nils-herrmann/clean_scirepeval huggingface dataset:
High-speed GaN micro-LED arrays for data communications

We report the modulation performance of micro-light-emitting diode arrays with peak emission ranging from 370 to 520 nm, and emitter diameters ranging from 14 to 84 μm. Bandwidths in excess of 400 MHz and error-free data transmission up to 1.1Gbit/s is shown. These devices are shown integrated with electronic drivers, allowing convenient control of individual array emitters. Transmission using such a device is shown at 512 Mbit/s.
Providing a Window of Opportunity for Converting eStore Visitors

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Principle and Realization of Micromouse Fast Dashing Turning Based onSTM32

Micromouse is an intelligent maze robot. Affected by the ability of MCU, micromouse based on MCS-51 can not guarantee the performance of quick dashing of the micromouse. High speed micromouse servo controller based on STM32 was designed. In order to meet the fast dashing of micromouse in a complicated maze environment, different speed-time curves were used. To realize the fast turning of micromouse in dashing, the turning trajectory was divided into five paths, different control parameters and the navigation sensors were used to track the turning. Experimental results show that five-path tracking method can not only realize micromouse dashing turning in high speed, which helps improve the dynamic performance and stability of micromouse, but also reduce its dashing time.
Prevalence of depression amongst medical students: a meta-analysis.

Medical schools are known to be stressful environments for students and hence medical students have been believed to experience greater incidences of depression than others. We evaluated the global prevalence of depression amongst medical students, as well as epidemiological, psychological, educational and social factors in order to identify high-risk groups that may require targeted interventions.



A systematic search was conducted in online databases for cross-sectional studies examining prevalences of depression among medical students. Studies were included only if they had used standardised and validated questionnaires to evaluate the prevalence of depression in a group of medical students. Random-effects models were used to calculate the aggregate prevalence and pooled odds ratios (ORs). Meta-regression was carried out when heterogeneity was high.



Findings for a total of 62 728 medical students and 1845 non-medical students were pooled across 77 studies and examined. Our analyses demonstrated a global prevalence of depression amongst medical students of 28.0% (95% confidence interval [CI] 24.2-32.1%). Female, Year 1, postgraduate and Middle Eastern medical students were more likely to be depressed, but the differences were not statistically significant. By year of study, Year 1 students had the highest rates of depression at 33.5% (95% CI 25.2-43.1%); rates of depression then gradually decreased to reach 20.5% (95% CI 13.2-30.5%) at Year 5. This trend represented a significant decline (B = - 0.324, p = 0.005). There was no significant difference in prevalences of depression between medical and non-medical students. The overall mean frequency of suicide ideation was 5.8% (95% CI 4.0-8.3%), but the mean proportion of depressed medical students who sought treatment was only 12.9% (95% CI 8.1-19.8%).



Depression affects almost one-third of medical students globally but treatment rates are relatively low. The current findings suggest that medical schools and health authorities should offer early detection and prevention programmes, and interventions for depression amongst medical students before graduation.
Data mining technique for identification of diagnostic biomarker to predict Schizophrenia disorder

In recent days, researchers are actively analysing the human brain to understand the underlying mechanism of heterogeneous psychiatric conditions. Schizophrenia is a severe neurological disorder which has been characterized by varying symptoms namely hallucinations, delusions and cognitive problems. In this paper, we have investigated the resting state fMRI images of 15 normal controls and 12 Schizophrenia patients by constructing functional connectome through image preprocessing techniques namely Realignment, temporal correction, filtering, etc., The parcellation of neuroimage is performed based on Automated Anatomical Labelling (AAL) atlas and 74 regions of interest (ROI) are identified. Functional connectome of each subject includes Pearson correlation values of mean time courses obtained between the regions. These region to region functional connectivity is considered as features and the feature selection technique namely Fisher filtering, ReliefF filtering and Runs filtering are applied. Then the features which are found by different filtering techniques are fed as input to the supervised non linear classifiers namely Random forest, C4.5, Cost sensitive classification and regression tree and K-Nearest Neighbour classification algorithm. These algorithms have produced classification rules which are used in the prediction of Schizophrenia disorder. C4.5 has achieved the higher predictive accuracy of 93% with leave-one out cross-validation and the predominant feature or diagnostic biomarker is obtained from the rule. This feature is one among the commonly identified feature of different feature selection techniques. The work has shown that the biomarker corresponds to the alterations in the functional connectivity between the brain regions namely Rolandic operculum and Postcentral gyrus of brain's left hemisphere which is involved in sensorimotor function of human.
Aspect-Oriented Web Development in PHP

