On the Approximability and Hardness of the Minimum Connected Dominating Set with Routing Cost Constraint

In the problem of minimum connected dominating set with routing cost constraint, we are given a graph $G=(V,E)$, and the goal is to find the smallest connected dominating set $D$ of $G$ such that, for any two non-adjacent vertices $u$ and $v$ in $G$, the number of internal nodes on the shortest path between $u$ and $v$ in the subgraph of $G$ induced by $D \cup \{u,v\}$ is at most $\alpha$ times that in $G$. For general graphs, the only known previous approximability result is an $O(\log n)$-approximation algorithm ($n=|V|$) for $\alpha = 1$ by Ding et al. For any constant $\alpha > 1$, we give an $O(n^{1-\frac{1}{\alpha}}(\log n)^{\frac{1}{\alpha}})$-approximation algorithm. When $\alpha \geq 5$, we give an $O(\sqrt{n}\log n)$-approximation algorithm. Finally, we prove that, when $\alpha =2$, unless $NP \subseteq DTIME(n^{poly\log n})$, for any constant $\epsilon > 0$, the problem admits no polynomial-time $2^{\log^{1-\epsilon}n}$-approximation algorithm, improving upon the $\Omega(\log n)$ bound by Du et al. (albeit under a stronger hardness assumption)

Microscopy of glazed layers formed during high temperature sliding wear at 750C

The evolution of microstructures in the glazed layer formed during high temperature sliding wear of Nimonic 80A against Stellite 6 at 750 ◦C using a speed of 0.314ms−1 under a load of 7N has been investigated using scanning electron microscopy (SEM), energy dispersive analysis by X-ray (EDX), X-ray diffraction (XRD) analysis, scanning tunnelling microscopy (STM) and transmission electron microscopy (TEM). The results indicate the formation of a wear resistant nano-structured glazed layer. The mechanisms responsible for the formation of the nano-polycrystalline glazed layer are discussed

Comment on "Self-Purification in Semiconductor Nanocrystals"

In a recent Letter [PRL 96, 226802 (2006)], Dalpian and Chelikowsky claimed that formation energies of Mn impurities in CdSe nanocrystals increase as the size of the nanocrystal decreases, and argued that this size dependence leads to "self-purification" of small nanocrystals. They presented density-functional-theory (DFT) calculations showing a strong size dependence for Mn impurity formation energies, and proposed a general explanation. In this Comment we show that several different DFT codes, pseudopotentials, and exchange-correlation functionals give a markedly different result: We find no such size dependence. More generally, we argue that formation energies are not relevant to substitutional doping in most colloidally grown nanocrystals.Comment: 1 page, 1 figur

Calibration of probabilistic quantitative precipitation forecasts with an artificial neural network

A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002-2003 cool season (1 November-31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as "truth." The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small. © 2007 American Meteorological Society

Short-range probabilistic quantitative precipitation forecasts over the southwest United States by the RSM ensemble system

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to produce twice-daily (0000 and 1200 UTC), high-resolution ensemble forecasts to 24 h. The forecasts are performed at an equivalent horizontal grid spacing of 12 km for the period 1 November 2002 to 31 March 2003 over the southwest United States. The performance of 6-h accumulated precipitation is assessed for 32 U.S. Geological Survey hydrologic catchments. Multiple accuracy and skill measures are used to evaluate probabilistic quantitative precipitation forecasts. NCEP stage-IV precipitation analyses are used as "truth," with verification performed on the stage-IV 4-km grid. The RSM ensemble exhibits a ubiquitous wet bias. The bias manifests itself in areal coverage, frequency of occurrence, and total accumulated precipitation over every region and during every 6-h period. The biases become particularly acute starting with the 1800-0000 UTC interval, which leads to a spurious diurnal cycle and the 1200 UTC cycle being more adversely affected than the 0000 UTC cycle. Forecast quality and value exhibit marked variability over different hydrologic regions. The forecasts are highly skillful along coastal California and the windward slopes of the Sierra Nevada Mountains, but they generally lack skill over the Great Basin and the Colorado basin except over mountain peaks. The RSM ensemble is able to discriminate precipitation events and provide useful guidance to a wide range of users over most regions of California, which suggests that mitigation of the conditional biases through statistical postprocessing would produce major improvements in skill. © 2007 American Meteorological Society

No association of CTLA-4 polymorphisms with susceptibility to Behcet disease

Background: Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) is a key negative regulator of T lymphocytes and has been shown to be associated with a number of autoimmune diseases. The present study was performed to assess the association between CTLA-4 polymorphisms and Behcet disease (BD) in Chinese patients. Methods: Two hundred and twenty-eight BD patients and 207 controls were analysed for four single nucleotide polymorphisms (SNPs) (21661A/G, 2318C/T, + 49G/A and CT60G/A) in the CTLA-4 gene by PCR-restriction fragment length polymorphism (RFLP) analysis. The association between SNP +49A/G and BD in Chinese population as well as other ethnic groups was analysed by meta-analysis. Results: No association could be detected between CTLA-4 SNPs or haplotypes and BD. Also, no association was observed between CTLA-4 polymorphisms and BD subgroups, stratified by clinical features. A meta-analysis showed that there was no heterogeneity between studies (p = 0.60, I-2 = 0%) and that CTLA-4 SNP + 49 was not associated with BD (overall effect: Z = 0.26, p = 0.79). Conclusion: This study and a meta-analysis failed to demonstrate any association between the tested CTLA-4 polymorphisms and B

A Game-Theory Based Incentive Framework for an Intelligent Traffic System as Part of a Smart City Initiative

Intelligent Transportation Systems (ITSs) can be applied to inform and incentivize travellers to help them make cognizant choices concerning their trip routes and transport modality use for their daily travel whilst achieving more sustainable societal and transport authority goals. However, in practice, it is challenging for an ITS to enable incentive generation that is context-driven and personalized, whilst supporting multi-dimensional travel goals. This is because an ITS has to address the situation where different travellers have different travel preferences and constraints for route and modality, in the face of dynamically-varying traffic conditions. Furthermore, personalized incentive generation also needs to dynamically achieve different travel goals from multiple travellers, in the face of their conducts being a mix of both competitive and cooperative behaviours. To address this challenge, a Rule-based Incentive Framework (RIF) is proposed in this paper that utilizes both decision tree and evolutionary game theory to process travel information and intelligently generate personalized incentives for travellers. The travel information processed includes travellers’ mobile patterns, travellers’ modality preferences and route traffic volume information. A series of MATLAB simulations of RIF was undertaken to validate RIF to show that it is potentially an effective way to incentivize travellers to change travel routes and modalities as an essential smart city service