LLukas22/scidocs huggingface dataset:
Sexual abuse history, alcohol intoxication, and women's sexual risk behavior.

We examined potential differences in women's likelihood of sexual risk taking in a laboratory setting based on alcohol intoxication and sexual abuse history. Participants (n = 64) were classified as non-sexually abused (NSA) or as having experienced sexual abuse in childhood only (CSA) or adulthood only (ASA) and randomly assigned to consume alcoholic (.06, .08, or .10% target blood alcohol content) or non-alcoholic drinks, after which participants read and responded to a risky sex vignette. Dependent measures included vaginal pulse amplitude, self-reported sexual arousal, likelihood of engaging in condom use and risky sexual behaviors described in the vignette, and mood. NSA and ASA women did not differ significantly on any dependent measures. CSA women reported significantly lower likelihood of condom use and unprotected intercourse relative to NSA and ASA women. Intoxicated women reported significantly greater sexual arousal, positive mood, and likelihood of risky sex relative to sober women. Intoxicated CSA women reported significantly more likelihood of unprotected oral sex and less likelihood of condom use relative to intoxicated NSA and ASA and sober CSA women. CSA women's increased risk of sexually transmitted infections (STIs) may be driven by non-condom use and behavioral changes while intoxicated. These findings provide preliminary insight into situational influences affecting CSA women's increased STI risk.
Introduction to simulation: introduction to simulation

Simulation is a powerful tool if understood and used properly. This introduction to simulation tutorial is designed to teach the basics of simulation, including structure, function, data generated, and its proper use. The introduction starts with a definition of simulation, goes through a talk about what makes up a simulation, how the simulation actually works, and how to handle data generated by the simulation. Throughout the paper, there is discussion on issues concerning the use of simulation in industry.
Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, X. Wang · Y. Huang (B) Department of Mechanical Engineering, Clemson University, Clemson, SC 29634-0921, USA e-mail: yongh@clemson.edu W. Wang College of Mechanical and Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China N. Nguyen · K. Krishnakumar Intelligent Systems Division, NASA Ames Research Center, Moffett Field, CA 94035, USA it is expected to be applicable to other tool wear modeling applications.
Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community

An online learning community enables learners to access up-to-date information via the Internet anytime–anywhere because of the ubiquity of the World Wide Web (WWW). Students can also interact with one another during the learning process. Hence, researchers want to determine whether such interaction produces learning synergy in an online learning community. In this paper, we take the Technology Acceptance Model as a foundation and extend the external variables as well as the Perceived Variables as our model and propose a number of hypotheses. A total of 436 Taiwanese senior high school students participated in this research, and the online learning community focused on learning English. The research results show that all the hypotheses are supported, which indicates that the extended variables can effectively predict whether users will adopt an online learning community. Finally, we discuss the implications of our findings for the future development of online English learning communities. 2009 Elsevier Ltd. All rights reserved.
The Basic Reproduction Number in a Multi-city Compartmental Epidemic Model

A directed graph with cities as vertices and arcs determined by outgoing (or return) travel represents the mobility component in a population of individuals who travel between n cities. A model with 4 epidemiological compartments in each city that describes the propagation of a disease in this population is formulated as a system of 4n ordinary differential equations. Terms in the system account for disease transmission, latency, recovery, temporary immunity, birth, death, and travel between cities. The basic reproduction number R0 is determined as the spectral radius of a nonnegative matrix product, and easily computable bounds on R0 are obtained.
A Route-Directed Hybrid Genetic Approach For The Vehicle Routing Problem With Time Windows

A route-directed hybrid genetic approach to address the Vehicle Routing Problem with Time Windows is presented. The proposed scheme relies on the concept of simultaneous evolution of two populations pursuing different objectives subject to partial constraint relaxation. The first population evolves individuals to minimize total traveled distance while the second focuses on minimizing temporal constraint violation to generate a feasible solution, both subject to a fixed number of tours. Genetic operators have been designed to incorporate key concepts emerging from recent promising techniques such as insertion heuristics and large neighborhood search to further explore the solution space. Results from a computational experiment over common benchmark problems show that the proposed technique matches or outperforms some of the best heuristic routing procedures, providing six new best-known solutions. In comparison, the method proved to be fast, cost-effective and highly competitive.
A Meta-Learning Approach for Robust Rank Learning

