nils-herrmann/clean_scirepeval huggingface dataset:
Joint Rate and Power Adaptation for Amplify and Forward Two Way Relaying Relying on Analog Network Coding
Venue: IEEE Access

Analog network coding (ANC) is a promising technique of improving the throughput of relaying, especially when combined with both rate and power adaptation in the context of amplify and forward two way relaying (AF TWR In particular, the adaptation is employed both during the multiple access stage, when a pair of terminals transmit simultaneously to a relay, and during the broadcast stage, where the relay transmits to both terminals. Based on our bit error rate (BER) bounds, we formulate the explicit relationship amongst the data rates, transmit powers, and BERs. Then, we conceive a rate and power adaptation scheme, operating both under specific average power constraints and BER constraints both at the relay node and at the two terminals. We observe that the transmit power of nodes does affect the data rate and hence using a fixed transmit power is not always optimal. We then derive a closed form solution for continuous rate ANC aided quadrature amplitude modulation/phase shift keying (ANC QAM/PSK) using the classic Karush Kuhn Tucker method and conceive discrete rate ANC QAM/PSK regimes for practical wireless systems. The performance of the proposed scheme is evaluated through extensive simulations. Our performance results confirm that the proposed ANC QAM/PSK outperforms its counterpart operating without power adaptation in a broad range of scenarios.
Accelerator Mass Spectrometry in Laboratory Nuclear Astrophysics

The extreme sensitivity and discrimination power of accelerator mass spectrometry (AMS) allows for the search and the detection of rare nuclides either in natural samples or produced in the laboratory. At Argonne National Laboratory, we are developing an AMS setup aimed in particular at the detection of medium and heavy nuclides, relying on the high ion energy achievable with the ATLAS superconducting linear accelerator and on gas filled magnet isobaric separation. The setup was recently used for the detection of the 146Sm p process nuclide and for a new determination of the 146Sm half life (68.7 My) AMS plays an important role in the measurement of stellar nuclear reaction cross sections by the activation method, extending thus the technique to the study of production of long lived radionuclides. Preliminary measurements of the 147Sm(g,n)146Sm are described. A measurement of the 142Nd(a,g)146Sm and 142Nd(a,n)145Sm reactions is in preparation. A new laser ablation method for the feeding of the Electron Cyclotron Resonance (ECR) ion source is described.
Revisiting the Nystrom Method for Improved Large scale Machine Learning
Venue: J. Mach. Learn. Res.

We reconsider randomized algorithms for the low rank approximation of SPSD matrices such as Laplacian and kernel matrices that arise in data analysis and machine learning applications. Our main results consist of an empirical evaluation of the performance quality and running time of sampling and projection methods on a diverse suite of SPSD matrices. Our results highlight complementary aspects of sampling versus projection methods, and they point to differences between uniform and nonuniform sampling methods based on leverage scores. We complement our empirical results with a suite of worst case theoretical bounds for both random sampling and random projection methods. These bounds are qualitatively superior to existing bounds e.g. improved additive error bounds for spectral and Frobenius norm error and relative error bounds for trace norm error.
Risk of Malnutrition Is an Independent Predictor of Mortality, Length of Hospital Stay, and Hospitalization Costs in Stroke Patients.
Venue: Journal of stroke and cerebrovascular diseases the official journal of National Stroke Association

Malnutrition is associated with poor outcomes after stroke. Nutrition screening tools (NSTs) are used to identify patients at risk of malnutrition, but so far no NST has been validated for use with patients who have had a stroke. This study aimed to determine the ability of the Malnutrition Universal Screening Tool (MUST) to predict poor outcomes in stroke patients, including mortality, cumulative length of hospital stay (LOS) and hospitalization costs.  Patients were recruited from consecutive admissions at 2 hyperacute stroke units in London and were screened for risk of malnutrition (low, medium, and high) according to MUST. Six month outcomes were obtained for each patient through a national database.  Of 543 recruited patients, 51% were males, the mean age was 75 years, and 87% had an ischemic stroke. Results showed a highly significant increase in mortality with increasing risk of malnutrition (P .001) This association remained significant after adjusting for age, severity of stroke, and a range of stroke risk factors (P .001) For those patients who survived, the LOS and hospitalization costs increased with increasing risk of malnutrition (P .001 and P .049, respectively) This association remained significant in the adjusted model (P .001 and P .001, respectively) CONCLUSIONS Risk of malnutrition is an independent predictor of mortality, LOS, and hospitalization costs at 6 months post stroke. Research is needed to determine if nutritional support for medium or high risk patients results in better outcomes. Routine screening of stroke patients for risk of malnutrition is recommended.
Using Longitudinal Data on Career Outcomes to Promote Improvements and Diversity in Graduate Education
Venue: Change

