Miracle-dz/UniFAR-FTUs huggingface dataset:
Intention to Choose Syariah Compliant Hospital: The Role of Religiosity

Syariah Compliant Healthcare Services in a rather unique trend that emerged in Malaysia. It started in few decades ago when certain ‘Islamic’ hospitals started offering Syariah compliant services with the tag line “Ibadah Friendly Hospital”. Syariah Hospital of Islamic Hospital defined as hospital that provides health services in accordance with Syariah principles or in other words applying maqasid Syariah in hospital governance hospital. But a question remains, is there a necessity of Syariah compliant hospitals? And why it became a choice in the market. The research methodology used is qualitative based on content analysis technique to obtain data. This study seeks to find out the reason that motivates this choice by looking into the role of religiosity that is presumed to be the main reason for the choice. Based on the theory of reason action, a literature has been reviewed to construct tools to investigate Muslim attitude towards their intention to choose Syariah compliant hospital.
TRAFFIC SIGN DETECTION USING DEEP LEARNING BY GROUP 36

This project includes implementation and comparison of deep learning based architectures for classiﬁcation of forty three different types of trafﬁc signals. Popular CNN based architectures were evaluated on the basis of classiﬁcation accuracy and the prediction speed. Based on the results obtained, a simple yet effective Convolutional Neural Network(CNN) architecture was proposed that gives high accuracy at a great prediction speed.
Changes Associated with Treatment of Plasmodium berghei Infected Mice with Momordica charantia, Xylopia aethiopica and Entandrophragma angolense Leaf Extract

Aim: Leaves of plants have been useful in the treatment of various diseases and infections. The study aims at determining the nephrotoxicity, hepatotoxicity, and hematological effect of Momordica charantia, Xylopia aethiopica, and Entandrophragma angolense on the Plasmodium berghei infected mice. Study Design: The plants’ leaves were air-dried and extracted. Forty-two Swiss male mice, 18 to 25 g, were grouped into six of seven mice each. Group I was uninfected but were administered with normal saline for four days, Group II to VI were all infected with P. berghei and administered with normal saline, 300 mg/b.w. of M. charantia leaf extract, 300 mg/b.w. of E. angolense leaf extract, 300 mg/b.w. of X. aethiopica leaf extract and 40 mg/b.w. of chloroquine injection for four days, respectively. Results: The treatment groups showed a lower level of toxicity when compared with chloroquine treatment. X. aethiopica has the greatest positive impact on the PCV level of the experimental animals of its treated group compared with other groups. Conclusion: Our findings confirmed the antimalarial potential of X. aethiopica and thus can be used to treat malaria without anemia as a side effect.
GC-MS Analysis of the Rauwolfia vomitoria Ethanol Extracts

Bioactive compounds are the frontline potent agents in both nutraceuticals and pharmaceutical industries. The bioactive compounds are gaining much importance for their ability in enhancing resistance to various diseases and to improve the health of people both by traditional and modern ways of administrations. R. vomitoria is one of the medicinal plants used traditionally to manage hypertension, diabetes and mental disorder. This present study sought to characterize the bioactive components of R. vomitoria leaf and root ethanol extracts using Gas-Chromatography-Mass Spectrophotometry (GC-MS). The results of the GC-MS analysis provide different peaks indicating the presence of 22 phytochemicals in the plant leaf and 16 phytochemicals in the root. The major bioactive compounds in the leaf were squalene (18.69%), phytol (16.47%), n-hexadecanoic acid (15.68%), 7-tetradecenal, (Z) (12.90%), 9,12,15-octadecatrienoic acid, ethyl ester, (Z,Z)-, (9.56%) and others, while the roots contains; cis-vaccenic acid (32.13%), n-hexadecanoic acid (15.41%), (E)-9-octadecenoic acid ethyl ester (9.83), cyclohexanecarbonitrile 1-(-4- chlorophenyl (9.45%), 8H-azeceno[5,4-b] indol-8-one, 5-ethylidene (7.66%) and other minor compounds. Pharmacological activities of these compounds indicated that the compounds present in the leaf of the plant can be used as a crude drug which could be developed into a novel drug. Some of these compounds have antimicrobial, antioxidant, hepatoprotective, hypocholesterolemic as well as cancer preventive activities amongst others. The findings suggest that there is an indication that both R. vomitoria leaves and roots contain potent bioactive compounds that may be linked to its beneficial effects on health, with the leaf taking the lead. It is therefore recommended as a plant of phytopharmaceutical significance.
Fingerprint Intramodal Biometric System Based on ABC Feature Fusion

Unimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system's classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful information from extracted feature sets being an optimization problem. Artificial Bee Colony (ABC) is an optimizing algorithm that has been frequently used in solving FS problems because of its simple concept, use of few control parameters, easy implementation and good exploration characteristics. ABC was proposed for optimized feature selection prior to the classification of Fingerprint Intramodal Biometric System (FIBS). Performance evaluation of ABC-based FIBS showed the system had a Sensitivity of 97.69% and RA of 96.76%. The developed ABC optimized feature selection reduced the high dimensionality of features space prior to classification tasks thereby increasing sensitivity and recognition accuracy of FIBS.
Effect of Mindfulness Meditation Practices on Students’ Behavior Change in Secondary Schools in Trashigang District, Bhutan

This research primarily focused on mindfulness meditation affecting students’ behaviour change in secondary schools, Trashigang district, Bhutan. The objectives of the research were to study the levels of students’ mindfulness meditation practice, to identify students’ behaviour change, and to examine mindfulness meditation practice affecting students’ behaviour change in secondary schools in Trashigang, Bhutan. The study administered questionnaires on mindfulness meditation and behaviour change to 367 students in two higher secondary schools and interviewed two students and two teachers in May 2013. The quantitative data were analyzed by descriptive statistics, while qualitative data were analysed by content analysis.
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Copy right Idea Grou p Inc. Copy right Idea Grou p Inc. ABSTRACT This paper discusses the barriers to knowledge sharing in an inter-organizational healthcare partnership in the UK public sector. Based on a case study incorporating participant observation, document analysis, interviews (n=30) and a survey (n=132), data are presented to highlight barriers associated with the learning culture, strategy deployment, information systems support and performance measurement. The paper discusses what must change in order to improve the knowledge-sharing environment. Knowledge Management In An Inter-Organizational Partnership: From Information Reporting to a Learning Culture
SOCIAL INNOVATIONS IN GEO-ICT EDUCATION AT TANZANIAN UNIVERSITIES FOR IMPROVED EMPLOYABILITY (GEOICT4E)

Abstract. Geospatial and ICT technologies are making an impact leap due to globally accessible open data solutions addressing environmental and social challenges, such as rapid urbanization, degradation of marine and land environments, and humanitarian crises. We are witnessing a rapid growth of innovations built on data and tools tackling local societal problems. At best, these can provide better opportunities for sustainable solutions and development. The need for geospatial expertise is growing globally, and the required skills and capabilities of experts are changing. Universities need to think that although the future jobs rely on experts’ geospatial data and technology skills, graduates need to have a strong conceptual and practical understanding of societal problems and capacity to co-develop solutions, which generate wellbeing and inclusive development. New generation university graduates need to master the interface between technologies’ potential and societies’ emerging needs, working in a multi-stakeholder environment and creating innovative and impactful solutions. In this paper, we present a model of institutional cooperation between five Tanzanian and three Finnish universities, aiming to tackle this transformative education challenge in Tanzania. GeoICT4e aims to develop innovative and scalable geospatial and ICT e-learning services for Tanzanian universities. Via this transformation, universities are aiming to enhance the future employment potential of the graduates with digital multi-competence skills. We present the overall methodology and key activities of the project cooperation, and discuss the opportunities and challenges related to this transformation, and use of open data and FOSS solutions particularly from the institutional and societal perspectives.
Real-time Queue Length Estimation Applying Shockwave Theory at Urban Signalized Intersections

Signal control is a basic need for urban traffic control; however, it is a very rough intervention in the free flow of traffic, which often results in queues in front of signal heads. The general goal is to reduce the delays caused, and to plan efficient traffic management on the network. For this, the exact knowledge of queue lengths on links is one of crucial importance. This article presents a link-based methodology for real-time queue length estimation in urban signalized road networks. The model uses a Kalman Filter-based recursive method and estimates the length of the queue in every cycle. The input of the filter, i.e. the dynamics of queue length is described by the traffic shockwave theory and the store and forward model. The method requires one loop-detector per link placed at the appropriate position, for which the article also provides suggestions.
On the Evolution of Edge Detectors for Robot Vision using Genetic Programming

Genetic programming has been applied to the task of evolving edge detectors. Using simple elementary functions we have evolved edge detectors using the squared di(cid:11)erence between the individual's response and Canny's edge detector as a (cid:12)tness measure. We present the best evolved individual and two individuals which specialized themselves to detect only vertical edges
Prevalence, Awareness and Risk Factors of Hypertension among Public Sector Workers Aged 40 Years and above in the Tamale Metropolis of Ghana

