The Processing of Aluminum Gasarites via Thermal Decomposition of Interstitial Hydrides

Gasarite structures are a unique type of metallic foam containing tubular pores. The original methods for their production limited them to laboratory study despite appealing foam properties. Thermal decomposition processing of gasarites holds the potential to increase the application of gasarite foams in engineering design by removing several barriers to their industrial scale production. The following study characterized thermal decomposition gasarite processing both experimentally and theoretically. It was found that significant variation was inherent to this process therefore several modifications were necessary to produce gasarites using this method. Conventional means to increase porosity and enhance pore morphology were studied. Pore morphology was determined to be more easily replicated if pores were stabilized by alumina additions and powders were dispersed evenly. In order to better characterize processing, high temperature and high ramp rate thermal decomposition data were gathered. It was found that the high ramp rate thermal decomposition behavior of several hydrides was more rapid than hydride kinetics at low ramp rates. This data was then used to estimate the contribution of several pore formation mechanisms to the development of pore structure. It was found that gas-metal eutectic growth can only be a viable pore formation mode if non-equilibrium conditions persist. Bubble capture cannot be a dominant pore growth mode due to high bubble terminal velocities. Direct gas evolution appears to be the most likely pore formation mode due to high gas evolution rate from the decomposing particulate and microstructural pore growth trends. The overall process was evaluated for its economic viability. It was found that thermal decomposition has potential for industrialization, but further refinements are necessary in order for the process to be viable.

Processing of Rosmarinus officinalis Linne extract on spray and spouted bed dryers

This article presents an investigation of the potential of spray and spouted bed technology for the production of dried extracts of Rosmarinus officinalis Linne, popularly known as rosemary. The extractive solution was characterized by loss on drying, extractable matter and total phenolic and flavonoid compounds (chemical markers). The product was characterized by determination of loss on drying, size distribution, morphology, flow properties and thermal degradation and thermal behavior. The spray and spouted bed dryer performance were assessed through estimation of thermal efficiency, product accumulation and product recovery. The parameters studied were the inlet temperature of the spouting gas (80 and 150 degrees C) and the feed mass flow rate of concentrated extract relative to the evaporation capacity of the dryer, W-s/W-max (15 to 75%). The atomizing air flow rate was maintained at 20 l/min with a pressure of 196.1 kPa. The spouting gas flow rate used in the drying runs was 40% higher than the gas flow under the condition of minimum spouting. The spray drying gas flow rate was fixed at 0.0118 kg/s. Under the conditions studied, performance in the spray and spouted bed drying of rosemary extract was poor, causing high degradation of the marker compounds (mainly the phenolic compounds). Thus, process improvements are required before use on an industrial scale.

Fermentative production of hydrogen from cassava processing wastewater by Clostridium acetobutylicum

This work reports on the effect of initial substrate concentration on COD consumption, pH, and H(2) production during cassava processing wastewater fermentation by Clostridium acetobutylicum ATCC 824. Five initial COD wastewater concentrations, namely 5.0, 7.5, 10.7, 15.0, and 30.0 g/L, were used. The results showed that higher substrate concentrations (30.0 and 15.0 COD/L) led to lower H(2) yield as well as less efficient substrate conversion into H(2). On the other hand, initial COD concentrations of 10.7, 7.5 and 5 g/L furnished 1.34, 1.2 and 2.41 mol H(2)/mol glucose, with efficiency of glucose conversion into H(2) of 34, 30, and 60% (mol/mol), respectively. These results demonstrate that cassava processing wastewater, a highly polluting effluent, can be successfully employed as substrate for H(2) production by C acetobutylicum at lower COD concentrations. (C) 2011 Elsevier Ltd. All rights reserved.

Aplicação do multi-layer stream mapping para a gestão da eficiência de recursos e produtividade de um sistema de produção

Mestrado em Engenharia Mecânica – Especialização Gestão Industrial

Evaluation and optimization of a session-based middleware for data management

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Probabilistic base calling of Solexa sequencing data.

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.

