Concise server-wide causality management for eventually consistent data stores

Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of net work partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conflicts. Another aspect of eventual consistency is detecting write conflicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a nonnegligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, perkey metadata cannot be permanently removed without coordination. Weintroduceanewcausalitymanagementframeworkforeventuallyconsistentdatastores,thatleveragesnodelogicalclocks(BitmappedVersion Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new efficient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.

Interrelationship between partition behavior of organic compounds and proteins in aqueous dextran-Polyethylene glycol and Polyethylene glycol-sodium sulfate two-phase systems

Partition behavior of adenosine and guanine mononucleotides was examined in aqueous dextran-polyethylene glycol (PEG) and PEG-sodium sulfate two-phase systems. The partition coefficients for each series of mononucleotides were analyzed as a functions of the number of phosphate groups and found to be dependent on the nature of nucleic base and on the type of \ATPS\ utilized. It was concluded that an average contribution of a phosphate group into logarithm of partition coefficient of a mononucleotide cannot be used to estimate the difference between the electrostatic properties of the coexisting phases of ATPS. The data obtained in this study were considered together with those for other organic compounds and proteins reported previously, and the linear interrelationship between logarithms of partition coefficients in dextran-PEG, PEG-Na2SO4 and PEG-Na2SO4-0.215 M NaCl (all in 0.01 M Na- or K/Na-phosphate buffer, pH 7.4 or 6.8) was established. Similar relationship was found for the previously reported data for proteins in Dex-PEG, PEG-600-Na2SO4, and PEG-8000-Na2SO4 ATPS. It is suggested that the linear relationships of the kind established in \ATPS\ may be observed for biological properties of compounds as well.

Sefficiency (sustainable efficiency) of water–energy–food entangled systems

Supplemental data for this article can be accessed at http://dx.doi.org/10.1080/07900627.2015.1070091. It includes an easy-to-use spreadsheet that calculates the efficiencies used in this paper, that is Sefficiency with energy considerations.

SOFHIA: a CAD environment to design digital control systems

"Series Title: IFIP - The International Federation for Information Processing, ISSN 1868-4238"

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.

In-line rheo-optical microstructural characterization of complex polymer systems

Tese de Doutoramento em Ciência e Engenharia de Polímeros e Compósitos.

Vapor - liquid equilibrium of systems containing perfluoromethyl-cyclohexane and hydrocarbons

Vapor - liquid equilibrium data for the binary systems: Perfluoromethylcyclohexane + n-Hexane and Perfluoromethylcyclohexane + 1-Hexene were determined at 93.3 KPa and 328.15 K. The vapor pressure for the pure components were also measured to calculate the Antoine constants. The data were correlated by using the Van-Laar, Margules, Wilson, NRTL and UNIQUAC equations. UNIFAC group-contribution parameters between CH, and CF,, and CH,=CH and CF, were also calculated.