El cuadro de financiación del Plan General de Contabilidad: ¿aporta nueva información a las cuentas anuales?

El Cuadro de Financiación es un estado contable que, como ya se indica en la Introducción del Plan General de Contabilidad, no es un documento independiente dentro de las cuentas anuales, sino que forma parte de la Memoria. Por este motivo hay que entender que, al igual que el resto de la información que aparece en la Memoria, debe completar la información ofrecida por el Balance y la Cuenta de Pérdidas y Ganancias. El objetivo del presente trabajo es analizar si el Cuadro de Financiación que presenta el Plan General de Contabilidad aporta al usuario externo una información adicional y distinta al resto de las Cuentas Anuales, que sirva para analizar los flujos financieros durante el ejercicio o si, por el contrario, dicha información es una reelaboración de la información que el resto de las Cuentas Anuales aportan y que podría ser deducida por los usuarios. En otras palabras, se trata de analizar si el Cuadro de Financiación puede ser confeccionado desde el exterior de la empresa con la información que la propia empresa aporta en sus Cuentas Anuales. Para ello se analizarán cada uno de los elementos informativos que configuran el Cuadro de Financiación así como su presencia en el resto de las Cuentas Anuales

A unified approach for hierarchical time series forecasting

In this paper an approach for hierarchical time series forecasting based on State Space modelling is proposed. Previous developments provide solutions to the hierarchical forecasting problem by algebra manipulations based on forecasts produced by independent models for each time series involved in the hierarchy. The solutions produce optimal reconciled forecasts for each individual forecast horizon, but the link along time that is implied by the dynamics of the models is completely ignored. Therefore, the novel approach in this paper improves upon past research at least in two key points. Firstly, the algebra is already encoded in the State Space system and the Kalman Filter algorithm, giving an elegant and clean solution to the problem. Secondly, the State Space approach is optimal both across the hierarchy, as expected, but also along time, something missing in past developments. In addition, the present approach provides an unified treatment of top-down, bottom-up, middle-out and reconciled approaches reported in the literature; it generalizes the optimization of hierarchies by proposing combined hierarchies which integrate the previous categories at different segments of the hierarchy; and it allows for multiple hierarchies to be simultaneously adjusted. The approach is assessed by comparing its forecasting performance to the existing methods, through simulations and using real data of a Spanish grocery retailer

Antecedentes históricos de la previsión social

El presente trabajo tiene tres partes claramente diferenciadas. En la primera de ellas, se contempla la evolución histórica del seguro hasta nuestros días. La segunda parte recoge el surgimiento del régimen de previsión social obligatoria, su nacimiento en el siglo XVII en el reino Unido, su evolución en Europa y en algunos países del resto del mundo. Existen dos corrientes del sistema de previsión social: Atlántico o universalista y el continental o individualista. La tercera parte se centra en el surgimiento de la Seguridad Social en España y la aparición del sistema de previsión social complementaria o Planes y Fondos de pensiones, haciendo especial reseña a su incidencia en variables biométricas, actuariales, sociales y financiera

Automatic selection of Unobserved Components models for supply chain forecasting

For many companies, automatic forecasting has come to be an essential part of Business Analytics applications. The large amounts of data available, the short life-cycle of the analysis and the acceleration of business operations make traditional handmade data analysis unfeasible in such environments. In this paper, an Automatic Forecasting Support System comprising several methods and models is developed in a general State Space framework built in the so called SSpace toolbox written for Matlab. Some of the models included such as Exponential Smoothing and ARIMA are well-known, but we propose a new model family that has very rarely been used before in this context, namely Unobserved Components models. Additional novelties are that Unobserved Components models are used in an automatic identification environment and that their forecasting performance is compared with Exponential Smoothing and ARIMA models estimated with different software packages. The daily sold units dataset of a franchise chain in Spain spanning 166 products and 517 days of sales is used to assess empirically the new system. The system works well in practice and the proposed automatic Unobserved Components models compare very favorably with other methods and other well-known software packages in forecasting terms

Diseño de estructura de pavimento articulado (adoquín) empleando el método AASHTO 93 del tramo empalme Hermanos Cruz- El Regadío (3km) Departamento de Estelí

Presenta un diseño de pavimento articulado, empleando el método AASHTO 93 del tramo empalme Hermanos Cruz, El Regadío 3 km, departamento de Estelí. Asimismo se realiza un estudio de suelo y de tránsit