Aim: To determine the prevalence, awareness and risk factors associated with hypertension among adults. Study Design: Cross-sectional study. Place and Duration of Study: Tamale Metropolis from January to March 2020. Methodology: 200 adults (101 men and 99 females) aged 40 years and above were recruited. Socio-demographic characteristics, anthropometric and blood pressure data of participants collected through face-to-face administered questionnaire and physical measurements were analyzed for prevalence, knowledge and awareness of hypertension. Binary and multivariate logistic regression was used to estimate the odds of association of risk factors with hypertension. Results: Overall age-standardized prevalence of hypertension was 46.00% (49.50% in males, 42.42% in females). The prevalence of both systolic and diastolic hypertension was higher in males than in females. 49.10% of hypertensive participants were unaware of their status at the time of this study and 83.3% of the hypertensive participants who were aware of their status were diagnosed incidentally. Multivariate logistic regression analysis revealed a significant positive association of being male [AOR = 2.39, (95% CI: 1.08–5.30)], aged between 50 – 65 years [AOR = 2.03, (95% CI: 1.03–4.01)], and being obese [AOR = 3.64, (95% CI: 1.43–9.29)] with hypertension. Being widowed [AOR = 0.06, (95% CI: 0.01–0.66)] was negatively associated with hypertension. Only obesity [AOR = 2.81, (95% CI: 1.29–6.14)] was independently associated with hypertension. Conclusion: Hypertension affects one in every two adults aged 40 years and above in the Tamale Metropolis of Ghana. Awareness of adult hypertension status in the Metropolis is very low with the most diagnoses of the disease occurring accidentally. Obesity, advancing age, being male and being widowed are risk factors associated with hypertension. The study suggests workplace BP screening and a scale-up of awareness campaigns in the Metropolis to curb the incidence of the disease and control associated risk factors.
IoT Air Quality Monitoring System

— Internet of Things (IoT) is an increasingly popular technology that enables physical devices, vehicles, home appliances, etc. to communicate and even inter-operate with one another. Air pollution in India is estimated to kill about 1.5 million people every year; it is the fifth largest killer in India. Existing monitoring systems have inferior precision, low responsivity, and require laboratory analysis, are the limitations of existing systems. Therefore, improved monitoring systems are needed. To overcome these problems, we propose a three-phase air pollution monitoring system. An IoT kit using gas sensors, Arduino IDE (Integrated Development Environment), and a Wi-Fi module was developed. This kit can be physically placed in various cities to monitoring air quality. The sensors gather data from surroundings and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud through the Wi-Fi module. We also developed an Android application that users can access air quality data from the cloud. If a user is traveling to a particular destination, the pollution level of the entire route is predicted, and a message is sent to user, if the pollution level is too high. The proposed system is analogous to Google Maps.
Impacts of the side ratio of isolated buildings on airflows and pollutant dispersion under different temperature stratifications: Numerical investigations

Based on the Reynolds-averaged Navier-Stokes (RANS) method, the standard k-ε turbulence model was used to simulate the effects of the side ratio (SR: building depth/building width, SR = 0.5, 1.0, 1.5, and 2.0, respectively) of isolated buildings on the flow field and pollutant of ethylene dispersion under unstable temperature stratification and neutral conditions. The results showed that under unstable temperature stratification, the updraft on the leeward side of the building caused the high temperature gas to flow upward and the temperature gradient to drop. Compared with the neutral condition, the length of the recirculation zone was shorter, and with the decrease in SR, the vortex in the recirculation zone became larger and more stable. Because the pollution source was located on the leeward side of the building, the pollutant gas was concentrated in the area between the pollution source and the leeward side of the building. The near-ground concentration of pollutants under unstable temperature stratification was lower than that under neutral conditions, and with the decrease in SR, the near-ground axis concentration decreased while the width of the pollutant plumes increased.
Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.
A Systematic Literature Review Towards Service Oriented Architecture Adoption from 2009 to 2015

Service Oriented Architecture (SOA) has appeared as an absorbing architectural approach that empowers the available systems to reveal their performance in the act of services, without creating important changes to the systems. This approach, due to its flexibility of adoption, has been widely recognized as an appropriate approach for implementing in organizations by many scholars and practitioners. Though, there are many studies that depict success factors of SOA; at the same time a few minor cases of failure has also been reported in the literature. In spite of the availability of rich material on SOA (in general), there is no systematic literature review on the potential aspect of SOA adoption factors. Hence, this article presents a systematic review of existing studies related to the SOA adoption and its success and failure factors from 2009 to 2015. The principle purpose of the study is to bring forward the existing issues and share our findings with researchers. Moreover, the findings of this paper would help IT experts in organizations to concentrate on the key factors highlighted in this study, so that they could decide whether it is advisable to adopt SOA or not in their context.
MODELAGEM DA REDUÇÃO DO T AMANHO DE P AR TÍCULAS NA ALIMENT AÇÃO DE RUMINANTES Modeling of feed particle size reduction in the ruminants nutrition

