LLukas22/scidocs huggingface dataset:
Lightning-Induced Overvoltages Transmitted Over Distribution Transformer With MV Spark-Gap Operation—Part I: High-Frequency Transformer Model

In Finland, distribution transformers are frequently subjected to lightning strokes. Therefore, it is worthy to study the voltage transferred to the secondary terminal of the transformer due to lightning strokes hitting the primary terminals. In order to do conduct this study, the high-frequency model of the distribution transformer is to be investigated. In this paper, an accurate and simplified high-frequency model of the distribution transformer under lightning strokes is presented. The proposed model is introduced based on two-port network theory where its parameters are calculated at two resonance frequencies experimentally measured. The model is suitable for unloaded and loaded conditions and it is experimentally verified under different balanced loads considering two different practical distribution transformers. Then, the model verification is extended to study the impact of medium-voltage spark-gap operation on the transmitted voltage. The experimental results have validated the proposed transformer model.
Persistent Systemic Inflammation is Associated with Poor Clinical Outcomes in COPD: A Novel Phenotype

BACKGROUND
Because chronic obstructive pulmonary disease (COPD) is a heterogeneous condition, the identification of specific clinical phenotypes is key to developing more effective therapies. To explore if the persistence of systemic inflammation is associated with poor clinical outcomes in COPD we assessed patients recruited to the well-characterized ECLIPSE cohort (NCT00292552).


METHODS AND FINDINGS
Six inflammatory biomarkers in peripheral blood (white blood cells (WBC) count and CRP, IL-6, IL-8, fibrinogen and TNF-α levels) were quantified in 1,755 COPD patients, 297 smokers with normal spirometry and 202 non-smoker controls that were followed-up for three years. We found that, at baseline, 30% of COPD patients did not show evidence of systemic inflammation whereas 16% had persistent systemic inflammation. Even though pulmonary abnormalities were similar in these two groups, persistently inflamed patients during follow-up had significantly increased all-cause mortality (13% vs. 2%, p<0.001) and exacerbation frequency (1.5 (1.5) vs. 0.9 (1.1) per year, p<0.001) compared to non-inflamed ones. As a descriptive study our results show associations but do not prove causality. Besides this, the inflammatory response is complex and we studied only a limited panel of biomarkers, albeit they are those investigated by the majority of previous studies and are often and easily measured in clinical practice.


CONCLUSIONS
Overall, these results identify a novel systemic inflammatory COPD phenotype that may be the target of specific research and treatment.
Verification of the analytical range of a new reagent for full-range C-reactive protein determination.

BACKGROUND
The aim of this study was to verify the analytical range of the DIAgam XS Cardio NanoGold C-reactive protein (CRP) method proposed as a full-range assay.


METHODS
For this purpose, analytical specifications were verified according to the Clinical and Laboratory Standards Institute specifications and a comparison was made with the reference method for the cardiovascular risk assessment, the CardioPhase high sensitivity CRP from Siemens Healthcare Diagnostics.


RESULTS
Our results showed that the immunoturbidimetric assay of CRP using reagents from DIAgam laboratory gave accurate and precise measurements from 0.42 to 265 mg/L. The performances in limits of detection and linearity allowed a full-range determination of CRP concentrations. The comparison with the reference method for the determination of cardiovascular risk was also accurate.


CONCLUSIONS
In conclusion, the DIAgam reagents might be used in a routine laboratory for the detection of acute inflammation as well as for evaluation of cardiovascular risk.
Estimation of Value-at-Risk and Expected Shortfall based on Nonlinear Models of Return Dynamics and Extreme Value Theory

We propose an estimation procedure for value-at-risk (VaR) and expected shortfall (TailVaR) for conditional distributions of a time series of returns on a financial asset. Our approach combines a local polynomial estimator of conditional mean and volatility functions in a conditional heterocedastic autoregressive nonlinear (CHARN) model with Extreme Value Theory for estimating quantiles of the conditional distribution. We investigate the finite sample properties of our method and contrast them with alternatives, including the method recently proposed by McNeil and Frey (2000), in an extensive Monte Carlo study. The method we propose outperforms the estimators currently available in the literature. An evaluation based on backtesting was also performed. ∗We would like to thank two anonymous referees for comments and suggestions. Department of Economics, Ballard Hall 303, Oregon State University, Corvallis, OR 97331-3612.
A pilot study evaluating the effect of mindfulness-based stress reduction on psychological status, physical status, salivary cortisol, and interleukin-6 among advanced-stage cancer patients and their caregivers.

PURPOSE
To investigate whether a mindfulness-based stress reduction program for cancer (MBSR-C) improved psychological and physical symptoms, quality of life (QOL), and stress markers among advanced-stage cancer patients and caregivers.


DESIGN
A pilot within-subject design was used.


