Explore the vast range of titles available.

Background Determining the type and degree of hearing loss is important in the treatment of loss or in the selection of assistive hearing aids to be used. In this study, it is aimed to distinguish the types and degrees of hearing loss with loops created by codes written using deep learning methods and conditional structures. Method A data set consisting of 1000 pure tone airway and pure tone bone conduction hearing tests performed in the audiology clinic was prepared for this study. The Spyder plugin of the Python program was used for the artificial neural network algorithm. While 800 of the tests in the dataset were used to train the machine, 200 test results were used to check the accuracy of the machine results. The audiogram types taught to the machine are interpreted with the artificial neural network algorithm and matched with each of the hearing loss types and degrees. Eclipse IDE for Java Developers-2021–03 program in Java programming language was used for the codes written with conditional structures. Hearing thresholds in each row in the dataset are looped with conditional constructs to determine the type and degree of hearing loss. After teaching with 800 audiogram results in artificial neural network modeling, the result was tested with 200 audiogram results. Results An accuracy of 94.5% was obtained in artificial intelligence learning when determining the type of hearing loss, and 95% when determining the degree of hearing loss. In the loop prepared using conditional constructs, an accuracy rate of 96.4% was obtained when determining the type of hearing loss and 100% when determining the degree of hearing loss. Conclusions It has been seen that computer-based programs can be used to determine the type and degree of hearing loss.

The form of the conditional syllogism resembles that of the categorical syllogism, while its subject matter is at least a conditional premise, but its conclusion is always conditional conjunctive or disjunctive. This mixed structure to which we apply the rules of the categorical syllogism, is a structure of which Aristotle did not have an idea, and which the Stoics did not conceive, and which the non-Arabian logicians did not know until in modern times. But what we have to notice here is the putting of a conditional matter in the form of the categorical syllogism, and it is this kind of hybridization, if we dare to say, which generated this mixed structure which appeared for the first time in the history of logic in the treatise on the logic of Ibn Sina and which can be considered a discovery by this author until proof to the contrary, and that the ancient Arabian logicians have taken the habit of exhibiting in their treatises.

The recovery of a random turbulent velocity field using Lagrangian tracers that move with the fluid flow is a practically important problem. This paper studies the filtering skill of L -noisy Lagrangian tracers in recovering random rotating compressible flows that are a linear combination of random incompressible geostrophically balanced (GB) flow and random rotating compressible gravity waves. The idealized random fields are defined through forced damped random amplitudes of Fourier eigenmodes of the rotating shallow-water equations with the rotation rate measured by the Rossby number ε . In many realistic geophysical flows, there is fast rotation so ε satisfies ε ≪ 1 and the random rotating shallow-water equations become a slow–fast system where often the primary practical objective is the recovery of the GB component from the Lagrangian tracer observations. Unfortunately, the L -noisy Lagrangian tracer observations are highly nonlinear and mix the slow GB modes and the fast gravity modes. Despite this inherent nonlinearity, it is shown here that there are closed analytical formulas for the optimal filter for recovering these random rotating compressible flows for any ε involving Ricatti equations with random coefficients. The performance of the optimal filter is compared and contrasted through mathematical theorems and concise numerical experiments with the performance of the optimal filter for the incompressible GB random flow with L -noisy Lagrangian tracers involving only the GB part of the flow. In addition, a sub-optimal filter is defined for recovering the GB flow alone through observing the L -noisy random compressible Lagrangian trajectories, so the effect of the gravity wave dynamics is unresolved but effects the tracer observations. Rigorous theorems proved below through suitable stochastic fast-wave averaging techniques and explicit formulas rigorously demonstrate that all these filters have comparable skill in recovering the slow GB flow in the limit ε → 0 for any bounded time interval. Concise numerical experiments confirm the mathematical theory and elucidate various new features of filter performance as the Rossby number ε , the number of tracers L and the tracer noise variance change.

This book studies insubordination using Germanic data. On a descriptive level, it distinguishes a wide number of (previously undescribed) types of complement and conditional insubordination in English, German, Dutch, Swedish, Danish and Icelandic. On a theoretical level, these data are used to investigate the boundaries of insubordination, and the degree to which insubordination is a constructionally and semantically unified phenomenon.

