Miracle-dz/UniFAR-FTUs huggingface dataset:
Work conditions, support, and changing personal priorities are perceived important for return to work and for stay at work after stroke – a qualitative study

Abstract Purpose To explore work related and personal facilitators and barriers for return to work (RTW) and stay at work after stroke. Materials and methods Twenty individuals post-stroke (median age 52 years; seven women) were interviewed in focus groups. Data were analyzed by using qualitative content analysis. Results An overall theme “Work conditions, support and changed personal priorities influenced RTW and stay at work after stroke” emerged and covered three categories: “Adjustments and flexibility at the work place facilitated RTW and a sustainable work situation”, “Psychosocial support and knowledge about stroke consequences facilitated work and reduced stress”, and “Changed view of work and other personal priorities”. Physical adjustments at the work place and flexibility in the work schedule were perceived facilitators. Support from family and colleagues were important, whereas lack of knowledge of stroke disabilities at the work place was perceived a barrier. Also changed personal priorities in relation to the work and the current life situation influenced RTW in various ways. Conclusions The individual’s opportunities to influence the work situation is a key factor for RTW and the ability to stay at work after stroke. Adjustments, flexibility, support, knowledge of stroke, and receptivity to a changed view of work are important for a sustainable work situation. Implications for rehabilitation Physical adjustments at the work place, a flexible work schedule and support increase the individual’s possibility to RTW and maintain a sustainable work situation after stroke. Changed work and life priorities after a stroke need attention in the RTW process. Rehabilitation professionals have an important role in providing knowledge about the disabilities following stroke, and how they impact work ability. Individually tailored recommendations for work place adjustments which enable RTW and a sustainable work situation are warranted.
Preeclamptic Women Have Decreased Circulating IL-10 (Interleukin-10) Values at the Time of Preeclampsia Diagnosis

Supplemental Digital Content is available in the text. A key immunomodulatory cytokine, IL-10 (interleukin-10), has been shown to be dysregulated in preeclampsia, a pregnancy-specific hypertensive disorder, further characterized by multi-system involvement. However, studies have reported inconsistent findings about circulating IL-10 levels in preeclamptic versus normotensive pregnancies. The aim of the present systematic review and meta-analysis was to assess circulating IL-10 levels in preeclamptic and normotensive pregnancies at 2 time points: before, and at the time of preeclampsia diagnosis. PubMED, EMBASE, and Web of Science databases were searched to include all published studies examining circulating IL-10 levels in preeclamptic and normotensive pregnancies. Differences in IL-10 levels were evaluated by standardized mean differences. Of 876 abstracts screened, 56 studies were included in the meta-analysis. Circulating IL-10 levels were not different before the time of active disease (standardized mean differences, −0.01 [95% CI, −0.11 to 0.08]; P=0.76). At the time of active disease, women with preeclampsia (n=1599) had significantly lower IL-10 levels compared with normotensive controls (n=1998; standardized mean differences, −0.79 [95% CI, −1.22 to −0.35]; P=0.0004). IL-10 levels were lower in both early/severe and late/mild forms of preeclampsia. Subgroup analysis revealed that IL-10 measurement methodology (ELISA or multiplex bead array) and the sample type (plasma or serum) significantly influenced the observed differences, with the use of sera paired with ELISA technology providing the best distinction in IL-10 levels between preeclamptic and normotensive pregnancies. These findings support the role of decreased IL-10 levels in the pathophysiology of preeclampsia. Future studies should address the therapeutic potential of IL-10 in preeclampsia.
Preservation and reunification for families of young children: case file review of a home-visiting program

ABSTRACT Objective The Home Parenting Education and Support (HoPES) programme is a new intensive 8-week home-visiting intervention supporting the preservation and reunification of families with young children (aged 0–4 years) receiving child protection services following child abuse and/or neglect in Australia. The aims of the study were to (a) describe families who had participated in HoPES, (b) describe the key education content and support activities of the programme, and (c) identify the enablers and challenges in implementing HoPES. Background Intensive home-based family preservation programs are designed to improve parenting skills, reduce specific abusive behaviours, and address child, parent and contextual risk factors for child maltreatment and its recurrence. Methods A casefile review and document analysis of 34 families who enrolled in HoPES was conducted. Results Families enrolled in HoPES were experiencing significant parent and child risk factors for child maltreatment and recurrence. Despite challenges in supporting families experiencing many complex social health issues and stressful life circumstances, there was evidence of intervention tailoring and use of strengths-based approaches to meet the needs of families. Conclusion This casefile review gathered rich evidence to inform further development of a trauma informed and culturally sensitive intervention to support family preservation and reunification, and to guide the next stage of evaluation research to generate a more robust level of evidence.
A BERT based Two-stage Fake News Spreader Profiling System

