Advances in understanding energy consumption behavior and the governance of its change - Outline of an integrated framework

Transforming today’s energy systems in industrialized countries requires a substantial reduction of the total energy consumption at the individual level. Selected instruments have been found to be effective in changing people’s behavior in single domains. However, the so far weak success story on reducing overall energy consumption indicates that our understanding of the determining factors of individual energy consumption as well as of its change is far from being conclusive. Among others, the scientific state of the art is dominated by analyzing single domains of consumption and by neglecting embodied energy. It also displays strong disciplinary splits and the literature often fails to distinguish between explaining behavior and explaining change of behavior. Moreover, there are knowledge gaps regarding the legitimacy and effectiveness of the governance of individual consumption behavior and its change. Against this backdrop, the aim of this paper is to establish an integrated interdisciplinary framework that offers a systematic basis for linking the different aspects in research on energy related consumption behavior, thus paving the way for establishing a better evidence base to inform societal actions. The framework connects the three relevant analytical aspects of the topic in question: (1) It systematically and conceptually frames the objects, i.e. the energy consumption behavior and its change (explananda); (2) it structures the factors that potentially explain the energy consumption behavior and its change (explanantia); (3) it provides a differentiated understanding of change inducing interventions in terms of governance. Based on the existing states of the art approaches from different disciplines within the social sciences the proposed framework is supposed to guide interdisciplinary empirical research.

Computer simulations of sample preconcentration in carrier-free systems and isoelectric focusing in microchannels using simple ampholytes.

In this work, electrophoretic preconcentration of protein and peptide samples in microchannels was studied theoretically using the 1D dynamic simulator GENTRANS, and experimentally combined with MS. In all configurations studied, the sample was uniformly distributed throughout the channel before power application, and driving electrodes were used as microchannel ends. In the first part, previously obtained experimental results from carrier-free systems are compared to simulation results, and the effects of atmospheric carbon dioxide and impurities in the sample solution are examined. Simulation provided insight into the dynamics of the transport of all components under the applied electric field and revealed the formation of a pure water zone in the channel center. In the second part, the use of an IEF procedure with simple well defined amphoteric carrier components, i.e. amino acids, for concentration and fractionation of peptides was investigated. By performing simulations a qualitative description of the analyte behavior in this system was obtained. Neurotensin and [Glu1]-Fibrinopeptide B were separated by IEF in microchannels featuring a liquid lid for simple sample handling and placement of the driving electrodes. Component distributions in the channel were detected using MALDI- and nano-ESI-MS and data were in agreement with those obtained by simulation. Dynamic simulations are demonstrated to represent an effective tool to investigate the electrophoretic behavior of all components in the microchannel.

Cooperation & Competition Between Navigation Systems in the Rat Brain: The Role of the Hippocampus and Striatum During a Dissociative Maze Task

While many tend to think of memory systems in the brain as a single process, in reality several experiments have supported multiple dissociations of different forms of learning, such as spatial learning and response learning. In both humans and rats, the hippocampus has long been shown to be specialized in the storage of spatial and contextual memory whereas the striatum is associated with motor responses and habitual behaviors. Previous studies have examined how damage to hippocampus or striatum has affected the acquisition of either a spatial or response navigation task. However even in a very familiar environment organisms must continuously switch between place and response strategies depending upon circumstances. The current research investigates how these two brain systems interact under normal conditions to produce navigational behavior. Rats were tested using a task developed by Jacobson and colleagues (2006) in which the two types of navigation could be controlled and studied simultaneously. Rats were trained to solve a plus maze using both a spatial and a response strategy. A cue (flashing light) was employed to indicate the correct strategy on a given trial. When no light was present, the animals were rewarded for making a 90º right turn (motor response). When the light was on, the animals were rewarded for going to a specific goal location (place strategy). After learning the task, animals had a sham surgery or dorsal striatum or hippocampus damaged. In order to investigate the individual role of each brain system and evaluate whether these brain regions compete or cooperate for control over strategy, we utilized a within-animal comparisons. The configuration of the maze allowed for the comparison of behavior in individual animals before and after specific brain areas were damaged. Animals with hippocampal lesions showed selective deficits on place trials after surgery and learned the reversal of the motor response more rapidly than striatal lesioned or sham rats. Unlike previous findings regarding maze learning, animals with striatal lesions showed deficits in both place and response trials and had difficulty learning the reversal of motor response. Therefore, the effects of lesions on the ability to switch back and forth between strategies were more complex than previously suggested. This work may reveal important new insight on the integration of hippocampal and striatal learning systems, and facilitate a better understanding of the brain dynamics underlying similar navigational processes in humans.

The effects of long (C20/22) and short (C18) chain omega-3 fatty acids on keel bone fractures, bone biomechanics, behavior, and egg production in free-range laying hens.

