Impact of Bottom Flange Confinement Reinforcement on Performance of Prestressed Concrete Bridge Girders

For many years AASHTO provided no recommendation to state DOT’s on bottom flange confinement reinforcement for their bridge superstructures. The 1996 edition of AASHTO Standard Specification for Highway Bridges stated that nominal reinforcement be placed to enclose the prestressing steel from the end of the girder for at least a distance equal to the girder’s height. A few years later the 2004 AASHTO LRFD Bridge Design Specification changed the distance over which the confinement was to be distributed from 1.0h to 1.5h, and gave minimum requirements for the amount of steel to be used, No.3 bars, and their maximum spacing, not to exceed 6”. Research was undertaken to study what impact, if any, confinement reinforcement has on the performance of prestressed concrete bridge girders. Of particular interest was the effect confinement had on the transfer length, development length, and vertical shear capacity of the fore mentioned members. First, an analytical investigation was performed on the subject, and then an experimental investigation followed which consisted of designing, fabricating, and testing eight tee-girders and three NU1100 girders with particular attention paid to the amount and distribution of confinement reinforcement placed at the end of each girder. The results of the study show: 1) neither the amount or distribution of confinement reinforcement had a significant effect on the initial or final transfer length of the prestress strands; 2) at the AASHTO calculated development length, no significant impact from confinement was found on either the nominal flexural capacity of bridge girders or bond capacity of the prestressing steel; 3) the effects from varied confinement reinforcement on the shear resistance of girders tested was negligible, however, distribution of confinement did show to have an impact on the prestressed strands’ bond capacity; 4) confinement distribution across the entire girder did increase ductility and reduced cracking under extreme loading conditions.

Use of policy, education, and enforcement to reduce binge drinking among university students: The NU Directions project

This paper describes a program, conducted over a 5-year period, that effectively reduced heavy drinking and alcohol-related harms among university students. The program was organized around strategies to change the environment in which binge drinking occurred and involved input and cooperation from officials and students of the university, representatives from the city and the neighborhood near the university, law enforcement, as well as public health and medical officials. In 1997, 62.5% of the university’s approximately 16,000 undergraduate student population reported binge drinking. This rate had dropped to 47% in 2003. Similar reductions were found in both self-reported primary and secondary harms related to alcohol consumption.

A transcriptional study in mice with different ethanol-drinking profiles: Possible involvement of the GABA(B) receptor

Previous studies have suggested that gamma-aminobutyric acid-B (GABA(B)) receptor agonists effectively reduce ethanol intake. The quantification using real-time polymerase chain reaction of Gabbr1 and Gabbr2 mRNA from the prefrontal cortex, hypothalamus, hippocampus, and striatum in mice exposed to an animal model of the addiction developed in our laboratory was performed to evaluate the involvement of the GABAB receptor in ethanol consumption. We used outbred, Swiss mice exposed to a three-bottle free-choice model (water, 5% v/v ethanol, and 10% v/v ethanol) that consisted of four phases: acquisition (AC), withdrawal (W), reexposure (RE), and quinine-adulteration (AD). Based on individual ethanol intake, the mice were classified into three groups: "addicted" (A group; preference for ethanol and persistent consumption during all phases), "heavy" (H group; preference for ethanol and a reduction in ethanol intake in the AD phase compared to AC phase), and "light" (L group; preference for water during all phases). In the prefrontal cortex in the A group, we found high Gabbr1 and Gabbr2 transcription levels, with significantly higher Gabbr1 transcription levels compared with the C (ethanol-naive control mice). L, and H groups. In the hippocampus in the A group, Gabbr2 mRNA levels were significantly lower compared with the C, L, and H groups. In the striatum, we found a significant increase in Gabbr1 transcription levels compared with the C, L, and H groups. No differences in Gabbr1 or Gabbr2 transcription levels were observed in the hypothalamus among groups. In summary, Gabbr1 and Gabbr2 transcription levels were altered in cerebral areas related to drug taking only in mice behaviorally classified as "addicted" drinkers, suggesting that these genes may contribute to high and persistent ethanol consumption. (C) 2012 Elsevier Inc. All rights reserved.

