Miracle-dz/UniFAR-FTUs huggingface dataset:
Statins and Death Due to Any Cause – All Doubts Removed?

Objective: To date, it is quite common to claim that some patient groups benefit from statin therapy in both primary and secondary prevention of cardiovascular disease while equally the use of higher-intensity statin therapies is emphasized. In this Review, the efficacy of statin therapy in light of the study data available is explored. Methods: All in all, 40 studies with a sample size of n = 88388 were re-analyzed. The exclusion relationship was used to test the null-hypothesis: a certain statin does exclude death due to any cause. The causal relationship k was used to test the data for causality. The level of significance was set to Alpha = 0,05. Results: The data of the studies reanalyzed provide convincing evidence that statins unfortunately do not exclude death due to any cause. An immediate statin therapy discontinuation should be considered. Conclusions: Overwhelming evidence suggests that the risk potential harmful effects of statin therapy far outweigh any real or perceived benefit. Keywords: Statins, death, causal relationship. Barukcic@t-online.de
Numerical analysis of thermal post-buckling strength of laminated skew sandwich composite shell panel structure including stretching effect

The computational post-buckling strength of the tilted sandwich composite shell structure is evaluated in this article. The computational responses are obtained using a mathematical model derived using the higher-order type of polynomial kinematic in association with the through-thickness stretching effect. Also, the sandwich deformation behaviour of the flexible soft-core sandwich structural model is expressed mathematically with the help of a generic nonlinear strain theory i.e. Green-Lagrange type strain-displacement relations. Subsequently, the model includes all of the nonlinear strain terms to account the actual deformation and discretized via displacement type of finite element. Further, the computer code is prepared (MATLAB environment) using the derived higher-order formulation in association with the direct iterative technique for the computation of temperature carrying capacity of the soft-core sandwich within the post-buckled regime. Further, the nonlinear finite element model has been tested to show its accuracy by solving a few numerical experimentations as same as the published example including the consistency behaviour. Lastly, the derived model is utilized to find the temperature load-carrying capacity under the influences of variable factors affecting the soft-core type sandwich structural design in the small (finite) strain and large deformation regime including the effect of tilt angle.
Two‐Step Estimation for Time Varying Arch Models

A time varying autoregressive conditional heteroskedasticity (ARCH) model is proposed to describe the changing volatility of a financial return series over long time horizon, along with two‐step least squares and maximum likelihood estimation procedures. After preliminary estimation of the time varying trend in volatility scale, approximations to the latent stationary ARCH series are obtained, which are used to compute the least squares estimator (LSE) and maximum likelihood estimator (MLE) of the ARCH coefficients. Under elementary and mild assumptions, oracle efficiency of the two‐step LSE for ARCH coefficients is established, that is, the two‐step LSE is asymptotically as efficient as the infeasible LSE based on the unobserved ARCH series. As a matter of fact, the two‐step LSE deviates from the infeasible LSE by opn−1/2 . The two‐step MLE, however, does not enjoy such efficiency, but n1/2 asymptotic normality is established for both the two‐step MLE as well as its deviation from the infeasible MLE. Simulation studies corroborate the asymptotic theory, and application to the S&P 500 index daily returns from 1950 to 2018 indicates significant change in volatility scale over time.
Research on wind turbine power performance based on the operation Data

The power characteristics are important for evaluating the operating state of the wind turbine. In order to accurately evaluate the performance of the actual 2.0MW wind turbine, this paper firstly collects the measured wind speed, power and other operational data reflecting the performance of the wind turbine, and analyzes the data according to the standard specification. On the basis of data analysis, this paper obtains the actual power curve of wind turbine, and then compares it with the theoretical power curve. Finally, this paper carries out comparative analysis and performance evaluation of wind turbine operation performance, and propose improvement measures on this basis.
Distributed sparse BSS for large-scale datasets

