Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Emotional and Physical Intimate Partner Violence Among Married Women in Pakistan

Intimate partner violence (IPV) affects millions of women across the world, and Pakistan is no exception. However, there is wide variance in reported frequencies worldwide and even within the same population. No standardized representative national and subnational estimates were available for IPV in Pakistan. We analyzed 3,666 ever-married women (15-49 years), from the most recent Pakistan Demographic and Health Survey (PDHS; 2012-2013), to determine the prevalence of emotional and physical violence among women in Pakistan and its major subpopulations. The Conflicts Tactics Scale (CTS) was used to ascertain violence. Furthermore, we examined the socioeconomic and demographic characteristics, which have the potential to increase the risk of emotional and physical violence. Weighted multivariate logistic regression was carried out to determine the association of IPV with the potential risk factors using Stata version 13.0. The prevalence of emotional and physical violence was, respectively, 36.4% (95% confidence interval [CI]: [33.8, 39.1]) and 18.4% (95% CI: [16.4, 20.6]) in Pakistan. The frequency of emotional and physical violence was highest in Khyber Pakhtunkhwa (KPK; 54.9% and 36.4%) followed by Balochistan (50.0% and 25.5%), Punjab (35.9% and 15.8%), and Sindh (24.7% and 13.3%) provinces. The prevalence of any form of violence (emotional or physical or both) was higher in rural than in urban areas (45.2% vs. 30.6%). Higher age of the husband and lower socioeconomic status were associated with emotional violence in KPK. Furthermore, alcohol intake by husbands and lower educational status of women or their husbands were associated with greater risk of emotional and physical violence across the provinces. IPV is high in Pakistan and large variations exist in the prevalence of violence across the provinces. The predictors of emotional and physical violence included occupation and education of the husband, alcohol intake, woman’s occupation, and wealth index. Further studies are needed to explore the underlying factors of violence and reasons for variation across different regions in the country for contextual interventions.
Power Sharing Between Microgrids Based on a Secondary Market

Microgrids are popular for severing local electricity loads. This paper considers the power-sharing problem between the microgrids. First, it proposes a secondary market (SM), which allows the users to submit their bids for buying or selling the power. The SM should be managed by an authoritative agent, so we let the main grid (MG) act as the administrator. In such a market, the buyers, sellers, and MG should get certain rewards. Otherwise, they have no incentive to join in the sharing process. For this reason, this paper first studies the optimal matching policy of the MG to maximize its rewards. Then, it explores the advantage of SM and shows that both the users and MG can gain certain benefit. In addition, the secondary market can encourage the users to consume more necessary power to improve their life quality. Finally, this paper proposes a multiple-biding policy, where the users can adjust the future bids based on the past transaction. Numerical results show that this policy can improve the satisfactory degree. When the utility by consuming the power is bigger, this policy is good for the users.
Comparison of Visual Field Point-Wise Event-Based and Global Trend-Based Analysis for Detecting Glaucomatous Progression

Purpose To compare global trend-based and point-wise event-based analysis for detecting visual field progression in eyes with glaucoma. Methods The study included a cohort of 367 glaucoma eyes from 265 participants seen over a mean follow-up period of 10 years to develop a computer simulation model of “real-world” visual field results. Progression was evaluated with point-wise event-based analysis using the Guided Progression Analysis (GPA) and global trend-based analysis using the mean deviation (MD) and visual field index (VFI) measures. The specificities of the methods were matched based on a simulated dataset of stable glaucoma eyes, to allow an adequate comparison of their sensitivities for detecting progression. Results The 5-year cumulative false-positive rate for the GPA alert of “possible progression” and “likely progression” (significant change from baseline at two and three consecutive visits, respectively) were 34.0% and 7.0%, respectively. At matched specificities, 27.7% eyes were detected as having progressed after 5 years using the GPA “likely progression” criterion, while 24.6% and 23.8% were detected as having progressed using the global trend–based analysis with MD and VFI, respectively. There was a moderate level of agreement between the GPA and global trend-based analyses. Conclusions Pointwise event-based and global trend–based methods had similar performances to detect glaucoma progression when rigorously matched for specificity. Translational Relevance Although both point-wise event- and global trend–based analyses perform similarly, they could provide complementary information that could be exploited to improve the overall detection of progression in clinical practice and clinical trials.
Participant satisfaction with clinical trial experience and post-trial transitioning to HIV care in Kenya

