nils-herrmann/clean_scirepeval huggingface dataset:
Prevalence and Predictors of Low Sexual Desire, Sexually Related Personal Distress, and Hypoactive Sexual Desire Dysfunction in a Community-Based Sample of Midlife Women.

Low desire is the most common sexual problem in women at midlife. Prevalence data are limited by lack of validated instruments or exclusion of un-partnered or sexually inactive women.



To document the prevalence of and factors associated with low desire, sexually related personal distress, and hypoactive sexual desire dysfunction (HSDD) using validated instruments.



Cross-sectional, nationally representative, community-based sample of 2,020 Australian women 40 to 65 years old.



Low desire was defined as a score no higher than 5.0 on the desire domain of the Female Sexual Function Index (FSFI); sexually related personal distress was defined as a score of at least 11.0 on the Female Sexual Distress Scale-Revised; and HSDD was defined as a combination of these scores. The Menopause Specific Quality of Life Questionnaire was used to document menopausal vasomotor symptoms. The Beck Depression Inventory-II was used to identify moderate to severe depressive symptoms (score ≥ 20).



The prevalence of low desire was 69.3% (95% CI = 67.3-71.3), that of sexually related personal distress was 40.5% (95% CI = 38.4-42.6), and that of HSDD was 32.2% (95% CI = 30.1-34.2). Of women who were not partnered or sexually active, 32.4% (95% CI = 24.4-40.2) reported sexually related personal distress. Factors associated with HSDD in an adjusted logistic regression model included being partnered (odds ratio [OR] = 3.30, 95% CI = 2.46-4.41), consuming alcohol (OR = 1.48, 95% CI = 1.16-1.89), vaginal dryness (OR = 2.08, 95% CI = 1.66-2.61), pain during or after intercourse (OR = 1.63, 95% CI = 1.27-2.09), moderate to severe depressive symptoms (OR = 2.69, 95% CI 1.99-3.64), and use of psychotropic medication (OR = 1.42, 95% CI = 1.10-1.83). Vasomotor symptoms were not associated with low desire, sexually related personal distress, or HSDD.



Given the high prevalence, clinicians should screen midlife women for HSDD.



Strengths include the large size and representative nature of the sample and the use of validated tools. Limitations include the requirement to complete a written questionnaire in English. Questions within the FSFI limit the applicability of FSFI total scores, but not desire domain scores, in recently sexually inactive women, women without a partner, and women who do not engage in penetrative intercourse.



Low desire, sexually related personal distress, and HSDD are common in women at midlife, including women who are un-partnered or sexually inactive. Some factors associated with HSDD, such as psychotropic medication use and vaginal dryness, are modifiable or can be treated with safe and effective therapies. Worsley R, Bell RJ, Gartoulla P, Davis SR. Prevalence and Predictors of Low Sexual Desire, Sexually Related Personal Distress, and Hypoactive Sexual Desire Dysfunction in a Community-Based Sample of Midlife Women.  2017;14:675-686.
Self-management education and regular practitioner review for adults with asthma.

A key component of many asthma management guidelines is the recommendation for patient education and regular medical review. A number of controlled trials have been conducted to measure the effectiveness of asthma education programmes. These programmes improve patient knowledge, but their impact on health outcomes is less well established. This review was conducted to examine the strength of evidence supporting Step 6 of the Australian Asthma Management Plan: "Educate and Review Regularly"; to test whether health outcomes are influenced by education and self-management programmes.



The objective of this review was to assess the effects of asthma self-management programmes, when coupled with regular health practitioner review, on health outcomes in adults with asthma.


SEARCH STRATEGY
We searched the Cochrane Airways Group trials register and reference lists of articles.



Randomised trials of self-management education in adults over 16 years of age with asthma.



Trial quality was assessed and data were extracted independently by two reviewers. Study authors were contacted for confirmation.



