Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiences of workers with post-COVID-19 symptoms can signpost suitable workplace accommodations

PurposeThe prevalence and multi-system nature of post-COVID-19 symptoms warrants clearer understanding of their work ability implications within the working age population. An exploratory survey was undertaken to provide empirical evidence of the work-relevant experiences of workers recovering from COVID-19.Design/methodology/approachA bespoke online survey based on a biopsychosocial framework ran between December 2020 and February 2021. It collected quantitative ratings of work ability and return-to-work status, qualitative responses about return-to-work experiences, obstacles and recommendations, along with views on employer benefits for making accommodations. A sample of 145 UK workers recovering from COVID-19 was recruited via social media, professional networks and industry contacts. Qualitative data was subject to thematic analysis. Participants were mainly from health/social care (50%) and educational settings (14%). FindingsJust over 90% indicated that they had experienced at least some post-COVID-19 symptoms, notably fatigue and cognitive effects. For 55%, symptoms lasted longer than six months. Only 15% had managed a full return-to-work. Of the 88 who provided workability ratings, just 13 and 18% respectively rated their physical and mental workability as good or very good. Difficulties in resuming work were attributed to symptom unpredictability, their interaction with job demands, managing symptoms and demands in parallel, unhelpful attitudes and expectations. Manager and peer support was reported as variable.Originality/valueWorkplace health management characterised by flexible long-term collaborative return-to-work planning, supported by more COVID-centric absence policies and organisational cultures, appear pivotal for sustaining the return-to-work of the large segments of the global workforce affected by post-COVID-19 symptoms.
Effectiveness of Repetitive Transcranial Magnetic Stimulation in the Treatment of Bipolar Disorder in Comparison to the Treatment of Unipolar Depression in a Naturalistic Setting

Repetitive transcranial magnetic stimulation (rTMS) is effective in the treatment of depression. However, for the subset of patients with bipolar disorder, less data is available and overall strength of evidence is weaker than for its use in unipolar depression. A cohort of 505 patients (of which 46 had a diagnosis of bipolar disorder) with depression who were treated with rTMS were analyzed retrospectively with regards to their response to several weeks of treatment. Hamilton Depression Rating Scale (HDRS) was assessed as main outcome. Unipolar and bipolar patients with depression did not differ significantly in baseline demographic variables or severity of depression. Both groups did not differ significantly in their response to treatment as indicated by absolute and relative changes in the HDRS and response and remission rates. On HDRS subitem-analysis, bipolar patients showed superior amelioration of the symptom “paranoid symptoms” in a statistically significant manner. In conclusion, depressed patients with a diagnosis of bipolar disorder benefit from rTMS in a similar fashion as patients with unipolar depression in a naturalistic setting. rTMS might be more effective in reducing paranoia in bipolar than in unipolar patients.
Machine Learning Model to Stratify the Risk of Lymph Node Metastasis for Early Gastric Cancer: A Single-Center Cohort Study

Simple Summary Endoscopic resection (ER) is a treatment option for clinically T1a early gastric cancer (EGC) without suspicion of lymph node metastasis (LNM). In patients with non-curative resection after ER, additional surgery is recommended owing to the LNM risk. However, of those patients treated with additional surgery after ER, the actual rate of LNM was about 5–10%; that is, the other patients underwent unnecessary surgeries. Therefore, it is crucial to estimate LNM risk in EGC patients to determine additional management after ER. We derived a machine learning (ML) model to stratify the LNM risk in EGC patients and validate its performance. The constructed ML model, which showed good performance with an area under the receiver operating characteristic of 0.85 or higher, could stratify LNM risk into very low (<1%), low (<3%), intermediate (<7%), and high (≥7%) risk categories. These findings suggest that the ML model can stratify the LNM risk in EGC patients. Abstract Stratification of the risk of lymph node metastasis (LNM) in patients with non-curative resection after endoscopic resection (ER) for early gastric cancer (EGC) is crucial in determining additional treatment strategies and preventing unnecessary surgery. Hence, we developed a machine learning (ML) model and validated its performance for the stratification of LNM risk in patients with EGC. We enrolled patients who underwent primary surgery or additional surgery after ER for EGC between May 2005 and March 2021. Additionally, patients who underwent ER alone for EGC between May 2005 and March 2016 and were followed up for at least 5 years were included. The ML model was built based on a development set (70%) using logistic regression, random forest (RF), and support vector machine (SVM) analyses and assessed in a validation set (30%). In the validation set, LNM was found in 337 of 4428 patients (7.6%). Among the total patients, the area under the receiver operating characteristic (AUROC) for predicting LNM risk was 0.86 in the logistic regression, 0.85 in RF, and 0.86 in SVM analyses; in patients with initial ER, AUROC for predicting LNM risk was 0.90 in the logistic regression, 0.88 in RF, and 0.89 in SVM analyses. The ML model could stratify the LNM risk into very low (<1%), low (<3%), intermediate (<7%), and high (≥7%) risk categories, which was comparable with actual LNM rates. We demonstrate that the ML model can be used to identify LNM risk. However, this tool requires further validation in EGC patients with non-curative resection after ER for actual application.
New Indirect Current Control Method for the Single-phase Dual Mode Time-sharing Grid-connected Inverter

