nils-herrmann/clean_scirepeval huggingface dataset:
Some anatomical considerations for implant supported restorations in edentulous patients.
Venue: Refu'at ha peh veha shinayim
Year: 2017

After the loss of the natural teeth, the alveolar process and part of the basal bone remodels, resulting in edentulous residual jaws. In edentulous patients, the residual arches provide the vital tissues on which to place the denture bases and the artificial teeth into their selected positions. The residual arches also furnish the foundation of osseo integrated implants for the planned restorations.  To examine some characteristics of the edentulous arches, the relations between the occluding jaws and their clinical importance.  212 stone casts mounted in centric relation, from one hundred and six edentulous patients were examined and surveyed. The measurements included the length, the width of the maxillary and mandibular edentulous arches and the inter jaw distances at anterior and posterior regions.  Of the one hundred and six participants, 62.3% were female and 37.7% male subjects. The maximallary arch length and width varied from 36 to 71mm, and between 36 to 58mm, respectively. The mandibular length and width differed from 32 to 55mm, and between 48 to 62mm. Higher width values of maxilla and mandibles were found among males as compared to females (p<0.001) The vertical inter jaw distances varied from 9 to 28mm in both anterior and posterior regions. The horizontal inter jaw distances and relation modes varied widely at both anterior and posterior regions.  There is a large disparity in sizes and shapes of the edentulous arches and varied spatial inter arch characteristics.  Before the placement of osseo integrated implants and rehabilitation of the edentulous patients, it is essential to identify the anatomy and relations of the occluding residual arches to contribute to a successful dental treatment.
Bayesian nonparametric regression analysis of data with random effects covariates from longitudinal measurements.
Venue: Biometrics
Year: 2011

We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data augmentation schemes. The approach allows flexible covariance structures for the random effects and within subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.
An Inclusive Study of Several Machine Learning Based Non functional Requirements Prediction Techniques
Year: 2019

Requirement Engineering is a critical area in the arena of Software Engineering. Non Functional Requirements (NFRs) are very important as Functional Requirements but often ignored. Especially the automated correct prediction and prioritization of Non Functional Requirements (NFRs) is highly needed. Non Functional Requirements can be predicted with the help of machine learning models. The Machine Learning models have shown more promising output than Natural Language Processing. In this paper, the comprehensive review on the recently proposed NFRs prediction techniques has been presented. The output of the review showed that the different authors have used different Clustering and Classification algorithms. But Naive Bayes algorithm has used by most of the researchers. The most of researchers have used Precision and Recall as their performance metric. Machine learning techniques like Ensembling and Parameter Tuning has neglected by researchers. The overall objective is to evaluate the numerous shortcomings in machine learning based NFRs prediction techniques and to draw suitable future directions.
Establishment and Research Status of Bronchial Asthma Animal Model
Year: 2014

Bronchial asthma is a chronic airway inflammatory disease participated by many cells and cellular elements,which continues to be a major health problem worldwide. Its mechanism include chronic airway inflammation,airway hyperresponsiveness,airway remolding,nervous system,allergic reaction and genetic problems,and they react with each other forming complex correlation. Establishing animial models is helpful to the research of the etiology and pathogenesis. Rodents are the most popular bronchial asthma models. Here is to make a review of recent asthma animal modeling research at home and aboard,to contribute to the further research about asthma.
Emergence and maintenance of menopause in humans: A game theory model.
Venue: Journal of theoretical biology
Year: 2017

Menopause, the permanent cessation of ovulation, occurs in women well before the end of their expected life span. Several adaptive hypotheses have been proposed to solve this evolutionary puzzle, each based on a possible fitness benefit derived from an early reproductive senescence, but no consensus has emerged. The construction of a game theory model allowed us to jointly study the main adaptive hypotheses in emergence and maintenance of menopause. Four classical hypotheses on the benefits of menopause were considered (decreased maternal mortality, increased grandmothering, decreased conflict over reproductive resources between older and younger females, and changes in their relatedness) plus a fifth one derived from a possible pleiotropic trade off. Interestingly, the conditions for the emergence of menopause are more restrictive than those for its maintenance due to the social and familial changes induced by the occurrence of non reproductive older women.
Cannabis Use and the Risk for Psychosis and Affective Disorders
Venue: Journal of dual diagnosis
Year: 2019

