Guia NIIF para Pymes

The International Accounting Standards Committee Foundation (IASCF), the authors and the publishers do not accept responsibility for loss caused to any person who acts or refrains from acting in reliance on the material in this publication, whether such loss is caused by negligence or otherwise.

6 7 131 CASOS SOBRE NIIF PARA PYMES 4 Formas: Ejemplos, Ejercicios, Casos Prácticos y Preguntas con Soluciones. Preguntas y respuestas 2. ¿Por qué es importante una norma internacional de información financiera para PYMES? Respuesta:

El presente documento proporciona un análisis del contenido de la sección 34 de las NIIF para pymes; el mismo que pretende facilitar el entendimiento y aplicación de esta norma a todas aquellas empresas cuya actividad principal es la actividad agrícola.

 Success in school, and life in general, depends on one's ability to efficiently manage time.

An ill-tempered doctor who only had one eye asked his patient how he was. "I am as you can see." "Well, as far as I can see, you are half dead!"1

Las dificultades del aprendizaje (DA) es un término genérico es utilizado para referirnos a un grupo bastante heterogéneo de trastornos que afectan al aprendizaje, y se manifiestan por dificultades importantes a la hora de hablar, leer, razonar, escribir, o comprender las matemáticas y aritmética. Las dificultades de aprendizaje pueden presentarse de diferentes formas: Problemas a la hora de pronunciar las palabras, de llamar a las cosas por su nombre, dificultades para aprender las letras y leer, dificultades con la escritura, caligrafía difícil de entender, problemas con los números, dificultades con el cálculo, no seguir las instrucciones que se le dan, etc…

International Journal of Rock Mechanics and …, 2009

An attempt has been made to evaluate and predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the artificial neural network (ANN) technique. A three-layer, feed-forward back-propagation neural network having 15 hidden neurons, 10 input parameters and two output parameters were trained using 154 experimental and monitored blast records from one of the major producing surface coal mines in India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) and frequency by ANN and other predictors. To develop more confidence in the proposed method, same data sets have also been used for the prediction of PPV by commonly used vibration predictors as well as by multivariate regression analysis (MVRA). Results were compared based on correlation and mean absolute error (MAE) between monitored and predicted values of PPV and frequency.