LLukas22/scidocs huggingface dataset:
Diagnosis of Motor Faults Using Sound Signature Analysis

The objective of this paper is to present recent developments in the field of Induction motor fault signature analysis with particular regard to Sound signature analysis of induction motor of fan. The different types of fan faults that can be identified from the sound signature analysis [1] are, for example, rotor faults, bearing faults, unbalances wings etc. Corresponding to the above-mentioned faults, many types of machine fault signature analysis techniques [2] have been proposed for motor faults detection and diagnosis. These techniques include vibration monitoring, motor current signature analysis (MCSA) [3–6], electromagnetic field monitoring [7], chemical analysis, temperature measurability [8, 9], infrared measurement, acoustic noise analysis [10], and partial discharge measurement [11, 12]. Among these methods, vibration analysis, current analysis and Sound signature analysis are the most popular due to their easy measurability, high accuracy, and reliability.
Oil price elasticities and oil price fluctuations

Studies identifying oil shocks using structural vector autoregressions (VARs) reach different conclusions on the relative importance of supply and demand factors in explaining oil market fluctuations. This disagreement is due to different assumptions on the oil supply and demand elasticities that determine the identification of the oil shocks. We provide new estimates of oil-market elasticities by combining a narrative analysis of episodes of large drops in oil production with country-level instrumental variable regressions. When the estimated elasticities are embedded into a structural VAR, supply and demand shocks play an equally important role in explaining oil prices and oil quantities. Published by Elsevier B.V.
Development of a Wearable Vibrotactile Feedback Suit for Accelerated Human Motor Learning

When a human learns a new motor skill from a teacher, they learn using multiple channels: They receive high level information aurally about the skill, visual information about how another performs the skill, and at times, tactile information, from a teacher's physical guidance of the student. This research proposes a novel approach, the application of this tactile feedback through a robotic wearable system, while a student tries to learn from a teacher. Initial tests on a 5-DOF robotic suit show a decrease in motion errors of over 20%, and an accelerated learning rate of 7%, both conservative given the system setup and statistically very significant (p les 0.01). This research is intended in use of sports training, motor rehabilitation after neurological damage, dance, postural retraining for health, and many other contexts.
PIF3 Is Involved in the Primary Root Growth Inhibition of Arabidopsis Induced by Nitric Oxide in the Light

PHYTOCHROME INTERACTING FACTOR3 (PIF3) is an important component in the phytochrome signaling pathway and mediates plant responses to various environmental conditions. We found that PIF3 is involved in the inhibition of root growth of Arabidopsis thaliana seedlings induced by nitric oxide (NO) in light. Overexpression of PIF3 partially alleviated the inhibitory effect of NO on root growth, whereas the pif3-1 mutant displayed enhanced sensitivity to NO in terms of root growth. During phytochrome signaling, the photoreceptor PHYB mediates the degradation of PIF3. We found that the phyB-9 mutant had a similar phenotype to that of PIF3ox in terms of responsiveness to NO. Furthermore, NO treatment promoted the accumulation of PHYB, and thus reduced PIF3 content. Our results further show that the activity of PIF3 is regulated by the DELLA protein RGL3[RGA (repressor of ga1-3) LIKE 3]. Therefore, we speculate that PIF3 lies downstream of PHYB and RGL3, and plays an important role in the inhibitory effect of NO on root growth of Arabidopsis seedlings in light.
Disease System Analysis: Basic Disease Progression Models in Degenerative Disease

To describe the disease status of degenerative diseases (i.e., type 2 diabetes mellitus, Parkinson’s disease) as function of disease process and treatment effects, a family of disease progression models is introduced. Disease progression is described using a progression rate (R dp ) acting on the synthesis or elimination parameters of the indirect response model. Symptomatic effects act as disease-dependent or -independent effects on the synthesis or elimination parameters. Protective drug effects act as disease dependent or -independent effects on R dp . Simulations with the ten disease models show distinctly different signature profiles of treatment effects on disease status. Symptomatic effects result in improvement of disease status with a subsequent deterioration. Treatment cessation results in a disease status equal to the situation where treatment had not been applied. Protective effects result in a lasting reduction, or even reversal, of the disease progression rate and the resulting disease status during the treatment period. After cessation of treatment the natural disease course will continue from the disease status at that point. Disease system analysis constitutes a scientific basis for the distinction between symptomatic versus protective drug effects in relation to specific disease processes as well as the identification of the exposure-response relationship during the time-course of disease.
Rural origin plus a rural clinical school placement is a significant predictor of medical students' intentions to practice rurally: a multi-university study.

