nils-herrmann/clean_scirepeval huggingface dataset:
Vitamin D: The "sunshine" vitamin
Venue: Journal of pharmacology pharmacotherapeutics
Year: 2012

Vitamin D insufficiency affects almost 50% of the population worldwide. An estimated 1 billion people worldwide, across all ethnicities and age groups, have a vitamin D deficiency (VDD) This pandemic of hypovitaminosis D can mainly be attributed to lifestyle (for example, reduced outdoor activities) and environmental (for example, air pollution) factors that reduce exposure to sunlight, which is required for ultraviolet B (UVB) induced vitamin D production in the skin. High prevalence of vitamin D insufficiency is a particularly important public health issue because hypovitaminosis D is an independent risk factor for total mortality in the general population. Current studies suggest that we may need more vitamin D than presently recommended to prevent chronic disease. As the number of people with VDD continues to increase, the importance of this hormone in overall health and the prevention of chronic diseases are at the forefront of research. VDD is very common in all age groups. As few foods contain vitamin D, guidelines recommended supplementation at suggested daily intake and tolerable upper limit levels. It is also suggested to measure the serum 25 hydroxyvitamin D level as the initial diagnostic test in patients at risk for deficiency. Treatment with either vitamin D2 or vitamin D3 is recommended for deficient patients. A meta analysis published in 2007 showed that vitamin D supplementation was associated with significantly reduced mortality. In this review, we will summarize the mechanisms that are presumed to underlie the relationship between vitamin D and understand its biology and clinical implications.
An investigation into the effect of parabolic speed hump profiles on ride comfort and driving safety under variable vehicle speeds: A campus experience
Venue: Sustainable Cities and Society
Year: 2019

This study aims to investigate the effect of parabolic speed hump profiles on ride comfort and driving safety under variable vehicle speeds. To this end, in situ and simulation tests were carried out in which root mean square values were evaluated. Field tests were performed on seven parabolic speed humps within the boundaries of Istanbul Technical University Ayazaga Campus Area. These tests were conducted twice with different loads. Five vehicle speeds were taken into account within the range of 15 km/h to 35 km/h with 5 km/h increments. As in situ tests focused on the measurement of the vertical acceleration when crossing the speed humps, maximum vertical accelerations and root mean square values were recorded by means of an accelerometer and a software. Two sets of parabolic speed hump profiles were considered for the simulation stage and then the results of the field tests and the simulation tests were compared. The results have yielded vehicle speed and parabolic speed hump height as the most important parameters affecting ride comfort and driving safety. It has also been demonstrated that the weight of the vehicle is negatively associated with motorists' sense of discomfort and an increase in the length of speed humps results in slightly better ride comfort.
Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation.
Venue: Journal of surgical education
Year: 2019

Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript.  The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise.  This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre.  The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review.  Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals.  This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education.
Detecting Laughter and Filler Events by Time Series Smoothing with Genetic Algorithms
Venue: SPECOM
Year: 2016

Social signal detection, where the aim is to identify vocalizations like laughter and filler events (sounds like "eh" "er" etc. is a popular task in the area of computational paralinguistics, a subfield of speech technology. Recent studies have shown that besides applying state of the art machine learning methods, it is worth making use of the contextual information and adjusting the frame level scores based on the local neighbourhood. In this study we apply a weighted average time series smoothing filter for laughter and filler event identification, and set the weights using genetic algorithms. Our results indicate that this is a viable way of improving the Area Under the Curve (AUC) scores: our resulting scores are much better than the accuracy of the raw likelihoods produced by both AdaBoost.MH and DNN, and we also significantly outperform standard time series filters as well.
Acute dissociative reaction to spontaneous delivery in a case of total denial of pregnancy: Diagnostic and forensic aspects
Venue: Journal of trauma dissociation the official journal of the International Society for the Study of Dissociation
Year: 2017