Learning effective feature-based ranking functions is a fundamental task for search engines, and has recently become an active area of research [10, 3, 2]. Many of these recent algorithms are based on the pairwise preference framework, in which instead of taking documents in isolation, document pairs are used as instances in the learning process. One disadvantage of this process is that a noisy relevance judgement on a single document can lead to a large number of mis-labeled document pairs. This can jeopardize robustness and deteriorate overall ranking performance. In this paper we study the effects of outlying pairs in rank learning with pairwise preferences and introduce a new meta-learning algorithm capable of suppressing these undesirable effects. This algorithm works as a second optimization step in which any linear baseline ranker can be used as input. Experiments on eight different ranking datasets show that this optimization step produces statistically significant performance gains over various state-of-the-art baseline rankers.
Computational Intelligence and Financial Markets: A Survey and Future Directions

Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. © 2016 Published by Elsevier Ltd.
Poor 10-year survivorship of hip resurfacing arthroplasty

Background and purpose - In a previous registry report, short-term implant survival of hip resurfacing arthroplasty (HRA) in Finland was found to be comparable to that of total hip arthroplasty (THA). Since then, it has become evident that adverse reactions to metal debris (ARMDs) may also be associated with HRA, not only with large-diameter head metal-on-metal THA. The aim of the study was to assess medium- to long-term survivorship of HRA based on the Finnish Arthroplasty Register (FAR). Patients and methods - 5,068 HRAs performed during the period 2001-2013 in Finland were included. Kaplan-Meier survival analysis was used to calculate survival probabilities and their 95% confidence intervals (CIs). Cox multiple regression, with adjustment for age, sex, diagnosis, femoral head size, and hospital volume was used to analyze implant survival of HRA devices with revision for any reason as endpoint. The reference group consisted of 6,485 uncemented Vision/Bimetric and ABG II THAs performed in Finland over the same time period. Results - The 8-year survival, with any revision as an endpoint, was 93% (CI: 92-94) for Birmingham Hip Resurfacing (BHR), 86% (CI: 78-94) for Corin, 91% (CI: 89-94) for ReCap, 92% (CI: 89-96) for Durom, and was 72% (CI: 69-76) for the Articular Surface Replacement (ASR). The 10-year survival, with any revision as an endpoint, for reference THAs was 92% (CI: 91-92) and for all HRAs it was 86% (CI: 84-87%). Female HRA patients had about twice the revision risk of male patients. ASR had an inferior outcome: the revision risk was 4-fold higher than for BHR, the reference implant. Interpretation - The 10-year implant survival of HRAs is 86% in Finland. According to new recommendations from NICE (The National Institute for Health and Care Excellence), an HRA/THA should have a revision rate of 5% or less at 10 years. None of the HRAs studied achieved this goal.
A hybrid KMV model, random forests and rough set theory approach for credit rating

In current credit ratings models, various accounting-based information are usually selected as prediction variables, based on historical information rather than the market’s assessment for future. In the study, we propose credit rating prediction model using market-based information as a predictive variable. In the proposed method, Moody’s KMV (KMV) is employed as a tool to evaluate the market-based information of each corporation. To verify the proposed method, using the hybrid model, which combine random forests (RF) and rough set theory (RST) to extract useful information for credit rating. The results show that market-based information does provide valuable information in credit rating predictions. Moreover, the proposed approach provides better classification results and generates meaningful rules for credit ratings. 2012 Elsevier B.V. All rights reserved.
User experience design and agile development: managing cooperation through articulation work

