nils-herrmann/clean_scirepeval huggingface dataset:
The distribution of pelvic organ support in a population of female subjects seen for routine gynecologic health care.

This study was undertaken to describe the distribution of pelvic organ support stages in a population of women seen at outpatient gynecology clinics for routine gynecologic health care.



This was an observational study. Women seen for routine gynecologic health care at four outpatient gynecology clinics were recruited to participate. After informed consent was obtained general biographic data were collected regarding obstetric history, medical history, and surgical history. Women then underwent a pelvic examination. Pelvic organ support was measured and described according to the pelvic organ prolapse quantification system. Stages of support were evaluated by variable for trends with Pearson chi(2) statistics.



A total of 497 women were examined. The average age was 44 years, with a range of 18 to 82 years. The overall distribution of pelvic organ prolapse quantification system stages was as follows: stage 0, 6.4%; stage 1, 43.3%; stage 2, 47.7%; and stage 3, 2.6%. No subjects examined had pelvic organ prolapse quantification system stage 4 prolapse. Variables with a statistically significant trend toward increased pelvic organ prolapse quantification system stage were advancing age, increasing gravidity and parity, increasing number of vaginal births, delivery of a macrosomic infant, history of hysterectomy or pelvic organ prolapse operations, postmenopausal status, and hypertension.



The distribution of the pelvic organ prolapse quantification system stages in the population revealed a bell-shaped curve, with most subjects having stage 1 or 2 support. Few subjects had either stage 0 (excellent support) or stage 3 (moderate to severe pelvic support defects) results. There was a statistically significant trend toward increased pelvic organ prolapse quantification system stage of support among women with many of the historically quoted etiologic factors for the development of pelvic organ prolapse.
Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

In this study, we present several image segmentation techniques for various image scales and modalities. We consider cellular-, organ-, and whole organism-levels of biological structures in cardiovascular applications. Several automatic segmentation techniques are presented and discussed in this work. The overall pipeline for reconstruction of biological structures consists of the following steps: image pre-processing, feature detection, initial mask generation, mask processing, and segmentation post-processing. Several examples of image segmentation are presented, including patient-specific abdominal tissues segmentation, vascular network identification and myocyte lipid droplet micro-structure reconstruction.
The analyzing of a cascaded multilevel class-D power amplifier with the carrier-based phase-shifted modulation

This paper presents a switch-mode power amplifier based on a cascaded multicell multilevel circuit topology. Amplifiers based on the single-phase inverter topology or H-bridge is most easily accomplished by the direct series, or parallel, connection of multiple H-bridge power-stages. Operating these with the phase-shifted PWM control gives higher output power and produces an improved output voltage spectrum and significantly lowers output current-ripple. Two carrier-based PWM schemes, phase-shifted and level-shifted modulations, are analyzed and their performance is compared. A multilevel class-D amplifier's mathematic model is analyzed. The results of measurements taken from a laboratory setup are presented which prove the proposed amplifier improves the dynamic system behavior and have wide bandwidth.
Information technology and organizational learning in knowledge alliances and networks: Evidence from U.S. pharmaceutical industry

Recent years have witnessed a change in firms’ innovation patterns, from closed to open, in which information technology (IT) has played an important role. This paper aims to open up the black box of ITenabled absorptive capacity by theorizing and testing the role of IT in two organizational learning processes, which are either interactive with partners in the knowledge alliance or non-interactive with others in the knowledge network. In particular, we formulate a model explaining how a firm’s IT investment moderates its organizational learning processes in knowledge alliances and networks, which sheds light on the role of IT as an enabler of absorptive capacity. Using a panel data set from the U.S. pharmaceutical industry, the results show the moderating role of IT in strengthening the organizational learning processes from knowledge alliance experience to coinvented knowledge and from knowledge network centrality to assimilated knowledge, which, in turn, improve firm competitiveness.  Corresponding author at: Department of Innovation Management and Strategy, Faculty of Economics and Business, University of Groningen, 9747 AE Groningen, The Netherlands. Tel.: +31 50 363 4839. E-mail address: john.dong@rug.nl (J.Q. Dong).
Analytic power factor calculation for vernier machines with concentrated windings

It is well-known that the power factor of Vernier machines is small compared to permanent magnet machines. However, the power factor equations already derived show a huge deviation to the finite-element analysis (FEA) when used for Vernier machines with concentrated windings. Therefore, this paper develops an analytic model to calculate the power factor of Vernier machines with concentrated windings and different numbers of flux modulating poles (FMPs) and stator slots. The established model bases on the winding function theory in combination with a magnetic equivalent circuit. Consequently, equations for the q-inductance and for the no-load back-EMF of the machine are derived, thus allowing the calculation of the power factor. Thereby, the model considers stator leakage effects, as they are crucial for a good power factor estimation. Comparing the results of the Vernier machine to those of a pm machine explains the decreased power factor of Vernier machines. In addition, a FEA confirms the results of the derived model.
The Argument Web: an Online Ecosystem of Tools, Systems and Services for Argumentation

