Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Machine Translation with Target-Attention Model

SUMMARY Attention mechanism , which selectively focuses on source-side information to learn a context vector for generating target words, has been shown to be an e ﬀ ective method for neural machine translation ( NMT ). In fact, generating target words depends on not only the source-side information but also the target-side information. Although the vanilla NMT can acquire target-side information implicitly by recurrent neural networks ( RNN ), RNN cannot adequately capture the global relationship between target-side words. To solve this problem, this paper proposes a novel target-attention approach to capture this information, thus enhancing target word predictions in NMT. Speciﬁcally, we propose three variants of target-attention model to directly obtain the global relationship among target words: 1) a forward target-attention model that uses a target attention mechanism to incorporate previous historical target words into the prediction of the current target word; 2) a reverse target-attention model that adopts a reverse RNN model to obtain the entire reverse target words information, and then to combine with source context information to generate target sequence; 3) a bidirectional target-attention model that combines the forward target-attention model and reverse target-attention model together, which can make full use of target words to further improve the performance of NMT. Our methods can be integrated into both RNN based NMT and self-attention based NMT, and help NMT get global target-side information to improve translation performance. Experiments on the NIST Chinese-to-English and the WMT English-to-German translation tasks show that the proposed models achieve signiﬁcant improvements over state-of-the-art baselines.
A Simple Algorithm for the Calculation of Hysteresis for Isotropic NdFeB Magnets

The calculation of hysteresis curves with the Stoner-Wohlfarth model is somewhat laborious. For the Nd2Fe14B phase, the second order magnetocrystalline anisotropy (K2) constant is relevant, and this case only has been discussed in some studies. In this article, a simple algorithm for the calculation of the Stoner-Wohlfarth model for isotropic NdFeB magnets is described. This algorithm makes easier the modeling of the hysteresis curves of bonded melt-spun NdFeB magnets.
Safety and efficacy of trial of labour after cesarean

Background: In recent years, there are increasing numbers of cases which report for delivery after previous LSCS. Trial of labour after lower segment cesarean section can save them from repeat LSCS and its complications. Aim of study was conducted to evaluate the safety and efficacy of trial of labour after LSCS. Methods: This is a prospective study carried out at Mahatma Gandhi Hospital. One hundred pregnant patients with previous LSCS giving informed consent for trial of labour after cesarean section (TOLAC) were enrolled. Case selection was done as per the ACOG guidelines. Continuous fetal and maternal monitoring done by WHO partograph and cardiotocography. If there appeared any indication for repeat cesarean then emergency LSCS was done. Results: In this study, 80% of the enrolled patients delivered vaginally and 20% underwent repeat emergency LSCS. 2% of the enrolled patients underwent scar dehiscence. The patients in the active phase of labour on admission had more chances of successful vaginal birth after cesarean section (VBAC). Birth weight of more than 3000 grams is associated with lower rates of successful VBAC. There was no maternal mortality but 1% intrauterine fetal death due to scar rupture was there. Conclusions: TOLAC is safe and the success rates are good. Proper selection of cases with adequate fetal and maternal monitoring is required. So, once cesarean always cesarean is not a true dictum.
Orthodontic treatment and patient’s expectations

Introduction: It is paramount to know the expectations of the patients in terms of the type of treatment provided, problems associated with treatment, duration and frequency of attendance, benefits of treatment, attitude of orthodontist and complications of treatment. Hence the aim of this study was to assess the expectations of patients from orthodontic treatment. Material and Methods: A total of 50 physically and mentally healthy participants in the age range of 15 to 30 years were included in the study. A valid and reliable questionnaire was used to measure the patients’ responses. Questions were asked about expectations of their initial visit, the type of treatment expected, problems associated with treatment, duration and frequency of attendance, benefits of treatment, attitude of orthodontist and complications of treatment. The statistical evaluation of the data was performed using the Statistical Package for the Social Sciences version 19.Descriptive analyses were used to calculate the responses of the patients. Results : Patient’s expectations regarding orthodontic treatment were different as shown by their responses. Conclusions : It was concluded from this study that patients were well aware of every aspect of the orthodontic treatment and their expectations could be easily met leading to more patient satisfaction.
Investment Decisions and Capital Budgeting Practices in Manufacturing Sector of Pakistan

