nils-herrmann/clean_scirepeval huggingface dataset:
Impact of peer teaching on nursing students: perceptions of learning environment, self-efficacy, and knowledge.

Peer teaching has been shown to enhance student learning and levels of self efficacy.



The purpose of the current study was to examine the impact of peer-teaching learning experiences on nursing students in roles of tutee and tutor in a clinical lab environment.



This study was conducted over a three-semester period at a South Central University that provides baccalaureate nursing education.



Over three semesters, 179 first year nursing students and 51 third year nursing students participated in the study.



This mixed methods study, through concurrent use of a quantitative intervention design and qualitative survey data, examined differences during three semesters in perceptions of a clinical lab experience, self-efficacy beliefs, and clinical knowledge for two groups: those who received peer teaching-learning in addition to faculty instruction (intervention group) and those who received faculty instruction only (control group). Additionally, peer teachers' perceptions of the peer teaching learning experience were examined.



Results indicated positive response from the peer tutors with no statistically significant differences for knowledge acquisition and self-efficacy beliefs between the tutee intervention and control groups. In contrast to previous research, students receiving peer tutoring in conjunction with faculty instruction were statistically more anxious about performing lab skills with their peer tutor than with their instructors. Additionally, some students found instructors' feedback moderately more helpful than their peers and increased gains in knowledge and responsibility for preparation and practice with instructors than with peer tutors.



The findings in this study differ from previous research in that the use of peer tutors did not decrease anxiety in first year students, and no differences were found between the intervention and control groups related to self efficacy or cognitive improvement. These findings may indicate the need to better prepare peer tutors, and research should be conducted using more complex skills.
Big Five personality and academic dishonesty : A meta-analytic review

Academic dishonesty is widespread within secondary and higher education. It can include unethical academic behaviors such as cheating, plagiarism, or unauthorized help. Researchers have investigated a number of individual and contextual factors in an effort to understand the phenomenon. In the last decade, there has been increasing interest in the role personality plays in explaining unethical academic behaviors. We used meta-analysis to estimate the relationship between each of the Big Five personality factors and academic dishonesty. Previous reviews have highlighted the role of neuroticism and extraversion as potential predictors of cheating behavior. However, our results indicate that conscientiousness and agreeableness are the strongest Big Five predictors, with both factors negatively related to academic dishonesty. We discuss the implications of our findings for both research and practice.
A Walk-based Model on Entity Graphs for Relation Extraction

We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All the entities in a sentence are placed as nodes in a fully-connected graph structure. The edges are represented with position-aware contexts around the entity pairs. In order to consider different relation paths between two entities, we construct up to l-length walks between each pair. The resulting walks are merged and iteratively used to update the edge representations into longer walks representations. We show that the model achieves performance comparable to the state-ofthe-art systems on the ACE 2005 dataset without using any external tools.
Synthetic-Aperture Radar Processing Using Fast Factorized Back-Projection

Synthetic aperture radar (SAR) is a general method for generating high-resolution radar maps from low-resolution aperture data which is based on using the relative motion between the radar antenna and the imaged scene. Originally conceived in the early 1950s , it is extensively used to image objects on the surface of the Earth and the planets . A synthetic aperture is formed using electromagnetic signals from a physical aperture located at different space-time positions. The synthetic aperture may therefore observe the scene over a large angular sector by moving the physical aperture. Hence, the technique can give a significant improvement in resolution, in principle limited only by the stability of the wave field and other restrictions imposed on the movement of the physical aperture. A physical aperture, on the other hand, provides angular resolution inversely proportional to aperture size such that the spatial resolution degrades with increasing distance to the scene. SAR images of the ground are often generated from pulse echo data acquired by an antenna moving along a nominally linear track. It is well known that the spatial resolution can be made independent of distance to the ground since the antenna can be moved along correspondingly longer tracks . It is therefore possible to produce radar maps with meteror decimeter-resolution from aircraft or spacecraft at very large distances. The resolution in these systems are limited by antenna illumination and system bandwidth but also by other factors, e.g. accuracy of antenna positioning, propagation perturbations, transmitter power, receiver sensitivity, clock stability, and dynamic range. The ultimate limit of SAR spatial resolution is proportional to the wavelength. The finest resolution is determined by the classical uncertainty principle applied to a band-limited wave packet. The area of a resolution cell can be shown to be related to radar system bandwidth B (= fmax fmin, where fmax and fmin are the maximum and minimum electromagnetic frequency, respectively) and aperture angle #2 #1 (the angle over which the antenna is moved and radiating as seen from the imaged ground) according to  ¢ASAR = ̧c 2(#2
Relaxed Wasserstein with Applications to GANs