Data from federal funding agencies demonstrate that the majority of doctoral alumni pursue careers outside academia. Most institutions have not tracked the career outcomes of their own doctoral alumni to get an accurate picture of placement of their graduates. Lack of data has resulted in continued use of the same training models that may not adequately support the needs of the majority of doctoral students who plan on pursuing careers outside academia.
Medical students' self efficacy in problem based learning and its relationship with self regulated learning
Venue: Medical education online

Problem based learning (PBL) is most commonly used in medical education to enhance self regulated learning (SRL) skills. Self efficacy beliefs affect students' motivation through self regulatory processes. The purpose of this study is to examine the relationship between medical students' self reported SRL skills and their self efficacy in PBL.  A cross sectional study was conducted with second (286; 83.1% and third (275; 80.2% year students at the Ankara University School of Medicine. The SRL perception (SRLP) scale and self efficacy for problem based learning (SPBL) scale were used in the study.  The SRLP subscales were positively correlated with the SPBL subscales. There was a weak but meaningful correlation between the subscales of SRLP (with the exception of the lack of self directedness scale) and the subscales of SPBL and the students' views on benefiting from PBL. The female students' mean score was higher for the 'planning and goal setting' subscale of SRLP (p=0.017) and the second year students' mean score was higher than that of the third year students for the 'lack of self directedness' subscale of SRLP (p=0.001) with small effect sizes (Cohen's d is 0.17 and 0.27) There was no statistically significant difference between the year and subscales of SPBL. With regard to gender, the female students had higher scores than the male students on the 'responsibility' subscale of SPBL (p=0.003; Cohen's d=0.26)  The study showed that medical students used SRL skills and believed in their ability to learn effectively in the PBL context and demonstrated the relationship between SRL skills and self efficacy beliefs. Monitoring students' development in these skills and giving them feedback could be beneficial for the cognitive achievement of students with learning difficulties and insufficient study skills. Further studies need to be undertaken to investigate issues such as the curriculum, learning environment, individual differences, and how these can affect the SRL process.
Identifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs.
Venue: Molecular bioSystems

Understanding the synergism of multiple microRNAs (miRNAs) in gene regulation can provide important insights into the mechanisms of complex human diseases caused by miRNA regulation. Therefore, it is important to identify miRNA synergism and study miRNA characteristics in miRNA synergistic regulatory networks. A number of methods have been proposed to identify miRNA synergism. However, most of the methods only use downstream target genes of miRNAs to infer miRNA synergism when miRNAs can also be regulated by upstream transcription factors (TFs) at the transcriptional level. Additionally, most methods are based on statistical associations identified from data without considering the causal nature of gene regulation. In this paper, we present a causality based framework, called mirSRN (miRNA synergistic regulatory network) to infer miRNA synergism in human molecular systems by considering both downstream miRNA targets and upstream TF regulation. We apply the proposed framework to two real world datasets and discover that almost all the top 10 miRNAs with the largest node degree in the mirSRNs are associated with different human diseases, including cancer, and that the mirSRNs are approximately scale free and small world networks. We also find that most miRNAs in the networks are frequently synergistic with other miRNAs, and miRNAs related to the same disease are likely to be synergistic and in a cluster linked to a biological function. Synergistic miRNA pairs show higher co expression level, and may have potential functional relationships indicating collaboration between the miRNAs. Functional validation of the identified synergistic miRNAs demonstrates that these miRNAs cause different kinds of diseases. These results deepen our understanding of the biological meaning of miRNA synergism.
Association of XbaI GLUT1 Polymorphism with Susceptibility to Type 2 Diabetes Mellitus and Diabetic Nephropathy

Diabetic nephropathy (DN) is one of the chronic microangiopathic complications of type 2 diabetes (T2DM) and has become the most frequent cause of end stage renal disease. The XbaI polymorphism in the glucose transporter (GLUT1) has been suggested in the development of DN. We examined the association between XbaI polymorphism of GLUT1 and susceptibility to T2DM and development of DN.  The study included 227 T2DM patients divided into 107 without DN (DM DN) and 120 with DN (DM DN) in addition to 100 apparently healthy controls. Genotyping was done by polymerase chain reaction restriction fragment length polymorphism (PCR RFLP)  The GLUT1 XbaI T allele was associated with increased susceptibility to T2DM, when comparing the healthy controls to the whole diabetic group, odds ratio (OR) 1.899, 95% confidence interval (CI) (1.149 3.136) p 0.011. This association was also significant between healthy controls and DM DN OR 1.997 (1.079 3.699) p 0.026 as well as between healthy controls and DM DN OR 1.818 (1.016 3.253) p 0.042. However there was no significant association of XbaI polymorphism with DN when comparing DM DN to DM DN OR 0.910 (0.474 1.747) p 0.777.  XbaI T allele is associated with increased susceptibility to T2DM, but not to development of DN. Further studies are needed to replicate such findings.
Fundamentals and Catalytic Applications of CeO2 Based Materials.
Venue: Chemical reviews

