Application of Multivariate Techniques for Spatial Drought Modelling using Satellite Rainfall Estimate in Fiji

Monitoring hydrological extremes is essential for developing risk-mitigation strategies. One of the limiting factors for this is the absence of reliable on the ground monitoring networks that capture data on climate variables, which is highly evident in developing states such as Fiji. Fortunately, increasing global coverage of satellite-derived datasets is facilitating utilisation of this information for monitoring dry and wet periods in data sparse regions. In this study, three global satellite rainfall datasets (CHIRPS, PERSIANN-CDR and CPC) were evaluated for Fiji. All satellite products had reasonable correlations with station data, and CPC had the highest correlation with minimum error values. The Effective Drought Index (EDI), a useful index for understanding hydrological extremes, was then calculated. Thereafter, a canonical correlation analysis (CCA) was employed to forecast the EDI using sea surface temperature anomaly (SSTa) data. A high canonical correlation of 0.98 was achieved between the PCs of mean SST and mean EDI, showing the influence of ocean–atmospheric interactions on precipitation regimes in Fiji. CCA was used to perform a hind cast and a short-term forecast. The training stage produced a coefficient of determinant (R2) value of 0.83 and mean square error (MSE) of 0.11. The results in the testing stage for the forecast were more modest, with an R2 of 0.45 and MSE of 0.26. This easy-to-implement system can be a useful tool used by disaster management bodies to aid in enacting water restrictions, providing aid, and making informed agronomic decisions such as planting dates or extents.

Identification of internal cracks in corn seed using convolutional neural networks

The identification of seed quality is very important for which the quality of seed is crucial to the yield and quality of crops. There are two main problems with the acquisition and identification of cracks inside corn seed. One is that most of the methods of near-infrared spectroscopy or X-ray are used to obtain images of cracks inside the seed, the acquisition equipment is expensive and the operation is complicated. The other is the identification of crack images, and the traditional image processing method is usually used which requires professionals to design different model parameters each time, resulting in poor model robustness and low model accuracy. In this study, we originally proposed a simple but effective method to obtain the picture of corn seed internal cracks, which is combined with visible light transmission and ordinary camera acquisition method. We also proposed using the transfer learning methods not only solving the problem of the small scale of our corn seed internal cracks dataset but also avoiding extracting features manually. Our proposed method achieved a promising result, which is able to correctly identify the cracked and intact corn seed 100% in our training stage and testing stage.

Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features

Insomnia disorder (ID) and obstructive sleep apnea (OSA) with respiratory arousal threshold (ArTH) phenotypes often coexist in patients, presenting similar symptoms. However, the typical diagnosis examinations (in-laboratory polysomnography (lab-PSG) and other alternatives methods may therefore have limited differentiation capacities. Hence, this study established novel models to assist in the classification of ID and low- and high-ArTH OSA. Participants reporting insomnia as their chief complaint were enrolled. Their sleep parameters and body profile were accessed from the lab-PSG database. Based on the definition of low-ArTH OSA and ID, patients were divided into three groups, namely, the ID, low- and high-ArTH OSA groups. Various machine learning approaches, including logistic regression, k-nearest neighbors, naive Bayes, random forest (RF), and support vector machine, were trained using two types of features (Oximetry model, trained with oximetry parameters only; Combined model, trained with oximetry and anthropometric parameters). In the training stage, RF presented the highest cross-validation accuracy in both models compared with the other approaches. In the testing stage, the RF accuracy was 77.53% and 80.06% for the oximetry and combined models, respectively. The established models can be used to differentiate ID, low- and high-ArTH OSA in the population of Taiwan and those with similar craniofacial features.

Which Vaccine? The Cost of Religious Freedom in Vaccination Policy

We discuss whether and under what conditions people should be allowed to choose which COVID-19 vaccine to receive on the basis of personal ethical views. The problem arises primarily with regard to some religious groups’ concerns about the connection between certain COVID-19 vaccines and abortion. Vaccines currently approved in Western countries make use of foetal cell lines obtained from aborted foetuses either at the testing stage (Pfizer/BioNTech and Moderna vaccines) or at the development stage (Oxford/AstraZeneca vaccine). The Catholic Church’s position is that, if there are alternatives, Catholic people have a moral obligation to request the vaccine whose link with abortion is more remote, which at present means that they should refuse the Oxford/AstraZeneca vaccine. We argue that any consideration regarding free choice of the vaccine should apply to religious and non-religious claims alike, in order to avoid religion-based discrimination. However, we also argue that, in a context of limited availability, considering the significant differences in costs and effectiveness profile of the vaccines available, people should only be allowed to choose the preferred vaccine if: 1) this does not risk compromising vaccination strategies; and 2) they internalize any additional cost that their choice might entail. The State should only subsidize the vaccine that is more cost-effective for any demographic group from the point of view of public health strategies.

