nils-herrmann/clean_scirepeval huggingface dataset:
Asset health management using predictive and prescriptive analytics for the electric power grid
Venue: IBM J. Res. Dev.

Electric utilities make up an asset intensive industry with a broad geographical spread of assets, such as poles, transformers, cables, and switchgear. The utilities face a backlog of aging assets that are pending replacement. Increasingly, a consensus has been reached on moving away from time based maintenance planning of assets to developing a proactive and smarter asset health management program to meet the competing constraints of reducing customer downtimes, meeting regulatory standards, and managing ever expanding infrastructure within budget. Incomplete information, fragmented data, and a diversity of asset classes collectively make a holistic assessment of the grid extremely challenging. Working with DTE Energy and Alliander N.V. IBM Research has developed advanced analytics to model asset health and network reliability by predicting the aging of assets, identifying the remaining lifecycle, and computing the network robustness. The analytics exploit data from multiple systems such as enterprise asset management, work management, geographic information systems, supervisory control and data acquisition systems, advanced metering infrastructure, weather systems, and outage management systems. The algorithms systematically evaluate asset health and prioritize preventive, proactive, and corrective maintenance strategies for all asset classes in the electrical network. We describe outcomes, summarizing an overall health score and risk ranking along with a suggested optimal maintenance strategy considering budgetary constraints.
Anatomy of the Pythagoras' Tree.

The grand architecture of nature can be seen at play in a tree: no two are alike. Nevertheless, there is an inescapable similarity that makes us identify a tree anywhere in the world. Just saying "tree" recalls words like green, root, leaves, still, strong, branches. The tree of primitive Pythagorean triples is no different  It has a root, or a beginning. It is rooted not on earth, but on the soil of our mind. It has branches that spring from that root as it grows with the action of nature and time. In this case, it is not the proverbial Mother Nature, but the human nature a nature formed by the human interpretation of reality. The Pythagoras' tree presented by Berggren in 1934 has stood still and strong for almost a century, but probably it is even older. Its leaves are triples, and they grow throughout its branches. Ultimately, when one looks at the Pythagoras' tree, one looks at a 'tree' The root is the triple (3, 4, 5) All branches and leafs emerge from, and are dependent, of this root. Like any tree, all it requires is a seed and soil, and all develops automatically. The architecture that defines the tree is present throughout the tree and is a reflection of the beginning the root. In other words, the root (3, 4, 5) plus the same movement repeated over and over again creates the tree. In this paper, we will look at how this basic geometrical and mathematical movement governs the birth and growth of the Pythagoras' tree. Pythagoras is included in secondary education around the world including in Australian Curriculum (ACARA, n.d. and hence this paper will be of interest to all.
Dual RNA seq unveils noncoding RNA functions in host pathogen interactions
Venue: Nature

Bacteria express many small RNAs for which the regulatory roles in pathogenesis have remained poorly understood due to a paucity of robust phenotypes in standard virulence assays. Here we use a generic 'dual RNA seq' approach to profile RNA expression simultaneously in pathogen and host during Salmonella enterica serovar Typhimurium infection and reveal the molecular impact of bacterial riboregulators. We identify a PhoP activated small RNA, PinT, which upon bacterial internalization temporally controls the expression of both invasion associated effectors and virulence genes required for intracellular survival. This riboregulatory activity causes pervasive changes in coding and noncoding transcripts of the host. Interspecies correlation analysis links PinT to host cell JAK STAT signalling, and we identify infection specific alterations in multiple long noncoding RNAs. Our study provides a paradigm for a sensitive RNA based analysis of intracellular bacterial pathogens and their hosts without physical separation, as well as a new discovery route for hidden functions of pathogen genes.
The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial
Venue: Journal of medical Internet research