Twenty-five trials were included. Self-management education was compared with usual care in 22 studies. Self-management education reduced hospitalisations (odds ratio 0.57, 95% confidence interval 0.38 to 0.88); emergency room visits (odds ratio 0.71, 95% confidence interval (0.57 to 0.90); unscheduled visits to the doctor (odds ratio 0.57, 95% confidence interval 0.40 to 0.82); days off work or school (odds ratio 0.55, 95% confidence interval 0.38 to 0. 79); and nocturnal asthma (odds ratio 0.53, 95% confidence interval 0.39 to 0.72). Measures of lung function were little changed. Self-management programmes that involved a written action plan showed a greater reduction in hospitalisation than those that did not (odds ratio 0.35, 95% confidence interval 0.18 to 0.68). People who managed their asthma by self-adjustment of their medications using an individualised written plan had better lung function than those whose medications were adjusted by a doctor.



Training in asthma self-management which involves self-monitoring by either peak expiratory flow or symptoms, coupled with regular medical review and a written action plan appears to improve health outcomes for adults with asthma. Training programmes which enable people to adjust their medication using a written action plan appear to be more effective than other forms of asthma self-management.
A Fault-Tolerant Scheduling Algorithm for Real-Time Periodic Tasks with Possible Software Faults

A hard real-time system is usually subject to stringent reliability and timing constraints since failure to produce correct results in a timely manner may lead to a disaster. One way to avoid missing deadlines is to trade the quality of computation results for timeliness, and software fault-tolerance is often achieved with the use of redundant programs. A deadline mechanism which combines these two methods is proposed to provide software faulttolerance in hard real-time periodic task systems. Specifically, we consider the problem of scheduling a set of realtime periodic tasks each of which has two versions:primary and alternate. The primary version contains more functions (thus more complex) and produces good quality results but its correctness is more difficult to verify because of its high level of complexity and resource usage. By contrast, the alternate version contains only the minimum required functions (thus simpler) and produces less precise but acceptable results, and its correctness is easy to verify. We propose a scheduling algorithm which (i) guarantees either the primary or alternate version of each critical task to be completed in time and (ii) attempts to complete as many primaries as possible. Our basic algorithm uses a fixed priority-driven preemptive scheduling scheme to pre-allocate time intervals to the alternates, and at run-time, attempts to execute primaries first. An alternate will be executed only (1) if its primary fails due to lack of time or manifestation of bugs, or (2) when the latest time to start execution of the alternate without missing the corresponding task deadline is reached. This algorithm is shown to be effective and easy to implement. This algorithm is enhanced further to prevent early failures in executing primaries from triggering failures in the subsequent job executions, thus improving efficiency of processor usage.
Optimal sizing and siting techniques for distributed generation in distribution systems : A review

To extract the maximum potential advantages in light of environmental, economical and technical aspects, the optimum installation and sizing of Distributed Generation (DG) in distribution network has always been challenging for utilities as well as customers. The installation of DG would be of maximum benefit where setting up of central power generating units are not practical, or in remote and small areas where the installation of transmission lines or availability of unused land is out of question. The objective of optimal installation of DG in distribution system is to achieve proper operation of distribution networks with minimization of the system losses, improvement of the voltage profile, enhanced system reliability, stability and loadability etc. In this respect analytical (classical) methods, although wellmatched for small systems, perform adversely for large and complex objective functions. Unlike the analytical (classical) methods, the intelligent techniques for optimal sizing and siting of DGs are speedy, possess good convergence characteristics, and are well suited for large and complex systems. However, to find a global optimal solution of complex multi-objective problems, a hybrid of two or more metaheuristic optimization techniques give more effective and reliable solution. This paper presents the fundamentals of DG and DG technologies review the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers. An attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal sizing and siting of DG in distribution networks. The present study can contribute meaningful knowledge and assist as a reference for investigators and utility engineers on issues to be considered for optimal sizing and siting of DG units in distribution systems. & 2015
Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

a r t i c l e i n f o a b s t r a c t A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
Model Predictive Control design for a convertible Quad Tilt-Wing UAV

