nils-herrmann/clean_scirepeval huggingface dataset:
Burnout prevention: a review of intervention programs.

To evaluate the effectiveness of intervention programs at the workplace or elsewhere aimed at preventing burnout, a leading cause of work related mental health impairment.



A systematic search of burnout intervention studies was conducted in the databases Medline, PsycINFO and PSYNDEX from 1995 to 2007. Data was also extracted from papers found through a hand search.



A total of 25 primary intervention studies were reviewed. Seventeen (68%) were person-directed interventions, 2 (8%) were organization-directed and 6 (24%) were a combination of both interventions types. Eighty percent of all programs led to a reduction in burnout. Person-directed interventions reduced burnout in the short term (6 months or less), while a combination of both person- and organization-directed interventions had longer lasting positive effects (12 months and over). In all cases, positive intervention effects diminished in the course of time.



Intervention programs against burnout are beneficial and can be enhanced with refresher courses. Better implemented programs including both person- and organization-directed measures should be offered and evaluated.



A combination of both intervention types should be further investigated, optimized and practiced. Institutions should recognize the need for and make burnout intervention programs available to employees.
The Prediction of Students' Academic Performance Using Classification Data Mining Techniques

Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students’ data which can be used to discover valuable pattern pertaining students’ learning behaviour. This paper proposes a framework for predicting students’ academic performance of first year bachelor students in Computer Science course. The data were collected from 8 year period intakes from July 2006/2007 until July 2013/2014 that contains the students’ demographics, previous academic records, and family background information. Decision Tree, Naïve Bayes, and Rule Based classification techniques are applied to the students’ data in order to produce the best students’ academic performance prediction model. The experiment result shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value of 71.3%. The extracted knowledge from prediction model will be used to identify and profile the student to determine the students’ level of success in the first semester.
Adaptive Learning in Tracking Control Based on the Dual Critic Network Design

In this paper, we present a new adaptive dynamic programming approach by integrating a reference network that provides an internal goal representation to help the systems learning and optimization. Specifically, we build the reference network on top of the critic network to form a dual critic network design that contains the detailed internal goal representation to help approximate the value function. This internal goal signal, working as the reinforcement signal for the critic network in our design, is adaptively generated by the reference network and can also be adjusted automatically. In this way, we provide an alternative choice rather than crafting the reinforcement signal manually from prior knowledge. In this paper, we adopt the online action-dependent heuristic dynamic programming (ADHDP) design and provide the detailed design of the dual critic network structure. Detailed Lyapunov stability analysis for our proposed approach is presented to support the proposed structure from a theoretical point of view. Furthermore, we also develop a virtual reality platform to demonstrate the real-time simulation of our approach under different disturbance situations. The overall adaptive learning performance has been tested on two tracking control benchmarks with a tracking filter. For comparative studies, we also present the tracking performance with the typical ADHDP, and the simulation results justify the improved performance with our approach.
Comparison of a four stage sequentially rotated wideband circularly polarized high gain microstrip patch array antennas at Ku-band

Two sequentially rotated, four stage, wideband circularly polarized high gain microstrip patch array antennas at Ku-band are investigated and compared by incorporating both unequal and equal power division based feeding networks. Four stages of sequential rotation is used to create 16×16 patch array which provides wider common bandwidth between the impedance matching (S11 < −10dB), 3dB axial ratio and 3dB gain of 12.3% for the equal power divider based feed array and 13.2% for the unequal power divider based feed array in addition to high polarization purity. The high peak gain of 28.5dBic is obtained for the unequal power division feed based array antennas compared to 26.8dBic peak gain in the case of the equal power division based feed array antennas. The additional comparison between two feed networks based arrays reveals that the unequal power divider based array antennas provide better array characteristics than the equal power divider based feed array antennas.
A Survey of Wide Bandgap Power Semiconductor Devices