Mobile apps for mental health have the potential to overcome access barriers to mental health care, but there is little information on whether patients use the interventions as intended and the impact they have on mental health outcomes.  The objective of our study was to document and compare use patterns and clinical outcomes across the United States between 3 different self guided mobile apps for depression.  Participants were recruited through Web based advertisements and social media and were randomly assigned to 1 of 3 mood apps. Treatment and assessment were conducted remotely on each participant's smartphone or tablet with minimal contact with study staff. We enrolled 626 English speaking adults =18 years old) with mild to moderate depression as determined by a 9 item Patient Health Questionnaire (PHQ 9) score =5, or if their score on item 10 was =2. The apps were (1) Project: EVO, a cognitive training app theorized to mitigate depressive symptoms by improving cognitive control, (2) iPST, an app based on an evidence based psychotherapy for depression, and (3) Health Tips, a treatment control. Outcomes were scores on the PHQ 9 and the Sheehan Disability Scale. Adherence to treatment was measured as number of times participants opened and used the apps as instructed.  We randomly assigned 211 participants to iPST, 209 to Project: EVO, and 206 to Health Tips. Among the participants, 77.0% (482/626) had a PHQ 9 score >10 (moderately depressed) Among the participants using the 2 active apps, 57.9% (243/420) did not download their assigned intervention app but did not differ demographically from those who did. Differential treatment effects were present in participants with baseline PHQ 9 score >10, with the cognitive training and problem solving apps resulting in greater effects on mood than the information control app (kh22=6.46, P=.04)  Mobile apps for depression appear to have their greatest impact on people with more moderate levels of depression. In particular, an app that is designed to engage cognitive correlates of depression had the strongest effect on depressed mood in this sample. This study suggests that mobile apps reach many people and are useful for more moderate levels of depression. ClinicalTrial Clinicaltrials.gov NCT00540865; https:/www.clinicaltrials.gov/ct2/show/NCT00540865 (Archived by WebCite at http:/www.webcitation.org/6mj8IPqQr)
Investigation into the Predictive Capability of Macro Economic Features in Modelling Credit Risk for Small Medium Enterprises

This research project investigates the predictive capability of macro economic features in modelling credit risk for small medium enterprises (SME/SMEs) There have been indications that there is strong correlation between economic growth and the size of the SME sector in an economy. However, since the financial crisis and consequent policies and regulations, SMEs have been hampered in attempts to access credit. It has also been noted that while there is a substantial amount of credit risk literature, there is little research on how macro economic factors affect credit risk. Being able to improve credit scoring by even a small amount can have a very positive effect on a financial institution's profits, reputation and ability to support the economy. Typically, in the credit scoring process two methods of scoring are carried out, application scoring model and behavioural scoring model. These models for predicting customers who are likely to default usually rely upon financial, demographic and transactional data as the predictive inputs. This research investigates the use of a much coarser source of data at a macro economic level by a low level and high level regions in Ireland. Features such as level of employment/unemployment, education attainment, consumer spending trends and default levels by different banking products will be evaluated as part of the research project. In the course of this research, techniques and methods are established for evaluating the usefulness of macro economic features. These are subsequently introduced into the predictive models to be evaluated. It was found that while employing coarse classification and subsequently choosing the macro economic features with the highest information value in the predictive model, the accuracy across all performance measures improved significantly. This has proven that macro economic features have the potential to be used in modelling credit risk for SMEs in the future.
Pre service teachers' competence to teach science through information and communication technologies in South Africa

Research suggests that there is a need for better training of prospective teachers on information and communication technologies (ICTs) in order to encourage their use for teaching and learning in schools. This paper presents findings on the self perceptions of competence by pre service teachers to use ICTs for teaching science content. A mixed method approach was used where 103 final year pre service teachers completed a questionnaire on their competence to use ICTs for teaching and 21 of them participated in focus group interviews concerning their experiences with ICTs during teaching practice. Results show that pre service teachers seem to be more competent in the non technology related skills compared to the technology related knowledge fields and that there are significant variations in their ICT competences. These variations largely result from the uneven opportunities to learn that are provided to the pre service teachers. More significantly, the directional trend shown by the correlations indicates that the more lecturers or mentor teachers use ICT tools to teach, the more pre service teachers learn to use ICT tools in their own teaching. The paper concludes with a discussion on the implication of these findings for policy and practice and specifically suggests that there is a need to review the policy guidelines on the development of the teacher education programmes to be more deliberate in their inclusion of ICTs. The key recommended contribution is for teacher preparation programmes at universities to be restructured in order to improve the training of future teachers on the use of ICTs to teach science.
Anatomical variation of celiac axis, superior mesenteric artery, and hepatic artery: Evaluation with multidetector computed tomography angiography
Venue: Journal of research in medical sciences the official journal of Isfahan University of Medical Sciences

The celiac axis, superior mesenteric artery (SMA) and hepatic artery are the most important branches of abdominal aorta due to their vascularization field. The aim of our study was to evaluate the prevalence of different anatomical variation of celiac axis, SMA, hepatic artery, and its branches with multidetector computed tomography (MDCT) angiography of upper abdomen arteries.  MDCT of 607 kidney donor and traumatic patients that referred to MDCT unit at Al Zahra Hospital in Isfahan from 2012 to 2015 were retrospectively evaluated. We excluded patients with history of abdominal vascular surgery and hepatic or pancreatic surgery. Computed tomography images of the patient were obtained with 64 row MDCT scanner and anatomical variations were analyzed.  Three hundred and eighty eight (63.9% of the 607 patients had classic arterial anatomy and 219 (36.1% patients had variant types. The most common type of variation was the origin of the right hepatic artery (RHA) from SMA (9.6% and the next common variation was the origin of the left hepatic artery (LHA) from the left gastric artery (6.9% Variations in the origin of the common hepatic artery (CHA) were seen in 16 (2.6% patients. Buhler arc was identified in two patients. The RHA originated from the celiac axis in 11 (1.8% patients and from the aorta in 8 (1.3% patients. Trifurcation of CHA into gastroduodenal artery, RHA, and LHA was detected in 11 (1.8% patients.  The results of the present study showed that anatomical variation occurs in a high percentage of patients. Detection of these variations can guide surgical and radiological interventional planning.
EXTRA HEPATIC BRANCHING PATTERN OF COMMON HEPATIC ARTERY AND ITS VARIATIONS: A HUMAN CADAVERIC STUDY

