nils-herrmann/clean_scirepeval huggingface dataset:
Power Factor Corrected Welding Power Supply Using Modified Zeta Converter

A power factor (PF) corrected single stage, two-switch isolated zeta converter is proposed for arc welding. This modified zeta converter is having two switches and two clamping diodes on the primary side of a high-frequency transformer. This, in turn, results in reduced switch stress. The proposed converter is designed to operate in a discontinuous inductor current mode (DICM) to achieve inherent PF correction at the utility. The DICM operation substantially reduces the complexity of the control and effectively regulates the output dc voltage. The proposed converter offers several features, such as inherent overload current limit and fast parametrical response, to the load and source voltage conditions. This, in turn, results in an improved performance in terms of power quality indices and an enhanced weld bead quality. The proposed modified zeta converter is designed and its performance is simulated in the MATLAB/Simulink environment. Simulated results are also verified experimentally on a developed prototype of the converter. The performance of the system is investigated in terms of its input PF, displacement PF, total harmonic distortion of ac mains current, voltage regulation, and robustness to prove its efficacy in overall performance.
Sex differences in aggression between heterosexual partners: a meta-analytic review.

Meta-analyses of sex differences in physical aggression to heterosexual partners and in its physical consequences are reported. Women were slightly more likely (d = -.05) than men to use one or more act of physical aggression and to use such acts more frequently. Men were more likely (d = .15) to inflict an injury, and overall, 62% of those injured by a partner were women. The findings partially support previous claims that different methods of measurement produce conflicting results, but there was also evidence that the sample was an important moderator of effect size. Continuous models showed that younger aged dating samples and a lower proportion of physically aggressive males predicted effect sizes in the female direction. Analyses were limited by the available database, which is biased toward young dating samples in the United States. Wider variations are discussed in terms of two conflicting norms about physical aggression to partners that operate to different degrees in different cultures.
Study of obstacle avoidance navigation robot control based on bland man tracing wall theory

In the field of robots' obstacle avoidance and navigation, indirect contact sensors such as visual, ultrasonic and infrared detection are widely used. However, the performance of these sensors is always influenced by the severe environment, especially under the dark, dense fog, underwater conditions. The obstacle avoidance robot based on tactile sensor is proposed in this paper to realize the autonomous obstacle avoidance navigation by only using three dimensions force sensor. In addition, the mathematical model and algorithm are optimized to make up the deficiency of tactile sensor. Finally, the feasibility and reliability of this study are verified by the simulation results.
Industrial-Organizational Psychology and Organizational Behavior Management : An Objective Comparison

This article compares traditional industrial-organizational psychology (I-O) research published in Journal of Applied Psychology (JAP) with organizational behavior management (OBM) research published in Journal of Organizational Behavior Management (JOBM). The purpose of this comparison was to identify similarities and differences with respect to research topics and methodologies, and to offer suggestions for what OBM researchers and practitioners can learn from I-O. Articles published in JAP from 1987-1997 were reviewed and compared to articles published during the same decade in JOBM (Nolan, Jarema, & Austin, 1999). This comparison includes Barbara R. Bucklin, Alicia M. Alvero, Alyce M. Dickinson, John Austin, and Austin K. Jackson are affiliated with Western Michigan University. Address correspondence to Alyce M. Dickinson, Department of Psychology, Western Michigan University, Kalamazoo, MI 49008-5052 (E-mail: alyce.dickinson@ wmich.edu.)  D ow nl oa de d by [ W es te rn M ic hi ga n U ni ve rs ity ] at 1 1: 14 0 3 Se pt em be r 20 12 JOURNAL OF ORGANIZATIONAL BEHAVIOR MANAGEMENT 28 (a) author characteristics, (b) authors published in both journals, (c) topics addressed, (d) type of article, and (e) research characteristics and methodologies. Among the conclusions are: (a) the primary relative strength of OBM is its practical significance, demonstrated by the proportion of research addressing applied issues; (b) the greatest strength of traditional I-O appears to be the variety and complexity of organizational research topics; and (c) each field could benefit from contact with research published in the other.
A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer

The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant process is highlighted as one of the important topics among cancer researches. In this article, we propose an algorithm for quantifying biological processes by using gene expression profiles over a sample population, which involves the idea of constructing principal curves to condense information of each biological process by a novel scoring scheme on an individualized manner. After applying our method on several large-scale breast cancer datasets in survival analysis, a subset of these biological processes extracted from corresponding survival model is then found to have significant associations with clinical outcomes. Further analyses of these biological processes enable the study of the interplays between biological processes and cancer phenotypes of interest, provide us valuable insights into cancer biology in biological process level and guide the precision treatment for cancer patients. And notably, prognosis predictions based on our method are consistently superior to the existing state of art methods with the same intention.
The Probabilistic Method