Cerium dioxide (CeO2, ceria) is becoming an ubiquitous constituent in catalytic systems for a variety of applications. 2016 sees the 40(th) anniversary since ceria was first employed by Ford Motor Company as an oxygen storage component in car converters, to become in the years since its inception an irreplaceable component in three way catalysts (TWCs) Apart from this well established use, ceria is looming as a catalyst component for a wide range of catalytic applications. For some of these, such as fuel cells, CeO2 based materials have almost reached the market stage, while for some other catalytic reactions, such as reforming processes, photocatalysis, water gas shift reaction, thermochemical water splitting, and organic reactions, ceria is emerging as a unique material, holding great promise for future market breakthroughs. While much knowledge about the fundamental characteristics of CeO2 based materials has already been acquired, new characterization techniques and powerful theoretical methods are deepening our understanding of these materials, helping us to predict their behavior and application potential. This review has a wide view on all those aspects related to ceria which promise to produce an important impact on our life, encompassing fundamental knowledge of CeO2 and its properties, characterization toolbox, emerging features, theoretical studies, and all the catalytic applications, organized by their degree of establishment on the market.
The Attention Grabbing Salience of Viscerally Engaging Images

Banner ads remain a core component of the marketing mix for companies in both the analog and digital domains. Consumers spend much of their time on websites, whether through mobile or desktop devices, and marketers need to target them where they are. Unfortunately, inattention blindness is a serious impediment to the effectiveness of banner advertising; consumers have become proficient at ignoring banners when they appear in areas predictably associated with advertising. Research has uncovered some strategies to overcome banner blindness, but many of these require customer analytics and can evoke privacy concerns. Others degrade the user experience by interfering with the user's task. One possible alternative is to use innately salient advertising content, but there is a dearth of research investigating the effectiveness of this approach. The current study fills this gap with an empirical investigation using established, innately salient images in banner ads and a primary task that involved reading text articles that were either intrinsically interesting or boring. Eye tracking was used to assess the impact on participants' visual scan patterns and visual attention. The results were mixed. Participants' visual attention was drawn to banner ads that contained sexually suggestive content, but not for other innately salient categories. The effect of the intrinsic salience of the content was complicated and requires further research to resolve.
Endothelial to mesenchymal transition contributes to arsenic trioxide induced cardiac fibrosis
Venue: Scientific reports

Emerging evidence has suggested the critical role of endothelial to mesenchymal transition (EndMT) in fibrotic diseases. The present study was designed to examine whether EndMT is involved in arsenic trioxide (As2O3) induced cardiac fibrosis and to explore the underlying mechanisms. Cardiac dysfunction was observed in rats after exposure to As2O3 for 15 days using echocardiography, and the deposition of collagen was detected by Masson's trichrome staining and electron microscope. EndMT was indicated by the loss of endothelial cell markers (VE cadherin and CD31) and the acquisition of mesenchymal cell markers (a SMA and FSP1) determined by RT PCR at the mRNA level and Western blot and immunofluorescence analysis at the protein level. In the in vitro experiments, endothelial cells acquired a spindle shaped morphology accompanying downregulation of the endothelial cell markers and upregulation of the mesenchymal cell markers when exposed to As2O3. As2O3 activated the AKT/GSK 3b/Snail signaling pathway, and blocking this pathway with PI3K inhibitor (LY294002) abolished EndMT in As2O3 treated endothelial cells. Our results highlight that As2O3 is an EndMT promoting factor during cardiac fibrosis, suggesting that targeting EndMT is beneficial for preventing As2O3 induced cardiac toxicity.
DRESS: A Rule Engine on Spark for Event Stream Processing
Venue: 2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT)

Rule based systems process event streams and trigger actions according to pre defined rule sets. Over the last three decades, such systems have been widely used in businesses, governments and organisations. However, with today's need to process larger event streams such as events produced in Internet of Things (IoT) current rule based systems face serious challenges in terms of speed, scalability and fault tolerance. Spark Streaming is emerging as a novel solution to address these challenges. This paper presents an approach for adapting rule based systems to work with Spark Streaming. We focus on Rete algorithm which is behind many of the current rule engines. We present DRESS (Distributed Rule Engine on Spark Streaming) an infrastructure for executing Rete algorithm on Spark Streaming. In addition, we present an automated method of transforming rules written in Drools' style to be executed on DRESS. The performance of our system was evaluated with the help of a case study.
A Survey of Current Valued Academic Leadership Qualities in Nursing
Venue: Nursing education perspectives