In this paper,a small signal modeling method for the dual mode time-sharing grid-connected inverter was carried out.It was revealed that the control-to-grid current transfer function of this inverter has a right-half-plane zero at low corner frequency during the "Boost" operating stage,which resulted in the difficulty in designing the compensator.For this reason,a novel indirect current control strategy was proposed,and therefore a satisfactory compensator with enough phase margin and amplitude margin can be obtained since the original control system turns into a simple minimum phase one.Simulation and experimental results of a 1 kW prototype proved the effectiveness of this novel control method.
Polestar++: An Intelligent Routing Engine for National-Wide Public Transportation

Public transportation plays a critical role in people's daily life. It has been proven that public transportation is more environmentally sustainable, efficient, and economical than any other forms of travel. However, due to the increasing expansion of transportation networks and more complex travel situations, people are having difficulties in efficiently finding the most preferred route from one place to another through public transportation systems for both intra-city and inter-city trips. To this end, in this paper, we present <inline-formula><tex-math notation="LaTeX">${\mathsf {Polestar\tt {++}}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Polestar</mml:mi><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-3153711.gif"/></alternatives></inline-formula>, a data-driven engine for intelligent and efficient public transportation routing. Specifically, we first propose a novel hierarchical public transportation graph (HPTG) to model both intra-city and inter-city public transportation in terms of various travel costs, such as time or distance. Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation. After that, we propose a two-pass route candidate ranking module to capture user preferences under dynamic travel situations. In particular, we propose two re-ranking models to decide the proper order of public routes: (1) a light-weight and explainable gradient boosting decision tree (GBDT) based model that integrates features from various urban data sources, and (2) a wide and deep learning (WDL) based model that automatically captures high order feature interactions from both inter-city and intra-city routes. Finally, experiments on two real-world data sets demonstrate the advantages of <inline-formula><tex-math notation="LaTeX">${\mathsf {Polestar\tt {++}}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Polestar</mml:mi><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq2-3153711.gif"/></alternatives></inline-formula> in terms of both efficiency and effectiveness. Indeed, in early 2019, <inline-formula><tex-math notation="LaTeX">${\mathsf {Polestar\tt {++}}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Polestar</mml:mi><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq3-3153711.gif"/></alternatives></inline-formula> has been deployed on Baidu Maps, one of the world's largest map services. To date, <inline-formula><tex-math notation="LaTeX">${\mathsf {Polestar\tt {++}}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">Polestar</mml:mi><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq4-3153711.gif"/></alternatives></inline-formula> is servicing over 330 cities, answers over a hundred millions of queries each day, and achieves substantial improvement of user click ratio.
Repeated Sprint Ability of Youth Football Players in the Same Age Category According to Playing Position and Competition Level

This study aims to examine repeated sprint performances of young soccer players in the same age category according to competition level and playing positions. 67 young soccer players in the U16 age category and from 4 different teams competing in two different competition levels participated voluntarily in this study. The participants performed the Bangsbo Sprint Test adapted by Wragg (7×34,2 m with 25-second recovery) to determine repeated sprint performance. The test variables were best sprint time, mean sprint time, and the fatigue index. The best sprint time and mean sprint time results varied according to competition level (p˂0,05) but the fatigue index did not differentiate according to competition level (p˃0,05). It is also determined that at a high competition level players have revealed better-repeated sprint performance. When examined the data in terms of game position, best sprint time and mean time values have varied (p˂0,05) but the fatigue index did not have significant differences (p˃0,05). Considering the data according to game positions, forwards, full-backs and wingers showed higher performance than central midfielders, central defenders, and goalkeepers. Consequently, our results suggest that performance in repeated-sprint the best sprint values and mean sprint values belonging to repeated performances of youth players from the same age category differ according to competition levels and game positions, whereas the fatigue index does not differ.
The paperboys of Russian messaging: RT/Sputnik audiences as vehicles for malign information influence