We propose a novel class of statistical divergences called Relaxed Wasserstein (RW) divergence. RW divergence generalizes Wasserstein divergence and is parametrized by a class of strictly convex and differentiable functions. We establish for RW divergence several probabilistic properties, which are critical for the success of Wasserstein divergence. In particular, we show that RW divergence is dominated by Total Variation (TV) and Wasserstein-L divergence, and that RW divergence has continuity, differentiability and duality representation. Finally, we provide a non-asymptotic moment estimate and a concentration inequality for RW divergence. Our experiments on image generation demonstrate that RW divergence is a suitable choice for GANs. The performance of RWGANs with Kullback-Leibler (KL) divergence is competitive with other state-of-the-art GANs approaches. Moreover, RWGANs possess better convergence properties than the existing WGANs with competitive inception scores. To the best of our knowledge, this new conceptual framework is the first to provide not only the flexibility in designing effective GANs scheme, but also the possibility in studying different loss functions under a unified mathematical framework.
Fooling Faraday : On-chip capacitor multipliers

While the first two functions are essential to switching power supplies, the latter has universal applications. Mixed-signal circuits, for instance, typically incur clock-synchronized load-current events that are faster than any active power supply circuit can supply, and do so while only surviving small variations in voltage. The result of these transient current excursions is noisy voltages, be they supply lines or data links. Capacitors are used to mitigate these effects, to supply and/or shunt the transient currents the power supply circuit is not quick enough to deliver, which is why a typical high performance system is sprinkled with many nanoand micro-Farad capacitors.
A Levy flight-random walk model for bioturbation.

Levy flights are employed in a lattice model of contaminant migration by bioturbation, the reworking of sediment by benthic organisms. The model couples burrowing, foraging, and conveyor-belt feeding with molecular diffusion. The model correctly predicts a square-root dependence on bioturbation rates over a wide range of biomass densities. The model is used to predict the effect of bioturbation on the redistribution of contaminants in laboratory microcosms containing pyrene-inoculated sediments and the tubificid oligochaete Limnodrilus hoffmeisteri. The model predicts the dynamic flux from the sediment and in-bed concentration profiles that are consistent with observations. The sensitivity of flux and concentration profiles to the specific mechanisms of bioturbation are explored with the model. The flux of pyrene to the overlying water was largely controlled by the simulated foraging activities.
Chronic gastritis in China: a national multi-center survey

Chronic gastritis is one of the most common findings at upper endoscopy in the general population, and chronic atrophic gastritis is epidemiologically associated with the occurrence of gastric cancer. However, the current status of diagnosis and treatment of chronic gastritis in China is unclear.



A multi-center national study was performed; all patients who underwent diagnostic upper endoscopy for evaluation of gastrointestinal symptoms from 33 centers were enrolled. Data including sex, age, symptoms and endoscopic findings were prospectively recorded.



Totally 8892 patients were included. At endoscopy, 4389, 3760 and 1573 patients were diagnosed to have superficial gastritis, erosive gastritis, and atrophic gastritis, respectively. After pathologic examination, it is found that atrophic gastritis, intestinal metaplasia and dysplasia were prevalent, which accounted for 25.8%, 23.6% and 7.3% of this patient population. Endoscopic features were useful for predicting pathologic atrophy (PLR = 4.78), but it was not useful for predicting erosive gastritis. Mucosal-protective agents and PPI were most commonly used medications for chronic gastritis.



The present study suggests non-atrophic gastritis is the most common endoscopic finding in Chinese patients with upper GI symptoms. Precancerous lesions, including atrophy, intestinal metaplasia and dysplasia are prevalent in Chinese patients with chronic gastritis, and endoscopic features are useful for predicting pathologic atrophy.
Linear Hamilton Jacobi Bellman Equations in high dimensions

The Hamilton Jacobi Bellman Equation (HJB) provides the globally optimal solution to large classes of control problems. Unfortunately, this generality comes at a price, the calculation of such solutions is typically intractible for systems with more than moderate state space size due to the curse of dimensionality. This work combines recent results in the structure of the HJB, and its reduction to a linear Partial Differential Equation (PDE), with methods based on low rank tensor representations, known as a separated representations, to address the curse of dimensionality. The result is an algorithm to solve optimal control problems which scales linearly with the number of states in a system, and is applicable to systems that are nonlinear with stochastic forcing in finite-horizon, average cost, and first-exit settings. The method is demonstrated on inverted pendulum, VTOL aircraft, and quadcopter models, with system dimension two, six, and twelve respectively.
Reality-Mining with Smartphones: Detecting and Predicting Life Events based on App Installation Behavior

