Speculation-Aware Resource Allocation for Cluster Schedulers

Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation.

In this thesis, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. The algorithm is model predictive in nature, i.e., at every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptimality of this model predictive algorithm vanishes as time horizon expands in the average case analysis. Further, we prove strong concentration results on the distribution of the load variance obtained by model predictive deferrable load control. These concentration results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance. Finally, we evaluate the algorithm via trace-based simulations.

Towards a standard methodology to evaluate internal cluster validity indices

The evaluation and comparison of internal cluster validity indices is a critical problem in the clustering area. The methodology used in most of the evaluations assumes that the clustering algorithms work correctly. We propose an alternative methodology that does not make this often false assumption. We compared 7 internal cluster validity indices with both methodologies and concluded that the results obtained with the proposed methodology are more representative of the actual capabilities of the compared indices.

Orígenes, elementos determinantes y resultados de un exitoso proceso de colaboración entre competidores y otros agentes: el cluster de la Alta Cocina Vasca

[ES] El País Vasco es internacionalmente reconocido por su gastronomía y sus grandes cocineros; de hecho, es el territorio del mundo con más estrellas Michelin por kilómetro cuadrado. Esta notoriedad e imagen repercuten muy positivamente en todo el sector gastronómico y en la imagen y proyección turística del País Vasco y se ha logrado gracias a la labor sostenida de un grupo inicial de cocineros, a los que siguieron otros, que realizan importantes esfuerzos de colaboración, sin dejar de competir entre ellos (tratándose de un claro ejemplo de coopetition). El análisis de la relación entre estos grandes cocineros vascos y su entorno, permite identificar un cluster que actualmente se encuentra en fase de madurez con un futuro esperanzador y que ha arrojado importantes beneficios al sector, a cada uno de sus integrantes y a la región en su conjunto muy especialmente en términos de innovación, notoriedad y reputación. Para la realización de este trabajo se ha utilizado, además de la revisión bibliográfica y documental pertinente, una metodología cualitativa, consistente en la realización de entrevistas en profundidad a los siete cocineros fundadores y patronos del Basque Culinary Center (primera Facultad Universitaria de Estudios Gastronómicos de Europa, dependiente de la Universidad de Mondragón). El trabajo es uno de los frutos extraídos de un contrato de colaboración entre el Instituto de Economía Aplicada a la empresa de la UPV/EHU e Innobasque (Agencia Vasca para la Innovación), en el que esta última fijó tanto los objetivos de la investigación como la metodología a utilizar.

Subtypes of Patients Experiencing Exacerbations of COPD and Associations with Outcomes

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition characterized by occasional exacerbations. Identifying clinical subtypes among patients experiencing COPD exacerbations (ECOPD) could help better understand the pathophysiologic mechanisms involved in exacerbations, establish different strategies of treatment, and improve the process of care and patient prognosis. The objective of this study was to identify subtypes of ECOPD patients attending emergency departments using clinical variables and to validate the results using several outcomes. We evaluated data collected as part of the IRYSS-COPD prospective cohort study conducted in 16 hospitals in Spain. Variables collected from ECOPD patients attending one of the emergency departments included arterial blood gases, presence of comorbidities, previous COPD treatment, baseline severity of COPD, and previous hospitalizations for ECOPD. Patient subtypes were identified by combining results from multiple correspondence analysis and cluster analysis. Results were validated using key outcomes of ECOPD evolution. Four ECOPD subtypes were identified based on the severity of the current exacerbation and general health status (largely a function of comorbidities): subtype A (n = 934), neither high comorbidity nor severe exacerbation; subtype B (n = 682), moderate comorbidities; subtype C (n = 562), severe comorbidities related to mortality; and subtype D (n = 309), very severe process of exacerbation, significantly related to mortality and admission to an intensive care unit. Subtype D experienced the highest rate of mortality, admission to an intensive care unit and need for noninvasive mechanical ventilation, followed by subtype C. Subtypes A and B were primarily related to other serious complications. Hospitalization rate was more than 50% for all the subtypes, although significantly higher for subtypes C and D than for subtypes A and B. These results could help identify characteristics to categorize ECOPD patients for more appropriate care, and help test interventions and treatments in subgroups with poor evolution and outcomes.

Investigation into the factors associated with poor water quality in intensively cultured prawn ponds

Water quality problems are reported to be the factor limiting prawn production in the local prawn farm. This investigation was carried out to monitor water quality and its relationship to physical, chemical and biological conditions in the ponds in order to establish what factors should be monitored in order to predict problems. Pond collapse was found to be associated with high concentrations of ammonium, high pH and blue-green algae dominated phytoplankton populations. There was no easy means of predicting the imminent collapse of ponds as the phenomenon was never associated with the extreme of any of the conditions monitored. Rather it seemed to be related to the stability of the pond's algal population, which was largely unaccounted for. Recommendations toward improving water quality are proposed.

