Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of coefficient of static friction with one-axis force sensor by observing peripheral local slips on elastic hemisphere

With the development of aging societies, medical costs due to slip-related-falls (SRFs) continue to increase. In order to monitor the accidents, wearable shoe systems to detect slips are promising tools to prevent SRFs in daily activities. However, a complex and heavy construction is a bottleneck of the shoe systems. In this paper, a simple sensor mechanism capable of detecting static coefficient of friction (SCOF) only by a one-axis force sensor with elastic hemisphere is proposed. In order to investigate design guidelines for the slip sensor, we experimentally evaluated (i) the Young's modulus of the elastic hemisphere and (ii) the ratio of the hemisphere diameter to the one-axis force sensor diameter. These results indicate that the proposed sensor detects the SCOF by only one-axis force sensor with soft elastic hemisphere. Furthermore, the best diameter ratio of one-axis force sensor and elastic hemisphere is 1/3 with a measurement range from 70 to 230 , which range is suitable for sensor-embedded systems.
Evaluation of a free-viewing task to measure distinct negative and positive biases in depression

Attentional bias has traditionally been inferred through the measurement of reaction-time-based tasks. Eye-tracking offers a way to measure attention bias directly, and free-viewing tasks with intricate stimuli presentations may capture the complexities and dynamics of attention bias in ways previous modalities have not. The present study developed a free-viewing task using a data-driven stimuli selection process. Two free-viewing tasks were created using sad and neutral stimuli, and happy and neutral stimuli to tease apart the distinct effects of negative and positive bias. In this study, eye tracking data was collected and analyzed from n = 130 participants using mixed-effect and generalized linear models. Results revealed the interaction term (depression severity and stimuli valence) influenced dwell time on emotional stimuli, such that with an increase of 1 SD in depression severity (7.87 points on the Beck Depression Inventory-II), participants spent less 60 ms less time viewing sad stimuli and 25 ms less viewing happy present examined measures of attentional bias derived from eye-tracking data collected via a free-viewing task. The task of and we two tasks: and Lazarov and similar free-viewing task for depression; however, the task combined both happy and sad faces within the matrix The authors highlighted that while this made the task more efficient, it restricted their ability to tease apart the independent effects of happy and sad biases. Thus, in the present study, we had two separate tasks consisting of happy and neutral faces, and sad and neutral faces, in order to examine the influence of these biases more clearly. Additionally, we felt it was important to keep the entire task as single valence (e.g. sad/neutral or happy/neutral) as opposed to alternating valence in each trial so that we could look at trial-by-trial fluctuations in attention. In addition to testing the psychometrics of the task with new stimuli, we also examined several trial-level outcomes within mixed-effects models: dwell time on emotional areas of interest (AOIs) per trial; latency to first fixation; and length of first fixation. Dwell time was calculated by summing up the fixations on either emotional or neutral AOIs in each trial. We also examined two summary-level outcomes (e.g. collapsing across trials): the proportion of trials where the first fixation was on emotional stimuli; and the proportion of trials where total fixation time for emotional stimuli exceeded neutral stimuli.
Reinforcement learning-based detection method for malware behavior in industrial control systems

Due to the popularity of intelligent mobile devices, malwares in the internet have seriously threatened the security of industrial control systems. Increasing number of malware attacks has become a major concern in the information security community. Currently, with the increase of malware variants in a wide range of application fields, some technical challenges must be addressed to detect malwares and achieve security protection in industrial control systems. Although many traditional solutions have been developed to provide effective ways of detecting malwares, some current approaches have their limitations in intelligently detecting and recognizing malwares, as more complex malwares exist. Given the success of machine learning methods and techniques in data analysis applications, some advanced algorithms can also be applied in the detection and analysis of complex malwares. To detect malwares and consider the advantages of machine learning algorithms, we developed a detection framework for malwares that threatens the network security of industrial control systems through the combination of an advanced machine learning algorithm, i.e., reinforcement learning. During the implementation process, according to the actual needs of malware behavior detection, key modules including feature extraction, policy, and classification networks were designed on the basis of the intelligent features of reinforcement learning algorithms in relation to sequence decision and dynamic feedback learning. Moreover, the training algorithms for the above key modules were presented while providing the detailed functional analysis and implementation framework. In the application experiments, after preprocessing the actual dataset of malwares, the developed method was tested and the satisfactory classification performance for malware was achieved that verified the efficiency and effectiveness of the reinforcement learning-based method. This method can provide an intelligent decision aid for general malware behavior detection.
Distinction between Natural and Anthropogenic Contaminants of Atmospheric Precipitates from Northeastern Kansas Based on Their Elemental Contents and Strontium Isotopic Signatures

