Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of antenatal care visits and their impact on the choice of birthplace among mothers in Uganda: a systematic review

Objective: The utilization of Antenatal Care (ANC) services to the recommended time by WHO is still low in developing countries. In Uganda, about 990,000 women become pregnant annually, 90 to 94% of whom attend at least the first ANC visit while 65% and 58% of them attend four or more times in urban and rural areas respectively with eventual health facility birth at about 62%, homebirth and TBA together at about 38%. The study determined antenatal care attendance and the mother’s choice of birthplace in Uganda. Materials and methods: Using electronic databases mainly Google Scholar, Science Direct, PubMed and African Journals Online, and journal articles of international Website, 70,195 articles were identified but only 19 met the Cochrane review inclusion criteria hence were reviewed. Results: Reviewed studies attribute a decline in antenatal care visits and subsequent choice of birthplace to institutional, demographic and socio-economic factors. The demographic factors are maternal age (mothers less than eighteen years are less likely to utilize maternal health services than those above), marital status, occupation, residency, distance from home to the health facility, and parity. The socio-economic factors are mothers’ and partners’ levels of education (less educated women and their partners are less likely to utilize ANC), household income which affects transportation and medical bills, cultural norms and taboos, patriarchy society, enrollment on ANC in the third trimester, and intrinsic factors of attending ANC such as obtaining ANC card to present in case of emergency. Institutional factors include quality of medical care, availability of logistics and supplies, and health workers’ influence in terms of attitude, referral, competences, and staffing. Conclusion: Pregnant women who attend ANC are more likely to deliver in the health facility than those who do not at all. To enable them to attend ANC to full term and have a health facility delivery, their empowerment for increased self-efficacy through education, sustainable livelihood training and provision of subsidized capital alongside partner involvement. The facilitation of health facilities would also attract them.
An Automatic Construction Approach for Sentiment Dictionary Based on Weibo Emoticons

Different from common texts, microblog texts have large amount of emotions and net-words. Its words and sentences expression are more colloquial and network popular, so traditional sentiment dictionary does not suit for the context of modern microblog short texts. This article puts forward an approach to automatically construct the sentiment dictionary based on microblog emoticons. Collected microblog texts are annotated by emoticons and form sentiment text corpus. To conduct consolidation according to existing sentiment dictionary, extract the sentiment words in the microblog texts per rule of part of speech, calculate the information added value of sentiment words in microblog texts as feature weight, and classify the sentiment words in the method of SVM to get the sentiment dictionary. This article improves the construction method of existing sentiment dictionary. The experimental result shows that the accuracy rate of sentiment dictionary after improvement reaches above 90%, and the overall F value reaches 85%, which are obviously better than existing dictionaries.
Wearable Electromechanical Sensors and Its Applications

Wearable electromechanical sensor transforms mechanical stimulus into electrical signals. The main electromechanical sensors we focus on are strain and pressure sensors, which correspond to two main mechanical stimuli. According to their mechanisms, resistive and capacitive sensor attracts more attentions due to their simple structures, mechanisms, preparation method, and low cost. Various kinds of nanomaterials have been developed to fabricate them, including carbon nanomaterials, metallic, and conductive polymers. They have great potentials on health monitoring, human motion monitoring, speech recognition, and related human-machine interface applications. Here, we discuss their sensing mechanisms and fabrication methods and introduce recent progress on their performances and applications.
Empirical examples of using Big Internet Data for Macroeconomic Nowcasting

In this paper we present results for nowcasting and one-month-ahead forecasting three key macroeconomic variables: inflation (measured by the month on month growth rate in the Harmonized Index of Consumer Prices), retail sales (measured by the Retail Trade Index), and the Unemployment Rate. The exercise is conducted recursively in a pseudo out of sample framework, using monthly data for three economies: Germany, Italy and the UK. We assess the relative performance of Big Data (proxied via weekly Google Trends) and standard indicators (based on a large set of economic and financial variables). We also evaluate the role of several econometric methods and alternative specifications for each of them (with or without big data), for a total of 279 models and model combinations. In general, we find that Google Trends tend to slightly improve the forecasts of factor models and penalized regressions. Furthermore, a data-driven automated model selection strategy, where the forecasts from a set of best performing models over the recent past are pooled, performs particularly well, with Big Data present in about 65% of the pooled models (on average across the cases where the strategy is the best model).
GENETIC EVALUATION OF SOME PRODUCTIVE LIFETIME TRAITS OF LOCALLY BORN FRIESIAN COWS IN EGYPT

