Structured robust stability and boundedness of nonlinear hybrid delay systems

Taking different structures in different modes into account, the paper has developed a new theory on the structured robust stability and boundedness for nonlinear hybrid stochastic differential delay equations (SDDEs) without the linear growth condition. A new Lyapunov function is designed in order to deal with the effects of different structures as well as those of different parameters within the same modes. Moreover, a lot of effort is put into showing the almost sure asymptotic stability in the absence of the linear growth condition

Delay dependent stability of highly nonlinear hybrid stochastic systems

There are lots of papers on the delay dependent stability criteria for differential delay equations (DDEs), stochastic differential delay equations (SDDEs) and hybrid SDDEs. A common feature of these existing criteria is that they can only be applied to delay equations where their coefficients are either linear or nonlinear but bounded by linear functions (namely, satisfy the linear growth condition). In other words, there is so far no delay-dependent stability criterion on nonlinear equations without the linear growth condition (we will refer to such equations as highly nonlinear ones). This paper is the first to establish delay dependent criteria for highly nonlinear hybrid SDDEs. It is therefore a breakthrough in the stability study of highly nonlinear hybrid SDDE

Boundedness and stability of highly nonlinear neutral stochastic systems with multiple delays

This paper reports the boundedness and stability of highly nonlinear hybrid neutral stochastic differential delay equations (NSDDEs) with multiple delays. Without imposing linear growth condition, the boundedness and exponential stability of the exact solution are investigated by Lyapunov functional method. In particular, using the M-matrix technique, the mean square exponential stability is obtained. Finally, three examples are presented to verify our results

Exponential stability of highly nonlinear neutral pantograph stochastic differential equations

In this paper, we investigate the exponential stability of highly nonlinear hybrid neutral pantograph stochastic differential equations(NPSDEs). The aim of this paper is to establish exponential stability criteria for a class of hybrid NPSDEs without the linear growth condition. The methods of Lyapunov functions and M-matrix are used to study exponential stability and boundedness of the hybrid NPSDEs

Stability of highly nonlinear neutral stochastic differential delay equations

Stability criteria for neutral stochastic differential delay equations (NSDDEs) have been studied intensively for the past several decades. Most of these criteria can only be applied to NSDDEs where their coefficients are either linear or nonlinear but bounded by linear functions. This paper is concerned with the stability of hybrid NSDDEs without the linear growth condition, to which we will refer as highly nonlinear ones. The stability criteria established in this paper will be dependent on delays

Development and validation of a preoperative MRI-based radiomics nomogram to predict progression-free survival in patients with clival chordomas

ObjectivesThe aim of this study was to establish and validate a MRI-based radiomics nomogram to predict progression-free survival (PFS) of clival chordoma.MethodsA total of 174 patients were enrolled in the study (train cohort: 121 cases, test cohort: 53 cases). Radiomic features were extracted from multiparametric MRIs. Intraclass correlation coefficient analysis and a Lasso and Elastic-Net regularized generalized linear model were used for feature selection. Then, a nomogram was established via univariate and multivariate Cox regression analysis in the train cohort. The performance of this nomogram was assessed by area under curve (AUC) and calibration curve.ResultsA total of 3318 radiomic features were extracted from each patient, of which 2563 radiomic features were stable features. After feature selection, seven radiomic features were selected. Cox regression analysis revealed that 2 clinical factors (degree of resection, and presence or absence of primary chordoma) and 4 radiomic features were independent prognostic factors. The AUC of the established nomogram was 0.747, 0.807, and 0.904 for PFS prediction at 1, 3, and 5 years in the train cohort, respectively, compared with 0.582, 0.852, and 0.914 in the test cohort. Calibration and risk score stratified survival curves were satisfactory in the train and test cohort.ConclusionsThe presented nomogram demonstrated a favorable predictive accuracy of PFS, which provided a novel tool to predict prognosis and risk stratification. Our results suggest that radiomic analysis can effectively help neurosurgeons perform individualized evaluations of patients with clival chordomas

Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller

Maximum power extraction for PV systems with multiple panels under partial shading conditions (PSCs) relies on the configuration of the system and the optimal searching algorithms used. This paper described a PV system with multiple PV panels in series. Each panel has a dc-dc step-down converter, hence allowing independent control of load and source power ratio corresponding to the irradiation levels. An H-bridge terminal inverter is also used for grid connection. An advanced searching algorithm (TSPSOEM) is proposed in the paper for the distributed maximum power point tracking (DMPPT). This applies the basic particle swarm optimization (PSO) procedure but with an extended memory and incorporating the grouping concept from shuffled frog leaping algorithm (SFLA). The new algorithm is applied simultaneously to all PV-converter modules in the chain. The system can exploit the variable converter ratios and reduces the effect of differential shading, both between panels and across panels. The paper presents the system and the proposed new algorithm and demonstrating superior results obtained when compared with other conventional methods

Recyclable Magnetic Titania Nanocomposite from Ilmenite with Enhanced Photocatalytic Activity

Using ilmenite as a raw material, iron was converted into Fe3O4 magnetic fluid, which further was combined with titanium filtrate by a solvothermal method. Finally Fe3O4/TiO2 nanocomposites with the uniform size of 100–200 nm were prepared. This approach uses rich, inexpensive ilmenite as a titanium and iron source, which effectively reduces the production cost. The crystal structure, chemical properties and morphologies of the products were characterized by SEM, TEM, XRD, FTIR, BET, UV-Vis, XPS and VSM. The novel photocatalyst composed of face-centered cubic Fe3O4 and body-centered tetragonal anatase–TiO2 exhibits a spherical shape with porous structures, superparamagnetic behavior and strong absorption in the visible light range. Using the degradation reaction of Rhodamine B (RhB) to evaluate the photocatalytic performance, the results suggest that Fe3O4/TiO2 nanocomposites exhibit excellent photocatalytic activities and stability under visible light and solar light. Moreover, the magnetic titania nanocomposites displayed good magnetic response and were recoverable over several cycles. Based on the trapping experiments, the main active species in the photocatalytic reaction were confirmed and the possible photocatalytic mechanism of RhB with magnetic titania was proposed. The enhanced photocatalytic activity and stability, combined with excellent magnetic recoverability, make the prepared nanocomposite a potential candidate in wastewater purification

T2DM Self-Management via Smartphone Applications: A Systematic Review and Meta-Analysis.

BACKGROUND:Mobile health interventions (mHealth) based on smartphone applications (apps) are promising tools to help improve diabetes care and self-management; however, more evidence on the efficacy of mHealth in diabetes care is needed. The objective of this study was to conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) assessing the effect of mHealth apps on changes in hemoglobin A1c (HbA1c), blood glucose, blood pressure, serum lipids, and body weight in type 2 diabetes mellitus (T2DM) patients. METHODS:Two independent reviewers searched three online databases (PubMed, the Cochrane Library, and EMBASE) to identify relevant studies published between January 2005 and June 2016. Of the 2,596 articles retrieved, 13 RCTs were included. We used random effects model to estimate the pooled results. RESULTS:Thirteen studies were selected for the systematic review, six of which with data available containing 1,022 patients were included for the meta-analysis. There was a moderate effect on glycemic control after the mHealth app-based interventions. The overall effect on HbA1c shown as mean difference (MD) was -0.40% (-4.37 mmol/mol) (95% confidence interval [CI] -0.69 to -0.11% [-7.54 to -1.20 mmol/mol]; p = 0.007) and standardized mean differences (SMD) was -0.40% (-4.37 mmol/mol) (95% confidence interval [CI] -0.69 to -0.10% [-7.54 to -1.09 mmol/mol]; p = 0.008). A subgroup analysis showed a similar effect with -0.33% (-3.61 mmol/mol) (95% CI -0.59 to -0.06% [-6.45 to -0.66 mmol/mol]; p = 0.02) in MD and -0.38% (-4.15 mmol/mol) (95% CI -0.71 to -0.05% [-7.76 to -0.55 mmol/mol]; p = 0.02) in SMD in studies where patients' baseline HbA1c levels were less than 8.0%. No effects of mHealth app interventions were found on blood pressure, serum lipids, or weight. Assessment of overall study quality and publication bias demonstrated a low risk of bias among the six studies. CONCLUSIONS:Smartphone apps offered moderate benefits for T2DM self-management. However, more research with valid study designs and longer follow-up is needed to evaluate the impact of mHealth apps for diabetes care and self-management