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Resguardo de protección internacional España.

Debido al ingente aumento en el número de solicitantes de Protección Internacional se ha incrementado la expedición de la documentación acreditativa…

Resguardo proteccion internacional España

 

Requerimiento de subsanación - (Requisitos para Solicitud de visa estudiante)

Visa Estudiante Requisitos

Larry Francis Obando – Technical Specialist – Educational Content Writer

Tutoría Académica / Emprendedores / Empresarial

Copywriting, Content Marketing, Tesis, Monografías, Paper Académicos, White Papers (Español – Inglés)

Escuela de Ingeniería Eléctrica de la Universidad Central de Venezuela, CCs.

Escuela de Ingeniería Electrónica de la Universidad Simón Bolívar, Valle de Sartenejas.

Escuela de Turismo de la Universidad Simón Bolívar, Núcleo Litoral.

Contacto: Jaén – España: Tlf. 633129287

WhatsApp: +34 633129287

Caracas, Quito, Guayaquil, Lima, México, Bogotá, Cochabamba, Santiago.

email: dademuchconnection@gmail.com

Twitter: @dademuch

FACEBOOK: DademuchConnection

The Learning Organization

Learning Organization – Conceptual Framework.

The basic meaning of a learning organization is an organization that is continually expanding its capacity to create its future.

The five "component technologies" to innovate learning organizations.

The ferment in management will continue until we build organizations that are more consistent with man’s higher aspirations beyond food, shelter and belonging.” Moreover, many who share these values are now in leadership positions. I find a growing number of organizational leaders who, while still a minority, feel they are part of a profound evolution in the nature of work as a social institution. “Why can’t we do good works at work?” asked Edward Simon, president of Herman Miller, recently.

Business is the only institution that has a chance, as far as I can see, to fundamentally improve the injustice that exists in the world. But first, we will have to move through the barriers that are keeping us from being truly vision-led and capable of learning.” Perhaps the most salient reason for building learning organizations is that we are only now starting to understand the capabilities such organizations must possess.

For a long time, efforts to build learning organizations were like groping in the dark until the skills, areas of knowledge, and paths for development of such organizations became known. What fundamentally will distinguish learning organizations from traditional authoritarian “controlling organizations” will be the mastery of certain basic disciplines. That is why the “disciplines of the learning organization” are vital.

Today, I believe, five new “component technologies” are gradually converging to innovate learning organizations. Though developed separately, each will, I believe, prove critical to the others’ success, just as occurs with any ensemble. Each provides a vital dimension in building organizations that can truly “learn,” that can continually enhance their capacity to realize their highest aspirations:

  1. Systems thinking is a conceptual framework, a body of knowledge and tools that has been developed over the past fifty years, to make the full patterns clearer, and to help us see how to change them effectively.
  2. Personal Mastery. People with a high level of personal mastery are able to consistently realize the results that matter most deeply to them— in effect, they approach their life as an artist would approach a work of art. They do that by becoming committed to their own lifelong learning. Personal mastery is the discipline of continually clarifying and deepening our personal vision, of focusing our energies, of developing patience, and of seeing reality objectively. As such, it is an essential cornerstone of the learning organization—the learning organization’s spiritual foundation. But surprisingly few organizations encourage the growth of their people in this manner. This results in vast untapped resources: “People enter business as bright, welleducated, high-energy people, full of energy and desire to make a difference,” says Hanover’s O’Brien. “By the time they are 30, a few are on the “fast track” and the rest ‘put in their time’ to do what matters to them on the weekend. They lose the commitment, the sense of mission, and the excitement with which they started their careers. We get damn little of their energy and almost none of their spirit.” And surprisingly few adults work to rigorously develop their own personal mastery. Here, I am most interested in  the connections between personal learning and organizational learning, in the reciprocal commitments between individual and organization, and in the special spirit of an enterprise made up of learners.
  3. Mental Models. The discipline of working with mental models starts with turning the mirror inward; learning to unearth our internal pictures of the world, to bring them to the surface and hold them rigorously to scrutiny. It also includes the ability to carry on “learningful” conversations that balance inquiry and advocacy, where people expose their own thinking effectively and make that thinking open to the influence of others.
  4. Building Shared Vision. The practice of shared vision involves the skills of unearthing shared “pictures of the future” that foster genuine commitment and enrollment rather than compliance. In mastering this discipline, leaders learn the counterproductiveness of trying to dictate a vision, no matter how heartfelt.
  5. Team Learning. How can a team of committed managers with individual IQs above 120 have a collective IQ of 63? The discipline of team learning confronts this paradox. We know that teams can learn; in sports, in the performing arts, in science, and even, occasionally, in business, there are striking examples where the intelligence of the team exceeds the intelligence of the individuals in the team, and where teams develop extraordinary capacities for coordinated action. When teams are truly learning, not only are they producing extraordinary results but the individual members are growing more rapidly than could have occurred otherwise. If a learning organization were an engineering innovation, such as the airplane or the personal computer, the components would be called “technologies.”