We conducted an exploratory analysis of former HIV Prevention Trials Network 052 (HPTN 052) clinical trial participants in 2016 to assess their (1) satisfaction with the HPTN 052 clinical trial care and treatment, and reasons for joining the trial; and (2) perspectives about the post-trial transition to public HIV care centers. Quantitative data showed that, of the 70 survey participants, 94.3% (n = 66) reported being very satisfied with the care and treatment they received while participating in the clinical trial and 51.4% (n = 36) reported they joined the study because they would receive information to improve their own or their partner’s health. Qualitative data (five in-depth interviews and two focus group discussions) analysis revealed the following themes: transition experiences; perceived superior clinical trial care; study benefits not offered at public HIV care centers; and the public HIV care centers’ indifference to the uninfected partner. For some HPTN 052 participants, transition to HIV care clinics was disappointing. Clinical trial investigators and local Institutional Review Boards should consider the need for safeguards and oversight of post-trial health care for trial participants after the trial ends, especially in resource-constrained settings, to avoid negative health outcomes.
Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within- and cross-document co-reference). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018).
Neural Compositional Denotational Semantics for Question Answering

Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a KG and a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituent spans, culminating in a grounding for the complete sentence which answers the question. For example, to interpret “not green”, the model represents “green” as a set of KG entities and “not” as a trainable ungrounded vector—and then uses this vector to parameterize a composition function that performs a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent from end-task supervision. The model learns a variety of challenging semantic operators, such as quantifiers, disjunctions and composed relations, and infers latent syntactic structure. It also generalizes well to longer questions than seen in its training data, in contrast to RNN, its tree-based variants, and semantic parsing baselines.
Identification of a distinct phenotype of elderly latent autoimmune diabetes in adults: LADA China Study 8

Latent autoimmune diabetes in adults (LADA) exhibits significant clinical heterogeneity, but the underlying causes remain unclear. The aim of this study was to investigate whether age of onset of LADA contributes to the observed clinical heterogeneity by comparing the clinical, metabolic, and immunogenetic characteristics between elderly and young LADA patients.
Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter Networks

The optical datacenter networks need periodical reconfiguration in response to traffic change. Before the networks reconfigure, the tenant requests are given and should be served within fixed transfer time and spectrum capacity. In this paper, the planning problem of serving the requests in optical orthogonal frequency division multiplexing datacenter networks is investigated. We introduce the knapsack-based spectrum and time allocation (KSTA) problem. The objective of this paper is to maximize the network throughput. We formulate the KSTA problem as an integer linear programming (ILP) model. However, ILP cannot find the optimal solution for large input requests within shorter time. To solve the problem, three fast heuristic algorithms, i.e., the most spectrum first, the most time first, and the most data volume first, are proposed to achieve suboptimal solutions. Furthermore, the simulated annealing (SA) algorithm is employed to yield a better suboptimal solution. The simulation results indicate that ILP provides an optimal solution for small input requests, whereas the three heuristic algorithms and SA can yield suboptimal solutions for large input requests. The results also show that the suboptimal solution to SA is better than those provided by the three heuristic algorithms.
Validity of thyromental height test as a predictor of difficult laryngoscopy: A prospective evaluation comparing modified Mallampati score, interincisor gap, thyromental distance, neck circumference, and neck extension

Background and Aims: Thyromental height test (TMHT) is a recently described anatomical bedside screening tool in predicting difficult laryngoscopy. It has been shown to be more accurate than the modified Mallampati score, thyromental distance (TMD), and sternomental distance with regard to sensitivity and positive predictive value (PPV). Airway assessment studies based on the anatomic parameters of the upper airway are limited in the subcontinent population. We attempted this study to evaluate and validate the predictive value of TMHT at 50 mm in an Indian population in predicting difficult laryngoscopy. Methods: This prospective observational study was conducted in a tertiary teaching hospital on 340 patients. TMHT along with other bedside predictors of difficult intubation, including modified Mallampati score, interincisor gap (IIG), TMD, neck circumference (NC), and neck extension were assessed. We compared the sensitivity, specificity, PPV, negative predictive value (NPV), and diagnostic accuracy of TMHT with other bedside tests such as the modified Mallampati score, IIG, TMD, NC, and neck extension individually in predicting difficult laryngoscopy. Any Cormack and Lehane's intubation grade II b and above was considered to be difficult laryngoscopy. Results: TMHT had the highest sensitivity (84.62%) and specificity (98.97%), and had the most PPV (88%) and NPV (98.63%) when compared with the modified Mallampati score, IIG, TMD, NC, and neck extension. TMHT was followed by the modified Mallampati score and IIG. Conclusion: TMHT appears promising as a single anatomical measure to predict the risk of difficult laryngoscopy, however, validation will require further studies in more diverse patient populations.
An Improved Torque Density Synchronous Reluctance Machine With a Combined Star–Delta Winding Layout