Use of SARIMA models to assess data-poor fisheries: a case study with a sciaenid fishery off Portugal

Research on assessment and monitoring methods has primarily focused on fisheries with long multivariate data sets. Less research exists on methods applicable to data-poor fisheries with univariate data sets with a small sample size. In this study, we examine the capabilities of seasonal autoregressive integrated moving average (SARIMA) models to fit, forecast, and monitor the landings of such data-poor fisheries. We use a European fishery on meagre (Sciaenidae: Argyrosomus regius), where only a short time series of landings was available to model (n=60 months), as our case-study. We show that despite the limited sample size, a SARIMA model could be found that adequately fitted and forecasted the time series of meagre landings (12-month forecasts; mean error: 3.5 tons (t); annual absolute percentage error: 15.4%). We derive model-based prediction intervals and show how they can be used to detect problematic situations in the fishery. Our results indicate that over the course of one year the meagre landings remained within the prediction limits of the model and therefore indicated no need for urgent management intervention. We discuss the information that SARIMA model structure conveys on the meagre lifecycle and fishery, the methodological requirements of SARIMA forecasting of data-poor fisheries landings, and the capabilities SARIMA models present within current efforts to monitor the world’s data-poorest resources.

Fish for the poor under a rising global demand and changing fishery regime

In the past, agricultural researchers tended to ignore the fisheries factor in global food and nutritional security. However, the role of fish is becoming critical as a result of changes in fisheries regimes, income distribution, demand and increasing international trade. Fish has become the fastest growing food commodity in international trade and this is raising concern for the supply of fish for poorer people. As a result, the impact of international trade regimes on fish supply and demand, and the consequences on the availability of fish for developing countries need to be studied. Policies aimed at increasing export earnings are in conflict with those aimed at increasing food security in third world countries. Fisheries policy research will need to focus on three primary areas which have an impact on the marginal and poorer communities of developing countries: increased international demand for low-value fish on the supply of poorer countries; improved aquaculture technologies and productivity on poorer and marginal farmers; and land and water allocation policy on productivity, food security and sustainability across farm, fishery and related sectors. The key to local food security is in the integration of agriculture, aquaculture and natural resources but an important focus on fisheries policy research will be to look at the linkages between societal, economic and natural systems in order to develop adequate and flexible solutions to achieve sustainable use of aquatic resources systems.

The future for fish in the food and livelihoods of the poor in Asia

This article is based on the study, Strategies and Options for Increasing and Sustaining Benefits from Fisheries and Aquaculture Production to Benefit Poor Households in Asia carried out under ADB-RETA 5945, and implemented by the WorldFish Center in partnership with nine participating Asian countries.

Growth, mortality and yield-per-recruit of the poor cod, Trisopterus minutus capelanus, from the Strait of Sicily

Length-based methods (LBMs) were used to study the growth of Trisopterus minutus capelanus in the Strait of Sicily (Messina Strait). A total of 16,304 'merluzzetto' or poor cod collected by experimental trawling off the southern coast of Sicily during spring, summer, autumn 1986 and winter 1987 were measured in order to estimate the length structure of the population. Length-frequency distribution were analyzed and normal components were discriminated. Von Bertalanffy growth parameters were derived from the mean length of the normal components. The growth parameters obtained by weighted non-linear regression were: K=0.462 (yr super(1)), L sub( infinity )=222.3 (TL,mm) and t sub(o)=-0.679 yr. The resulting growth performance index ( Phi ') was 4.36, a value slightly lower than those derived for Western Mediterranean (mean Phi '=4.45) and Adriatic ( Phi '=4.58) populations and slightly higher than that derived for Hellenic waters ( Phi '=4.27). On the basis of the von Bertalanffy parameters estimated, an array of age-specific instantaneous natural mortality rate (M sub(t)=0.5-1.1) and an average value of total natural mortality rate (Z=2.1 yr super(1)) were estimated and used in the Thompson and Bell yield per recruit (Y/R) analysis in order to evaluate the status of the fishery and forecast the effects of changes in the fishing pattern. Results indicate that this resource is overexploited and that Y/R could be increased by postponing the age at first capture from 0.5 to 1.0 yr. Even a slight reduction in fishing mortality could improve the performance of the fishery. At the present level of exploitation, and assuming a constant recruitment, the spawning stock biomass per recruit (SPR) is well below the conservative threshold of 30% of the pristine or unexploited SPR.