nils-herrmann/clean_scirepeval huggingface dataset:
Theoretical analysis and optimal design of LLC resonant converter

A LLC resonant topology is analyzed to derive efficiency and cost optimal design for wide input ranges and load variations. In the LLC converter, a wide range of output power is controlled with only a narrow variation in operating frequency since this converter is capable of both step-up and step-down. In addition, ZVS turn-on and ZCS turn-off of MOSFETs and diode rectifiers can be achieved over the entire operating range. Finally, the inductance of a resonant tank in the primary side can be merged in the main power transformer by resonant inductance and the absence of the secondary filter inductor makes low voltage stress on secondary rectifier and cost-effective property. DC characteristics and input-output response in frequency domain are obtained with the equivalent circuit derived by first harmonic approximation (FHA) method. In addition, operational principles are explained to show the ZVS and ZCS conditions of primary switches and output diode rectifiers, respectively. Efficiency and cost optimal design rules of the LLC resonant converter are derived by a primary resonant network, operating frequency, and dead time duration. Proposed analysis and designation are proved by experimental results with a 400 W LLC resonant converter.
Comparison of strategies for scalable causal discovery of latent variable models from mixed data

Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data (“Mixed Data”), and essential variables may be unobserved in this data due to the complex nature of biomedical phenomena. Causal inference algorithms can identify important relationships from biomedical data; however, handling the challenges of causal inference over mixed data with unmeasured confounders in a scalable way is still an open problem. Despite recent advances into causal discovery strategies that could potentially handle these challenges; individually, no study currently exists that comprehensively compares these approaches in this setting. In this paper, we present a comparative study that addresses this problem by comparing the accuracy and efficiency of different strategies in large, mixed datasets with latent confounders. We experiment with two extensions of the Fast Causal Inference algorithm: a maximum probability search procedure we recently developed to identify causal orientations more accurately, and a strategy which quickly eliminates unlikely adjacencies in order to achieve scalability to high-dimensional data. We demonstrate that these methods significantly outperform the state of the art in the field by achieving both accurate edge orientations and tractable running time in simulation experiments on datasets with up to 500 variables. Finally, we demonstrate the usability of the best performing approach on real data by applying it to a biomedical dataset of HIV-infected individuals.
Research of Sorting Technology Based on Industrial Robot of Machine Vision

Aimed at the industrial sorting technology problems, this paper researched correlative algorithm of image processing and analysis, and completed the construction of robot vision sense. the operational process was described as follows: the camera acquired image sequences of the metal work piece in the sorting region. Image sequence was analyzed to use algorithms of image pre-processing, Hough circle detection, corner detection and contour recognition. in the mean time, this paper also explained the characteristics of three main function model (image pre-processing, corner detection and contour recognition), and proposed algorithm of multi-objective center and a corner recognition. the simulated results show that the sorting system can effectively solve the sorting problem of regular geometric work piece, and accurately calculate center and edge of geometric work piece to achieve the sorting purpose.
Effective Microorganisms : Myth or reality ?

Th e environmentally friendly Eff ective Microorganisms (EM) technology claims an enormous amount of benefi ts (claimed by the companies). Th e use of EM as an addictive to manure or as a spray directly in the fi elds may increase the microfauna diversity of the soil and many benefi ts are derived from that increase. It seems that suffi cient information is available about this new technology.
Clinical and Radiological Classification of the Jawbone Anatomy in Endosseous Dental Implant Treatment

The purpose of present article was to review the classifications suggested for assessment of the jawbone anatomy, to evaluate the diagnostic possibilities of mandibular canal identification and risk of inferior alveolar nerve injury, aesthetic considerations in aesthetic zone, as well as to suggest new classification system of the jawbone anatomy in endosseous dental implant treatment.



Literature was selected through a search of PubMed, Embase and Cochrane electronic databases. The keywords used for search were mandible; mandibular canal; alveolar nerve, inferior; anatomy, cross-sectional; dental implants; classification. The search was restricted to English language articles, published from 1972 to March 2013. Additionally, a manual search in the major anatomy and oral surgery books were performed. The publications there selected by including clinical and human anatomy studies.



In total 109 literature sources were obtained and reviewed. The classifications suggested for assessment of the jawbone anatomy, diagnostic possibilities of mandibular canal identification and risk of inferior alveolar nerve injury, aesthetic considerations in aesthetic zone were discussed. New classification system of the jawbone anatomy in endosseous dental implant treatment based on anatomical and radiologic findings and literature review results was suggested.