The aim of this research is to study the investment decisions and capital budgeting practices in manufacturing sector of Pakistan. To examine the investment decisions in manufacturing sector in Pakistan. To determine the capital budgeting practices in Pakistan.Capital budgeting is very important for firm its importance cannot be overemphasize because it has long term benefits for the validity and operational functionality of the firm. Capital budgeting is followed by techniques which are helpful in decision making. In planning process these techniques play key role for choosing worth funding project. These techniques are used to get clear view of proposed projects. Previous studies work on overall financial/non-financial sector according to my limited knowledge there is no specific study in manufacturing sector. This study helps the mangers to take the corrective measures. Questionnaire is conducted by using self-delivery collection method. It was found that the net present value technique is the most preferred by the big size firms companies. Moreover, significant differences between companies of different sizes are identified. In smaller companies, earning and cost comparison are
Risk Factors for Coronary Artery Diseases: A Study Among Patients With Ischemic Heart Disease in India (Kerala)

Summary Coronary artery disease (CAD) is a condition that develops due to accumulation of atherosclerotic plaque in the epicardial coronary arteries, leading to myocardial ischemia. It is the leading cause of death worldwide and is a common complex disease. A study was carried out in a group of 496 patients with acute coronary syndrome or with angiographic or stress test evidence for coronary artery disease, admitted to the Department of Cardiology at Lourdes Heart Institute and Neuro Centre during the period June-August 2012. The risk factors studied were hypertension, diabetes mellitus, dyslipidemia, body mass index, smoking and family history of CAD. The results demonstrated that in both males and females of the Indian population studies, diabetes and dyslipidemia were major risk factors for CAD, while hypertension was not a major risk factor. Therefore, early detection and treatment of diabetes mellitus and dyslipidemia play a vital role in prevention of CAD in Indian population.
A European, multicentre, observational, post-authorisation safety study of oral sulphate solution: compliance and safety

Abstract Background and study aims Oral sulphate solution (OSS) is a sulphate-based, low-volume bowel cleansing preparation taken in two doses of 500 mL, each followed by 1000mL of water or clear liquid. The primary objective of this observational study was to document compliance with the recommended hydration guidelines in a representative sample of the European population. Patients and methods Prospective, non-interventional, multicentre study (NCT02630680, EUPAS9361) in patients prescribed OSS for colonoscopy preparation in routine clinical practice in Europe. Patients were included according to pre-agreed consecutive enrolment rules. Patients recorded the volume of OSS and water or clear liquid intake, and occurrence of adverse events (AEs). Compliance with hydration was calculated as a ratio of actual volume of water/clear liquid taken versus prescribed 2,000 mL, and non-compliance defined as < 75 % intake. Colon cleansing level was assessed on a 4-point scale. Results Between October 2015 and January 2017, 1,281 patients were recruited in 16 centres in four European countries (safety population n = 1,206; registry population n = 1,177). Of patients, 94.5 % were ≥ 75 % and 86.8 % 100 % compliant with hydration guidelines. Patients took an average of 96.8 % of the recommended OSS volume; 46 patients (3.9 %) were non-compliant. Colon cleansing levels were good-to-excellent in 87.6 % of patients. Three hundred and twenty-nine patients (27.3 %) experienced 758 treatment-related AEs, mostly gastrointestinal (82.9 %), all were mild-to-moderate. Non-compliant patients had no AEs suggestive of dehydration. Conclusion In this non-interventional study in a real-life setting, treatment compliance with hydration guidelines was good-to-excellent in 94.5 % of patients receiving OSS. The safety profile of OSS was similar to the prescribing information. TRIAL REGISTRATION: Observational study EUPAS9361 at www.encepp. eu
Deformation and fracture of unidirectional GFRP composites at high strain rate tension

This work deals with experimental investigation of the high strain rate behaviour of unidirectional (UD) glass fibre-reinforced (GFRP) composite materials. The related experiments were performed with a high rate test machine to determine the mechanical properties of UD E-glass/epoxy composites. The specimens were tested separately under quasi-static and high-speed conditions with loading rates of up to 20 m/s. All specimens were tested to failure in order to characterise the effect of high strain rate on failure strength of the material. The results show that high strain rates have a significant effect on the properties of UD GFRP composites. The increment of an ultimate strength with high strain rate is proportional to the strain rate in a given range with remaining elastic modulus.
MODIFICATION POLYMER MATRIX COMPOSITES BY ADDITION GRAPHENE

