nils-herrmann/clean_scirepeval huggingface dataset:
Emotional intelligence as a predictor of academic performance in first-year accelerated graduate entry nursing students.

To examine the association between trait emotional intelligence and learning strategies and their influence on academic performance among first-year accelerated nursing students.



The study used a prospective survey design.



A sample size of 81 students (100% response rate) who undertook the accelerated nursing course at a large university in Sydney participated in the study. Emotional intelligence was measured using the adapted version of the 144-item Trait Emotional Intelligence Questionnaire. Four subscales of the Motivated Strategies for Learning Questionnaire were used to measure extrinsic goal motivation, peer learning, help seeking and critical thinking among the students. The grade point average score obtained at the end of six months was used to measure academic achievement.



The results demonstrated a statistically significant correlation between emotional intelligence scores and critical thinking (r = 0.41; p < 0.001), help seeking (r = 0.33; p < 0.003) and peer learning (r = 0.32; p < 0.004) but not with extrinsic goal orientation (r = -0.05; p < 0.677). Emotional intelligence emerged as a significant predictor of academic achievement (β = 0.25; p = 0.023).



In addition to their learning styles, higher levels of awareness and understanding of their own emotions have a positive impact on students' academic achievement. Higher emotional intelligence may lead students to pursue their interests more vigorously and think more expansively about subjects of interest, which could be an explanatory factor for higher academic performance in this group of nursing students.


 TO CLINICAL PRACTICE
The concepts of emotional intelligence are central to clinical practice as nurses need to know how to deal with their own emotions as well as provide emotional support to patients and their families. It is therefore essential that these skills are developed among student nurses to enhance the quality of their clinical practice.
How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis.

Instrumental learning involves corticostriatal circuitry and the dopaminergic system. This system is typically modeled in the reinforcement learning (RL) framework by incrementally accumulating reward values of states and actions. However, human learning also implicates prefrontal cortical mechanisms involved in higher level cognitive functions. The interaction of these systems remains poorly understood, and models of human behavior often ignore working memory (WM) and therefore incorrectly assign behavioral variance to the RL system. Here we designed a task that highlights the profound entanglement of these two processes, even in simple learning problems. By systematically varying the size of the learning problem and delay between stimulus repetitions, we separately extracted WM-specific effects of load and delay on learning. We propose a new computational model that accounts for the dynamic integration of RL and WM processes observed in subjects' behavior. Incorporating capacity-limited WM into the model allowed us to capture behavioral variance that could not be captured in a pure RL framework even if we (implausibly) allowed separate RL systems for each set size. The WM component also allowed for a more reasonable estimation of a single RL process. Finally, we report effects of two genetic polymorphisms having relative specificity for prefrontal and basal ganglia functions. Whereas the COMT gene coding for catechol-O-methyl transferase selectively influenced model estimates of WM capacity, the GPR6 gene coding for G-protein-coupled receptor 6 influenced the RL learning rate. Thus, this study allowed us to specify distinct influences of the high-level and low-level cognitive functions on instrumental learning, beyond the possibilities offered by simple RL models.
Construction and Usage of the Semantic Interaction Pipeline

Semantic interaction techniques in visual data analytics allow users to indirectly adjust model parameters by directly manipulating the visual output of the models. Many existing tools that support semantic interaction do so with a number of similar features, including using an underlying bidirectional pipeline, using a series of statistical models, and performing inverse computations to transform user interactions into model updates. We propose a visual analytics pipeline that captures these necessary features of semantic interactions. Our flexible, multi-model, bidirectional pipeline has modular functionality to enable rapid prototyping. This enables quick alterations to the type of data being visualized, models for transforming the data, semantic interaction methods, and visual encodings. To demonstrate how this pipeline can be used, we developed a series of applications that employ semantic interactions. We also discuss how the pipeline can be used or extended for future research on semantic interactions in visual analytics.
Leadership in Academic Health Centers: Transactional and Transformational Leadership