Life events are often described as major forces that are going to shape tomorrow's consumer need, behavior and mood. Thus, the prediction of life events is highly relevant in marketing and sociology. In this paper, we propose a data-driven, real-time method to predict individual life events, using readily available data from smartphones. Our large-scale user study with more than 2000 users shows that our method is able to predict life events with 64.5% higher accuracy, 183.1% better precision and 88.0% higher specificity than a random model on average.
A Low Power Dual CDS for a Column-Parallel CMOS Image Sensor

In this paper, a 320 × 240 pixel, 80 frame/s CMOS image sensor with a low power dual correlated double sampling (CDS) scheme is presented. A novel 8-bit hold-and-go counter in each column is proposed to obtain 10-bit resolution. Furthermore, dual CDS and a configurable counter scheme are also discussed to realize efficient power reduction. With these techniques, the digital counter consumes at least 43% and at most 61% less power compared with the column-counters type, and the frame rate is approximately 40% faster than the double memory type due to a partial pipeline structure without additional memories. The prototype sensor was fabricated in a Samsung 0.13 μm 1P4M CMOS process and used a 4T APS with a pixel pitch of 2.25 μm. The measured column fixed pattern noise (FPN)
Automatic tongue image segmentation based on histogram projection and matting

This paper mainly discusses how to use histogram projection and LBDM (Learning Based Digital Matting) to extract a tongue from a medical image, which is one of the most important steps in diagnosis of traditional Chinese Medicine. We firstly present an effective method to locate the tongue body, getting the convinced foreground and background area in form of trimap. Then, use this trimap as the input for LBDM algorithm to implement the final segmentation. Experiment was carried out to evaluate the proposed scheme, using 480 samples of pictures with tongue, the results of which were compared with the corresponding ground truth. Experimental results and analysis demonstrated the feasibility and effectiveness of the proposed algorithm.
Suvanine Sesterterpenes from a Tropical Sponge Coscinoderma sp. Inhibit Isocitrate Lyase in the Glyoxylate Cycle

While the NAND flash memory is widely used as the storage medium in modern sensor systems, the aggressive shrinking of process geometry and an increase in the number of bits stored in each memory cell will inevitably degrade the reliability of NAND flash memory. In particular, it’s critical to enhance metadata reliability, which occupies only a small portion of the storage space, but maintains the critical information of the file system and the address translations of the storage system. Metadata damage will cause the system to crash or a large amount of data to be lost. This paper presents Asymmetric Programming, a highly reliable metadata allocation strategy for MLC NAND flash memory storage systems. Our technique exploits for the first time the property of the multi-page architecture of MLC NAND flash memory to improve the reliability of metadata. The basic idea is to keep metadata in most significant bit (MSB) pages which are more reliable than least significant bit (LSB) pages. Thus, we can achieve relatively low bit error rates for metadata. Based on this idea, we propose two strategies to optimize address mapping and garbage collection. We have implemented Asymmetric Programming on a real hardware platform. The experimental results show that Asymmetric Programming can achieve a reduction in the number of page errors of up to 99.05% with the baseline error correction scheme. OPEN ACCESS Sensors 2014, 14 18852
The mixed workload CH-benCHmark

While standardized and widely used benchmarks address either operational or real-time Business Intelligence (BI) workloads, the lack of a hybrid benchmark led us to the definition of a new, complex, mixed workload benchmark, called mixed workload CH-benCHmark. This benchmark bridges the gap between the established single-workload suites of TPC-C for OLTP and TPC-H for OLAP, and executes a complex mixed workload: a transactional workload based on the order entry processing of TPC-C and a corresponding TPC-H-equivalent OLAP query suite run in parallel on the same tables in a single database system. As it is derived from these two most widely used TPC benchmarks, the CH-benCHmark produces results highly relevant to both hybrid and classic single-workload systems.
1987 Volvo award in clinical sciences. A new clinical model for the treatment of low-back pain.