This paper investigates the performance of synchronous reluctance motors when the stator is equipped with a combined star–delta winding layout. The conventional star winding is used as a benchmark in this study to compare different possible single-layer winding layouts. Among these different winding layouts, those which maximize the fundamental magneto-motive force component are selected. A simple mathematical formula is then derived to calculate the equivalent winding factor for different shares between star and delta subwindings. It has been proved that for the same copper volume and line current magnitude, the star–delta connection can offer an enhancement in the torque density of approximately 5.2% over the conventional star case under rated conditions. However, this gain is affected by the employed number of poles and stator slots. On the other hand, the effect of the winding layout on either power factor or core loss can be merely neglected over a wide range of speeds and currents. Nevertheless, the machine efficiency under a combined star–delta connection is relatively improved under light loading as a result to the machine torque/current ratio enhancement. The theoretical findings are experimentally validated using two identical 5.5 kW prototype machines, having star and combined star–delta winding connections.
Talking About Feelings: Mother–Child Emotion Dialogues Among Sexually Abused Children

Mother–child dialogues about children’s emotional experiences are associated with children’s adaptive coping with stressful situations and mental health. Despite these findings, dialogues have not been examined yet in the context of child sexual abuse. This gap is surprising given that mother–child dialogues may particularly promote children’s recovery from traumatic events. The current exploratory study examined the quality of mother–child emotion dialogues, as well as the quality of child and maternal contributions to dialogues, among dyads with sexually abused children (n = 30; 60% female; M age = 8.03), as compared with dyads with nonabused children (n = 30; 60% female; M age = 8.20). Quality of dialogues was assessed using the Autobiographical Emotional Events Dialogue. Mothers reported on their own childhood maltreatment history and psychopathological symptoms. Results showed that dyads with abused children were more likely to engage in overwhelming/excessive dialogues and dialogues lacking content, as compared with dyads with nonabused children. After controlling for differences in background characteristics, mothers of abused children showed lower sensitive guidance. Although mothers of abused children had more experiences of childhood maltreatment and higher levels of psychopathology, they did not add to explain group differences in maternal sensitive guidance. Our findings suggest that the ability to discuss emotional experiences may be impaired among mother–child dyads with sexually abused children. This may be an important target in the treatment of sexually abused children and their families.
Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks

Summary Objectives: To review the latest scientific challenges organized in clinical Natural Language Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data, and the sharing strategies. Methods: We harvested the literature by using Google Scholar and PubMed Central to retrieve all shared tasks organized since 2015 on clinical NLP problems on English data. Results: We surveyed 17 shared tasks. We grouped the data into four types (synthetic, drug labels, social data, and clinical data) which are correlated with size and sensitivity. We found named entity recognition and classification to be the most common tasks. Most of the methods used to tackle the shared tasks have been data-driven. There is homogeneity in the methods used to tackle the named entity recognition tasks, while more diverse solutions are investigated for relation extraction, multi-class classification, and information retrieval problems. Conclusions: There is a clear trend in using data-driven methods to tackle problems in clinical NLP. The availability of more and varied data from different institutions will undoubtedly lead to bigger advances in the field, for the benefit of healthcare as a whole.
Associations of Emotional, Physical, or Sexual Intimate Partner Violence and Depression Symptoms Among South African Women in a Prospective Cohort Study

