The neuronal and molecular basis of quinine-dependent bitter taste signaling in Drosophila larvae.

The sensation of bitter substances can alert an animal that a specific type of food is harmful and should not be consumed. However, not all bitter compounds are equally toxic and some may even be beneficial in certain contexts. Thus, taste systems in general may have a broader range of functions than just in alerting the animal. In this study we investigate bitter sensing and processing in Drosophila larvae using quinine, a substance perceived by humans as bitter. We show that behavioral choice, feeding, survival, and associative olfactory learning are all directly affected by quinine. On the cellular level, we show that 12 gustatory sensory receptor neurons that express both GR66a and GR33a are required for quinine-dependent choice and feeding behavior. Interestingly, these neurons are not necessary for quinine-dependent survival or associative learning. On the molecular receptor gene level, the GR33a receptor, but not GR66a, is required for quinine-dependent choice behavior. A screen for gustatory sensory receptor neurons that trigger quinine-dependent choice behavior revealed that a single GR97a receptor gene expressing neuron located in the peripheral terminal sense organ is partially necessary and sufficient. For the first time, we show that the elementary chemosensory system of the Drosophila larva can serve as a simple model to understand the neuronal basis of taste information processing on the single cell level with respect to different behavioral outputs

Automatic Generation Control and its Implementation in Real Time

In power systems, the control mechanism responsible for maintaining the system frequency to the nominal value and the real power interchange between balancing authority areas to the scheduled values is referred to as automatic generation control (AGC). The purpose of this paper is to present a systematic way to determine, in real time, the power allocated to each generator participating in AGC by taking into account the cost and quality of the AGC service provided. To this end, we formulate the economic dispatch process and gain insights into the economic characteristics of the generating units. We value the quality of AGC service by taking into consideration the ramping constraints of the generating units. The proposed methodology is illustrated in the WECC system and is compared with other allocation methods

Residential Load Variability and Diversity at Different Sampling Time and Aggregation Scales

The increasing use of large-scale intermittent distributed renewable energy resources on the electrical power system introduces uncertainties in both network planning and management. In addition to architectural changes to the power system, the applications of demand side response (DSR) also add a dimension of complexity - thereby converting the traditionally passive customers into active prosumers (customers that both produce and consume electricity). It has therefore become important to conduct detailed studies on system load profiles to uncover the nature of the system load. These studies could help distribution network operators (DNOs) to adopt relevant strategies that can accommodate new resources such as distributed generation and energy storage on the evolving distribution network and ensure updated design and management approaches. This paper investigates the relationship between both the system load diversity and variability when different customers are aggregated at different scales. Additionally, the implication of sampling time scales is investigated to capture its effect on load diversity and variability. The study looks at the diversity and variability that is observable from the viewpoint of higher power levels, when interconnecting different sized groupings of customers, at different sampling resolutions. The paper thus concludes that the per-customer capacity requirement of the network decreases as the size of customer groupings increases. The load variability also decreases as the aggregation level increases. For active network management, faster time scales are required at lower aggregation scales due to high load variability

Balancing Authority Area Coordination with Limited Exchange of Information

In this paper, we propose a coordination scheme between balancing authority (BA) areas in an interconnected power system that decreases the regulation amount needed as well as the associated costs. Our approach aims at mimicking the behavior of the automatic generation control (AGC) system in a scenario where the whole interconnected system is assumed to be operated by a single BA area. To this end, we modify the area control error (ACE), which is fed into the AGC system of each BA area, and determine the AGC allocation based on a distributed algorithm that identifies the least expensive generators, with the mismatch of the total regulation needed being the only information exchanged between BA areas. We demonstrate the proposed ideas with the 3-machine 9-bus Western Electricity Coordinating Council (WECC) system, and compare the performance of our method with other three existing coordination approaches

An Assessment of the Impact of Uncertainty on Automatic Generation Control Systems

This paper proposes a framework to quantify the impact of uncertainty that arises from load variations, renewable-based generation, and noise in communication channels on the automatic generation control (AGC) system. To this end, we rely on a model of the power system that includes the synchronous generator dynamics, the network, and the AGC system dynamics, as well as the effect of various sources of uncertainty. Then, we develop a method to analytically propagate the uncertainty from the aforementioned sources to the system frequency and area control error (ACE), and obtain expressions that approximate their probability distribution functions. We make use of this framework to obtain probabilistic expressions for the frequency performance criteria developed by the North American Electric Reliability Corporation (NERC); such expressions may be used to determine the limiting values of uncertainty that the system may withstand. The proposed ideas are illustrated through the Western Electricity Coordination Council (WECC) 9-bus 3-machine system and a 140-bus 48-machine system

Balancing Authority Area Model and its Application to the Design of Adaptive AGC Systems

In this paper, we develop a reduced-order model for synchronous generator dynamics via selective modal analysis. Then, we use this reduced-order model to formulate a balancing authority (BA) area dynamic model. Next, we use the BA area model to design an adaptive automatic generation control (AGC) scheme, with self-tuning gain, that decreases the amount of regulation needed and potentially reduces the associated costs. In particular, we use the BA area model to derive a relationship between the actual frequency response characteristic (AFRC) of the BA area, the area control error, the system frequency, and the total generation. We make use of this relationship to estimate the AFRC online, and set the frequency bias factor equal to the online estimation. As a result, the AGC system is driven by the exact number of MW needed to restore the system frequency and the real power interchange to the desired values. We demonstrate the proposed ideas with a single machine infinite bus, the 9-bus 3-machine Western Electricity Coordinating Council (WECC), and a 140-bus 48-machine systems

Composition of agarose substrate affects behavioral output of Drosophila larvae.

