nils-herrmann/clean_scirepeval huggingface dataset:
Development and Assessment of a Web Based Patient Simulation Program
Year: 2011

The development of clinical reasoning skills is dependant on substantial exposure to realistic patient experiences. Academic programs are constrained by several factors, including the the amount of time they can expose students to real patient cases. Typically, text cases and standardized patient experiences are used to increase student exposure; however, both of these methods have limitations. Computer simulated patients may be an alternative or supplemental pedagogy that can be used to expose students to realistic patient cases with greater frequency. Model Description and Evaluation. Existing computer simulated cases used to teach clinical reasoning to medical studients were modified to include the Guide to Physical Therapist Practice to add examination and interventions used by physical therapists. First, 5 faculty and second year students performed a usability analysis. Second, they conducted a feasibility study to determine if the software could be integrated into a specific class. Third, a pilot study was conducted that compared the effectiveness and efficiency of the a Web based simulation compared to a traditional small group discussion. Outcomes for the pilot study included the health science reasoning test (HSRT) and implementation costs.  The outcome of this project was the design and testing of a computerized patient simulation program that can be used to teach clinical reasoning to physical therapy students.  Existing clinical reasoning software was modified for use by physical therapist students. Incorporating software into a physcial therapy class was proved to be feasible. When compared to a traditional teaching group, the Web based simulated group had signficant improvements in some aspects of clinical reasoning and cost less to implement. However, the software requires further changes for greater congruency with physical therapist clinical reasoning. Additionally, questions of dosing and integration into a physical therapist education curriculum rather than a discrete class need to be addressed.
Burnout, boredom and engagement at the workplace
Year: 2014

This chapter is about how employees feel at work. In other words, it is concerned with their psychological well being, which can be either negative or positive. For instance, employees may feel worn out, cynical or bored, or in contrast, they may feel enthused and full of pep. The way employees feel has not only to do with 'who they are' i.e. their personality but also with 'where they are' i.e. in their jobs. In essence, employee well being results from the interaction between person and (work) environment. It depends on the interplay of person related factors such as temperament and past experiences, and job related factors such as job characteristics and interpersonal relations at work. More particularly, this chapter Chapter
A Cross Sectional Study on Prevalence of Anaemia and its Determinant Factors among Pregnant Mothers in a Rural Community
Venue: Journal of Pharmaceutical Research International
Year: 2021

Anemia is the greatest common nutritional deficiency disorders affecting the pregnant women in the developing countries and determine the prevalence of anaemia among pregnant women in a rural community and determine the socio demographic factors and obstetric factors associated with anaemia. The objective of the study was to evaluation the prevalence of anemia between pregnant women and to regulate its association with maternal and fetal consequences. The care given by the health care provider to a pregnant woman during her pregnancy period (a period of 280 days) is known as antenatal care. This research was carried out in rural field practicing area, Sripuram Kanchepuram district of Tamilnadu. The pregnant mothers residing in this area were taken as the study subject. This study was conducted for a period of four month from June 2014 to September 2014. Anaemia is considered a severe public health problem by World Health Organization when anaemia prevalence is equal to or greater than 40% in the population. Iron deficiency is highest population subgroups that are at peak growth rates that is infant, children and pregnant mothers. Anaemia in pregnancy, the most common micro nutritional deficiency (disorder) It is estimated that more than 50% of pregnant women are anaemic and majority (90% belongs to iron deficiency. The great incidence of anemia in pregnant women speciously increases the maternal and fetal risks. To increase maternal and fetal effect, it is suggested that the primary health care has to be strengthened, prevention, initial diagnosis, and treatment of anemia in pregnancy to be given priority.
Bank erosion processes measured with UAV SfM along complex banklines of a straight mid sized river reach
Year: 2018