Dairy Food Consumption, Production, and Policy in Japan, August 2005

We explore and investigate Japanese dairy markets. We first provide an overview of consumer demand and how it evolved after World War II. Using historical data and econometric estimates of Japanese dairy demand, we identify economic, cultural, and demographic forces that have been shaping consumption patterns. Then we summarize the characteristics of Japanese milk production and dairy processing and policies affecting them. We next describe the import regime and trade flows in dairy products. The analysis of the regulatory system of the dairy sector shows how its incentive structure affects the long-term prospects of various segments of the industry. The paper concludes with policy recommendations of how to reform the Japanese dairy sector.

Order-flow Standardization with Product Data Management Ideology

Tämän diplomityön päämääränä oli kuvata tilaus-toimitusprosessin eri toimintojen työnkulku, kun tuotetiedonhallintajärjestelmä on osa työympäristöä. Työn teoreettisessa osassa tarkasteltiin liiketoimintaprosessien uudistamista ja prosessien määrittämistä sekä esiteltiin tuotetiedonhallinnan (PDM) keskeiset osa-alueet. Kohdeyrityksen tausta ja strategiat esiteltiin, minkä jälkeen muutoksia arvioitiin suhteessa teoriaosuuden tuloksiin. Nykyisten toimintatapojen määrittämistä varten haastateltiin henkilöitä jokaisesta tilaus-toimitusprosessin vaiheesta tuotantoyksikön sisällä. Lopuksi kuvattiin yrityksen tuotetiedonhallintaperiaatteet ja määritettiin työnkulku prosessin eri vaiheissa. Samalla kuin uusi tuotetiedonhallintajärjestelmä otetaan käyttöön, on yrityksessä omaksuttava tuotetiedonhallinnan ajatusmalli. Tuoterakenteen hallinta jakautuu nyt eri toimintojen kesken, jolloin suunnittelun rakenne, tuotannon rakenne ja huoltorakenne ovat eri ihmisten vastuulla. Näiden eri rakenteiden konfigurointi tilaus-toimitus prosessin aikana määrää missä järjestyksessä toiminnot on suoritettava eri järjestelmien välillä. Monikansallinen suunnitteluorganisaatio on myös otettava huomioon tilauksenkulun aikana. Tuotetiedonhallintajärjestelmää käytetään yhdessä tuttujen suunnitteluohjelmien sekä toiminnanohjausjärjestelmän (ERP) kanssa. Työnkulkukaaviossa määritellään koko yritystä koskeva malli siitä, miten ja missä järjestyksessä tehtävät on suoritettava eri järjestelmissä tilaus-toimitus prosessin aikana. Tässä työssä tutkittiin tuotteen määrittelyn ja suunnittelutiedon hallinnan kannalta oleellisimmat tilaus-toimitusprosessiin kuuluvat toiminnot; myynti, myynnin tuki, tuotannon ohjaus, sovellussuunnittelu ja dokumentointi. Tulevaisuudessa on suositeltavaa pohtia tuotetiedonhallintajärjestelmän käyttöönottoa myös tuotannossa ja ostoissa. Tilaus-toimitusprosessiin liittyvät kehitysmahdollisuudet kannattaisi seuraavaksi kohdistaa tilauksen määrittelyvaiheeseen myyjä-asiakas rajapinnassa, jossa tehdyt virheet kertautuvat jokaisessa prosessin vaiheessa.

Project owner’s and outside engineering consultant’s role in power plant implementation projects

The purpose of this study was to investigate the nature of co-operation between a project owner and an outside engineering consultant in combined heat and power plant implementation projects. Moreover, as another focal subject of the study was to familiarize the purchasing behavior of the energy producer and how an outside engineering consultant participated into different stages of the purchasing process. The study was carried out as a multiple case study including altogether six Finnish power plant implementation projects that had been taken into commercial use during 1995 – 2015. By adjusting the findings of empirical interview data and comparing those to the theoretical framework concerning, among others, Finnish energy production, engineering consulting businesses, delivery methods of construction project and finally the purchasing process, it can be concluded that especially in the power plant implementation projects in the past have a great influence to decisions made during the project. The role of the main engineering consultant is to act as an assistant, who helps to achieve the project goals successfully rather than an advisor who only knows how the project should be conducted. At least in these five project cases this was the case, meaning that the final decision power always remaining with project owner.