The probabilistic method comes up in various fields in mathematics. In these notes, we will give a brief introduction to graph theory and applications of the probabilistic method in proving bounds for Ramsey numbers and a theorem in graph cuts. This method is based on the following idea: in order to prove the existence of an object with some desired property, one defines a probability space on some larger class of objects, and then shows that an element of this space has the desired property with positive probability. The elements contained in this probability space may be of any kind. We will illustrate the probabilistic method by giving applications in graph theory.
Vitamin D3 and calcium to prevent hip fractures in elderly women.

Hypovitaminosis D and a low calcium intake contribute to increased parathyroid function in elderly persons. Calcium and vitamin D supplements reduce this secondary hyperparathyroidism, but whether such supplements reduce the risk of hip fractures among elderly people is not known.



We studied the effects of supplementation with vitamin D3 (cholecalciferol) and calcium on the frequency of hip fractures and other nonvertebral fractures, identified radiologically, in 3270 healthy ambulatory women (mean [+/- SD] age, 84 +/- 6 years). Each day for 18 months, 1634 women received tricalcium phosphate (containing 1.2 g of elemental calcium) and 20 micrograms (800 IU) of vitamin D3, and 1636 women received a double placebo. We measured serial serum parathyroid hormone and 25-hydroxyvitamin D (25(OH)D) concentrations in 142 women and determined the femoral bone mineral density at base line and after 18 months in 56 women.



Among the women who completed the 18-month study, the number of hip fractures was 43 percent lower (P = 0.043) and the total number of nonvertebral fractures was 32 percent lower (P = 0.015) among the women treated with vitamin D3 and calcium than among those who received placebo. The results of analyses according to active treatment and according to intention to treat were similar. In the vitamin D3-calcium group, the mean serum parathyroid hormone concentration had decreased by 44 percent from the base-line value at 18 months (P < 0.001) and the serum 25(OH)D concentration had increased by 162 percent over the base-line value (P < 0.001). The bone density of the proximal femur increased 2.7 percent in the vitamin D3-calcium group and decreased 4.6 percent in the placebo group (P < 0.001).



Supplementation with vitamin D3 and calcium reduces the risk of hip fractures and other nonvertebral fractures among elderly women.
Temporal Data Mining for Educational Applications

Intelligent tutoring systems (ITSs) acquire rich data about students behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. This paper surveys a variety of data mining techniques for analyzing how students interact with ITSs, including methods for handling hidden state variables, and for testing hypotheses. To illustrate these methods we draw on data from two ITSs for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.
Cardiac Rehabilitation: Improving Function and Reducing Risk.

Cardiac rehabilitation is a comprehensive multidisciplinary program individually tailored to the needs of patients with cardiovascular disease. The overall goals focus on improving daily function and reducing cardiovascular risk factors. Cardiac rehabilitation includes interventions aimed at lowering blood pressure and improving lipid and diabetes mellitus control, with tobacco cessation, behavioral counseling, and graded physical activity. The physical activity component typically involves 36 sessions over 12 weeks, during which patients participate in supervised exercise under cardiac monitoring. There are also intensive programs that include up to 72 sessions lasting up to 18 weeks, although these programs are not widely available. Additional components of cardiac rehabilitation include counseling on nutrition, screening for and managing depression, and assuring up-to-date immunizations. Cardiac rehabilitation is covered by Medicare and recommended for patients following myocardial infarction, bypass surgery, and stent placement, and for patients with heart failure, stable angina, and several other conditions. Despite proven benefits in mortality rates, depression, functional capacity, and medication adherence, rates of referral for cardiac rehabilitation are suboptimal. Groups less likely to be referred are older adults, women, patients who do not speak English, and persons living in areas where cardiac rehabilitation is not locally available. Additionally, primary care physicians refer patients less often than cardiologists and cardiothoracic surgeons.
Rich Short Text Conversation Using Semantic-Key-Controlled Sequence Generation

With the recent advances of the sequence-to-sequence framework, generation approaches for the short text conversation STC become attractive. The traditional sequence-to-sequence approaches for the STC often suffer from poor diversity and general reply without substantiality. It is also hard to control the topic or semantics of the selected reply from multiple generated candidates. In this paper, a novel external-memory-driven sequence-to-sequence learning approach is proposed to address these problems. A tensor of the external memory is constructed to represent interpretable topics or semantics. During generation, a controllable memory trigger is extracted given the input sequence, and a reply is then generated using the memory trigger as well as the sequence-to-sequence model. Experiments show that the proposed approach can generate much richer diversity than the traditional sequence-to-sequence training with attention. Meanwhile, it achieves better quality score in human evaluation. It is also observed that by manually manipulating the memory trigger, it is possible to interpretably guide the topics or semantics of the reply.
Acoustic, phonetic, and discriminative approaches to automatic language identification