We apply structure from motion (SfM) photogrammetry with imagery from an unmanned aerial vehicle (UAV) to measure bank erosion processes along a mid sized river reach. This technique offers a unique set of characteristics compared to previously used methods to monitor banks, such as high resolution and relatively fast deployment in the field. We analyse the retreat of a 1.2 km restored bank of the Meuse River which has complex vertical scarps laying on a straight reach, features that present specific challenges to the UAV SfM application. We surveyed eight times within a year with a simple approach, combining different photograph perspectives and overlaps to identify an effective UAV flight. The accuracy of the digital surface models (DSMs) was evaluated with real time kinematic (RTK) GPS points and airborne laser scanning of the whole reach. An oblique perspective with eight photo overlaps and 20 m of cross sectional ground control point distribution was sufficient to achieve the relative precision to observation distance of ~1 1400 and 3 cm root mean square error (RMSE) complying with the required accuracy. A complementary nadiral view increased coverage behind bank toe vegetation. Sequential DSMs captured signatures of the erosion cycle such as mass failures, slump block deposition, and bank undermining. Although UAV SfM requires low water levels and banks without dense vegetation as many other techniques, it is a fast in the field alternative to survey reach scale riverbanks in sufficient resolution and accuracy to quantify bank retreat and identify morphological features of the bank failure and erosion processes. Improvements to the adopted approach are recommended to achieve higher accuracies.
Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion
Venue: Comput. Ind. Eng.
Year: 2020

The demand for a particular product or service is typically associated with different uncertainties that can make them volatile and challenging to predict. Demand unpredictability is one of the managers' concerns in the supply chain that can cause large forecasting errors, issues in the upstream supply chain and impose unnecessary costs. We investigate 843 real demand time series with different values of coefficient of variations (CoV) where promotion causes volatility over the entire demand series. In such a case, forecasting demand for different CoV require different models to capture the underlying behavior of demand series and pose significant challenges due to very different and diverse demand behavior. We decompose demand into baseline and promotional demand and propose a hybrid model to forecast demand. Our results indicate that our proposed hybrid model generates robust and accurate forecast across series with different levels of volatilities. We stress the necessity of decomposition for volatile demand series. We also model demand series with a number of well known statistical and machine learning (ML) models to investigate their performance empirically. We found that ARIMA with covariate (ARIMAX) works well to forecast volatile demand series, but exponential smoothing with covariate (ETSX) has a poor performance. Support vector regression (SVR) and dynamic linear regression (DLR) models generate robust forecasts across different categories of demands with different CoV values.
Linear Codes With Two or Three Weights From Weakly Regular Bent Functions
Venue: IEEE Transactions on Information Theory
Year: 2016

Linear codes with a few weights have applications in consumer electronics, communication, data storage system, secret sharing, authentication codes, association schemes, and strongly regular graphs. This paper first generalizes the method of constructing two weight and three weight linear codes of Ding et al. and Zhou et al. to general weakly regular bent functions and determines the weight distributions of these linear codes. It solves an open problem proposed by Ding et al. Furthermore, this paper constructs new linear codes with two or three weights and presents their weight distributions. They contain some optimal codes meeting certain bound on linear codes.
Aportaciones teoricas y practicas para la autogestion de la escritura academica en la UNED a traves de los MOOCs para docentes, tutores y alumnos
Year: 2018

espanolFruto de nuestra investigacion sobre el desarrollo de competencias, hemos creado el Centro de Escritura Academica y Profesional en la OTRI. El centro se ha creado a semejanza del compromiso institucional asumido en las universidades del ambito anglosajon por el desarrollo de la escritura academica. El proposito es abordar la competencia escrita desde la pedagogia, por lo que el planteamiento anade a la orientacion linguistica, la cognitiva y la social. Desde esta perspectiva pedagogica hemos elaborado dos MOOCS focalizados en la autogestion de la escritura academica. El primer MOOC marca el nuevo enfoque pedagogico. El segundo, impulsa la creatividad y la expresion artistica en los trabajos academicos. Desde esta perspectiva, la motivacion y la autoestima se consideran las bases para una correcta pedagogia de la escritura academica desde los distintos generos. Ademas de UNED abierta, los MOOCs se han incorporado como material audiovisual gratuito a la asignatura de Practicas II de Pedagogia. Como transferencia, podemos senalar la invitacion de la UAM a presentar el centro en el seminario internacional de centros de escritura en nuestro territorio, que tendra lugar el 10 de diciembre (actualmente se han creado siete en Espana) Los MOOCs han llegado a los alumnos de la universidad a distancia de Mexico. EnglishThe research carried out concerning skills development provided us with the framework to set up the Academic and Professional Writing Center at the OTRI. This institution has been inspired by the anglosaxon experiences where the institutional commitment with writing has fostered the setting up of writing centers. The purpose of the center is to foster academic writing from pedagogical premises including not only linguistic skills but also the cognitive and social ones. From this approach we have designed two MOOCs focused on academic writing selfmanagement. The first MOOC defines the pedagogical premises. The second one fosters creativity and academic writing as an artistic ability. Both selfesteem and motivation should be the basis for a correct academic gender pedagogy. The MOOCs are placed at UNED abierta so that these materials are offered free of charges to the students registered at the subject of the Education Degree Practicas Profesionales II. The transfer of the content will be spread at the UAM writing seminar that will take place on the 10th of December (at present seven centers have been at up at the Spanish universities) The MOOC have been included as material for students of the Distance Mexican University.
Chapter 4 System management and record keeping
Year: 2017