Leadership is a crucial component to the success of academic health science centers (AHCs) within the shifting U.S. healthcare environment. Leadership talent acquisition and development within AHCs is immature and approaches to leadership and its evolution will be inevitable to refine operations to accomplish the critical missions of clinical service delivery, the medical education continuum, and innovations toward discovery. To reach higher organizational outcomes in AHCs requires a reflection on what leadership approaches are in place and how they can better support these missions. Transactional leadership approaches are traditionally used in AHCs and this commentary suggests that movement toward a transformational approach is a performance improvement opportunity for AHC leaders. This commentary describes the transactional and transformational approaches, how they complement each other, and how to access the transformational approach. Drawing on behavioral sciences, suggestions are made on how a transactional leader can change her cognitions to align with the four dimensions of the transformational leadership approach.
Broadband dual-polarized antenna with high port isolation and polarization purity

This paper presents a novel high symmetry balun which significantly improves the performance of dipole-based dual-polarized antennas. The new balun structure provides enhanced differential capability leading to high performance in terms of port-to-port isolation and far-field cross polarization. An example antenna using this balun is proposed. The simulated results show 53.5% of fractional bandwidth within the band 1.71-2.96 GHz (VSWR<;1.5) and port-to-port isolation >59 dB. The radiation characteristic shows around 9 dBi of gain and far-field cross polarization <;-48 dBi over the entire bandwidth. The detailed balun functioning and full antenna measurements will be presented during the conference. Performance comparison with similar structures will be also provided.
Improving Nanofiber Membrane Characteristics and Membrane Distillation Performance of Heat-Pressed Membranes via Annealing Post-Treatment

Electrospun membranes are gaining interest for use in membrane distillation (MD) due to their high porosity and interconnected pore structure; however, they are still susceptible to wetting during MD operation because of their relatively low liquid entry pressure (LEP). In this study, post-treatment had been applied to improve the LEP, as well as its permeation and salt rejection efficiency. The post-treatment included two continuous procedures: heat-pressing and annealing. In this study, annealing was applied on the membranes that had been heat-pressed. It was found that annealing improved the MD performance as the average flux reached 35 L/m2·h or LMH (>10% improvement of the ones without annealing) while still maintaining 99.99% salt rejection. Further tests on LEP, contact angle, and pore size distribution explain the improvement due to annealing well. Fourier transform infrared spectroscopy and X-ray diffraction analyses of the membranes showed that there was an increase in the crystallinity of the polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) membrane; also, peaks indicating the α phase of polyvinylidene fluoride (PVDF) became noticeable after annealing, indicating some β and amorphous states of polymer were converted into the α phase. The changes were favorable for membrane distillation as the non-polar α phase of PVDF reduces the dipolar attraction force between the membrane and water molecules, and the increase in crystallinity would result in higher thermal stability. The present results indicate the positive effect of the heat-press followed by an annealing post-treatment on the membrane characteristics and MD performance.
Hierarchical Reinforcement Learning for Robot Navigation

For complex tasks, such as manipulation and robot navigation, reinforcement learning (RL) is well-known to be difficult due to the curse of dimensionality. To overcome this complexity and making RL feasible, hierarchical RL (HRL) has been suggested. The basic idea of HRL is to divide the original task into elementary subtasks, which can be learned using RL. In this paper, we propose a HRL architecture for learning robot’s movements, e.g. robot navigation. The proposed HRL consists of two layers: (i) movement planning and (ii) movement execution. In the planning layer, e.g. generating navigation trajectories, discrete RL is employed while using movement primitives. Given the movement planning and corresponding primitives, the policy for the movement execution can be learned in the second layer using continuous RL. The proposed approach is implemented and evaluated on a mobile robot platform for a
Performance analysis of stacked RFID tags

Most RFID tag performance analysis is based on the assumption that the tag is stand-alone when interacting with the readers. However, in the real world, RFID tags are often stacked together when deployed, i.e., RFID-labeled carton boxes stacked on the pallet. When multiple RFID tag antennas are placed close to each other, each antenna behaves like a shielding and reflecting object to others and the radiation pattern and other EM properties will be detuned. The stacking impact often leads to performance degradation and in many cases, read and write failure. In this paper, we first derive an RCS model to generalize a loaded antenna as a shielding and reflecting object. We then introduce a normalized mutual impedance matrix of multiple antennas to formulate the coupling effects among stacked antennas. To quantify and visualize the stacking impact in a real world scenario, we also construct a 3-D EM model in Agilent® AMDS consisting of 27 (3 × 3 × 3) stacked carton boxes, each with Alien® tags attached on the surface. Our theoretical calculation agrees with the simulation and shows that both tag radiation pattern and other EM properties will be greatly altered by neighboring tags. Compared with tags in stand-alone scenarios, the degradation at different positions could be very significant (more than 6dB in some directions).
CONSTRUCTED IDENTITIES AND PERPETUATED INEQUALITIES IN APP DATING By