This review discusses the relationship between cannabis use and psychotic, bipolar, depressive, and anxiety disorders, as well as suicide. It summarizes epidemiological evidence from cross sectional and long term prospective studies and considers possible etiological mechanisms.  Systematic reviews and methodologically robust studies in the field (from inception to February 2019) were identified using a comprehensive search of Medline, PsychINFO, and Embase and summarized using a narrative synthesis.  Consistent evidence, both from observational and experimental studies, has confirmed the important role of cannabis use in the initiation and persistence of psychotic disorders. The size of the effect is related to the extent of cannabis use, with greater risk for early cannabis use and use of high potency varieties and synthetic cannabinoids. Accumulating evidence suggests that frequent cannabis use also increases the risk for mania as well as for suicide. However, the effect on depression is less clear and findings on anxiety are contradictory with only a few methodologically robust studies. Furthermore, the relationship with common mental disorders may involve reverse causality, as depression and anxiety are reported to lead to greater cannabis consumption in some studies. Pathogenetic mechanisms focus on the effect of tetrahydrocannabinol (THC, the main psychoactive ingredient of cannabis) interacting with genetic predisposition and perhaps other environmental risk factors. Cannabidiol (CBD) the other important ingredient of traditional cannabis, ameliorates the psychotogenic effects of THC but is absent from the high potency varieties that are increasingly available.  The evidence that heavy use of high THC/low CBD types of cannabis increases the risk of psychosis is sufficiently strong to merit public health education. Evidence of similar but smaller effects in mania and suicide is growing, but is not convincing for depression and anxiety. There is much current interest in the possibility that CBD may be therapeutically useful.
Writing with Impunity in a Space of Their Own: On Cultural Appropriation, Imaginative Play, and a New Ethics of Slash in Harry Potter Fan Fiction
Year: 2019

As defined by Ika Willis, slash is "fiction written by women involving man on man (m/m) sexual and/or romantic relationships" (290) Refracted through the contemporary theories of moral philosophy, this paper names such slash as cultural appropriation; however, it further contends that such cultural appropriation is not inherently unethical but instead represents a generative imaginative space in which new configurations of gender and sexuality might be theorized. Building upon this premise, this paper argues that slash's appropriative nature only becomes problematic when it generates misrepresentations that decouple the gay community from its histories, both joyous and painful.
Modeling and Planning for Dual Objective Selective Disassembly Using and/or Graph and Discrete Artificial Bee Colony
Venue: IEEE Transactions on Industrial Informatics
Year: 2019

Disassembly sequencing is important for remanufacturing and recycling used or discarded products. AND/OR graphs (AOGs) have been applied to describe practical disassembly problems by using "AND" and "OR" nodes. An AOG based disassembly sequence planning problem is an NP hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While precedence and "AND" relationship issues can be addressed, OR (exclusive OR) relations are not well addressed by the existing heuristic methods. Thus, an ineffective result may be obtained in practice. A conflict matrix is introduced to cope with the exclusive OR relation in an AOG graph. By using it together with precedence and succession matrices in the existing work, this work proposes an effective triple phase adjustment method to produce feasible disassembly sequences based on an AOG graph. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with the traditional economical criterion leads to a novel dual objective optimization model such that disassembly profit is maximized and disassembly energy consumption is minimized. An improved artificial bee colony algorithm is developed to effectively generate a set of Pareto solutions for this dual objective disassembly optimization problem. This methodology is employed to practical disassembly processes of two products to verify its feasibility and effectiveness. The results show that it is capable of rapidly generating satisfactory Pareto results and outperforms a well known genetic algorithm.
Application of two under researched typologies in crisis communication: Ethics of justice vs. care and public relations vs. legal strategies
Year: 2017