Previous discussions of how User Experience (UX) designers and Agile developers can work together have focused on bringing the disciplines together by merging their processes or adopting specific techniques. This paper reports in detail on one observational study of a mature Scrum team in a large organization, and their interactions with the UX designers working on the same project. The evidence from our study shows that Agile development and UX design practice is not explained by rationalized accounts dealing with processes or techniques. Instead, understanding practice requires examining the wider organizational setting in which the Agile developers and UX designers are embedded. Our account focuses on the situatedness of the work by making reference to values and assumptions in the organizational setting, and the consequences that those values and assumptions had for practice. In this organizational setting, cooperation between the Agile developers and UX designers was achieved through ongoing articulation work by the developers, who were compelled to engage a culturally distinct UX design division. Based on this study, insights into culture, self-organization and purposeful work highlight significant implications for practice. Copyright q 2011 John Wiley & Sons, Ltd.
A Motivational Model of Performance-Enhancing Substance Use in Elite Athletes

Donahue and Vallerand are with the Laboratoire de Recherche sur le Comportement Social, Département de psychologie, Université du Québec à Montréal, Montréal, Québec, Canada; Miquelon is with the Psychology Department, McGill University, Montréal, Québec, Canada; Valois is with the Département des fondements et pratiques en éducation, Faculté dʼéducation, Université Laval, Québec, Canada; Goulet and Buist are with the Direction de la promotion de la sécurité, Ministère de lʼÉducation, du Loisir et du Sport, Québec Government. Journal of Sport & Exercise Psychology, 2006, 28, 511-520 © 2006 Human Kinetics, Inc.
Biochemical Parameters and Oxidative Resistance to Thermal Treatment of Refined and Unrefined Vegetable Edible Oils

Vidrih R., Vidakovič S., Abramovič H. (2010): Biochemical parameters and oxidative resistance to thermal treatment of refined and unrefined vegetable edible oils. Czech J. Food Sci., 28: 376–384. In human nutrition fats are physiologically important food constituents but also the components most liable to oxidative degradation. The oils included in the study were refined (sunflower, extra-sunflower, soybean, and rapeseed) as well as unrefined (olive and pumpkin-seed) oils. The aim of our study was to determine the fatty acid composition, tocopherol content, and quality parameters such as the free fatty acid content, peroxide value, and induction time. Extra virgin olive oil had the highest average peroxide value, while unrefined pumpkin seed oil had the lowest one. The acid value of the unrefined oils was higher on average than that of the refined oils. Soybean oil had the highest total tocopherol content and extra virgin olive oil the lowest one. The refined oils with higher contents of saturated and monounsaturated fatty acids and lower polyunsaturated fatty acid contents had a high oxidative stability. A negative correlation has been found in the oils between the induction time and polyunsaturated fatty acid content. Among the oils investigated, unrefined pumpkin seed oil was the most oxidatively stable, the other oils following in the decreasing order: extra virgin olive > high oleic sunflower > rapeseed > soybean > sunflower oil. The oxidative stability of the unrefined oils was better than that of the refined oils.
Evolution of the prokaryotes.

Abstract A scheme for the evolution of the prokaryotes is proposed. This is based on the assumptions that (1) the main course of early prokaryotic evolution after the development of an effective genetic apparatus was determined by the problem of generating metabolically usable energy under anerobic conditions and (2) that each of the major metabolic types of anerobic bacteria existing today is mainly composed of surviving representatives of the corresponding stages in prokaryote evolution. In this paper, known species of bacteria are arranged in a postulated phylogenetic sequence which begins with the simplest anerobes and progresses through a series of relatively small, discrete steps to the photosynthetic bacteria and the blue-green algae. The latter, by introducing substantial amounts of oxygen into the biosphere, radically altered the possibilities available for energy production, and led to a second major era of prokaryote evolution with the appearance of the aerobes. Some of the implications of this hypothesis for the phylogeny and taxonomy of the prokaryotes are discussed.
BFH-OST, a new predictive screening tool for identifying osteoporosis in postmenopausal Han Chinese women

PURPOSE
To develop a simple new clinical screening tool to identify primary osteoporosis by dual-energy X-ray absorptiometry (DXA) in postmenopausal women and to compare its validity with the Osteoporosis Self-Assessment Tool for Asians (OSTA) in a Han Chinese population.