Gender differences in drinking patterns and alcohol-related problems in a community sample in Sao Paulo, Brazil

OBJECTIVE: To investigate drinking patterns and gender differences in alcohol-related problems in a Brazilian population, with an emphasis on the frequency of heavy drinking. METHODS: A cross-sectional study was conducted with a probability adult household sample (n = 1,464) in the city of Sao Paulo, Brazil. Alcohol intake and ICD-10 psychopathology diagnoses were assessed with the Composite International Diagnostic Interview 1.1. The analyses focused on the prevalence and determinants of 12-month non-heavy drinking, heavy episodic drinking (4-5 drinks per occasion), and heavy and frequent drinking (heavy drinking at least 3 times/week), as well as associated alcohol-related problems according to drinking patterns and gender. RESULTS: Nearly 22% (32.4% women, 8.7% men) of the subjects were lifetime abstainers, 60.3% were non-heavy drinkers, and 17.5% reported heavy drinking in a 12-month period (26.3% men, 10.9% women). Subjects with the highest frequency of heavy drinking reported the most problems. Among subjects who did not engage in heavy drinking, men reported more problems than did women. A gender convergence in the amount of problems was observed when considering heavy drinking patterns. Heavy and frequent drinkers were twice as likely as abstainers to present lifetime depressive disorders. Lifetime nicotine dependence was associated with all drinking patterns. Heavy and frequent drinking was not restricted to young ages. CONCLUSIONS: Heavy and frequent episodic drinking was strongly associated with problems in a community sample from the largest city in Latin America. Prevention policies should target this drinking pattern, independent of age or gender. These findings warrant continued research on risky drinking behavior, particularly among persistent heavy drinkers at the non-dependent level.

Chemical and physical factors influencing lead and copper contamination in drinking water: approach for a case study in analytical chemistry

CHEMICAL AND PHYSICAL FACTORS INFLUENCING LEAD AND COPPER CONTAMINATION IN DRINKING WATER: APPROACH FOR A CASE STUDY IN ANALYTICAL CHEMISTRY. Lead and copper concentrations in drinking water increase considerably on going from municipality reservoirs to the households sampled in Ribeirao Preto (SP-Brazil). Flushing of only 3 liters of water reduced metal concentrations by more than 50%. Relatively small changes in water pH rapidly affected corrosion processes in lead pipes, while water hardness appeared to have a long-term effect. This approach aims to encourage University teachers to use its content as a case study in disciplines of Instrumental Analytical Chemistry and consequently increase knowledge about drinking water contamination in locations where no public monitoring of trace metals is in place.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

N-Acetylcysteine Protects Rats with Chronic Renal Failure from Gadolinium-Chelate Nephrotoxicity

The aim of this study was to evaluate the effect of Gd-chelate on renal function, iron parameters and oxidative stress in rats with CRF and a possible protective effect of the antioxidant N-Acetylcysteine (NAC). Male Wistar rats were submitted to 5/6 nephrectomy (Nx) to induced CRF. An ionic - cyclic Gd (Gadoterate Meglumine) was administrated (1.5 mM/KgBW, intravenously) 21 days after Nx. Clearance studies were performed in 4 groups of anesthetized animals 48 hours following Gd-chelate administration: 1 - Nx (n = 7); 2 - Nx+NAC (n = 6); 3 - Nx+Gd (n = 7); 4 - Nx+NAC+Gd (4.8 g/L in drinking water), initiated 2 days before Gd-chelate administration and maintained during 4 days (n = 6). This group was compared with a control. We measured glomerular filtration rate, GFR (inulin clearance, ml/min/kg BW), proteinuria (mg/24 hs), serum iron (mu g/dL); serum ferritin (ng/mL); transferrin saturation (%), TIBC (mu g/dL) and TBARS (nmles/ml). Normal rats treated with the same dose of Gd-chelate presented similar GFR and proteinuria when compared with normal controls, indicating that at this dose Gd-chelate is not nephrotoxic to normal rats. Gd-chelate administration to Nx-rats results in a decrease of GFR and increased proteinuria associated with a decrease in TIBC, elevation of ferritin serum levels, transferrin oversaturation and plasmatic TBARS compared with Nx-rats. The prophylactic treatment with NAC reversed the decrease in GFR and the increase in proteinuria and all alterations in iron parameters and TBARS induced by Gd-chelate. NAC administration to Nx rat did not modify the inulin clearance and iron kinetics, indicating that the ameliorating effect of NAC was specific to Gd-chelate. These results suggest that NAC can prevent Gd-chelate nephrotoxicity in patients with chronic renal failure.