This paper describes our two-stage classification approach to the CLEF 2020 lab: Profiling Fake News Spreaders on Twitter. The task can be briefly defined as: Given a Twitter feed, determine whether its author is keen to be a spreader of fake news. Our approach is to adopt the pretrained model BERT as a tweet classifier and to spot potential spreaders whose tweets are strongly suspected as fake news. The performance of our approach can reach 0.71 on the English data set during developing the system. However, the performance drop to 0.56 in the final PAN at CLEF 2020 shared task.
Enhancing Integrative Cognitive-Affective Therapy with ecological momentary interventions: A pilot trial.

OBJECTIVE Although current treatments are effective for some patients with eating disorders, a large number of patients remain partially or fully symptomatic post-treatment. This may be related to poor utilization of treatment skills outside of the therapy office. Smartphone applications that can detect and intervene during moments of need could facilitate such skill use between sessions. METHOD Individuals (N = 16) participated in a small pilot open trial where they received 21 sessions of in-person Integrative Cognitive-Affective Therapy (ICAT) therapy an app (iCAT+) that delivers ecological momentary interventions (EMI) in response to user-entered data. Data were collected on the feasibility and acceptability of this treatment approach and on preliminary indicators of treatment outcomes. RESULTS Participants found iCAT+ as a treatment augmentation acceptable and indicated it had clinical utility as an adjunct to in-person therapy, although analyses indicated poor compliance with data entry needed to trigger EMI delivery. This suggests that long-term use of EMI requiring ongoing data entry is infeasible. CONCLUSIONS We describe lessons learned from our initial pilot trial and future directions for the development of impactful EMI systems that can be used to augment in-person therapies.
Determinants of Gender-based Violence Against Women in Spain: An Asymmetric Bayesian Model

The number of fatalities in Spain due to gender-based violence has increased in recent years, with a new rise in 2019, reaching the highest figure since 2015, a year that registered a peak with 60 victims. This article analyzes a database obtained from a survey on gender violence conducted by the Spanish Centre for Sociological Research. The survey, prepared by the Government Delegation for Gender Violence, consisted of interviews with women aged over 15 years living in 858 municipalities distributed over 50 provinces in Spain. The data reveal that most of the women interviewed have not suffered any type of physical, sexual, or psychological abuse. Hence, the application of standard logistic methodologies which suppose symmetric responses, can lead to a poor specification of the model, a misinterpretation of marginal effects and unidentified predictors. It seems more appropriate to consider an asymmetric link function to explain the probability of abuse (physical, sexual, or psychological). The Bayesian methodology allows the incorporation of such an asymmetric function improving the specification of the model. In this article, we compare both methodologies and prove that Bayesian asymmetric performs better results by considering several diagnostic criteria. Furthermore, this methodology detects some significative factors that are not revealed by the classical method, e.g., the partner’s nationality for sexual abuse or the women’s total number of intimate partners for psychological abuse. Bayesian asymmetric estimations reveal no significance concerning to the lowest partner’s level of education for physical abuse but if the intimate partner is currently studying this reduces the probability of sexual abuse. The woman’s level of education is not relevant to the physical, sexual, or psychological abuses suffered. Therefore, the findings may help identify economic and sociological factors not previously considered in this area and highlight policies that may be adopted or revised to help overcome this social problem.
“Those Plates That Save Me”: Experiences of Italian Patients With Implantable Cardioverter Defibrillator

This study aimed to investigate and understand the experiences and feelings lived by people with an ICD. Qualitative study, semi-structured interviews were conducted with a sample of 16 patients, during the annual follow-up. A hermeneutical analysis of the interviews was carried out to identify the themes. Four main themes were classified: “My heart falls asleep,” highlighting a reduced awareness of the pathology and ICD installation; “But what is this thing. ” underlining the lack of information and fears related to the device and its functioning; “I cannot hug the microwave” which describes the changes brought about by the surgery; “And with this one I am alright for the rest of my life”, involving hopes and life expectations. This study highlighted the patients’ need for major support from health professionals while processing their new life conditions.
APPLICATION OF TLBO ALGORITHM IN POWER SYSTEM