Wide bandgap semiconductors show superior material properties enabling potential power device operation at higher temperatures, voltages, and switching speeds than current Si technology. As a result, a new generation of power devices is being developed for power converter applications in which traditional Si power devices show limited operation. The use of these new power semiconductor devices will allow both an important improvement in the performance of existing power converters and the development of new power converters, accounting for an increase in the efficiency of the electric energy transformations and a more rational use of the electric energy. At present, SiC and GaN are the more promising semiconductor materials for these new power devices as a consequence of their outstanding properties, commercial availability of starting material, and maturity of their technological processes. This paper presents a review of recent progresses in the development of SiC- and GaN-based power semiconductor devices together with an overall view of the state of the art of this new device generation.
Modified Perturb and Observe MPPT Algorithm for Drift Avoidance in Photovoltaic Systems

The perturb and observe (P&O) maximum power point tracking (MPPT) algorithm is a simple and efficient tracking technique. However, the P&O tracking method suffers from drift in case of an increase in insolation (G), and this drift effect is severe in case of a rapid increase in insolation. Drift occurs due to the incorrect decision taken by the conventional P&O algorithm at the first step change in duty cycle during increase in insolation. A modified P&O technique is proposed to avoid the drift problem by incorporating the information of change in current (ΔI) in the decision process in addition to change in power (ΔP) and change in voltage (ΔV ). The drift phenomena and its effects are clearly demonstrated in this paper for conventional P&O algorithm with both fixed and adaptive step size technique. A single-ended primary inductance converter is considered to validate the proposed drift-free P&O MPPT using direct duty ratio control technique. MATLAB/Simulink is used for simulation studies, and for experimental validation, a microcontroller is used as a digital platform to implement the proposed algorithm. The simulation and experimental results showed that the proposed algorithm accurately tracks the maximum power and avoids the drift in fast changing weather conditions.
Trust in multi-agent systems

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.
Compact High-Performance Reflector-Antenna Feeds and Feed Networks for Space Applications [Antenna Applications]

Reflector antennas are widely used on satellites to communicate with ground stations. They simultaneously transmit and receive RF signals using separate downlink and uplink frequency bands. These antennas require compact and high-performance feed assemblies with small size, low mass, low passive intermodulation (PIM) products , low insertion loss, high power handling, and low cross-polar levels. The feeds must also be insensitive to large thermal variations, and must survive the launch environment. In order to achieve these desirable features without prototyping and/or bench tuning, Custom Microwave Inc. (CMI) has combined integrated RF design, precision CAD, and a precision manufacturing technique known as electroforming to closely integrate the various components of a feed or feed network, thereby achieving small size while maintaining high RF performance . In addition to close integration, electroforming eliminates split joints and minimizes flanges by allowing several components to be realized in a single piece, making it the ideal manufacturing technique for ultra-low passive-intermodulation applications. This paper describes the use of precision design CAD tools along with electroforming to realize high-performance feed assemblies for various communication frequency bands for fixed satellite, broadcast satellite, and broadband satellite services.
Familicide : The Killing of Spouse and Children

A familicide is a multiple-victim homicide incident in which the killer’s spouse and one or more children are slain. National archives of Canadian and British homicides, containing 109 familicide incidents, permit some elucidation of the characteristic and epidemiology of this crime. Familicides were almost exclusively perpetrated by men, unlike other spouse-killings and other filicides. Half the familicidal men killed themselves as well, a much higher rate of suicide than among other uxoricidal or filicidal men. De facto unions were overrepresented, compared to their prevalence in the populations-atlarge, but to a much lesser extent in familicides than in other uxoricides. Stepchildren were overrepresented as familicide victims, compared to their numbers in the populations-at-large, but to a much lesser extent than in other filicides; unlike killers of their genetic offspring, men who killed their stepchildren were rarely suicidal. An initial binary categorization of familicides as accusatory versus despondent is tentatively proposed. @ 19% wiley-Liss, Inc.
Automatic Segmentation of Brain Tumor using K-Means Clustering and its Area Calculation