ABSTRACT This article examines the reception and dissemination of ‘malign information influence’ (MII) in a liberal democracy; information sponsored by authoritarian regimes or other hostile actors and projected through international broadcasting outlets across borders. The study contributes to the scarce research on the reception of narratives transmitted by the Russian state-supported media platforms RT and Sputnik, exposing characteristics, political attitudes, and sharing behaviors of RT/Sputnik consumers. A nationwide, representative survey (n: 3033) from November 2020 revealed a surprisingly high number of Swedish RT/Sputnik consumers (7%), with an overrepresentation of young, men and supports of non-parliamentarian parties and the right wing, nationalist Sweden Democratic Party. These consumers are somewhat more willing than non-consumers to disseminate news on social media and in real life despite being distrustful of the sources. The findings strengthen previous research in demonstrating the attractiveness of identity grievance narratives among alternative media consumers, yet the results show that RT/Sputnik consumers also aligned with narratives that contrasts with national security policy. They state less trust in politicians, institutions, the media, news, and journalism, yet are comparatively prone to share unreliable or untrue news content on social media and in real life. The analysis thus identified a section of media consumers who can function as vehicles for the dissemination of MII. The article contributes to the under-researched problem of the potential of MII to take root and provides a basis for future qualitative research that can refine and provide nuance to the knowledge of reception of MII.
Implementation of Inertia Sensor and Machine Learning Technologies for Analyzing the Behavior of Individual Laying Hens

Simple Summary Poultry-welfare regulations have caused a shift from cage housing towards more welfare-friendly systems with more possibilities for the birds to meet their natural behavioural needs. The welfare-friendly systems with litter allow and encourage the hens to perform natural behavior including activities that lead to increases in the amount of airborne dust particles emission from such poultry houses. For successful management of these systems, the behavior of the hens needs to be considered, which is more challenging and time-consuming for the farmer. The main objective of this study was to show a proof of principle to identify, classify and analyze the behaviors of laying hens in three levels of activity by using an inertia sensor and machine learning techniques. The model was able to predict the laying hen behaviors with an accuracy of 90%. The results of such monitoring could be used by farmers in the management of poultry houses. Abstract Welfare-oriented regulations cause farmers worldwide to shift towards more welfare-friendly, e.g., loose housing systems such as aviaries with litter. In contrast to the traditional cage housing systems, good technical results can only be obtained if the behavior of hens is considered. With increasing flock sizes, the automation of behavioural assessment can be beneficial. This research aims to show a proof of principle of tools for analyzing laying-hen behaviors by using wearable inertia sensor technology and a machine learning model (ML). For this aim, the behaviors of hens were classified into three classes: static, semi-dynamic, and highly dynamic behavior. The activities of hens were continuously recorded on video and synchronized with the sensor signals. Two hens were equipped with sensors, one marked green and one blue, for five days to collect the data. The training data set indicated that the ML model can accurately classify the highly dynamic behaviors with a one-second time window; a four-second time window is accurate for static and semi-dynamic behaviors. The Bagged Trees model, with an overall accuracy of 89% was the best ML model with the F1-scores of 89%, 91%, and 87% for static, semi-dynamic, and highly dynamic behaviors. The Bagged Trees model also performed well in classifying the behaviors of the hen in the validation data set with an overall F1-score of 0.92 (uniform either % or decimals). This research illustrates that the combination of wearable inertia sensors and machine learning is a viable technique for analyzing the laying-hen behaviors and supporting farmers in the management of hens in loose housing systems.
500kV Man-Made Three-Phase Earthing Short Circuit Experiment in Northeast Power Grid