For an innovation in human behavior, the components need to be seen as disciplines. By “discipline,” I do not mean an “enforced order” or “means of punishment,” but a body of theory and technique that must be studied and mastered to be put into practice. A discipline is a developmental path for acquiring certain skills or competencies. As with any discipline, from playing the piano to electrical engineering, some people have an innate “gift,” but anyone can develop proficiency through practice.

System Thinking - The Fifth Discipline

It is vital that the five disciplines develop as an ensemble. This is why systems thinking is the fifth discipline. It is the discipline that integrates the disciplines, fusing them into a coherent body of theory and practice. It keeps them from being separate gimmicks or the latest organization change fads. Without a systemic orientation, there is no motivation to look at how the disciplines interrelate. By enhancing each of the other disciplines, it continually reminds us that the whole can exceed the sum of its parts.

But systems thinking also needs the disciplines of building shared vision, mental models, team learning, and personal mastery to realize its potential. Building shared vision fosters a commitment to the long term. Mental models focus on the openness needed to unearth shortcomings in our present ways of seeing the world. Team learning develops the skills of groups of people to look for the larger picture that lies beyond individual perspectives. And personal mastery fosters the personal motivation to continually learn how our actions affect our world. Without personal mastery, people are so steeped in the reactive mindset (“someone/something else is creating my problems”) that they are deeply threatened by the systems perspective.

At the heart of a learning organization is a shift of mind—from seeing ourselves as separate from the world to connected to the world, from seeing problems as caused by someone or something “out there” to seeing how our own actions create the problems we experience. A learning organization is a place where people are continually discovering how they create their reality. And how they can change it. People talk about being part of something larger than themselves, of being connected, of being generative.

Metanoia

The word is “metanoia” and it means a shift of mind. In the early (Gnostic) Christian tradition, it took on a special meaning of awakening shared intuition and direct knowing of the highest, of God.

To grasp the meaning of “metanoia” is to grasp the deeper meaning of “learning,” for learning also involves a fundamental shift or movement of mind.

Real learning gets to the heart of what it means to be human. Through learning we re-create ourselves. Through learning we become able to do something we never were able to do. Through learning we reperceive the world and our relationship to it. Through learning we extend our capacity to create, to be part of the generative process of life. There is within each of us a deep hunger for this type of learning.

This, then, is the basic meaning of a “learning organization”—an organization that is continually expanding its capacity to create its future. For such an organization, it is not enough merely to survive. “Survival learning” or what is more often termed “adaptive learning” is important—indeed it is necessary. But for a learning organization, “adaptive learning” must be joined by “generative learning,” learning that enhances our capacity to create.

A few brave organizational pioneers are pointing the way, but the territory of building learning organizations is still largely unexplored. It is my fondest hope that this book can accelerate that exploration: The-Fifth-Discipline.

Source:

  • The Fifth Discipline – The Art and Practices of Learning Organizations. By Peter M. Senge – 1990

Literature Review made by:

Prof. Larry Francis Obando – Technical Specialist – Educational Content Writer

Twitter: @dademuch

Copywriting, Content Marketing, Tesis, Monografías, Paper Académicos, White Papers (Español – Inglés)

Escuela de Ingeniería Eléctrica de la Universidad Central de Venezuela, UCV CCs

Escuela de Ingeniería Electrónica de la Universidad Simón Bolívar, USB Valle de Sartenejas.

Escuela de Turismo de la Universidad Simón Bolívar, Núcleo Litoral.

