Analysis of wind characteristics and wind energy resource assessment for Tonga using eleven methods of estimating Weibull parameters.

of wind characteristics and assessment of wind energy resource is carried out at a location in Tonga with the help of twelve months of measurements carried out at 34 m and 20 m heights above ground level. The daily, monthly and annual averages are computed. The wind shear analysis and its diurnal variation were studied and compared with the temperature variation. The turbulence levels at the two heights were estimated for the entire measurement period as well as for some typical days. For estimating the Weibull parameters, eleven methods were employed along with goodness of fit and error estimates to find the best method. The overall averaged wind speed for the entire period of study is estimated to be 4.41 m/s at 34 m above ground level. The predominant wind direction was south-east for Tonga. 'Moments' is seen to be the best method to determine accurate Weibull parameters. The average net annual energy production from one Vergnet 275 kW wind turbine is 198.57 MWh. A payback period of 8.95 years by installing five turbines near the measurement location was estimated, which is very encouraging in terms of investment. Installing wind turbines will lower the heavy reliance on the imported fossil fuels in the country and also help in achieving Sustainable Development Goal 7.

Constructing efficient strata boundaries in stratified sampling using survey cost.

For maximum precision in population parameter estimation under the Stratified sampling design, the optimum strata boundaries (OSB) could be constructed based on a continuous study variable rather than a set of categorical variables. If constructed optimally, the OSB results in homogenous units within each stratum leading to optimal stratum sample sizes (OSS) as well. The OSB and OSS may not remain optimum if the problem is considered in terms of a fixed total sample size, especially when a survey design involves a fixed budget. This article suggests a methodology for computing the OSB and OSS when the per unit stratum measurement costs for the survey or its probability density function are known. To plan for such a stratified survey, we demonstrate a design-based stratification empirically by using Wave 18 of the HILDA Survey general release dataset where we estimate the mean level of Gamma-distributed annual total disposable income in Australia, which could potentially be an important variable for policy decision-making. We also provide numerical illustrations for hypothetical study variables that follow exponential and right-triangular distributions respectively. The findings indicate that the suggested method is satisfactory in the sense that it is either more efficient or relatively comparable with other methods aimed at improving the accuracy of population parameter estimates. The proposed technique has been implemented in the updated stratifyR package.

MERN Stack Web-Based Education Management Information Systems for Pacific Island Countries.

Generation of quality data to aid planning, assessing the education performance, monitoring programs implementation and learning outcomes are the basic functions of the Education Management Information System (EMIS). This study examines the existing EMIS in the Pacific Island Countries PICs and proffers solution. A SWOT analysis on the selected PICs EMIS through the published technical reports and policy documents from government and donors' between years 2000 and 2021, revealed that EMIS in PICs have not progressed beyond the stages of collecting demographic data and generating basic indicators. Fiji EMIS which has prospect of leading other PICs EMIS could only generate few indicators manually, and the findings indicate that these indicators are not being considered in decision making. To solve these defects, we proposed data-driven microservices architecture developed with MERN (MongoDB, ExpressJS, ReactJS, and NodeJS) stack on 13 NoSQL collections, tested with pseudonymised data from Fiji Ministry of Education (consisting of 98.6% Learners, 100.2% Schools and 99.5% Teachers in post). Our solution renders dynamic visualized production-ready education data and 28 UNESCO standard indicators to guide decision making and this may serve as a model for PICs EMIS.

The Attitudes of Tongan Senior Secondary Students Toward Science.

As with other Pacific Island nations, the scientific efforts of the Kingdom of Tonga have been hampered through a lack of local scientists and science graduates. As observed globally, the region appears to face a steady decline in student interest and achievement in science, resulting in reduced uptake of science subjects in schools and universities. This study aims to provide insight into the attitudes of Tongan senior secondary students toward science, using the validated Test of Scientific Related Attitudes (TOSRA) instrument. The sample population comprised 2636 students of approximately 15-18 years of age, from 26 schools across Tonga. Overall, the mean attitudes of Tongan senior secondary students toward science were lower than that previously observed for Australian secondary students (Grades 7-10) and Fijian senior secondary students. A significant reduction in attitude was found between forms 5 and 6 for female students from rural areas, but not those from urban areas. No significant changes across different form levels were found for male students. The greatest difference between students' perspectives was found for the normality of scientists, which may be indicative of cultural views toward this topic. Combined with the similar results of previous research in Fijian students, this may point toward broader differences in the attitudes toward science between students in Pacific Island countries more generally.