METHODS
A cross-sectional study was conducted, enrolling 1,721 community-dwelling postmenopausal Han Chinese women. All the subjects completed a structured questionnaire and had their bone mineral density measured using DXA. Using logistic regression analysis, we assessed the ability of numerous potential risk factors examined in the questionnaire to identify women with osteoporosis. Based on this analysis, we build a new predictive model, the Beijing Friendship Hospital Osteoporosis Self-Assessment Tool (BFH-OST). Receiver operating characteristic curves were generated to compare the validity of the new model and OSTA in identifying postmenopausal women at increased risk of primary osteoporosis as defined according to the World Health Organization criteria.


RESULTS
At screening, it was found that of the 1,721 subjects with DXA, 22.66% had osteoporosis and a further 47.36% had osteopenia. Of the items screened in the questionnaire, it was found that age, weight, height, body mass index, personal history of fracture after the age of 45 years, history of fragility fracture in either parent, current smoking, and consumption of three of more alcoholic drinks per day were all predictive of osteoporosis. However, age at menarche and menopause, years since menopause, and number of pregnancies and live births were irrelevant in this study. The logistic regression analysis and item reduction yielded a final tool (BFH-OST) based on age, body weight, height, and history of fracture after the age of 45 years. The BFH-OST index (cutoff =9.1), which performed better than OSTA, had a sensitivity of 73.6% and a specificity of 72.7% for identifying osteoporosis, with an area under the receiver operating characteristic curve of 0.797.


CONCLUSION
BFH-OST may be a powerful and cost-effective new clinical risk assessment tool for prescreening postmenopausal women at increased risk for osteoporosis by DXA, especially for Han Chinese women.
Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

Muscle fatigue is the decline in ability of a muscle to create force. Electromyography (EMG) is a medical technique for measuring muscle response to nervous stimulation. During a sustained muscle contraction, the power spectrum of the EMG shifts towards lower frequencies. These effects are due to muscle fatigue. Muscle fatigue is often a result of unhealthy work practice. In this research, the effectiveness of the wavelet transform applied to the surface EMG (SEMG) signal as a means of understanding muscle fatigue during walk is presented. Power spectrum and bispectrum analysis on the EMG signal getting from right rectus femoris muscle is executed utilizing various wavelet functions (WFs). It is possible to recognize muscle fatigue appreciably with the proper choice of the WF. The outcome proves that the most momentous changes in the EMG power spectrum are symbolized by WF Daubechies45. Moreover, this research has compared bispectrum properties to the other WFs. To determine muscle fatigue during gait, Daubechies45 is used in this research to analyze the SEMG signal.
Knowledge Extraction From Trained Neural Networks

Received Jul 16 th , 2012 Revised Aug 01 th , 2012 Accepted Sept 02 th , 2012 Artificial neural networks (ANN) are very efficient in solving various kinds of problems But Lack of explanation capability (Black box nature of Neural Networks) is one of the most important reasons why artificial neural networks do not get necessary interest in some parts of industry. In this work artificial neural networks first trained and then combined with decision trees in order to fetch knowledge learnt in the training process. After successful training, knowledge is extracted from these trained neural networks using decision trees in the forms of IF THEN Rules which we can easily understand as compare to direct neural network outputs. We use decision trees to train on the results set of trained neural network and compare the performance of neural networks and decision trees in knowledge extraction from neural networks. Weka machine learning simulator with version 3.7.5 is used for research purpose. The experimental study is done on bank customers‟ data which have 12 attributes and 600 instances. The results study show that although neural networks takes much time in training and testing but are more accurate in classification then decision trees. Keyword:
Hybrid diagnostic techniques

The Automated Maintenance Procedures at the Boeing Company's Huntsville Hybrid Facility were the result of a joint development effort by the Boeing Company and International Business Machines. (IBM). The analog and linkage portions are the responsibility of Boeing while the Digital System is maintained by IBM.