Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary protein and amino acids intake and its relationship with blood pressure in adolescents: the HELENA STUDY.

OBJECTIVE To analyze the association between dietary protein and amino acids intake and systolic (SBP) and diastolic (DBP) blood pressure in European adolescents. METHODS Participants were from the cross-sectional study performed in Europe, Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA study; n = 1605; 12.5-17.5 years; 833 girls) selected by complex sampling. The associations between dietary protein and amino acids intake and SBP/DBP were examined by multilevel linear regression models (context variable by school); the analysis being stratified by sex. Cities, seasonality, age, socioeconomic level, parental education level, body mass index, waist circumference, Tanner stage and physical activity were used as covariates. RESULTS In boys, we found an inverse association between protein (animal and vegetable) intake and DBP; and a positive association between histidine and SBP. In girls, we observed a positive association among tryptophan, histidine with SBP and methionine with DBP. On the other hand, we observed an inverse association between tyrosine and both SBP and DBP levels in girls. CONCLUSIONS The association between amino acids and BP levels is controversial and depends on the type of amino acids, and protein intake can help control the DBP in boys.
Correlates of species richness in the largest Neotropical amphibian radiation

*Department of Integrative Ecology, Estació n Bioló gica de Doñ ana-CSIC, Sevilla, Spain †Department of Evolution, Genomics and Systematics, Evolutionary Biology Centre (EBC), Uppsala University, Uppsala, Sweden ‡Departamento de Ciencias Bioló gicas, Instituto de Genética, Universidad de los Andes, Bogotá , Colombia §Department of Biodiversity and Evolutionary Biology, Museo Nacional de Ciencias Naturales-CSIC, Madrid, Spain
Application of cepstrum analysis and linear predictive coding for motor imaginary task classification

In this paper, classification of electroencephalography (EEG) signals of motor imaginary tasks is studied using cepstrum analysis and linear predictive coding (LPC). The Brain-Computer Interface (BCI) competition III dataset IVa containing motor imaginary tasks for right hand and foot of five subjects are used. The data was preprocessed by applying whitening and then filtering the signal followed by feature extraction. A random forest classifier is then trained using the cepstrum and LPC features to classify the motor imaginary tasks. The resulting classification accuracy is found to be over 90%. This research shows that concatenating appropriate different types of features such as cepstrum and LPC features hold some promise for the classification of motor imaginary tasks, which can be helpful in the BCI context.
Reporting peak expiratory flow in older persons.

BACKGROUND Peak expiratory flow ("peak flow") predicts important outcomes in older persons. Nevertheless, its clinical application is uncertain because prior strategies for reporting peak flow may not be valid. We thus determined the frequency distribution of peak flow by the conventional strategy of percent predicted (%predicted) and by an alternative method termed standardized residual (SR) percentile, and evaluated how these two metrics relate to health status in older persons. METHODS Participants included 754 community-dwelling persons aged >/= 70 years. Data included chronic conditions, frailty indicators, and peak flow. RESULTS Mean age was 78.4 years, with 63.7% reporting a smoking history, 17.4% chronic lung disease, and 77.1% having one or more frailty indicators. Peak flow >/= 80 %predicted was recorded in 67.5% of participants, whereas peak flow >/= 80th SR percentile was only noted in 15.9%. A graded relationship was observed between peak flow and health status, but %predicted yielded health risk at peak flows currently considered normal (80-100 %predicted), whereas SR percentile conferred health risk only at severely reduced peak flows (< 50th SR percentile). CONCLUSIONS Peak flow expressed as SR percentile attains a frequency distribution more consistent with the characteristics of our elderly cohort, and establishes health risk at more appropriate levels of reduced peak flow. These findings establish the need for longitudinal studies based on SR percentile to further evaluate the use of peak flow as a risk assessment tool in older persons and to determine if pulmonary function, in general, is better reported in older persons as SR percentile, rather than as %predicted.
Approximate Feedback Linearization Control for Spatial 6-DOF Hydraulic Parallel Manipulator

Traditional feedback linearization approach (TFL) requires a priori knowledge of plant, which is difficult and the computational efficiency of controller is low due to the complex dynamics of spatial 6-DOF hydraulic parallel manipulator. In order to improve the tracking performance of spatial 6-DOF hydraulic parallel manipulator and to conquer the drawbacks of TFL, a novel approximate feedback linearization approach, non-model based method, is proposed in this paper. The mathematical model of spatial hydraulic parallel manipulator is established. The approximate feedback linearization control is designed for the parallel manipulator in joint space, with position and stored force in the previous time step are employed, as a learning tool to yield improved performance. Under Lyapunov theorems, the stability of the presented algorithm is confirmed in the presence of uncertainties. Simulation results show the proposed control is easy and effective to realize path tracking, and it exhibits excellent performance and high efficiency without a precision dynamics of plant. Furthermore, the presented algorithm is well suitable for most industrial applications.
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009