A new SVM-based ensemble approach for time series forecasting

Time series analysis has remained as an extremely active research area for decades, receiving a great deal of attention from very different domains like econometrics, statistics, engineering, mathematics, medicine and social sciences. To say nothing about its importance in real-world applications in a wide variety of industrial and business scenarios. However, as hardware becomes ubiquitous, the amounts of data collected is more and more overwhelming, bringing us all the so-called big data era. It is in this context where automatic time series analysis deserves especial attention as a mean of making sense of such enormous databases. Nevertheless, the automatic identification of the appropriate data modelling techniques stands in the middle as a compulsory stage of any big data implementation. Research on model selection and combination points out the benefits of such techniques in terms of forecast accuracy and reliability. This study proposes a novel ensemble approach for automatic time series forecasting as a part of a big data implementation. Given a set of alternative models, a Support Vector Machine (SVM) is trained at each forecasting origin to select the best model, according to the computed features and the past performance. The feature space embeds information of the time series itself as well as responses and parameters of the alternative models. This approach will help to reduce the risk of misusing modelling techniques when dealing with big datasets, and at the same time will provide a mechanism to assert the appropriateness of the underlying models used to analyse such data. The effects of the proposed approach are explored empirically using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care UK manufacturer. Findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts

Temporal regulation of the Mus81-Mms4 endonuclease ensures cell survival under conditions of DNA damage

The structure-specific Mus81-Eme1/Mms4 endonuclease contributes importantly to DNA repair and genome integrity maintenance. Here, using budding yeast, we have studied its function and regulation during the cellular response to DNA damage and show that this endonuclease is necessary for successful chromosome replication and cell survival in the presence of DNA lesions that interfere with replication fork progression. On the contrary, Mus81-Mms4 is not required for coping with replicative stress originated by acute treatment with hydroxyurea (HU), which causes fork stalling. Despite its requirement for dealing with DNA lesions that hinder DNA replication, Mus81-Mms4 activation is not induced by DNA damage at replication forks. Full Mus81-Mms4 activity is only acquired when cells finish S-phase and the endonuclease executes its function after the bulk of genome replication is completed. This post-replicative mode of action of Mus81-Mms4 limits its nucleolytic activity during S-phase, thus avoiding the potential cleavage of DNA substrates that could cause genomic instability during DNA replication. At the same time, it constitutes an efficient fail-safe mechanism for processing DNA intermediates that cannot be resolved by other proteins and persist after bulk DNA synthesis, which guarantees the completion of DNA repair and faithful chromosome replication when the DNA is damagedSpanish Ministry of Economy and Competitiveness [BFU2010-16989 and Consolider Ingenio CSD2007-00015 to J.A.T.]; Fundación Ramón Areces (Institutional Grant to the Centro de Biologıa Molecular Severo Ochoa); Spanish Ministry of Economy and Competitiveness (predoctoral fellowships to M.V.V and M.A.O-B.); Universidad Autónoma de Madrid (predoctoral fellowship to M.G-F.); Consejo Superior de Investigaciones Cientıficas (JAE-Doc contract to M.S.). Funding for open access charge: Spanish Ministry of Economy and Competitiveness [BFU2010-16989 and Consolider Ingenio CSD2007-00015]Peer Reviewe

Hemodynamic Tolerance of Virtual Reality Intradialysis Exercise Performed during the Last 30 Minutes versus the Beginning of the Hemodialysis Session

[EN] Background: Exercise improves the physical function of people suffering from chronic kidney disease on hemodialysis (HD). Virtual reality is a new type of intradialysis exercise that has a positive impact on physical function. Intradialysis exercise is recommended during the first 2 h, but its safety in the last part of the dialysis session is unknown. Methods: This was a pilot sub-study of a clinical trial. Several hemodynamic control variables were recorded, including blood pressure, heart rate, and intradialytic hypotensive events. These variables were recorded during three different HD sessions, one HD session at rest, another HD session with exercise during the first two hours, and one HD session with exercise during the last 30 min of dialysis. The intradialysis virtual reality exercise was performed for a maximum of 30 min. Results: During exercise sessions, there was a significant increase in heart rate (6.65 (4.92, 8.39) bpm; p < 0.001) and systolic blood pressure (6.25 (0.04,12.47) mmHg; p < 0.05). There was no difference in hemodynamic control between the sessions with exercise during the first two hours and the sessions with exercise during the last 30 min. There was no association between intra-dialytic hypotensive events at rest (five events) or exercise at any point (two vs. one event(s), respectively). Conclusion: performing exercise with virtual reality at the end of a hemodialysis session is not associated with hemodynamic instability.Funding included a research project (PID2019-108814RA-I00) supported by the Spanish Government 'Ministerio de Ciencia e Innovacion', a research prize awarded by the nonprofit organization Fundacion Renal Tomas de Osma, as well as from a research grant (IDOC 17-19 and PPC14/2017) awarded by the Universidad Cardenal Herrera CEU.García-Testal, A.; Martínez-Olmos, FJ.; Gil-Gómez, J.; López-Tercero, V.; Lahoz-Cano, L.; Hervás-Marín, D.; Cana-Poyatos, A.... (2023). Hemodynamic Tolerance of Virtual Reality Intradialysis Exercise Performed during the Last 30 Minutes versus the Beginning of the Hemodialysis Session. Healthcare. 11(1). https://doi.org/10.3390/healthcare1101007911

Vocabulario de la enfermedad por el virus del ébola (EN-ES)

Se trata de un estudio terminológico sistemático sobre la enfermedad por el virus del Ébola (Ebola virus disease)