Blind Source Separation (BSS) [1] is widely used to analyze multichannel data stemming from origins as wide as astrophysics to medicine. However, existent methods do not efficiently handle very large datasets. In this work, we propose a new method coined DGMCA (Distributed Generalized Morphological Component Analysis) in which the original BSS problem is decomposed into subproblems that can be tackled in parallel, alleviating the large-scale issue. We propose to use the RCM (Riemannian Center of Mass-[6][7]) to aggregate during the iterative process the estimations yielded by the different subproblems. The approach is made robust both by a clever choice of the weights of the RCM and the adaptation of the heuristic parameter choice proposed in [4] to the parallel framework. The results obtained show that the proposed approach is able to handle large-scale problems with a linear acceleration performing at the same level as GMCA and maintaining an automatic choice of parameters. I. LARGE-SCALE BLIND SOURCE SEPARATION Given m row observations of size t stacked in a matrix Y assumed to follow a linear model Y = AS + N, the objective of BSS [1] is to estimate the matrices A (size m × n) and S (size n × t) up to a mere permutation and scaling indeterminacy. In this model, A mixes the n row sources in S, the observations being entached by some unknwown noise N (size m × t). We will assume that n ≤ m. While ill-posed, this problem can be regularized assuming the sparsity of S [2]. The estimation will then turn into the minization of: ˆ A, ˆ S = arg min A,S 1 2 Y − AS 2 F +Λ S 1 +i X:X k 2 =1, ∀k (A) , (1) with ·· F the Frobenius norm, Λ the regularization parameters and iC(·) the indicator function of the set C. The first term is a data fidelity one, the second enforces the sparsity and the last avoids degenerated solutions with A 2 F → 0 by enforcing unit columns. To tackle Eq. (1), the GMCA [4] algorithm has known a tremendous success due to an automatic decreasing parameter strategy making it robust. However, in this work we will assume that the data Y are large-scale in the sense that t can have huge values (e.g. up to 10 9 samples), which make the treatement of Y as a whole intractable. In this context, using GMCA or most other algorithms is prohibitive. II. PROPOSED METHOD This difficulty motivates the construction of J subproblems (j) of the type Yj = ASj + Nj where j denotes a subset of tj columns of the corresponding matrices. We use disjoints sets with j |tj| = t.
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

ABSTRACT Short-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues, this paper develops a residual graph convolution long short-term memory (RGC-LSTM) model for spatial-temporal data forecasting considering the network topology. This model integrates a new graph convolution operator for spatial modelling on networks and a residual LSTM structure for temporal modelling considering multiple periodicities. The proposed model has few parameters, low computational complexity, and a fast convergence rate. The framework is evaluated on both the 10-min traffic speed data from Shanghai, China and the 5-min Caltrans Performance Measurement System (PeMS) traffic flow data. Experiments show the advantages of the proposed approach over various state-of-the-art baselines, as well as consistent performance across different datasets.
URIKA, continuous ultrasound monitoring for the detection of a full bladder in children with dysfunctional voiding: a feasibility study

Objectives. To assess the feasibility of a new wearable, wireless ultrasonic device, the URIKA bladder monitor (UBM), in the detection of a full bladder in children with dysfunctional voiding (DV). Methods. This observational study included 14 children with DV who were subjected to an UBM monitoring session of 1.5–2 h. Transabdominal ultrasound (TUS) images were made as reference. The UBM measured the anterior–posterior bladder dimension by an ultrasound transducer, mounted in an elastic belt around the lower abdomen. Level of agreement between both methods was estimated by Bland–Altman analysis. Receiver operating characteristics (ROC) analysis was performed to determine a full bladder threshold for the studied population. Results. In 13 out of 14 patients, the UBM measured properly. Maximum bladder dimensions detected by the UBM and TUS were 6.69 ± 1.53 cm and 4.79 ± 0.99 cm respectively. Bland–Altman analysis showed a negative bias of −0.90 cm (limits of agreement: −4.1/+2.3 cm). ROC analysis resulted in a sensitivity and specificity of 78.3% and 100%, for a bladder dimension threshold of 5.03 cm. When this threshold was implemented a priori, the full bladder detection rate would have been 71%. In children younger than 10 years, this would be 100% (n = 5). Conclusion. The UBM is able to detect a full bladder with a detection rate of 71%, if a 5.03 cm threshold would be implemented. In patients younger than 10 years, the detection rate would be 100%. Future research will focus on increasing the UBM’s accuracy and investigating the effect of UBM alarm treatment in children with urinary incontinence.
Inhibition of Nonessential Bacterial Targets: Discovery of a Novel Serine O-Acetyltransferase Inhibitor.