This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime of the algorithms, measured in number of function evaluations, is investigated and a connection between a single convergence graph and the runtime distribution is uncovered. Performance is investigated for different dimensions up to 40-D, for different target precision values, and in different subgroups of functions. Searching in larger dimension and multi-modal functions appears to be more difficult. The choice of the best algorithm also depends remarkably on the available budget of function evaluations.
MeCP2 in the Rostral Striatum Maintains Local Dopamine Content Critical for Psychomotor Control

Methyl-CpG binding protein 2 (MeCP2) is a chromatin regulator highly expressed in mature neurons. Mutations of MECP2 gene cause >90% cases of Rett syndrome, a neurodevelopmental disorder featured by striking psychomotor dysfunction. In Mecp2-null mice, the motor deficits are associated with reduction of dopamine content in the striatum, the input nucleus of basal ganglia mostly composed of GABAergic neurons. Here we investigated the causal role of MeCP2 in modulation of striatal dopamine content and psychomotor function. We found that mice with selective removal of MeCP2 in forebrain GABAergic neurons, predominantly in the striatum, phenocopied Mecp2-null mice in dopamine deregulation and motor dysfunction. Selective expression of MeCP2 in the striatum preserved dopamine content and psychomotor function in both males and females. Notably, the dopamine deregulation was primarily confined to the rostral striatum, and focal deletion or reactivation of MeCP2 expression in the rostral striatum through adeno-associated virus effectively disrupted or restored dopamine content and locomotor activity, respectively. Together, these findings demonstrate that striatal MeCP2 maintains local dopamine content in a non-cell autonomous manner in the rostral striatum and that is critical for psychomotor control.
Comparative Study of Efficacy of Dopaminergic Neuron Differentiation between Embryonic Stem Cell and Protein-Based Induced Pluripotent Stem Cell

In patients with Parkinson's disease (PD), stem cells can serve as therapeutic agents to restore or regenerate injured nervous system. Here, we differentiated two types of stem cells; mouse embryonic stem cells (mESCs) and protein-based iPS cells (P-iPSCs) generated by non-viral methods, into midbrain dopaminergic (mDA) neurons, and then compared the efficiency of DA neuron differentiation from these two cell types. In the undifferentiated stage, P-iPSCs expressed pluripotency markers as ES cells did, indicating that protein-based reprogramming was stable and authentic. While both stem cell types were differentiated to the terminally-matured mDA neurons, P-iPSCs showed higher DA neuron-specific markers' expression than ES cells. To investigate the mechanism of the superior induction capacity of DA neurons observed in P-iPSCs compared to ES cells, we analyzed histone modifications by genome-wide ChIP sequencing analysis and their corresponding microarray results between two cell types. We found that Wnt signaling was up-regulated, while SFRP1, a counter-acting molecule of Wnt, was more suppressed in P-iPSCs than in mESCs. In PD rat model, transplantation of neural precursor cells derived from both cell types showed improved function. The present study demonstrates that P-iPSCs could be a suitable cell source to provide patient-specific therapy for PD without ethical problems or rejection issues.
Integrating Data Mining With Decision Support Systems To Support Engineering Decision Making

Traditional engineering decision support systems (EDSS) cannot be well applied in the practice of construction management due to their inherent limitations in knowledge acquisition. In this paper, a new approach named DMDS is proposed, which integrates data mining technologies with decision support systems. DMDS aims to automatically extract knowledge from a large amount of historic data generated in engineering practice and then reason with the extracted knowledge to support engineering decision makings in construction. A prototype system named DM-EDSS has also been designed and developed to demonstrate the feasibility and effectiveness of the DMDS model. The findings demonstrate that at least the following benefits can be achieved: (1) providing a new approach for EDSS to automatically acquire knowledge from a large mount of engineering data; (2) enabling insights to be gained on factors that have impacts on construction problems; and (3) providing a uniform decision support process for different decision-making problems.
E-business adoption and application portfolio management in remanufacturing small and medium enterprises

