Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable step-size algorithm implemented two phase soft-switched interleaved boost converter

This paper presents the study of maximum power point tracking (MPPT) controller implemented two phase soft-switched interleaved boost converter and focuses on the effectiveness of putting a solar panel instead of an ordinary dc source. The proposed system raises the overall efficiency by incorporating soft-switching technique in addition to MPPT. The algorithm used to track maximum possible power from the PV panel was variable step size algorithm, being superior over other methods due to its ability to change the step size according to the MPP. The above mentioned converter with MPPT is found to be highly efficient (around 96%) compared to other converters with MPPT because of reduced ripple and reduced switching losses. So it can be applied for high voltage as well as high power applications such as in space crafts, hybrid electric vehicles, dc back up energy systems, power factor correctors etc. It can also be used for grid connected and micro-grid applications because of low output voltage ripple. In this work, design and simulation has been done in MATLAB software to verify the performance of the converter with MPPT.
How prioritized is self-prioritization during stimulus processing?

ABSTRACT Recent research has suggested that self-relevance automatically enhances stimulus processing (i.e., the self-prioritization effect). Notably, information associated with one’s self elicits faster responses than comparable material associated with other targets (e.g., friend, stranger). Challenging the assertion that self-prioritization is an obligatory process, here we hypothesized that self-relevance only facilitates performance when task sets draw attention to previously formed target-object associations. The results of two experiments were consistent with this viewpoint. Compared with arbitrary objects owned by a friend, those owned by the self were classified more rapidly when participants were required to report either the owner or identity of the items (i.e., semantic task set). In contrast, self-relevance failed to facilitate performance when participants judged the orientation of the stimuli (i.e., perceptual task set). These findings demonstrate the conditional automaticity of self-prioritization during stimulus processing.
Identification and Estimation of 'Maximal' Affine Term Structure Models: An Application to Stochastic Volatility

We propose a canonical representation for affine term structure models where the state vector is comprised of the first few Taylor-series components of the yield curve and their quadratic (co-)variations. With this representation: (i) the state variables have simple physical interpretations such as level, slope and curvature, (ii) their dynamics remain affine and tractable, (iii) the model is by construction 'maximal' (i.e., it is the most general model that is econometrically identifiable), and (iv) model-insensitive estimates of the state vector process implied from the term structure are readily available. (Furthermore, this representation may be useful for identifying the state variables in a squared-Gaussian framework where typically there is no one-to-one mapping between observable yields and latent state variables). We find that the 'unrestricted' A1(3) model of Dai and Singleton (2000) estimated by 'inverting' the yield curve for the state variables generates volatility estimates that are negatively correlated with the time series of volatility estimated using a standard GARCH approach. This occurs because the 'unrestricted' A1(3) model imposes the restriction that the volatility state variable is simultaneously a linear combination of yields (i.e., it impacts the cross-section of yields), and the quadratic variation of the spot rate process (i.e., it impacts the time-series of yields). We then investigate the A1(3) model which exhibits 'unspanned stochastic volatility' (USV). This model predicts that the cross section of bond prices is independent of the volatility state variable, and hence breaks the tension between the time-series and cross-sectional features of the term structure inherent in the unrestricted model. We find that explicitly imposing the USV constraint on affine models significantly improves the volatility estimates, while maintaining a good fit cross-sectionally.
Bullying language: behind the aggression

Este estudio busco una compresion del lenguaje del bullying en un contexto colombiano con estudiantes de septimo grado quienes unen y/o relacionan sus experiencias sociales y linguisticas para crear un sistema de lenguaje unico alrededor de bullying, el cual estaba afectando su convivencia y el proceso de aprendizaje de la lengua extranjera. El problema surge al observar que el tiempo de las clases se reducia porque se debia dar solucion a situaciones de bullying encontrando que ellos no concebian ciertas situaciones como bullying a diferencia de los adultos como pueden ser los docentes y padres de familia. El estudio presentado es de tipo cualitativo, descriptivo, interpretativo siguiendo los principios de una investigacion etnografica y esta disenado para entender, describir y descubrir las estrategias que los estudiantes usan en sus actos habla inmersos en el lenguaje del bullying. Relata como los estudiantes a traves de sus clases del segundo idioma se concientizaron de sus creaciones linguisticas y como algunas de ellas fueron usadas violenta y agresivamente para mantener una jerarquia social o para sostener relaciones de poder entre ellos. Al categorizar sus comportamientos y reacciones se encuentran posibles roles que los estudiantes tienen frente a una situacion de bullying. Por ultimo, se plantea como a traves del ‘Community Language Learning - CLL’ se pueden crear ambientes motivantes para el aprendizaje del ingles a traves del trabajo de problemas cotidianos en la escuela.
Parallel Connections and their Effect on the Battery Consumption of a Mobile Phone