Contact: Caracas, Quito, Guayaquil, Cuenca. telf – 0998524011

WhatsApp: +593998524011   +593981478463 

Twitter: @dademuch

FACEBOOK: DademuchConnection

email: dademuchconnection@gmail.com

Matemática aplicada - Appd Math, Probabilidades

Modelo probabilístico – Axiomas.

Un modelo probabilístico es una descripción matemática de una situación incierta. Debe estar de acuerdo con un marco teórico fundamental que tenga dos ingredientes principales: null

Introducción

Un modelo probabilístico es una descripción cuantitativa de una situación, un fenómeno o un experimento cuyo resultado es incierto. Elaborar un modelo de este tipo implica dos pasos claves.

Primero, necesitamos describir los posibles resultados del experimento. Esto se hace especificando un Espacio Muestral del experimento y se denota con Ω.

En segundo lugar, especificamos una ley de probabilidad, que asigna probabilidades a los resultados o a colecciones de resultados. La ley de probabilidad nos dice, por ejemplo, si un resultado es mucho más probable que otro resultado.

Las probabilidades tienen que satisfacer ciertas propiedades básicas para ser significativas. Estos son los axiomas de la teoría de la probabilidad. Por ejemplo, las probabilidades no pueden ser negativas. Curiosamente, habrá muy pocos axiomas, pero son poderosos y veremos que tienen muchas consecuencias. Veremos que implican muchas otras propiedades que no eran parte de los axiomas.

Espacio de muestra Ω y Evento (The sample space Ω)

Cada modelo probabilístico implica un proceso subyacente, llamado experimento, que producirá exactamente uno de varios resultados posibles. El conjunto de todos los resultados posibles se denomina Espacio Muestral del experimento y se denota con Ω. Un subconjunto del espacio muestral, es decir, una recopilación de posibles resultados, se denomina Evento. Es importante tener en cuenta que en nuestra formulación de un modelo probabilístico, solo hay un experimento.

El espacio muestral de un experimento puede consistir en un número finito o infinito de resultados posibles. Los espacios muestrales finitos son conceptualmente y matemáticamente más simples. Aún así, los espacios muestrales con un número infinito de elementos son bastante comunes. Como ejemplo, considere lanzar un dardo sobre un objetivo cuadrado y ver el punto de impacto como resultado.

Independientemente de su número, los diferentes elementos del espacio muestral deben ser distintos y mutuamente excluyentes, de modo que, cuando se lleve a cabo el experimento, haya un resultado único.

Generalmente, el espacio muestral elegido para un modelo probabilístico debe ser colectivamente exhaustivo, en el sentido de que no importa lo que suceda en el experimento, siempre obtenemos un resultado que se ha incluido en el espacio muestral. Además, el espacio de muestra debe tener suficientes detalles para distinguir entre todos los resultados de interés para el modelador, mientras se evitan los detalles irrelevantes.

Para resumir: este conjunto denominado Espacio Muestral debe ser tal que, al final del experimento, siempre se pueda señalar uno y exactamente uno de los posibles resultados y decir que este es el resultado que se produjo. Los resultados físicamente diferentes deben distinguirse en el espacio muestral y corresponder a puntos distintos. Pero cuando decimos resultados físicamente diferentes, ¿qué queremos decir? Realmente queremos decir diferente en todos los aspectos relevantes, pero quizás no diferente en aspectos irrelevantes.

Leyes de Probabilidad

Supongamos que nos hemos asentado en el espacio muestral Ω asociado con un experimento en particular, proceso esbozado en el apartado anterior. Para completar el modelo probabilístico, ahora debemos introducir una Ley de Probabilidad.