RESUMOO tamanho de particula influencia o valor nutricional do alimento porque pode afetar o consumo de materia seca, a digestaoe utilizacao de nutrientes pelos microor ganismos ruminais e, o desempenho animal. Alguns modelos sao apresentados para ilustrar aevolucao da representacao da reducao do tamanho de particula. Em geral, as diferentes propostas se comportam adequadamente eproveem informacao util para producao ou pesquisa. Porem, todos os modelos demonstram inexatidao em certos pontos. Nestarevisao sao discutidas as razoes para esses problemas e e proposta a inclusao de novas variaveis relacionadas com a cinetica departiculas, ingestao descontinua de alimentos e liberacao do conteudo celular com o objetivo de aumentar a precisao de futurosmodelos de consumo e digestao.Termos para indexacao: cinetica ruminal, consumo, digestibilidade, modelos de simulacao.ABSTRACTParticle size seems to be an important factor influencing the nutritional value of the feed because it may affect dry matterintake, microbial digestion and utilization of nutrients and animal performance. Some models are described to present differentattempts to represent particle size reduction. In general, the diverse approaches behave adequately and provide useful informationfor production or research. All models, however, demonstrate inaccuracy in some points. The reasons for these failures arediscussed and the inclusion of new variables related to particle kinetics, pulses of feed and cell content release to increase accuracyof predictions in future models of feed intake and digestion of ruminants is proposed.Index terms: digestibility , feed intake, ruminal kinetics, simulation models. (Recebido para publicacao em 6 de abril de 2004 e aprovado em 18 de marco de 2005)
Application of evidence-based nursing in patients after cataract surgery and its impacts on visual acuity recovery and psychological status.

OBJECTIVE To probe into the evidence-based nursing in patients after cataract surgery and its impact on their visual acuity recovery and psychological status. METHODS Ninety-seven patients with cataract who underwent surgery in our hospital were randomly divided into control group (n=48, receiving conventional nursing) and observation group (n=49, receiving evidence-based nursing) for a prospective study. Postoperative visual acuity recovery, incidence of complications, recurrence rate within 1 year, psychological status before and after surgery and quality of life were compared between the two groups. RESULTS The best corrected visual acuity of the affected eye of patients at 3 and 6 months after surgery in both groups was significantly higher than that before surgery, with the best corrected visual acuity of the same period in the observation group higher than that in the control group (all P<0.05). The incidence of postoperative complications and recurrence rate within 1 year in the observation group were lower than those in the control group (all P<0.05). Three months after operation, the scores of Hamilton Anxiety Scale and Hamilton Depression Scale in the two groups were lower than those before operation, with the scores in the observation group lower than those in the control group (all P<0.05); and the scores of all items in Generic Quality of Life Inventory in the two groups were higher than those before operation, with the scores in the observation group higher than those in the control group (all P<0.05). CONCLUSION Evidence-based nursing for patients after cataract surgery can significantly improve the recovery of patients' postoperative visual acuity, reduce the incidence of postoperative complications as well as the recurrence rate. It also helps to improve the adverse psychological status of patients and their postoperative quality of life.
The impact of collaborative planning of an oral task on learners’ language performance in an EFL context

Learning a new language is permeated by several cognitive processes which are believed to demand a high load of attentional resources during oral performance (SKEHAN, 2014). Strategic (ELLIS, 2005) and collaborative planning (SWAIN, 2000) appear as an alternative to lower these demands. In the light of Task Based Language Teaching (TBLT) (COOK, 2011; EAST, 2017) and aiming at contributing to this background, in this study, an oral task was elaborated with the purpose to analyze the performance of basic speakers of English as a Foreign Language from a language course in an oral task under two different conditions: after individual and collaborative strategic planning. Fourteen students were divided into two groups: four pairs planned collaboratively, and six students planned individually. Both groups had ten minutes to plan, followed by a few minutes to record their messages. Participants also answered a perception questionnaire concerning the activity. Statistical results indicated that students performing under the peer-planning condition had higher scores in all three measures of adequacy: organization, convincingness, and clarity than students performing under the individual-planning condition, which was confirmed by the results of the independent t-test. Furthermore, qualitative results showed that most students appeared to enjoy the task, especially the ones who performed under the peer-planning condition. These contributions bring important implications for classroom, allowing teachers to acknowledge collaborative strategic planning as a valuable tool.