nils-herrmann/clean_scirepeval huggingface dataset:
Congruence of perceived effective clinical teaching characteristics between students and preceptors of nurse anesthesia programs.

This study continues landmark research, by Katz in 1984 and Hartland and Londoner in 1997, on characteristics of effective teaching by nurse anesthesia clinical instructors. Based on the literature review, there is a highlighted gap in research evaluating current teaching characteristics of clinical nurse anesthesia instructors that are valuable and effective from an instructor's and student's point of view. This study used a descriptive, quantitative research approach to assess (1) the importance of 24 characteristics (22 effective clinical teaching characteristics identified by Katz, and 2 items added for this study) of student registered nurse anesthetists (SRNAs) and clinical preceptors, who are Certified Registered Nurse Anesthetists, and (2) the congruence between the student and preceptor perceptions. A Likert-scale survey was used to assess the importance of each characteristic. The study was conducted at a large Midwestern hospital. The findings of this study did not support the results found by Hartland and Londoner based on the Friedman 2-way analysis. The rankings of the 24 characteristics by the students and the clinical preceptors in the current research were not significantly congruent based on the Kendall coefficient analysis. The results can help clinical preceptors increase their teaching effectiveness and generate effective learning environments for SRNAs.
Impact of Employees Motivation on Organizational Effectiveness

The purpose of this paper is to identify the factors that effects employee motivation and examining the relationship between organizational effectiveness and employee motivation. A model was designed based on the literature, linking factors of employee motivation with employee motivation and organizational effectiveness. Three hypotheses were build based on the literature and the model and were tested in perspective of the previous studies and literature. The literature and various studies concluded that factors: empowerment and recognition have positive effect on employee motivation. More the empowerment and recognition of employees in an organization is increased, more will their motivation to work will enhance. Also there exists a positive relationship between employee motivation and organizational effectiveness. The more the employees are motive to tasks accomplishment higher will the organizational performance and success. The study focuses on the practice and observance of the two central factors, empowerment and employee recognition for enhancing employee motivation which leads to organizational effectiveness. The organizations should design their rules, policies and organizational structures that give space to the employee to work well and appreciate them on their tasks fulfillment and achievements. This will surely lead to organizational growth.
Formative Assessment: A Critical Review

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Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial—Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial

The lowering of cholesterol concentrations in individuals at high risk of cardiovascular disease improves outcome. No study, however, has assessed benefits of cholesterol lowering in the primary prevention of coronary heart disease (CHD) in hypertensive patients who are not conventionally deemed dyslipidaemic.



Of 19342 hypertensive patients (aged 40-79 years with at least three other cardiovascular risk factors) randomised to one of two antihypertensive regimens in the Anglo-Scandinavian Cardiac Outcomes Trial, 10305 with non-fasting total cholesterol concentrations 6.5 mmol/L or less were randomly assigned additional atorvastatin 10 mg or placebo. These patients formed the lipid-lowering arm of the study. We planned follow-up for an average of 5 years, the primary endpoint being non-fatal myocardial infarction and fatal CHD. Data were analysed by intention to treat.



Treatment was stopped after a median follow-up of 3.3 years. By that time, 100 primary events had occurred in the atorvastatin group compared with 154 events in the placebo group (hazard ratio 0.64 [95% CI 0.50-0.83], p=0.0005). This benefit emerged in the first year of follow-up. There was no significant heterogeneity among prespecified subgroups. Fatal and non-fatal stroke (89 atorvastatin vs 121 placebo, 0.73 [0.56-0.96], p=0.024), total cardiovascular events (389 vs 486, 0.79 [0.69-0.90], p=0.0005), and total coronary events (178 vs 247, 0.71 [0.59-0.86], p=0.0005) were also significantly lowered. There were 185 deaths in the atorvastatin group and 212 in the placebo group (0.87 [0.71-1.06], p=0.16). Atorvastatin lowered total serum cholesterol by about 1.3 mmol/L compared with placebo at 12 months, and by 1.1 mmol/L after 3 years of follow-up.