Intuitivamente, una ley de probabilidad especifica la “probabilidad” de cualquier resultado , o de cualquier conjunto de posibles resultados (un evento, como lo llamamos antes) que forman parte del espacio muestral Ω. Más precisamente, la ley de probabilidad asigna a cada evento A, un número P (A), denominado probabilidad de A, que satisface los siguientes axiomas:

1. No negatividad.

null

2. Aditividad. Si A y B son dos conjuntos disjuntos, entonces la probabilidad de su unión satisface lo siguiente:

null

Más genéricamente, si el espacio muestral  tiene un número infinito de eventos y A1, A2, A3, A4,… es una secuencia de conjuntos disjuntos de eventos, entonces la probabilidad de su unión satisface lo siguiente:

null

3. Normalización. La probabilidad de todo el espacio muestral  es igual a 1:

null

Para visualizar en que consiste la ley de probabilidad, considere una unidad de masa que se “extiende” sobre todo el espacio muestral Ω. Entonces, P (A) es simplemente la masa total que fue asignada colectivamente a los elementos de A. En términos de esta analogía, el axioma de aditividad se vuelve bastante intuitivo: la masa total en una secuencia de eventos (o conjunto de eventos) separados es la suma de sus masas individuales

Hay muchas propiedades naturales que pueden derivarse de los anteriores enunciados. Por ejemplo, utilizando los axiomas de normalización y aditividad podemos encontrar la probabilidad del evento vacío (o conjunto vacío) P (Ø) como sigue

null

Esto implica que:

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Modelo Discreto - Ley de probabilidad discreta

Si el espacio muestral consiste en un número finito de resultados posibles, entonces la ley de probabilidad se especifica por las probabilidades de los eventos que consisten en un solo elemento. En particular, la probabilidad de cualquier  evento {s1, s2, …., sn} es la suma de las probabilidades de cada uno de sus elementos:

null

En el caso especial donde las probabilidades P(s1), P(s2), …, P(sn) son todas de un mismo valor, tomando en cuenta el axioma de normalización, obtenemos la siguiente ley.

Discrete Uniform Probability Law 

Si el espacio muestral consta de n resultados posibles que son igualmente probables (es decir, todos los eventos de un solo elemento tienen la misma probabilidad), la probabilidad de cualquier evento A nos es dada por:

null

Modelo Continuo

Los modelos discretos son conceptualmente mucho más fáciles. Los modelos continuos implican algunos conceptos más sofisticados. Los modelos probabilísticos con espacio muestral continuo se diferencian de sus homólogos discretos en que las probabilidades de los eventos de un solo elemento pueden no ser suficientes para caracterizar la ley de probabilidad.

Propiedades de las leyes de probabilidad

Las leyes de probabilidad tienen una serie de propiedades, que pueden deducirse de los axiomas. Algunos de ellas se resumen a continuación.:

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El rol de la teoría de probabilidades.

La teoría de la probabilidad puede ser una herramienta muy útil para hacer predicciones y decisiones que se aplican al mundo real. Ahora, si sus predicciones y decisiones serán buenas dependerá de si ha elegido un buen modelo. ¿Has elegido un modelo que proporcione una representación suficientemente buena del mundo real? ¿Cómo se asegura de que este sea el caso? Existe todo un campo, el campo de las estadísticas, cuyo propósito es complementar la teoría de la probabilidad utilizando datos para obtener buenos modelos. Y así tenemos el siguiente diagrama que resume la relación entre el mundo real, las estadísticas y la probabilidad. El mundo real genera datos. El campo de estadística e inferencia utiliza estos datos para generar modelos probabilísticos. Una vez que tenemos un modelo probabilístico, utilizamos la teoría de la probabilidad y las herramientas de análisis que nos proporciona. Y los resultados que obtenemos de este análisis conducen a predicciones y decisiones sobre el mundo real. Video sugerido: Interpretation and uses of Probability

 

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Fuentes:

  1. Introduction to probability (bertsekas, 2nd, 2008)
  2. Probability – The Science of Uncertainty and Data (MITx – 6.431x)

Revisión literaria hecha por:

Prof. Larry Francis Obando – Technical Specialist – Educational Content Writer

WhatsApp: +34 633129287 +593998524011 Atención Inmediata!!

Twitter: @dademuch

Copywriting, Content Marketing, Tesis, Monografías, Paper Académicos, White Papers (Español – Inglés)

Escuela de Ingeniería Eléctrica de la Universidad Central de Venezuela, UCV CCs

Escuela de Ingeniería Electrónica de la Universidad Simón Bolívar, USB Valle de Sartenejas.

Escuela de Turismo de la Universidad Simón Bolívar, Núcleo Litoral.

Contacto: España +34 633129287

Caracas, Quito, Guayaquil, Cuenca.