The classification system proposed here based on anatomical and radiological jawbone quantity and quality evaluation is a helpful tool for planning of treatment strategy and collaboration among specialists. Further clinical studies should be conducted for new classification validation and reliability evaluation.
Text Categorization of Movie Reviews for Sentiment Analysis

Text classification is an important task in many text mining applications. Text data generated from the reviews have been growing tremendously. People are participating largely in internet to give their opinion about various subjects and topics. A branch of text mining that deals with people’s views about a subject is opinion mining, in which the data in the form of reviews is mined in order to analyze their sentiment. This study of people’s opinion is sentiment analysis and is a popular research area in text mining. In this paper, movie reviews are classified for sentiment analysis in weka. There are 2000 movie reviews in a dataset obtained from Cornell university dataset repository. The dataset is preprocessed and various filters have been applied to reduce the feature set. Feature selection methods are widely used for gathering most valuable words for each category in text mining processes. They help to find most distinctive words for each category by calculating some variables on data. The mostly employed methods are Chi-Square, Information Gain, and Gain Ratio. In this study, information gain method was employed because of its simplicity, less computational costs and its efficiency. The effects of reduced feature set have been proved to improve the performance of the classifier. Two popular classifiers namely naïve bayes and svm have been experimented with the movie review dataset. The results show that naïve bayes performs better than svm for classification of movie reviews.
Graph Minors for Preserving Terminal Distances Approximately - Lower and Upper Bounds

Given a graph where vertices are partitioned into k terminals and non-terminals, the goal is to compress the graph (i.e., reduce the number of non-terminals) using minor operations while preserving terminal distances approximately. The distortion of a compressed graph is the maximum multiplicative blow-up of distances between all pairs of terminals. We study the trade-off between the number of non-terminals and the distortion. This problem generalizes the Steiner Point Removal (SPR) problem, in which all non-terminals must be removed. We introduce a novel black-box reduction to convert any lower bound on distortion for the SPR problem into a super-linear lower bound on the number of non-terminals, with the same distortion, for our problem. This allows us to show that there exist graphs such that every minor with distortion less than 2 / 2.5 / 3 must have Ω(k2) / Ω(k5/4) / Ω(k6/5) non-terminals, plus more trade-offs in between. The black-box reduction has an interesting consequence: if the tight lower bound on distortion for the SPR problem is super-constant, then allowing any O(k) non-terminals will not help improving the lower bound to a constant. We also build on the existing results on spanners, distance oracles and connected 0-extensions to show a number of upper bounds for general graphs, planar graphs, graphs that exclude a fixed minor and bounded treewidth graphs. Among others, we show that any graph admits a minor with O(log k) distortion and O(k2) non-terminals, and any planar graph admits a minor with 1 + ε distortion and Õ((k/ε)2) non-terminals.
Developing ZigBee Deployment Guideline Under WiFi Interference for Smart Grid Applications

Smart grid is an intelligent power generation, distribution, and control system. ZigBee, as a wireless mesh networking scheme low in cost, power, data rate, and complexity, is ideal for smart grid applications, e.g., real-time system monitoring, load control, and building automation. Unfortunately, almost all ZigBee channels overlap with wireless local area network (WLAN) channels, resulting in severe performance degradation due to interference. In this paper, we aim to develop practical ZigBee deployment guideline under the interference of WLAN. We identify the “Safe Distance” and “Safe Offset Frequency” using a comprehensive approach including theoretical analysis, software simulation, and empirical measurement. In addition, we propose a frequency agility-based interference avoidance algorithm. The proposed algorithm can detect interference and adaptively switch nodes to “safe” channel to dynamically avoid WLAN interference with small latency and small energy consumption. Our proposed scheme is implemented with a Meshnetics ZigBit Development Kit and its performance is empirically evaluated in terms of the packet error rate (PER) using a ZigBee and Wi-Fi coexistence test bed. It is shown that the empirical results agree with our analytical results. The measurements demonstrate that our design guideline can efficiently mitigate the effect of WiFi interference and enhance the performance of ZigBee networks.
Miniature robots for space and military missions

