Does technological progress magnify regional disparities?

We study how technological progress in manufacturing and transportation to-gether with migration costs interact to shape the space-economy. Rising labor productivity in the manufacturing sector fosters the agglomeration of activities, whereas falling transport costs associated with technological and organizational in-novations fosters their dispersion. Since these two forces have been at work for a long time, the final outcome must depend on how drops in the costs of producing and trading goods interact with the various costs borne by migrants. Finally, when labor is heterogeneous, the most efficient workers of the less productive region are the first to move to the more productive region.

Forecasting inbound tourists in Cambodia

Forecasting tourism demand is crucial for management decisions in the tourism sector. Estimating a vector autoregressive (VAR) model for monthly visitor arrivals disaggregated by three entry points in Cambodia for the years 2006–2015, I forecast the number of arrivals for years 2016 and 2017. The results show that the VAR model fits well with the data on visitor arrivals for each entry point. Ex post forecasting shows that the forecasts closely match the observed data for visitor arrivals, thereby supporting the forecasting accuracy of the VAR model. Visitor arrivals to Siem Reap and Phnom Penh airports are forecast to increase steadily in future periods, with varying fluctuations across months and origin countries of foreign tourists.

Competence Training in Higher Education: The case of the Technological Master "Prestación de Servicios Profesionales" from the Colegio de Postgraduados (México).

The Competency-Based Education in the context of training is intended as a comprehensive approach that seeks to link education with the productive sector and increase the potential of individuals, in the face of social, economic, political and cultural transformations that suffers the world and the contemporary society; this is how educational services associated to the rural area takes part of the global revalorization of the role of learning and knowledge. Under the competence approach and taking into account the CONOCER model, we design a Technological Master from the “Colegio de Postgraduados” identifying the competences needed so that the students, professional from different areas of knowledge, managed to develop them, but mainly to achieve the goal of developing the capacities of producers in Mexican rural area.

Towards a Transfer Model of Scientific and Technological Knowledge: the Case of Mexico

This paper shows the development of a science-technological knowledge transfer model in Mexico, as a means to boost the limited relations between the scientific and industrial environments. The proposal is based on the analysis of eight organizations (research centers and firms) with varying degrees of skill in the practice of science-technological knowledge transfer, and carried out by the case study approach. The analysis highlights the synergistic use of the organizational and technological capabilities of each organization, as a means to identification of the knowledge transfer mechanisms best suited to enabling the establishment of cooperative processes, and achieve the R&D and innovation activities results.

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Forecasting Electricity Prices and their volatilities using Unobserved Components.

The liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities, both in the short and long term. Thus, there is a need of providing methodology that is able to deal with the most important features of electricity price series, which are well known for presenting not only structure in conditional mean but also time-varying conditional variances. In this work we propose a new model, which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices. These common factors are jointly estimated as well as their relationship with the original vector of series, and the dynamics affecting both their conditional mean and variance. The estimation of the model is carried out under the state-space formulation. The new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the Spanish zone of the Iberian Market. Several simplified/alternative models are also considered as benchmarks to illustrate that the proposed approach is superior to all of them in terms of explanatory and predictive power.

An Analysis of the Influence of Students´ Technological Culture on Their Technology

decade has raised the interest among the research community on the acceptance and use of these systems by both teachers and students. At first, the implementation of LMS was based on their technical design and the adaptation of the learning processes to the virtual environment, neglecting students’ characteristics when the systems were deployed, which led to expensive and failing implementations. The Unified Theory of Acceptance and Use of Technology (UTAUT) proposes a framework which allows the study of the acceptance and use of technology that takes into consideration the students’ characteristics and how they affect the acceptance and the degree of use of educational technology. This study questions the role of the user’s attitude towards use of LMS and uses the UTAUT to examine the moderating effect of technological culture in the adoption of LMS in Spain. The results from the comparison and analysis of three different models confirm the relevance of attitude towards use as an antecedent of intention to use the system, as well as the important moderating effect of gender and technological culture. The discussion of results suggests the need for a more in-depth analysis and interrelations of cultural dimensions in the adoption of educational technologies and learning management systems

Service needs forecasting: an approach for the automotive industry using analogies with medical ER management models.

During the past years, the industry has shifted position and moved towards “the luxury universe” whose customers are demanding, treating individuals as unique and valued customer for the business, offering vehicles produced with the state of the art technologies and implementing the highest finishing standards. Due to the competitive level in the market, car makers enable processes which equalizes customer services to E.R. management, being dealt with the maximum urgency that allows the comparison between both, car workshops and emergency rooms, where workshop bays or ramps will be equal to emergency boxes and skilled technicians are equivalent to the health care specialist, who will carry out tests and checks prior to afford any final operation, keeping the “patient” under control before it is back to normal utilization. This paper establishes a valid model for the automotive industry to estimate customer service demand forecasting under variable demand conditions using analogies with patient demand models used for the medical ER.