In this paper, a simple algorithm for detecting the range and shape of tumor in brain MR Images is described. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection and diagnosis of brain tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor based on the combination of two algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MR image and its exact position and the shape also determined. The stage of the tumor is displayed based on the amount of area calculated from the cluster.
STL tutorial and reference guide - C++ programming with the standard template library

"The second edition is clearer and adds more examples on how to use STL in a practical environment. Moreover, it is more concerned with performance and tools for its measurement. Both changes are very welcome."--Lawrence Rauchwerger, Texas A&M University "So many algorithms, so little time! The generic algorithms chapter with so many more examples than in the previous edition is delightful! The examples work cumulatively to give a sense of comfortable competence with the algorithms, containers, and iterators used."--Max A. Lebow, Software Engineer, Unisys Corporation The STL Tutorial and Reference Guide is highly acclaimed as the most accessible, comprehensive, and practical introduction to the Standard Template Library (STL). Encompassing a set of C++ generic data structures and algorithms, STL provides reusable, interchangeable components adaptable to many different uses without sacrificing efficiency. Written by authors who have been instrumental in the creation and practical application of STL, STL Tutorial and Reference Guide, Second Edition includes a tutorial, a thorough description of each element of the library, numerous sample applications, and a comprehensive reference. You will find in-depth explanations of iterators, generic algorithms, containers, function objects, and much more. Several larger, non-trivial applications demonstrate how to put STL's power and flexibility to work. This book will also show you how to integrate STL with object-oriented programming techniques. In addition, the comprehensive and detailed STL reference guide will be a constant and convenient companion as you learn to work with the library. This second edition is fully updated to reflect all of the changes made to STL for the final ANSI/ISO C++ language standard. It has been expanded with new chapters and appendices. Many new code examples throughout the book illustrate individual concepts and techniques, while larger sample programs demonstrate the use of the STL in real-world C++ software development. An accompanying Web site, including source code and examples referenced in the text, can be found at http://www.cs.rpi.edu/~musser/stl-book/index.html.
Usage , barriers and measurement of social media marketing : an exploratory investigation of small and medium B 2 B brands

• NOTICE: this is the author's version of a work that was accepted for publication in Industrial Marketing Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at: http://dx.doi.org/10.1016/j.indmarman.2011.09.009
Efficient service recommendation system for cloud computing market

In recent years, Cloud computing is gaining much popularity as it can efficiently utilize the computing resources and hence can contribute to the issue of Green IT to save energy. So to make the Cloud services commercialized, Cloud markets are necessary and are being developed. As the increasing numbers of various Cloud services are rapidly evolving in the Cloud market, how to select the best and optimal services will be a great challenge. In this paper we present a Cloud service selection framework in the Cloud market that uses a recommender system (RS) which helps a user to select the best services from different Cloud providers (CP) that matches user requirements. The RS recommends a service based on the network QoS and Virtual Machine (VM) platform factors of difference CPs. The experimental results show that our Cloud service recommender system (CSRS) can effectively recommend a good combination of Cloud services to consumers.
Holonic multi-agent system for traffic signals control

Agent-based technologies are rapidly growing as a powerful tool for modelling and developing largescale distributed systems. Recently, multi-agent systems are largely used for intelligent transportation systems modelling. Traffic signals control is a challenging issue in this area, especially in a large-scale urban network. In a large traffic network, where each agent represents a traffic signals controller, there are many entities interacting with each other and hence it is a complex system. An approach to reduce the complexity of such systems is using organisation-based multi-agent system. In this paper, we use an organisation called holonic multi-agent system (HMAS) to model a large traffic network. A traffic network containing fifty intersections is partitioned into a number of regions and holons are assigned to control each region. The holons are hierarchically arranged in two levels, intersection controller holons in the first level and region controller holons in the second level. We introduce holonic Q-learning to control the signals in both levels. The inter-level interactions between the holons in the two levels contribute to the learning process. Experimental results show that the holonic Q-learning prevents the network to be over-saturated while it causes less average delay time and higher flow rate. & 2013 Elsevier Ltd. All rights reserved.
Neural network architectures and learning algorithms