Involvement of the basolateral complex and central nucleus of amygdala in the omission effects of different magnitudes of reinforcement

Evidence from appetitive Pavlovian and instrumental conditioning studies suggest that the amygdala is involved in modulation of responses correlated with motivational states, and therefore, to the modulation of processes probably underlying reinforcement omission effects. The present study aimed to clarify whether or not the mechanisms related to reinforcement omission effects of different magnitudes depend on basolateral complex and central nucleus of amygdala. Rats were trained on a fixed-interval 12 s with limited hold 6 s signaled schedule in which correct responses were always followed by one of two reinforcement magnitudes. Bilateral lesions of the basolateral complex and central nucleus were made after acquisition of stable performance. After postoperative recovery, the training was changed from 100% to 50% reinforcement schedules. The results showed that lesions of the basolateral complex and central nucleus did not eliminate or reduce, but interfere with reinforcement omission effects. The response from rats of both the basolateral complex and central nucleus lesioned group was higher relative to that of the rats of their respective sham-lesioned groups after reinforcement omission. Thus, the lesioned rats were more sensitive to the omission effect. Moreover, the basolateral complex lesions prevented the magnitude effect on reinforcement omission effects. Basolateral complex lesioned rats showed no differential performance following omission of larger and smaller reinforcement magnitude. Thus, the basolateral complex is involved in incentive processes relative to omission of different reinforcement magnitudes. Therefore, it is possible that reinforcement omission effects are modulated by brain circuitry which involves amygdala. (C) 2012 Elsevier B.V. All rights reserved.

Enhancing spiral-wound ultrafiltration performance for direct drinking water treatment through operational procedures improvement: A feasible option for the Sao Paulo Metropolitan Region

This work aimed to evaluate the influence of specific operational conditions on the performance of a spiral-wound ultrafiltration pilot plant for direct drinking water treatment, installed at the Guarapiranga's reservoir, in the Sao Paulo Metropolitan Region. Results from operational tests showed that the volume of permeate produced in the combination of periodic relaxation with flushing and chlorine dosage procedures was 49% higher than the volume obtained when these procedures were not used. Two years of continuous operation demonstrated that the ultrafiltration pilot plant performed better during fall and winter seasons, higher permeate flow production and reduced chemical cleanings frequency. Observed behavior seems to be associated with the algae bloom events in the reservoir, which are more frequent during spring and summer seasons, confirmed by chlorophyll-a analysis results. Concentrate clarification using ferric chloride was quite effective in removing NOM and turbidity, allowing its recirculation to the ultrafiltration feed tank. This procedure made it possible to reach almost 99% water recovery considering a single 54-hour recirculation cycle. Water quality monitoring demonstrated that the ultrafiltration pilot plant was quite efficient, and that potential pathogenic organisms, Escherichia coil and total coliforms, turbidity and apparent color removals were 100%, 95.1%, and 91.5%, respectively. (C) 2012 Elsevier B.V. All rights reserved.

Gender differences in drinking patterns and alcohol-related problems in a community sample in São Paulo, Brazil

OBJECTIVE: To investigate drinking patterns and gender differences in alcohol-related problems in a Brazilian population, with an emphasis on the frequency of heavy drinking. METHODS: A cross-sectional study was conducted with a probability adult household sample (n = 1,464) in the city of São Paulo, Brazil. Alcohol intake and ICD-10 psychopathology diagnoses were assessed with the Composite International Diagnostic Interview 1.1. The analyses focused on the prevalence and determinants of 12-month nonheavy drinking, heavy episodic drinking (4-5 drinks per occasion), and heavy and frequent drinking (heavy drinking at least 3 times/week), as well as associated alcohol-related problems according to drinking patterns and gender. RESULTS: Nearly 22% (32.4% women, 8.7% men) of the subjects were lifetime abstainers, 60.3% were non-heavy drinkers, and 17.5% reported heavy drinking in a 12-month period (26.3% men, 10.9% women). Subjects with the highest frequency of heavy drinking reported the most problems. Among subjects who did not engage in heavy drinking, men reported more problems than did women. A gender convergence in the amount of problems was observed when considering heavy drinking patterns. Heavy and frequent drinkers were twice as likely as abstainers to present lifetime depressive disorders. Lifetime nicotine dependence was associated with all drinking patterns. Heavy and frequent drinking was not restricted to young ages. CONCLUSIONS: Heavy and frequent episodic drinking was strongly associated with problems in a community sample from the largest city in Latin America. Prevention policies should target this drinking pattern, independent of age or gender. These findings warrant continued research on risky drinking behavior, particularly among persistent heavy drinkers at the non-dependent level.

