nils-herrmann/clean_scirepeval huggingface dataset:
Transfer Torque Performance Comparison in Coaxial Magnetic Gears With Different Flux Modulator Shapes
Venue: IEEE Transactions on Magnetics

Coaxial magnetic gears, which are capable of transferring torque without physical contact or a change in speed, have sufficient advantages, including high efficiency and reliability, allowing them to replace mechanical gears. Such the coaxial magnetic gears have therefore been receiving attention for various applications, including wind power generation and electric vehicles. However, when two rotors of a coaxial magnetic gear, which are attached using permanent magnets (PMs) rotate, a high transfer torque ripple is generated owing to the difference in magnetic resistance of the flux modulator. In particular, the torque ripple of the inner rotor is relatively higher than that of the outer rotor. These torque ripples should be reduced as much as possible during the design process because they cause vibrations and noises in the machinery. Therefore, in this paper, various flux modulator shapes are proposed to compare the transfer torque characteristics of a coaxial magnetic gear. The transfer torque characteristics resulting from the proposed shape were derived using nonlinear finite element analysis based on a 2 D numerical analysis.
The effect of triple A supply chain on performance applied to the Egyptian textile industry

The purpose of this paper is to examine the effect of triple A supply chain (TSC) on performance by collecting and analysing data from the textile industry in Egypt, the proposed research model describes the impact of TSC on organisational performance. The proposed research model and hypotheses were tested using correlation analysis and structural equation modelling based on data collected from 153 companies in the Egyptian textile industry. According to the results of the study, organisational performance is positively associated with the three dimensions of the TSC, the data used in this study were collected from 153 companies in the Egyptian textile industry. However, the generalisation of the study results may be limited by the size of the sample. This study provides a useful working model in the textile industry. The results suggest that the successful implementation of TSC will contribute to improve organisational performance.
How to ask about patient satisfaction? The visual analogue scale is less vulnerable to confounding factors and ceiling effect than a symmetric Likert scale.
Venue: Journal of advanced nursing

To study the effects of scale type (visual analogue scale vs. Likert) item order (systematic vs. random) item non response and patient related characteristics (age, gender, subjective health, need for assistance with filling out the questionnaire and length of stay) on the results of patient satisfaction surveys.  Although patient satisfaction is one of the most intensely studied issues in the health sciences, research information about the effects of possible instrument related confounding factors on patient satisfaction surveys is scant.  A quasi experimental design was employed. A non randomized sample of 150 surgical patients was gathered to minimize possible alterations in care quality.  Data were collected in May September 2014 from one tertiary hospital in Finland using the Revised Humane Caring Scale instrument. New versions of the instrument were created for the present purposes. In these versions, items were either in a visual analogue format or Likert scaled, in systematic or random order. The data were analysed using an analysis of covariance and a paired samples t test.  The visual analogue scale items were less vulnerable to bias from confounding factors than were the Likert scaled items. The visual analogue scale also avoided the ceiling effect better than Likert and the time needed to complete the visual analogue scale questionnaire was 28% shorter than that needed to complete the Likert scaled questionnaire.  The present results supported the use of visual analogue scale rather than Likert scaling in patient satisfaction surveys and stressed the need to account for as many potential confounding factors as possible.
The Likert scale analysis using parametric based Structural Equation Modeling (SEM)

The Likert scale is commonly used in survey research using primary and secondary data to measure the respondent attitude by asking insofar to which they agree or disagree with a particular questions. In generals, Likert scale would be preferred in the questionnaire development stage to ascertain the researchers conducting their research needed. However, the researchers nowadays are abuse to understand the nature of measurement scale in data analysis and thus causes the finding obtained are meaningless. This article is aimed to compare the performance of two categories of measurement scales which are 5 point and 10 points of Likert scales using the same sample size and research subject that would pave the way to understand the real different between both of these ranges using Structural Equation Modeling (SEM) Moreover, this study also interested to clarify briefly between two types of measurement scale namely ordinal and interval data. The findings reveal that 10 points of Likert scale is more efficient than 5 points of Likert scale in operating of measurement model.
Legal Yes/No Question Answering System Using Case Role Analysis
Venue: JSAI isAI Workshops