This article presents the history of a 21 year old female college student with total denial of pregnancy who experienced an acute dissociative reaction during the spontaneous delivery at home without medical assistance where the newborn died immediately. Psychiatric examination, self report questionnaires, legal documents, and witness reports have been reviewed in evaluation of the case. Evidence pointed to total denial of pregnancy, that is, until delivery. The diagnoses of an acute dissociative reaction to stress (remitted) and a subsequent PTSD were established in a follow up examination conducted 7 months after the delivery. Notwithstanding the inherently dissociative nature of total denial of pregnancy, no other evidence has been found about pre existing psychopathology. For causing the newborn's death, the patient faced charges for "aggravated murder," which were later on reduced into "involuntary manslaughter." Given the physical incapacity to perform voluntary acts due to the loss of control over her actions during the delivery, and the presence of an acute dissociative reaction to unexpected delivery, the legal case represents an intricate overlap between "insanity" and "incapacitation" defenses. The rather broad severity spectrum of acute dissociative conditions requires evaluation of the limits and conditions of appropriate legal defenses by mental health experts and lawyers. Denial of pregnancy as a source of potential stress has attracted little interest in psychiatric literature although solid research exists which documented that it is not infrequent. Arguments are presented to introduce this condition as a diagnostic category of female reproductive psychiatry with a more neutral label: "unperceived pregnancy."
Effects of surface roughness on shear viscosity.
Venue: Physical review. E
Year: 2017

This paper investigates the effect of surface roughness on fluid viscosity using molecular dynamics simulations. The three dimensional model consists of liquid argon flowing between two solid walls whose surface roughness was modeled using fractal theory. In tandem with previously published experimental work, our results show that, while the viscosity in smooth channels remains constant across the channel width, in the presence of surface roughness it increases close to the walls. The increase of the boundary viscosity is further accentuated by an increase in the depth of surface roughness. We attribute this behavior to the increased momentum transfer at the boundary, a result of the irregular distribution of fluid particles near rough surfaces. Furthermore, although the viscosity in smooth channels has previously been shown to be independent of the strength of the solid liquid interaction, here we show that in the presence of surface roughness, the boundary viscosity increases with the solid's wettability. The paper concludes with an analytical description of the viscosity as a function of the distance from the channel walls, the walls' surface roughness, and the solid's wetting properties. The relation can potentially be used to adjust the fluid dynamics equations for a more accurate description of microfluidic systems.
Female medical students are estimated to have a higher risk for developing eating disorders than male medical students.
Venue: Danish medical bulletin
Year: 2011

Studies show that university students are at risk for eating disorders. However, risk behaviour has not been studied among Danish medical students, nor have the gender differences in risk behaviour been described in a Danish context.  All first year medical students (n 979) received a questionnaire related to body perception, exercise habits, eating habits, height and weight in the fall of 2006 and 2007. The response rate was 57% (n 561) The gender distribution of the study population was 71.8% females and 28.2% males and the average age was 21.5 years.  More males (89.8% than females (73.1% were satisfied with their body and more females (34.8% than males (10.9% felt too fat. More females (42.7% than males (19.9% felt guilty when eating unhealthy food. 2.3% (all females) claimed to feel anxiety when they were about to eat. More males (48.4% than females (28.6% stated that they could not keep themselves from exercising. 13.5% of the underweight females (body mass index 20 kg/m2) felt too fat, while none of the underweight males had this perception. In average, females and males displayed 2.8 and 2.1 risk behaviours, respectively.  Female medical students have a drive for thinness and male medical students want to be muscular. More female than male students have a negative body perception. Female medical students are estimated to have a higher risk for developing eating disorders than male students. Future research in this area should address the causes of such behaviour.
Collaborative Reinforcement Learning Model for Sustainability of Cooperation in Sequential Social Dilemmas
Venue: AAMAS
Year: 2019

Learning the emergence of cooperation in conflicting scenarios such as social dilemmas is a centerpiece of research. Many reinforcement learning based theories exist in the literature to address this problem. The well known fact about RL based model's very slow learning capabilities coupled with large state space exhibit significant negative effects especially in repeated version of social dilemma settings such as repeated Public Goods Game (PGG) and thereby making them ineffective to model sustainability of cooperation. In this paper, we address this research challenge by augmenting the reinforcement learning based models with a notion of collaboration among the agents, motivated by the fact that humans learn not only through their own actions but also by following the actions of other agents who also continuously learn about the environment. In particular, we propose a novel model, which we refer to as Collaborative Reinforcement Learning (CRL) wherein we define collaboration among the agents as the ability of agents to fully follow other agent's actions/decisions. This is also termed as social learning. The proposed CRL model significantly influences the speed of individual learning, which eventually has a large effect on the collective behavior as compared to that of RL only models and thereby effectively explaining the sustainability of cooperation in repeated PGG settings. We also extend the CRL model for PGGs over different generations where agents die out and new agents are born following a birth death process.
Investigating Medical Parallels between Two Works of Mythology and Epics; Ferdosi's Shanameh from Iran and Feng Shen Yan Yi from China
Year: 2016