The attractive properties of graphene and its compo sites have led to the study of numerous application s such as transistors, biosensors, energy storage devices, na no-electro-mechanical systems and others; the past decade has witnessed the rapid growth of carbon nanotechnology. More res earch in the area will help the development of next generation graphene based composites and hybrid materials. In this study, samples composite materials used for manufacturing by hand layout technique which caste d into cylindrical pellets. The matrix materials of these composites are: epoxy resin, reinforced with graphe ne particles which are added in three percentages (1, 3, and 6) % wt to th e matrix. Additionally there are pellet without rei nforced with graphene particles, then measured Shor-D hardness, diametric al compressive strength and thermal conductivity fo r all the samples, its found from the results there is improvement in mechanical properties and thermal conductivity of t he samples that reinforced with graphene specially at 6%wt.
Investing in Women’s Mental Health: Strengthening the Foundations for Women, Families and the Australian Economy

The AHPC policy paper Investing in women's mental health, strengthening the foundations for women, families and the Australian economy discusses the extensive evidence that women's mental health needs are significantly different from those of men. Certain mental illnesses are more prevalent in women, they use mental health services more frequently than men, and they want a broader range of treatment options than are currently available. Mental disorders represent the leading cause of disability and the highest burden of non-fatal illnesses for women in Australia. -43% of Australian women (3.5 million) have experienced mental illness at some time. -Australian women are more likely than men to have experienced symptoms of a mental disorder during the previous 12 months (22% of women compared to 18% of men). -Young women report the highest rates of mental disorder of any population group (30% for women aged 16 to 24). -One in five mothers of children aged 24 months or less are diagnosed with depression. This represents an estimated -111,000 Australian mothers being diagnosed with depression, and 56,000 with perinatal depression annually. -Women are 1.6 times as likely as men to suffer coexisting mental and physical illness. These multimorbidities are associated with increased severity of mental illness and increased disability. -The number of hospital admissions for specialised psychiatric care following a principal diagnosis of recurrent depressive disorders and specific personality disorders was substantially higher for females than males during 2007-08. In 2007-08, females aged 35-44 were the highest consumers of Medicare-subsidised mental health-related GP services (external link). -Women in Aboriginal and Torres Strait Islander communities have much poorer physical and mental health than other Australians. The suicide rate of Aboriginal and Torres Strait Islander women is highest within the 20-24 years old age group (21.8 per 100,000), which is more than five times higher than their corresponding non-indigenous counterparts (4.0 per 100,000). The burden of poor mental health amongst women results in adverse social and economic consequences, including disability, reduced life expectancy, impoverishment of individual women, unemployment, reduced productivity, and increasing costs of healthcare and welfare. Women's mental health is influenced by biological, psychosocial, economic and environmental factors. The high prevalence of mental distress amongst women and girls of all ages is bound up in the totality of women's experiences including social and economic inequality. There is an urgent need to counter the gender-blindness of current mental health policy which serves to make these experiences invisible. Policies which reinforce this invisibility are not only ineffective; they are part of the problem. This paper argues that it is time for a new approach aimed at tackling these gendered risks and enhancing protections across the life course.
Sex Ratio and Religion in Vietnam

We examine whether the probability of having a boy aged below 5 years in households and communes is associated with religious individuals in Vietnam using data from the 1999 Population and Housing Census (on 76 million people) and 2007 Establishment Census (on religious establishments). Our results show low probability of having a boy aged below 5 years among religious households. Moreover, using Vietnam’s 1955–1974 North-South division that resulted in different religious developments, we apply a commune-level instrumental-variable approach. From this analysis, we find a higher serious follower ratio associated with a lower boy ratio within communes and certain non-believer communities.
An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines

In this study, a new and rapid hidden resource decomposition method has been proposed to determine noisy pixels by adopting the extreme learning machines (ELM) method. The goal of this method is not only to determine noisy pixels, but also to protect critical structural information that can be used for disease diagnosis. In order to facilitate the diagnosis and also the treatment of patients in medicine, two-dimensional (2-D) images were calculated tomography (CT) which is obtained using medical imaging techniques. Utilizing a large number of CT images, promising results have been obtained from these experiments. The proposed method has shown a significant improvement on mean squared error and peak signal-to-noise ratio. The experimental results indicate that the proposed method is statistically efficient, and it has a good performance with a high learning speed. In the experiments, the results demonstrated that remarkable successive rates were obtained through the ELM method.
The effect of Multimedia Method of Education on Weight loss in Hemodialysis Patients

