Effective scheduling algorithm for on-demand XML data broadcasts in wireless environments

The organization of data on wireless channels, which aims to reduce the access time of mobile clients, is a key problem in data broadcasts. Many scheduling algorithms have been designed to organize flat data on air. However, how to effectively schedule semi-structured information such as XML data on wireless channels is still a challenge. In this paper, we firstly propose a novel method to greatly reduce the tuning time by splitting query results into XML snippets and to achieve better access efficiency by combining similar ones. Then we analyze the data broadcast scheduling problem of on-demand XML data broadcasts and define the efficiency of a data item. Based on the definition, a Least Efficient Last (LEL) scheduling algorithm is also devised to effectively organize XML data on wireless channels. Finally, we study the performance of our algorithms through extensive experiments. The results show that our scheduling algorithms can reduce both access time and tuning time signifcantly when compared with existing work

A New Confidence Interval for the Difference Between Two Binomial Proportions of Paired Data

Motivated by a study on comparing sensitivities and specificities of two diagnostic tests in a paired design when the sample size is small, we first derived an Edgeworth expansion for the studentized difference between two binomial proportions of paired data. The Edgeworth expansion can help us understand why the usual Wald interval for the difference has poor coverage performance in the small sample size. Based on the Edgeworth expansion, we then derived a transformation based confidence interval for the difference. The new interval removes the skewness in the Edgeworth expansion; the new interval is easy to compute, and its coverage probability converges to the nominal level at a rate of O(n-1/2). Numerical results indicate that the new interval has the average coverage probability that is very close to the nominal level on average even for sample sizes as small as 10. Numerical results also indicate this new interval has better average coverage accuracy than the best existing intervals in finite sample sizes

Improved Confidence Intervals for the Sensitivity at a Fixed Level of Specificity of a Continuous-Scale Diagnostic Test

For a continuous-scale test, it is an interest to construct a confidence interval for the sensitivity of the diagnostic test at the cut-off that yields a predetermined level of its specificity (eg. 80%, 90%, or 95%). IN this paper we proposed two new intervals for the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity. We then conducted simulation studies to compare the relative performance of these two intervals with the best existing BCa bootstrap interval, proposed by Platt et al. (2000). Our simulation results showed that the newly proposed intervals are better than the BCa bootstrap interval in terms of coverage accuracy and interval length

New Intervals for the Difference Between Two Independent Binomial Proportions

In this paper we gave an Edgeworth expansion for the studentized difference of two binomial proportions. We then proposed two new intervals by correcting the skewness in the Edgeworth expansion in a direct and an indirect way. Such the bias-correct confidence intervals are easy to compute, and their coverage probabilities converge to the nominal level at a rate of O(n-½), where n is the size of the combined samples. Our simulation results suggest tat in finite samples the new interval based on the indirect method have the similar performance to the two best existing intervals in terms of coverage accuracy and average interval length and that another new interval based on the direct method had the best average coverage accuracy but could have poor coverage when two true binomial proportions are close to the boundary points

New Confidence Intervals for the Difference between Two Sensitivities at a Fixed Level of Specificity

For two continuous-scale diagnostic tests, it is of interest to compare their sensitivities at a predetermined level of specificity. In this paper we propose three new intervals for the difference between two sensitivities at a fixed level of specificity. These intervals are easy to compute. We also conduct simulation studies to compare the relative performance of the new intervals with the existing normal approximation based interval proposed by Wieand et al (1989). Our simulation results show that the newly proposed intervals perform better than the existing normal approximation based interval in terms of coverage accuracy and interval length

Inferences in Censored Cost Regression Models with Empirical Likelihood

In many studies of health economics, we are interested in the expected total cost over a certain period for a patient with given characteristics. Problems can arise if cost estimation models do not account for distributional aspects of costs. Two such problems are 1) the skewed nature of the data and 2) censored observations. In this paper we propose an empirical likelihood (EL) method for constructing a confidence region for the vector of regression parameters and a confidence interval for the expected total cost of a patient with the given covariates. We show that this new method has good theoretical properties and compare its finite-sample properties with the existing method. Our simulation results demonstrate that the new EL-based method performs equally well with the existing method when cost data are not so skewed, and outperforms the existing method when cost data are highly skewed. Finally, we illustrate the application of our method in a real data set

Hypothesis test on a mixture forward-incubation-time epidemic model with application to COVID-19 outbreak

The distribution of the incubation period of the novel coronavirus disease that emerged in 2019 (COVID-19) has crucial clinical implications for understanding this disease and devising effective disease-control measures. Qin et al. (2020) designed a cross-sectional and forward follow-up study to collect the duration times between a specific observation time and the onset of COVID-19 symptoms for a number of individuals. They further proposed a mixture forward-incubation-time epidemic model, which is a mixture of an incubation-period distribution and a forward time distribution, to model the collected duration times and to estimate the incubation-period distribution of COVID-19. In this paper, we provide sufficient conditions for the identifiability of the unknown parameters in the mixture forward-incubation-time epidemic model when the incubation period follows a two-parameter distribution. Under the same setup, we propose a likelihood ratio test (LRT) for testing the null hypothesis that the mixture forward-incubation-time epidemic model is a homogeneous exponential distribution. The testing problem is non-regular because a nuisance parameter is present only under the alternative. We establish the limiting distribution of the LRT and identify an explicit representation for it. The limiting distribution of the LRT under a sequence of local alternatives is also obtained. Our simulation results indicate that the LRT has desirable type I errors and powers, and we analyze a COVID-19 outbreak dataset from China to illustrate the usefulness of the LRT.Comment: 34 pages, 2 figures, 2 table

Transport critical current density in Fe-sheathed nano-SiC doped MgB2 wires

The nano-SiC doped MgB2/Fe wires were fabricated using a powder-in-tube method and an in-situ reaction process. The depression of Tc with increasing SiC doping level remained rather small due to the counterbalanced effect of Si and C co-doping. The high level SiC co-doping allowed creation of the intra-grain defects and nano-inclusions, which act as effective pinning centers, resulting in a substantial enhancement in the Jc(H) performance. The transport Jc for all the wires is comparable to the magnetic Jc at higher fields despite the low density of the samples and percolative nature of current. The transport Ic for the 10wt% SiC doped MgB2/Fe reached 660A at 5K and 4.5T (Jc = 133,000A/cm2) and 540A at 20K and 2T (Jc = 108,000A/cm2). The transport Jc for the 10wt% SiC doped MgB2 wire is more than an order of magnitude higher than for the state-the-art Fe-sheathed MgB2 wire reported to date at 5K and 10T and 20K and 5T respectively. There is a plenty of room for further improvement in Jc as the density of the current samples is only 50%.Comment: 4 pages, 7 figures, presented at ASC 2002, Housto

New Therapeutic Approaches for the Treatment of Rheumatoid Arthritis may Rise from the Cholinergic Anti-Inflammatory Pathway and Antinociceptive Pathway

Due to the complex etiology of rheumatoid arthritis (RA), it is difficult to be completely cured at the current stage although many approaches have been applied in clinics, especially the wide application of nonsteroidal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs). New drug discovery and development via the recently discovered cholinergic anti-inflammatory and antinociceptive pathways should be promising. Based on the above, the nicotinic acetylcholine receptor agonists maintain the potential for the treatment of RA. Therefore, new therapeutic approaches may rise from these two newly discovered pathways. More preclinical experiments and clinical trials are required to confirm our viewpoint