nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of carvedilol and metoprolol on clinical outcomes in patients with chronic heart failure in the Carvedilol Or Metoprolol European Trial (COMET): randomised controlled trial

Beta blockers reduce mortality in patients who have chronic heart failure, systolic dysfunction, and are on background treatment with diuretics and angiotensin-converting enzyme inhibitors. We aimed to compare the effects of carvedilol and metoprolol on clinical outcome.



In a multicentre, double-blind, and randomised parallel group trial, we assigned 1511 patients with chronic heart failure to treatment with carvedilol (target dose 25 mg twice daily) and 1518 to metoprolol (metoprolol tartrate, target dose 50 mg twice daily). Patients were required to have chronic heart failure (NYHA II-IV), previous admission for a cardiovascular reason, an ejection fraction of less than 0.35, and to have been treated optimally with diuretics and angiotensin-converting enzyme inhibitors unless not tolerated. The primary endpoints were all-cause mortality and the composite endpoint of all-cause mortality or all-cause admission. Analysis was done by intention to treat.



The mean study duration was 58 months (SD 6). The mean ejection fraction was 0.26 (0.07) and the mean age 62 years (11). The all-cause mortality was 34% (512 of 1511) for carvedilol and 40% (600 of 1518) for metoprolol (hazard ratio 0.83 [95% CI 0.74-0.93], p=0.0017). The reduction of all-cause mortality was consistent across predefined subgroups. The composite endpoint of mortality or all-cause admission occurred in 1116 (74%) of 1511 on carvedilol and in 1160 (76%) of 1518 on metoprolol (0.94 [0.86-1.02], p=0.122). Incidence of side-effects and drug withdrawals did not differ by much between the two study groups.



Our results suggest that carvedilol extends survival compared with metoprolol.
Optimizing network attacks by artificial bee colony

Over the past few years, the task of conceiving effective attacks to complex networks has arisen as an optimization problem. Attacks are modelled as the process of removing a number k of vertices, from the graph that represents the network, and the goal is to maximise or minimise the value of a predefined metric over the graph. In this work, we present an optimization problem that concerns the selection of nodes to be removed to minimise the maximum betweenness centrality value of the residual graph. This metric evaluates the participation of the nodes in the communications through the shortest paths of the network. To address the problem we propose an artificial bee colony algorithm, which is a swarm intelligence approach inspired in the foraging behaviour of honeybees. In this framework, bees produce new candidate solutions for the problem by exploring the vicinity of previous ones, called food sources. The proposed method exploits useful problem knowledge in this neighbourhood exploration by considering the partial destruction and heuristic reconstruction of selected solutions. The performance of the method, with respect to other models from the literature that can be adapted to face this problem, such as sequential centrality-based attacks, module-based attacks, a genetic algorithm, a simulated annealing approach, and a variable neighbourhood search, is empirically shown. E-mail addresses: lozano@decsai.ugr.es (M. Lozano), cgarcia@uco.es (C. GarćıaMart́ınez), fjrodriguez@unex.es (F.J. Rodŕıguez), humberto@ugr.es (H.M. Trujillo).  2016 *Manuscript (including abstract) Click here to view linked References
CasCADe: A Novel 4D Visualization System for Virtual Construction Planning

Building Information Modeling (BIM) provides an integrated 3D environment to manage large-scale engineering projects. The Architecture, Engineering and Construction (AEC) industry explores 4D visualizations over these datasets for virtual construction planning. However, existing solutions lack adequate visual mechanisms to inspect the underlying schedule and make inconsistencies readily apparent. The goal of this paper is to apply best practices of information visualization to improve 4D analysis of construction plans. We first present a review of previous work that identifies common use cases and limitations. We then consulted with AEC professionals to specify the main design requirements for such applications. These guided the development of CasCADe, a novel 4D visualization system where task sequencing and spatio-temporal simultaneity are immediately apparent. This unique framework enables the combination of diverse analytical features to create an information-rich analysis environment. We also describe how engineering collaborators used CasCADe to review the real-world construction plans of an Oil & Gas process plant. The system made evident schedule uncertainties, identified work-space conflicts and helped analyze other constructability issues. The results and contributions of this paper suggest new avenues for future research in information visualization for the AEC industry.
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the current object detection field, which uses fully convolutional neural network to detect all scaled objects in an image. Deconvolutional Single Shot Detector (DSSD) is an approach which introduces more context information by adding the deconvolution module to SSD. And the mean Average Precision (mAP) of DSSD on PASCAL VOC2007 is improved from SSD’s 77.5% to 78.6%. Although DSSD obtains higher mAP than SSD by 1.1%, the frames per second (FPS) decreases from 46 to 11.8. In this paper, we propose a single stage end-to-end image detection model called ESSD to overcome this dilemma. Our solution to this problem is to cleverly extend better context information for the shallow layers of the best single stage (e.g. SSD) detectors. Experimental results show that our model can reach 79.4% mAP, which is higher than DSSD and SSD by 0.8 and 1.9 points respectively. Meanwhile, our testing speed is 25 FPS in Titan X GPU which is more than double the original DSSD.
Male antifertility effect of Ocimum sanctum Linn . : A study in albino rat

