Wind power investment in thermal system and emissions reduction

This paper presents an analytical model for wind power investment. Most generation planning problems are formulated in multiperiod mixed integer programming with cost minimization as objective. We try to resort to finance literature for models able to systematically characterize return and risk. Real option theory is chosen. A primitive function is defined for the fuel cost able to be saved as the revenue of a wind power project. Subsequently the real project is described as a contingent claim on the stochastic fuel prices. Theoretical valuation of the project is thus given by the solution of a partial differential equation derived by Ito lemma. This formulation avoids the ambiguity in analyzing wind power investment based on non-market-based tariffs, but focuses on the welfare to the system as a whole. Finally a hypothetical scenario of carbon emission price is included to demonstrate the incentive it could offer to renewable generation. ©2010 IEEE.published_or_final_versionThe IEEE Power and Energy Society (PES) General Meeting, Minneapolis, MN., 25-29 July 2010. In Proceedings of PES, 2010, p. 1-

Analysis of several key factors influencing deep learning-based inter-residue contact prediction

Motivation: Deep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated. Results: We analyzed the results of our three deep learning-based contact prediction methods (MULTICOMCLUSTER, MULTICOM-CONSTRUCT and MULTICOM-NOVEL) in the CASP13 experiment and identified several key factors [i.e. deep learning technique, multiple sequence alignment (MSA), distance distribution prediction and domain-based contact integration] that influenced the contact prediction accuracy. We compared our convolutional neural network (CNN)-based contact prediction methods with three coevolution-based methods on 75 CASP13 targets consisting of 108 domains. We demonstrated that the CNN-based multi-distance approach was able to leverage global coevolutionary coupling patterns comprised of multiple correlated contacts for more accurate contact prediction than the local coevolution-based methods, leading to a substantial increase of precision by 19.2 percentage points. We also tested different alignment methods and domain-based contact prediction with the deep learning contact predictors. The comparison of the three methods showed deeper sequence alignments and the integration of domain-based contact prediction with the full-length contact prediction improved the performance of contact prediction. Moreover, we demonstrated that the domain-based contact prediction based on a novel ab initio approach of parsing domains from MSAs alone without using known protein structures was a simple, fast approach to improve contact prediction. Finally, we showed that predicting the distribution of inter-residue distances in multiple distance intervals could capture more structural information and improve binary contact prediction. Availability and implementation: https://github.com/multicom-toolbox/DNCON2/

Modeling mode choice behavior incorporating household and individual sociodemographics and travel attributes based on rough sets theory

Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem reflected from the explanatory variables of determining the choices between alternatives. The paper applies the knowledge discovery technique of rough sets theory to model travel mode choices incorporating household and individual sociodemographics and travel information, and to identify the significance of each attribute. The study uses the detailed travel diary survey data of Changxing county which contains information on both household and individual travel behaviors for model estimation and evaluation. The knowledge is presented in the form of easily understood IF-THEN statements or rules which reveal how each attribute influences mode choice behavior. These rules are then used to predict travel mode choices from information held about previously unseen individuals and the classification performance is assessed. The rough sets model shows high robustness and good predictive ability. The most significant condition attributes identified to determine travel mode choices are gender, distance, household annual income, and occupation. Comparative evaluation with the MNL model also proves that the rough sets model gives superior prediction accuracy and coverage on travel mode choice modeling

The Symbolic Meaning Effect on Smartphone Repurchase: A Comparison of Android and IOS

With Android and iOS as the dominating operation systems, the growth of one player’s influence in the market translates into the loss of its opponent’s market share. In the marketing discipline, expectation confirmation theory is used to study how a consumer’s satisfaction influences his/her willingness to repurchase the product. Scholars extended the theory and developed a post-acceptance model of information system continuance and applied the model to consumers’ continuance with information technological products. This paper will analyse consumers’ repurchase with Android and iOS-based smartphones. More, satisfied customers may still be switching to competitor\u27s product/service. This study therefore explores possible moderators between user satisfaction and repurchase

