capacity of the equipment in the system, multi-source power, as well as voltage and power stability benefits