Search

THERE WILL BE NO more early-morning breakfasts with Luis at the Green Street Café. Luis would be waiting for me curbside in front of his condo at precisely 7:00am (he [...]

Purpose: To investigate the influence of maturation on match running performance in elite male youth soccer players. Methods: A total of 37 elite male youth soccer participants from an English professional soccer academy from the U14s, U15s, and U16s age groups were assessed over the course of 1 competitive playing season (2018–2019). Relative biological maturity was assessed using percentage of predicted adult height. A global positioning system device was used between 2 and 30 (mean = 8 [5]) times on each outfield player. The position of each player in each game was defined as defender, midfielder, or attacker and spine or lateral. A total of 5 match-running metrics were collected total distance covered, high-speed running distance, very high-speed running distance, maximum speed attained, and number of accelerations. Results: Relative biological maturity was positively associated with all global positioning system running metrics for U14s. The U15/16s showed variation in the associations among the global positioning system running metrics against maturity status. A multilevel model which allowed slopes to vary was the best model for all parameters for both age groups. In the U14 age group, advanced maturation was associated with greater high-speed running distance. However, maturation did not contribute toward variance in any of the indices of running performance in the U15/16s. In the U15/16 age group, significance was observed in the spine/lateral playing positions when undertaking actions that required covering distance at high speeds. Conclusions: Maturation appeared to have an impact on match-running metrics within the U14s cohort. However, within the U15/16s, the influence of maturation on match-running metrics appeared to have less of an impact. [ABSTRACT FROM AUTHOR]

Thermospheric density is one of the main sources of uncertainty in the estimation of satellites' position and velocity in low‐Earth orbit. This has negative consequences in several space domains, including space traffic management, collision avoidance, re‐entry predictions, orbital lifetime analysis, and space object cataloging. In this paper, we investigate the prediction accuracy of empirical density models (e.g., NRLMSISE‐00 and JB‐08) against black‐box machine learning (ML) models trained on precise orbit determination‐derived thermospheric density data (from CHAMP, GOCE, GRACE, SWARM‐A/B satellites). We show that by using the same inputs, the ML models we designed are capable of consistently improving the predictions with respect to state‐of‐the‐art empirical models by reducing the mean absolute percentage error (MAPE) in the thermospheric density estimation from the range of 40%–60% to approximately 20%. As a result of this work, we introduce Karman: an open‐source Python software package developed during this study. Karman provides functionalities to ingest and preprocess thermospheric density, solar irradiance, and geomagnetic input data for ML readiness. Additionally, it facilitates developing and training ML models on the aforementioned data and benchmarking their performance at different altitudes, geographic locations, times, and solar activity conditions. Through this contribution, we offer the scientific community a comprehensive tool for comparing and enhancing thermospheric density models using ML techniques. Plain Language Summary: Accurately modeling the density of the thermosphere is pivotal for spacecraft operations such as collision avoidance, re‐entry prediction, and orbital lifetime analysis. In this study, our aim is twofold. First, we want to study and compare the performance of data‐driven machine learning (ML) models in predicting thermospheric density data against standard empirical models used in the field, which are used as baseline. By training ML models using precise orbit determination‐derived satellite data, we show that they can achieve significant performance improvement compared to empirical models, with a reduction of 61% in the mean absolute percentage error. Second, we also provide the community with a shared software framework that supports the ingestion of solar irradiance, geomagnetic, and thermospheric density data, as well as a training and benchmarking framework to develop ML models. This framework allows researchers and operators to both train their ML models and to compare them at different periods of the solar cycle, geomagnetic storm conditions, geographical locations, and times. Key Points: Machine learning (ML) models can significantly outperform existing physics‐based thermospheric neutral density models on exactly the same inputsML models can improve over NRLMSISE‐00 and JB‐08 empirical density models by 61% and 39% respectively in the mean absolute percentage errorThe software allows the creation of ML‐ready data for training and benchmarking new models, supporting solar irradiance and geomagnetic data [ABSTRACT FROM AUTHOR]