In the last decade the Drosophila larva has evolved into a simple model organism offering the opportunity to integrate molecular genetics with systems neuroscience. This led to a detailed understanding of the neuronal networks for a number of sensory functions and behaviors including olfaction, vision, gustation and learning and memory. Typically, behavioral assays in use exploit simple Petri dish setups with either agarose or agar as a substrate. However, neither the quality nor the concentration of the substrate is generally standardized across these experiments and there is no data available on how larval behavior is affected by such different substrates. Here, we have investigated the effects of different agarose concentrations on several larval behaviors. We demonstrate that agarose concentration is an important parameter, which affects all behaviors tested: preference, feeding, learning and locomotion. Larvae can discriminate between different agarose concentrations, they feed differently on them, they can learn to associate an agarose concentration with an odor stimulus and change locomotion on a substrate of higher agarose concentration. Additionally, we have investigated the effect of agarose concentration on three quinine based behaviors: preference, feeding and learning. We show that in all cases examined the behavioral output changes in an agarose concentration-dependent manner. Our results suggest that comparisons between experiments performed on substrates differing in agarose concentration should be done with caution. It should be taken into consideration that the agarose concentration can affect the behavioral output and thereby the experimental outcomes per se potentially due to the initiation of an escape response or changes in foraging behavior on more rigid substrates

Impact of Climate Change on the Heating Demand of Buildings. A District Level Approach

There is no doubt that during recent years, the developing countries are in urgent demand of energy, which means the energy generation and the carbon emissions increase accumulatively. The 40 % of the global energy consumption per year comes from the building stock. Considering the predictions regarding future climate due to climate change, a good understanding on the energy use due to future climate is required. The aim of this study was to evaluate the impact of future weather in the heating demand and carbon emissions for a group of buildings at district level, focusing on two areas of London in the United Kingdom. The methodological approach involved the use of geospatial data for the case study areas, processed with Python programming language through Anaconda and Jupyter notebook, generation of an archetype dataset with energy performance data from TABULA typology and the use of Python console in QGIS to calculate the heating demand in the reference weather data, 2050 and 2100 in accordance with RCP 4.5 and RCP 8.5 scenarios. A validated model was used for the district level heating demand calculation. On the one hand, the results suggest that a mitigation of carbon emissions under the RCP4.5 scenario will generate a small decrease on the heating demand at district level, so slightly similar levels of heating generation must continue to be provided using sustainable alternatives. On the other hand, following the RCP 8.5 scenario of carbon emission carrying on business as usual will create a significant reduction of heating demand due to the rise on temperature but with the consequent overheating in summer, which will shift the energy generation problem. The results suggest that adaptation of the energy generation must start shifting to cope with higher temperatures and a different requirement of delivered energy from heating to cooling due to the effect of climate change

A Novel Modeling Approach to Stochastically Evaluate the Impact of Pore Network Geometry, Chemistry and Topology on Fluid Transport

Fine-grained sandstones, siltstones, and shales have become increasingly important to satisfy the ever-growing global energy demands. Of particular current interest are shale rocks, which are mudstones made up of organic and inorganic constituents of varying pore sizes. These materials exhibit high heterogeneity, low porosity, varying chemical composition and low pore connectivity. Due to the complexity and the importance of such materials, many experimental, theoretical and computational eforts have attempted to quantify the impact of rock features on fuids difusivity and ultimately on permeability. In this study, we introduce a stochastic kinetic Monte Carlo approach developed to simulate fuid transport. The features of this approach allow us to discuss the applicability of 2D vs 3D models for the calculation of transport properties. It is found that a successful model should consider realistic 3D pore networks consisting of pore bodies that communicate via pore throats, which however requires a prohibitive amount of computational resources. To overcome current limitations, we present a rigorous protocol to stochastically generate synthetic 3D pore networks in which pore features can be isolated and varied systematically and individually. These synthetic networks do not correspond to real sample scenarios but are crucial to achieve a systematic evaluation of the pore features on the transport properties. Using this protocol, we quantify the contribution of the pore network’s connectivity, porosity, mineralogy, and pore throat width distribution on the difusivity of supercritical methane. A sensitivity analysis is conducted to rank the signifcance of the various network features on methane difusivity. Connectivity is found to be the most important descriptor, followed by pore throat width distribution and porosity. Based on such insights, recommendations are provided on possible technological approaches to enhance fuid transport through shale rocks and equally complex pore networks. The purpose of this work is to identify the signifcance of various pore network characteristics using a stochastic KMC algorithm to simulate the transport of fuids. Our fndings could be relevant for applications that make use of porous media, ranging from catalysis to radioactive waste management, and from environmental remediation to shale gas production