Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term

To overcome the noise sensitiveness of conventional fuzzy c-means (FCM) clustering algorithm, a novel extended FCM algorithm for image segmentation is presented in this paper. The algorithm is developed by modifying the objective function of the standard FCM algorithm with a penalty term that takes into account the influence of the neighboring pixels on the centre pixels. The penalty term acts as a regularizer in this algorithm, which is inspired from the neighborhood expectation maximization algorithm and is modified in order to satisfy the criterion of the FCM algorithm. The performance of our algorithm is discussed and compared to those of many derivatives of FCM algorithm. Experimental results on segmentation of synthetic and real images demonstrate that the proposed algorithm is effective and robust

Chaotic time series prediction using wavelet transform and multi-model hybrid method

In order to further improve the prediction accuracy of the chaotic time series and overcome the defects of the single model, a multi-model hybrid model of chaotic time series is proposed. First, the Discrete Wavelet Transform (DWT) is used to decompose the data and obtain the approximate coefficients (low-frequency sequence) and detailed coefficients (high-frequency sequence) of the sequence. Secondly, phase space reconstruction is performed on the decomposed data. Thirdly, the chaotic characteristics of each sequence are judged by correlation integral and Kolmogorov entropy. Fourthly, in order to explore the deeper features of the time series and improve the prediction accuracy, a sequence of Volterra adaptive prediction models is established for the components with chaotic characteristics according to the different characteristics of each component. For the components without chaotic characteristics, a JGPC prediction model without chaotic feature sequences is established. Finally, the multi-model fusion prediction of the above multiple sequences is carried out by the LSTM algorithm, and the final prediction result is obtained through calculation, which further improves the prediction accuracy. Experiments show that the multi-model hybrid method of Volterra-JGPC-LSTM is more accurate than other comparable models in predicting chaotic time series

Schmeissneria: A missing link to angiosperms?

BACKGROUND: The origin of angiosperms has been under debate since the time of Darwin. While there has been much speculation in past decades about pre-Cretaceous angiosperms, including Archaefructus, these reports are controversial. The earliest reliable fossil record of angiosperms remains restricted to the Cretaceous, even though recent molecular phylogenetic studies suggest an origin for angiosperms much earlier than the current fossil record. RESULTS: In this paper, after careful SEM and light microscopic work, we report fossils with angiospermous traits of the Jurassic age. The fossils were collected from the Haifanggou Formation (middle Jurassic) in western Liaoning, northeast China. They include two female structures and an associated leaf on the same slab. One of the female structures is physically connected to the apex of a short shoot. The female organs are borne in pairs on short peduncles that are arranged along the axis of the female structure. Each of the female organs has a central unit that is surrounded by an envelope with characteristic longitudinal ribs. Each central unit has two locules completely separated by a vertical septum. The apex of the central unit is completely closed. The general morphology places these fossils into the scope of Schmeissneria, an early Jurassic genus that was previously attributed to Ginkgoales. CONCLUSION: Because the closed carpel is a character only found in angiosperms, the closed apex of the central unit suggests the presence of angiospermy in Schmeissneria. This angiospermous trait implies either a Jurassic angiosperm or a new seed plant group parallel to angiosperms and other known seed plants. As an angiosperm, the Liassic age (earliest Jurassic) of Schmeissneria microstachys would suggest an origin of angiosperms during the Triassic. Although still uncertain, this could have a great impact on our perspective of the history, diversity and systematics of seed plants and angiosperms

A switched reluctance motor torque ripple reduction strategy with deadbeat current control and active thermal management

This paper presents a switched reluctance motor (SRM) torque ripple reduction strategy with deadbeat current control and active thermal management. In this method, the SRM torque is indirectly controlled by the phase current. A deadbeat current control method is used to improve the SRM phase current control accuracy, so that SRM torque control error can be reduced significantly. According to the online measurement of the power switching device temperature, the switching frequency will be reduced to prevent the SRM power converter from being damaged by over-temperature. The feasibility and effectiveness of the proposed strategy have been verified in both simulation and experimental studies

A new coordination tetra­mer of copper(I) iodide and benzyl­dimethyl­amine: tetra-μ3-iodido-tetra­kis[(benzyl­dimethyl­amine-κN)copper(I)]