The role of e-business or the use of information and communication technology (ICT) for business activities in advancing sustainable industrial development is well acknowledged. As one of the key drivers in manufacturing growth, e-business can potentially enable increased productivity and industrial competitiveness. The adoption of e-business in remanufacturing, an emerging strategy in sustainable manufacturing, has been lagging especially among small and medium enterprises (SMEs) in developing countries. SMEs face challenges in capitalizing on growth opportunity in remanufacturing, which include demand forecasting and inventory management. These challenges, if left unaddressed, could pose threats to SMEs business viability. Exploratory in nature, this study presents the state-of-the-art in e-business for remanufacturing industries including accounts on the hurdles that contribute to the lack of adoption. A number of factors have been identified as potential hurdles to adoption include, among others, knowledge and competencies in e-business and financial support. To address the issue of lack of adoption, potential strategies are also discussed. Furthermore, a value chain analysis of a remanufacturing SME is used to provide latest insights into how e-business applications portfolio could be used to counter aforementioned challenges faced by remanufacturing SMEs.
An Evaluation of Taxes on Air Pollutants Emissions: An Applied General Equilibrium Approach

All political parties in Germany agree that pollution controls by market instruments should become an increasingly important part of government regulatory activities. As environmental quality goals are sometimes in conflict with other objectives such as full employment, economic growth orprice stability, there arises the question of the economic impact of these activities. At the current state of the debate, the ecological reform of the German tax system should be neutral in its effect on the income distribution, on government budget deficit, on the international competitiveness of the German industry, on location decisions and to foreign direct investment. It should, of course, provide an incentive to conserve energy, to reduce discharge of waste to the atmosphere and water, to stimulate substitution in the structure of goods and inputs; and it should be a signal for world-wide effort in environmental policy. The objective of this paper is to identify and to measure the traderoffs between the various goals of environmental and economic policy. This requires information on the economic impacts of various environmental objectives. Since environmental quality programs have impacts on prices, and hence on supply and demand, a general equilibrium model with pollution specifications is an appropriate methodological approach to identify the economic impacts of pollution control policies. By integrating environmental variables directly into models of producers' and consumers' behavior, structural changes arising from environmental programs can be properly taken into account Such an approach models price-responsiveness on the demand side for consumption goods and production inputs as well as endogenous changes in factor incomes in a fully closed and consistent way. In contrast to the fixed-price approach in the Leontief model where abatement activities have in any case a positive impact on growth^ in a price-responsive approach higher abatement costs can dampen the growth effect or even reverse iL Incorporating environmental regulation and its impact in general equilibrium models has by now a twenty years long tradition. In 1970, LEONTIEF extended his input-output approach to quantify environmental repercussions on the economic structure. Since then generalized input-output programming models have been developed for quantifying environmental policy packages (see THOSS and DÖLLEKES [1974], THOSS [1976], MÜLLER [1979]). The shortcomings of these models like fixed coefficients in technologi-
Gender and International Migration in Europe: Employment, Welfare and Politics

"Gender and international migration in Europe" is a unique work which introduces a gender dimension into theories of contemporary migrations. As the European Union seeks to extend equal opportunities, increasingly restrictionist immigration policies and the persistence of racism, deny autonomy and choice to migrant women. This work demonstrates how processes of globalisation and change in state policies on employment and welfare have maintained a demand for diverse forms of gendered immigration. The authors examine state and European Union policies of immigration control, family reunion, refugees and the management of immigrant and ethnic minority communities. Most importantly this work considers the opportunities created for political activity by migrant women and the extent to which they are able to influence and participate in mainstream policy-making. This volume will be essential reading for anyone involved in or interested in modern European immigration policy.
A Delay Slot Scheduling Framework for VLIW Architectures in Assembly-Level

A delay slot scheduling framework for VLIW architectures is presented in this paper. In this framework, an assembly data dependence graph is proposed to describe the data dependences among instructions in a basic block. An assembly control flow graph is also proposed to describe control relations among basic blocks. With the help of predicate analysis technology, majority invalid control flows are detected. Along the edges in these two graphs, instructions are selected to be filled into delay slots. Additionally, a scheme to balance the local scheduling and the global scheduling is proposed in this paper. This framework is evaluated by several experiments on the SuperV-DSP processor. And the results demonstrate the effectiveness of the new approach.
The process of convergence towards the euro for the Visegrad-4 countries

The aim of the paper is to analyze the foreign transmission mechanism between each of the Visegrad-4 countries and the eurozone, through an empirical analysis of the basic international parity conditions linking Czech, Hungarian, Polish and Slovakian inflations and interest rates with the ones of the current euro area members. The focus of the analysis is to show the differences among these catching-up economies, with particular attention to their process of convergence towards the eurozone economy. For reasons due to the availability of data, the sample covers the last decade. We use the cointegrated VAR model to define longrun stationary relations as well as common stochastic trends. The methodology adopted is properly apt to uncover the dynamic structure underlying the stochastic behaviour of prices, interest rates and exchange rate. Of particular interest is the empirical finding that the parities do not hold on their own, as expected, but that weaker form of the same parities, or linear combinations of them, hold in our data set, with some differences for each country. Also the process of convergence is different: the Czech Republic seems to have reached a relative convergence, while for the other countries we have that the process show a tendency towards convergence.
Interface Development for Advanced Vehicle Teleoperation