Some believe that ancient mythological and literary texts solely aimed to amuse and entertain, and were sprouted from their feelings and thoughts, and do not regard the study of the ancient and mythological stories as having the definiteness and clarity which science is known for it. Hence, this is why mythological stories in reality convey the beliefs, nature, attitudes and the extent of human knowledge at some point in history. They account for one of the most important signs of civilization and an important factor in creating cultural ties and dominance in the past. It is on this basis that literature is considered as a symbol of cultural competence and civilization of various nations. Although Ferdosi's Shanameh from Iran and Feng Shen Yan Yi from China are independent works, numerous similarities in various fields including mythological and medical elements are to be found. It can be concluded that the effects of each of these works have acknowledged the signs of old historical relations between Iran and China.
Feature Enhancement in Medical Ultrasound Videos Using Contrast Limited Adaptive Histogram Equalization
Venue: Journal of Digital Imaging
Year: 2019

Speckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this requires the enhancement of features and fine details. We propose a novel framework for both multiplicative noise suppression and robust contrast enhancement and demonstrate its effectiveness using a wide range of clinical ultrasound scans. Our approach to noise suppression uses a novel algorithm based on a convolutional neural network that is first trained on synthetically modeled ultrasound images and then applied on real ultrasound videos. The feature improvement stage uses an improved contrast limited adaptive histogram equalization (CLAHE) method for enhancing texture features, contrast, resolvable details, and image structures to which the human visual system is sensitive in ultrasound video frames. The proposed CLAHE algorithm also considers an automatic system for evaluating the grid size using entropy, and three different target distribution functions (uniform, Rayleigh, and exponential) and interpolation techniques (B spline, cubic, and Lanczos 3) An extensive comparative study has been performed to find the most suitable distribution and interpolation techniques and also the optimal clip limit for ultrasound video feature enhancement after speckle suppression. Subjective assessments by four radiologists and experimental validation using three quality metrics clearly indicate that the proposed framework generates superior performance compared with other well established methods. The processing pipeline reduces speckle effectively while preserving essential information and enhancing the overall visual quality and therefore could find immediate applications in real time ultrasound video segmentation and classification algorithms.
Wolf dog hybridization in Croatia
Year: 2018

Wolf dog hybridization is considered as one of the main threats for wolf conservation since the admixture and introgression of domestic genes may disrupt local adaptations and threaten the long term survival of wild wolf populations. We investigated the occurrence of wolf dog hybridization in Croatia by analyzing a panel of 12 autosomal microsatellite markers using Bayesian admixture tests, and assessed its directionality by the use of maternally and paternally inherited markers in combination with morphometric data and morphological features. A systematic analysis of morphologic features and morphometric data was used to rank the studied individuals into either phenotypic wild type wolves or suspected hybrids. By combining Bayesian assignment results with phenotypic features, we set three thresholds which differentiated wolves from hybrids with maximized hybrid detection and a minimized chance for false positive hybrid identification. On the basis of phenotype, out of 176 wild canids, 157 (89.2% were categorized as wolves and 19 (10.8% as suspected hybrids. On the basis of the Bayesian admixture tests and phenotype together, five (2.8 percent) animals were classified as wolf dog hybrids, four of them as backcrosses with wolves, and one as a backcross with a dog. Mitochondrial DNA suggested that all hybrids originated from the mating of female wolves and male dogs. Two male hybrids had Y chromosome haplotypes common to both wolves and dogs, while the other two had wolf private Y chromosome haplotypes. One wolf had a dog Y haplotype, indicating a past introgression of dog genes. All hybrids were found in Dalmatia, where wolves settled recently, and where they live close to humans, with a high rate of human caused mortality. These conditions are considered as favorable for wolf dog hybridization. However, we found a low hybridization prevalence in Croatia, which is nonetheless expected to persist as long as the conditions favoring its occurrence are met. The ecological, sociological, conservation and management implications of hybrid occurrence are yet to be determined.
Tracking urban sustainability transition: An eco efficiency analysis on eco industrial development in Ulsan, Korea
Year: 2020