Standards covering food transportation system management and record keeping are similar in content and design to many previously established standards, such as those found in International Standards Organization (ISO) and other certification standards. This chapter includes TransCert management standards, as well as examples of electronic, paper, and real time data collection strategies designed to help companies plan for meeting new rules. While many large companies are currently embracing the evolving GFSI (Global Food Safety Initiative) approach, others still struggle with the myriad of standards imposed on them by their customer base. All such standards tend to ignore the food transportation sector's need for improved and more objective data, such as those provided by slow and costly bacterial contaminant testing. New lower cost and faster testing for transportation container bacterial, chemical, and radiation needs to be developed by the food industry and associated laboratory entities to provide a more preventive and risk reducing approach. Systems that provide cost analysis capabilities are needed to provide ROI and adequate food transportation controls.
Shared Experience Actor Critic for Multi Agent Reinforcement Learning
Venue: NeurIPS
Year: 2020

Exploration in multi agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents. Our proposed algorithm, called Shared Experience Actor Critic (SEAC) applies experience sharing in an actor critic framework. We evaluate SEAC in a collection of sparse reward multi agent environments and find that it consistently outperforms two baselines and two state of the art algorithms by learning in fewer steps and converging to higher returns. In some harder environments, experience sharing makes the difference between learning to solve the task and not learning at all.
Disentangling Factors of Variation with Cycle Consistent Variational Auto Encoders
Venue: ECCV
Year: 2018

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle consistent adversarial architectures, we use cycle consistency in a variational auto encoder framework. Our non adversarial approach is in contrast with the recent works that combine adversarial training with auto encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training.
Deep Learning for Segmentation Using an Open Large Scale Dataset in 2D Echocardiography
Venue: IEEE Transactions on Medical Imaging
Year: 2019

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state of the art encoder decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e. segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi structure ultrasound segmentation dataset, the largest publicly available and fully annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder decoder based architectures outperform state of the art non deep learning methods and faithfully reproduce the expert analysis for the end diastolic and end systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6% Although these results are below the inter observer scores, they remain slightly worse than the intra observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully automatic analysis of 2D echocardiographic images.
Tumor Segmentation in Brain MRI using Fully Convolutional Network
Venue: 2020 International Conference on Advances in Computing, Communication Materials (ICACCM)
Year: 2020

In biomedical image processing and cancer studies tumor segmentation is one of the most indispensable tasks. Early diagnosis of tumorous cells aids substantially in early treatment planning and enhances the chance of survival of the patient. Manual segmentation of tumor cells in Brain MRI is difficult and time consuming and also requires expertise in this field. In this paper, we have presented a deep Fully Convolutional Network designed using the TensorFlow library, which can successfully perform segmentation tasks in medical images. Brain MRI images of 225 patients are used as a training data set and a separate set from 20 patients is used to test the performance of the network. Two different loss functions: dice loss and weighted cross entropy are used in the learning algorithm. We obtained a 0.76 Dice Score coefficient (i.e. 76% of the overlapping between the predicted image and ground truth) for a training of 100 epochs.
Sequential Quadratic Programming Method for Nonlinear Least Squares Estimation and Its Application
Venue: Mathematical Problems in Engineering
Year: 2019