Neural networks are the topic of this paper. Neural networks are very powerful as nonlinear signal processors, but obtained results are often far from satisfactory. The purpose of this article is to evaluate the reasons for these frustrations and show how to make these neural networks successful. The following are the main challenges of neural network applications: (1) Which neural network architectures should be used? (2) How large should a neural network be? (3) Which learning algorithms are most suitable? The multilayer perceptron (MLP) architecture is unfortunately the preferred neural network topology of most researchers. It is the oldest neural network architecture, and it is compatible with all training softwares. However, the MLP topology is less powerful than other topologies such as bridged multilayer perceptron (BMLP), where connections across layers are allowed. The error-back propagation (EBP) algorithm is the most popular learning algorithm, but it is very slow and seldom gives adequate results. The EBP training process requires 100-1,000 times more iterations than the more advanced algorithms such as Levenberg-Marquardt (LM) or neuron by neuron (NBN) algorithms. What is most important is that the EBP algorithm is not only slow but often it is not able to find solutions for close-to-optimum neural networks. The paper describes and compares several learning algorithms.
Embodied hands: modeling and capturing hands and bodies together

Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes at http://mano.is.tue.mpg.de.
Self and social identity.

In this chapter, we examine the self and identity by considering the different conditions under which these are affected by the groups to which people belong. From a social identity perspective we argue that group commitment, on the one hand, and features of the social context, on the other hand, are crucial determinants of central identity concerns. We develop a taxonomy of situations to reflect the different concerns and motives that come into play as a result of threats to personal and group identity and degree of commitment to the group. We specify for each cell in this taxonomy how these issues of self and social identity impinge upon a broad variety of responses at the perceptual, affective, and behavioral level.
Analysis of non-orthogonal multiple access for 5G

The major challenge faced by the fifth generation (5G) mobile network is higher spectral efficiency and massive connectivity, i.e., the target spectrum efficiency is 3 times over 4G, and the target connection density is one million devices per square kilometer. These requirements are difficult to be satisfied with orthogonal multiple access (OMA) schemes. Non-orthogonal multiple access (NOMA) has thus been proposed as a promising candidate to address some of the challenges for 5G. In this paper, a comprehensive survey of different candidate NOMA schemes for 5G is presented, where the usage scenarios of 5G and the application requirements for NOMA are firstly discussed. A general framework of NOMA scheme is established and the features of typical NOMA schemes are analyzed and compared. We focus on the recent progress and challenge of NOMA in standardization of international telecommunication union (ITU), and 3rd generation partnership project (3GPP). In addition, prototype development and future research directions are also provided respectively.
Breast Tumor Analysis in Dynamic Contrast Enhanced MRI Using Texture Features and Wavelet Transform

Dynamic contrast enhanced MRI (DCE-MRI) is an emerging imaging protocol in locating, identifying and characterizing breast cancer. However, due to image artifacts in MR, pixel intensity alone cannot accurately characterize the tissue properties. We propose a robust method based on the temporal sequence of textural change and wavelet transform for pixel-by-pixel classification. We first segment the breast region using an active contour model. We then compute textural change on pixel blocks. We apply a three-scale discrete wavelet transform on the texture temporal sequence to further extract frequency features. We employ a progressive feature selection scheme and a committee of support vector machines for the classification. We trained the system on ten cases and tested it on eight independent test cases. Receiver-operating characteristics (ROC) analysis shows that the texture temporal sequence (Az: 0.966 and 0.949 in training and test) is much more effective than the intensity sequence (Az: 0.871 and 0.868 in training and test). The wavelet transform further improves the classification performance (Az: 0.989 and 0.984 in training and test).