This study was designed to identify and possibly evaluate the changing occurrence of major pollutants in different atmospheric precipitates (rain, snow and hail) that were collected in northeastern Kansas next to the Konza Prairie Preservation site by analyzing their elemental and Sr isotope compositions. Potential pollutants like the local soils and their clay material, as well as the fly ash of a nearby coal-burning power plant were also analyzed. Positively correlated with K in the analyzed precipitates, the Na contents of the precipitates suggest a supply of fertilizers and/or natural plant organics. Combining the four identified pollutants of the precipitates that is to say the soils, the fly ash, the fertilizers and the plants allows evaluation of their changing contribution during a single precipitation event. The duration of the rain events monitors also the changing contribution of the identified pollutants. Variations of the 87Sr/86Sr ratio from precipitates during lasting events are confirmed by changing distribution patterns of the REEs. In fact, soil minerals contribute mostly at the beginning of the precipitation events and are replaced progressively by the industrial fly ash that becomes dominant towards the end of the precipitation events, depending on the duration and wind directions. At last not least, the different contaminants are recognizable by changing elemental contributions, REE distribution patterns and 87Sr/86Sr ratios. Their variable occurrence can be followed in the wet solutes, but a strict quantification cannot be provided due to elemental and isotopic interconnections between the natural and the anthropogenic contributors.
Clinical and Subclinical Bovine Mastitis and Associated Risk factors in Small-scale Dairy Farms in Bahir Dar and its envirion, Amhara region

Background Cross-sectional study was conducted from June 2017 to January 2018 with the objective of determining the prevalence of mastitis and identify the associated risk factors in and around Bahir dar dairy farms. Totally 302 lactating cows were selected using simple random sampling method. Initially, mastitis cases were diagnosed based on California Mastitis Test (MCT) results and the nature of gel formation. Then positive samples were subjected to bacterial culture and isolation using blood agar, MacConkey agar, colony morphology, Gram stain, and biochemical tests. Statistical analysis was carried out using STATA software and association of mastitis with risk factors was analyzed using chi square test. The association was considered as significant when p-value was less than 0.05. Result The overall cow level prevalence of mastitis using CMT was 25.16% (76/302) with clinical and sub clinical mastitis of 3.64% and 21.52%, respectively. Similarly, the quarter level mastitis was 10.43% (126/1208) with clinical and sub clinical cases of 1.49% and 8.94%, respectively. Bacteria identified were Staphylococci Spp, Escherichia coli, Bacillus Spp, and Streptococcus Spp. with a proportion of 66.07% . 94%, 8.0% and 7.14%, respectively. Body condition score, cow Hygiene, Parity, history of mastitis, stage of lactation, teat lesion, use of same clothing, lactating mastitis positive animal last were found to be significant risk factors. Conclusion The current study revealed moderate prevalence of mastitis warranting application of appropriate hygienic practices during milking and awareness creation to the dairy farmers.
rLH/rFSH Combination Improved Cumulative Live Birth Rate in Patients With LH Over-Suppression Following GnRH Agonist Pituitary Down-Regulation Compared to rFSH or hMG Alone: A Retrospective Cohort Study With Propensity Score Matching

