The Effects of Olympic Weightlifting Derivatives on Muay Thai Roundhouse Kicking Performance

The purpose of this study was to identify performance measures in roundhouse striking, by the implementation of Olympic Weightlifting Derivatives (OWLD) with Muay Thai fighters. Forty male subjects were recruited, with twenty male subjects placed in the experimental group (EG)and twenty male subjects in the control group (CG) and were randomly assigned to their groups. Mean 26 years 3 (± 3.2), weight and height 82.4 kg (± 12.2), and 180.7cm (± 3). Pre- and post-intervention and control group testing included roundhouse strike impact power, measured using the PowerKubeTM, a dynamometer that identifies the impact power used to produce striking potential. Countermovement jumps were recorded using a single PASCO force platform one axis PS2141. The EG subjects were prescribed an eight-week training programme that was carried out prior to their sparring with twenty minutes rest to recover. The control group were instructed to carry out traditional training that involved the same volume of sparring, cardiovascular fitness, and circuit training as the EG. Within-group post test results showed the EG and CG group demonstrated significantly different results in RHK performance (p ≤ 0.01); however, no significant differences were observed between groups. Results in the countermovement jump were, however, highly significant (p ≤ 0.01) in the EG (within group), but not significant in the CG (p ≥ 0.05); again, no significant differences were observed between groups. Meaningful differences were seen in the EG with a 7.41% increase in roundhouse kicking performance and 7.54% in countermovement jump height, compared to the CG that elicited negligible differences of only 1.56% in the roundhouse kicking performance and 0.33% in countermovement jumper, demonstrating OWLD would improve the performance of the Muay Thai fighter

Determination of the size, mass, and density of "exomoons" from photometric transit timing variations

Precise photometric measurements of the upcoming space missions allow the size, mass, and density of satellites of exoplanets to be determined. Here we present such an analysis using the photometric transit timing variation ($TTV_p$). We examined the light curve effects of both the transiting planet and its satellite. We define the photometric central time of the transit that is equivalent to the transit of a fixed photocenter. This point orbits the barycenter, and leads to the photometric transit timing variations. The exact value of $TTV_p$ depends on the ratio of the density, the mass, and the size of the satellite and the planet. Since two of those parameters are independent, a reliable estimation of the density ratio leads to an estimation of the size and the mass of the exomoon. Upper estimations of the parameters are possible in the case when an upper limit of $TTV_p$ is known. In case the density ratio cannot be estimated reliably, we propose an approximation with assuming equal densities. The presented photocenter $TTV_p$ analysis predicts the size of the satellite better than the mass. We simulated transits of the Earth-Moon system in front of the Sun. The estimated size and mass of the Moon are 0.020 Earth-mass and 0.274 Earth-size if equal densities are assumed. This result is comparable to the real values within a factor of 2. If we include the real density ratio (about 0.6), the results are 0.010 Earth-Mass and 0.253 Earth-size, which agree with the real values within 20%.Comment: 6 pages, 5 figures, to appear in Astronomy and Astrophysic

Evaluation of New Dwarf Elephant Grass Genotypes for Grazing

Elephant grass stands out for its production potential, forage quality and acceptance by animals. However, its tall size makes management under grazing difficult and dwarf cultivars have been selected and evaluated to overcome this limitation. The objective was to characterize agronomic aspects of dwarf elephant grass genotypes submitted to two defoliation intensities. The experiment was installed in a 5 x 2 factorial scheme, with five elephant grass genotypes (2022, 1810, 2111, 2035, BRS Kurumi) and two residue heights after defoliation (25 and 45 cm). A randomized block design with three replications in 4x3m plots was used. Forage above the residue height was cut whenever the canopy reached 93-95% light interception. Residue heights did not influence leaf/stem ratio, basal and total tillering, and dry matter production. On the other hand, influence of residue height on canopy height, aerial tillering and forage accumulation rate was observed. The forage accumulation rate increased by 19% for the 45 cm residue compared to the 25 cm residue. No interaction was observed between genotype and residue height for the variables canopy height, leaf:stem ratio, basal tillering, aerial tillering, forage mass and forage accumulation rate. In relation to tillering, BRS kurumi showed greater total and aerial tiller number, 31% higher than the average of the other materials. Although the cultivar BRS kurumi has more vigorous tillering, the variables leaf:stem ratio and forage accumulation rate were higher in the new materials, especially material 1810, which presented better performance compared to the control. In view of this, it is concluded that the new grass genotypes have a higher proportion of leaves and forage accumulation rate than BRS Kurumi, and that the residue height of 45 cm provides a higher forage accumulation rate

