A checkpointing-enabled and resource-aware Java Virtual Machine for efficient and robust e-Science applications in grid environments

Object-oriented programming languages presently are the dominant paradigm of application development (e. g., Java,. NET). Lately, increasingly more Java applications have long (or very long) execution times and manipulate large amounts of data/information, gaining relevance in fields related with e-Science (with Grid and Cloud computing). Significant examples include Chemistry, Computational Biology and Bio-informatics, with many available Java-based APIs (e. g., Neobio). Often, when the execution of such an application is terminated abruptly because of a failure (regardless of the cause being a hardware of software fault, lack of available resources, etc.), all of its work already performed is simply lost, and when the application is later re-initiated, it has to restart all its work from scratch, wasting resources and time, while also being prone to another failure and may delay its completion with no deadline guarantees. Our proposed solution to address these issues is through incorporating mechanisms for checkpointing and migration in a JVM. These make applications more robust and flexible by being able to move to other nodes, without any intervention from the programmer. This article provides a solution to Java applications with long execution times, by extending a JVM (Jikes research virtual machine) with such mechanisms. Copyright (C) 2011 John Wiley & Sons, Ltd.

Lighting conditions in assembling electrical industry

Proper lighting is a prerequisite for obtaining a good working environment. Good lighting includes quantity and quality requirements, and should necessarily be appropriate to the activity/task being carried out, bearing in mind the comfort and visual efficiency of the worker. Apart from the advantages in the health and welfare for the workers, good lighting also leads to better job performance (faster), less errors, better safety, fewer accidents and less absenteeism. The overall effect is: better productivity.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Mergers and acquisitions as strategies of business expansion in the pharmaceutical industry

Introduction: The present paper deals with the issue of the increasing usage of corporation mergers and acquisitions strategies within pharmaceutical industry environment. The aim is to identify the triggers of such business phenomenon and the immediate impact on the financial outcome of two powerful biopharmaceutical corporations: Pfizer and GlaxoSmithKline, which have been sampled due to their successful approach of the tactics in question. Materials and Methods: In order to create an overview of the development steps through mergers and acquisitions, the historical data of the two corporations has been consulted, from their official websites. The most relevant events were then associated with adequate information from the financial reports and statements of the two corporations indulged by web-based financial data providers. Results and Discussions: In the past few decades Pfizer and GlaxoSmithKline have purchased or merged with various companies in order to monopolize new markets, diversify products and services portfolios, survive and surpass competitors. The consequences proved to be positive although this approach implies certain capital availability. Conclusions: Results reveal the fact that, as far as the two sampled companies are concerned, acquisitions and mergers are reactions at the pressure of the highly competitive environment. Moreover, the continuous diversification of the market’s needs is also a consistent motive. However, the prevalence and the eminence of mergers and acquisition strategies are conditioned by the tender offer, the announcer’s caliber, research and development status and further other factors determined by the internal and external actors of the market.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Zonal prices analysis supported by a data mining based methodology

A methodology based on data mining techniques to support the analysis of zonal prices in real transmission networks is proposed in this paper. The mentioned methodology uses clustering algorithms to group the buses in typical classes that include a set of buses with similar LMP values. Two different clustering algorithms have been used to determine the LMP clusters: the two-step and K-means algorithms. In order to evaluate the quality of the partition as well as the best performance algorithm adequacy measurements indices are used. The paper includes a case study using a Locational Marginal Prices (LMP) data base from the California ISO (CAISO) in order to identify zonal prices.

Ant colony system based approach to single machine scheduling problems: weighted tardiness scheduling problem

The paper introduces an approach to solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System based algorithm is validated with benchmark problems available in the OR library. The obtained results were compared with the best available results and were found to be nearer to the optimal. The obtained computational results allowed concluding on their efficiency and effectiveness.

Desenvolvimento do sistema de protecção, monitorização e gestão de baterias de iões de lítio do tipo LiFePO4 para aplicação em veículos eléctricos