Address for Correspondence: Dr. Ashwini C Appaji, Associate Professor, Department of Anatomy, M. S. Ramaiah Medical College, MSRIT Post, Bangalore. 560054, Karnataka, India.  ashwinishivaprasad@gmail.com  The hepatic arterial anatomy is variable. Pre operative evaluation of extra hepatic arterial pattern is relevant for the surgeons during hepatic surgery as well as for the radiologists while doing different procedures. The main purpose of the study was to identify the normal variable branching pattern of common hepatic artery.  Extra hepatic arterial pattern was observed in 45 adult cadavers (male female) by in situ dissection. The point of origin branching pattern of common hepatic artery (CHA) till the porta hepatis were observed and also looked for presence of aberrant/accessory hepatic arteries. The branching pattern of hepatic artery before entering the liver was photographed.  Normal extra hepatic arterial pattern was found in 46.67% livers. The variable branching patterns was seen in the remaining livers (53.3% The common variations were: presence of accessory left hepatic artery (LHA) from left gastric artery (LGA) (20% trifurcation of common hepatic artery (CHA) into gastro duodenal artery (GDA) right hepatic artery (RHA) left hepatic artery (LHA) (13.33% origin of right gastric artery (RGA) from left hepatic artery (LHA) (13.33% Some rare variations were also observed.  The knowledge of extra hepatic arterial anatomy is important in the field of hepatic surgery especially during split living donor liver transplantations. It will help to reduce the post operative complications such as hepatic necrosis and bleeding during surgery.
A Multicriteria Weighted Vote Based Classifier Ensemble for Heart Disease Prediction
Venue: Comput. Intell.

The availability of a large amount of medical data leads to the need of intelligent disease prediction and analysis tools to extract hidden information. A large number of data mining and statistical analysis tools are used for disease prediction. Single data mining techniques show acceptable level of accuracy for heart disease diagnosis. This article focuses on prediction and analysis of heart disease using weighted vote based classifier ensemble technique. The proposed ensemble model overcomes the limitations of conventional data mining techniques by employing the ensemble of five heterogeneous classifiers: naive Bayes, decision tree based on Gini index, decision tree based on information gain, instance based learner, and support vector machines. We have used five benchmark heart disease data sets taken from UCI repository. Each data set contains different set of feature space that ultimately leads to the prediction of heart disease. The effectiveness of proposed ensemble classifier is investigated by comparing the performance with different researchers' techniques. Tenfold cross validation is used to handle the class imbalance problem. Moreover, confusion matrices and analysis of variance statistics are used to show the prediction results of all classifiers. The experimental results verify that the proposed ensemble classifier can deal with all types of attributes and it has achieved the high diagnosis accuracy of 87.37% sensitivity of 93.75% specificity of 92.86% and F measure of 82.17% The F ratio higher than the F critical and p value less than 0.01 for a 95% confidence interval indicate that the results are statistically significant for all the data sets.
Kouching v povyshenii motivatsii patsientov k provedeniiu lechebno profilakticheskikh meropriiatii pri zabolevaniiakh parodonta

The phenomenon of "coaching" in dentistry is a fundamentally new direction of medical science and practice. Coach ing differs from counseling by motivation orientation. From the standpoint of coaching the doctor's art is to awaken the patient's interest to his health, to increase the level of his motivation when a person assumes responsibility for the outcome of the treatment and the level of health even for a short appointment. With the help of a specially modified coaching method, increasing of motivation to treatment and prevention of periodontal disease in 115 patients is conducted. In the controlling group (105 people) treatment is accompanied by tra ditional counseling including motivation for a healthy lifestyle, teaching the standard method of cleaning teeth and infor mation on plans and predictable treatment results. It has been found that the level of dental motivation is sensitive to the applied techniques of formation. In the observ ing group, where the technique of coaching is applied, there is a significant migration of patients from the number of less "motivated" to more "motivated" Obtained results make possible to recommend the introduction of coaching techniques into the work of periodontal centers and specialized units in order to increase motivation of patients to prevention and treatment of periodontal dis eases.
Analysis of the Structural Relationships among Self efficacy, Experience, Mobile Learning Quality, and Learner Satisfaction in Universities