Pengembangan Media Articulate Storyline dalam Pembelajaran Menulis Puisi pada Kelas X SMA Negeri 2 Sukabumi

Learning media is one of the supporting facilities in teaching and learning activities. In this time of the COVID-19 pandemic, online-based learning media is very necessary. The lack of teacher ability in using interactive learning media makes online learning even more boring, especially in learning to write poetry. The articulate storyline is one software that can produce more interesting learning media. Using many features can produce learning media that are more varied, innovative, and easy to use. The research method used is RND, which has ten research steps that the researchers then simplified into four main steps, namely the research and data collection stage, the planning stage, the product development stage, and the validation and testing stage. Media development results are a product of a poem made by class X students at SMA Negeri 2 Sukabumi. The product was produced after learning poetry material using online interactive learning media. Three validators assessed the media feasibility level. The material validator is given a feasibility value of 82%, the medium validator is given a value of 80%, and the Indonesian language learning validator is given 84%. Meanwhile, 89% of students responded "Ya" in student responses, and 11% of students responded "Tidak". Based on the validator's assessment results, the media was considered very feasible to be used in schools and received a positive response from students.

Current state of the evidence on community treatments for people with complex emotional needs: a scoping review

BackgroundImproving the quality of care in community settings for people with ‘Complex Emotional Needs’ (CEN - our preferred working term for services for people with a “personality disorder” diagnosis or comparable needs) is recognised internationally as a priority. Plans to improve care should be rooted as far as possible in evidence. We aimed to take stock of the current state of such evidence, and identify significant gaps through a scoping review of published investigations of outcomes of community-based psychosocial interventions designed for CEN.MethodsWe conducted a scoping review with systematic searches. We searched six bibliographic databases, including forward and backward citation searching, and reference searching of relevant systematic reviews. We included studies using quantitative methods to test for effects on any clinical, social, and functioning outcomes from community-based interventions for people with CEN.ResultsWe included 226 papers in all (209 studies). Little relevant literature was published before 2000. Since then, publications per year and sample sizes have gradually increased, but most studies are relatively small, including many pilot or uncontrolled studies. Most studies focus on symptom and self-harm outcomes of various forms of specialist psychotherapy: most result in outcomes better than from inactive controls and similar to other specialist psychotherapies. We found large evidence gaps.Adaptation and testing of therapies for significant groups (e.g. people with comorbid psychosis, bipolar disorder, post-traumatic stress disorder or substance misuse; older and younger groups; parents) have for the most part only reached a feasibility testing stage. We found little evidence regarding interventions to improve social aspects of people’s lives, peer support or ways of designing effective services.ConclusionsCompared with other longer term mental health problems that significantly impair functioning, the evidence base on how to provide high quality care for people with CEN is very limited. There is good evidence that people with CEN can be effectively helped when specialist therapies are available and they are able to engage with them. However, a much more methodologically robust and substantial literature addressing a much wider range of research questions is urgently needed to optimise treatment and support across this group.

CNN for Image Identification of Hiragana Based on Pattern Recognition using CNN

Hiragana is one of the letters in Japanese. In this study, CNN (Convolutional Neural Network) method used as identication method, while he preprocessing used thresholding. Then carry out the normalization stage and the filtering stage to remove noise in the image. At the training stage use maxpooling and danse methods as a liaison in the training process, wherea in testing stage using the Adam Optimizer method. Here, we use 1000 images from 50 hiragana characters with a ratio of 950: 50, 950 as training data and 50 data as testing data. Our experiment yield accuracy in 95%.

ZAKAT AS TAX REDUCTION: STUDY OF MUSLIM COMMUNITY PERCEPTION IN INDONESIA AND MALAYSIA

This study aims to examine the model adopted based on Muslim consumer perceptions of taxes through the zakat system. This research uses three stages of comprehensive technical analysis through demographic depiction of respondents based on distribution frequently, then tests the adopted factors using Exploratory Factor Analysis (EFA) to select and determine the number of factors and related items. In the final stage, data analysis is carried out in the form of the modeling technique using Structure Equations Model (SEM) to test the quality of the models and hypotheses produced. 152 respondents were collected who were sampled in this study, the majority of respondents are 77 Malaysian citizens and 75 Indonesian residents. At the testing stage of the model through the Structural Equation Model (SEM), based on the results of the formation factors in the test, it can be said that only the knowledge about tax, religious, and service variables have an impact on perception toward through zakat system positively and significantly, but through testing the serviceability of a model results in a determinant coefficient of 0.668, which it was relatively strong to explaining independent variable.

An Efficient Real Time Object Tracking Based on HSV Value

This study presents a strategy to automate the process to recognize and track objects using color and motion. Video Tracking is the approach to detect a moving item using a camera across the long distance. The basic goal of video tracking is in successive video frames to link target objects. When objects move quicker in proportion to frame rate, the connection might be particularly difficult. This work develops a method to follow moving objects in real-time utilizing HSV color space values and OpenCV in distinct video frames.. We start by deriving the HSV value of an object to be tracked and then in the testing stage, track the object. It was seen that the objects were tracked with 90% accuracy. Keywords: HSV, OpenCV, Object tracking,