The Teaching Learning Based Optimization (TLBO) is a newer human-based optimization algorithm that has involved a lot of consideration since scholars due to its great possible for resolving optimization problem.TLBO is a great quantity of requests in various area of technology or science. The main uses are in many fields like electrical, civil, thermal mechanical engineering, physics manufacturing technology, also used in computer and electronics, and economics. TLBO algorithm, a new heuristic optimization technique which motivated by an educational atmosphere in the classroom. It has providing facilitate the method of resolving optimization problems. The main features of the algorithm is that it only wants mutual control factors for its process and does not need an algorithmic parameter. This contributes to the efficacy of the algorithm for the failure caused by the not suitable adjustment of the detailed strictures is eliminated. This article provides an overview of the uses of the TLBO algorithm in power system.
Exploring the Influence of Critical Parameters for the Effective Synthesis of High-Quality 2D MXene

Recently, a new class of two-dimensional (2D) materials, called MXene, consisting of layers of transition-metal carbides and nitrides/carbonitrides has been introduced. MXene, a multifunctional material with hydrophilic nature and excellent electrical conductivity and chemical stabilities, can be applied in diverse research areas such as energy harvesting and its storage, water purification, thermal dissipation, and gas sensing. To achieve the best quality of MXene, optimization of some important synthetic parameters is highly required such as an optimized etchant concentration to remove an “A” element from the MAX phase and sonication time for the efficient exfoliation of MXene flakes. Besides, there is a need to disclose that particular solvent through which intercalation can easily be achieved. In this work, we optimized the abovementioned critical parameters for the synthesis of good-quality MXene. Our results clearly explain the variations in the quality of MXene under applied etchant concentrations, solvents for better intercalation, and optimization of sonication time for better exfoliation. The obtained results suggest that 30% HF as an etchant, dimethyl sulfoxide (DMSO) as a solvent, and 135 min as the sonication time are effective parameters for the synthesis of good-quality MXene. We expect that this report will be helpful for the young research community to synthesize good-quality MXene with the required properties.
Exploiting Neural Query Translation into Cross Lingual Information Retrieval

As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency. To this end, existing CLIR systems mainly exploit statistical-based machine translation (SMT) rather than the advanced neural machine translation (NMT), limiting the further improvements on both translation and retrieval quality. In this paper, we investigate how to exploit neural query translation model into CLIR system. Specifically, we propose a novel data augmentation method that extracts query translation pairs according to user clickthrough data, thus to alleviate the problem of domain-adaptation in NMT. Then, we introduce an asynchronous strategy which is able to leverage the advantages of the real-time in SMT and the veracity in NMT. Experimental results reveal that the proposed approach yields better retrieval quality than strong baselines and can be well applied into a real-world CLIR system, i.e. Aliexpress e-Commerce search engine. Readers can examine and test their cases on our website: this https URL .
Multiwavelength Analysis and the Difference in the Behavior of the Spectral Features during the 2010 and 2014 Flaring Periods of the Blazar 3C 454.3

Over the years, the flat-spectrum radio quasar 3C 454.3 has presented phases of very high activity (flares) in which the different wave bands increase their flux dramatically. In this work, we perform multiwavelength analysis from radio to gamma-rays and study the Mg ii λ2798 emission line and the UV Fe ii band from 2008 to 2018. We found that an increase in the 43 GHz flux density of the quasi-stationary component C coincides with the estimated time at which a superluminal blob ejected from the radio core (which caused the brightest flare of 2010) collides with the quasi-stationary component (at a projected distance of ∼4.6 pc from the radio core). The different behavior of the spectral index in the first (5000 < JD − 2,450,000 < 5600) and second (6600 < JD − 2,450,000 < 7900) flaring periods suggests changes in the physical conditions. The complex nature of the second period can be a result of a superposition of multiple events at different locations. The Mg ii line has an anticorrelation with the UV continuum while Fe ii correlates positively, except at the time of the brightest flare of 2010, when both have a strong response at high continuum luminosities. Our results suggest that the dominant gamma-ray emission mechanism for the first flaring period is external Compton. For the second flaring period the region emitting seed photons is co-spatial with that emitting gamma-rays. However, a study of the spectral energy distribution using a multizone jet emission model is required to confirm the nature of each significant flare during the second period.
Paired Representation Learning for Event and Entity Coreference