The Argument Web is maturing as both a platform built upon a synthesis of many contemporary theories of argumentation in philosophy and also as an ecosystem in which various applications and application components are contributed by different research groups around the world. It already hosts the largest publicly accessible corpora of argumentation and has the largest number of interoperable and cross compatible tools for the analysis, navigation and evaluation of arguments across a broad range of domains, languages and activity types. Such interoperability is key in allowing innovative combinations of tool and data reuse that can further catalyse the development of the field of computational argumentation. The aim of this paper is to summarise the key foundations, the recent advances and the goals of the Argument Web, with a particular focus on demonstrating the relevance to, and roots in, philosophical argumentation theory.
A Mutual Agreement Signature Scheme for Secure Data Provenance

Derived from the field of art curation, digital provenance is an unforgeable record of a digital object’s chain of successive custody and sequence of operations performed on it. Digital provenance forms an immutable directed acyclic graph (DAG) structure. Recent works in digital provenance have focused on provenance generation, storage and management frameworks in different fields. In this paper, we address two important aspects of digital provenance that have not been investigated thoroughly in existing works: 1) capturing the DAG structure of provenance and 2) supporting dynamic information sharing. We propose a scheme that uses signature-based mutual agreements between successive users to clearly delineate the transition of responsibility of the document as it is passed along the chain of users. In addition to preserving the properties of confidentiality, immutability and availability for a digital provenance chain, it supports the representation of DAG structures of provenance. Our scheme supports dynamic information sharing scenarios where the sequence of users who have custody of the document is not predetermined. Security analysis and empirical results indicate that our scheme improves the security of the existing Onion and PKLC provenance schemes with comparable performance. Provenance, cryptography, signatures, integrity, confidentiality, availability
Effect of 1000 ppm relative to 250 ppm fluoride toothpaste. A meta-analysis.

To estimate the overall effect of 1000 ppm F relative to 250 ppm F toothpaste.



Experimental caries increment studies from the dental literature, which compared 1000 ppm with 250 ppm fluoride toothpastes, were summarized using meta-analytic methods.



The overall caries reduction of 1000 ppm F relative to 250 ppm F paste was estimated to be 0.142 (95%-CL: 0.074-0.210) when applying a fixed effects model and 0.129 (95%-CL: 0.012-0.230) when applying a random effects model.



The present analysis found slightly lower caries increments (14%, 13%) in children using 1000 ppm F toothpastes compared to children using 250 ppm F pastes. On the other hand, the use of 1000 ppm F pastes is associated with dental fluorosis. Considering these effects it seems justifiable to the authors to keep the use of 250 ppm F pastes for preschool children in Switzerland.
Prevention of violence against women and girls: what does the evidence say?

In this Series paper, we review evidence for interventions to reduce the prevalence and incidence of violence against women and girls. Our reviewed studies cover a broad range of intervention models, and many forms of violence--ie, intimate partner violence, non-partner sexual assault, female genital mutilation, and child marriage. Evidence is highly skewed towards that from studies from high-income countries, with these evaluations mainly focusing on responses to violence. This evidence suggests that women-centred, advocacy, and home-visitation programmes can reduce a woman's risk of further victimisation, with less conclusive evidence for the preventive effect of programmes for perpetrators. In low-income and middle-income countries, there is a greater research focus on violence prevention, with promising evidence on the effect of group training for women and men, community mobilisation interventions, and combined livelihood and training interventions for women. Despite shortcomings in the evidence base, several studies show large effects in programmatic timeframes. Across different forms of violence, effective programmes are commonly participatory, engage multiple stakeholders, support critical discussion about gender relationships and the acceptability of violence, and support greater communication and shared decision making among family members, as well as non-violent behaviour. Further investment in intervention design and assessment is needed to address evidence gaps.
Multiple paired forward and inverse models for motor control

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. In this paper, we propose a modular approach to such motor learning and control. We review the behavioral evidence and benefits of modularity, and propose a new architecture based on multiple pairs of inverse (controller) and forward (predictor) models. Within each pair, the inverse and forward models are tightly coupled both during their acquisition, through motor learning, and use, during which the forward models determine the contribution of each inverse model's output to the final motor command. This architecture can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment. Finally, we describe specific predictions of the model, which can be tested experimentally.
Language Identification in Mixed Script Social Media Text