Background & Aim: Overweight and its consequences are the most important complications in hemodialysis patients. Weight Control is an important educational need in hemodialysis patient. Multimedia technology provides easy and stable learning by various forms of interaction. The present study carried out to determine the effect of multimedia training method to weight management in hemodialysis patients. Material & Methods: In a quasi-experimental research 30 hemodialysis patients were divided based on random allocation in two intervention and control groups. Data were collected by demographic data form and standardized weighing scale. In the intervention group training were given about methods of weight control. Weight measurement of patients performed before and after training. Data were analyzed using SPSS software ver. 18.0 while descriptive statistics and independent t-test were applied. Results: Comparison of means demonstrated that change in the body weight in intervention group is more than control group (1.63±1.55 VS 0.43±1.07). The average weight change between intervention and control group was significant (P<0.05). Conclusion: Multimedia training method was effective on weight loss in hemodialysis patients as a result this method may improve the weight management of hemodialysis patients.
Training deep segmentation networks on texture-encoded input: application to neuroimaging of the developing neonatal brain

Standard practice for using convolutional neural networks (CNNs) in semantic segmentation tasks assumes that the image intensities are directly used for training and inference. In natural images this is performed using RGB pixel intensities, whereas in medical imaging, e.g. magnetic resonance imaging (MRI), gray level pixel intensities are typically used. In this work, we explore the idea of encoding the image data as local binary textural maps prior to the feeding them to CNNs, and show that accurate segmentation models can be developed using such maps alone, without learning any representations from the images themselves. This questions common consensus that CNNs recognize objects from images by learning increasingly complex representations of shape, and suggests a more important role to image texture, in line with recent ﬁndings on natural images. We illustrate this for the ﬁrst time on neuroimaging data of the developing neonatal brain in a tissue segmentation task, by analyzing large, publicly available T2-weighted MRI scans (n=558, range of postmenstrual ages at scan: 24.3 - 42.2 weeks) obtained retrospectively from the Developing Human Connectome Project cohort. Rapid changes in visual characteristics that take place during early brain development make it important to establish a clear understanding of the role of visual texture when training CNN models on neuroimaging data of the neonatal brain; this yet remains a largely understudied but important area of research. From a deep learning perspective, the results suggest that CNNs could simply be capable of learning representations from structured spatial information, and may not necessarily require conventional images as input.
Forecasting Exchange Rates using Neural Neworks

The foreign exchange (FOREX) market is larger, in terms of trading volume than any other market, financial or otherwise. Developments in the foreign exchange market determine the levels and chages in exchange rates, which have significant implications for business and exonomics. In this paper an attempt has been made forecasting the daily exchange rates of Indian Rupee (INR) to U.S. Dollar (USD) using Box-Jenkins methodology for building ARIMA model and artificial neural networks (ANN) for building Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) models. The sample data is used from April 2011 to March 2015. RMSE, MAE and MAPE are used for the comparison of the models. Result of this study shows that RNN is performing better than ARIMA and MLP in forecasting the daily exchange rates INR to USD.
Improving the Quality of Neural Machine Translation

—Over the last few years, neural machine translation has become the major approach to the problem of automatic translation. Nonetheless, even though current models are able to output ﬂuent translations, they often lack adequacy. In this thesis we target adequacy issues with two different causes: poor coverage of source words during translation, which lead to unnecessary repetitions and erroneously untranslated words; rare words and out-of-domain sentences. In order to mitigate coverage issues we propose a fertility-based approach to neural machine translation, which couples the concept of fertility with sparse and constrained attention transformations. Furthermore, we present two machine translation metrics that allow us to understand how much the problems of over and under-translations are affecting the model. To deal with rare words and out-of-domain sentences, we implement an existing approach in the literature, that makes use of the concept of translation pieces to guide the decoding step of NMT models. We further extend this method by solving some identiﬁed issues, and by applying it to the problem of domain adaptation, something which had not been done in the past. Finally, we provide an empirical evaluation in three language pairs, presenting an extensive error analysis. This makes it possible to understand the strengths and weaknesses of each of the models, and how they may be improved in the future.
A Review of Gamification in Virtual Reality (VR) Sport

PURPOSE: To identify the main findings from research on gamified virtual reality in sport METHODS: The digital databases for this review were searched and visited on 19 May 2019. Gamification AND (“virtual reality” OR sports fitness) were used as a search string or keyword in the paper selection. RESULTS: Thirty -three papers were found after the preliminary searches to narrow down the selections that align with the goals of this study. The selected papers were examined to investigate the characteristics of gamification in a broad context of sports. These findings suggest that the use of gamification methods help physical exercise in the immersive VR setting. CONCLUSION: Through the outcomes of user experience, it can prove that gamification components are boosting the pleasure of the user throughout the physical activity and provide a set of rules to encourage the athlete or user to perform better. Besides, generality of impacts with VR and gamification in sports has been discussed in this paper by listing the advantages and limitations. However, findings need more varied populations, particularly elements, athletes, kids, and older people.