ABSTUCT Miniature robots enable low-cost planetary surface exploration missions, and new military missions in urban terrain where small robots provide critical assistance to human operations. These space and military missions have many similar technological challenges. Robots can be deployed in environments where it may not be safe or affordable to send humans, or where robots can reduce the risk to humans. Small size is needed in urban terrain to make the robot easy to carry and deploy by military personnel. Technology to sense and perceive the environment, and to autonomously plan and execute navigation maneuvers and other remote tasks, is an important requirement for both planetary and surface robots and for urban terrain robotic assistants. Motivated by common technological needs and by a shared vision about the great technological potential, a strong, collaborative relationship exists between the NASNJPL and DARPA technology development in miniaturized robotics. This paper describes the technologies under development, the applications where these technologies are relevant to both space and military missions, and the status of the most recent technology demonstrations in terrestrial scenarios.
A Data Set of Authentic and Spliced Image Blocks

Image splicing is a simple process that crops and pastes regions from the same or separate sources. It is a fundamental step used in digital photomontage, which refers to a paste-up produced by sticking together images using digital tools such as Photoshop. Examples of photomontages can be seen in several infamous news reporting cases involving the use of faked images. Searching for technical solutions for image authentication, researchers have recently started development of new techniques aiming at blind passive detection of image splicing. However, like most other research communities dealing with data processing, we need an open data set with diverse content and realistic splicing conditions in order to expedite the progresses and facilitate collaborative studies. In this report, we describe with details a data set of 1845 image blocks with a fixed size of 128 pixels x 128 pixels. The image blocks are extracted from images in the CalPhotos collection [CalPhotos'00], with a small number of additional images captured by digital cameras. The data set include about the same number of authentic and spliced image blocks, which are further divided into different subcategories (smooth vs. textured, arbitrary object boundary vs. straight boundary).
On Convergence Properties of the EM Algorithm for Gaussian Mixtures

We build up the mathematical connection between the Expectation-Maximization (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of gaussian mixture models.
A Survey of Public Auditing for Secure Data Storage in Cloud Computing

Cloud computing has been popular as the IT architecture. Cloud service providers offer many services based on cloud computing. Cloud storage service is the cloud services which can provide a huge storage space to solve the bottleneck of the storage space of local end users. However, cloud storage service may have data security because the users’ data is not stored in their own storage. In this paper, we will focus on data integrity in the cloud storage service. Public auditability is a model of outsourcing data integrity verification, which can achieve efficiency and security. Therefore, we survey the previous researches of data integrity based on public auditability which includes collecting the basic requirements and evaluation metrics, providing the representative with approaches to analyze security and efficiency. Finally, we propose some future developments.
A Real Time Survey On Attacks And Detection Strategies For Primary User Emulator In Cognitive Radio Networks

The increase in the wireless users has led to the spectrum shortage problem. Federal Communication Commission (FCC) showed that licensed spectrum bands are underutilized, specially TV bands. The IEEE 802.22 standard was proposed to exploit these white spaces in the (TV) frequency spectrum. Cognitive Radio allows unlicensed users to use licensed bands while safeguarding the priority of licensed users. Cognitive Radio is composed of two types of users, licensed users also known as Primary Users(PUs) and unlicensed users also known as Secondary Users(SUs).SUs use the resources when spectrum allocated to PU is vacant, as soon as PU become active, the SU has to leave the channel for PU. Hence the opportunistic access is provided by CR to SUs whenever the channel is vacant. Cognitive Users sense the spectrum continuously and share this sensing information to other SUs, during this spectrum sensing, the network is vulnerable to so many attacks. One of these attacks is Primary User Emulation Attack (PUEA), in which the malicious secondary users can mimic the characteristics of primary users thereby causing legitimate SUs to erroneously identify the attacker as a primary user, and to gain access to wireless channels. PUEA is of two types: Selfish and Malicious attacker. A selfish attacker aims in stealing Bandwidth form legitimate SUs for its own transmissions while malicious attacker mimic the characteristics of PU.
Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model

The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.
Design of an X-band pulsed SSPA based on a cascade technique