Methods for teaching foreign-language literature actually teach students how to approach, understand, and develop their own method of literary criticism through scholarship, interpretation, and theory. (21 references) (CB)

Both maturity and relative age selection biases are entrenched within professional academy soccer programmes. Lay opinion, and that of some scholars, holds that relative age effects exist as a product of advanced biological maturity, that is relatively older players succeed as a consequence of the physical and athletic advantages afforded by earlier maturation. There is, however, a growing body of evidence to suggest that this is not the case, and that relative age and maturation should be considered and treated as independent constructs. To avoid a disconnect between contemporary academic evidence and practitioner practice, the aim of this commentary is to provide a discussion of pre-existing and new evidence relating to maturity and relative age selection biases in soccer. It is hoped that this commentary will provide an overview of new insight regarding the differences between the two selection phenomena and enable practitioners who are responsible for the (de)selection of academy soccer players for talent development programmes to make more informed decisions regarding their retention/selection strategies. [ABSTRACT FROM AUTHOR]

WITH DUE DEFERENCE TO Umberto Eco and Miguel de Cervantes, this brief arable through the fictional woods is divided into four parts. The first is a preamble; the second is [...]

These remarks are prompted by Howard Mancing's article on "Cervantes as Narrator of Don Quijote" in this journal. Howard and I have known each other for many years, so my [...]

Multidirectional speed (MDS) can be defined as [DOUBLE HIGH-REVERSED-9 QUOTATION MARK]the competency and capacity to accelerate, decelerate, change direction, and maintain speed in multiple directions and movements, within the context of sport-specific scenarios." The components of MDS are linear speed, change of direction speed, curvilinear speed, contextual speed, and agility. A MDS development framework is provided for the practitioner who considers the complexities of the growing athlete within a progressive sequence of skill learning and adaptation. Practical examples for each MDS component are provided and discussed within weekly microcycle examples that represent different stages of development for the youth athlete. [ABSTRACT FROM AUTHOR]

The heat capacity change, partition coefficients and enthalpy for the partitioning of a series of n-alcohols were measured using titration calorimetry to understand the thermodynamics and determine the function of the hydrophobic effects in the partitioning of small amphiphilic molecules into lipib bilayers. The data were then compared with those of a model compound for partitioning into bulk hydrocarbon solvents. The results revealed that the thermodynamic state was dependent on dissolved solutes, temperature and lipid composition.

Pakistan depends on the importation of agricultural products since production is not keeping up with population growth. A study on how to increase production demonstrates that in regions where urea supergranules (USG) and farmyard manure (FYM) are costly or scarce, green manures can act as a cheep N source and will help small-scale farmers to record good yields in rice-wheat rotation. Rice growth, output and N-uptake is the maximum with USG and green manures (GM) plus prilled urea (PU).

Isothermal titration calorimetry was used to determine the pH dependence of the enthalpy of binding of imidazole and analogues of the human carbonic anhydrase I. A differential and competitive approach was devised in which the enthalpy of binding of the strong sulfonamide inhibitor methazolamide was determined in the absence and presence of a constant high concentration of the competing imidazole or its analogues. It was observed that appreciable pH changes in the standard enthalpy of binding of imidazole was absent. This may be due to the absence of enthalpically significant structural changes in the complex or to compensating heats of binding and ionization that nearly cancel out.

History shows the value of recycling organic waste in agriculture. Agricultural research done in land-grant colleges since the 1862 reveals that the regular use of organic fertilizer is important in keeping soil fertile and productive and protects against erosion and the loss of soil nutrients from leaching and run-offs. Historically, agriculturalists from the time of 2000 BC have used farmyard manure, crop residues, sewage, ash and other organic materials as fertilizers to keep soils fertile and productive, and the ancient Greeks and Romans that developed modern agriculture made extensive use of organic resources.

The environment protection agency's clean water act was required to shift the public wastewater treatment plants from primary to secondary treatment plants, which has resulted in the increase of organic materials being dumped everywhere. A number of ways to handle organic materials, odor management, compost quality and public acceptance are listed.