Keel fractures in the laying hen are the most critical animal welfare issue facing the egg production industry, particularly with the increased use of extensive systems in response to the 2012 EU directive banning conventional battery cages. The current study is aimed at assessing the effects of 2 omega-3 (n3) enhanced diets on bone health, production endpoints, and behavior in free-range laying hens. Data was collected from 2 experiments over 2 laying cycles, each of which compared a (n3) supplemented diet with a control diet. Experiment 1 employed a diet supplemented with a 60:40 fish oil-linseed mixture (n3:n6 to 1.35) compared with a control diet (n3:n6 to 0.11), whereas the n3 diet in Experiment 2 was supplemented with a 40:60 fish oil-linseed (n3:n6 to 0.77) compared to the control diet (n3:n6 to 0.11). The n3 enhanced diet of Experiment 1 had a higher n3:n6 ratio, and a greater proportion of n3 in the long chain (C20/22) form (0.41 LC:SC) than that of Experiment 2 (0.12 LC:SC). Although dietary treatment was successful in reducing the frequency of fractures by approximately 27% in Experiment 2, data from Experiment 1 indicated the diet actually induced a greater likelihood of fracture (odds ratio: 1.2) and had substantial production detriment. Reduced keel breakage during Experiment 2 could be related to changes in bone health as n3-supplemented birds demonstrated greater load at failure of the keel, and tibiae and humeri that were more flexible. These results support previous findings that n3-supplemented diets can reduce fracture likely by increasing bone strength, and that this can be achieved without detriment to production. However, our findings suggest diets with excessive quantities of n3, or very high levels of C20/22, may experience health and production detriments. Further research is needed to optimize the quantity and type of n3 in terms of bone health and production variables and investigate the potential associated mechanisms.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Recent developments in the quantum dynamical characterization of unimolecular resonances

We give a selective review of quantum mechanical methods for calculating and characterizing resonances in small molecular systems, with an emphasis on recent progress in Chebyshev and Lanczos iterative methods. Two archetypal molecular systems are discussed: isolated resonances in HCO, which exhibit regular mode and state specificity, and overlapping resonances in strongly bound HO2, which exhibit irregular and chaotic behavior. Recent progresses for non-zero total angular momentum J calculations of resonances including parallel computing models are also included and future directions in this field are discussed.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)

Simultaneous Control of Chaotic Systems Using RBF Networks

Chaos control is a concept that recently acquiring more attention among the research community, concerning the fields of engineering, physics, chemistry, biology and mathematic. This paper presents a method to simultaneous control of deterministic chaos in several nonlinear dynamical systems. A radial basis function networks (RBFNs) has been used to control chaotic trajectories in the equilibrium points. Such neural network improves results, avoiding those problems that appear in other control methods, being also efficient dealing with a relatively small random dynamical noise.

Characterizing the transition dynamics for multi-pulsing in mode-locked lasers

We consider experimentally and theoretically a refined parameter space near the transition to multi-pulse modelocking. Near the transition, the onset of instability is initiated by a Hopf (periodic) bifurcation. As cavity energy is increased, the band of unstable, oscillatory modes generates a chaotic behavior between single- and multi-pulse operation. Both theory and experiment are in good qualitative agreement and they suggest that the phenomenon is of a universal nature in mode-locked lasers at the onset of multi-pulsing from N to N + 1 pulses per round trip. This is the first theoretical and experimental characterization of the transition behavior, made possible by a highly refined tuning of the gain pump level. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Phase behavior and far-from-equilibrium gelation in charged attractive colloids

In this Rapid Communication we demonstrate the applicability of an augmented Gibbs ensemble Monte Carlo approach for the phase behavior determination of model colloidal systems with short-ranged depletion attraction and long-ranged repulsion. This technique allows for a quantitative determination of the phase boundaries and ground states in such systems. We demonstrate that gelation may occur in systems of this type as the result of arrested microphase separation, even when the equilibrium state of the system is characterized by compact microphase structures.

The Role of Dopaminergic Systems in the Neurobehavioral Teratology of Organophosphates in Zebrafish

Background: Organophosphate (OP) pesticides are well-known developmental neurotoxicants that have been linked to abnormal cognitive and behavioral endpoints through both epidemiological studies and animal models of behavioral teratology, and are implicated in the dysfunction of multiple neurotransmitters, including dopamine. Chemical similarities between OP pesticides and organophosphate flame retardants (OPFRs), a class of compounds growing in use and environmental relevance, have produced concern regarding whether developmental exposures to OPFRs and OP pesticides may share behavioral outcomes, impacts on dopaminergic systems, or both. Methods: Using the zebrafish animal model, we exposed developing fish to two OPFRs, TDCIPP and TPHP, as well as the OP pesticide chlorpyrifos, during the first 5 days following fertilization. From there, the exposed fish were assayed for behavioral abnormalities and effects on monoamine neurochemistry as both larvae and adults. An experiment conducted in parallel examined how antagonism of the dopamine system during an identical window of development could alter later life behavior in the same assays. Finally, we investigated the interaction between developmental exposure to an OPFR and acute dopamine antagonism in larval behavior. Results: Developmental exposure to all three OP compounds altered zebrafish behavior, with effects persisting into adulthood. Additionally, exposure to an OPFR decreased the behavioral response to acute D2 receptor antagonism in larvae. However, the pattern of behavioral effects diverged substantially from those seen following developmental dopamine antagonism, and the investigations into dopamine neurochemistry were too variable to be conclusive. Thus, although the results support the hypothesis that OPFRs, as with OP pesticides such as chlorpyrifos, may present a risk to normal behavioral development, we were unable to directly link these effects to any dopaminergic dysfunction.