A mobilidade é considerada um dos factores chave na sustentabilidade e desenvolvimento de qualquer economia. Em Portugal essa realidade não é diferente. Em 2011 verifica-se que 41% do consumo global de combustíveis pertence ao sector rodoviário [1] o que evidencia a sua relevância na economia do país. No que concerne aos veículos de tracção eléctrica, começaram a surgir nos finais do séc. XIX, e no início do séc. XX nos Estados Unidos da América representavam 38% dos veículos [2]. Diversos factores económicos e tecnológicos conduziram a um crescente desinteresse por parte da indústria em investir na produção deste tipo de veículos. Contudo com a introdução de baterias de iões de lítio em veículos de tracção eléctrica, torna-os viáveis e competitivos. Neste trabalho é proposto o desenvolvimento de um sistema de gestão de baterias de iões de lítio do tipo LiFePO4 para aplicação em veículos eléctricos. O sistema deverá assegurar a protecção das baterias e indicar o estado de carga das mesmas. Este sistema permitirá uma optimização no uso deste género de baterias, proporcionará uma melhor utilização, aumentando a sua vida útil. O sistema irá ser aplicado e testado experimentalmente no veículo eléctrico ecológico (Veeco). No âmbito do projecto Veeco foi projectado e construído um banco de ensaios utilizado na análise do comportamento das baterias, e determinar quais os requisitos necessários para o sistema de gestão desenvolvido. Foi também projectado e realizado um sistema de aquisição e processamento de dados que permite obter informações acerca da bateria, dados que estarão disponíveis no interface Homem-máquina do Veeco.

Planning, Cooperation and Competition: A case study in industry and construction in a Portuguese Region

In the present paper we analyzed the behavior of firms in the construction and manufacturing sectors, located in the region of Vale do Sousa, in the north of Portugal. From the literature, even existing some disagreements, it is possible to conclude that planning is crucial for firms survival and growth. Cooperation is another aspect that the literature presents as an important factor for firms sustainability. It also plays a major role in competition, since firms are adopting coopetition strategies. By studying a sample of 251 firms, it was possible to realize, that the majority started their business without a formal planning, and they keep going without using it. In cooperation aspects, there is a lack of cooperation. It was possible to verify, that existing cooperation has some evidence but at a vertical level. These vertical relations were also identified in stakeholder’s involvement.

Nanofiltration of Cork Wastewaters and Their Possible Use in Leather Industry as Tanning Agents

Cork processing wastewater is a very complex mixture of vegetal extracts and has, among other natural compounds, a very high content of phenolic/tannic colloidal matter that is responsible for severe environmental problems. In the present work, the concentration of this wastewater by nanofiltration was investigated with the aim of producing a cork tannin concentrate to be utilized in tanning. Permeation results showed that the permeate fluxes are controlled by both osmotic pressure and fouling/gel layer phenomena, leading to a rapid decrease of permeate fluxes with the concentration factor. The rejection coefficients to organic matter were higher than 95%, indicating that nanofiltration has a very good ability to concentrate the tannins and produce a permeate stream depleted from organic matter. The cork tannin concentrate obtained by nanofiltration and evaporation had total solids concentration of 34.8 g/l. The skins tanned by this concentrate were effectively converted to leather with a shrinking temperature of 7 degrees C.

Cork industry wastewater partition by ultra/nanofiltration: A biodegradation and valorisation study

Wastewater from cork processing industry present high levels of organic and phenolic compounds, such as tannins, with a low biodegradability and a significant toxicity. These compounds are not readily removed by conventional municipal wastewater treatment, which is largely based on primary sedimentation followed by biological treatment. The purpose of this work is to study the biodegradability of different cork wastewater fractions, obtained through membrane separation, in order to assess its potential for biological treatment and having in view its valorisation through tannins recovery, which could be applied in other industries. Various ultrafiltration and nanofiltration membranes where used, with molecular weight cut-offs (MWCO) ranging from 0.125 to 91 kDa. The wastewater and the different permeated fractions were analyzed in terms of Total Organic Carbon (TOC), Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Phenols (TP), Tannins, Color, pH and Conductivity. Results for the wastewater shown that it is characterized by a high organic content (670.5-1056.8 mg TOC/L, 2285-2604 mg COD/L, 1000-1225 mg BOD/L), a relatively low biodegradability (0.35-0.38 for BODs/COD and 0.44-0.47 for BOD20/COD) and a high content of phenols (360-410 mg tannic acid/L) and tannins (250-270 mg tannic acid/L). The results for the wastewater fractions shown a general decrease on the pollutant content of permeates, and an increase of its biodegradability, with the decrease of the membrane MWCO applied. Particularly, the permeated fraction from the membrane MWCO of 3.8 kDa, presented a favourable index of biodegradability (0.8) and a minimized phenols toxicity that enables it to undergo a biological treatment and so, to be treated in a municipal wastewater treatment plant. Also, within the perspective of valorisation, the rejected fraction obtained through this membrane MWCO may have a significant potential for tannins recovery. Permeated fractions from membranes with MWCO lower than 3.8 kDa, presented a particularly significant decline of organic matter and phenols, enabling this permeates to be reused in the cork processing and so, representing an interesting perspective of zero discharge for the cork industry, with evident environmental and economic advantages. (C) 2010 Elsevier Ltd. All rights reserved.