Data on 2186 lactation records of 541 pure Friesian cows presenting 43 sires and 372 dams born at the Dairy Unit of Milk and Meat Project of the Faculty of Agriculture, Alexandria University, between 1983-2006 were utilized to evaluate lifetime milk yield (LMY), days in lactation (LDL), daily milk yield (LDMY) and number of lactations (NL) of Friesian cows. Also, the effects of season and year of calving and age at first calving on the previous traits were studied. The least square means of LMY, LDL, LDMY and NL were 18310 kg, 1318 day, 13.6 kg and 4.04 lactations, respectively. Season of calving had no significant effect on all studied traits. Year of calving had significant effect (P<0.01) on all studied traits. Age at first calving had significant effect (P<0.05) on LDMY, but had no significant effect on LMY, LDL and NL. The heritability estimates from univariate animal models were 0.272, 0.137, 0.117, and 0.116 for LMY, LDL, LDMY and NL, respectively. The moderate heritability for LMY emphasized the possibility of realizing a considerable rate of genetic improvement in this trait through selection programme. The genetic corrletions estimated from bivariate animal models were positive ranging from 0.264 to 0.993, except that between LMY and LDMYwas negative (-0.163). All correlations among all traits were significant (P<0.01). The positive genetic correlations between LMY and each of LDL and NL indicate that selection for any traits associated with genetic improvement in other traits. Phenotypic correlations among all traits were positive ranging from 0.099 to 0.966 and significant (P<0.01 or P<0.05). The breeding values for LMY, LDL, LDMY and NL of cows ranged between -759 and 1139 kg, -101 and 105 day, -1.77 and 1.82 kg and between -01.64 and 2.69 lactations, respectively, the corresponding values for dams were between -814 and 1107 kg, -122 and 100 day, -2.38 and 1.53 kg and between -1.74 and 3.24 lactations, respectively. The breeding values for sires were between -560 and 748 kg, -117 and 83 day, -1.79 and 1.26 kg and between -1.44 and 3.12 lactations for the respective traits. The genetic trends estimated by the regression of sires breeding values on time were positive and nonsignificant for LMY, LDL and NL and negative approached zero for LDMY. Generally, the results indicate that improvement of productive lifetime traits of Friesian cows could be obtained through both selection programme and improvement of management conditions.
Effectiveness of Auricular Acupuncture in Treating Depressive Disorders: a Systematic Review Protocol

Background: Depression is a chronic condition of high prevalence in the world population and associated with functional disability and compromises the physical health of affected individuals. According to Traditional Chinese Medicine (TCM), depression is associated with disability, there is not enough energy for positive feelings or associated with stagnation, when there is energy, but the flow of energy and emotions is blocked. Deficiency is also associated with excess in manic depression or irregularity in depression associated with anxiety. Auricular acupuncture, also known as auriculotherapy, it is part of a set of techniques based on TCM. For this, the auricular pavilion is related to 12 meridians, stimulating points in the ear and restoring the balance between blood and Qi (energy or vital force). Methods: This is a systematic review protocol for clinical trials prepared in accordance with the guidelines of the protocol Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA-P). Studies in which participants have any depressive disorder, diagnosed through standardized criteria such as the Diagnostic and Statistical Manual of Mental Disorders (DSM), the International Classification of Diseases (ICD) and other diagnostic criteria used internationally will be included. There will be no restrictions on age, gender, ethnicity, education or economic status. Studies will be accepted that have as experimental intervention any type of auricular acupuncture (using needles, seeds, magnetic stones, lasers, ultrasound, bleeding or electrical treatment). The control interventions that will be considered for the studies will be conventional medical treatment for each depressive disorder, no treatment, placebo and other active therapies. Discussion: Auricular acupuncture can be a potential alternative complementary treatment, it is low cost and has minimal side effects presented until now. With this review we hope to complete gaps in the knowledge of auricular acupuncture, promoting an updated and comprehensive synthesis of research on this topic. Record of systematic review: In accordance with the guidelines, our systematic review protocol was registered with the International Prospective Registry of systematic reviews (PROSPERO) on 10/27/2020 (registration number CRD42020211302).
PC-VINS-Mono: A Robust Mono Visual-Inertial Odometry with Photometric Calibration

Feature detection and Tracking, which heavily rely on the gray value information of images, is a very importance procedure for Visual-Inertial Odometry (VIO) and the tracking results significantly affect the accuracy of the estimation results and the robustness of VIO. In high contrast lighting condition environment, images captured by auto exposure camera shows frequently change with its exposure time. As a result, the gray value of the same feature in the image show vary from frame to frame, which poses large challenge to the feature detection and tracking procedure. Moreover, this problem further been aggravated by the nonlinear camera response function and lens attenuation. However, very few VIO methods take full advantage of photometric camera calibration and discuss the influence of photometric calibration to the VIO. In this paper, we proposed a robust monocular visual-inertial odometry, PC-VINS-Mono, which can be understood as an extension of the opens-source VIO pipeline, VINS-Mono, with the capability of photometric calibration. We evaluate the proposed algorithm with the public dataset. Experimental results show that, with photometric calibration, our algorithm achieves better performance comparing to the VINS-Mono.
Tensile behavior of a Brazilian Disk Containing non-persistent Joint Sets Subjected to Diametral Loading: An Experimental Investigation