This paper deals with the design of a Model Predictive Control (MPC) approach for the altitude and attitude stabilization and tracking of a Quad Tilt Wing (QTW) type of Unmanned Aerial Vehicles (UAVs). This Vertical Take-Off and Landing (VTOL) aircraft can take-off and landing vertically such as helicopters and is convertible to the fixed-wing configuration for horizontal flight using a tilting mechanism for its rotors/wings. A nonlinear dynamical model, relating to the vertical flight mode of this QTW, is firstly developed using the Newton-Euler formalism, in describing the aerodynamic forces and moments acting on the aircraft. This established model, linearized around an equilibrium operating point, is then used to design a MPC approach for the stabilization and tracking of the QTW attitude and altitude. In order to show the performance superiority of the proposed MPC technique, a comparison with the known Linear Quadratic (LQ) strategy is carried out. All simulation results, obtained for both MPC and LQ approaches, are presented and discussed.
Mobile Application Development for Quran Verse Recognition and Interpretations

Mobile learning or “m-learning” is the process of learning when learners are not at a fixed location or time and can exploit the advantage of learning opportunities using mobile technologies. Nowadays, speech recognition is being used in many mobile applications.!Speech recognition helps people to interact with the device as if were they talking to another person. This technology helps people to learn anything using computers by promoting self-study over extended periods of time. The objective of this study focuses on designing and developing a mobile application for the Arabic recognition of spoken Quranic verses. The application is suitable for Android-based devices. The application is called Say Quran and is available on Google Play Store. Moreover, this paper presents the results of a preliminary study to gather feedback from students regarding the developed application.
AutoElastic: Automatic Resource Elasticity for High Performance Applications in the Cloud

Elasticity is undoubtedly one of the most striking characteristics of cloud computing. Especially in the area of high performance computing (HPC), elasticity can be used to execute irregular and CPU-intensive applications. However, the on- the-fly increase/decrease in resources is more widespread in Web systems, which have their own IaaS-level load balancer. Considering the HPC area, current approaches usually focus on batch jobs or assumptions such as previous knowledge of application phases, source code rewriting or the stop-reconfigure-and-go approach for elasticity. In this context, this article presents AutoElastic, a PaaS-level elasticity model for HPC in the cloud. Its differential approach consists of providing elasticity for high performance applications without user intervention or source code modification. The scientific contributions of AutoElastic are twofold: (i) an Aging-based approach to resource allocation and deallocation actions to avoid unnecessary virtual machine (VM) reconfigurations (thrashing) and (ii) asynchronism in creating and terminating VMs in such a way that the application does not need to wait for completing these procedures. The prototype evaluation using OpenNebula middleware showed performance gains of up to 26 percent in the execution time of an application with the AutoElastic manager. Moreover, we obtained low intrusiveness for AutoElastic when reconfigurations do not occur.
Efficacy of High Intensity Exercise on Disease Activity and Cardiovascular Risk in Active Axial Spondyloarthritis: A Randomized Controlled Pilot Study

Physical therapy is recommended for the management of axial spondyloarthritis (axSpA) and flexibility exercises have traditionally been the main focus. Cardiovascular (CV) diseases are considered as a major health concern in axSpA and there is strong evidence that endurance and strength exercise protects against CV diseases. Therefore, the aim of this study was to investigate the efficacy of high intensity endurance and strength exercise on disease activity and CV health in patients with active axSpA.



In a single blinded randomized controlled pilot study the exercise group (EG) performed 12 weeks of endurance and strength exercise while the control group (CG) received treatment as usual. The primary outcome was the Ankylosing Spondylitis (AS) Disease Activity Score (ASDAS). Secondary outcomes included patient reported disease activity (Bath AS Disease Activity Index [BASDAI]), physical function (Bath AS Functional Index [BASFI]), and CV risk factors measured by arterial stiffness (Augmentation Index [Alx]) and Pulse Wave Velocity [PWV]), cardiorespiratory fitness (VO2 peak) and body composition. ANCOVA on the post intervention values with baseline values as covariates was used to assess group differences, and Mann Whitney U-test was used for outcomes with skewed residuals.



Twenty-eight patients were included and 24 (EG, n = 10, CG, n = 14) completed the study. A mean treatment effect of -0.7 (95%CI: -1.4, 0.1) was seen in ASDAS score. Treatment effects were also observed in secondary outcomes (mean group difference [95%CI]): BASDAI: -2.0 (-3.6, -0.4), BASFI: -1.4 (-2.6, -0.3), arterial stiffness (estimated median group differences [95% CI]): AIx (%): -5.3 (-11.0, -0.5), and for PVW (m/s): -0.3 (-0.7, 0.0), VO2 peak (ml/kg/min) (mean group difference [95%CI]: 3.7 (2.1, 5.2) and trunk fat (%): -1.8 (-3.0, -0.6). No adverse events occurred.