In this paper, we analyze the end-to-end communication activities of a modern mobile phone, Nokia N95, to understand how much energy different communication alternatives consume. In particular, we investigate the interactions when multiple connections are used in parallel. Parallel connections save energy but the gains vary depending on the technology. TCP downloads during 3G voice calls result into 75%-90% energy savings, TCP downloads during VoIP calls result into 30%-40% savings, and TCP downloads when other TCP streams are active at the same interface result into 0%- 20% savings. The results indicate that there is a significant potential to save energy if applications are engineered to take advantage of this phenomenon.
Altered States of Consciousness during an Extreme Ritual

Extreme rituals (body-piercing, fire-walking, etc.) are anecdotally associated with altered states of consciousness—subjective alterations of ordinary mental functioning (Ward, 1984)—but empirical evidence of altered states using both direct and indirect measures during extreme rituals in naturalistic settings is limited. Participants in the “Dance of Souls”, a 3.5-hour event during which participants received temporary piercings with hooks or weights attached to the piercings and danced to music provided by drummers, responded to measures of two altered states of consciousness. Participants also completed measures of positive and negative affect, salivary cortisol (a hormone associated with stress), self-reported stress, sexual arousal, and intimacy. Both pierced participants (pierced dancers) and non-pierced participants (piercers, piercing assistants, observers, drummers, and event leaders) showed evidence of altered states aligned with transient hypofrontality (Dietrich, 2003; measured with a Stroop test) and flow (Csikszentmihalyi, 1990; Csikszentmihalyi & Csikszentmihalyi, 1990; measured with the Flow State Scale). Both pierced and non-pierced participants also reported decreases in negative affect and psychological stress and increases in intimacy from before to after the ritual. Pierced and non-pierced participants showed different physiological reactions, however, with pierced participants showing increases in cortisol and non-pierced participants showing decreases from before to during the ritual. Overall, the ritual appeared to induce different physiological effects but similar psychological effects in focal ritual participants (i.e., pierced dancers) and in participants adopting other roles.
A correlation based SVM-recursive multiple feature elimination classifier for breast cancer disease using microarray

Support Vector Machine (SVM), is most widely popular learning algorithm used for classification of large dataset. Our project aims to generate a classifier for breast cancer genes microarray by using modified-SVM-RFE algorithm. This breast cancer microarray contains a large number of genes and its expression, so it necessary to reduce the number of genes before applying for classification. So the most efficient algorithm that can be applied for classification of microarray is SVM-RFE, which is an embedded method, which performs backward single gene elimination as well as classification of the dataset. A new modified algorithm is proposed with less computation over SVM-RFE. SVM-RFE generates the rank of the features and eliminates one lowest rank irrelevant feature, in each iteration. Since our microarray contains 47,294 genes its very computational overhead to reduce the dimension. So the modified algorithm which removes more than one irrelevant genes in single iteration of SVM-RFE algorithm. And also this algorithm only removes irrelevant gene, it does not remove the correlated genes. So before applying SVM-RFE, our research focuses on finding out the correlated genes and extracting a new gene from the two, and then apply SVM-RFE on the new set of genes. So our proposed method is Correlation based Support Vector Machine Recursive Multiple Feature Elimination (CSVM-RMFE) algorithm which first extracts a new genes from two correlated genes called virtual gene and then apply SVM-RMFE to generate a classifier. This SVM-RMFE algorithm eliminate multiple feature so that the classification time can be reduced and its accuracy can be increased.
Synergistic Effect of Indole Acetic Acid and Kinetin on Performance, Some Biochemical Constituents and Yield of Faba Bean Plant Grown under Newly Reclaimed Sandy Soil

Two field experiments were conducted at the Research and Production Station, National Research Centre, Nubaria Province, Behaira Governorate, Egypt during two successive winter seasons 2011/2012 and 2012/2013 to study the effect of indole acetic acid (IAA) at 0, 50 and 75 mg/l and kinetin (Kin) at 0, 50, 75 and 100 mg/l individually or in combination on two faba bean cultivars (Nubaria 1 and Giza 843). Results revealed that IAA and/or Kin treatments had positive effects on growth criteria (plant height, leaves number, fresh and dry weight per plant), photosynthetic pigments (chlorophyll a, b and carotenoids), total carbohydrate, polysaccharide, free amino acid, proline and total phenolic contents in the two cultivars. Furthermore, endogenous hormones (IAA, GA and zeatin) were increased concomitantly with decrease in ABA content 3 particularly at treatment of 50 mg/l IAA + 75 mg/l kinetin in the two cultivars. Seed yield and yield components (number of pods/plant, pods yield /plant, 100 seeds weight and biological yield/plant) as well as total carbohydrates and proteins in the yielded seeds were significantly improved at all treatments in the two cultivars. Meanwhile, vicine contents were significantly decreased at all treatments. It is worthy to mention that, performance of Nubaria 1 cultivar was more pronounced than that of Giza 843 cultivar when grown under newly reclaimed sandy soil conditions i.e. Under control treatment, seed yield/faddan (one faddan= 0.42 ha) of Nubaria 1 cultivar was 47.17% over that of Giza 843 cultivar. Meanwhile, response of Giza 843 cultivar to all applied treatments was more effective than Nubaria 1 cultivar.
Development of a microcontroller-based automatic control system for the electrohydraulic total artificial heart