AIMAn informal survey was used to identify nurse faculty leadership qualities currently valued and relevant. BACKGROUNDThe accelerating retirement rate for seasoned leaders has created a need for nurse educators and academic leaders. Our school was concerned that we were not meeting students' needs for today's leadership challenges. We were also interested in the experiences of leadership preparation. METHODThis was a cross sectional, online survey of faculty at top nursing schools as determined by US News World Report. FINDINGSThe top leadership qualities identified were integrity, communication clarity, and problem solving ability. Current challenges for leaders were finding qualified faculty, obtaining resources, and team building. CONCLUSIONThe results may guide curricular adjustments and the transition to a new generation of nurse academic leaders.
On stability and application of extremum seeking control without steady state oscillation
Venue: Autom.

To enhance the dynamic and static performance of the extremum seeking control (ESC) scheme, a novel fast ESC scheme without steady state oscillation is proposed, in which the structure of the classic ESC scheme is adjusted to make the sinusoidal excitation signal amplitude locally exponentially converge to zero. The improved ESC scheme can speed up the convergence to shorten the seeking time dramatically, and enlarge the search area to avoid falling to local extrema effectively. It can also reduce the sinusoidal excitation signal amplitude to eliminate the adverse effects of the steady state oscillation eventually. The rigorous stability analysis and proof of the improved ESC scheme are provided in detail, and the simulation results are presented to illustrate its effectiveness and superiority. Finally, the application of the improved ESC scheme to antilock braking systems (ABS) has been discussed through comparing with classical perturbation based ESC scheme and sliding mode based ESC scheme to illustrate the practicability of the improved scheme.
Phasor extremum seeking control with adaptive perturbation amplitude
Venue: 2016 IEEE 55th Conference on Decision and Control (CDC)

In this paper, we propose a perturbation amplitude adaption scheme for phasor extremum seeking control based on the plant's estimated gradient. By using phasor extremum seeking instead of classical extremum seeking, the problem of algebraic loops in the controller formulation is avoided. Furthermore, a stability analysis for the proposed method is provided, which is the first stability analysis for extremum seeking controllers using adaptive amplitudes. The proposed method is illustrated using numerical examples and it is found that changes in optimum can be tracked accurately while the steady state perturbations can be reduced significantly.
Wilderness protected areas management guidelines for IUCN category 1b protected areas

Sarah A. Casson, Vance G. Martin, Alan Watson, Angie Stringer, Cyril F. Kormos, Harvey Locke, Sonali Ghosh, Steve Carver, Tom McDonald, Sharon Shay Sloan, Ilarion Merculieff, John Hendee, Chad Dawson, Susan Moore, David Newsome, Steve McCool, Roger Semler, Steven Martin, Robert Dvorak, Chris Armatas, Ralph Swain, Brad Barr, David Krause, Nicole Whittington Evans, Lawrence S. Hamilton, Joel Holtrop, James Tricker, Peter Landres, Elizabeth Mejicano, Trimble Gilbert, Brendan Mackey, Toby Aykroyd, Barbara Zimmerman and Jim Thomas
A measurement based approach for power system instability early warning

Early warning of impending instability in a power system under disturbance conditions is important for preventing of system collapse. A measurement based approach is proposed to assess the potential power system transient instability problem under cascading outages. Where a measurement based index is obtained as the estimation accuracy of a linear autoregressive exogenous (ARX) model to estimate the dynamic response of the power system and indicate the system stability to some extent after a disturbance. The proposed approach was verified using a set of marginally stable cases in a 179 bus WECC equivalent power system. Then the instability early warning threshold for this system is obtained as 0.44.
Measurement based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network

Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications. Wide area measurement systems (WAMS) based on synchrophasors make power system dynamics visible to system operators, delivering an accurate picture of overall operating conditions. However, in actual field implementations, some measurements can be inaccessible for various reasons, e.g. most notably communication failure. To reconstruct these inaccessible measurements, in this paper, the radial basis function artificial neural network (RBF ANN) is used to estimate the system dynamics. In order to find the best input features of the RBF ANN model, geometric template matching (GeTeM) and quality threshold (QT) clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system. The proposed method is tested and verified on the Eastern Interconnection (EI) transmission system in the United States. The results obtained indicate that the proposed approach provides a compact and efficient RBF ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.
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The Ability Model of Emotional Intelligence: Principles and Updates

This article presents seven principles that have guided our thinking about emotional intelligence, some of them new. We have reformulated our original ability model here guided by these principles, clarified earlier statements of the model that were unclear, and revised portions of it in response to current research. In this revision, we also positioned emotional intelligence amidst other hot intelligences including personal and social intelligences, and examined the implications of the changes to the model. We discuss the present and future of the concept of emotional intelligence as a mental ability.