Structural defects are part of the inherent characteristics of rock masses. They can be found in the form of fishers, joints, and beddings and can be divided into persistent or non-persistent one. The coalescence of non-persistent cracks may lead to the formation of persistent joints under the tensile stress field, leading to instability of rock mass. The mechanical behavior of non-persistent jointed disks under tensile stress has essential implications for rock engineering structures. In this paper, concrete Brazilian disks containing open non-persistent joints were constructed and subjected to diametral loading to investigate the effect of this kind of joint parameters on the tensile strength and stiffness of disks. The effect of some parameters, such as joint continuity factor (the relationship between joint length and rock bridge length), bridge angle, joint spacing, and loading direction with respect to joint angle were investigated to estimate the tensile strength and stiffness as well as failure pattern. The results of experiments revealed that the tensile strength, stiffness, and failure pattern of Brazilian disks are highly affected by non-persistent pre-existing crack parameters. The increase of joint continuity factor and loading direction leads to an increase in tensile strength and a decrease in stiffness. However, when bridge angle and spacing increase tensile strength rises, and the former decreases stiffness while the latter results in its reduction. Finally, all the parameters significantly affect the failure pattern, and some failure patterns such as step-path failure, splitting, or sliding may occur as a function of non-persistent joint parameters.
Multiattribute judgment: Acceptance of a new COVID-19 vaccine as a function of price, risk, and effectiveness

This paper illustrates how to apply the RECIPE design to evaluate multiattribute judgment, reporting an experiment in which participants judged intentions to receive a new vaccine against COVID-19. The attributes varied were Price of the vaccine, Risks of side effects as reported in trials, and Effectiveness of the vaccine in preventing COVID. The RECIPE design is a union of factorial designs in which each of three attributes is presented alone, in pairs with each of the other attributes, and in a complete factorial with all other information. Consistent with previous research with analogous judgment tasks, the additive and relative weight averaging models with constant weights could be rejected in favor of a configural weight averaging model in which the lowest-valued attribute receives additional weight. That is, people are unlikely to accept vaccination if Price is too high, Risk is too high, or Effectiveness is too low. The attribute with the greatest weight was Effectiveness, followed by Risk of side-effects, and Price carried the least weight.
Accelerating Nonconvex Learning via Replica Exchange Langevin diffusion

Langevin diffusion is a powerful method for nonconvex optimization, which enables the escape from local minima by injecting noise into the gradient. In particular, the temperature parameter controlling the noise level gives rise to a tradeoff between ``global exploration'' and ``local exploitation'', which correspond to high and low temperatures. To attain the advantages of both regimes, we propose to use replica exchange, which swaps between two Langevin diffusions with different temperatures. We theoretically analyze the acceleration effect of replica exchange from two perspectives: (i) the convergence in \chi^2-divergence, and (ii) the large deviation principle. Such an acceleration effect allows us to faster approach the global minima. Furthermore, by discretizing the replica exchange Langevin diffusion, we obtain a discrete-time algorithm. For such an algorithm, we quantify its discretization error in theory and demonstrate its acceleration effect in practice.
Decentralized Access Control Technique with Anonymous Authentication

In this paper, a new decentralized access control scheme for secure data storage in clouds that supports anonymous authentication is proposed. In the proposed scheme, the cloud verifies the authenticity of the series without knowing the user’s identity before storing data. This scheme also has the added feature of access control in which only valid users are able to decrypt the stored information. This system also prevents replay attacks and supports creating, modifying, and reading of data stored in the cloud. This paper also address user revocation. Our authentication and access control scheme is more robust and decentralized, unlike other access control schemes designed for clouds which are centralized. When compared to decentralized approaches the computation, communication and storage overheads are improved. Index Terms — Access control, authentication, attribute-based signatures, attribute-based encryption, cloud storage.
Health Systems Interventions to Prevent Firearm Injuries and Death

The Board on Population Health and Public Health Practice of the National Academies of Sciences, Engineering, and Medicine convened a workshop that examined the roles that health systems can play in addressing the prevalence of firearm violence, including suicide, in the United States. This publication summarizes the discussions held during the workshop and highlighted the lessons, practical strategies and opportunities for healthcare systems that were identified by the speakers to prevent firearm injury and death.
Scheduling stochastic tasks on heterogeneous cloud platforms under budget and deadline constraints