Co-reference of Events and of Entities are commonly formulated as binary classification problems, given a pair of events or entities as input. Earlier work addressed the main challenge in these problems -- the representation of each element in the input pair by: (i) modelling the representation of one element (event or entity) without considering the other element in the pair; (ii) encoding all attributes of one element (e.g., arguments of an event) into a single non-interpretable vector, thus losing the ability to compare cross-element attributes. In this work we propose paired representation learning (PairedRL) for coreference resolution. Given a pair of elements (Events or Entities) our model treats the pair's sentences as a single sequence so that each element in the pair learns its representation by encoding its own context as well the other element's context. In addition, when representing events, PairedRL is structured in that it represents the event's arguments to facilitate their individual contribution to the final prediction. As we show, in both (within-document & cross-document) event and entity coreference benchmarks, our unified approach, PairedRL, outperforms prior state of the art systems with a large margin.
Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble

The 12-lead electrocardiogram (ECG) is a commonly used tool for detecting cardiac abnormalities such as atrial fibrillation, blocks, and irregular complexes. For the Phy-sioNet/CinC 2020 Challenge, we built an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis. For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal wave-form. We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to each class. We train a phase one set of feature importance determining models to isolate the top 1,000 most important features to use in our phase two diagnosis prediction models. We use repeated random sub-sampling by splitting our dataset of 43,101 records into 100 independent runs of 85:15 training/validation splits for our internal evaluation results. Our methodology generates us an official phase validation set score of 0.476 and test set score of − 0.080 under the team name, CVC, placing us 36 out of 41 in the rankings.
Application of Deep Learning to Sphere Decoding for Large MIMO Systems

Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO systems, where a large number of antennas are employed. To overcome this challenge, we propose fast deep learning (DL)-aided SD (FDL-SD) and fast DL-aided <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a <inline-formula> <tex-math notation="LaTeX">$24 \times 24$ </tex-math></inline-formula> MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than 90% without any performance loss compared to conventional SD schemes. For a <inline-formula> <tex-math notation="LaTeX">$32 \times 32$ </tex-math></inline-formula> MIMO system with QPSK, the proposed FDL-KSD only requires <inline-formula> <tex-math notation="LaTeX">$K = 32$ </tex-math></inline-formula> to attain the performance of the conventional KSD with <inline-formula> <tex-math notation="LaTeX">$K=256$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> is the number of survival paths in KSD. This implies a dramatic improvement in the performance–complexity tradeoff of the proposed FDL-KSD scheme.
Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings

Presentation attacks are posing major challenges to most of the biometric modalities. Iris recognition, which is considered as one of the most accurate biometric modality for person identification, has also been shown to be vulnerable to advanced presentation attacks such as 3D contact lenses and textured lens. While in the literature, several presentation attack detection (PAD) algorithms are presented; a significant limitation is the generalizability against an unseen database, unseen sensor, and different imaging environment. To address this challenge, we propose a generalized deep learning-based PAD network, MVANet, which utilizes multiple representation layers. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks. The computational complexity is an essential factor in training deep neural networks; therefore, to reduce the computational complexity while learning multiple feature representation layers, a fixed base model has been used. The performance of the proposed network is demonstrated on multiple databases such as IIITD-WVU MUIPA and IIITD-CLI databases under cross-database training-testing settings, to assess the generalizability of the proposed algorithm.
Multifunctional oxides for topological magnetic textures by design

Several challenges in designing an operational skyrmion racetrack memory are well known. Among those challenges, a few contradictions can be identified if researchers are to rely only on metallic materials. Hence, expanding the exploration of skyrmion physics into oxide materials is essential to bridge the contradicting gap. In this topical review, we first briefly revise the theories and criteria involved in stabilizing and manipulating skyrmions, followed by studying the behaviors of dipolar-stabilized magnetic bubbles. Next, we explore the properties of multiferroic skyrmions with magnetoelectric coupling, which can only be stabilized in Cu2OSeO3 thus far, as well as the rare bulk Néel-type skyrmions in some polar materials. As an interlude section, we review the theory of the anomalous and topological Hall effect (THE), before going through the recent progress of THE in oxide thin films. The debate about an alternative interpretation is also discussed. Finally, this review ends with a future outlook regarding the promising strategies of using interfacial charge transfer and (111)-orientation of perovskites to benefit the field of skyrmion research.