Predicting protein-peptide binding sites with a deep convolutional neural network.

MOTIVATION: Interactions between proteins and peptides influence biological functions. Predicting such bio-molecular interactions can lead to faster disease prevention and help in drug discovery. Experimental methods for determining protein-peptide binding sites are costly and time-consuming. Therefore, computational methods have become prevalent. However, existing models show extremely low detection rates of actual peptide binding sites in proteins. To address this problem, we employed a two-stage technique - first, we extracted the relevant features from protein sequences and transformed them into images applying a novel method and then, we applied a convolutional neural network to identify the peptide binding sites in proteins. RESULTS: We found that our approach achieves 67% sensitivity or recall (true positive rate) surpassing existing methods by over 35%.

Protein secondary structure prediction using neural networks and deep learning: A review.

Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. An informative introduction to protein secondary structure and artificial neural networks is also included for context. This review will be valuable in designing future methods to improve protein secondary structure prediction accuracy. The various neural network methods found in this problem domain employ varying architectures and feature spaces, and a handful stand out due to significant improvements in prediction. Neural networks with larger feature scope and higher architecture complexity have been found to produce better protein secondary structure prediction. The current prediction accuracy lies around the 84% marks, leaving much room for further improvement in the prediction of secondary structures in silico. It was found that the estimated limit of 88% prediction accuracy has not been reached yet, hence further research is a timely demand.

Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm.

Nuclear Magnetic Resonance Spectroscopy (most commonly known as NMR Spectroscopy) is used to generate approximate and partial distances between pairs of atoms of the native structure of a protein. To predict protein structure from these partial distances by solving the Euclidean distance geometry problem from the partial distances obtained from NMR Spectroscopy, we can predict three-dimensional (3D) structure of a protein. In this paper, a new genetic algorithm is proposed to efficiently address the Euclidean distance geometry problem towards building 3D structure of a given protein applying NMR's sparse data. Our genetic algorithm uses (i) a greedy mutation and crossover operator to intensify the search; (ii) a twin removal technique for diversification in the population; (iii) a random restart method to recover from stagnation; and (iv) a compaction factor to reduce the search space. Reducing the search space drastically, our approach improves the quality of the search. We tested our algorithms on a set of standard benchmarks. Experimentally, we show that our enhanced genetic algorithms significantly outperforms the traditional genetic algorithms and a previously proposed state-of-the-art method. Our method is capable of producing structures that are very close to the native structures and hence, the experimental biologists could adopt it to determine more accurate protein structures from NMR data.

SOCS1 inhibits migration and invasion of prostate cancer cells, attenuates tumor growth and modulates the tumor stroma.

BACKGROUND: The suppressor of cytokine signaling 1 (SOCS1) gene is repressed in prostate cancer (PCa) by epigenetic silencing and microRNA miR30d. Increased expression of the SOCS1-targeting miR30d correlates with higher biochemical recurrence, suggesting a tumor suppressor role of SOCS1 in PCa, but the underlying mechanisms are unclear. We have shown that SOCS1 inhibits MET receptor kinase signaling, a key oncogenic pathway in cancer progression. Here we evaluated the role of SOCS1 in attenuating MET signaling in PCa cells and tumor growth in vivo. METHODS: MET-overexpressing human DU145 and PC3 PCa cell lines were stably transduced with SOCS1, and their growth, migration and invasion of collagen matrix were evaluated in vitro. Cells expressing SOCS1 or the control vector were evaluated for tumor growth in NOD.scid.gamma mice as xenograft or orthotopic tumors. RESULTS: HGF-induced MET signaling was attenuated in SOCS1-expressing DU145 and PC3 cells. Compared with vector control cells, SOCS1-expressing cells showed reduced proliferation and impaired migration following HGF stimulation. DU145 and PC3 cells showed marked ability to invade the collagen matrix following HGF stimulation and this was attenuated by SOCS1. As xenografts, SOCS1-expressing PCa cells showed significantly reduced tumor growth compared with vector control cells. In the orthotopic tumor model, SOCS1 reduced the growth of primary tumors and metastatic spread. Intriguingly, the SOCS1-expressing DU145 and PC3 tumors showed increased collagen deposition, associated with increased frequency of myofibroblasts. CONCLUSIONS: Our findings support the tumor suppressor role of SOCS1 in PCa and suggest that attenuation of MET signaling is one of the underlying mechanisms. SOCS1 in PCa cells also appears to prevent the tumor-promoting functions of cancer-associated fibroblasts.