In ϒ-proteobacteria and Actinomycetales, cysteine biosynthetic enzymes are indispensable during persistence and become dispensable during growth or acute infection. The biosynthetic machinery required to convert inorganic sulfur into cysteine is absent in mammals; therefore, it is a suitable drug target. We searched for inhibitors of Salmonella serine acetyltransferase (SAT), the enzyme that catalyzes the rate-limiting step of l-cysteine biosynthesis. The virtual screening of three ChemDiv focused libraries containing 91 243 compounds was performed to identify potential SAT inhibitors. Scaffold similarity and the analysis of the overall physicochemical properties allowed the selection of 73 compounds that were purchased and evaluated on the recombinant enzyme. Six compounds displaying an IC50 <100 μM were identified via an indirect assay using Ellman's reagent and then tested on a Gram-negative model organism, with one of them being able to interfere with bacterial growth via SAT inhibition.
RF modeling and parameter extraction for GaAs‐based on‐chip inductors

RF modeling and equivalent circuit model parameter extraction for GaAs‐based on‐chip inductors are presented in this article. An improved model that takes into account of the substrate loss effects based on the S parameters on wafer measurement is proposed. The parameter extraction method combines the analytical expressions and the empirical optimization procedure, which does not require any additional test structures. The intrinsic elements determined by utilizing the analytical method are described as functions of the parasitic elements. After that, an advanced design system is used to optimize the parasitic parameters with very small dispersion of initial values. Good agreement is obtained between the simulated and measured data up to 50 GHz.
Estimating uncompensated medical care cost as a result of adverse events in a university hospital in Thailand

Abstract Background The impact of adverse events on costs of medical care in developing countries is more limited than in developed countries. Objective To estimate uncompensated medical care costs as a result of adverse events in hospitalized patients. Method Retrospective study based on a panel reviews of the medical records and hospital charges relating to 574 inpatients in the electronic database of a tertiary-care university hospital in Bangkok, Thailand, of which 138 were identified to have adverse events in 2009. The main outcome measure was hospital charges for medical care standardized by Thai diagnosis-related groups (Thai-DRGs). Result After controlling for personal and clinical factors, the average medical cost of an adverse event was estimated at US $128.98 per adjusted relative weight of Thai-DRGs, equivalent to 52% of the standard payment rate paid to the hospital set by a Universal Coverage Scheme. Conclusion Adverse events in the hospital increased the cost of medical care despite adjustment for the case-mix based on DRG. Under the close-ended DRG-based payments for hospitals in Thailand, this meant that adverse events resulted in significant amounts of uncompensated care.
Application of Intelligent Search Algorithms in Proportional-Integral-Derivative Control of Direct-Current Motor System

Proportional-Integral-Derivative (PID) control is one of the famous controllers applied in industrial and robotics world. It is quite easy to understand and to be implemented. It is known that, with a proper combination of its gain parameters, a system with good performance can be obtained. This parameter tuning can be tricky since the number of the combination is almost infinite. Hence, methods to tune the parameter gains of proportional, integral and derivative is needed. Some optimization methods that can be used in tuning parameters are Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The paper will analyse the performance of those methods by applying them in PID Controller to control a Direct Current (DC) motor system by comparing the iteration time, the number of parameters, and augmented system performance. The comparison is done on MATLAB simulation by using the same computer. It results as DE as the best among them with the least number of parameters and best DC motor system performances.
Below the magic mountain: a social history of tuberculosis in Twentieth-Century Britain.