Food systems in a zero-deforestation world: Dietary change is more important than intensification for climate targets in 2050

Global food systems contribute to climate change, the transgression of planetary boundaries and deforestation. An improved understanding of the environmental impacts of different food system futures is crucial for forging strategies to sustainably nourish a growing world population. We here quantify the greenhouse gas (GHG) emissions of global food system scenarios within a biophysically feasible “option space” in 2050 comprising all scenarios in which biomass supply – calculated as function of agricultural area and yields – is sufficient to cover biomass demand – derived from human diets and the feed demand of livestock. We assessed the biophysical feasibility of 520 scenarios in a hypothetical no-deforestation world. For all feasible scenarios, we calculate (in) direct GHG emissions related to agriculture. We also include (possibly negative) GHG emissions from land-use change, including changes in soil organic carbon (SOC) and carbon sinks from vegetation regrowth on land spared from food production. We identify 313 of 520 scenarios as feasible. Agricultural GHG emissions (excluding land use change) of feasible scenarios range from 1.7 to 12.5 Gt CO2e yr−1. When including changes in SOC and vegetation regrowth on spare land, the range is between −10.7 and 12.5 Gt CO2e yr−1. Our results show that diets are the main determinant of GHG emissions, with highest GHG emissions found for scenarios including high meat demand, especially if focused on ruminant meat and milk, and lowest emissions for scenarios with vegan diets. Contrary to frequent claims, our results indicate that diets and the composition and quantity of livestock feed, not crop yields, are the strongest determinants of GHG emissions from food-systems when existing forests are to be protected

Hierarchical semantic representations of online news comments for emotion tagging using multiple information sources

With the development of online news services, users now can actively respond to online news by expressing subjective emotions, which can help us understand the predilections and opinions of an individual user, and help news publishers to provide more relevant services. Neural network methods have achieved promising results, but still have challenges in the field of emotion tagging. Firstly, these methods regard the whole document as a stream or bag of words and can't encode the intrinsic relations between sentences. So these methods cannot properly express the semantic meaning of the document in which sentences may have logical relations. Secondly, these methods only use semantics of the document itself, while ignoring the accompanying information sources, which can significantly influence the interpretation of the sentiment contained in documents. Therefore, this paper presents a hierarchical semantic representation model of news comments using multiple information sources, called Hierarchical Semantic Neural Network (HSNN). In particular, we begin with a novel neural network model to learn document representation in a bottom-up way, capturing not only the semantics within sentence but also semantics or logical relations between sentences. On top of this, we tackle the task of predicting emotions for online news comments by exploiting multiple information sources including the content of comments, the content of news articles, and the user-generated emotion votes. A series of experiments and tests on real-world datasets have demonstrated the effectiveness of our proposed approach

Moagem e sapeco/secagem em forno de microondas na classificação sensorial de erva-mate no infravermelho próximo.

bitstream/CNPF-2009-09/44430/1/com_tec203.pd

Cardiorespiratory Progression Over 5 Years and Role of Corticosteroids in Duchenne Muscular Dystrophy: A Single-Site Retrospective Longitudinal Study