This research is an endeavour to examine problems EFL learners encountered in learning English conditional structures at University for Natural Resources and Environment, Ho Chi Minh City (UNREHCMC) as well as causes behind these problems. These two aims were reached through responses to the survey questionnaire and the interviews. The influence of the mother tongue on learning English conditional structures should be taken into due consideration in the course of teaching and learning English conditional structures. [PUBLICATION ABSTRACT]

Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions. The three approaches will be reviewed, compared, and contrasted in relation to various joint probability structures frequently encountered in applications. In particular, the relevance of the approaches to calculating Bayesian posterior densities for a variety of structured models will be discussed and illustrated.

En este trabajo se presentan resultados de una indagación que inquirió el efecto que, sobre estudiantes ingresantes a la universidad, tuvieron cursos universitarios iniciales de Matemática para adquirir conocimientos que faciliten la comprensión y el uso de estructuras deductivas de uso frecuente en razonamientos o argumentaciones. Este tema es de interés actual en Uruguay, debido a la preocupación que generan las altas tasas de rezago y abandono en primer año. Como antecedentes se efectuó un análisis de libros de texto usuales y de registros estudiantiles de clases, en busca de evidencias acerca del grado de explicitación de estas estructuras en la enseñanza. Entre los resultados observados destacan que los ingresantes muestran conocimientos acerca de las estructuras deductivas para comprender o construir razonamientos, y que la sola participación en cursos de Matemática, sin mediar una enseñanza intencional, no parece suficiente para adquirir habilidades en esta área.

During the acquisition of programming concepts prior knowledge may serve as «precursor» and interacts with the informatical concepts; nevertheless the integration of prior knowledge in the new informatical frame requires forming mental representations on this device. Two experiments on conditional structures (on 10th grade students) are reported: they show that logical knowledge is a prerequisite to the acquisition of conditional structures but is not sufficient to insure acquisition. Properties of natural communication are used by many beginners when they need to handle dialog with an informatical device in programming tasks. A model, termed the PRES-model is defined, which accounts for the discrepancy observed between logical knowledge and success on programming tasks. Lors des acquisitions en programmation des connaissances préalables peuvent servir de précurseurs; elles interagissent alors avec les concepts informatiques. Toutefois l'intégration de ces connaissances antérieures dans le nouveau cadre informatique exige la construction de représentations mentales sur le dispositif informatique. Les auteurs présentent deux expériences sur l'acquisition des structures conditionnelles par des élèves de seconde (15-16 ans). Les résultats montrent d'une part que des connaissances logiques sont des prérequis pour cette acquisition mais ne sont pas suffisantes, d'autre part que de nombreux élèves utilisent des caractères de la communication naturelle lors de la communication avec le dispositif informatique. Un modèle de présupposition (le modèle PRES) est introduit par les auteurs pour rendre compte de la différence entre les connaissances logiques et la réussite dans des tâches de programmation.

Conditional Cash Transfer (CCT) programs, started in the late 1990s in Latin America, have become the antipoverty program of choice in many developing countries in the region and beyond. This paper reviews the literature on their long-term impacts on human capital and related outcomes observed after children have reached a later stage of their life cycle, focusing on two life-cycle transitions. The first includes children exposed to CCTs in utero or during early childhood who have reached school ages. The second includes children exposed to CCTs during school ages who have reached young adulthood. Most studies find positive long-term effects on schooling, but fewer find positive impacts on cognitive skills, learning, or socio-emotional skills. Impacts on employment and earnings are mixed, possibly because former beneficiaries were often still too young. A number of studies find estimates that are not statistically different from zero, but for which it is often not possible to be confident that this is due to an actual lack of impact rather than to the methodological challenges facing all long-term evaluations. Developing further opportunities for analyses with rigorous identification strategies for the measurement of long-term impacts should be high on the research agenda. As original beneficiaries age, this should also be increasingly possible, and indeed important before concluding whether or not CCTs lead to sustainable poverty reduction.