List of illustrations, figures, tables. Abbreviations. Introduction. A national problem 1898-1918 "pickaxe cure for consumptives" the national tuberculosis service 1918-1939 causal factors in tuberculosis fighting infection work versus collapse theory the patient's view tuberculosis and the state, 1939 and beyond. Bibliography. Index.
Does the Takeover Market Deter Opportunistic Non-GAAP Reporting?

We exploit the Foreign Investment and National Security Act (FINSA) to examine the effect of an important managerial disciplining mechanism, the takeover market, on the quality of non-GAAP reporting. FINSA significantly reduced the likelihood of takeover for a large fraction of the CRSP-Compustat universe. We draw inferences using a difference-in-differences research design by contrasting non-GAAP disclosures by FINSAaffected firms with those of unaffected firms, before and after FINSA. We find that FINSA-affected firms more often exclude recurring expenses, more often exclude expenses incremental to those excluded by analysts, and more often use non-GAAP earnings to convert a GAAP-based miss of an earnings forecast to a beat, after FINSA. This effect accentuates predictably with the extent to which FINSA-affected firms are susceptible to the takeover market in the pre-FINSA period, and with the extent to which non-GAAP earnings determine manager compensation. We conclude by documenting a decline in non-GAAP earnings persistence and the value-relevance of non-GAAP earnings after FINSA. Our evidence demonstrates the role of the takeover market in curbing opportunistic non-GAAP reporting. JEL Classification: F52; G14; G34; G38
Various edge response of ground beetles in edges under natural versus anthropogenic influence: A meta-analysis using life-history traits

Edges are on the increase world-wide due to increasing fragmentation and loss of natural habitats. After formation, edges are maintained by various processes (natural vs. contin ued anthropogenic interventions: forestry, agriculture, urbanization) which influence the reaction of individual species to edge effects (history-based edge effect hypothesis), and this will be reflected in the diversity of assemblages. Diversity, however, is not the most appropriate indicator of the edge effect because species with different traits may respond differently to the edges. To further articulate the history-based edge effect hypothesis, we evaluated the edge effect on one of the most commonly used life-history traits, the feed ing habit of ground beetles in forest edges. A meta-analysis based on 28 publications and 422 comparisons showed that natural vs. continued anthropogenic interventions as edge-maintaining processes reflected at the trait level. Abundance of herbivorous, omnivorous, and predatory ground beetle species were all higher in the natural edges than in the forest interiors, while no similar pattern occurred in edges with continued anthropogenic influ -ence. These results suggest that structural and environmental changes at edges sustained by repeated anthropogenic influence adversely influencing ecosystem functions, with neg ative effects on ecosystem services like pest or weed control. To heterogeneity, complementary measures of Q and I 2 calculated renstein Q is the weighted sum of squares within a data set. For significant het erogeneity, it can be tested against the expected deviation assuming that all studies share a common effect size. I 2 measures the proportion of the observed variance that reflects real differences in effect size between studies. Total variance ( Q total ) was partitioned into within- ( Q within ) and between group ( Q between ) variances and these were tested for statistical significance (Borenstein et al. 2009). Significant variance between groups ( Q between ) means that edge effect on abundance significantly differed according to the maintaining processes of edges. During the calculations, subgroups with less than five cases were excluded from analyses. Publication bias was tested using funnel plots and the Egger test (Borenstein et al. 2009). In the case of significant asymmetry, the trim and fill method was used (Duval & Tweedie 2000). During the calculations, the MAd (version 0.8-2, Del Re & Hoyt 2014) and metafor packages (version 1.9-9, Viechtbauer 2010) were used in R programming environ ment (version 3.4.3; R Core Team 2017).
Nowcasting con Google Trends: Dinámica de la Actividad Económica Mensual, el Consumo Privado y la Inversión basada en datos de Google Trends y un Modelo Bayesiano Estructural de Series de Tiempo