This work introduces scheduling strategies to maximize the expected number of independent tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The cloud platform is composed of several types of virtual machines (VMs), where each type has a unit execution cost that depends upon its characteristics. The amount of budget spent during the execution of a task on a given VM is the product of its execution length by the unit execution cost of that VM. The execution length of tasks follow an exponential, uniform or lognormal probability distribution whose mean and standard deviation both depend upon the VM type. Finally, there is a global available budget and a deadline constraint, and the goal is to successfully execute as many tasks as possible before the deadline is reached or the budget is exhausted (whichever comes first). On each VM, the scheduler can decide at any instant to interrupt the execution of a (long) running task and to launch a new one, but the budget already spent for the interrupted task is lost. The main questions are which VMs to enroll, and whether and when to interrupt tasks that have been executing for some time. We assess the complexity of the problem by showing its NP-completeness and providing a 2-approximation for the asymptotic case where budget and deadline both tends to infinity. We introduce several heuristics and compare their performance by running an extensive set of simulations.
Dynamic survival prediction for high dimensional data

In this thesis, we consider models for survival data with a high-dimensional covariate space. Most models used for such datasets are based on the Cox regression model, of which a critical assumption is that the hazard functions are proportional between individuals. The purpose of this thesis is to develop a way of analysing these datasets that does not require that the proportional hazards assumption is valid. In search of such a method, we study the concept of landmarking and try to develop a way of fitting what van Houwelingen and Putter [2011] refers to as sliding landmark models that works when we have a high number of covariates. An essential part of our strategy is the ‘bet on sparsity principle’ [Hastie et al., 2001], where one assumes that only some of the variables in the dataset have an effect on the outcome. We seek out to implement this using regularisation techniques, such as penalised regression and boosting. In particular, we develop a boosting algorithm for sliding landmark models, based on the likelihood boosting algorithm for Cox regression [Binder and Schumacher, 2008]. The thesis is concluded by a simulation study, where the different models and methods of estimation we consider are used to analyse different simulated datasets, and are compared via a dynamic Brier score [van Houwelingen and Putter, 2011].
Face Recognition Using Transform Domain and Overlapping LBP Techniques

Face Recognition is one of the popular research topics with different applications. In real-world application, human identification and verification is needed to ensure security. In this paper transform and spatial domain techniques are used for face recognition. The face images of orl_faces and JAFEE databases are pre processed using Histogram Equalisation (HE). The Discrete Wavelet Transform (DWT) is applied on Histogram Equalised images to generate four sub bands. The Overlapping Local Binary Pattern (OLBP) is applied on LL sub band to generate final features. The features of test images are compared with the features of database images using Euclidian Distance (ED) to compute performance parameters such as False Acceptance Rate (FAR), False Rejection Rate (FRR) and Total Success Rate (TSR). Keywords— Face Recognition, Histogram Equalisation (HE), Discrete Wavelet Transform (DWT), Overlapping Local Binary Pattern (OLBP), Euclidian Distance (ED).
Research Synthesis on Educational Models

Objective - The purpose of this research is to evaluate research on educational models, to study the effect of the research characteristics on educational models, and to synthesize the research methods and presentations about educational models through qualitative synthesis by content analysis. Methodology/Technique – The sample was research on educational models on the type of thesis at the graduate level from 2013 to present. The research instruments took three forms. The data analysis method used was the ATLAS.ti program using content analysis, frequency, and percentage. Finding - The results show that: 1) The research evaluation on educational models the most of the research used to synthesize research at a very good level, 2) The most research characteristics: research issue was personality traits, research problem presentation was the characteristic of the research problem presentation reflecting the importance of the study, setting research objectives was the study the composition/indicators, develop the model and study the use of the model, and research conceptual framework was the bringing each concept/component together, 3) The most common research methodology applied in the research was research and development (method), focus group discussion (technique), an expert (sample), purposive sampling (sampling), interviews (tool), mean and standard deviation (data analysis), one-group pre-test/post-test design (research design), and 4) there were 8 types of research result presentations on educational models which were descriptive characteristics, causal relationship, the proposed process steps as objective, design characteristics on standard/indicators/factors of model. Novelty - The qualitative characteristics, system approach characteristics, experimental characteristics, and mixed-method between quantitative and qualitative methods. Type of Paper: Empirical. JEL Classification: A20, A39. Keywords: Research Synthesis; Educational Model; Evaluative Research; Research Characteristics; Research Methodology; Research Result Presentation. Reference to this paper should be made as follows: Chianchana, C. 2020. Research Synthesis on Educational Model, Global J. Bus. Soc. Sci. Review 8(3): 181 – 190. https://doi.org/10.35609/gjbssr.2020. 8.3(5)