The reductions in major cardiovascular events with atorvastatin are large, given the short follow-up time. These findings may have implications for future lipid-lowering guidelines.
Review on Tuberculosis Detection Using Various Data Mining Techniques

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Data Mining is one of the most important and inspiring area of research with the goal of discovering purposeful information from massive data sets. Data mining plays an important role in healthcare field to detect causation of various diseases, their treatment methods .Tuberculosis is one of the well-known disorders among all the persons in the nation including India. Tuberculosis is a virus which strikes the immune system of an individual, usually transmits through air. It is primarily occurs in lungs. It is the typical cause of necrosis. This paper discussed about various data mining techniques to detect tuberculosis such as Classification, Clustering and Association. There are some parameters that are also useful to detect Tuberculosis like Age, Cough, Fever, Chest pain and Weight Loss etc.
Dropout Improves Recurrent Neural Networks for Handwriting Recognition

Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition. We show that their performance can be greatly improved using dropout - a recently proposed regularization method for deep architectures. While previous works showed that dropout gave superior performance in the context of convolutional networks, it had never been applied to RNNs. In our approach, dropout is carefully used in the network so that it does not affect the recurrent connections, hence the power of RNNs in modeling sequences is preserved. Extensive experiments on a broad range of handwritten databases confirm the effectiveness of dropout on deep architectures even when the network mainly consists of recurrent and shared connections.
Mobile Augmented Reality : the potential for education

The rapid evolution of technology has changed the face of education, especially when technology was combined with adequate pedagogical foundations. This combination has created new opportunities for improving the quality of teaching and learning experiences. Until recently, Augmented Reality (AR) is one of the latest technologies that offer a new way to educate. Due to the rising popularity of mobile devices globally, the widespread use of AR on mobile devices such as smartphones and tablets has become a growing phenomenon. Therefore, this paper reviews several literatures concerning the information about mobile augmented reality and exemplify the potentials for education.  Selection and peer-review under responsibility of The Association of Science, Education and Technology-TASET, Sakarya Universitesi, Turkey.
OpenSample: A Low-Latency, Sampling-Based Measurement Platform for Commodity SDN

In this paper we propose, implement and evaluate OpenSample: a low-latency, sampling-based network measurement platform targeted at building faster control loops for software-defined networks. OpenSample leverages sFlow packet sampling to provide near-real-time measurements of both network load and individual flows. While OpenSample is useful in any context, it is particularly useful in an SDN environment where a network controller can quickly take action based on the data it provides. Using sampling for network monitoring allows OpenSample to have a 100 millisecond control loop rather than the 1-5 second control loop of prior polling-based approaches. We implement OpenSample in the Floodlight Open Flow controller and evaluate it both in simulation and on a test bed comprised of commodity switches. When used to inform traffic engineering, OpenSample provides up to a 150% throughput improvement over both static equal-cost multi-path routing and a polling-based solution with a one second control loop.
String Stable Heterogeneous Vehicle Platoon Using Cooperative Adaptive Cruise Control

In this paper, a heterogeneous vehicle platoon equipped with Cooperative Adaptive Cruise Control (CACC) systems is studied. First, various causes of heterogeneity are reviewed. A selection of parameters is made, for which string stability is analyzed. The influence of controller parameters and headway time on string stability is studied. Numerical simulation results provide guidelines on how to choose controller parameters and headway time for different vehicles using CACC.
Effect of virtual reality training on laparoscopic surgery: randomised
controlled trial

To assess the effect of virtual reality training on an actual laparoscopic operation.



Prospective randomised controlled and blinded trial.



Seven gynaecological departments in the Zeeland region of Denmark.



24 first and second year registrars specialising in gynaecology and obstetrics.



Proficiency based virtual reality simulator training in laparoscopic salpingectomy and standard clinical education (controls).



The main outcome measure was technical performance assessed by two independent observers blinded to trainee and training status using a previously validated general and task specific rating scale. The secondary outcome measure was operation time in minutes.



The simulator trained group (n=11) reached a median total score of 33 points (interquartile range 32-36 points), equivalent to the experience gained after 20-50 laparoscopic procedures, whereas the control group (n=10) reached a median total score of 23 (22-27) points, equivalent to the experience gained from fewer than five procedures (P<0.001). The median total operation time in the simulator trained group was 12 minutes (interquartile range 10-14 minutes) and in the control group was 24 (20-29) minutes (P<0.001). The observers' inter-rater agreement was 0.79.