High intensity exercise improved disease activity and reduced CV risk factors in patients with active axSpA. These effects will be further explored in a larger trial.
Fusing Inertial Sensor Data in an Extended Kalman Filter for 3D Camera Tracking

In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.
Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.
Long-term survival of porcelain laminate veneers using two preparation designs: a retrospective study.

This study evaluated the long-term survival of anterior porcelain laminate veneers placed with and without incisal porcelain coverage.



Two prosthodontists in a private dental practice placed 110 labial feldspathic porcelain veneers in 50 patients; 46 veneers were provided with incisal porcelain coverage, and 64 were not. The veneers were evaluated retrospectively from case records for up to 7 years (mean 4 years).



At 5, 6, and 7 years, the cumulative survival estimates were 95.8% for veneers with incisal porcelain coverage and 85.5% for those without incisal coverage. The difference was not statistically significant. Six of the nine failures occurred from porcelain fracture in the veneers without incisal coverage.



Although there was a trend for better long-term survival of the veneers with incisal porcelain coverage, this finding was not statistically significant.
Warthin tumor of the upper lip: an unusual location of a benign salivary gland tumor.

Warthin tumor (papillary cystadenoma lymphomatosum) is a benign salivary gland tumor involving almost exclusively the parotid gland. The lip is a very unusual location for this type of tumor, which develops only rarely in minor salivary glands. The case of 42-year-old woman with Warthin tumor arising in minor salivary glands of the upper lip is reported.
Dynamic Non-Parametric Mixture Models and the Recurrent Chinese Restaurant Process: with Applications to Evolutionary Clustering

Clustering is an important data mining task for exploration and visualization of different data types like news stories, scientific publications, weblogs, etc. Due to the evolving nature of these data, evolutionary clustering, also known as dynamic clustering, has recently emerged to cope with the challenges of mining temporally smooth clusters over time. A good evolutionary clustering algorithm should be able to fit the data well at each time epoch, and at the same time results in a smooth cluster evolution that provides the data analyst with a coherent and easily interpretable model. In this paper we introduce the temporal Dirichlet process mixture model (TDPM) as a framework for evolutionary clustering. TDPM is a generalization of the DPM framework for clustering that automatically grows the number of clusters with the data. In our framework, the data is divided into epochs; all data points inside the same epoch are assumed to be fully exchangeable, whereas the temporal order is maintained across epochs. Moreover, The number of clusters in each epoch is unbounded: the clusters can retain, die out or emerge over time, and the actual parameterization of each cluster can also evolve over time in a Markovian fashion. We give a detailed and intuitive construction of this framework using the recurrent Chinese restaurant process (RCRP) metaphor, as well as a Gibbs sampling algorithm to carry out posterior inference in order to determine the optimal cluster evolution. We demonstrate our model over simulated data by using it to build an infinite dynamic mixture of Gaussian factors, and over real dataset by using it to build a simple non-parametric dynamic clustering-topic model and apply it to analyze the NIPS12 document collection.
A Comparison Based Study on Biometrics for Human Recognition

A biometric system provides automatic recognition of an individual based on a unique feature or characteristic possessed by the individual. These biometric characteristic may physiological or behavioral. Unlike other identification methods such as id proof, tokens and password, the distinct aspect of biometric recognition comes into light from randomly distributed features in human being. In this paper, I describe the novel comparison based upon various aspects to make easy selection for biometric device deployment in specific environment. This paper proposes a comparison among all kind of biometric system available in the society. The existing computer security systems used at various places like banking, passport, credit cards, smart cards, PIN , access control and network security are using username and passwords for person identification. Biometric systems also introduce an aspect of user convenience; it means one can be authorized by representing himself or herself. In this paper, the main focus is on working principal of biometric technique, the various biometrics systems and their comparisons.