With the spurt in usage of smart devices, large amounts of unstructured text is generated by numerous social media tools. This text is often filled with stylistic or linguistic variations making the text analytics using traditional machine learning tools to be less effective. One of the specific problem in Indian context is to deal with large number of languages used by social media users in their roman form. As part of FIRE-2015 shared task on mixed script information retrieval, we address the problem of word level language identification. Our approach consists of a two stage algorithm for language identification. First level classification is done using sentence level character n-grams and second level consists of word level character n-grams based classifier. This approach effectively captures the linguistic mode of author in social texting enviroment. The overall weighted F-Score for the run submitted to FIRE Shared task is 0.7692. The sentence level classification algorithm which is used in achiving this result has an accuracy of 0.6887. We could further improve the accuracy of sentence level classifier further by 1.6% using additional social media text crawled from other sources. Naive Bayes classifier showed largest improvement (5.5%) in accuracy level by the addition of supplementary tuples. We also observed that using semi-supervised learning algorithm such as Expectation Maximization with Naive Bayes, the accuracy could be improved to 0.7977.
Phytochemical Analysis of Vitex altissima L . using UV-VIS , FTIR and GC-MS

The present study was carried out to characterize the bioactive constituents present in different leaf extracts of Vitex altissima L. using UV-VIS, FTIR and GC-MS. The crude extracts were scanned in the wavelength ranging from 200-1100 nm by using Perkin Elmer Spectrophotometer and the characteristic peaks were detected. For GC-MS analysis, 10 g sample is extracted with 30 ml ethanol, filtered in ash less filter paper with 2 g sodium sulphate and the extract is concentrated to 1 ml by bubbling nitrogen into the solution. The compound detection employed the NIST Ver. 2.0 Year 2005 library. The biological activities are based on Dr. Duke’s Phytochemical and Ethnobotanical Databases by Dr. Jim Duke of the Agricultural Research Service/USDA. The UV-VIS profile showed different peaks ranging from 400-700 nm with different absorption respectively. The FTIR spectrum confirmed the presence of alcohols, phenols, alkanes, alkynes, alkyl halides, aldehydes, carboxylic acids, aromatics, nitro compounds and amines in different extracts. The results of the GC-MS analysis provide different peaks determining the presence of 21 phytochemical compounds with different therapeutic activities. The major phytoconstituents were n-Hexadecanoic acid (23.74%), 9, 12-Octadecadienoic acid [Z, Z] (23.41%) and Squalene (14.74%). Hence, this study offers a base of using V. altissima as herbal alternative for the synthesis of antimicrobial agents.
Mobile device interfaces illiterate

The development of Graphical User Interface (GUI) is meant to significantly increase the ease of usability of software applications so that the can be used by users from different backgrounds and knowledge level. Such a development becomes even more important and challenging when the users are those that have limited literacy capabilities. Although the progress of development for standard software interface has increased significantly, similar progress has not been available in interface for illiterate people. To fill this gap, this paper presents our research on developing interface of software application devoted to illiterate people. In particular, the proposed interface was designed for mobile application and combines graphic design and linguistic approaches. With such feature, the developed interface is expected to provide easy to use application for illiterate people.
Multiclass support vector machine for classification spatial data from satellite image

This paper presents a method for the classification of Landsat Multi-Spectral Scanner (MSS) satellite images to identify the areas of land use. The image is pre-processed and classified using Support Vector Machine (SVM) with the Radial Basis Function (RBF) Kernel as it is an efficient supervised-classification technique. In this research, pixel — base classification method is performed according to the value of spectral pixels with Multi-Spectral Scanner satellite image and used data corresponds to a 3×3 square neighborhoods. The research work consists of two main stages. At the first stage, the optimal parameter, sigma value of RBF kernel, for SVM is studied. At the second stage, the obtained classification result is compared with other classification methods. In this study, sigma value, a parameter of RBF kernel, is varied between 1.0 and 2.0. The sigma value at 1.7 lead to the best classification result which has over 90% accuracy. The result from this SVM method has a higher accuracy compared to other methods.
Multi Class Adult Image Classification Using Neural Networks

As the Internet became popular, the volume of digital multimedia data is exponentially increased in all aspects of our life. This drastic increment in multimedia data causes unwelcome deliveries of adult image contents to the Internet. Consequently, a large number of children are wide-open to these harmful contents. In this paper, we propose an efficient classification system that can categorize the images into multiple classes such as swimming suit, topless, nude, sexual act, and normal. The experiment shows that this system achieved more than 80% of the success rate. Thus, the proposed system can be used as a framework for web contents rating systems.
Face Alignment by Local Deep Descriptor Regression