Skills in laparoscopic surgery can be increased in a clinically relevant manner using proficiency based virtual reality simulator training. The performance level of novices was increased to that of intermediately experienced laparoscopists and operation time was halved. Simulator training should be considered before trainees carry out laparoscopic procedures.
USING LOGISTIC REGRESSION TO PREDICT CUSTOMER RETENTION

Logistic regression is an increasingly popular statistical technique used to model the probability of discrete (i.e., binary or multinomial) outcomes. When properly applied, logistic regression analyses yield very powerful insights in to what attributes (i.e., variables) are more or less likely to predict event outcome in a population of interest. These models also show the extent to which changes in the values of the attributes may increase or decrease the predicted probability of event outcome.
Speed Daemon: Experience-Based Mobile Robot Speed Scheduling

A time-optimal speed schedule results in a mobile robot driving along a planned path at or near the limits of the robot's capability. However, deriving models to predict the effect of increased speed can be very difficult. In this paper, we present a speed scheduler that uses previous experience, instead of complex models, to generate time-optimal speed schedules. The algorithm is designed for a vision-based, path-repeating mobile robot and uses experience to ensure reliable localization, low path-tracking errors, and realizable control inputs while maximizing the speed along the path. To our knowledge, this is the first speed scheduler to incorporate experience from previous path traversals in order to address system constraints. The proposed speed scheduler was tested in over 4 km of path traversals in outdoor terrain using a large Ackermann-steered robot travelling between 0.5 m/s and 2.0 m/s. The approach to speed scheduling is shown to generate fast speed schedules while remaining within the limits of the robot's capability.
A Survey on Multi-label Classification for Images

The area of an image multi-label classification is increase continuously in last few years, in machine learning and computer vision. Multi-label classification has attracted significant attention from researchers and has been applied to an image annotation. In multi-label classification, each instance is assigned to multiple classes; it is a common problem in data analysis. In this paper, represent general survey on the research work is going on in the field of multi-label classification. Finally, paper is concluded towards challenges in multi-label classification for images for future research.
Attacking Binarized Neural Networks

Neural networks with low-precision weights and activations offer compelling efficiency advantages over their full-precision equivalents. The two most frequently discussed benefits of quantization are reduced memory consumption, and a faster forward pass when implemented with efficient bitwise operations. We propose a third benefit of very low-precision neural networks: improved robustness against some adversarial attacks, and in the worst case, performance that is on par with full-precision models. We focus on the very low-precision case where weights and activations are both quantized to ±1, and note that stochastically quantizing weights in just one layer can sharply reduce the impact of iterative attacks. We observe that non-scaled binary neural networks exhibit a similar effect to the original defensive distillation procedure that led to gradient masking, and a false notion of security. We address this by conducting both black-box and white-box experiments with binary models that do not artificially mask
Final report : Dynamic Asset Allocation Using Reinforcement Learning

The role of portfolio managers is to optimize the allocation of capital across various financial assets such as stocks, bonds or funds to maximize a chosen performance metric like expected returns or risk-adjusted returns. This task is traditionally performed in two steps as described in . First, it is necessary to predict the expected returns, and the covariance matrix of the returns of the assets considered. Then a quadratic programming problem can be solved to either minimize the risk of the portfolio for a constrained return or maximize its return for a given risk. Finding an optimal way of allocating assets in a portfolio in order to outperform the market is still a very active research area in finance as recently reviewed in . Reinforcement learning, as a global procedure to maximize cumulative reward from a random underlying environment, fits perfectly with the problem of asset allocation [6], . Hence we need to model our problem as a Markov Decision process (MDP). Finding the optimal policy from this model can then be solved using model based reinforcement learning  or Q-learning , .
Reference-Dependent Risk Attitudes

Daniel Kahneman and Amos Tversky’s (1979) prospect theory, and the literature building from it, provide theories of risk attitudes based on a few regularities. Most importantly, evaluation of an outcome is influenced by how it compares to a reference point, with people exhibiting both a significantly greater aversion to losses than appreciation of gains, and a diminishing sensitivity to changes in an outcome as it moves farther from the reference point. In addition, people weight the probability of a prospect nonlinearly, overweighting small probabilities and underweighting high probabilities. The implications of prospect theory have been studied with several different specifications of the reference point, including the status quo, lagged status quo, and the mean of the chosen lottery. These various approaches explain many risk attitudes that are inconsistent with the classical diminishing-marginal-utility-of-wealth Reference-Dependent Risk Attitudes
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction

Deep convolutional neural networks (DCNNs) have led to breakthrough results in numerous practical machine learning tasks, such as classification of images in the ImageNet data set, control-policy-learning to play Atari games or the board game Go, and image captioning. Many of these applications first perform feature extraction and then feed the results thereof into a classifier. The mathematical analysis of DCNNs for feature extraction was initiated by Mallat, 2012. Specifically, Mallat considered so-called scattering networks based on a wavelet transform followed by the modulus non-linearity in each network layer, and proved translation invariance (asymptotically in the wavelet scale parameter) and deformation stability of the corresponding feature extractor. This paper complements Mallat’s results by developing a theory that encompasses general convolutional transforms, or in more technical parlance, general semi-discrete frames (including Weyl–Heisenberg filters, curvelets, shearlets, ridgelets, wavelets, and learned filters), general Lipschitz-continuous non-linearities (e.g., rectified linear units, shifted logistic sigmoids, hyperbolic tangents, and modulus functions), and general Lipschitz-continuous pooling operators emulating, e.g., sub-sampling and averaging. In addition, all of these elements can be different in different network layers. For the resulting feature extractor, we prove a translation invariance result of vertical nature in the sense of the features becoming progressively more translation-invariant with increasing network depth, and we establish deformation sensitivity bounds that apply to signal classes such as, e.g., band-limited functions, cartoon functions, and Lipschitz functions.
Meaning and Use of not...until

Time adverbials introduced by until impose restrictions on the aspectual class of the main clause they combine with: they only combine with durative sentences. In negative sentences, the situation is more complex. The question arises whether negative sentences are durative, or whether there is a separate use of until as a negative polarity item. In this paper, I discuss the three treatments of not. . . until that are characterized in the literature as the scope analysis, the ambiguity thesis and the lexical composition approach. I work out the interpretation of the three approaches in an event-based semantics, and argue that they are truth-conditionally equivalent in sentences containing an explicit negation. Furthermore, they generate the same pragmatic implicatures. A separate negative polarity use of until is motivated by sentences containing NPI-licensers different from explicit negation, though. The observation that the scope analysis, the ambiguity thesis and the lexical composition approach are semantically and pragmatically equivalent in sentences containing an explicit negation helps us describe the similarities and differences between the expression of exclusion of a range of values on the rime axis in a variety of languages.
Fields and Objects in Space, Time, and Space-time

The well-known distinction between field-based and objectbased approaches to spatial information is generalised to arbitrar y locational frameworks, including in particular space, time and space-time. W systematically explore the different ways in which these approaches can be c ombined, and address the relative merits of a fully four-dimensional app roach as against a more conventional ‘three-plus-one’-dimensional approac h. We single out as especially interesting in this respect a class of phenomena , here calledmultiaspect phenomena , which seem to present different aspects when considered from different points of view. Such phenomena (e.g., floods, wildfires, processions) are proposed as the most natural candidates for tr eatment as fully four-dimensional entities (‘hyperobjects’), but it remai ns problematic how to model them so as to do justice to their multi-aspectual nat ure. The paper ends with a range of important researchable questions aimed at clearing up some of the difficulties raised.
Studies with psychedelic drugs in human volunteers

Scientific curiosity and fascination have played a key role in human research with psychedelics along with the hope that perceptual alterations and heightened insight could benefit well-being and play a role in the treatment of various neuropsychiatric disorders. These motivations need to be tempered by a realistic assessment of the hurdles to be cleared for therapeutic use. Development of a psychedelic drug for treatment of a serious psychiatric disorder presents substantial although not insurmountable challenges. While the varied psychedelic agents described in this chapter share some properties, they have a range of pharmacologic effects that are reflected in the gradation in intensity of hallucinogenic effects from the classical agents to DMT, MDMA, ketamine, dextromethorphan and new drugs with activity in the serotonergic system. The common link seems to be serotonergic effects modulated by NMDA and other neurotransmitter effects. The range of hallucinogens suggest that they are distinct pharmacologic agents and will not be equally safe or effective in therapeutic targets. Newly synthesized specific and selective agents modeled on the legacy agents may be worth considering. Defining therapeutic targets that represent unmet medical need, addressing market and commercial issues, and finding treatment settings to safely test and use such drugs make the human testing of psychedelics not only interesting but also very challenging. This article is part of the Special Issue entitled 'Psychedelics: New Doors, Altered Perceptions'.
Progress in wetland restoration ecology.

It takes more than water to restore a wetland. Now, scientists are documenting how landscape setting, habitat type, hydrological regime, soil properties, topography, nutrient supplies, disturbance regimes, invasive species, seed banks and declining biodiversity can constrain the restoration process. Although many outcomes can be explained post hoc, we have little ability to predict the path that sites will follow when restored in alternative ways, and no insurance that specific targets will be met. To become predictive, bolder approaches are now being developed, which rely more on field experimentation at multiple spatial and temporal scales, and in many restoration contexts.