Experiments were carried out to study the effect of high dose of “tulsi” (Ocimum sanctum Linn.) pellets on testis and epididymis in male albino rat. Wheat flour, oil and honey pellets of tulsi leaves were fed to albino rat, at 400mg/ 100g body weight per day, along with normal diet, for a period of 72 days. One group of tulsi-fed rats was left for recovery, after the last dose fed on day 72, up to day 120. This high dose of tulsi was found to cause durationdependant decrease of testis weight and derangements in the histo-architecture of testis as well as epididymis. The diameter of seminiferous tubules decreased considerably, with corresponding increase in the interstitium. Spermatogenesis was arrested, accompanied by degeneration of seminiferous epithelial elements. Epididymal tubules regressed, and the luminal spermatozoa formed a coagulum. In the recovery group, testis and epididymis regained normal weights, where as spermatogenesis was partially restored. Thus, high dose of tulsi leaf affects testicular and epididymyal structure and function reversibly.
Effect of daily chlorhexidine bathing on hospital-acquired infection.

Results of previous single-center, observational studies suggest that daily bathing of patients with chlorhexidine may prevent hospital-acquired bloodstream infections and the acquisition of multidrug-resistant organisms (MDROs).



We conducted a multicenter, cluster-randomized, nonblinded crossover trial to evaluate the effect of daily bathing with chlorhexidine-impregnated washcloths on the acquisition of MDROs and the incidence of hospital-acquired bloodstream infections. Nine intensive care and bone marrow transplantation units in six hospitals were randomly assigned to bathe patients either with no-rinse 2% chlorhexidine-impregnated washcloths or with nonantimicrobial washcloths for a 6-month period, exchanged for the alternate product during the subsequent 6 months. The incidence rates of acquisition of MDROs and the rates of hospital-acquired bloodstream infections were compared between the two periods by means of Poisson regression analysis.



A total of 7727 patients were enrolled during the study. The overall rate of MDRO acquisition was 5.10 cases per 1000 patient-days with chlorhexidine bathing versus 6.60 cases per 1000 patient-days with nonantimicrobial washcloths (P=0.03), the equivalent of a 23% lower rate with chlorhexidine bathing. The overall rate of hospital-acquired bloodstream infections was 4.78 cases per 1000 patient-days with chlorhexidine bathing versus 6.60 cases per 1000 patient-days with nonantimicrobial washcloths (P=0.007), a 28% lower rate with chlorhexidine-impregnated washcloths. No serious skin reactions were noted during either study period.



Daily bathing with chlorhexidine-impregnated washcloths significantly reduced the risks of acquisition of MDROs and development of hospital-acquired bloodstream infections. (Funded by the Centers for Disease Control and Prevention and Sage Products; ClinicalTrials.gov number, NCT00502476.).
Anthropometry of the Breast Region: How to Measure?

Breast region measurements are important for research, but they may also become significant in the legal field as a quantitative tool for preoperative and postoperative evaluation. Direct anthropometric measurements can be taken in clinical practice. The aim of this study was to compare direct breast anthropometric measurements taken with a tape measure and a compass. Forty women, aged 18–60 years, were evaluated. They had 14 anatomical landmarks marked on the breast region and arms. The union of these points formed eight linear segments and one angle for each side of the body. The volunteers were evaluated by direct anthropometry in a standardized way, using a tape measure and a compass. Differences were found between the tape measure and the compass measurements for all segments analyzed (p > 0.05). Measurements obtained by tape measure and compass are not identical. Therefore, once the measurement tool is chosen, it should be used for the pre- and postoperative measurements in a standardized way. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Multi-site meta-analysis of image-wide genome-wide associations of morphometry

150/150). Large-scale distributed analyses of over 30,000 MRI scans recently detected common genetic variants associated with the volumes of subcortical brain structures. Scaling up these efforts, still greater computational challenges arise in screening the genome for statistical associations at each voxel in the brain, localizing effects using “image-wide genome-wide” testing (voxelwise GWAS, vGWAS). Here we benefit from distributed computations at multiple sites to meta-analyze genome-wide image-wide data, allowing private genomic data to stay at the site where it was collected. Site-specific tensorbased morphometry (TBM) is performed with a custom template for each site, using a multi channel registration. A single vGWAS testing 10 variants against 2 million voxels can yield hundreds of TB of summary statistics, which would need to be transferred and pooled for meta-analysis. We propose a 2-step method, which reduces data transfer for each site to a subset of SNPs and voxels guaranteed to contain all significant hits.
Critical Success Factors to Improve the Game Development Process from a Developers Perspective

