Bidding For Parking: The Impact of University Affiliation on Predicting Bid Values in Dutch Auctions of On-Campus Parking Permits

Parking is often underpriced and expanding its capacity is expensive; universities need a better way of reducing congestion outside of building costly parking garages. Demand based pricing mechanisms, such as auctions, offer a possible solution to the problem by promising to reduce parking at peak times. However, faculty, students, and staff at universities have systematically different parking needs, leading to different parking valuations. In this study, I determine the impact university affiliation has on predicting bid values cast in three Dutch Auctions of on-campus parking permits sold at Chapman University in Fall 2010. Using clustering techniques crosschecked with university demographic information to detect affiliation groups, I ran a log-linear regression, finding that university affiliation had a larger effect on bid amount than on lot location and fraction of auction duration. Generally, faculty were predicted to have higher bids whereas students were predicted to have lower bids.

Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modeled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets Assisted by CPS

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy assisted by a cyber-physical system for supporting management decisions to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a stochastic linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modelled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

Bidding Decision of Wind-Thermal GenCo in Day-Ahead Market

This paper deals with the self-scheduling problem of a price-taker having wind and thermal power production and assisted by a cyber-physical system for supporting management decisions in a day-ahead electric energy market. The self-scheduling is regarded as a stochastic mixed-integer linear programming problem. Uncertainties on electricity price and wind power are considered through a set of scenarios. Thermal units are modelled by start-up and variable costs, furthermore constraints are considered, such as: ramp up/down and minimum up/down time limits. The stochastic mixed-integer linear programming problem allows a decision support for strategies advantaging from an effective wind and thermal mixed bidding. A case study is presented using data from the Iberian electricity market.

Bidding strategy of wind-thermal energy producers

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

Optimal Wind Bidding Strategies in Day-Ahead Markets

This paper presents a computer application for wind energy bidding in a day-ahead electricity market to better accommodate the variability of the energy source. The computer application is based in a stochastic linear mathematical programming problem. The goal is to obtain the optimal bidding strategy in order to maximize the revenue. Electricity prices and financial penalties for shortfall or surplus energy deliver are modeled. Finally, conclusions are drawn from an illustrative case study, using data from the day-ahead electricity market of the Iberian Peninsula.

Optimal bidding strategies of an electric vehicle aggregator in an electricity market

In recent years Electric Vehicles (EVs) are getting more importance as future transport systems, due to the increase of the concerns relevant to the greenhouse gases emission and the use fossil fuel. The management of the charging and discharging process of EVs could provide new business model for participating in the electricity markets. Moreover, vehicle to grid systems have the potential of increasing utility system flexibility. This thesis develops some models for the optimal integration of the EVs in the electricity market. In particular, the thesis focuses on the optimal bidding strategy of an EV aggregator participating to both the day ahead market and the secondary reserve market. The aggregator profit is maximized taking into account the energy balance equation, as well as the technical constraints of energy settlement, power supply and state of charge of the EVs. The results obtained by using the GAMS (General Algebraic Modelling System) environment are presented and discussed.

Unbalanced R-loops and micronuclei induced by DNA topoisomerase I poisons in cancer cells

Topoisomerase I (Top1) poisons are among the most clinically-effective drugs used for colon, ovary and lung cancers. Unpublished data from our lab have recently revealed that the structurally-unrelated Top1 poisons, Camptothecin (CPT) and Indimitecan (LMP776), induce the formation of micronuclei (MNi) in human cancer cells. In addition, MNi trigger an innate immune gene response by stimulating the cGAS/STING pathway. As the mechanisms of MNi formation are not fully determined, our aim is here to establish how MNi form after Top1 poisoning. Using immunofluorescence assays and EdU labelling of nascent DNAs, our results show that, after 24 hours of recovery, a short treatment with sub-cytotoxic doses of Top1 poisons induces the formation of MNi that do not contain newly synthetized (EdU+) DNA. We also saw that Top1 poisons delay replication machinery reducing EdU incorporation and produce significant levels of the damage markers γH2AX and p53BP1 in S-phase cells but not in G1 and G2/M cells. The results also show that MNi formation is dependent on R-loops, as RNaseH1 overexpression markedly reduces Top1 induced MNi. Genome-wide mapping of R-loops by DRIP-seq technique revealed that R-loop levels are both decreased and increased by CPT. In particular, increased R-loops are mainly found at active genes and always overlapped with Top1cc sites. We also found that increased R-loops overlap with lamina-associated chromatin domains while decreased R-loops correlate with replication origin sites. Overall, our data are consistent with the formation of MNi due to R-loop increase and under-replication at specific regions caused by Top1 poisons. These results will eventually help in developing new strategies for effective personalized interventions by using Top1-targeted compounds as immuno-modulators in cancer patients.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

Genomic Copy Number Alterations Of Primary And Secondary Metastasizing Pleomorphic Adenomas.