An automatic physiological control system for the actively filled, alternately pumped ventricles of the volumetrically coupled, electrohydraulic total artificial heart (EHTAH) was developed for long-term use. The automatic control system must ensure that the device: (1) maintains a physiological response of cardiac output, (2) compensates for any nonphysiological condition, and (3) is stable, reliable, and operates at a high power efficiency. The developed automatic control system met these requirements both in vitro, in week-long continuous mock circulation tests, and in vivo, in acute open-chested animals (calves). Satisfactory results were also obtained in a series of chronic animal experiments, including 21 days of continuous operation of the fully automatic control mode, and 138 days of operation in a manual mode, in a 159-day calf implant.
Deadtime block compensation sliding mode control of linear system with delay

Abstract This paper applies the block control method to form a decomposed control law suitabal for multivariable linear time-delay systems. A block controllable form is introduced and a non-singular transformation that reduces the system to this form, is proposed. A block deadtime compensation algorithm wich gives a sliding manifold, is derived. An example of the application of the proposed control strategy is illustrated.
Effects of tip-gap size on the tip-leakage flow in a turbomachinery cascade

The effects of tip-gap size on the tip-leakage vortical structures and velocity and pressure fields are investigated using large-eddy simulation, with the objective of providing guidelines for controlling tip-leakage cavitation and viscous losses associated with the tip-leakage flow. The effects of tip-gap size on the generation and evolution of the end-wall vortical structures are discussed by investigating their evolutionary trajectories and the mean velocity field. The tip-leakage jet and tip-leakage vortex are found to produce significant mean velocity gradients, leading to the production of vorticity and turbulent kinetic energy. Inside the cascade passage, the peak streamwise velocity deficit and magnitudes of vorticity and turbulent kinetic energy in the tip-leakage vortex are reduced as the tip-gap size decreases. The present analysis indicates that the mechanisms for the generation of vorticity and turbulent kinetic energy are mostly unchanged by the tip-gap size variation. However, larger tip-ga.
Tracking dynamic sparse signals with hierarchical Kalman filters: A case study

Tracking and recovering dynamic sparse signals using traditional Kalman filtering techniques tend to fail. Compressive sensing (CS) addresses the problem of reconstructing signals for which the support is assumed to be sparse but is not fit for dynamic models. This paper provides a study on the performance of a hierarchical Bayesian Kalman (HB-Kalman) filter that succeeds in promoting sparsity and accurately tracks time varying sparse signals. Two case studies using real-world data show how the proposed method outperforms the traditional Kalman filter when tracking dynamic sparse signals. It is shown that the Bayesian Subspace Pursuit (BSP) algorithm, that is at the core of the HB-Kalman method, achieves better performance than previously proposed greedy methods.
Time series prediction for evolutions of complex systems: A deep learning approach

Evolutions of complex systems are expressions of the dynamic and uncertain behaviors and ever-changing causalities of the potential multivariate processes in a system, which exhibit the characteristics of uncertainties, nonlinear dynamics, and aperiodic fluctuation. These characteristics pose a major challenge for predicting the states of evolutions of complex systems accurately. Supervised learning methods in artificial intelligence, such as the artificial neural networks (ANNs) and the support vector machine (SVM), are suitable for nonlinear time series prediction. However, due to suffering from the issue of feature extraction relying on prior knowledge, and the weakness of shallow learning, traditional ANNs and SVM are hard to guarantee the prediction accuracy and stability. In this study, we propose a deep learning approach, which hybridizes a deep belief networks (DBNs) and a nonlinear kernel-based parallel evolutionary SVM (ESVM), to predict evolution states of complex systems in a classification manner. The proposed approach is tested by two case studies regarding the mobile robot navigation and the early-warning-of-risk of harmful algal blooms. Computational results are compared with those of SVM and DBNs approaches, which demonstrate that the proposed approach outperforms them, and can offer an intelligent decision support tool for predicting evolutions of complex systems.
Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