The growth of the software game development industry is enormous and is gaining importance day by day. This growth imposes severe pressure and a number of issues and challenges on the game development community. Game development is a complex process, and one important game development choice is to consider the developer perspective to produce good-quality software games by improving the game development process. The objective of this study is to provide a better understanding of the developer’s dimension as a factor in software game success. It focusses mainly on an empirical investigation of the effect of key developer factors on the software game development process and eventually on the quality of the resulting game. A quantitative survey was developed and conducted to identify key developer factors for an enhanced game development process. For this study, the developed survey was used to test the research model and hypotheses. The results provide evidence that game development organizations must deal with multiple key factors to remain competitive and to handle high pressure in the software game industry. The main contribution of this paper is to investigate empirically the influence of key developer factors on the game development process.
A Review on Tissue Segmentation and Feature Extraction of MRI Brain images

Magnetic resonance imaging (MRI) is an important diagnostic imaging technique for the early detection of brain cancer. Brain cancer is one of the most dangerous diseases occurring commonly among human beings. The chances of survival can be increased if the cancer is detected at its early stage. MRI brain image plays a vital role in assisting radiologists to access patients for diagnosis and treatment. Studying of medical image by the Radiologist is not only a tedious and time consuming process but also accuracy depends upon their experience. So, the use of computer aided systems becomes very necessary to overcome these limitations. Even though several automated methods are available, still segmentation of MRI brain image remains as a challenging problem due to its complexity and there is no standard algorithm that can produce satisfactory results. In this review paper, various current methodologies of brain image segmentation using automated algorithms that are accurate and requires little user interaction are reviewed and their advantages, disadvantages are discussed. This review paper guides in combining two or more methods together to produce accurate results.
An SFS Berger check prediction ALU and its application to self-checking processor designs

A strongly fault secure (SFS) ALU design based on the Berger check prediction (BCP) technique is presented in this paper. The fault and error models of a large class of VLSI ALU designs are discussed. The proposed design is proved to be fault-secure and self-testing with respect to any single fault in the ALU part. Then, the proposed BCP ALU is proved to be SFS with any design of BCP circuit. Consequently, a selfchecking processor whose data path is encoded entirely in a Berger code can be achieved. An efficient self-checking processor can then be designed.
Energy Optimal VM Placement in the Cloud

In this paper, we investigate the issue of minimizing data center energy usage. In particular, we formulate a problem of virtual machine placement with the objective of minimizing the total power consumption of all the servers. To do this, we examine a CPU power consumption model and then incorporate the model into an mixed integer programming formulation. In order to find optimal or near-optimal solutions fast, we resolve two difficulties: non-linearity of the power model and integer decision variables. We first show how to linearize the problem, and then give a relaxation and iterative rounding algorithm. Computation experiments have shown that the algorithm can solve the problem much faster than the standard integer programming algorithms, and it consistently yields near-optimal solutions. We also provide a heuristic min-cost algorithm, which finds less optimal solutions but works even faster.
Full-Frame Scene Coordinate Regression for Image-Based Localization

Image-based localization, or camera relocalization, is a fundamental problem in computer vision and robotics, and it refers to estimating camera pose from an image. Recent state-ofthe-art approaches use learning based methods, such as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress for each pixel in the image its corresponding position in the scene’s world coordinate frame, and solve the final pose via a RANSAC-based optimization scheme using the predicted correspondences. In this paper, instead of in a patch-based manner, we propose to perform the scene coordinate regression in a full-frame manner to make the computation efficient at test time and, more importantly, to add more global context to the regression process to improve the robustness. To do so, we adopt a fully convolutional encoder-decoder neural network architecture which accepts a whole image as input and produces scene coordinate predictions for all pixels in the image. However, using more global context is prone to overfitting. To alleviate this issue, we propose to use data augmentation to generate more data for training. In addition to the data augmentation in 2D image space, we also augment the data in 3D space. We evaluate our approach on the publicly available 7-Scenes dataset, and experiments show that it has better scene coordinate predictions and achieves state-of-the-art results in localization with improved robustness on the hardest frames (e.g., frames with repeated structures).
FPGA-Based Real-Time Simulation of Finite-Element Analysis Permanent Magnet Synchronous Machine Drives