Adversarial Camouflage for Node Injection Attack on Graphs

Node injection attacks against Graph Neural Networks (GNNs) have received emerging attention as a practical attack scenario, where the attacker injects malicious nodes instead of modifying node features or edges to degrade the performance of GNNs. Despite the initial success of node injection attacks, we find that the injected nodes by existing methods are easy to be distinguished from the original normal nodes by defense methods and limiting their attack performance in practice. To solve the above issues, we devote to camouflage node injection attack, i.e., camouflaging injected malicious nodes (structure/attributes) as the normal ones that appear legitimate/imperceptible to defense methods. The non-Euclidean nature of graph data and the lack of human prior brings great challenges to the formalization, implementation, and evaluation of camouflage on graphs. In this paper, we first propose and formulate the camouflage of injected nodes from both the fidelity and diversity of the ego networks centered around injected nodes. Then, we design an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve the camouflage while ensuring the attack performance. Several novel indicators for graph camouflage are further designed for a comprehensive evaluation. Experimental results demonstrate that when equipping existing node injection attack methods with our proposed CANA framework, the attack performance against defense methods as well as node camouflage is significantly improved

Search for new physics from B→πϕB\to\pi \phi

We investigate the pure penguin process $B^-\to \pi^-\phi$ using QCD factorization approach to calculate hadronic matrix elements to the $\alpha_s$ order in some well-known NP models. It is shown that the NP contributions in R-parity conserved SUSY models and 2HDMs are not enough to saturate the experimental upper bounds for $B\to \phi \pi$. We have shown that the flavor changing $Z^\prime$ models can make the branching ratios of $B\to \phi \pi$ to saturate the bound under all relevant experimental constraints.Comment: No figure

Cloning and selection of reference genes for gene expression studies in Ananas comosus

Full length mRNA sequences of Ac-β-actin and Ac-gapdh, and partial mRNA sequences of Ac-18SrRNA and Ac-ubiquitin were cloned from pineapple in this study. The four genes were tested as housekeeping genes in three experimental sets. GeNorm and NormFinder analysis revealed that β-actin was the most appropriate reference gene for qPCR analysis of callus under induction conditions and in different tissue types, meanwhile, 18SrRNA was the most stable reference gene during organ development. Gapdh was the most unstable gene in all tested experimental sets. Transcript level analysis result of AcSERK1 in stressed callus normalized by β-actin and 18SrRNA further confirmed that reference genes selected in this study were suitable for transcript level analysis of pineapple. The expression pattern of AcSERK1 during somatic embryogenesis normalized by β-actin coincided with the cytological features of calluses during somatic embryogenesis. These results will enable more accurate and reliable normalization of qPCR results for transcription analysis in pineapple. Keywords: Reference genes, qPCR, pineapple, geNorm, NormFinder African Journal of Biotechnology Vol. 11(29), pp. 7424-7433, 10 April, 201

Implications for new physics from Bˉ0→π0π0{\bar B}^0\to \pi^0\pi^0 and Bˉ0→Kˉ0K0{\bar B}^0\to {\bar K}^0K^0

We have analyzed the ${\bar B}^0\to \pi^0\pi^0$ puzzle in three kinds of models beyond the standard model (SM). It is shown that the minimal flavor violation (MFV) models, the minimal supersymmetric standard model (MSSM), and the two Higgs doublet models (2HDM) I and II can not give an explanation of the ${\bar B}^0\to \pi^0\pi^0$ puzzle within $1 \sigma$ experimental bounds and the model III 2HDM can explain the puzzle without a conflict with other experimental measurements. If the constraint on $C_{8g}$ from $b\to s g$ is not imposed, for all kinds of insertions considered there are regions of parameter space, where the scalar quark mass is larger (much larger) than the gluino mass in the case of LR or RL (LL or RR), in which the puzzle can be resolved within $1 \sigma$ experimental bounds.Comment: 7 pages, 3 eps files. Add some more explicit analysis version appeared in PL