From controlling or moving a remote object in a chemical laboratory to driving an unmanned spacecraft in Mars, teleoperation research has come a long way. Although a lot had been accomplished in this field, researchers always face the daunting task of overcoming lag caused during communication and avoiding loss of situational awareness. The constant demand to improve teleoperation interfaces has resulted in numerous research works which use augmented and virtual reality based interfaces for teleoperation. With most of the works partially satisfying the requirements of a teleoperation interface and leaving the remaining, there is a need to study and understand the current teleoperation demands. This would in turn help us arrive at a specific set of guidelines for future teleoperation interface design. The paper presents a review on various teleoperation methods that are currently used and provides a critical comparative analysis on the usage of augmented and virtual reality based interfaces in teleoperation.
Discrimination between Nested Two- and Three-Parameter Distributions: An Application to Models of Air Pollution

The purpose of this paper is to discriminate between 2and 3-parameter nested alternatives for the gamma, Weibull and lognormal distributions. Monte Carlo expetiments are conducted to evaluate the likelihood ratio test, Akaike's information criterion, Schwarz's information criterion, the Chi-square test and the KolmogorovSmirnov test. The performance of the tests and criteria depends on the types of nested distributions under consideration, the parametric values of the parent diatributions, the confidence levels used (if applicable), and the sample sizes. The practical usefulnesa of the techniques is illustrated by observing the errora of the models in fitting the upper percentilea of the parent distribution. Two sete of air pollution data, namely hourly pollutant observati~ns of p-scattering and nitrogen dioxide,from an urban airshed are used to examine the similarities and differences in fitting 2and 3-parameter distributions where historical practice suggesta there is a preference for the more parsimonious model.
Maximum power point tracking with fuzzy logic approach for grid connected photovoltaic system

This paper proposes the maximum power tracking from the Photovoltaic system, which is interfaced with utility grid through a three phase inverter, by using a fuzzy logic controller. In this arrangement, three phase step up transformer is used to synchronize the inverter output with the grid ratings. Controller tracks and feeds maximum power to the utility grid. The linguistic variables have been selected appropriately to modulate the firing angle of the inverter for tracking the maximum power. MATLAB/SIMULINK has been used to model fuzzy logic controller. Complete model of the system has been presented in detail. Also, simulation results are presented and described.
1 Support Vector Machine Solvers

The support vector machine (SVM) algorithm (Cortes and Vapnik, 1995) is probably the most widely used kernel learning algorithm. It achieves relatively robust pattern recognition performance using well-established concepts in optimization theory. Despite this mathematical classicism, the implementation of efficient SVM solvers has diverged from the classical methods of numerical optimization. This divergence is common to virtually all learning algorithms. The numerical optimization literature focuses on the asymptotic performance: how quickly the accuracy of the solution increases with computing time. In the case of learning algorithms, two other factors mitigate the impact of optimization accuracy.
A leakage tolerant high fan-in dynamic circuit design technique

We present a leakage tolerant dynamic circuit called Source Following Evaluation Gate (SFEG) which has high DC noise margin. Simulation results in 0. 13µm and 0. 1µm CMOS process technologies [1] show considerable degradation in noise immunity of domino circuits with threshold voltage scaling and/or increase of fan-in. Comparison with standard domino circuits indicates the noise immunity of 16-input OR SFEG is 37% higher than domino circuit with same delay. We also show that the noise characteristic of SFEG is insensitive to fan-in, and hence, the difference of noise immunity between SFEG and domino becomes larger with the increase in fan-in.
Modularizing Software Systems using PSO optimized hierarchical clustering

Software modularization is an automated process for restructuring software entities into modules to refine the software's design. Software systems are required to evolve in order to accommodate the changes relating to their functionalities, performance, and the supporting platforms. As software undergoes the required changes over the time, its structure deteriorates. In recent times, various clustering techniques have been applied to improve the architecture of such systems. Weighted Combined Algorithm (WCA) is a hierarchical clustering-based technique for restructuring software systems, which provides a multi-level architectural view of the system. In this paper, we propose an approach for optimizing WCA using Particle Swarm Optimization (PSO) for software modularization. To analyze the performance of the proposed algorithm, five open source java software systems were considered under the experimental study. The results of this experimental study show that proposed approach outperforms both WCA and PSO clustering techniques when applied to software modularization.