We present an algorithm for extracting key-point descriptors using deep convolutional neural networks (CNN). Unlike many existing deep CNNs, our model computes local features around a given point in an image. We also present a face alignment algorithm based on regression using these local descriptors. The proposed method called Local Deep Descriptor Regression (LDDR) is able to localize face landmarks of varying sizes, poses and occlusions with high accuracy. Deep Descriptors presented in this paper are able to uniquely and efficiently describe every pixel in the image and therefore can potentially replace traditional descriptors such as SIFT and HOG. Extensive evaluations on five publicly available unconstrained face alignment datasets show that our deep descriptor network is able to capture strong local features around a given landmark and performs significantly better than many competitive and state-of-the-art face alignment algorithms.
A model for coordinating nuclear mechanics and membrane remodeling to support nuclear integrity.

A polymer network of intranuclear lamin filaments underlies the nuclear envelope and provides mechanical stability to the nucleus in metazoans. Recent work demonstrates that the expression of A-type lamins scales positively with the stiffness of the cellular environment, thereby coupling nuclear and extracellular mechanics. Using the spectrin-actin network at the erythrocyte plasma membrane as a model, we contemplate how the relative stiffness of the nuclear scaffold impinges on the growing number of interphase-specific nuclear envelope remodeling events, including recently discovered, nuclear envelope-specialized quality control mechanisms. We suggest that a stiffer lamina impedes these remodeling events, necessitating local lamina remodeling and/or concomitant scaling of the efficacy of membrane-remodeling machineries that act at the nuclear envelope.
Which Teaching Strategy is Better for Enhancing Anti-Phishing Learning Motivation and Achievement? The Concept Maps on Tablet PCs or Worksheets?

The purpose of this study is to evaluate the feasibility of the integration of concept maps and tablet PCs in anti-phishing education for enhancing students’ learning motivation and achievement. The subjects were 155 students from grades 8 and 9. They were divided into an experimental group (77 students) and a control group (78 students). To begin with, the two groups received identical anti-phishing training: the teacher explained the concept of anti-phishing and asked the students questions; the students then used tablet PCs for polling and answering the teachers’ questions. Afterwards, the two groups performed different group activities: the experimental group was divided into smaller groups, which used tablet PCs to draw concept maps; the control group was also divided into groups which completed worksheets. The study found that the use of concept maps on tablet PCs during the anti-phishing education significantly enhanced the students’ learning motivation when their initial motivation was already high. For learners with low initial motivation or prior knowledge, the use of worksheets could increase their posttest achievement and motivation. This study therefore proposes that motivation and achievement in teaching the anti-phishing concept can be effectively enhanced if the curriculum is designed based on the students’ learning preferences or prior knowledge, in conjunction with the integration of mature and accessible technological media into the learning activities. The findings can also serve as a reference for anti-phishing educators and researchers.
Direction of Arrival (DOA) Estimation Using Electrically Small Tuned Dipole Antennas

We present a methodology for the direction of arrival (DOA) estimation using the induced voltages that are measured at the loads connected to electrically small tuned dipole antenna arrays illuminated by the signal of interest (SOI). The matrix pencil method is applied directly to the induced voltages to estimate the DOA of the various signals. Using electrically small tuned antennas can be advantageous as they can be placed in close proximity of each other saving the real estate and, thus, making it possible to deploy phased arrays on small footprints. When dealing with closely spaced tuned electrically small antennas, it is necessary to use a transformation matrix to compensate for the strong mutual coupling that may exist between the antenna elements. The transformation matrix converts the voltages that are induced at the loads corresponding to the feed point of the array operating in the presence of mutual coupling and other near field scatterers to an equivalent set of voltages that will be induced by the same incident wave in an uniform linear virtual array (ULVA) consisting of omnidirectional isotropic point radiators equally spaced and operating in free space. For any given incident field, the open circuit voltage developed across the feed-point of the small dipole will always be less than the open circuit voltage developed across the feed-point of the half-wavelength dipole. The difference is in the voltage developed across the loads connected to the dipole's feed-point. With the small dipole antenna, the voltage developed across the load impedance will be orders of magnitude greater than the voltage developed across the load connected to the half-wavelength dipole, even though the power captured by any conjugately matched antenna is approximately the same irrespective of their lengths. Three different scenarios are presented to illustrate the methodology. First, we consider resonant dipole elements spaced half wavelength apart, electrically small tuned antenna elements spaced half wavelength apart and electrically small tuned antenna elements placed in close proximity of each other to reduce the footprint without affecting the performance of the phased array. In addition, we consider the possibility of DOA estimation using a combination of different type of electrically small antennas both uniformly and nonuniformly spaced. Numerical examples are presented to illustrate the principles of this methodology