An automated microreactor for semi-continuous biosensor measurements.

Living bacteria or yeast cells are frequently used as bioreporters for the detection of specific chemical analytes or conditions of sample toxicity. In particular, bacteria or yeast equipped with synthetic gene circuitry that allows the production of a reliable non-cognate signal (e.g., fluorescent protein or bioluminescence) in response to a defined target make robust and flexible analytical platforms. We report here how bacterial cells expressing a fluorescence reporter ("bactosensors"), which are mostly used for batch sample analysis, can be deployed for automated semi-continuous target analysis in a single concise biochip. Escherichia coli-based bactosensor cells were continuously grown in a 13 or 50 nanoliter-volume reactor on a two-layered polydimethylsiloxane-on-glass microfluidic chip. Physiologically active cells were directed from the nl-reactor to a dedicated sample exposure area, where they were concentrated and reacted in 40 minutes with the target chemical by localized emission of the fluorescent reporter signal. We demonstrate the functioning of the bactosensor-chip by the automated detection of 50 μgarsenite-As l(-1) in water on consecutive days and after a one-week constant operation. Best induction of the bactosensors of 6-9-fold to 50 μg l(-1) was found at an apparent dilution rate of 0.12 h(-1) in the 50 nl microreactor. The bactosensor chip principle could be widely applicable to construct automated monitoring devices for a variety of targets in different environments.

Estudo químico-computacional da reatividade de aziridinona e diaziridinona através de cálculos semi-empíricos

The results of semiempirical molecular orbital calculations performed on aziridinone and diaziridinone employing the MNDO, AM1, and PM3 molecular models are presented. The AM1 method, which best reproduces ground-state molecular properties, is used to calculate electronic parameters and the use of these parameters for the evaluation of reactivity is discussed.

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

A concurrent region growing algorithm guided by circumscribed contours

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach

Pork Barrel politics in semi-democracies: the Spanish 'Parlamentary roads', 1874-1915

This paper analyzes the effects of parliamentary representation on road infrastructure expenditure during the Spanish Restoration. Using a panel dataset of Spanish provinces in 1880-1914 we find that the allocation of administrative resources among provinces depended both on the delegation characteristics (such as the share of MPs with party leadership positions, and their degree of electoral independence), and the regime"s global search for stability. These results point to the importance of electoral dynamics within semi-democratic political systems, and offer an example of the influence of government tactics on infrastructure allocation.

Pork Barrel politics in semi-democracies: the Spanish 'Parlamentary roads', 1874-1915

This paper analyzes the effects of parliamentary representation on road infrastructure expenditure during the Spanish Restoration. Using a panel dataset of Spanish provinces in 1880-1914 we find that the allocation of administrative resources among provinces depended both on the delegation characteristics (such as the share of MPs with party leadership positions, and their degree of electoral independence), and the regime"s global search for stability. These results point to the importance of electoral dynamics within semi-democratic political systems, and offer an example of the influence of government tactics on infrastructure allocation.

Nitração do fenol, um método em escala semi-micro para disciplina prática de 4 horas

A procedure for the nitration of phenol in a semi-micro scale, followed by separation of the formed orto- and para-nitrophenol isomers by column chromatography, is described. All the experiment, including determination of the melting point of the isolated products, require a period of 4 hours, and it is suitable for organic chemistry laboratory undergraduate courses.

Avaliação da salinização de açudes no semi-árido brasileiro por ICP-AES

The salt accumulation process in some reservoirs of regular and irregular use (from 10 to 50 years of constrution), located in the Southeast of Bahia State was evaluated. Inductively coupled plasma atomic emission spectrometry was used to evaluate the concentrations of Na, K, Ca and Mg in water samples from inside and upstream of the reservoirs. The results showed that for reservoirs of irregular use, the salt accumulation, indicated by the tracer Na, increases with the age of the reservoirs, however for the reservoirs of regular use the hydraulic retention time is the main parameter.

Estudo semi-empírico de ácidos hidroxâmicos: ácido formoidroxâmico e derivados do aleloquímico dimboa

Open chain hydroxamic acid (Hx) can exist as Z and E diastereomers of two tautomers, hydroxamic acid and hydroximic acid. The conformational stability of the formohydroxamic acid isomers evaluated by PM3 compared better to ab initio results from the literature than AM1 results. Structural data of the cyclic Hx 2,4-dihydroxy-7-metoxy-2H-1,4-benzoxazin-3(4 H)-one (DIMBOA) obtained by both semiempirical methods compared well to ab initio results. pKa data from the literature for derivatives of the aldolic isomer of DIMBOA were compared to the stability of the anions resulting from the loss of protons of their phenol and hydroxamic acid groups, determined as the difference in heat of formation between anionic and neutral forms, calculated by AM1 and PM3 methods. Good correlations between theoretical and experimental data were obtained for both semiempirical methods.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Monivaiheisen höyryturbiinin optimointi