The title compound, [Cu4I4(C9H13N)4], has a distorted cubane-like [Cu4I4] core structure. Each CuI atom is tetra­hedrally coordinated by three I atoms and one N atom of an benzyl­dimethyl­amine ligand. Each I atom acts as a μ3-ligand, linking three CuI atoms. The Cu—I bond distances vary between 2.6328 (7) and 2.7121 (6) Å, while the Cu—N bond distances vary between 2.107 (3) and 2.122 (3) Å

Local Volterra multivariable chaotic time series multi-step prediction based on phase points clustering

To solve the multivariable multi-step prediction problem in chaotic complex systems, this paper proposes a local Volterra model based on phase points clustering. Firstly, reconstruct the phase space of the data and calculate the similarity of the evolution trajectories. According to the similarity, the initial clustering center of the observation point is calculated and the clustering is carried out by means of K mean. We find the cluster class nearest to the prediction phase, compare the predicted phase point with the evolutionary trajectory similarity of all the observed points in the cluster, select the optimal neighboring phase point, and the optimal neighboring phase point is used for training and multi-step prediction of the multivariable local Volterra model. The proposed model method can greatly reduce the time of multi-step prediction and improve the efficiency of prediction. Finally, by experimenting with the data of Beijing PM2.5 acquired from UCI machine learning database, the experimental results show that this model method has better predictive performance

A randomized, three-period crossover study of umeclidinium as monotherapy in adult patients with asthma

SummaryBackgroundTo our knowledge, no studies in patients with asthma have assessed a long-acting muscarinic antagonist in the absence of inhaled corticosteroids (ICS).ObjectiveEvaluate the dose–response, efficacy, and safety of umeclidinium (UMEC) in patients with asthma not receiving ICS.MethodsIn this double-blind, three-period crossover study, 350 subjects were randomized to a sequence of three of eight inhaled treatments: UMEC 15.6, 31.25, 62.5, 125, or 250 mcg once daily (OD), UMEC 15.6 or 31.25 mcg twice daily (BID), or placebo, administered for 14 days (12–14-day washout). Trough forced expiratory volume in one second (FEV1), 0–24-h weighted mean (WM) FEV1, and safety were assessed. Serial spirometry and pharmacokinetic assessments were performed in a subgroup.ResultsSubjects had a mean baseline pre- and post-bronchodilator FEV1 of 71% and 88% predicted, respectively. Significant improvements in change from baseline trough FEV1 were observed for UMEC 15.6 OD (0.066 L; p = 0.036) and UMEC 125 OD (0.088 L; p = 0.005) versus placebo, but not other OD or BID doses. UMEC increased 0–24-h WM FEV1 versus placebo (0.068–0.121 L [p ≤ 0.017] with no clear dose–response). Treatment differences were similar for corresponding OD and BID doses in serial assessments. UMEC was rapidly absorbed, with evidence of some accumulation. The incidence of on-treatment adverse events was 9–21% for UMEC and 12% for placebo. There were no treatment-related effects on laboratory parameters.ConclusionThe modest trough FEV1 improvements did not conclusively support a therapeutic benefit of UMEC in non-ICS treated patients with asthma.ClinicalTrials.govNCT01641692

A systematic search for SNPs/haplotypes associated with disease phenotypes using a haplotype-based stepwise procedure

<p>Abstract</p> <p>Background</p> <p>Genotyping technologies enable us to genotype multiple Single Nucleotide Polymorphisms (SNPs) within selected genes/regions, providing data for haplotype association analysis. While haplotype-based association analysis is powerful for detecting untyped causal alleles in linkage-disequilibrium (LD) with neighboring SNPs/haplotypes, the inclusion of extraneous SNPs could reduce its power by increasing the number of haplotypes with each additional SNP.</p> <p>Methods</p> <p>Here, we propose a haplotype-based stepwise procedure (HBSP) to eliminate extraneous SNPs. To evaluate its properties, we applied HBSP to both simulated and real data, generated from a study of genetic associations of the bactericidal/permeability-increasing (BPI) gene with pulmonary function in a cohort of patients following bone marrow transplantation.</p> <p>Results</p> <p>Under the null hypothesis, use of the HBSP gave results that retained the desired false positive error rates when multiple comparisons were considered. Under various alternative hypotheses, HBSP had adequate power to detect modest genetic associations in case-control studies with 500, 1,000 or 2,000 subjects. In the current application, HBSP led to the identification of two specific SNPs with a positive validation.</p> <p>Conclusion</p> <p>These results demonstrate that HBSP retains the essence of haplotype-based association analysis while improving analytic power by excluding extraneous SNPs. Minimizing the number of SNPs also enables simpler interpretation and more cost-effective applications.</p