METHOD
Patients previously diagnosed with advanced-stage breast, colon, lung, or prostate cancer and on treatment were recruited from the Moffitt Cancer Center and Research Institute. Twenty-six patient-caregiver dyads completed a modified 6-week, self-study MBSR-C program based on the Kabat-Zinn model. Psychological and physical symptoms and QOL were compared pre- and post-MBSR-C sessions. Salivary cortisol and interleukin-6 were assessed pre- and post-MBSR-C session at 1, 3, and 6 weeks.


FINDINGS
Following the 6-week MBSR program, patients showed improvements in stress and anxiety (p < .05); caregivers' psychological and QOL also improved but were not statistically significant. Both patients and caregivers had decreases in cortisol at Weeks 1 and 3 (p < .05) but not at Week 6. Similar to cortisol levels at Week 6, salivary interleukin-6 levels were lower overall (before/after an MBSR-C session), compared with Week 1 for patients and caregivers.


CONCLUSIONS
MBSR-C may be a beneficial intervention for reducing stress, anxiety, cortisol levels, and symptoms in advanced-stage cancer patients and may also benefit caregivers.
Automated Monitoring Suspicious Discussions on Online Forums Using Data Mining Statistical Corpus Based Approach

M.Suruthi Murugesan, R. Pavitha Devi, S. Deepthi, V.Sri Lavanya & Dr. Annie Princy Ph.D Department of information technology (B-tech), Panimalar Engineering College. Chennai, India . Abstract: The Forum is a huge virtual space where to express and share individual opinions, influencing any aspect of life, with implications for marketing and communication alike. Forums are influencing users preferences by shaping their attitudes and behaviours. Monitoring the suspicious activities is a good way to measure users loyalty, keeping a track on their sentiment towards their posts. The exponential advancement in information and communication technology has fostered the creation of new online forums for much online discussion and has also reduced distances between people. Unfortunately, malicious people use these online forums for illegal purposes. In online forums, the users produce several and various formats of suspicious posts (text, image, video...) and exchange them online with other people .The law enforcement agencies are looking for solutions to monitor these discussion forums for possible illegal activities and download suspected postings that are in text formats as evidence for investigation .The data in most online forums are stored in text format, so this system will focus only on text posts.
Capital structure and Ownership Structure Relationship : A Comparative Study of Textile and Non-textile Manufacturing Sectors of Pakistan

The current study explores the impact of ownership structure on capital structure in textile sector and rest of the manufacturing sectors (non-textile) in Pakistan using regression analysis with fixed effect model. As textile sector is the largest manufacturing sector in Pakistan and having diversified financial characteristics, however, there exists a gap whether textile sector’s ownership and capital structure relationship matches with other manufacturing sectors or not. Current study tries to fill this gap. The results indicate that in textile sector, no significant relationship exists between ownership concentration and capital structure whereas a significant negative relationship is found between these two variables in case of non-textile firms in Pakistan. However, institutional ownership variable was found to be non-significant in both textile and non-textile sectors. Other control variables were found to have the results as hypothesized. Period of study used in this study is 2006-2009 and sample comprises of KSE listed firms.
Nanotechnology and its Applications in Medicine

Advancement in the field of nanotechnology and its applications to the field of medicines and pharmaceuticals has revolutionized the twentieth century. Nanotechnology [1] is the study of extremely small structures. The prefix “nano” is a Greek word which means “dwarf ”. The word “nano” means very small or miniature size. Nanotechnology is the treatment of individual atoms, molecules, or compounds into structures to produce materials and devices with special properties. Nanotechnology involve work from top down i.e. reducing the size of large structures to smallest structure e.g. photonics applications in nano electronics and nano engineering, top-down or the bottom up, which involves changing individual atoms and molecules into nanostructures and more closely resembles chemistry biology.
Adaptive Dynamic Walking of a Quadruped Robot on Irregular Terrain Based on Biological Concepts

We have been trying to induce a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. We propose the necessary conditions for stable dynamic walking on irregular terrain in general, and we design the mechanical system and the neural system by comparing biological concepts with those necessary conditions described in physical terms. A PD controller at the joints can construct the virtual spring–damper system as the visco-elasticity model of a muscle. The neural system model consists of a central pattern generator (CPG) and reflexes. A CPG receives sensory input and changes the period of its own active phase. The desired angle and P-gain of each joint in the virtual spring– damper system is switched based on the phase signal of the CPG. CPGs, the motion of the virtual spring–damper system of each leg and the rolling motion of the body are mutually entrained through the rolling motion feedback to CPGs, and can generate adaptive walking. We report on our experimental results of dynamic walking on terrains of medium degrees of irregularity in order to verify the effectiveness of the designed neuro-mechanical system. We point out the trade-off problem between the stability and the energy consumption in determining the cyclic period of walking on irregular terrain, and we show one example to solve this problem. MPEG footage of these experiments can be seen at http://www.kimura.is.uec.ac.jp. KEY WORDS—quadruped, central pattern generator (CPG), walking on irregular terrain The International Journal of Robotics Research Vol. 22, No. 3–4, March–April 2003, pp. 187-202, ©2003 Sage Publications
Maternal psychosocial factors determining the concentrations of transforming growth factor-beta in breast milk.