INTRODUCTION
Health workforce shortages are a major problem in rural areas. Australian medical schools have implemented a number of rural education and training interventions aimed at increasing medical graduates' willingness to work in rural areas. These initiatives include recruiting students from rural backgrounds, delivering training in rural areas, and providing all students with some rural exposure during their medical training. However there is little evidence regarding the impact of rural exposure versus rural origin on workforce outcomes. The aim of this study is to identify and assess factors affecting preference for future rural practice among medical students participating in the Australian Rural Clinical Schools (RCS) Program.


METHODS
Questionnaires were distributed to 166 medical students who had completed their RCS term in 2006; 125 (75%) responded. Medical students were asked about their preferred location and specialty for future practice, their beliefs about rural work and life, and the impact of the RCS experience on their future rural training and practice preferences.


RESULTS
Almost half the students (47%; n=58) self-reported a 'rural background'. Significantly, students from rural backgrounds were 10 times more likely to prefer to work in rural areas when compared with other students (p<0.001). For those preferring general practice, 80% (n=24) wished to do so rurally. Eighty-five per cent (n=105) of students agreed that their RCS experience increased their interest in rural training and practice with 62% (n=75) of students indicating a preference for rural internship/basic training after their RCS experience. A substantial percentage (86%; n=108) agreed they would consider rural practice after their RCS experience.


CONCLUSIONS
This baseline study provides significant evidence to support rural medical recruitment and retention through education and training, with important insights into the factors affecting preference for future rural practice. By far the most significant predictor of rural practice intention is recruitment of students with a rural background who also undertake an RCS placement. This research also demonstrates significant demand for post-graduate rural training places, including specialty places, as RCS graduates become junior doctors and vocational trainees.
Self-efficacy moderates the mediation of intentions into behavior via plans.

OBJECTIVE
To examine the putative moderating role of self-efficacy in the intention-planning-behavior relationship.


METHODS
In N=812 individuals, intentions (independent variable) were assessed at baseline, whereas action plans (mediator), self-efficacy (moderator), and physical activity (dependent variable) were measured again 4 weeks later. We examined a moderated-mediation model.


RESULTS
Self-efficacy moderates the mediation process: the strength of the mediated effect increased along with levels of self-efficacy. The results remain valid after accounting for baseline physical activity.


CONCLUSIONS
For plans to mediate the intention-behavior relation, people must hold sufficiently high levels of self-efficacy. If they lack self-efficacy, planning may be in vain.
When nurse emotional intelligence matters: How transformational leadership influences intent to stay.

AIM
The purpose of this study was to examine the role of staff nurse emotional intelligence between transformational leadership and nurse intent to stay.


BACKGROUND
Nurse intent to stay and transformational leadership are widely recognized as vital components of nurse retention. Staff nurse emotional intelligence that has been confirmed improvable has been recently recognized in the nursing literature as correlated with retention. Yet, the nature of the relationships among these three variables is not known.


METHODS
Cross-sectional data for 535 Chinese nurses were analysed using Structural Equation Modelling.


RESULTS
Transformational leadership and staff nurse emotional intelligence were significant predictors of nurse intent to stay, accounting for 34.3% of the variance in nurse intent to stay. Staff nurse emotional intelligence partially mediates the relationship between transformational leadership and nurse intent to stay.


CONCLUSION
The findings of the study emphasized the importance of transformational leadership in enhancing nurse emotional intelligence and to provide a deeper understanding of the mediating role of emotional intelligence in the relationship between nurse manager's transformational leadership and nurse's intent to stay.


IMPLICATIONS FOR NURSING MANAGEMENT
Nurse leaders should develop training programmes to improve nursing manager transformational leadership and staff nurse emotional intelligence in the workplace.
Analytical results about the robustness of FMT modulation with several prototype pulses in time-frequency selective fading channels

In this paper we study the performance of Filtered Multitone (FMT) modulation systems in time-frequency selective fading channels. FMT generalizes the OFDM scheme through the deployment of a sub-channel shaping pulse. A general analysis framework is reported, and it is specialized to the case of using a sine pulse, a root-raised cosine pulse, and Gaussian pulse. Quasi-closed form expressions for the signal-to-interference (SIR) power ratio are derived. The results allow to benchmark the multitone system and understand how robust it is to frequency selective time-variant fading. We make a comparison with OFDM both in terms of SIR and bit-error-rate when single-tap subchannel equalization is used. It shows that FMT can have better performance than OFDM due to the better sub-channel spectral containment. We also discuss the usefulness of the SIR results on the design of the prototype pulse in a time-frequency selective fading channel.
Modeling and optimal design of ground air collector for heating in controlled environment greenhouse