Aspect-Oriented Programming (AOP) provides another way of thinking about program structure that allows developers to separate and modularize concerns like crosscutting concerns. These concerns are maintained in aspects that allows to easily maintain both the core and crosscutting concerns. Much research on this area has been done focused on traditional software development. Although little has been done in the Web development context. In this paper is presented an overview of existing AOP PHP development tools identifying their strengths and weaknesses. Then we compare the existing AOP PHP development tools presented in this paper. We then discuss how these tools can be effectively used in the Web development. Finally, is discussed how AOP can enhance the Web development and are presented some future work possibilities on this area.
Scheduler-based Defenses against Cross-VM Side-channels

Public infrastructure-as-a-service clouds, such as Amazon EC2 and Microsoft Azure allow arbitrary clients to run virtual machines (VMs) on shared physical infrastructure. This practice of multi-tenancy brings economies of scale, but also introduces the threat of malicious VMs abusing the scheduling of shared resources. Recent works have shown how to mount crossVM side-channel attacks to steal cryptographic secrets. The straightforward solution is hard isolation that dedicates hardware to each VM. However, this comes at the cost of reduced efficiency. We investigate the principle of soft isolation: reduce the risk of sharing through better scheduling. With experimental measurements, we show that a minimum run time (MRT) guarantee for VM virtual CPUs that limits the frequency of preemptions can effectively prevent existing Prime+Probe cache-based side-channel attacks. Through experimental measurements, we find that the performance impact of MRT guarantees can be very low, particularly in multi-core settings. Finally, we integrate a simple per-core CPU state cleansing mechanism, a form of hard isolation, into Xen. It provides further protection against side-channel attacks at little cost when used in conjunction with an MRT guarantee.
On the Class Imbalance Problem

The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years. In such a problem, almost all the examples are labeled as one class, while far fewer examples are labeled as the other class, usually the more important class. In this case, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class since traditional classifiers seeking an accurate performance over a full range of instances. This paper reviewed academic activities special for the class imbalance problem firstly. Then investigated various remedies in four different levels according to learning phases. Following surveying evaluation metrics and some other related factors, this paper showed some future directions at last.
A Comparative Study of Ontology building Tools in Semantic Web Applications

Ontologies have recently received popularity in the area of knowledge management and knowledge sharing, especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d) market status and penetration. The results of the review in ontologies are analyzed for each application area, such as transport, tourism, personal services, health and social services, natural languages and other HCI-related domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks. Although each tool provides different functionalities, most of the users just use only one, because they are not able to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different ontologies with different development and management tools. The paper is also concerns the detection of commonalities and differences between the examined ontologies, both on the same domain (application area) and among different domains.
Improved training for disasters using 3-D virtual reality simulation.

The purpose of this study was to examine the effects of virtual reality simulation (VRS) on learning outcomes and retention of disaster training. The study used a longitudinal experimental design using two groups and repeated measures. A convenience sample of associate degree nursing students enrolled in a disaster course was randomized into two groups; both groups completed web-based modules; the treatment group also completed a virtually simulated disaster experience. Learning was measured using a 20-question multiple-choice knowledge assessment pre/post and at 2 months following training. Results were analyzed using the generalized linear model. Independent and paired t tests were used to examine the between- and within-participant differences. The main effect of the virtual simulation was strongly significant (p < .0001). The VRS effect demonstrated stability over time. In this preliminary examination, VRS is an instructional method that reinforces learning and improves learning retention.
The biochemical alterations underlying post-burn hypermetabolism.

A severe burn can trigger a hypermetabolic state which lasts for years following the injury, to the detriment of the patient. The drastic increase in metabolic demands during this phase renders it difficult to meet the body's nutritional requirements, thus increasing muscle, bone and adipose catabolism and predisposing the patient to a host of disorders such as multi-organ dysfunction and sepsis, or even death. Despite advances in burn care over the last 50 years, due to the multifactorial nature of the hypermetabolic phenomenon it is difficult if not impossible to precisely identify and pharmacologically modulate the biological mediators contributing to this substantial metabolic derangement. Here, we discuss biomarkers and molecules which play a role in the induction and mediation of the hypercatabolic condition post-thermal injury. Furthermore, this thorough review covers the development of the factors released after burns, how they induce cellular and metabolic dysfunction, and how these factors can be targeted for therapeutic interventions to restore a more physiological metabolic phenotype after severe thermal injuries. This article is part of a Special Issue entitled: Immune and Metabolic Alterations in Trauma and Sepsis edited by Dr. Raghavan Raju.
Atkins and other low-carbohydrate diets: hoax or an effective tool for weight loss?