PurposeThe suppression of luteinizing hormone (LH) in patients undergoing gonadotropin-releasing hormone agonist (GnRH-a) pituitary down-regulation may cause LH deficiency which may impact follicular development. However, little is known about effect of LH adding in patients with LH over-suppression. This study to investigate the effects of different gonadotropins on the cumulative live birth rate (CLBR) in the patients with LH over-suppression after GnRH-a pituitary down-regulation. MethodsThis retrospective study used propensity score-matching methodology to compare CLBR, as the primary endpoint, in the patients with LH over-suppression after different GnRH-a pituitary down-regulation regimens, including recombinant follicle stimulating hormone (rFSH) combined with recombinant LH (rLH), or using rFSH alone, or human menopausal gonadotropin (hMG) alone. The secondary endpoints included biochemical pregnancy rate, clinical pregnancy rate, and live birth rate in fresh embryo transfer cycles. ResultsA total of 88 patients were enrolled after matching: 22 patients in the rFSH+rLH group, 44 in the rFSH group, and 22 in the hMG group. The CLBR of the rFSH+rLH group was significantly higher than that of the rFSH and hMG groups (19/22, 86.4% vs. 25/44, 56.8%, P = 0.014; vs. 7/22, 31.8%, P < 0.001). Moreover, the rFSH group had a higher CLBR than the hMG group (P = 0.048). There were no significant differences in any of the secondary endpoints (all P > 0.05). Conclusion﻿Exogenous rLH supplementation achieved a higher CLBR than rFSH or hMG alone among patients with LH over-suppression; furthermore, rFSH alone was superior to hMG alone for CLBR.
A cross-sectional online survey of an impact of COVID-19 lockdown on smartphone addiction and nomophobia among undergraduate health sciences students of a rural tertiary health-care center from Maharashtra, India

Introduction: Increased use of smartphones can lead to smartphone addiction and “no-mobile-phone-phobia” known as nomophobia. During an ongoing coronavirus disease 2019 (COVID-19) lockdown, to tackle an issue of boredom, many people including undergraduate health sciences students (HSSs) are using smartphones, leading to an increased risk of smartphone addiction and nomophobia. During lockdown, many universities have shifted the mode of learning to e-classes by using an internet-based technology on smartphones, which may contribute to increased exposure to smartphones, leading to an increased risk of smartphone addiction among students. Objective: To assess an impact of COVID-19 lockdown on smartphone addiction and severity of nomophobia among UG HSSs. Materials and Methods: The present study was conducted at a rural tertiary health-care center in Maharashtra state of India with a sample size of 412. Smartphone Addiction Scale–Short Version was used to assess smartphone addiction and Nomophobia Questionnaire was used to assess nomophobia. Results: Majority (69.7%) reported inability to concentrate on studies at home due to increased use of smartphone during the COVID-19 lockdown. Boredom (32.8%) was the most bothering thing during lockdown, and to tackle it, 55.8% reported of using smartphone. 97.6% had nomophobia and 45.1% had smartphone addiction. There was increase in the prevalence of smartphone addiction and nomophobia during the COVID-19 lockdown as compared to the prevalence from studies which were conducted in the pre-COVID-19 lockdown period. Conclusion: Appropriate measures should be taken by health sciences universities and authorities to address an issue of smartphone addiction and nomophobia so that students can concentrate on their studies at home during the COVID-19 lockdown.
Microbial Pathogenesis' Damage-Response Framework

Numerous infectious illnesses produced by bacteria that seldom cause disease in normal, healthy immunocompetent hosts emerged in the late twentieth century. The development of these illnesses demonstrates that a conventional notion of pathogenicity and virulence fail to account for the reality that microbial pathogenesis is influenced by both the bacterium and the host. To overcome this barrier to studying host–microbe interactions, we offer the 'damage-response' framework, a novel theoretical paradigm for understanding microbial pathogenesis.
Aggregate productivity slowdown in Europe: New evidence from corporate balance sheets