Integrated Environment for Launch Vehicle Telemetry Data Management

The telemetry data processing operation intended for a given mission are pre-defined by an onboard telemetry configuration, mission trajectory and overall telemetry methodology have stabilized lately for ISRO vehicles. The given problem on telemetry data processing is reduced through hierarchical problem reduction whereby the sequencing of operations evolves as the control task and operations on data as the function task. The function task Input, Output and execution criteria are captured into tables which are examined by the control task and then schedules when the function task when the criteria is being met.

Offline Emergence Engineering For Agent Societies

Many examples for emergent behaviors may be observed in self-organizing physical and biological systems which prove to be robust, stable, and adaptable. Such behaviors are often based on very simple mechanisms and rules, but artificially creating them is a challenging task which does not comply with traditional software engineering. In this article, we propose a hybrid approach by combining strategies from Genetic Programming and agent software engineering, and demonstrate that this approach effectively yields an emergent design for given problems.

Applicability of Emergence Engineering to Distributed Systems Scenarios

Genetic Programming can be effectively used to create emergent behavior for a group of autonomous agents. In the process we call Offline Emergence Engineering, the behavior is at first bred in a Genetic Programming environment and then deployed to the agents in the real environment. In this article we shortly describe our approach, introduce an extended behavioral rule syntax, and discuss the impact of the expressiveness of the behavioral description to the generation success, using two scenarios in comparison: the election problem and the distributed critical section problem. We evaluate the results, formulating criteria for the applicability of our approach.

Conceptual knowledge processing with formal concept analysis and ontologies

Among many other knowledge representations formalisms, Ontologies and Formal Concept Analysis (FCA) aim at modeling ‘concepts’. We discuss how these two formalisms may complement another from an application point of view. In particular, we will see how FCA can be used to support Ontology Engineering, and how ontologies can be exploited in FCA applications. The interplay of FCA and ontologies is studied along the life cycle of an ontology: (i) FCA can support the building of the ontology as a learning technique. (ii) The established ontology can be analyzed and navigated by using techniques of FCA. (iii) Last but not least, the ontology may be used to improve an FCA application.

Large-Size AlGaN/GaN HEMT Large-Signal Electrothermal Characterization and Modeling for Wireless Digital Communications

The rapid growth in high data rate communication systems has introduced new high spectral efficient modulation techniques and standards such as LTE-A (long term evolution-advanced) for 4G (4th generation) systems. These techniques have provided a broader bandwidth but introduced high peak-to-average power ratio (PAR) problem at the high power amplifier (HPA) level of the communication system base transceiver station (BTS). To avoid spectral spreading due to high PAR, stringent requirement on linearity is needed which brings the HPA to operate at large back-off power at the expense of power efficiency. Consequently, high power devices are fundamental in HPAs for high linearity and efficiency. Recent development in wide bandgap power devices, in particular AlGaN/GaN HEMT, has offered higher power level with superior linearity-efficiency trade-off in microwaves communication. For cost-effective HPA design to production cycle, rigorous computer aided design (CAD) AlGaN/GaN HEMT models are essential to reflect real response with increasing power level and channel temperature. Therefore, large-size AlGaN/GaN HEMT large-signal electrothermal modeling procedure is proposed. The HEMT structure analysis, characterization, data processing, model extraction and model implementation phases have been covered in this thesis including trapping and self-heating dispersion accounting for nonlinear drain current collapse. The small-signal model is extracted using the 22-element modeling procedure developed in our department. The intrinsic large-signal model is deeply investigated in conjunction with linearity prediction. The accuracy of the nonlinear drain current has been enhanced through several issues such as trapping and self-heating characterization. Also, the HEMT structure thermal profile has been investigated and corresponding thermal resistance has been extracted through thermal simulation and chuck-controlled temperature pulsed I(V) and static DC measurements. Higher-order equivalent thermal model is extracted and implemented in the HEMT large-signal model to accurately estimate instantaneous channel temperature. Moreover, trapping and self-heating transients has been characterized through transient measurements. The obtained time constants are represented by equivalent sub-circuits and integrated in the nonlinear drain current implementation to account for complex communication signals dynamic prediction. The obtained verification of this table-based large-size large-signal electrothermal model implementation has illustrated high accuracy in terms of output power, gain, efficiency and nonlinearity prediction with respect to standard large-signal test signals.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.