Metastasizing pleomorphic adenoma (MPA) is a rare tumour, and its mechanism of metastasis still is unknown. To date, there has been no study on MPA genomics. We analysed primary and secondary MPAs with array comparative genomic hybridization to identify somatic copy number alterations and affected genes. Tumour DNA samples from primary (parotid salivary gland) and secondary (scalp skin) MPAs were subjected to array comparative genomic hybridization investigation, and the data were analysed with NEXUS COPY NUMBER DISCOVERY. The primary MPA showed copy number losses affecting 3p22.2p14.3 and 19p13.3p123, and a complex pattern of four different deletions at chromosome 6. The 3p deletion encompassed several genes: CTNNB1, SETD2, BAP1, and PBRM1, among others. The secondary MPA showed a genomic profile similar to that of the primary MPA, with acquisition of additional copy number changes affecting 9p24.3p13.1 (loss), 19q11q13.43 (gain), and 22q11.1q13.33 (gain). Our findings indicated a clonal origin of the secondary MPA, as both tumours shared a common profile of genomic copy number alterations. Furthermore, we were able to detect in the primary tumour a specific pattern of copy number alterations that could explain the metastasizing characteristic, whereas the secondary MPA showed a more unbalanced genome.

Exercise training changes IL-10/TNF-alpha ratio in the skeletal muscle of post-MI rats

Heart failure (HF) is associated with changes in the skeletal muscle (SM) which might be a consequence of the unbalanced local expression of pro- (TNF-alpha) and anti- (IL-10) inflammatory cytokines, leading to inflammation-induced myopathy, and SM wasting. This local effect of HF on SM may, on the other hand, contribute to systemic inflammation, as this tissue actively secretes cytokines. Since increasing evidence points out to an anti-inflammatory effect of exercise training, the goal of the present study was to investigate its effect in rats with HF after post-myocardial infarction (MI), with special regard to the expression of TNF-alpha and IL-10 in the soleus and extensor digitorum longus (EDL), muscles with different fiber composition. Wistar rats underwent left thoracotomy with ligation of the left coronary artery, and were randomly assigned to either a sedentary (Sham-operated and MI sedentary) or trained (Sham-operated and MI trained) group. Animals in the trained groups ran on a treadmill (0% grade at 13-20 m/min) for 60 min/day, 5 days/week, for 8-10 weeks. The training protocol was able to reverse the changes induced by MI, decreasing TNF-alpha protein (26%, P < 0.05) and mRNA (58%, P < 0.05) levels in the soleus, when compared with the sedentary MI group. Training also increased soleus IL-10 expression (2.6-fold, P < 0.001) in post-MI HF rats. As a consequence, the IL-10/TNF-alpha ratio was increased. This ""anti-inflammatory effect"" was more pronounced in the soleus than in the EDL, suggesting a fiber composition dependent response. (C) 2009 Elsevier Ltd. All rights reserved.

Distribution systems fault analysis considering fault resistance estimation

Fault resistance is a critical component of electric power systems operation due to its stochastic nature. If not considered, this parameter may interfere in fault analysis studies. This paper presents an iterative fault analysis algorithm for unbalanced three-phase distribution systems that considers a fault resistance estimate. The proposed algorithm is composed by two sub-routines, namely the fault resistance and the bus impedance. The fault resistance sub-routine, based on local fault records, estimates the fault resistance. The bus impedance sub-routine, based on the previously estimated fault resistance, estimates the system voltages and currents. Numeric simulations on the IEEE 37-bus distribution system demonstrate the algorithm`s robustness and potential for offline applications, providing additional fault information to Distribution Operation Centers and enhancing the system restoration process. (C) 2011 Elsevier Ltd. All rights reserved.

Extended Fault-Location Formulation for Power Distribution Systems

In this paper, an extended impedance-based fault-location formulation for generalized distribution systems is presented. The majority of distribution feeders are characterized by having several laterals, nonsymmetrical lines, highly unbalanced operation, and time-varying loads. These characteristics compromise traditional fault-location methods performance. The proposed method uses only local voltages and currents as input data. The current load profile is obtained through these measurements. The formulation considers load variation effects and different fault types. Results are obtained from numerical simulations by using a real distribution system from the Electrical Energy Distribution State Company of Rio Grande do Sul (CEEE-D), Southern Brazil. Comparative results show the technique robustness with respect to fault type and traditional fault-location problems, such as fault distance, resistance, inception angle, and load variation. The formulation was implemented as embedded software and is currently used at CEEE-D`s distribution operation center.

Three-phase harmonic distortion state estimation algorithm based on evolutionary strategies

This paper presents a new methodology to estimate unbalanced harmonic distortions in a power system, based on measurements of a limited number of given sites. The algorithm utilizes evolutionary strategies (ES), a development branch of evolutionary algorithms. The problem solving algorithm herein proposed makes use of data from various power quality meters, which can either be synchronized by high technology GPS devices or by using information from a fundamental frequency load flow, what makes the overall power quality monitoring system much less costly. The ES based harmonic estimation model is applied to a 14 bus network to compare its performance to a conventional Monte Carlo approach. It is also applied to a 50 bus subtransmission network in order to compare the three-phase and single-phase approaches as well as the robustness of the proposed method. (C) 2010 Elsevier B.V. All rights reserved.

Nondestructive inspection of plastic deformation in commercial carbon steels using magnetic Barkhausen noise

The present work shows measurements of the Magnetic Barkhausen Noise (MBN) in commercial AISI/SAE 1045 and ASTM 36 steel deformed samples. The correlation between the MBN root mean square, Barkhausen signal profile and MBN power spectrum with the plastic deformation is established. The results show that the power spectral density of the Barkhausen signal is more effective as nondestructive evaluator than root mean square of Barkhausen signal. The Outcomes also suggest the presence of unbalanced tensions between the surface and the bulk of sample due to the presence of plastic deformation.