A central issue of yes/no question answering is the usage of knowledge source given a question. While yes/no question answering has been studied for a long time, legal yes/no question answering largely differs from other domains. The most distinguishing characteristic is that legal issues require precise analysis of roles and relationships of agents named in sentences. We have developed a yes/no question answering system for answering questions about a statute legal domain. Our system uses case role analysis, in order to find correspondences of roles and relationships between given problem sentences and knowledge source sentences. We applied our system to the JURISIN's COLIEE (Competition on Legal Information Extraction/Entailment) 2016 task. Our system performance was better than systems of previous task participants and shared first place in current year's task in Phase Two. This result shows the importance of the points described above, while revealing opportunities to continue further work on improving our system's accuracy.
Question Answering of Bar Exams by Paraphrasing and Legal Text Analysis
Venue: JSAI isAI Workshops

Our legal question answering system combines legal information retrieval and textual entailment, and exploits paraphrasing and sentence level analysis of queries and legal statutes. We have evaluated our system using the training data from the competition on legal information extraction/entailment (COLIEE) 2016. The competition focuses on the legal information processing required to answer yes/no questions from Japanese legal bar exams, and it consists of three phases: legal ad hoc information retrieval (Phase 1) textual entailment (Phase 2) and a combination of information retrieval and textual entailment (Phase 3) Phase 1 requires the identification of Japan civil law articles relevant to a legal bar exam query. For this phase, we have used an information retrieval approach using TF IDF and a Ranking SVM. Phase 2 requires decision on yes/no answer for previously unseen queries, which we approach by comparing the approximate meanings of queries with relevant articles. Our meaning extraction process uses a selection of features based on a kind of paraphrase, coupled with a condition/conclusion/exception analysis of articles and queries. We also identify synonym relations using word embedding, and detect negation patterns from the articles. Our heuristic selection of attributes is used to build an SVM model, which provides the basis for ranking a decision on the yes/no questions. Experimental evaluation show that our method outperforms previous methods. Our result ranked highest in the Phase 3 in the COLIEE 2016 competition.
Building The Learning Organization Mastering The 5 Elements For Corporate Learning

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Benchmark report reveals trends in corporate learning and development

An outline of the 2015 2016 industry benchmark report produced by Towards Maturity, a benchmarking research company that provides independent advice and support to help organisations use learning technologies to accelerate business performance. The purpose of this paper is to examine the report and highlights some of the key findings from the research and the conclusions drawn.  A report, making use of research data from more than 600 learning and development (L D) professionals in 55 countries and inputs from 1,600 learners. It also sets out comments and conclusions by those closely involved in the research.  Among the many findings contained in the report are that: L D professionals have high aspirations for their role; top performing organisations actively support the self directed learner, as well as equip their L D teams to cope with and lead change; evidence from top performing organisations highlights that business leaders now expect more and different things from their L D professionals; and all L D professionals are responding to the changing corporate learning climate by looking to improve their: efficiency; processes; organisational productivity and engagement; business responsiveness and learning culture.  As with all surveys even those, like this one, which include a significant sample this report is only as valuable as those surveyed are representative of the L D sector and of corporate learning. However, the benchmarking survey covers 55 countries and over 2,000 people.  This is the 12th year of the Towards Maturity benchmarking study, so this year's data rests on a wealth of previous data to provide information on trends in learning and L D. Moreover, the study's results show how L D is changing in its relationship with businesses and business leaders and how this needs to develop further.  lower costs through discovering more cost effective processes and systems; improved quality in terms of such things as products, services and customer care; and increasing sales and profits through understanding how to improve functions, operations, products, services, pre and post sales customer care, as well as lower costs.  No organisation other than Towards Maturity carries out a benchmark survey such as this.
Triple negative breast cancer has worse overall survival and cause specific survival than non triple negative breast cancer
Venue: Breast Cancer Research and Treatment