BACKGROUND
Cytokines in breast milk may play crucial roles in the beneficial effects of breastfeeding in protecting against allergic and infectious diseases in infants. In particular, breast milk-borne transforming growth factor-beta (TGF-β) has an important potential role in developing the mucosal immune system in infants. However, little is known about what factors influence TGF-β expression in human milk. We investigated whether the behavioral and psychosocial characteristics of mothers affect breast milk TGF-β levels.


METHODS
We conducted a survey of all 139 mothers who were lactating between February and October 2010 in Koshu City, Japan. Participants completed a questionnaire and provided breast milk at the health checkups for their 3-month-old child (N = 129, 93%). Breast milk was assayed for total TGF-β2 levels by ELISA. We took an exploratory approach based on linear and ordered logistic regressions to model TGF-β2 concentrations with their multiple potential determinants.


RESULTS
Mothers with depression or poor self-rated health had higher TGF-β2 concentrations than mothers without depression (odds ratio for a higher TGF-β2 quartile: 3.11, 95% confidence intervals: 1.03-9.37) or those reporting better health (odds ratio: 2.34, 1.21-4.55). Smoking, drinking alcohol, probiotics supplementation, social support, and maternal history of allergic diseases were not associated with milk TGF-β2 levels. Milk gathered between August and October or later in the afternoon (3-4 pm vs. 12-2 pm) contained less TGF-β2.


CONCLUSION
Depression, as the consequence of psychosocial stress, may be a strong determinant of TGF-β levels in breast milk. Seasonal and daily fluctuations in milk TGF-β2 concentrations warrant further study.
Using collaborative learning to improve diabetes care and outcomes: the VIDA project.

UNLABELLED
The prevalence of diabetes in Mexico among those 20-64 years of age has increased from 7.2% in 1993 to 10.7% in 2000. National population-based surveys in Mexico demonstrated that 50% of the total population with diabetes had blood glucose levels of 200mg/dl or higher. Thus, diabetes care has become one of the most important public health challenges in this country. The aim of the study was to improve the quality of diabetes care in primary health care centers using the chronic care model and the breakthrough series (BTS) collaborative methodology.


METHODS
Ten public health centers in the cities of Xalapa and Veracruz were randomly selected to participate in the project. Five of the health centers were randomly assigned to receive the intervention (intervention group) and the other five followed usual care (usual care group). The intervention was evaluated by A1c test before and after the intervention in both groups of patients. Patients were followed for 18 months from November 2002 to May 2004. Results were adjusted for the clustering of patients within practices and baseline measure.


RESULTS
The proportion of people with good glycemic control (A1c<7%) among those in the intervention group increased from 28% before the intervention to 39% after the intervention. The proportion of patients achieving three or more quality improvement goals increased from 16.6% to 69.7% (p<0.001) among the intervention group while the usual care group experienced a non-significant decrease from 12.4% to 5.9% (p=0.118). The focus on the primary care team and the participation of people with diabetes were strategic elements incorporated into the methodology, expected to ensure sustainability of continued improvement of health outcomes.


CONCLUSIONS
The intervention introduced modifications to solve problems identified by health teams in their practice and improved process and outcome measures of quality diabetes care. Most of the actions were directed at four components of the chronic care model: self-management support, decision support, delivery system design, and clinical information systems.
Visual Text Analytics: Techniques for Linguistic Information Visualization

Visual Text Analytics has been an active area of interdisciplinary research (http://textvis.lnu.se/). This interactive tutorial is designed to give attendees an introduction to the area of information visualization, with a focus on linguistic visualization. After an introduction to the basic principles of information visualization and visual analytics, this tutorial will give an overview of the broad spectrum of linguistic and text visualization techniques, as well as their application areas [3]. This will be followed by a hands-on session that will allow participants to design their own visualizations using tools (e.g., Tableau), libraries (e.g., d3.js), or applying sketching techniques [4]. Some sample datasets will be provided by the instructor. Besides general techniques, special access will be provided to use the VisArgue framework [1] for the analysis of selected datasets.
Power and moral leadership: role of self‐other agreement

The relationships between moral leadership, transformational, transactional, and laissez·faire leaderships, and certain outcomes were studied using a sample of 116 managers of a large manufacturing organization in eastern India. Results reveal that transformational leadership partially mediates moral leadership's relationship with follower's extra effort and satisfaction, and leader's effectiveness, and it fully mediates moral leadership's relationship with leader's power. Based on whether leader's self-rating was more than, same as, or less than follower's rating of leader's transformational leadership, leader-follower dyads were classified into three categories overestimation, agreement, and underestimation. Findings show that moral leadership is lower in overestimation than in agreement, and is lower in agreement than in underestimation. Correlation between moral leadership and power is also the highest in the case of underestimation. Leader's power, however, does not differ across
Local cortical thinning links to resting-state disconnectivity in major depressive disorder.