The Atkins diet books have sold more than 45 million copies over 40 years, and in the obesity epidemic this diet and accompanying Atkins food products are popular. The diet claims to be effective at producing weight loss despite ad-libitum consumption of fatty meat, butter, and other high-fat dairy products, restricting only the intake of carbohydrates to under 30 g a day. Low-carbohydrate diets have been regarded as fad diets, but recent research questions this view.



A systematic review of low-carbohydrate diets found that the weight loss achieved is associated with the duration of the diet and restriction of energy intake, but not with restriction of carbohydrates. Two groups have reported longer-term randomised studies that compared instruction in the low-carbohydrate diet with a low-fat calorie-reduced diet in obese patients ( 2003; 348: 2082-90;  2004; 140: 778-85). Both trials showed better weight loss on the low-carbohydrate diet after 6 months, but no difference after 12 months. WHERE NEXT?: The apparent paradox that ad-libitum intake of high-fat foods produces weight loss might be due to severe restriction of carbohydrate depleting glycogen stores, leading to excretion of bound water, the ketogenic nature of the diet being appetite suppressing, the high protein-content being highly satiating and reducing spontaneous food intake, or limited food choices leading to decreased energy intake. Long-term studies are needed to measure changes in nutritional status and body composition during the low-carbohydrate diet, and to assess fasting and postprandial cardiovascular risk factors and adverse effects. Without that information, low-carbohydrate diets cannot be recommended.
Hybrid Neuro-Fuzzy Controller based Adaptive Neuro-Fuzzy Inference System Approach for Multi-Area Load Frequency Control of Interconnected Power System

This paper concentrated on the design and analysis of Neuro-Fuzzy controller based Adaptive Neuro-Fuzzy inference system (ANFIS) architecture for Load frequency control of interconnected areas, to regulate the frequency deviation and power deviations. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. So there is necessity robust controller required to maintain the nominal system frequency. The proposed ANFIS controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of artificial neural network however the control technology implemented with sugeno rule to obtain the optimum performance. In order to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters near its optimum. This ANFIS replaces the original conventional proportional Integral (PI) controller and a fuzzy logic (FL) controller were also utilizes the same area criteria error input. The advantage of this controller is that it can handle the nonlinarites at the same time it is faster than other conventional controllers. Simulation results show that the performance of the proposed ANFIS based NeuroFuzzy controller damps out the frequency deviation and reduces the overshoot of the different frequency deviations.
Classification of ECoG motor imagery tasks based on CSP and SVM

This paper mainly studies about the data processing of brain computer interface(BCI) and presents a kind of method for classifying the ECoG motor imagery tasks. Both the training and testing ECoG datasets were filtered with the frequency band of 8–30Hz according to the event-related desynchronization and synchronization(ERD/ERS) phenomenon. The features were extracted by using Common Spatial Pattern(CSP) and then the best starting and ending points were decided through a ten-fold cross validation(CV) of the training dataset. All the features were fed into a linear support vector machine(SVM), and the final classification accuracy of testing dataset is 90%.
Accelerated Degradation Tests Applied to Software Aging Experiments

In the past ten years, the software aging phenomenon has been systematically researched, and recognized by both academic, and industry communities as an important obstacle to achieving dependable software systems. One of its main effects is the depletion of operating system resources, causing system performance degradation or crash/hang failures in running applications. When conducting experimental studies to evaluate the operational reliability of systems suffering from software aging, long periods of runtime are required to observe system failures. Focusing on this problem, we present a systematic approach to accelerate the software aging manifestation to reduce the experimentation time, and to estimate the lifetime distribution of the investigated system. First, we introduce the concept of ¿aging factor¿ that offers a fine control of the aging effects at the experimental level. The aging factors are estimated via sensitivity analyses based on the statistical design of experiments. Aging factors are then used together with the method of accelerated degradation test to estimate the lifetime distribution of the system under test at various stress levels. This approach requires us to estimate a relationship model between stress levels and aging degradation. Such models are called stress-accelerated aging relationships. Finally, the estimated relationship models enable us to estimate the lifetime distribution under use condition. The proposed approach is used in estimating the lifetime distribution of a web server with software aging symptoms. The main result is the reduction of the experimental time by a factor close to 685 in comparison with experiments executed without the use of our technique.
An alternative intervention for urinary incontinence: retraining diaphragmatic, deep abdominal and pelvic floor muscle coordinated function.