PurposeThe current American Joint Committee on Cancer (AJCC) staging manual uses tumor size, lymph node, and metastatic status to stage breast cancer across different subtypes. We examined the prognosis of triple negative breast cancer (TNBC) versus non TNBC within the same stages and sub stages to evaluate whether TNBC had worse prognosis than non TNBC.MethodsWe reviewed the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) data and identified 158,358 patients diagnosed with breast cancer from 2010 to 2012. The overall survival (OS) time and breast cancer cause specific survival time were compared between patients with TNBC and non TNBC in each stage and sub stages. The results were validated using a dataset of 2049 patients with longer follow up from our institution.ResultsCompared with patients with non TNBC, patients with TNBC had worse OS and breast cancer cause specific survival time in every stage and sub stage in univariate and multivariate analyses adjusting for age, race, tumor grade, and surgery and radiation treatments in the SEER data. The worse OS time in patients with TNBC was validated in our institutional dataset.ConclusionsPatients with TNBC have worse survival than patients with non TNBC. The new AJCC staging manual should consider breast cancer biomarker information.
Bikeshare Trip Generation in New York City

Cities around the world and in the US are implementing bikesharing systems, which allow users to access shared bicycles for short trips, typically in the urban core. Yet few scholars have examined the determinants of bikeshare station usage using a fine grained approach. The authors estimate a series of Bayesian regression models of trip generation at stations, examining the effects bicycle infrastructure, population and employment, land use mix, and transit access separately by season of the year, weekday/weekend, and user type (subscriber versus casual) The authors find that bikeshare stations located near busy subway stations and bicycle infrastructure see greater utilization, and that greater population and employment generally predict greater usage. The authors' findings are more nuanced, however; for instance, those areas with more residential population are associated with more trips by subscribers and on both weekdays and non working days; however, the effect is much stronger on non working days. Additional nuances can be found in how various land use variables affect bikeshare usage. The authors discuss these nuances in detail and elaborate on how these findings may be of use to transportation planners.
Grandparental childcare in Australia: gender differences in the correlates of providing regular grandparental care while parents work

Grandparents' regular care for children while parents work has been mostly studied from the parental perspective. This paper focuses on the grandparents. Using the Australian Bureau of Statistics Time Use Survey 2006 (N 7672) we investigate regular caring grandparents' demographic characteristics, which childcare activities they undertake, and how regular childcare provision relates to their time in other activities, subjective time pressure and satisfaction. Results indicate the correlates and nature of regular care differ by gender. Regular and non regular caring grandmothers' relative time allocation to different childcare tasks barely differs, while regular caring grandfathers' care includes a much higher proportion of active care and travel than non regular caring grandfathers' Regular care provision is associated with less leisure than non regular caring counterparts for both genders, but with only grandmothers' housework, personal care and sleep time. Providing regular care doubles the likelihood of grandmothers reporting high subjective time pressure compared to non regular caring grandmothers; there is no association between regular care and time pressure for grandfathers. We conclude that in taking on regular care, grandparents echo the gender patterns found among parents, namely that it is women who are disproportionately impacted by meeting family care needs.
Energy efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model
Venue: Inf. Sci.

One important way to extend the lifetime of wireless sensor networks is to deploy the sensors in a dense manner. The redundancy among the sensed data demonstrates that it is not efficient to collect raw data from all nodes in the network if we further consider that the data is generally spatial correlated. The node scheduling strategy aims at selecting a set of representative nodes to provide the required data service in a periodic manner with accuracy guarantee. This strategy can effectively reduce the number of active nodes and the amount of messages in the network, and extend the network lifetime accordingly. In this paper, we firstly introduce how to model the spatial correlation among sensed data by Markov Random Field (MRF) model. Secondly, we formulate the problem definitions, namely, the data amendment problem which maximizes the data coverage range for a given node by amending the raw noise corrupted data from the neighbors, while the representative nodes selection problem focuses on reducing the number of representative nodes and covering all nodes in the network, and the node scheduling problem aims at maximizing the network lifetime. Thirdly, we propose a novel Data Amendment Procedure (DAP) Representative node Selection Procedure (RSP) and energy efficient Node Scheduling Algorithm (NSA) respectively for these above problems. Finally, extensive experiments demonstrate that the proposed node scheduling algorithm can significantly improve the network lifetime compared with related works with an average increment of about 80%
Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine
Venue: Appl. Soft Comput.