Capitalising on the productivity decomposition proposed by Olley and Pakes (1996), we analyse the role of financial factors behind the relatively muted post-crisis rebound in productivity compared to previous upturns in Europe. Firstly, we provide an OLS-consistent framework to decompose sector-level productivity into trend and allocative efficiency components. We then extend our approach to estimate the contribution of firm-level confounders to the sector-level allocative component. Secondly, we find that financial leverage played an important role in explaining the change in aggregate productivity growth in Europe between 2004 and 2017. Thirdly, focusing on Northern and Western Europe, we show that the productivity potential could not be fully exploited due to access to credit conditions. Specifically, reducing collateral bottlenecks could more than double the effectiveness of financial leverage in spurring productivity growth in this region between 2014-17.
Analisis Pengaruh Bahasa Pertama terhadap Bahasa Target pada Siswa Multilingual di Kampung Inggris

<div align="center"><table class="TableGrid1" width="645" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="439"><p><strong>Abstract:</strong> This study aims to determine language errors that are conducted by multilingual students that are influenced by some languages that they have mastered before. This research is using a descriptive qualitative research. Data collection of the study uses field observation, providing questionnaires, interview, and documentation. The results of this study indicate that multilingual learners who still have low language skill in target language tend to make mistakes and that are influenced by their first language (Interlingual interference). </p><p><strong>Abstrak:</strong> Penelitian ini bertujuan untuk mengetahui kesalahan berbahasa yang dilakukan oleh peserta didik multilingual yang dipengaruhi oleh bahasa yang pernah mereka kuasai sebelumnya. Penelitian ini merupakan penelitian kualitatif deskriptif. Teknik pengumpulan data dalam penelitian ini dengan menggunakan pengamatan lapangan, membagaikan kuisoner, wawancara, dan dokumentasi. Adapun hasil penelitian ini menunjukkan jika peserta didik multilingual yang masih memiliki kemampuan bahasa yang rendah cenderung melakukan kesalahan dan bahasa yang dipengaruhi oleh bahasa pertama mereka (Interlingual interference). </p></td></tr></tbody></table></div>
Evaluating Performance of Insurance Sector in Pakistan Through Benchmark Performance Indicators

Benchmarking has been very important in measurement, comparison, and improvement. Important benchmarking indicators such as leverage, liquidity, stock performance, profitability, asset utilization and growth are used to examine the performance of various companies in different businesses. The current study is based on secondary data, which were collected from annual reports of 14 insurance companies of the Listed Pakistan Stock Exchange during the period of 2014-2018. For this purpose, the Technique for order Preferences by the Similarity to Ideal Solution (TOPSIS) based on Grey Relation Analysis (GRA) has been used. The findings of the study help managers of moderate growth and low growth insurance companies to formulate policies to improve their performance by looking at the policies of highly growing Insurance companies. The results concluded that overall ranking of insurance companies from 2014 to 2018, JLIL ranked 1st in overall performance in the year 2014. It is due to high net sales and total investment returns as compared to other insurance companies.
Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17

Evapotranspiration (ET) is the water-vapor flux trans ported from the surface of the Earth into the atmosphere and is the sum of surface water directly evaporated and subsurface water transpired by plants. ET rates are commonly estimated by using potential or reference ET, which might differ from actual ET rates. Actual evapotranspiration (ETa) rates can be estimated by using the Operational Simplified Surface Energy Balance (SSEBop) model. This report evaluates SSEBop ETa rates at the point and basin scales in Florida and parts of Alabama and Georgia for 2000–17. ETa rates computed by using data from 24 micrometeorological stations in Florida are referred to as mETa rates and were used to quantify biases in the SSEBop ETa rates, stratified by generalized land-use type. Bias was computed as mETa minus SSEBop ETa rates for given generalized land-use types, and bias-correction equations were computed by using least-squares regressions. In addition to mETa rates at station locations, annual average ETa rates calculated from the application of a water-balance method to 55 basins in Florida and parts of Alabama and Georgia were used to assess the accuracy of the annual SSEBop ETa rates at the basin scale. Another independent model used to simulate ETa rates was based on monthly reference ET from the statewide daily reference evapotranspiration (ETo) gridded dataset for Florida computed by using Geostationary Operational Environmental Satellite estimates of solar radiation (GOES ETo). ETa at grid points was computed as monthly GOES ETo multiplied by ratios of monthly mETa to GOES ETo, computed at micrometeorological stations and stratified by each generalized land-use type. The coefficient of determination (R 2 ) between monthly mETa and SSEBop ETa rates for all stations combined improved from 0.37 before bias correction of SSEBop ETa rates to 0.79 after the bias correction stratified by land-use type. For individual land-uses types, R 2 varied from 0.59 for the monthly mETa stations in land-use type of forested wetland with low to medium canopy density. [mETa, actual evapotranspiration computed by using micrometeorological station data; u , uncorrected Operational Simplified Surface Energy Balance; Simplified Surface
Category-Controlled Encoder-Decoder for Fake News Detection