Because there is increasing concern about low-back disability and its current medical management, this analysis attempts to construct a new theoretic framework for treatment. Observations of natural history and epidemiology suggest that low-back pain should be a benign, self-limiting condition, that low back-disability as opposed to pain is a relatively recent Western epidemic, and that the role of medicine in that epidemic must be critically examined. The traditional medical model of disease is contrasted with a biopsychosocial model of illness to analyze success and failure in low-back disorders. Studies of the mathematical relationship between the elements of illness in chronic low-back pain suggest that the biopsychosocial concept can be used as an operational model that explains many clinical observations. This model is used to compare rest and active rehabilitation for low-back pain. Rest is the commonest treatment prescribed after analgesics but is based on a doubtful rationale, and there is little evidence of any lasting benefit. There is, however, little doubt about the harmful effects--especially of prolonged bed rest. Conversely, there is no evidence that activity is harmful and, contrary to common belief, it does not necessarily make the pain worse. Experimental studies clearly show that controlled exercises not only restore function, reduce distress and illness behavior, and promote return to work, but actually reduce pain. Clinical studies confirm the value of active rehabilitation in practice. To achieve the goal of treating patients rather than spines, we must approach low-back disability as an illness rather than low-back pain as a purely physical disease. We must distinguish pain as a purely the symptoms and signs of distress and illness behavior from those of physical disease, and nominal from substantive diagnoses. Management must change from a negative philosophy of rest for pain to more active restoration of function. Only a new model and understanding of illness by physicians and patients alike makes real change possible.
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks

We provide theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the convex linear regression loss. We prove that the rate of convergence of an ideal curriculum learning method is monotonically increasing with the difficulty of the examples. Moreover, among all equally difficult points, convergence is faster when using points which incur higher loss with respect to the current hypothesis. We then analyze curriculum learning in the context of training a CNN. We describe a method which infers the curriculum by way of transfer learning from another network, pre-trained on a different task. While this approach can only approximate the ideal curriculum, we observe empirically similar behavior to the one predicted by the theory, namely, a significant boost in convergence speed at the beginning of training. When the task is made more difficult, improvement in generalization performance is also observed. Finally, curriculum learning exhibits robustness against unfavorable conditions such as excessive regularization.
Perceived discrimination and health: a meta-analytic review.

Perceived discrimination has been studied with regard to its impact on several types of health effects. This meta-analysis provides a comprehensive account of the relationships between multiple forms of perceived discrimination and both mental and physical health outcomes. In addition, this meta-analysis examines potential mechanisms by which perceiving discrimination may affect health, including through psychological and physiological stress responses and health behaviors. Analysis of 134 samples suggests that when weighting each study's contribution by sample size, perceived discrimination has a significant negative effect on both mental and physical health. Perceived discrimination also produces significantly heightened stress responses and is related to participation in unhealthy and nonparticipation in healthy behaviors. These findings suggest potential pathways linking perceived discrimination to negative health outcomes.
The use of cluster analysis to derive dietary patterns: methodological considerations, reproducibility, validity and the effect of energy mis-reporting.

Over the last three decades, dietary pattern analysis has come to the forefront of nutritional epidemiology, where the combined effects of total diet on health can be examined. Two analytical approaches are commonly used: a priori and a posteriori. Cluster analysis is a commonly used a posteriori approach, where dietary patterns are derived based on differences in mean dietary intake separating individuals into mutually exclusive, non-overlapping groups. This review examines the literature on dietary patterns derived by cluster analysis in adult population groups, focusing, in particular, on methodological considerations, reproducibility, validity and the effect of energy mis-reporting. There is a wealth of research suggesting that the human diet can be described in terms of a limited number of eating patterns in healthy population groups using cluster analysis, where studies have accounted for differences in sex, age, socio-economic status, geographical area and weight status. Furthermore, patterns have been used to explore relationships with health and chronic diseases and more recently with nutritional biomarkers, suggesting that these patterns are biologically meaningful. Overall, it is apparent that consistent trends emerge when using cluster analysis to derive dietary patterns; however, future studies should focus on the inconsistencies in methodology and the effect of energy mis-reporting.
Rethinking ferrule – a new approach to an old dilemma

The 'ferrule effect' is a long standing, accepted concept in dentistry that is a foundation principle for the restoration of teeth that have suffered advanced structure loss. A review of the literature based on a search in PubMed was performed looking at the various components of the ferrule effect, with particular attention to some of the less explored dimensions that influence the effectiveness of the ferrule when restoring severely broken down teeth. These include the width of the ferrule, the effect of a partial ferrule, the influence of both, the type of the restored tooth and the lateral loads present as well as the well established 2 mm ferrule height rule. The literature was collaborated and a classification based on risk assessment was derived from the available evidence. The system categorises teeth according to the effectiveness of ferrule effect that can be achieved based on the remaining amount of sound tooth structure. Furthermore, risk assessment for failure can be performed so that the practitioner and patient can better understand the prognosis of restoring a particular tooth. Clinical recommendations were extrapolated and presented as guidelines so as to improve the predictability and outcome of treatment when restoring structurally compromised teeth. The evidence relating to restoring the endodontic treated tooth with extensive destruction is deficient. This article aims to rethink ferrule by looking at other aspects of this accepted concept, and proposes a paradigm shift in the way it is thought of and utilised.