Violence against women remains a significant public health problem globally. The majority of longitudinal studies documenting the negative impact of intimate partner violence (IPV) on the mental health of women come from high-income countries. The aim of this study was to investigate the longitudinal association between emotional, physical, or sexual IPV and depression symptoms among South African women in a prospective cohort study. Participants were 981 South African women enrolled in the Drakenstein Child Health Study—a cohort study investigating the early life determinants of child health. Interview data from four time-points (antenatal care visit, 6 months, 12 months, and 18 months postpartum) were included. The primary independent variable was self-reported emotional, physical, and sexual IPV in the past 12 months. Depressive symptoms were assessed at each time-point with the Edinburgh Postnatal Depression Scale (EPDS); a cutoff score of ⩾13 was used to define significant depression symptoms. We used pooled-multivariable logistic regression models to determine associations between the three different forms of IPV and significant depression symptoms while adjusting for time-fixed and time-updated covariates. The mean age of the sample at antenatal care visit was 27 years (standard deviation = 6.0). In the adjusted model including all forms of IPV and adjusting for sociodemographic and clinical characteristics, substance use, and childhood trauma, emotional (adjusted odds ratio [aOR] =1.55, 95% confidence interval (CI): [1.02, 2.34]; p = . 039)] and sexual (aOR = 2.02, 95% CI: [1.10, 3.72]; p < .001) IPV were significantly associated with significant depression symptoms. The relationship between physical IPV and significant depression symptoms was not statistically significant (aOR = 0.68, 95% CI: [0.44, 1.05]; p = .485). Our study confirms findings from high-income countries of the association between IPV and depressive symptoms among women in South Africa. Routine screening for IPV, including emotional IPV and intervention programs for IPV among women, is needed in South Africa.
Validation of a Blood Biomarker for Identification of Individuals at High Risk for Gastric Cancer

Background: Helicobacter pylori is the leading cause of gastric cancer, yet the majority of infected individuals will not develop neoplasia. Previously, we developed and replicated serologic H. pylori biomarkers for gastric cancer risk among prospective cohorts in East Asia and now seek to validate the performance of these biomarkers in identifying individuals with premalignant lesions. Methods: This cross-sectional study included 1,402 individuals from Linqu County screened by upper endoscopy. H. pylori protein-specific antibody levels were assessed using multiplex serology. Multivariable-adjusted logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for prevalent intestinal metaplasia, indefinite dysplasia, or dysplasia, compared with superficial or mild atrophic gastritis. Results: Compared with individuals seronegative to Omp and HP0305, individuals seropositive to both were seven times more likely to have precancerous lesions (OR, 7.43; 95% CI, 5.59–9.88). A classification model for precancerous lesions that includes age, smoking, and seropositivity to H. pylori, Omp, and HP0305 resulted in an area under the curve (AUC) of 0.751 (95% CI, 0.725–0.777), which is significantly better than the same model, including the established gastric cancer risk factor CagA (AUC, 0.718; 95% CI, 0.691–0.746, Pdifference = 0.0002). Conclusions: The present study of prevalent precancerous gastric lesions provides support for two new serum biomarkers of gastric cancer risk, Omp and HP 0305. Impact: Our results support further research into the serological biomarkers Omp and HP0305 as possible improvements over the established virulence marker CagA for identifying individuals with precancerous lesions in East Asia.
Machine Learning Based Method to Predict Influence Spread

Influence maximization problem is an extensively studied problem among researchers from diverse disciplines due to its broad range of applications i.e. maximizing information propagation, identification of most influential node, stopping disease spread etc. It deals with identifying most crucial nodes in the network, which once activated trigger a widespread of that behavior throughout the network. Influence maximization is an NP-Hard problem. Of various approximation algorithms available, heuristic based seed selection has attracted much attention due to its computational efficiency. Influence spread which is evaluated based on diffusion process is mostly measured using Monte Carlo simulations by many researchers. Diffusion is a stochastic process containing multiple iterations of estimations. However, Monte Carlo predictions are computationally complex for greater efficiency. Hence for complex network, this approach is inadequate for influence maximization. Even a modern computer will take days to solve influence maximization problem on complex networks. In this paper, we present an analysis of various heuristic based influence maximization techniques and also propose a machine learning based approach to find the spread of information in the network.
Considerations on the Effects That Core Material Machining Has on an Electrical Machine's Performance

An often-overlooked aspect during the development process of electrical machines is the validity and accuracy of the machine material properties being used at the design stage. Designers usually consider the data provided by the material supplier, which are measured on material in an unprocessed state. However, the fact that the machining processes required to produce the finished product (e.g., the stator core) can permanently vary the material properties is very often neglected. This paper, therefore, deals with and investigates the effects that such processes can have on the overall machine performance. To do this, three sets of material data, the material suppliers’ data, material data based on conventional characterization methods, and material data based on test samples that include the manufacturing processes, are used to develop three versions of the same baseline machine. The results of these three machines are then compared, and the resulting variations of the machine's performance are presented and described. The chosen baseline machine is a high-performance and relatively high speed, aerospace, electrical machine. Special attention is focused on the efficiency maps of the machine, as this aspect is highly dependent on the material properties that are the most sensitive to manufacturing processes such as the material's anhysteretic B–H curve and its specific core loss.