WhatsApp: +34 633129287 +593998524011 

Twitter: @dademuch

FACEBOOK: DademuchConnection

email: dademuchconnection@gmail.com

Matemática aplicada - Appd Math, Probability

Probabilistic model – models and axioms.

A probabilistic model is a mathematical description of an uncertain situation. It must be in accordance with a fundamental framework which has two main ingredients:

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Introduction

A probabilistic model is a quantitative description of a situation, a phenomenon, or an experiment whose outcome is uncertain. Putting together such a model involves two key steps.

First, we need to describe the possible outcomes of the experiment. This is done by specifying a so-called sample space .

Second, we specify a probability law, which assigns probabilities to outcomes or to collections of outcomes. The probability law tells us, for example, whether one outcome is much more likely than some other outcome.

Probabilities have to satisfy certain basic properties in order to be meaningful. These are the axioms of probability theory. For example probabilities cannot be negative. Interestingly, there will be very few axioms, but they are powerful, and we will see that they have lots of consequences. We will see that they imply many other properties that were not part of the axioms.

Sample space and Events

Every probabilistic model involves an underlying process, called the experiment,  that will produce exactly one out of several possible outcomes. The set of all possible outcomes is called the sample space of the experiment, and is denoted by . A subset of the sample space, that is, a collection of possible outcomes, is called an Event. It is important to note that in our formulation of a probabilistic model, there is only one experiment.

The sample space of an experiment may consist of a finite or an infinite number of possible outcomes. Finite sample spaces are conceptually and mathematically simpler. Still, sample spaces with an infinite number of elements are quite common. As an example, consider throwing a dart on a square target and viewing the point of impact as the outcome.

Regardless of their number, different elements of the sample space should be distinct and mutually exclusive, so that, when the experiment is carried out, there is a unique outcome.

Generally, the sample space chosen for a probabilistic model must be collectively exhaustive, in the sense that no matter what happens in the experiment, we always obtain an outcome that has been included in the sample space. In addition, the sample space should have enough detail to distinguish between all outcomes of interest to the modeler, while avoiding irrelevant details.

To summarize– this set should be such that, at the end of the experiment, you should be always able to point to one, and exactly one, of the possible outcomes and say that this is the outcome that occurred. Physically different outcomes should be distinguished in the sample space and correspond to distinct points. But when we say physically different outcomes, what do we mean? We really mean different in all relevant aspects but perhaps not different in irrelevant aspects.

Probability Laws

Suppose we have settled on the sample space associated with an experiment. To complete the probabilistic model, we must now introduce a Probability Law.

Intuitively, a probability law specifies the “likelihood” of any outcome, or of any set of possible outcomes (an event, as we called it early). More precisely, the probability law assigns to every event A, a number P(A), called the probability of A, satisfying the following axioms:

1. Nonnegativity.

null

2. Additivity. If A and B are two disjoints events, then the probability of their union satisfies the following:

null

More generally, if the sample space has an infinite number of elements and A1, A2, A3, A4,… is a sequence of disjoint events, then the probability of their union satisfies:

null

3. The probability of the entire sample space is equal to 1, that is:

null

In order to visualize a probability law, consider a unity of mass which is “spread” over the sample space . Then, P(A) is simply the total mass that was assigned collectively to the elements of A. In terms of this analogy, the additivity axiom becomes quite intuitive: the total mass in a sequence of disjoint events is the sum of their individual masses.

There are many natural properties of a probability law which can be derived from them. For example, using the normalization and additivity axioms we may find out the probability of the empty event P(Ø) as following:

null

This implies that:

null

Discrete Model - Discrete Probability Law 

If the sample space consists of a finite number of possible outcomes, then the probability law is specified by the probabilities of the events that consist of a single element. In particular, the probability of any event {s1, s2, …., sn} is the sum of the probabilities of its elements:

null

In the special case where the probability P(s1), P(s2), …, P(sn) are all the same, in view of the normalization axiom we obtain the following law.

Discrete Uniform Probability Law 

If the sample space consists of n possible outcomes which are equally likely (i.e., all single-element events have the same probability), the probability of any event A us given by:

null

Continuous Model

Discrete models are conceptually much easier. Continuous models involve some more sophisticated concepts.