SERVICE NEEDS FORECASTING: An Approach for the Motor Industry Using Analogies with Medical ER Management Models.

During the past years, the industry has shifted position and moved towards “the luxury universe” whose customers are demanding, treating individuals as unique and valued customer for the business, offering vehicles produced with the state of the art technologies and implementing the highest finishing standards. Due to the competitive level in the market, motor makers enable processes which equalizes customer services to E.R. management, being dealt with the maximum urgency that allows the comparison between both, car workshops and emergency rooms, where workshop bays or ramps will be equal to emergency boxes and skilled technicians are equivalent to the health care specialist, who will carry out tests and checks prior to afford any final operation, keeping the “patient” under control before it is back to normal utilization. This paper ratify a valid model for the automotive industry to estimate customer service demand forecasting under variable demand conditions using analogies with patient demand models used for the medical ER

Applications of Infrared Thermography as innovative technological solution in sports injuries

Infrared thermography IR is a technique, which allows us to get rapidly and non-invasive thermal images from objects or human beings. (Barnes, 1967). In Medicine, its usefulness as diagnosis tool was accepted decades ago (BenEliyahu, 1990), but other techniques with a higher efficiency -such as magnetic resonance or x-rays- ousted it. Nevertheless, the technological improvements on thermographic cameras and new studies on sport injuries are reinforcing new applications (Ring, 2006)

Influence of local wind speed and direction on wind power dynamics - Application to offshore very short-term forecasting

Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some of these effects by means of statistical models. To this end, a benchmarking between two different families of varying-coefficient models (regime-switching and conditional parametric models) is carried out. The case of the offshore wind farm of Horns Rev in Denmark has been considered. The analysis is focused on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power transformation process can be introduced by considering the local wind speed. In both cases, conditional parametric models showed a better performance than the one achieved by the regime-switching strategy. The results attained reinforce the idea that each explanatory variable allows the modelling of different underlying effects in the dynamics of wind power time series.

Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

CO2 soil flux baseline at the technological development plant for CO2 injection at Hontomin (Burgos, Spain)

From the end of 2013 and during the following two years, 20 kt of CO2sc are planned to be injected in a saline reservoir (1500 m depth) at the Hontomín site (NE Spain). The target aquifers are Lower Jurassic limestone formations which are sealed by Lower Cretaceous clay units at the Hontomín site (NE Spain). The injection of CO2 is part of the activities committed in the Technology Development phase of the EC-funded OXYCFB300 project (European Energy Program for Recovery – EEPR, http://www.compostillaproject.eu), which include CO2 injection strategies, risk assessment, and testing and validating monitoring methodologies and techniques. Among the monitoring works, the project is intended to prove that present-day technology is able to monitor the evolution of injected CO2 in the reservoir and to detect potential leakage. One of the techniques is the measurement of CO2 flux at the soil–atmosphere interface, which includes campaigns before, during and after the injection operations. In this work soil CO2 flux measurements in the vicinity of oil borehole, drilled in the eighties and named H-1 to H-4, and injection and monitoring wells were performed using an accumulation chamber equipped with an IR sensor. Seven surveys were carried out from November 2009 to summer 2011. More than 4000 measurements were used to determine the baseline flux of CO2 and its seasonal variations. The measured values were low (from 5 to 13 g m−2 day−1) and few outliers were identified, mainly located close to the H-2 oil well. Nevertheless, these values cannot be associated to a deep source of CO2, being more likely related to biological processes, i.e. soil respiration. No anomalies were recognized close to the deep fault system (Ubierna Fault) detected by geophysical investigations. There, the CO2 flux is indeed as low as other measurement stations. CO2 fluxes appear to be controlled by the biological activity since the lowest values were recorded during autumn-winter seasons and they tend to increase in warm periods. Two reference CO2 flux values (UCL50 of 5 g m−2 d−1 for non-ploughed areas in autumn–winter seasons and 3.5 and 12 g m−2 d−1 for in ploughed and non-ploughed areas, respectively, in spring–summer time, and UCL99 of 26 g m−2 d−1 for autumn–winter in not-ploughed areas and 34 and 42 g m−2 d−1 for spring–summer in ploughed and not-ploughed areas, respectively) were calculated. Fluxes higher than these reference values could be indicative of possible leakage during the operational and post-closure stages of the storage project.

FRe building technological products

Nowadays "Flood Resilient (FRe) Building Technological Products" is an undefined concept, and concerned FRe solutions cannot be even easily identified. There is an interest in offering an identification and classification of the referred products, since it will be useful for stakeholders and populations at flood risk for adopting the most adequate protections when facing floods. There are many barriers for the implementation of "FRe building technological products", and particularly their standardization is still a major challenge. To put into contact such solutions with final customers, it appears necessary to protocolize them all. The classification effort achieved in this document shall be considered as a necessary preliminary step in order to open the road to the market to FRe building technological solutions.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.