This paper presents a real-time simulator of a permanent magnet synchronous motor (PMSM) drive based on a finite-element analysis (FEA) method and implemented on an FPGA card for HIL testing of motor drive controllers. The proposed PMSM model is a phase domain model with inductances and flux profiles computed from the JMAG-RT finite element analysis software. A 3-phase IGBT inverter drives the PMSM machine. Both models are implemented on an FPGA chip, with no VHDL coding, using the RT-LAB real-time simulation platform from Opal-RT and a Simulink blockset called xilinx system generator (XSG). The PMSM drive, along with an open-loop test source for the pulse width modulation, is coded for an FPGA card. The PMSM drive is completed with various encoder models (quadrature, Hall effects and resolver). The overall model compilation and simulation is entirely automated by RT-LAB. The drive is designed to run in a closed loop with a HIL-interfaced controller connected to the I/O of the real-time simulator. The PMSM drive model runs with an equivalent 10 nanosecond time step (100 MHz FPGA card) and has a latency of 300 ns (PMSM machine and inverter) with the exception of the FEA-computed inductance matrix routines which are updated in parallel on a CPU of the real-time simulator at a 40 us rate. The motor drive is directly connected to digital inputs and analog outputs with 1 microsecond settling time on the FPGA card and has a resulting total hardware-in-the-loop latency of 1.3 microseconds.
Pipeline-based processing of the deep learning framework caffe

Many life-log analysis applications, which transfer data from cameras and sensors to a Cloud and analyze them in the Cloud, have been developed with the spread of various sensors and Cloud computing technologies. However, difficulties arise because of the limitation of the network bandwidth between the sensors and the Cloud. In addition, sending raw sensor data to a Cloud may introduce privacy issues. Therefore, we propose distributed deep learning processing between sensors and the Cloud in a pipeline manner to reduce the amount of data sent to the Cloud and protect the privacy of the users. In this paper, we have developed a pipeline-based distributed processing method for the Caffe deep learning framework and investigated the processing times of the classification by varying a division point and the parameters of the network models using data sets, CIFAR-10 and ImageNet. The experiments show that the accuracy of deep learning with coarse-grain data is comparable to that with the default parameter settings, and the proposed distributed processing method has performance advantages in cases of insufficient network bandwidth with actual sensors and a Cloud environment.
Keratin expression in human tissues and neoplasms.

Keratin expression in human tissues and neoplasms Keratin filaments constitute type I and type II intermediate filaments (IFs), with at least 20 subtypes named keratin 1-20. Since certain keratin subtypes are only expressed in some normal human tissues but not others, and vice versa, various tissues have been subclassified according to the pattern of keratin staining. Simple epithelia generally express the simple epithelial keratins 7, 18, 19, and 20, while complex epithelia express complex epithelial keratins 5/6, 10, 14, and 15. When an epithelium undergoes malignant transformation, its keratin profile usually remains constant. The constitution and expression patterns of keratin filaments in human epithelial neoplasms are complex and often distinctive. In this article, we first briefly review the molecular and cell biology of keratin filaments. We then focus on the expression patterns of keratin filaments in various human neoplasms.
Histograms of optical flow orientation for abnormal events detection

In this paper, we propose an algorithm to detect abnormal events based on video streams. The algorithm is based on histograms of the orientation of optical flow descriptor and one-class SVM classifier. We introduce grids of Histograms of the Orientation of Optical Flow (HOF) as the descriptors for motion information of the monolithic video frame. The one-class SVM, after a learning period characterizing normal behaviors, detects the abnormality which is considered as the event needed to be recognized in the current frame. Extensive testing on dataset corroborates the effectiveness of the proposed detection method.
Socio-Economic Status and Physical Attractiveness in Mate Selection Choice Smt

Selection the ideal mate is the most confusing process in the life of most people. To explore these issues to examine differences under graduates socio-economic status have on their preference of marriage partner selection in terms of their personality traits, socio-economic status and physical attractiveness. A total of 770 respondents participated in this study. The respondents were mainly college students studying in final year degree in professional and non professional courses. The result revealed that the respondents socio-economic status significantly influence preferences in marriage partners selection in terms of personality traits, socio-economic status and physical attractiveness.
Spam mail detection through data mining techniques

In todays electronic world a huge part of communication, both professional and private, takes place in the form of electronic mails or emails. However, due to advertising agencies and social networking websites most of the emails circulated contain unwanted information which is not relevant to the user. Spam emails are a type of electronic mail where the user receives unsolicited messages via email. Spam emails cause inconvenience and financial loss to the recipients so there is a need to filter them and separate them from the legitimate emails. Many algorithms and filters have been developed to detect the spam emails but spammers continuously evolve and sophisticate their spamming techniques due to which the existing filters are becoming less effective. The method proposed in this paper involves creating a spam filter using binary and continuous probability distributions. The algorithms implemented in building the classifier model are Naive Bayes and Decision Trees. The effect of overfitting on the performance and accuracy of decision trees is analyzed. Finally, the better classifier model is identified based on its accuracy to correctly classify spam and non-spam emails.