This study seeks to investigate the ways in which individuals make sense of the nearly overwhelming amount of information they are presented with in an application like Tinder, and the ways in which their experiences at the confluence of the digital and “real” worlds are informed by various forces, including race and ethnicity. In particular, it seeks to determine how the experiences of marginalized groups are represented (and underrepresented) through the design and use of the app. By engaging with students at the University of Pennsylvania through a series of semi-structured interviews, this study reveals that users are enmeshed in a constant and ever-evolving relationship with the app. They appear to be endlessly renegotiating how they make sense of their own expectations and desires, as well as the means by which they attempt to speak to something broader than themselves.
A survey of mindset theories of intelligence and medical error self-reporting among pediatric housestaff and faculty.

Intelligence theory research has illustrated that people hold either "fixed" (intelligence is immutable) or "growth" (intelligence can be improved) mindsets and that these views may affect how people learn throughout their lifetime. Little is known about the mindsets of physicians, and how mindset may affect their lifetime learning and integration of feedback. Our objective was to determine if pediatric physicians are of the "fixed" or "growth" mindset and whether individual mindset affects perception of medical error reporting. 



We sent an anonymous electronic survey to pediatric residents and attending pediatricians at a tertiary care pediatric hospital. Respondents completed the "Theories of Intelligence Inventory" which classifies individuals on a 6-point scale ranging from 1 (Fixed Mindset) to 6 (Growth Mindset). Subsequent questions collected data on respondents' recall of medical errors by self or others.



We received 176/349 responses (50 %). Participants were equally distributed between mindsets with 84 (49 %) classified as "fixed" and 86 (51 %) as "growth". Residents, fellows and attendings did not differ in terms of mindset. Mindset did not correlate with the small number of reported medical errors.



There is no dominant theory of intelligence (mindset) amongst pediatric physicians. The distribution is similar to that seen in the general population. Mindset did not correlate with error reports.
Wearable Endfire Textile Antenna for On-Body Communications at 60 GHz

A textile endfire antenna operating in the 60-GHz band is proposed for wireless body area networks (BANs). The permittivity of the textile substrate has been accurately characterized, and the Yagi-Uda antenna has been fabricated using an ad hoc manufacturing process. Its performance in terms of reflection coefficient, radiation pattern, gain, and efficiency has been studied in free space and on a tissue-equivalent phantom representing the human body. It is shown that the antenna is matched in the 57-64-GHz band. Its measured on-body efficiency and maximum gain equal 48.0% and 11.9 dBi, respectively. To our best knowledge, this is the first textile antenna for on-body wireless communications reported at millimeter waves.
"What happens if..." Learning to Predict the Effect of Forces in Images

What happens if one pushes a cup sitting on a table toward the edge of the table? How about pushing a desk against a wall? In this paper, we study the problem of understanding the movements of objects as a result of applying external forces to them. For a given force vector applied to a specific location in an image, our goal is to predict long-term sequential movements caused by that force. Doing so entails reasoning about scene geometry, objects, their attributes, and the physical rules that govern the movements of objects. We design a deep neural network model that learns long-term sequential dependencies of object movements while taking into account the geometry and appearance of the scene by combining Convolutional and Recurrent Neural Networks. Training our model requires a large-scale dataset of object movements caused by external forces. To build a dataset of forces in scenes, we reconstructed all images in SUN RGB-D dataset in a physics simulator to estimate the physical movements of objects caused by external forces applied to them. Our Forces in Scenes (ForScene) dataset contains 10,335 images in which a variety of external forces are applied to different types of objects resulting in more than 65,000 object movements represented in 3D. Our experimental evaluations show that the challenging task of predicting longterm movements of objects as their reaction to external forces is possible from a single image.
Sense of Community : A Definition and Theory

For several years many of us at Peabody College have participated in the evolution of a theory of community, the first conceptualization of which was presented in a working paper (McMillan, 1976) of the Center for Community Studies. To support the proposed definition, McMillan focused on the literature on group cohesiveness, and we build here on that original definition. This article attempts to describe the dynamics of the sense-ofcommunity force-to identify the various elements in the force and to describe the process by which these elements work together to produce the experience of sense of community.
Visual object-action recognition: Inferring object affordances from human demonstration