BACKGROUND
Local structural and metabolic as well as inter-regional connectivity abnormalities have been implicated in the neuropathology of major depressive disorder (MDD). How local tissue properties affect intrinsic functional connectivity is, however, unclear. Using a cross-sectional, multi-modal imaging approach, we investigated the relationship between local cortical tissue abnormalities and intrinsic resting-state functional connectivity (RSFC) in MDD.


METHOD
A total of 20 MDD in-patients and 20 healthy controls underwent magnetic resonance imaging at 3 T for structural and functional imaging. Whole-brain cortical thickness was calculated and compared between groups. Regions with reduced cortical thickness defined seeds for subsequent whole-brain RSFC analyses. Contributions of structural tissue abnormalities on inter-regional RSFC were explicitly investigated.


RESULTS
Lower cortical thickness was observed in MDD in the right dorsomedial prefrontal cortex (PFC), superior temporal gyrus/temporal pole, middle-posterior cingulate cortex, and dorsolateral PFC. No differences in local fractional amplitude of low-frequency fluctuations were observed. Lower thickness in patients' dorsomedial PFC further directly and selectively affected its RSFC with the precuneus, which was unaffected by symptom severity. No effects of cortical thickness in other regions showing abnormal thickness were observed to influence functional connectivity.


CONCLUSIONS
Abnormal cortical thickness in the dorsomedial PFC in MDD patients was observed to selectively and directly affect its intrinsic connectivity with the precuneus in MDD patients independent of depression severity, thereby marking a potential vulnerability for maladaptive mood regulation. Future studies should include an unmedicated sample and replicate findings using independent component analysis to test for morphometric effects on network integrity.
An Open-Source Dialog System with Real-Time Engagement Tracking for Job Interview Training Applications

In complex conversation tasks, people react to their interlocutor’s state, such as uncertainty and engagement to improve conversation effectiveness [2]. If a conversational system reacts to a user’s state, would that lead to a better conversation experience? To test this hypothesis, we designed and implemented a dialog system that tracks and reacts to a user’s state, such as engagement, in real time. We designed and implemented a conversational job interview task based on the proposed framework. The system acts as an interviewer and reacts to user’s disengagement in real-time with positive feedback strategies designed to re-engage the user in the job interview process. Experiments suggest that users speak more while interacting with the engagement-coordinated version of the system as compared to a noncoordinated version. Users also reported the former system as being more engaging and providing a better user experience.
Simultaneous Localization and Mapping : Literature Survey

One of the initial solutions to the SLAM problem was proposed by Smith and Cheeseman who used the Extended Kalman Filter (EKF) to jointly represent the landmark position with the pose [38]. Guivant and Nebot [18] developed compressed EKF which performed mapping in local space using a reduced number of landmarks and only performed global update when the robot moved from one local map to another local map. The worst case complexity in this case is O(kn) required for full global update. Paskin [36] proposed thin junction tree filter (TJTF) to maintain manageable complexity. It is an assumed density filtering algorithm where the belief state is represented using a junction tree and proposed a thinning operation to maintain an ever-increasing tree width caused by filtering operations. Estimation in this case is performed in O(kn) time by passing marginalized distribution along the edges of the junction tree. According to Frese et al. [13] “From the perspective of “linear equation solving” this approach is complementary to the approach proposed in this paper. In the Gaussian case the junction tree algorithm is a direct, i.e., exact equation solver based on Schur-complements (corresponding to the marginalized distributions) and approximation is performed on the equation level. In contrast Multilevel Relaxation is an iterative, i.e., approximate equation solver but does not approximate the equations themselves.”
Convolutional Neural Networks for Figure Extraction in Historical Technical Documents

We present a method of extracting figures and images from the pages of scanned documents, especially from technical research articles. Our approach is novel in two key ways. First, we treat this as a computer vision problem, and train convolutional neural networks to recognize figures in scanned pages. Second, we generate our training data from 'born-digital' structured documents, allowing us to automatically produce labels for our training set using PDF figure extractors. This avoids the otherwise tedious task of hand-labelling thousands of document pages. Our convolutional neural networks achieve precision and recall of close to 85% in identifying figures from a test set consisting of modern journal papers and conference proceedings, and obtain precision and recall above 80% on an application data set comprised of historical technical documents scanned from the Bell Labs Records. Our results show that models trained on digital documents transfer very well to historical scans. Finally, it is easy to extend our models to identify other document elements such as tables and captions.