Background: Duchenne muscular dystrophy (DMD) boys treated with corticosteroids (CS) have prolonged survival and respiratory function when compared to CS-naïve. /\ud Research question: The differential impact of frequently used corticosteroids and their regimens on long-term (>5 years) cardiorespiratory progression in DMD children is unknown. / Study Design and Methods: Retrospective longitudinal study including DMD children followed at Dubowitz Neuromuscular Centre (Great Ormond Street Hospital London), May 2000-June 2017. Patients enrolled in any interventional clinical trials were excluded. We collected patients’ anthropometrics, respiratory (forced vital capacity, FVC% predicted and absolute FVC, non-invasive ventilation requirement, NIV) and cardiac (left ventricular shortening function, LVFS%) function. CS-naïve patients had never received CS. CS-treated took either deflazacort or prednisolone, daily or intermittently (10 days on/10 days off) for >1 month. Average longitudinal models were fitted for yearly respiratory (FVC%P) and cardiac (LVFS%) progression. A time-to-event analysis to FVC%P<50%, NIV start and cardiomyopathy (LVFS<28%) was performed in CS-treated (daily and intermittent) vs CS-naïve patients. / Results: There were 270 patients, mean age at baseline 6.2 (±2.3) years. Median follow-up 5.6 (± 3.5) years. At baseline, 263 were ambulant. Sixty-six were CS-daily, 182 CS-intermittent >60% treatment, 22 CS-naïve. Yearly FVC%P declined similarly from 9 years (5.9% and 6.9%/year, p=0.27) in CS-daily and CS-intermittent. CS-daily declined from a higher FVC%P than CS-intermittent (p2 years later than CS-treated. LVFS% declined by 0.53%/year in CS-treated irrespective of CS regimen, significantly slower (p<0.01) than CS-naïve progressing by 1.17%/year. Age at cardiomyopathy was 16.6 in CS-treated (p<0.05) irrespective of regimen and 13.9 years in CS-naïve. / Interpretation: CS irrespective of their regimen significantly improved respiratory function and delayed NIV requirement and cardiomyopathy

An experiment with association rules and classification: post-bagging and conviction

In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and X². We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.Programa de Financiamento Plurianual de Unidades de I & D.Comunidade Europeia (CE). Fundo Europeu de Desenvolvimento Regional (FEDER).Fundação para a Ciência e a Tecnologia (FCT) - POSI/SRI/39630/2001/Class Project

Sediment infill of tropical floodplain lakes: rates, controls, and implications for ecosystem services.

Shallow lakes in tropical floodplains provide significant ecosystem services that can be altered by natural and anthropogenic forces. Despite their importance, little is known about the infill patterns and timescales and the magnitude of these changes in tropical floodplain lakes. Here, we present a global meta-analysis of sediment core-derived accumulation rate data for shallow floodplain lakes in tropical lowlands to quantify the timescales of basin infill. Environmental variables (e.g., sediment accumulation rates, bathymetry, surface area) were compiled from the literature or derived from remote sensing imagery, resulting in a database (n = 76 lakes) that includes various lake morphologies. Our results show an exponential increase in sediment accumulation rates in many of these lakes over the past 50 years, which we interpret as a response to growing human populations and deforestation, particularly in topographically steep watersheds with pronounced seasonal rainfall. Over centennial periods, tropical floodplain lakes accumulate sediment faster than many other extratropical lakes. The dataset suggests that complete infill of some tropical floodplain lakes will occur in as little as a few centuries. Our findings also reveal the critical environmental and human factors that influence sediment accumulation patterns and affect ecosystem services in shallow tropical floodplain lakes. These findings have important implications for water and sediment management in low latitude watersheds, many of which are located in densely populated and/or developing nations

Predicting physical properties of woven fabrics via automated machine learning and textile design and finishing features

This paper presents a novel Machine Learning (ML) approach to support the creation of woven fabrics. Using data from a textile company, two CRoss-Industry Standard Process for Data Mining (CRISP-DM) iterations were executed, aiming to compare three input feature representation strategies related with fabric design and finishing processes. During the modeling stage of CRISP-DM, an Automated ML (AutoML) procedure was used to select the best regression model among six distinct state-of-the-art ML algorithms. A total of nine textile physical properties were modeled (e.g., abrasion, elasticity, pilling). Overall, the simpler yarn representation strategy obtained better predictive results. Moreover, for eight fabric properties (e.g., elasticity, pilling) the addition of finishing features improved the quality of the predictions. The best ML models obtained low predictive errors (from 2% to 7%) and are potentially valuable for the textile company, since they can be used to reduce the number of production attempts (saving time and costs).This work was carried out within the project “TexBoost: less Commodities moreSpecialities” reference POCI-01-0247-FEDER-024523, co-funded byFundo Eu-ropeu de Desenvolvimento Regional(FEDER), through Portugal 2020 (P2020)