Tässä diplomityössä optimoitiin nelivaiheinen 1 MWe höyryturbiinin prototyyppimalli evoluutioalgoritmien avulla sekä tutkittiin optimoinnista saatuja kustannushyötyjä. Optimoinnissa käytettiin DE – algoritmia. Optimointi saatiin toimimaan, mutta optimoinnissa käytetyn laskentasovelluksen (semiempiirisiin yhtälöihin perustuvat mallit) luonteesta johtuen optimoinnin tarkkuus CFD – laskennalla suoritettuun tarkastusmallinnukseen verrattuna oli jonkin verran toivottua pienempi. Tulosten em. epätarkkuus olisi tuskin ollut vältettävissä, sillä ongelma johtui puoliempiirisiin laskentamalleihin liittyvistä lähtöoletusongelmista sekä epävarmuudesta sovitteiden absoluuttisista pätevyysalueista. Optimoinnin onnistumisen kannalta tällainen algebrallinen mallinnus oli kuitenkin välttämätöntä, koska esim. CFD-laskentaa ei olisi mitenkään voitu tehdä jokaisella optimointiaskeleella. Optimoinnin aikana ongelmia esiintyi silti konetehojen riittävyydessä sekä sellaisen sopivan rankaisumallin löytämisessä, joka pitäisi algoritmin matemaattisesti sallitulla alueella, muttei rajoittaisi liikaa optimoinnin edistymistä. Loput ongelmat johtuivat sovelluksen uutuudesta sekä täsmällisyysongelmista sovitteiden pätevyysalueiden käsittelyssä. Vaikka optimoinnista saatujen tulosten tarkkuus ei ollut aivan tavoitteen mukainen, oli niillä kuitenkin koneensuunnittelua edullisesti ohjaava vaikutus. DE – algoritmin avulla suoritetulla optimoinnilla saatiin turbiinista noin 2,2 % enemmän tehoja, joka tarkoittaa noin 15 000 € konekohtaista kustannushyötyä. Tämä on yritykselle erittäin merkittävä konekohtainen kustannushyöty. Loppujen lopuksi voitaneen sanoa, etteivät evoluutioalgoritmit olleet parhaimmillaan prototyyppituotteen optimoinnissa. Evoluutioalgoritmeilla teknisten laitteiden optimoinnissa piilee valtavasti mahdollisuuksia, mutta se vaatii kypsän sovelluskohteen, joka tunnetaan jo entuudestaan erinomaisesti tai on yksinkertainen ja aukottomasti laskettavissa.

Shout and Act: an Algorithm for Digital Objects Preservation Inspired from Rescue Robots

We adapt the Shout and Act algorithm to Digital Objects Preservation where agents explore file systems looking for digital objects to be preserved (victims). When they find something they “shout” so that agent mates can hear it. The louder the shout, the urgent or most important the finding is. Louder shouts can also refer to closeness. We perform several experiments to show that this system works very scalably, showing that heterogeneous teams of agents outperform homogeneous ones over a wide range of tasks complexity. The target at-risk documents are MS Office documents (including an RTF file) with Excel content or in Excel format. Thus, an interesting conclusion from the experiments is that fewer heterogeneous (varying skills) agents can equal the performance of many homogeneous (combined super-skilled) agents, implying significant performance increases with lower overall cost growth. Our results impact the design of Digital Objects Preservation teams: a properly designed combination of heterogeneous teams is cheaper and more scalable when confronted with uncertain maps of digital objects that need to be preserved. A cost pyramid is proposed for engineers to use for modeling the most effective agent combinations

An energy efficient vertical handover decision algorithm

As wireless communications evolve towards heterogeneousnetworks, mobile terminals have been enabled tohandover seamlessly from one network to another. At the sametime, the continuous increase in the terminal power consumptionhas resulted in an ever-decreasing battery lifetime. To that end,the network selection is expected to play a key role on howto minimize the energy consumption, and thus to extend theterminal lifetime. Hitherto, terminals select the network thatprovides the highest received power. However, it has been provedthat this solution does not provide the highest energy efficiency.Thus, this paper proposes an energy efficient vertical handoveralgorithm that selects the most energy efficient network thatminimizes the uplink power consumption. The performance of theproposed algorithm is evaluated through extensive simulationsand it is shown to achieve high energy efficiency gains comparedto the conventional approach.

Síntese de sulfadiazina e sulfadiazina de prata em escala semi-micro: prática experimental em síntese de fármacos

The total synthesis of sulfadiazine and silver sulfadiazine from readily available starting materials was adapted to semi-micro laboratory scale and is proposed as an experiment in drug synthesis for undergraduate courses.