A mathematical model is devised to study the thermal behavior of a greenhouse while heating with a ground air collector (GAC). A computer program based on MatLab software has been used to predict the plant and room temperatures as a function of various design parameters of the ground air collector. Extensive experiments have been conducted during December 2000 to March 2001 for an even span greenhouse of effective floor area of 24 m with a GAC and having a brick north wall. The model was validated experimentally in the climate of Delhi for the winter season. A parametric study involves the area of the GAC, mass flow rate and heat capacity. The predicted plant and room temperatures show fair agreement with the experimental values.
Healthcare Resource Use and Costs in Long-Term Survivors of Acute Respiratory Distress Syndrome: A 5-Year Longitudinal Cohort Study.

OBJECTIVE
To evaluate the time-varying relationship of annual physical, psychiatric, and quality of life status with subsequent inpatient healthcare resource use and estimated costs.


DESIGN
Five-year longitudinal cohort study.


SETTING
Thirteen ICUs at four teaching hospitals.


PATIENTS
One hundred thirty-eight patients surviving greater than or equal to 2 years after acute respiratory distress syndrome.


INTERVENTIONS
None.


MEASUREMENTS AND MAIN RESULTS
Postdischarge inpatient resource use data (e.g., hospitalizations, skilled nursing, and rehabilitation facility stays) were collected via a retrospective structured interview at 2 years, with prospective collection every 4 months thereafter, until 5 years postacute respiratory distress syndrome. Adjusted odds ratios for hospitalization and relative medians for estimated episode of care costs were calculated using marginal longitudinal two-part regression. The median (interquartile range) number of inpatient admission hospitalizations was 4 (2-8), with 114 patients (83%) reporting greater than or equal to one hospital readmission. The median (interquartile range) estimated total inpatient postdischarge costs over 5 years were $58,500 ($19,700-157,800; 90th percentile, $328,083). Better annual physical and quality of life status, but not psychiatric status, were associated with fewer subsequent hospitalizations and lower follow-up costs. For example, greater grip strength (per 6 kg) had an odds ratio (95% CI) of 0.85 (0.73-1.00) for inpatient admission, with 23% lower relative median costs, 0.77 (0.69-0.87).


CONCLUSIONS
In a multisite cohort of long-term acute respiratory distress syndrome survivors, better annual physical and quality of life status, but not psychiatric status, were associated with fewer hospitalizations and lower healthcare costs.
Modular and reconfigurable mobile robotics

With increasing demand on reliable robotic platforms that can alleviate the burden of daily painstaking tasks, researchers have focused their effort towards developing robotic platforms that possess a high level of autonomy and versatility in function. These robots, capable of operating either individually or in a group, also possess the structural modular morphology that enables them to adapt to the unstructured nature of a real environment. Over the past two decades, significant work has been published in this field, particularly in the aspects of autonomy, mobility and docking. This paper reviews the primary methods in the literature related to the fields of modular and reconfigurable mobile robotics. By bringing together aspects of modularity, including docking and autonomy, and synthesizing the most relevant findings, there is optimism that a more complete understanding of this field will serve as a starting ground for innovation and integration of such technology in the urban environment. © 2012 Elsevier B.V. All rights reserved.
A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

BACKGROUND
With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain.


OBJECTIVE
This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset.


METHODS
We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models.


RESULTS
The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average.


CONCLUSIONS
In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge.
Heated intra-operative intraperitoneal oxaliplatin plus irinotecan after complete resection of peritoneal carcinomatosis: pharmacokinetics, tissue distribution and tolerance.

BACKGROUND
The purpose of this study was to report the pharmacokinetics (PK) and tolerance profile of intraoperative intraperitoneal chemo-hyperthermia (IPCH) with oxaliplatin and irinotecan.


PATIENTS AND METHODS
Thirty-nine patients with peritoneal carcinomatosis (PC) of either gastrointestinal or peritoneal origin underwent complete cytoreductive surgery followed by IPCH with a stable dose of oxaliplatin (460 mg/m(2)), plus one among seven escalating doses of irinotecan (from 300 to 700 mg/m(2)). IPCH was carried out with the abdomen open, for 30 min at 43 degrees C, with 2 l/m(2) of a 5% dextrose instillation in a closed continuous circuit. Patients received intravenous leucovorin (20 mg/m(2)) and 5-fluorouracil (400 mg/m(2)) just before IPCH to maximize the effect of oxaliplatin and irinotecan.