This study investigates organizations' crisis responses by adopting two extant, under researched typologies public relations vs. legal strategies and ethics of justice vs. care approaches. Through content analyzing organizational responses to three well known crises, the study observes that the public relations strategy is used more often than the legal strategy. It also finds that when the examined organizations respond to their crises, they tend not to emphasize ethical approaches, though when they do it is more often the ethics of justice approach than that of the ethics of care. Therefore, this study urges organizations to reflect on whether their crisis communication and management decisions lack sufficient emphasis on ethical approaches, especially on the interpersonal and situational aspects of involved publics in crises. If such deficiency exits, it would be problematic because these interpersonal and situational aspects, when addressed strategically, can help an organization to rebuild post crisis reputation and maintain positive relationships with its publics in the long run.
Flexo photovoltaic effect
Venue: Science
Year: 2018

Poking a semiconductor Noncentrosymmetric crystal structure can lead to a peculiar kind of charge separation under illumination called the bulk photovoltaic (BPV) effect. Solar cells made of such materials, however, typically have low efficiency. Yang et al. expanded the class of materials capable of exhibiting the BPV effect by making ordinarily centrosymmetric materials, such as SrTiO3 and TiO2, lose their inversion symmetry. The authors accomplished this by applying a point force on the surface of the material. This induced a strain gradient and the loss of inversion symmetry, resulting in large photovoltaic currents under illumination. The mechanism, dubbed the flexo photovoltaic effect, is expected to apply to most semiconductors. Science, this issue p. 904 A bulk photoelectric effect is induced in centrosymmetric semiconductors by applying a point force on the materials' surfaces. It is highly desirable to discover photovoltaic mechanisms that enable enhanced efficiency of solar cells. Here we report that the bulk photovoltaic effect, which is free from the thermodynamic Shockley Queisser limit but usually manifested only in noncentrosymmetric (piezoelectric or ferroelectric) materials, can be realized in any semiconductor, including silicon, by mediation of flexoelectric effect. We used either an atomic force microscope or a micrometer scale indentation system to introduce strain gradients, thus creating very large photovoltaic currents from centrosymmetric single crystals of strontium titanate, titanium dioxide, and silicon. This strain gradient induced bulk photovoltaic effect, which we call the flexo photovoltaic effect, functions in the absence of a p n junction. This finding may extend present solar cell technologies by boosting the solar energy conversion efficiency from a wide pool of established semiconductors.
Instructional control of reinforcement learning: A behavioral and neurocomputational investigation
Venue: Brain Research
Year: 2009

Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub optimal outcomes (Hayes, S. ( Rule governed behavior: cognition, contingencies, and instructional control. Plenum Press. Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction driven and reinforcement driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC) and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q learning" and Bayesian models to subject data. The best fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.
Risk factors for fall related injuries among community dwelling men and women over 70 years of age, based on social cognitive theory: results from a population study
Year: 2020

Healthcare professionals' especially physiotherapists' role as promoters, preventers and rehabilitators play a significant role in prevention of falls. The purpose of this study was to gain knowledge on the risk factors which are associated with fall related injuries among community dwelling men and women, based on Social Cognitive Theory (SCT) concepts.  The study includes 13,151 people who responded to a postal survey questionnaire in 2017. The random sample consisted of men and women aged 70 years and older. Multivariate regression models were used to explore associations between theory based risk factors and fall related injuries.  A total of 16% (1951 people) reported a fall related injury during the past 12 months. For men, a total of seven risk factors were associated with fall related injury: age, pain (neck/shoulder) incontinence, depression, help in daily living, education and sedentary behaviour. For women, a total of nine risk factors were associated with fall related injury: age, pain (extremities and neck/shoulder) tiredness, incontinence, appetite, economy, accommodation and participation in social activities.  This study identified risk factors from all aspects of the SCT, serving as a clinically useful theory in addition to traditional fall preventive actions. Gender differences should be considered when designing fall prevention strategies.
Determination of organochlorine compounds in fish liver by ultrasound assisted dispersive liquid liquid microextraction based on solidification of organic droplet coupled with gas chromatography electron capture detection.
Venue: Journal of chromatography. A
Year: 2018