This study was a randomized controlled trial to investigate the effect of treating women with stress or mixed urinary incontinence (SUI or MUI) by diaphragmatic, deep abdominal and pelvic floor muscle (PFM) retraining. Seventy women were randomly allocated to the training (n = 35) or control group (n = 35). Women in the training group received 8 individual clinical visits and followed a specific exercise program. Women in the control group performed self-monitored PFM exercises at home. The primary outcome measure was self-reported improvement. Secondary outcome measures were 20-min pad test, 3-day voiding diary, maximal vaginal squeeze pressure, holding time and quality of life. After a 4-month intervention period, more participants in the training group reported that they were cured or improved (p < 0.01). The cure/improved rate was above 90%. Both amount of leakage and number of leaks were significantly lower in the training group (p < 0.05) but not in the control group. More aspects of quality of life improved significantly in the training group than in the control group. Maximal vaginal squeeze pressure, however, decreased slightly in both groups. Coordinated retraining diaphragmatic, deep abdominal and PFM function could improve symptoms and quality of life. It may be an alternative management for women with SUI or MUI.
Performance Evaluation of Multicore Processors

Multicore processors represent the latest significant development in microprocessor technology. Computer System Performance and Evaluation deal with the investigation of computer components (both hardware and software) with a view to establish the level of their performances. This research work carried out performance evaluation studies on AMD dual-core and Intel dual-core processor to know which of the processor has better execution time and throughput. The architecture of AMD and Intel duo-core processor were studied. SPEC CPU2006 benchmarks suite was used to measure the performance of AMD and Intel duo core processors. The overall execution and throughput time measurement of AMD and Intel duo core processors were reported and compared to each other. Results showed that the execution time of CQ56 Intel Pentium Dual-Core Processor is about 6.62% faster than AMD Turion II P520 Dual-Core Processor while the throughput of Intel Pentium Dual-Core Processor was found to be 1.06 times higher than AMD Turion (tm) II P520 Dual Core Processor. Therefore, Intel Pentium Dual-Core Processors exhibit better performance probably due to the following architectural features: faster core-to-core communication, dynamic cache sharing between cores and smaller size of level 2 cache.
Managing cyber-bullying in online educational virtual worlds

Online Educational Virtual Worlds offer promise that is currently not being realised because of potential threats to the child's safety and wellbeing. This paper seeks to better understand the behavioural and psychological issues, particularly relating to cyber-bullying, faced by school children when they are online and what solutions currently exist. As an outcome of this understanding we make recommendations regarding how cyber-bullying should be managed in educational virtual worlds taking a hybrid approach involving policy, technology and non-technology based solutions.
Acute oxalate nephropathy due to ‘Averrhoa bilimbi’ fruit juice ingestion

Irumban puli (Averrhoa bilimbi) is commonly used as a traditional remedy in the state of Kerala. Freshly made concentrated juice has a very high oxalic acid content and consumption carries a high risk of developing acute renal failure (ARF) by deposition of calcium oxalate crystals in renal tubules. Acute oxalate nephropathy (AON) due to secondary oxalosis after consumption of Irumban puli juice is uncommon. AON due to A. bilimbi has not been reported before. We present a series of ten patients from five hospitals in the State of Kerala who developed ARF after intake of I. puli fruit juice. Seven patients needed hemodialysis whereas the other three improved with conservative management.
A Comprehensive Active Contour Model

Image segmentation is one of the substantial techniques in the field of image processing. It is excessively used in the field of medicine provides visual means for identification, inspection and tracking of diseases for surgical planning and simulation. Active contours or snakes are used extensively for image segmentation and processing applications, particularly to locate object boundaries. Active contours are regarded as promising and vigorously researched model-based approach to computer assisted medical image analysis. However, its utility is limited due to poor convergence of concavities and small capture range. Many subsequent models have been introduced in order to overcome these problems. This paper reviews the traditional model, the Gradient vector flow (GVF) model and the balloon model for different images and proposes a model which can provide the most accurate segmentation.