The ecologically efficient transformation of natural resources into economic output is a critical pathway to achieve urban and industrial sustainability. Eco efficiency approach offers an effective tool to uncover both the status and trends of such a transformation. This paper applied a modified eco efficiency approach in combination with data envelopment analysis, to analyze the sustainability transition of Ulsan metropolis, Korea, under a fifteen years' promotion (2000 2015) of eco industrial development (EID) In the design for analysis, eco efficiency change both at industrial park level and the regional level was investigated to analyze whether eco efficiency improvements at the park level coincide with those at the regional level. Our main findings were highlighted as: At the regional level, from 2000 to 2015, eco efficiency of industrial waste generation and energy use has improved by 35.0% and 21.4% respectively, driven by a significant reduction in waste and energy intensities attributable to technological improvements (EID promotion and urban industrial symbiosis) At the industrial park/complex level, two national eco industrial parks (EIPs) in Ulsan showed highest eco efficiency (pure technical efficiency was 1.0) in analytical years, compared with nine regional industrial complexes. At both levels of analysis, EIPs stood out as eco efficient and their contribution to urban sustainability transition was overwhelming. Moreover, EIP implementation and urban industrial symbiosis in Ulsan were identified as the major drivers of regional EID policy. Based on our critical findings, EID was highlighted as a preventative and regenerative approach to improve eco efficiency at the regional and industrial park level, enlightening other regional and local initiatives towards urban sustainability.
Ego GNNs: Exploiting Ego Structures in Graph Neural Networks
Venue: ICASSP 2021 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021

Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph structured data. However, GNNs are fundamentally limited by their tree structured inductive bias: the WL subtree kernel formulation bounds the representational capacity of GNNs, and polynomial time GNNs are provably incapable of recognizing triangles in a graph. In this work, we propose to augment the GNN message passing operations with information de fined on ego graphs (i.e. the induced subgraph surrounding each node) We term these approaches Ego GNNs and show that Ego GNNs are provably more powerful than standard message passing GNNs. In particular, we show that Ego GNNs are capable of recognizing closed triangles, which is essential given the prominence of transitivity in real world graphs. We also motivate our approach from the perspective of graph signal processing as a form of multiplex graph convolution. Experimental results on node classification using synthetic and real data highlight the achievable performance gains using this approach.
Good manufacturing and hygiene practices in small scaled food processing enterprise: a case study
Year: 2017

The purpose of this study is to determine the compliance of small food processing enterprise to Good Manufacturing Practices (GMPs) and Good Hygiene practices (GHPs) stipulated by the Ministry of Health, Malaysia. This study has performed an audit at the food processing enterprise in a rural area. The results of the audit report suggested that three major non compliances are found. They are categories into three components; the infrastructure of the premise and facilities, cleanliness of processing equipment and personal hygiene behavior. The study shows that the burden of the adoption of GMP and GHP in small enterprise is the cost. The enterprise has limited basic knowledge of GMP and GHP requirements and unaware of poor hygiene practices can affect the safety and quality of finished product. The most important factor noted during the audit is that the enterprise has excess to the government water supply and stable supply of electricity. Another positive point that the study found is the location of the premise is sited away from flood prone or polluted area suggesting minimal contamination from the environment and less prone to insects and rats infestation. Beside financial constraint, the obstacles that hamper the implementation are knowledge in designing the premise and workers attitude to prevent food safety problems. Effective continuous training intervention program is essential to educate workers to implement personal hygiene practices. Proper implementation of best practices can reduce or prevent the risk of food borne diseases or illness. Therefore, effective knowledge transfer especially to small and medium enterprise could reduce quality rejection rate and increase the customer confidence in their products and increase market opportunities.