Las tecnicas de proyeccion basadas en nowcasting1 han adoptado cada vez mayor preponderancia, gracias a su bondad para pronosticar determinadas variables macroeconomicas por medio de informacion de alta frecuencia y dada la posibilidad de publicar dicha informacion sin rezagos. El objetivo primordial de este estudio es generar proyecciones basadas en datos nano-economicos procedentes de la plataforma Google Trends, con frecuencia mensual y semanal, y empleando un Modelo Bayesiano Estructural de Series de Tiempo (BSTS, por sus siglas en ingles) para el Indice de Actividad Economica (IMAE), el Consumo Privado y la Inversion, los cuales son publicados por el Banco Central de la Republica Dominicana con periodicidad mensual y trimestral, respectivamente. Los resultados exhiben una convergencia entre los indicadores adelantados estimados a traves de la metodologia propuesta y las variables observadas, i.g. IMAE, Consumo Privado e Inversion. Se llega a la conclusion de que la concatenacion de la informacion de Google Trends con un modelo especializado como el BSTS, para el manejo de grandes conjuntos de datos propicia una mejora en la exactitud de los resultados en comparacion con los valores efectivamente observados. La importancia y el valor de las herramientas inherentes a la teoria de machine learning continuan en ascenso y su desempeno refleja su alto potencial para reforzar los procesos de toma de decisiones.
Longitudinal study of PTSD and depression in a war-exposed sample – comorbidity increases distress and suicide risk

Abstract Background Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are the most common mental disorders following traumatic experiences. The aim of this study was to investigate the extent to which PTSD and depression co-occurred in Serbian general population at baseline and 1 year after the follow-up, as well as how this co-occurrence was associated with sociodemographic factors, personal distress, suicidality and quality of life. Subjects and methods The sample consisted of 159 subjects, who fulfilled the IES criteria for PTSD, and were taken from a larger sample of 640 participants, which was chosen by a random walk technique in five regions of the country affected by major trauma. The assessment was carried out by the following instruments: Mini International Neuropsychiatric Interview 5 (MINI 5), Life Stressor Checklist-Revised (BSC-R), Brief Symptom Inventory (BSI), Impact of Event Scale-Revised (IES-R) and Manchester Short Assessment of Quality of Life Scale (MANSA). The follow-up study was carried out 1 year after the baseline. Results In the initial phase, PTSD was found in 100 out of 159 participants (62.9%), while 81 (51%) fulfilled the diagnostic criteria for MDD. Comorbidity of PTSD and depression was identified in 65 (40.9%) subjects of the sample. After 1 year, PTSD was found in 56 (35.2%) and MDD in 73 (45.9%) participants. Comorbidity of PTSD and depression in the follow-up phase was identified in 41 (25.8%) subjects of the sample. The subjects with comorbidity had significantly higher level of post-traumatic stress symptoms, general psychological distress as well as suicide risk and lower level of quality of life than participants with either condition alone. Conclusion PTSD–depression comorbidity is a common post-traumatic condition. Complex psychopathology, severity of symptoms and their consequences, both at individual and community levels, require attention to be paid to early diagnostics and treatment of affected persons.
Design optimisation for a novel underactuated robotic finger by genetic algorithms

ABSTRACT This paper presents a novel design of the robotic finger as a development of the Otto bock hand design, especially in the index finger of the hand. Namely, make it composed from three phalanges instead of a single one. In addition, in order to improve the grasping dexterity, a four-bar mechanism for each phalanx was used. This results in finding the optimum dimensions of the linkages to drive the robotic finger. To achieve such aim, a Genetic Algorithm (G.A) was employed. Two criteria were chosen for finding the optimal solution; Grasping force criterion and Squeezing force criterion. The optimisation criteria are based on the force analysis equations that derived to calculate the contact forces during power grasping action. The prosthetic finger was built from PLA (Polylactic Acid) plastic using a 3D printer and tested experimentally through a special measuring mechanism provided with a sensory system. The experimental and the theoretical results of the normal force of the finger were in good agreement.