Probabilistic models with continuous sample space differ from their discrete counterparts in that the probabilities of the single-element events may not be sufficient to characterize the probability law.

Properties of Probability Laws

Probability laws have a number of properties, which can be deduced from the axioms. Some of them are summarized below:

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The role of Probability Theory

Probability theory can be a very useful tool for making predictions and decisions that apply to the real world. Now, whether your predictions and decisions will be any good will depend on whether you have chosen a good model. Have you chosen a model that’s provides a good enough representation of the real world? How do you make sure that this is the case? There’s a whole field, the field of statistics, whose purpose is to complement probability theory by using data to come up with good models. And so we have the following diagram that summarizes the relation between the real world, statistics, and probability. The real world generates data. The field of statistics and inference uses these data to come up with probabilistic models. Once we have a probabilistic model, we use probability theory and the analysis tools that it provides to us. And the results that we get from this analysis lead to predictions and decisions about the real world.  Suggested video: Interpretation and uses of Probability

null

 

Sources:

  1. Introduction to probability (bertsekas, 2nd, 2008)
  2. Probability – The Science of Uncertainty and Data (MITx – 6.431x)

Literature review made by:

Prof. Larry Francis Obando – Technical Specialist – Educational Content Writer

Twitter: @dademuch

Copywriting, Content Marketing, Tesis, Monografías, Paper Académicos, White Papers (Español – Inglés)

Escuela de Ingeniería Eléctrica de la Universidad Central de Venezuela, UCV CCs

Escuela de Ingeniería Electrónica de la Universidad Simón Bolívar, USB Valle de Sartenejas.

Escuela de Turismo de la Universidad Simón Bolívar, Núcleo Litoral.

Contact: Caracas, Quito, Guayaquil, Cuenca. telf – 0998524011

WhatsApp: +593998524011   +593981478463 

Twitter: @dademuch

FACEBOOK: DademuchConnection

email: dademuchconnection@gmail.com

Travel Writing

Requisitos Visa de Estudio España – Lo más importante.

Resulta insuficiente la información que aparece en la página web del Ministerio de Asuntos Exteriores del Gobierno de España, referida a la solicitud de Visa de Estudio para un tiempo mayor de 90 días. En Guayaquil, hoy 4 de julio del 2019, mi esposa entregó en el Consulado Español todos los documentos solicitados en la siguiente dirección:

http://www.exteriores.gob.es/Consulados/GUAYAQUIL/es/InformacionParaExtranjeros/Paginas/VisadosGuayaquil/Visado_Estudios.aspx

Sin embargo, sólo al pagar la cita y la tasa ($70) y asistir a dicha cita, fue correctamente atendida e informada de los requisitos para obtener la Visa, entre los cuales destaca el requerimiento imprescindible de contar con al menos 9.000 dólares en una cuenta bancaria (o tarjeta de crédito, beca, préstamo..por la misma cantidad).

La funcionaria que le atendió, hizo caso omiso al resto de los documentos presentados: seguro médico, boleto aéreo (sólo se requiere la reserva), matrícula de estudio pagada, reserva de alojamiento, certificado médico, antecedentes penales…Ella fue directamente al estado de cuenta y de inmediato informó a mi esposa que no contaba con los recursos financieros suficientes, al menos 9.000 dólares, para un postgrado de un año en Andalucía.

Por tanto, es lo más importante para ellos, el capital. No vale la pena engañarse invirtiendo tiempo y dinero en el resto de los requisitos si no se cuenta con el principal. A continuación, una copia del documento donde aparecen los requisitos…el que debería circular en internet para evitar pecar de ingenuo:

Requerimientos de subsanación..

Visa Estudiante Requisitos

A modo de advertencia, cabe acotar que a pesar de consultar a varios asesores que pululan como moscas alrededor de los consulados españoles en América, ninguno nos advirtió de ese requisito principal, resaltando que en la mayoría de dichos asesores priva la necesidad de cobrar primero por gestiones como la reserva aérea o el seguro médico, antes que espantar al cliente potencial con la información esencial sobre el capital.

Nuevo resguardo de solicitud de protección internacional - España

Resguardo proteccion internacional España

Para mayor información:

Prof. Larry

whatsapp +34 633129287