Mode locking in a spatially extended neuron model: active soma and compartmental tree

Understanding the mode-locked response of excitable systems to periodic forcing has important applications in neuroscience. For example it is known that spatially extended place cells in the hippocampus are driven by the theta rhythm to generate a code conveying information about spatial location. Thus it is important to explore the role of neuronal dendrites in generating the response to periodic current injection. In this paper we pursue this using a compartmental model, with linear dynamics for each compartment, coupled to an active soma model that generates action potentials. By working with the piece-wise linear McKean model for the soma we show how the response of the whole neuron model (soma and dendrites) can be written in closed form. We exploit this to construct a stroboscopic map describing the response of the spatially extended model to periodic forcing. A linear stability analysis of this map, together with a careful treatment of the non-differentiability of the soma model, allows us to construct the Arnol'd tongue structure for 1:q states (one action potential for q cycles of forcing). Importantly we show how the presence of quasi-active membrane in the dendrites can influence the shape of tongues. Direct numerical simulations confirm our theory and further indicate that resonant dendritic membrane can enlarge the windows in parameter space for chaotic behavior. These simulations also show that the spatially extended neuron model responds differently to global as opposed to point forcing. In the former case spatio-temporal patterns of activity within an Arnol'd tongue are standing waves, whilst in the latter they are traveling waves.

Essays in Vertical Agreements and Consumer Behavior

This thesis collects three independent essays and a literature review. Two of them relate to vertical agreements. The first essay explores a retailer's choice in allocating control rights over the decision of retail prices. Results show that retailers adopt a hybrid configuration as a middle ground between two extremes, where pricing decisions are delegated, for all products, either to retailer or manufacturers. The second essay investigates the make-it-or-license-it choice of a brand owner under the risk of moral hazard when licensing the extension product to a third party. Brand licensing emerges as an equilibrium choice under brand dilution (respectively, enhancement) when the consumer perceives a large (small) distance between the extension product and parent brand. The third essay explores the issue of rating bubbles within online feedback systems by means of a field experiment. The analysis found the presence of positive social influence bias, in that high ratings affect the individual rating behavior in a significant way. The last paper is accompanied by a thorough and deep review of the literature about the consequences of online user ratings on product sales/performance (economic dimension) and product adoption/rating behavior (behavioral dimension). The topic is increasingly investigated by academic researchers and industry professionals alike. This overview presents established results and insights as issues for future research.

Mechanisms Of Insulin Resistance In The Amygdala: Influences On Food Intake.

Obesity is increasing worldwide and is triggered, at least in part, by enhanced caloric intake. Food intake is regulated by a complex mechanism involving the hypothalamus and hindbrain circuitries. However, evidences have showing that reward systems are also important in regulating feeding behavior. In this context, amygdala is considered a key extra-hypothalamic area regulating feeding behavior in human beings and rodents. This review focuses on the regulation of food intake by amygdala and the mechanisms of insulin resistance in this brain area. Similar to the hypothalamus the anorexigenic effect of insulin is mediated via PI3K (phosphoinositide 3-kinase)/Akt (protein kinase B) pathway in the amygdala. Insulin decreases NPY (neuropeptide Y) and increases oxytocin mRNA levels in the amygdala. High fat diet and saturated fatty acids induce inflammation, ER (endoplasmic reticulum) stress and the activation of serine kinases such as PKCθ (protein kinase C theta), JNK (c-Jun N-terminal kinase) and IKKβ (inhibitor of nuclear factor kappa-B kinase beta) in the amygdala, which have an important role in insulin resistance in this brain region. Overexpressed PKCθ in the CeA (central nucleus of amygdala) of rats increases weight gain, food intake, insulin resistance and hepatic triglycerides content. The inhibition of ER stress ameliorates insulin action/signaling, increases oxytocin and decreases NPY gene expression in the amygdala of high fat feeding rodents. Those data suggest that PKCθ and ER stress are main mechanisms of insulin resistance in the amygdala of obese rats and play an important role regulating feeding behavior.

Electrical transport in single-crystalline Li(0.9)Mo(6)O(17): A two-band Luttinger liquid exhibiting Bose metal behavior

Temperature-dependent electrical resistance in quasi-one-dimensional Li(0.9)Mo(6)O(17) is described by two Luttinger liquid anomalous exponents alpha, each associated with a distinct one dimensional band. The band with alpha < 1 is argued to crossover to a higher dimension below the temperature T(M'), leading to superconductivity. Disorder and magnetic fields are shown to induce the Bose metal behavior in this bulk compound.