This study was designed to determine the factors affecting learner satisfaction and examine the relationships of these factors in mobile learning linked to pre existing e learning in universities. In the structural model used, three mobile learning quality factors are the endogenous variables, namely, system quality (SYQ) information quality (INQ) and service quality (SEQ) perceived by students, and learner satisfaction (LS) whereas students' self efficacy (SE) and experience (EX) in mobile learning are the exogenous variables. The subjects were 900 students who registered for mobile learning courses offered by a private university in Seoul, Korea. The results indicated that SE in mobile learning had positive effects on SYQ, INQ, and SEQ. Furthermore, SE influenced LS when analyzed without quality factors as parameters. Mobile learning EX directly affected INQ, but not SYQ or SEQ. EX likewise had a direct effect on LS when analyzed without quality factors as parameters. Meanwhile, both SYQ and INQ showed a positive effect on LS, but not SEQ. SE and EX affected LS indirectly when SYQ and INQ were used as parameters. This study addresses the importance of increasing SE, EX, SYQ, and INQ to increase LS in mobile learning in universities
Comparing dataset characteristics that favor the Apriori, Eclat or FP Growth frequent itemset mining algorithms
Venue: SoutheastCon 2016

Frequent itemset mining is a popular data mining technique. Apriori, Eclat, and FP Growth are among the most common algorithms for frequent itemset mining. Considerable research has been performed to compare the relative performance between these three algorithms, by evaluating the scalability of each algorithm as the dataset size increases. While scalability as data size increases is important, previous papers have not examined the performance impact of similarly sized datasets that contain different itemset characteristics. This paper explores the effects that two dataset characteristics can have on the performance of these three frequent itemset algorithms. To perform this empirical analysis, a dataset generator is created to measure the effects of frequent item density and the maximum transaction size on performance. The generated datasets contain the same number of rows. This provides some insight into dataset characteristics that are conducive to each algorithm. The results of this paper's research demonstrate Eclat and FP Growth both handle increases in maximum transaction size and frequent itemset density considerably better than the Apriori algorithm.
An enhancement of the mapreduce apriori algorithm using vertical data layout and set theory concept of intersection

The process of Association Rule Generation is an important task in Data Mining. It is widely used for the Market Basket Analysis. The data that is used for the generation of Association Rules is usually distributed and complex. This can be efficiently implemented using the Hadoop Framework as it can process large datasets with less cost and good performance. We have proposed an efficient algorithm for MapReduce Apriori based on Hadoop MapReduce model using Vertical Database Layout and Set Theory concept of Intersection.
Aging causes decreased resistance to multiple stresses and a failure to activate specific stress response pathways
Venue: Aging

In this work, we examine the relationship between stress resistance and aging. We find that resistance to multiple types of stress peaks during early adulthood and then declines with age. To dissect the underlying mechanisms, we use C. elegans transcriptional reporter strains that measure the activation of different stress responses including: the heat shock response, mitochondrial unfolded protein response, endoplasmic reticulum unfolded protein response, hypoxia response, SKN 1 mediated oxidative stress response, and the DAF 16 mediated stress response. We find that the decline in stress resistance with age is at least partially due to a decreased ability to activate protective mechanisms in response to stress. In contrast, we find that any baseline increase in stress caused by the advancing age is too mild to detectably upregulate any of the stress response pathways. Further exploration of how worms respond to stress with increasing age revealed that the ability to mount a hormetic response to heat stress is also lost with increasing age. Overall, this work demonstrates that resistance to all types of stress declines with age. Based on our data, we speculate that the decrease in stress resistance with advancing age results from a genetically programmed inactivation of stress response pathways, not accumulation of damage.
A Network Performance Analysis of LoRa Modulation for LPWAN Sensor Devices
Venue: UCAmI

The rise in low power devices has created a necessity for connectivity among systems, in some cases across great geographic lengths. Current wireless communication protocols for these devices cover only small areas or require several hops to communicate. LPWAN have surfaced to cover this necessity offering greater transmission range in energy efficient protocols. Among these can we find the LoRa technology, offering wide spread spectrum modulation for WSN. The most common implementations of LoRa work at 868 MHz frequency range, and there is few information for other frequency bands. Given the nature of this modulation, it is of great interest to analyse lower frequencies. In this article a direct comparison of LoRa in the 868 MHz and 433 MHz will be done. Additionally, several parameters will be modified to find the best configuration available. This will help validate the possibility of transmitting at longer distances than current 868 MHz implementations.