Formal evaluations conducted by NIST in 1996 demonstrated that systems that used parallel banks of tokenizer-dependent language models produced the best language identification performance. Since that time, other approaches to language identification have been developed that match or surpass the performance of phone-based systems. This paper describes and evaluates three techniques that have been applied to the language identification problem: phone recognition, Gaussian mixture modeling, and support vector machine classification. A recognizer that fuses the scores of three systems that employ these techniques produces a 2.7% equal error rate (EER) on the 1996 NIST evaluation set and a 2.8% EER on the NIST 2003 primary condition evaluation set. An approach to dealing with the problem of out-of-set data is also discussed.
Strategies for preventing defection based on the mean time to defection and their implementations on a self-organizing map

Customer retention is a critical issue for the survival of any business in today’s competitive marketplace. In this paper, we propose a dynamic procedure utilizing self-organizing maps and a Markov process for detecting and preventing customer defection that uses data of past and current customer behavior. The basic concept originates from empirical observations that identified that a customer has a tendency to change behavior (i.e. trim-out usage volumes) before eventual withdrawal and defection. Our explanatory model predicts when potential defectors are likely to withdraw. Two strategies are suggested to respond to the question of where to lead potential defectors for the next stage, based on anticipating when the potential defector will leave. Our model predicts potential defectors with little deterioration of prediction accuracy compared with that of the multilayer perceptron neural network and decision trees. Moreover, it performs reasonably well in a controlled experiment using an online game.
Antioxidant properties of various solvent extracts of total phenolic constituents from three different agroclimatic origins of drumstick tree (Moringa oleifera Lam.) leaves.

Water, aqueous methanol, and aqueous ethanol extracts of freeze-dried leaves of Moringa oleifera Lam. from different agroclimatic regions were examined for radical scavenging capacities and antioxidant activities. All leaf extracts were capable of scavenging peroxyl and superoxyl radicals. Similar scavenging activities for different solvent extracts of each collection were found for the stable 1,1-diphenyl 2-picrylhydrazyl (DPPH(*)) radical. Among the three different moringa samples, both methanol and ethanol extracts of Indian origins showed the highest antioxidant activities, 65.1 and 66.8%, respectively, in the beta-carotene-linoleic acid system. Nonetheless, increasing concentration of all the extracts had significantly (P < 0.05) increased reducing power, which may in part be responsible for their antioxidant activity. The major bioactive compounds of phenolics were found to be flavonoid groups such as quercetin and kaempferol. On the basis of the results obtained, moringa leaves are found to be a potential source of natural antioxidants due to their marked antioxidant activity. This is the first report on the antioxidant properties of the extracts from freeze-dried moringa leaves. Overall, both methanol (80%) and ethanol (70%) were found to be the best solvents for the extraction of antioxidant compounds from moringa leaves.
Machine learning for financial market prediction

The usage of machine learning techniques for the prediction of financial time series is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative methods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful at predicting daily and minutely prices from a wide range of asset classes. Committees of discriminative techniques (Support Vector Machines (SVM), Relevance Vector Machines and Neural Networks) are found to perform well when incorporating sophisticated exogenous financial information in order to predict daily FX carry basket returns. The higher dimensionality that Electronic Communication Networks make available through order book data is transformed into simple features. These volumebased features, along with other price-based ones motivated by common trading rules, are used by Multiple Kernel Learning (MKL) to classify the direction of price movement for a currency over a range of time horizons. Outperformance relative to both individual SVM and benchmarks is found, along with an indication of which features are the most informative for financial prediction tasks. Fisher kernels based on three popular market microstructural models are added to the MKL set. Two subsets of this full set, constructed from the most frequently selected and highest performing individual kernels are also investigated. Furthermore, kernel learning is employed optimising hyperparameter and Fisher feature parameters with the aim of improving predictive performance. Significant improvements in out-of-sample predictive accuracy relative to both individual SVM and standard MKL is found using these various novel enhancements to the MKL algorithm.
Psoriasis patients with psoriasis Area and Severity Index (PASI) 90 response achieve greater health-related quality-of-life improvements than those with PASI 75-89 response: results from two phase 3 studies of secukinumab.