Since the interconnection of Northeast Power Grid with North China Power Grid, the dynamic properties of Northeast Power Grid were evidently worsened and its stability level was greatly decreased, in addition, different models of generators and loads significantly affected the results of stability calculation. Although the measures such as the simulation method with detailed generator model in which the dynamic behavior of exciting system is taken into account and installing power system stabilizers (PSSs) in main generators of Northeast Power Grid were adopted, it was still unable to effectively verify the reasonableness of parameter setting of PSS as well as its integrated matching result, and there was not enough scientific foundation for the selection of power grid model, especially load models. Moreover, it was hard to make accurate quantitative analysis, even though the qualitative analysis, on the close extent of selected models to actual operation of power grid. For this reason, the Northeast Power Grid Corp., Ltd., proposed the thinking of researching stability level of Northeast Power Grid under the interconnection mode of areal power grids, and on March 25, 2004 March 29, 2005, two man-made three-phase metallic ground short-circuit experiments were implemented in 500kV power grid being operated respectively and desired results were achieved. Experiment results show that based on the measured data from large disturbance tests, it is feasible and effective in practice to verify and modify load models as well as establish the research method and technical line suitable to load models of power grids in China.
Influence of the COVID-19 lockdown on Spanish professional soccer teams’ external demands according to their on-field ranking

The main objective of this study was to analyse the changes in external demand parameters (e.g., total distance, high-speed running distance, accelerations/decelerations) in Spanish professional soccer teams after the COVID-19 lockdown considering their on-field ranking (i.e., teams whose ranking worsened after the COVID-19 lockdown [WRS] vs. teams that improved their ranking after the COVID-19 lockdown [IMP]). A total of 23,527 individual match observations were collected on players competing during the 2019/20 season in the First Spanish Professional soccer League (LaLiga). Goalkeepers and players who participated for less than 10 minutes in each match were excluded. Relative total distance (TD/min), distance covered at 21–24 km · h-1 (HIRD/min) and > 24 km · h-1 per minute (VHIRD/min), high metabolic load distance (HMLD), and the number of accelerations (3 m/s2) and decelerations (< 3 m/s2) performed were analysed by the ChryonHego video-tracking system. These variables were analysed during two differentiated periods, before the COVID-19 lockdown (i.e., 27 matches) and after the COVID-19 lockdown (i.e., 11 matches), and teams were classified into two groups according to their ranking (i.e., WRS vs. IMP). R-Studio was employed for data analysis and a mixed linear model was conducted. A decrease in external demands in all teams after the COVID-19 lockdown was observed, and this decrease was greater in WRS. These results suggest that, after an inactive period (i.e., the COVID-19 lockdown), teams that return with better physical performance, mainly related to high-intensity actions, have more possibilities of improving their final qualifying position.
Intimate Partner Violence and Mental Health during Lockdown of the COVID-19 Pandemic

Background: This study took place in the context of the COVID-19 pandemic. The present research assesses the association between lockdown conditions (such as time spent at home, living environment, proximity to contamination and social contacts), mental health (including intolerance of uncertainty, anxiety and depression) and intimate partner violence within the community. This study evaluates the indirect effect of anxiety and depression on the relationship between intolerance of uncertainty and intimate partner violence (physical assault and psychological aggression). Methods: 1532 adults (80.8% of women, Mage = 35.34) were recruited from the Belgian general population through an online self-report questionnaire completed during the lockdown (from April 17 to 1 May 2020). Results: The results demonstrate that the prevalence of physical assault (including both perpetration and victimization) was significantly higher in men, whereas the prevalence of psychological aggression was significantly higher in women. Men reported significantly more violence during lockdown. Women, on the other hand, were more anxious and more intolerant of uncertainty. No difference between men and women was found for depression. Anxiety and depression significantly mediated the relationship between intolerance of uncertainty and physical assault and psychological aggression. Sex did not moderate the mediation. Conclusion: Clinical implications for public health policy are highlighted.
The Investigation of Lattice Properties for Group-IV Sigesn Ternary Alloy: By Using Machine Learning Method

Group-IV SiGeSn ternary alloy is a hot spot in the field of fabricating high-efficient Si-based light source due to its large lattice constant and bandgap variation range. However, due to the high cost and low speed of experimental and computational research, it is difficult to obtain their lattice constants comprehensively and quickly. Machine learning prediction based on statistics is an advanced method to solve this problem. In this paper, based on the existing data of group IV alloys, three machine learning methods such as Random Forest (RF), Support Vector Regression (SVR) and Gradient Boosting Decision Tree (GBDT) have been built to predict the lattice constants of SiGeSn. Firstly, the lattice constants of Group-IV alloys are collected to construct data set; Then, the data set are used to train the machine learning models which describe the quantitative relationship between concentrations and lattice constants; Finally, the prediction performance of these models are compared with each other, and the concentrations with appropriate lattice constants are predicted. The results show the comprehensive performance of SVR model is better than the other two, which means the SVR model can be used to directly predict the lattice constants of SiGeSn.