Aktionenlernen mit Selbstorganisierenden Karten und Reinforcement Learning

Die vorliegende Arbeit beschäftigt sich mit der Entwicklung eines Funktionsapproximators und dessen Verwendung in Verfahren zum Lernen von diskreten und kontinuierlichen Aktionen: 1. Ein allgemeiner Funktionsapproximator – Locally Weighted Interpolating Growing Neural Gas (LWIGNG) – wird auf Basis eines Wachsenden Neuralen Gases (GNG) entwickelt. Die topologische Nachbarschaft in der Neuronenstruktur wird verwendet, um zwischen benachbarten Neuronen zu interpolieren und durch lokale Gewichtung die Approximation zu berechnen. Die Leistungsfähigkeit des Ansatzes, insbesondere in Hinsicht auf sich verändernde Zielfunktionen und sich verändernde Eingabeverteilungen, wird in verschiedenen Experimenten unter Beweis gestellt. 2. Zum Lernen diskreter Aktionen wird das LWIGNG-Verfahren mit Q-Learning zur Q-LWIGNG-Methode verbunden. Dafür muss der zugrunde liegende GNG-Algorithmus abgeändert werden, da die Eingabedaten beim Aktionenlernen eine bestimmte Reihenfolge haben. Q-LWIGNG erzielt sehr gute Ergebnisse beim Stabbalance- und beim Mountain-Car-Problem und gute Ergebnisse beim Acrobot-Problem. 3. Zum Lernen kontinuierlicher Aktionen wird ein REINFORCE-Algorithmus mit LWIGNG zur ReinforceGNG-Methode verbunden. Dabei wird eine Actor-Critic-Architektur eingesetzt, um aus zeitverzögerten Belohnungen zu lernen. LWIGNG approximiert sowohl die Zustands-Wertefunktion als auch die Politik, die in Form von situationsabhängigen Parametern einer Normalverteilung repräsentiert wird. ReinforceGNG wird erfolgreich zum Lernen von Bewegungen für einen simulierten 2-rädrigen Roboter eingesetzt, der einen rollenden Ball unter bestimmten Bedingungen abfangen soll.