RESULTS
Irinotecan concentration in tumoral tissue increased until 400 mg/m(2) and then remained stable despite dose escalations. It was 16-23 times higher than in non-bathed tissues. Increasing doses of intraperitoneal irinotecan did not modify the PK of intraperitoneal oxaliplatin, and the drug concentration ratio was 17.8 higher in tumoral tissue (bathed) than in non-bathed tissues. The hospital mortality rate was 2.5% and the non-hematological complication rate was 25%. However, grade 3-4 hematological toxicity rate was 58%.


CONCLUSION
Intraperitoneal heated oxaliplatin (460 mg/m(2)) plus irinotecan (400 mg/m(2)) presented an advantageous PK profile and was tolerated by patients, despite a high hematological toxicity rate.
Efficient and Scalable Processing of String Similarity Join

The string similarity join is a basic operation of many applications that need to find all string pairs from a collection given a similarity function and a user-specified threshold. Recently, there has been considerable interest in designing new algorithms with the assistant of an inverted index to support efficient string similarity joins. These algorithms typically adopt a two-step filter-and-refine approach in identifying similar string pairs: 1) generating candidate pairs by traversing the inverted index; and 2) verifying the candidate pairs by computing the similarity. However, these algorithms either suffer from poor filtering power (which results in high verification cost), or incur too much computational cost to guarantee the filtering power. In this paper, we propose a multiple prefix filtering method based on different global orderings such that the number of candidate pairs can be reduced significantly. We also propose a parallel extension of the algorithm that is efficient and scalable in a MapReduce framework. We conduct extensive experiments on both centralized and Hadoop systems using both real and synthetic data sets, and the results show that our proposed approach outperforms existing approaches in both efficiency and scalability.
Stress Distributions in Plasma-Sprayed Thermal Barrier Coatings Under Thermal Cycling in a Temperature Gradient

The residual stress distribution in plasma-sprayed zirconia thermal barrier coatings subjected to cyclic thermal gradient testing was evaluated using Raman piezospectroscopy and finite element computation. The thermal gradient testing (approximately 440°C /mm at temperature), consisted of repeated front-side heating with a flame and constant cooling of the back-side of the substrate either with front-side radiative cooling only or with additional forced air cooling between the heating cycles. The coatings exhibited characteristic “mud-cracking” with the average crack spacing dependent on the cooling treatment. This is consistent with finite element calculations and Raman spectroscopy measurements in which the sudden drop in coating surface temperature on initial cooling leads to a large biaxial tension at the surface. The key to proper interpretation of the Raman shifts is that the stress-free Raman peaks need to be corrected for shifts associated with the evolution of the metastable tetragonal phase with aging. DOI: 10.1115/1.4002209
The Medical Imaging Interaction Toolkit: challenges and advances

   The Medical Imaging Interaction Toolkit (MITK) has been available as open-source software for almost 10 years now. In this period the requirements of software systems in the medical image processing domain have become increasingly complex. The aim of this paper is to show how MITK evolved into a software system that is able to cover all steps of a clinical workflow including data retrieval, image analysis, diagnosis, treatment planning, intervention support, and treatment control.    MITK provides modularization and extensibility on different levels. In addition to the original toolkit, a module system, micro services for small, system-wide features, a service-oriented architecture based on the Open Services Gateway initiative (OSGi) standard, and an extensible and configurable application framework allow MITK to be used, extended and deployed as needed. A refined software process was implemented to deliver high-quality software, ease the fulfillment of regulatory requirements, and enable teamwork in mixed-competence teams.    MITK has been applied by a worldwide community and integrated into a variety of solutions, either at the toolkit level or as an application framework with custom extensions. The MITK Workbench has been released as a highly extensible and customizable end-user application. Optional support for tool tracking, image-guided therapy, diffusion imaging as well as various external packages (e.g. CTK, DCMTK, OpenCV, SOFA, Python) is available. MITK has also been used in several FDA/CE-certified applications, which demonstrates the high-quality software and rigorous development process.    MITK provides a versatile platform with a high degree of modularization and interoperability and is well suited to meet the challenging tasks of today’s and tomorrow’s clinically motivated research.