The emergence of new biological therapies showing high and sustained level of Psoriasis Area and Severity Index (PASI) 90 response has provided the possibility of both greater skin clearance and increased quality of life (QOL).



To evaluate the association of greater response in skin clearance with improvements in skin-related QOL up to 52 weeks.



Subjects achieving various levels of skin clearance (PASI 90-100 or PASI 75-89) and Dermatology Life Quality Index (DLQI) (0/1) response were compared using ERASURE and FIXTURE trial data. Similar analyses with IGA ratings of Clear or Almost Clear were performed.



Significantly more PASI 90-100 responders at week 12 had DLQI 0/1 response than PASI 75-89 (69.4% vs. 47.1%; p < .001) and sustained DLQI 0/1 response at week 52 (74.0% vs. 56.7%; p < .001). IGA results were similar.



These results show that PASI 90-100 is a relevant therapeutic goal in moderate to severe psoriasis compared to PASI 75-89 when considering patients' QOL.
A text visualization method for cross-domain research topic mining

Cross-domain research topic mining can help users find relationships among related research domains and obtain a quick overview of these domains. This study investigates the evolution of crossdomain topics of three interdisciplinary research domains and uses a visual analytic approach to determine unique topics for each domain. This study also focuses on topic evolution over 10 years and on individual topics of cross domains. A hierarchical topic model is adopted to extract topics of three different domains and to correlate the extracted topics. A simple yet effective visualization interface is then designed, and certain interaction operations are provided to help users more deeply understand the visualization development trend and the correlation among the three domains. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method.
Faster variational inducing input Gaussian process classification

Gaussian processes (GP) provide an elegant and effective approach to learning in kernel machines. This approach leads to a highly interpretable model and allows using the bayesian framework for model adaptation and incorporating the prior knowledge about the problem. GP framework is successfully applied to regression, classification and dimensionality reduction problems. Unfortunately, the standard methods for both GP-regression and GP-classification scale as O(n 3), where n is the size of the dataset, which makes them inapplicable to big data problems. A variety of methods have been proposed to overcome this limitation both for regression and classification problems. The most successful recent methods are based on the concept of inducing inputs. These methods reduce the computational complexity to O(nm 2), where m is the number of inducing inputs with m typically much less, than n. In this work we focus on classification. The current state-of-the-art method for this problem is based on stochastic optimization of an evidence lower bound, that depends on O(m 2) parameters. For complex problems, the required number of inducing points m is fairly big, making the optimization in this method challenging.  We analyze the structure of variational lower bound that appears in inducing input GP classification. First we notice that using quadratic approximation of several terms in this bound, it is possible to obtain analytical expressions for optimal values of most of the optimization parameters, thus sufficiently reducing the dimension of optimization space. Then we provide two methods for constructing necessary quadratic approximations. One is based on Jaakkola-Jordan bound for logistic function and the other one is derived using Taylor expansion.  We propose two new variational lower bounds for inducing input GP classification that depend on a number of parameters. Then we propose several methods for optimization of these bounds and compare the resulting algorithms with the state-of-the-art approach based on stochastic optimization. Experiments on a bunch of classification datasets show that the new methods perform as well or better than the existing one. However, new methods don't require any tunable parameters and can work in settings within a big range of n and m values thus significantly simplifying training of GP classification models.
Nanosecond laser therapy reverses pathologic and molecular changes in age-related macular degeneration without retinal damage.

Age-related macular degeneration (AMD) is a leading cause of vision loss, characterized by drusen deposits and thickened Bruch's membrane (BM). This study details the capacity of nanosecond laser treatment to reduce drusen and thin BM while maintaining retinal structure. Fifty patients with AMD had a single nanosecond laser treatment session and after 2 yr, change in drusen area was compared with an untreated cohort of patients. The retinal effect of the laser was determined in human and mouse eyes using immunohistochemistry and compared with untreated eyes. In a mouse with thickened BM (ApoEnull), the effect of laser treatment was quantified using electron microscopy and quantitative PCR. In patients with AMD, nanosecond laser treatment reduced drusen load at 2 yr. Retinal structure was not compromised in human and mouse retina after laser treatment, with only a discrete retinal pigment epithelium (RPE) injury, and limited mononuclear cell response observed. BM was thinned in the ApoEnull mouse 3 mo after treatment (ApoEnull treated 683 ± 38 nm, ApoEnull untreated 890 ± 60 nm, C57Bl6J 606 ± 43 nm), with the expression of matrix metalloproteinase-2 and -3 increased (>260%). Nanosecond laser resolved drusen independent of retinal damage and improved BM structure, suggesting this treatment has the potential to reduce AMD progression.