This paper investigates object categorization according to function, i.e., learning the affordances of objects from human demonstration. Object affordances (functionality) are inferred from observations of humans using the objects in different types of actions. The intended application is learning from demonstration, in which a robot learns to employ objects in household tasks, from observing a human performing the same tasks with the objects. We present a method for categorizing manipulated objects and human manipulation actions in context of each other. The method is able to simultaneously segment and classify human hand actions, and detect and classify the objects involved in the action. This can serve as an initial step in a learning from demonstration method. Experiments show that the contextual information improves the classification of both objects and actions.
Bitcoin Covenants

This paper presents an extension to Bitcoin’s script language enabling covenants, a primitive that allows transactions to restrict how the value they transfer is used in the future. Covenants expand the set of financial instruments expressible in Bitcoin, and enable new powerful and novel use cases. We illustrate two novel security constructs built using covenants. The first, vaults, focuses on improving the security of private cryptographic keys. Historically, maintaining these keys securely and reliably has been a critical vulnerability for Bitcoin users. We show how covenants enable vaults, which disincentivize key theft by preventing an attacker from gaining full access to stolen funds. The second construct, poison transactions, is a generally useful mechanism for penalizing double-spending attacks. Bitcoin-NG, a protocol that has been recently proposed to improve Bitcoin’s throughput, latency and overall scalability, requires this feature. We show how covenants enable poison transactions, and detail how Bitcoin-NG can be implemented progressively as an overlay on top of the Bitcoin blockchain.
Deep Learning for Image-Based Cassava Disease Detection

Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.
A Review of More-Electric Aircraft

The More-Electric Aircraft (MEA) underlines the utilization of the electrical power to power the non-propulsive aircraft systems. Adopting the MEA achieves numerous advantages such as optimizing the aircraft performance and decreasing operating and maintenance costs. Moreover, the MEA reduces the emission of the air pollutant gases from the aircraft, which can contribute in solving the problem of climate change. However, the MEA put some challenge on the aircraft electrical system either in the amount of the required power or the processing and management of this power. This paper introduces a review for the MEA. The review includes the different options of generation and power system architectures.
A Study on Big Data Thinking of the Internet of Things-Based Smart-Connected Car in Conjunction with Controller Area Network Bus and 4G-Long Term Evolution

A smart connected car in conjunction with the Internet of Things (IoT) is an emerging topic. The fundamental concept of the smart connected car is connectivity, and such connectivity can be provided by three aspects, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X). To meet the aspects of V2V and V2I connectivity, we developed modules in accordance with international standards with respect to On-Board Diagnostics II (OBDII) and 4G Long Term Evolution (4G-LTE) to obtain and transmit vehicle information. We also developed software to visually check information provided by our modules. Information related to a user’s driving, which is transmitted to a cloud-based Distributed File System (DFS), was then analyzed for the purpose of big data analysis to provide information on driving habits to users. Yet, since this work is an ongoing research project, we focus on proposing an idea of system architecture and design in terms of big data analysis. Therefore, our contributions through this work are as follows: (1) Develop modules based on Controller Area Network (CAN) bus, OBDII, and 4G-LTE; (2) Develop software to check vehicle information on a PC; (3) Implement a database related to vehicle diagnostic codes; (4) Propose system architecture and design for big data analysis.
CrescendoNet: A New Deep Convolutional Neural Network with Ensemble Behavior

We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convolution paths with increased depths. The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all networks without residual connections on benchmark datasets, CIFAR10, CIFAR100, and SVHN. Given sufficient amount of data as in SVHN dataset, CrescendoNet with 15 layers and 4.1M parameters can match the performance of DenseNet-BC with 250 layers and 15.3M parameters. CrescendoNet provides a new way to construct high performance deep convolutional neural networks with simple network architecture. Moreover, by investigating a various combination of subnetworks in CrescendoNet, we note that the high performance of CrescendoNet may come from its implicit ensemble behavior, which gives CrescendoNet an anytime classification property. Furthermore, the independence between paths in CrescendoNet allows us to introduce a new path-wise training procedure, which can reduce the memory needed for training.