This article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning in Learning Networks. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system with a specific purpose from one context and transfer it to another context or domain. The article describes a number of distinctive differences for personalised recommendation to learners when compared to recommendations for consumers. Similarities and differences for informal and formal learning are discussed and used to define the recommendation goal that recommender systems in informal learning networks have to address. The article further suggests an evaluation approach for recommender systems in Learning Networks.
Measuring and forecasting financial variability using realised variance with and without a model

We use high frequency financial data to proxy, via the realised variance, each day's financial variability. Based on a semiparametric stochastic volatility process, a limit theory shows you can represent the proxy as a true underlying variability plus some measurement noise with known characteristics. Hence filtering, smoothing and forecasting ideas can be used to improve our estimates of variability by exploiting the time series structure of the realised variances. This can be carried out based on a model or without a model. A comparison is made between these two methods.
Producing and broadcasting non-linear art-based content through open source interactive internet-tv

The development of an interactive Internet-based TV system designed to cover the broadcasting needs for new media artwork introduces various complexities across the organisation, production and interaction forefronts, as live interactive installation art and multi-sourced multimedia content poses particular presentation requirements. Our work presents and discusses real-life strategies and solutions employed during the development of iMediaTV, an open-source academic broadcasting system that permits various presentation modes to be employed, extending the presentation needs for artistic and non-uniform content. We demonstrate how an open-source Internet-based production/broadcasting system may be customised to include advanced content enhancing features, enabling interactive new media arts presentation over the Web.
Intuitionistic model constructions and normalization proofs

The traditional notions of strong and weak normalization refer to properties of a binary reduction relation. In this paper we explore an alternative approach to normalization, in which we bypass the reduction relation and instead focus on the normalization function, that is, the function that maps a term to its normal form. We work in an intuitionistic metalanguage, and characterize a normalization function as an algorithm that picks a canonical representative from the equivalence class of convertible terms. This means that we also get a decision algorithm for convertibility. Such a normalization function can be constructed by building an appropriate model and a function quote, which inverts the interpretation function. The normalization function is then obtained by composing the quote function with the interpretation function. We also discuss how to get a simple proof of the property that constructors are one-to-one, which is usually obtained as a corollary of Church–Rosser and normalization in the traditional sense. We illustrate this approach by showing how a glueing model (closely related to the glueing construction used in category theory) gives rise to a normalization algorithm for a combinatory formulation of Gödel System T. We then show how the method extends in a straightforward way when we add cartesian products and disjoint unions (full intuitionistic propositional logic under a Curry–Howard interpretation) and transfinite inductive types such as the Brouwer ordinals.
Hardware implementation for a hand recognition system on FPGA

This paper presents the implementation for hand tacking and gesture recognition on FPGA. For hand tracking, an improved hand segmentation algorithm combined YCbCr color space and region growing algorithm is used for valid region separation, and then morphological operations including erosion and dilation are applied to this system in order to remove extra noise. The three-frame-difference motion detection method is used to detect moving hand. Finally the centroid of hand can be located. For gesture recognition, we develop a hand model based on finger state, converting pose recognition to finger state detection. The hand recognition system has been entirely implemented on Cyclone II FPGA.
Scale-Space Approach for Color Image Segmentation

This paper presents a new method for color image segmentation based on a scale-space clustering of the image pixels. Unlike standard image scale-spaces, which smooth the images, this approach consider an augmented space which combines spatial and color dimensions. The clustering relies on the mean-shift algorithm to find the modes of the position and color distribution of the pixels. In order to produce a non-hierarchical segmentation, we also present a method to prune the computed hierarchy by suppressing nodes that are not stable enough.
Balanced-to-Balanced Gysel Power Divider With Bandpass Filtering Response

In this paper, the balanced-to-balanced Gysel power divider is proposed with bandpass filtering response for the first time. The component is based on a ring structure and designed with an extended coupled-resonator circuit model. The characteristic impedances, electrical lengths, and isolation resistance are calculated analytically by our derived equations, according to the design specifications of the filtering power divider. The achievable bandwidth is from about 3% to 12%. As an example, a fabricated prototype occupies an area of 0.97 × 0.87 λg2, whose bandwidth is 8.4%. It has low differential-mode in-band insertion loss, good magnitude, and phase balances between two output ports, good isolation, and good suppression of common-mode noises and mode conventions. Based on stub-loaded sections, another prototype is realized for miniaturization, whose occupied area is reduced to 0.67 × 0.63 λg2. In comparison with the original prototype, the central insertion loss is increased by 0.13 dB, and the rejection band of common-mode and mode-conversion noises is narrowed by 21%.