Reinforcement Lernen mit Regularisierungsnetzwerken

Die Arbeit behandelt das Problem der Skalierbarkeit von Reinforcement Lernen auf hochdimensionale und komplexe Aufgabenstellungen. Unter Reinforcement Lernen versteht man dabei eine auf approximativem Dynamischen Programmieren basierende Klasse von Lernverfahren, die speziell Anwendung in der Künstlichen Intelligenz findet und zur autonomen Steuerung simulierter Agenten oder realer Hardwareroboter in dynamischen und unwägbaren Umwelten genutzt werden kann. Dazu wird mittels Regression aus Stichproben eine Funktion bestimmt, die die Lösung einer "Optimalitätsgleichung" (Bellman) ist und aus der sich näherungsweise optimale Entscheidungen ableiten lassen. Eine große Hürde stellt dabei die Dimensionalität des Zustandsraums dar, die häufig hoch und daher traditionellen gitterbasierten Approximationsverfahren wenig zugänglich ist. Das Ziel dieser Arbeit ist es, Reinforcement Lernen durch nichtparametrisierte Funktionsapproximation (genauer, Regularisierungsnetze) auf -- im Prinzip beliebig -- hochdimensionale Probleme anwendbar zu machen. Regularisierungsnetze sind eine Verallgemeinerung von gewöhnlichen Basisfunktionsnetzen, die die gesuchte Lösung durch die Daten parametrisieren, wodurch die explizite Wahl von Knoten/Basisfunktionen entfällt und so bei hochdimensionalen Eingaben der "Fluch der Dimension" umgangen werden kann. Gleichzeitig sind Regularisierungsnetze aber auch lineare Approximatoren, die technisch einfach handhabbar sind und für die die bestehenden Konvergenzaussagen von Reinforcement Lernen Gültigkeit behalten (anders als etwa bei Feed-Forward Neuronalen Netzen). Allen diesen theoretischen Vorteilen gegenüber steht allerdings ein sehr praktisches Problem: der Rechenaufwand bei der Verwendung von Regularisierungsnetzen skaliert von Natur aus wie O(n**3), wobei n die Anzahl der Daten ist. Das ist besonders deswegen problematisch, weil bei Reinforcement Lernen der Lernprozeß online erfolgt -- die Stichproben werden von einem Agenten/Roboter erzeugt, während er mit der Umwelt interagiert. Anpassungen an der Lösung müssen daher sofort und mit wenig Rechenaufwand vorgenommen werden. Der Beitrag dieser Arbeit gliedert sich daher in zwei Teile: Im ersten Teil der Arbeit formulieren wir für Regularisierungsnetze einen effizienten Lernalgorithmus zum Lösen allgemeiner Regressionsaufgaben, der speziell auf die Anforderungen von Online-Lernen zugeschnitten ist. Unser Ansatz basiert auf der Vorgehensweise von Recursive Least-Squares, kann aber mit konstantem Zeitaufwand nicht nur neue Daten sondern auch neue Basisfunktionen in das bestehende Modell einfügen. Ermöglicht wird das durch die "Subset of Regressors" Approximation, wodurch der Kern durch eine stark reduzierte Auswahl von Trainingsdaten approximiert wird, und einer gierigen Auswahlwahlprozedur, die diese Basiselemente direkt aus dem Datenstrom zur Laufzeit selektiert. Im zweiten Teil übertragen wir diesen Algorithmus auf approximative Politik-Evaluation mittels Least-Squares basiertem Temporal-Difference Lernen, und integrieren diesen Baustein in ein Gesamtsystem zum autonomen Lernen von optimalem Verhalten. Insgesamt entwickeln wir ein in hohem Maße dateneffizientes Verfahren, das insbesondere für Lernprobleme aus der Robotik mit kontinuierlichen und hochdimensionalen Zustandsräumen sowie stochastischen Zustandsübergängen geeignet ist. Dabei sind wir nicht auf ein Modell der Umwelt angewiesen, arbeiten weitestgehend unabhängig von der Dimension des Zustandsraums, erzielen Konvergenz bereits mit relativ wenigen Agent-Umwelt Interaktionen, und können dank des effizienten Online-Algorithmus auch im Kontext zeitkritischer Echtzeitanwendungen operieren. Wir demonstrieren die Leistungsfähigkeit unseres Ansatzes anhand von zwei realistischen und komplexen Anwendungsbeispielen: dem Problem RoboCup-Keepaway, sowie der Steuerung eines (simulierten) Oktopus-Tentakels.

Processes influencing the nitrate concentration in an aquifer for drinking water production in Bremen-Nord - Northern Germany

Groundwater represents the most important raw material. Germany struggles to maintain the best water quality possible by providing advanced monitoring systems and legal measures to prevent further pollution. In areas involved in the intensive growing of plantations, one of the major contamination factors derives from nitrate. The aim of this master thesis is the characterisation of the Water Protection Area of Bremen (Germany). Denitrification is a natural process, representing the best means of natural reduction of the hazardous nitrate ion, which is dangerous both for human health and for the development of eutrophication. The study has been possible thanks to the collaboration with the University of Bremen, the Geological Service of Bremen (GDfB) and Peter Spiedt (Water Supply Company of Bremen). It will be defined whether nitrate amounts in the groundwater still overcome the threshold legally imposed, and state if the denitrification process takes place, thanks to new samples collected in 2015 and their integration with historical data. Gas samples have been gathered to test them with the “N2/Ar method”, which is able to estimate the denitrification rate quantitatively. Analyses stated the effective occurrence of the reaction, nevertheless showing that it only affects the chemical of the deep aquifers and not shallow ones. Temporal trends concentrations of nitrate have shown that no real improvement took place in the past years. It will be commented that despite the denitrification being responsible for an efficacious lowering in the nitrate ion, it needs reactive materials to take place. Since the latter are finite elements, it is not an endless process. It is thus believed that is clearly necessary to adopt a better attitude in order to maintain the best chemical qualities possible in such an important area, providing drinking water.

Reinforcement learning in dendritic structures

The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.

Reinforcement learning in dendritic structures

The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.