Display Omitted A new status box method is proposed to perform the stock trend prediction.A new feature construction approach for status box is presented.A new hybrid classifier that integrates AdaBoost algorithm, genetic algorithm and probabilistic support vector machine.The status box method can solve the unbalance problem of the stock turning points classification. Stock trend prediction is regarded as one of the most challenging tasks of financial time series prediction. Conventional statistical modeling techniques are not adequate for stock trend forecasting because of the non stationarity and non linearity of the stock market. With this regard, many machine learning approaches are used to improve the prediction results. These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price. In this paper, we concentrate on the evaluation of the current trend of stock price and the prediction of the change orientation of the stock price in future. Then, a new approach named status box method is proposed. Different from the prediction issue of the turning points, the status box method packages some stock points into three categories of boxes which indicate different stock status. And then, some machine learning techniques are used to classify these boxes so as to measure whether the states of each box coincides with the stock price trend and forecast the stock price trend based on the states of the box. These results would support us to make buying or selling strategies. Comparing with the turning points prediction that only considered the features of one day, each status box contains a certain amount of points which represent the stock price trend in a certain period of time. So, the status box reflects more information of stock market. To solve the classification problem of the status box, a special features construction approach is presented. Moreover, a new ensemble method integrated with the AdaBoost algorithm, probabilistic support vector machine (PSVM) and genetic algorithm (GA) is constructed to perform the status boxes classification. To verify the applicability and superiority of the proposed methods, 20 shares chosen from Shenzhen Stock Exchange (SZSE) and 16 shares from National Association of Securities Dealers Automated Quotations (NASDAQ) are applied to perform stock trend prediction. The results show that the status box method not only have the better classification accuracy but also effectively solve the unbalance problem of the stock turning points classification. In addition, the new ensemble classifier achieves preferable profitability in simulation of stock investment and remarkably improves the classification performance compared with the approach that only uses the PSVM or back propagation artificial neural network (BPN)
Probabilistic back analysis based on Bayesian and multi output support vector machine for a high cut rock slope

Uncertainty of geomechanical parameters is an important consideration for rock engineering and has a very important influence on safety evaluation, design, and construction. Back analysis is a common method of determining geomechanical parameters but traditional deterministic back analysis cannot allow for consideration of this uncertainty. In this study, a new probabilistic back analysis method is proposed that integrates Bayesian methods and a multi output support vector machine (B MSVM) In this B MSVM back analysis method, Bayesian was used to deal with the uncertainty of geomechanical parameters and a multi output support vector machine (MSVM) was adopted to build the relationships between displacements and those parameters. The proposed method was applied to a high abutment rock slope at the Longtan hydropower station, China. At Longtan, the uncertainty of the two types of geomechanical parameters, Young's modulus and lateral pressure coefficients of in situ stress, were modeled as random variables. Based on the parameters identified by probabilistic back analysis, the computed displacements agreed closely with the measured displacement data monitored in the field. The result showed that B MSVM presented the uncertainty of the geomechanical parameters reasonably. Further study indicated that the performance of B MSVM could be improved greatly by updating field monitoring information regularly. The proposed method provides a significant new approach for probabilistic back analysis and contributes to the determination of realistic geomechanical parameters.
The High Cost of Harsh Discipline and Its Disparate Impact.

School suspension rates have been rising since the early 1970s, especially for children of color. One body of research has demonstrated that suspension from school is harmful to students, as it increases the risk of retention and school dropout. Another has demonstrated that school dropouts impose huge social costs on their states and localities, due to lost wages and taxes, increased crime, higher welfare costs, and poorer health. Although it is estimated that reducing school suspension rates in Texas would save the state up to $1 billion in social costs, only one study to date has linked these two bodies of research. The current study addresses some of the limitations of that study by (1) estimating a stronger causal model of the effects suspension has on dropping out of school, (2) calculating a more comprehensive set of the social costs associated with dropping out, and (3) estimating the cost of school suspensions in Florida and California, and for the U.S. as a whole. The results show that suspensions in 10th grade alone produced more than 67,000 dropouts in the U.S. and generated social costs to the nation of more than $35 billion. These results are undoubtedly conservative, since the California and U.S. estimates were limited to 10th grade students, while the Florida estimates were limited to 9th grade students. Thus, they did not capture the effects of suspensions in earlier grades.