The existing data-driven approaches typically capture credibility-indicative representations from relevant articles for fake news detection, such as skeptical and conflicting opinions. However, these methods still have several drawbacks: 1) Due to the difficulty of collecting fake news, the capacity of the existing datasets is relatively small; and 2) there is considerable unverified news that lacks conflicting voices in relevant articles, which makes it difficult for the existing methods to identify their credibility. Especially, the differences between true and fake news are not limited to whether there are conflict features in their relevant articles, but also include more extensive hidden differences at the linguistic level, such as the perspectives of emotional expression (like extreme emotion in fake news), writing style (like the shocking title in clickbait), etc., the existing methods are difficult to fully capture these differences. To capture more general and wide-ranging differences between true and fake news, in this paper, directly from the different categories of news itself, we propose a Category-controlled Encoder-Decoder model (CED) to generate examples with category-differentiated features and extend the dataset capacity to achieve data enhancement effect, thus enhancing fake news detection. Specifically, to make the generated examples enrich more news features, we develop news-guided encoder to guide relevant articles to generate news-semantic context representations. To drive the generated examples to contain more category-differentiated features, we devise category-controlled decoder which relies on pattern-shared unit to respectively capture intra-category shared features within true or fake news, and employs restriction unit to force the two types of shared features to be more different for highlighting inter-category differentiated features. The experimental results on three datasets demonstrate the superiority of CED.
Reconstructing Z3 Proofs With KeY

KeY dient zur formalen Verifikation spezifizierter Eigenschaften von Java-Programmen.Dafur werden aus der formalen Spezifikation sowie dem Programm-Code Beweisverpflichtungen generiert. Diese werden dann Schritt fur Schritt in eine Menge von Formeln der Pradikatenlogik erster Stufe uberfuhrt. Da diese allerdings unentscheidbar ist, ist es eine grose Herausforderung, einen Beweis fur diese Formelmenge zu finden. Moderne SMT-Solver, wie zum Beispiel Z3, sind genau auf diesen Anwendungszweck hin optimiert. Daher ist schon lange in KeY die Moglichkeit eingebaut, (Teil-)Probleme fur SMT-Solver zu ubersetzen. Das Ergebnis bei diesem Vorgehen ist ein partieller Beweis in KeY, der von (moglicherweise mehreren) SMT-Antworten komplettiert wird. Da Z3 aber auch Beweise fur seine Antworten liefern kann, gibt es hier Verbesserungspotential: In dieser Thesis wird eine Technik zum Nachspielen der Z3 Beweise in KeY vorgestellt, sodass man als Ergebnis einen geschlossenen Beweis in KeY erhalt und die SMT-Antworten verworfen werden konnen. Herausforderungen sowohl systematischer als auch technischer Natur werden identifiziert and Losungen dafur vogestellt. Schlieslich wird auch eine prototypische Implementierung der Technik zum Nachspielen der Beweise zur Verfugung gestellt. Im Evaluations-Teil der Arbeit wird die Leistungsfahigkeit dieser Implementierung sowie die zukunfigen Moglichkeiten erortert.