A simple and rapid method for the extraction of organochlorine compounds (OCs) including poly chlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in fish liver using ultrasound assisted dispersive liquid liquid microextraction based on the solidification of floating organic droplet (US DLLME SFO) was developed. For reducing the complexity of the matrix, the sample was pre treated prior to microextraction. Factors affecting US DLLME SFO were optimized by using statistical design of experiment (DoE) The analysis was carried out by Gas Chromatography (GC) equipped with micro electron capture detector (m ECD) The optimized parameters were 4.8 min of ultrasound, 3.0 mL of Milli Q and 24 mL of 1 undecanol as an extraction solvent as determined by DoE. US DLLME SFO was validated in terms of limit of detection, limit of quantification, dynamic linearity range, coefficient of determination (linearity) and extraction recovery in fish liver for OCs, and the respective values were (1.06 3.84 ng g 1) (3.50 12.67 ng g 1) (1.0 500 ng g 1) (R2 0.994 0.999) (88.5 108.4% Interday and intraday precisions were evaluated as relative standard deviation RSD) and the values were =10%
Digital Currency: An International Legal and Regulatory Compliance Guide
Year: 2018

Digital or 'virtual' currencies pose significant challenges for government, financial and legal institutions because of their non physical nature and their relative anonymity to physical currency. These attributes make this form of exchange extremely volatile and, at the same time, attractive to criminals. Many countries around the world have, therefore issued warnings against the use of digital currencies and have enacted laws to regulate and in some cases, restrict their use among members under their respective jurisdictions.
ML ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG
Venue: Comput. Methods Programs Biomed.
Year: 2020

Myocardial infarction (MI) is one of the most threatening cardiovascular diseases for human beings, which can be diagnosed by electrocardiogram (ECG) Automated detection methods based on ECG focus on extracting handcrafted features. However, limited by the performance of traditional methods and individual differences between patients, it's difficult for predesigned features to detect MI with high performance.  The paper presents a novel method to detect and locate MI combining a multi lead residual neural network (ML ResNet) structure with three residual blocks and feature fusion via 12 leads ECG records. Specifically, single lead feature branch network is trained to automatically learn the representative features of different levels between different layers, which exploits local characteristics of ECG to characterize the spatial information representation. Then all the lead features are fused together as global features. To evaluate the generalization of proposed method and clinical utility, two schemes including the intra patient scheme and inter patient scheme are all employed.  Experimental results based on PTB (Physikalisch Technische Bundesanstalt) database shows that our model achieves superior results with the accuracy of 95.49% the sensitivity of 94.85% the specificity of 97.37% and the F1 score of 96.92% for MI detection under the inter patient scheme compared to the state of the art. By contrast, the accuracy is 99.92% and the F1 score is 99.94% based on 5 fold cross validation under the intra patient scheme. As for five types of MI location, the proposed method also yields an average accuracy of 99.72% and F1 of 99.67% in the intra patient scheme.  The proposed method for MI detection and location has achieved superior results compared to other detection methods. However, further promotion of the performance based on MI location for the inter patient scheme still depends significantly on the mass data and the novel model which reflects spatial location information of different leads subtly.
RSSI based Outdoor Localization with Single Unmanned Aerial Vehicle
Venue: ArXiv
Year: 2020

Localization of a target object has been performed conventionally using multiple terrestrial reference nodes. This paradigm is recently shifted towards utilization of unmanned aerial vehicles (UAVs) for locating target objects. Since locating of a target using simultaneous multiple UAVs is costly and impractical, achieving this task by utilizing single UAV becomes desirable. Hence, in this paper, we propose an RSSI based localization method that utilizes only a single UAV. The proposed approach is based on clustering method along with the Singular Value Decomposition (SVD) The performance of the proposed method is verified by the experimental measurements collected by a UAV that we have designed and computer simulations. The results show that the proposed method can achieve location accuracy as low as 7m depending on the number of iterations.