In this study, we propose a direction controlled nonlinear least squares estimation model that combines the penalty function and sequential quadratic programming. The least squares model is transformed into a sequential quadratic programming model, allowing for the iteration direction to be controlled. An ill conditioned matrix is processed by our model; the least squares estimate, the ridge estimate, and the results are compared based on a combination of qualitative and quantitative analyses. For comparison, we use two equality indicators: estimated residual fluctuation of different methods and the deviation between estimated and true values. The root mean squared error and standard deviation are used for quantitative analysis. The results demonstrate that our proposed model has a smaller error than other methods. Our proposed model is thereby found to be effective and has high precision. It can obtain more precise results compared with other classical unwrapping algorithms, as shown by unwrapping using both simulated and real data from the Jining area in China.
Comparative Analysis of Autism Spectrum Disorders and Schizophrenia in Childhood
Venue: Human Physiology
Year: 2020

Several problems arise during the diagnosis of children with autism spectrum disorders (ASD) or children with child schizophrenia due to the similarity of mechanisms and manifestations of these two types of pathology. In this paper we analyzed the literature data to determine the differences in the diagnosis of ASD and schizophrenia. This issue is important for understanding the fundamental patterns of these two types of disorders during child development and for appropriate correction and rehabilitation procedures. We identified some consistent indicators of bioelectric activity typical of schizophrenic and autistic types of development. The indicators of the schizophrenic type include "hypofrontality" (i.e. a large representation of slow rhythms in frontal areas) and dextral dominance of the a rhythm. The indicators of the autistic type include the maximum reduction of the a rhythm accompanied by an increase in the g rhythm representation, changes in synchronization of slow waves during cognitive task performance, maximal activation of visual and acoustical areas of the cortex (which may be associated with abnormally low thresholds in autistic children) Verbal function is more affected in ASD than in schizophrenia; the recognition of mental condition of other people is more disordered. Significant differences between ASD and schizophrenia were detected in comparing attention function disturbances. Marked and vigorous changes in the mirror neuron system were observed in ASD; data on mirror neurons in schizophrenia are contradictory.
Modulated electron multiplied fluorescence lifetime imaging microscope: all solid state camera for fluorescence lifetime imaging
Venue: Journal of biomedical optics
Year: 2012

We have built an all solid state camera that is directly modulated at the pixel level for frequency domain fluorescence lifetime imaging microscopy (FLIM) measurements. This novel camera eliminates the need for an image intensifier through the use of an application specific charge coupled device design in a frequency domain FLIM system. The first stage of evaluation for the camera has been carried out. Camera characteristics such as noise distribution, dark current influence, camera gain, sampling density, sensitivity, linearity of photometric response, and optical transfer function have been studied through experiments. We are able to do lifetime measurement using our modulated, electron multiplied fluorescence lifetime imaging microscope (MEM FLIM) camera for various objects, e.g. fluorescein solution, fixed green fluorescent protein (GFP) cells, and GFP actin stained live cells. A detailed comparison of a conventional microchannel plate (MCP) based FLIM system and the MEM FLIM system is presented. The MEM FLIM camera shows higher resolution and a better image quality. The MEM FLIM camera provides a new opportunity for performing frequency domain FLIM.
Dynamic Changes in Amygdala Psychophysiological Connectivity Reveal Distinct Neural Networks for Facial Expressions of Basic Emotions
Venue: Scientific reports
Year: 2017

The quest to characterize the neural signature distinctive of different basic emotions has recently come under renewed scrutiny. Here we investigated whether facial expressions of different basic emotions modulate the functional connectivity of the amygdala with the rest of the brain. To this end, we presented seventeen healthy participants (8 females) with facial expressions of anger, disgust, fear, happiness, sadness and emotional neutrality and analyzed amygdala's psychophysiological interaction (PPI) In fact, PPI can reveal how inter regional amygdala communications change dynamically depending on perception of various emotional expressions to recruit different brain networks, compared to the functional interactions it entertains during perception of neutral expressions. We found that for each emotion the amygdala recruited a distinctive and spatially distributed set of structures to interact with. These changes in amygdala connectional patters characterize the dynamic signature prototypical of individual emotion processing, and seemingly represent a neural mechanism that serves to implement the distinctive influence that each emotion exerts on perceptual, cognitive, and motor responses. Besides these differences, all emotions enhanced amygdala functional integration with premotor cortices compared to neutral faces. The present findings thus concur to reconceptualise the structure function relation between brain emotion from the traditional one to one mapping toward a network based and dynamic perspective.