An X-band pulsed solid-state power amplifier (PSSPA) with high output power and high power added efficiency (PAE) is reported in this article. The high power amplifier (HPA) was implemented by a cascade approach, including an MMIC driving amplifier, an internally matched medium-power and a high-power GaAs FET. To achieve optimum electrical performance of the proposed PSSPA, some considerations of the Grounding, DC Blocking Circuit, bias network, microwave absorber, and the isolation blocks, have been taken in our design. Under the pulse condition of 8 KHz pulse repeat frequency (PRF) and 10% of duty cycle, the pulse output power ranges between 45.8 and 46.6 dBm, and the PAE varies between 35.8% and 40.5% from 9.5 to 10.5 GH.
THE PEOPLING OF THE NEW WORLD : Perspectives from Molecular Anthropology

■  A number of important insights into the peopling of the New World have been gained through molecular genetic studies of Siberian and Native American populations. These data indicate that the initial migration of ancestral Amerindian originated in south-central Siberia and entered the New World between 20,000–14,000 calendar years before present (cal yr BP). These early immigrants probably followed a coastal route into the New World, where they expanded into all continental regions. A second migration that may have come from the same Siberian region entered the Americas somewhat later, possibly using an interior route, and genetically contributed to indigenous populations from North and Central America. In addition, Beringian populations moved into northern North America after the last glacial maximum (LGM) and gave rise to Aleuts, Eskimos, and Na-Dené Indians.
Control of a self-balancing robot driven by DC motors via IDA-PBC

The self-balancing robot is an example of underactuated mechanical systems; a class of mechanisms which have been very popular as benchmarks for control methods in the last years. The Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) is a control methodology designed to solve the stabilization problem of underactuated systems. In this paper, we present an application of this control methodology to a self-balancing robot, by using an extension of the IDA-PBC method, which includes actuator dynamics. Based on the Lyapunov direct method we carry out stability analysis and, by invoking the theorem of Barbashin---Krasovskii, we arrive to asymptotic stability conditions. Experimental results are shown to illustrate the performance and viability of the proposed control.
Are the effects of Unreal violent video games pronounced when playing with a virtual reality system?

This study was conducted to analyze the short-term effects of violent electronic games, played with or without a virtual reality (VR) device, on the instigation of aggressive behavior. Physiological arousal (heart rate (HR)), priming of aggressive thoughts, and state hostility were also measured to test their possible mediation on the relationship between playing the violent game (VG) and aggression. The participants--148 undergraduate students--were randomly assigned to four treatment conditions: two groups played a violent computer game (Unreal Tournament), and the other two a non-violent game (Motocross Madness), half with a VR device and the remaining participants on the computer screen. In order to assess the game effects the following instruments were used: a BIOPAC System MP100 to measure HR, an Emotional Stroop task to analyze the priming of aggressive and fear thoughts, a self-report State Hostility Scale to measure hostility, and a competitive reaction-time task to assess aggressive behavior. The main results indicated that the violent computer game had effects on state hostility and aggression. Although no significant mediation effect could be detected, regression analyses showed an indirect effect of state hostility between playing a VG and aggression.
Study on the Waste Water Treatment Technology for Steel Industry : Recycle And Reuse

The steel industry is one of the most important and vital Industry of the present and the future. It is the asset of a nation. Steel plants use a tremendous amount of water for waste transfer, cooling and dust control. The steel plants have sintering mills, coke plants, blast furnaces, chemical byproducts and chemical processes, water cooled rolls, pumps, extrusion experiment, transfer lines for sludges and slurries. All these plants use a tremendous amount of water to cool the products and flush the impurities away from the finished stock. Wastewater is generated in huge quantity in steel industries. It contains many dissolved, undisclosed substances and chemicals in the wastewater. The steel industries produce wastewater and sludge during different industrial processes. The development of innovative technologies for treatment of wastewaters from steel industries is a matter of alarming concern for us. Although many research papers have been reported on wastewater pollution control studies, but a very few research work is carried out for treatment of wastewater of steel industries, especially in reference to development of design of industrial effluent Treatment Plants (ETP) system. Another beneficial aspect of this research work will be recycling, reuse of water and sludge from steel industry The whole technologies for treating industrial wastewater can be divided into four categories: Chemical, Physical, Biological and mathematical approaches. Physical treatment methods include sedimentation, Floatation , filtering , stripping, ion – exchange, adsorption and other processes that accomplish removal of dissolved and undisclosed substances without necessarily changing their chemical structure. The mathematical approaches are very useful and more realistic for developing a well operating cost–effective treatment system for industrial wastewater treatment.