On the asymptotic behavior of highly nonlinear hybrid stochastic delay differential equations

In this paper, under a local Lipschitz condition and a monotonicity condition, the problems on the existence and uniqueness theorem as well as the almost surely asymptotic behavior for the global solution of highly nonlinear stochastic differential equations with time-varying delay and Markovian switching are discussed by using the Lyapunov function and some stochastic analysis techniques. Two integral lemmas are firstly established to overcome the difficulty stemming from the coexistence of the stochastic perturbation and the time-varying delay. Then, without any redundant restrictive condition on the time-varying delay, by utilizing the integral inequality, the exponential stability in pth(p ≥ 1)-moment for such equations is investigated. By employing the nonnegative semi-martingale convergence theorem, the almost sure exponential stability is analyzed. Finally, two examples are given to show the usefulness of the results obtained.National Natural Science Foundation of ChinaNatural Science Foundation of Jiangxi Province of ChinaFoundation of Jiangxi Provincial Educations of ChinaMinisterio de Economía y Competitividad (MINECO). EspañaJunta de Andalucí

A Distribution Evolutionary Algorithm for Graph Coloring

Graph Coloring Problem (GCP) is a classic combinatorial optimization problem that has a wide application in theoretical research and engineering. To address complicated GCPs efficiently, a distribution evolutionary algorithm based on population of probability models (DEA-PPM) is proposed. Based on a novel representation of probability model, DEA-PPM employs a Gaussian orthogonal search strategy to explore the probability space, by which global exploration can be realized using a small population. With assistance of local exploitation on a small solution population, DEA-PPM strikes a good balance between exploration and exploitation. Numerical results demonstrate that DEA-PPM performs well on selected complicated GCPs, which contributes to its competitiveness to the state-of-the-art metaheuristics

Systematic analysis of the Hippo pathway organization and oncogenic alteration in evolution.

The Hippo pathway is a central regulator of organ size and a key tumor suppressor via coordinating cell proliferation and death. Initially discovered in Drosophila, the Hippo pathway has been implicated as an evolutionarily conserved pathway in mammals; however, how this pathway was evolved to be functional from its origin is still largely unknown. In this study, we traced the Hippo pathway in premetazoan species, characterized the intrinsic functions of its ancestor components, and unveiled the evolutionary history of this key signaling pathway from its unicellular origin. In addition, we elucidated the paralogous gene history for the mammalian Hippo pathway components and characterized their cancer-derived somatic mutations from an evolutionary perspective. Taken together, our findings not only traced the conserved function of the Hippo pathway to its unicellular ancestor components, but also provided novel evolutionary insights into the Hippo pathway organization and oncogenic alteration

A novel control system design for automatic feed drilling operation of the PLC-based oil rig

Aiming at the difficulties in realizing the accurate control due to the nonlinearity of the automatic drilling system of oil drilling rig, a design scheme is proposed by giving a constant drilled-pressure to the rig for fuzzy control. Sampling error with changes in the signal was sent into the fuzzy controller, which turned the signal into a fuzzy volume. Subsequently, a precise volume was obtained accordingly and then added to an actuator for the motor control. According to the MATLAB simulation results, the response could be faster and more stable compared with the traditional control

Evolution of digestive enzymes and dietary diversification in birds

As the most species-rich class of tetrapod vertebrates, Aves possesses diverse feeding habits, with multiple origins of insectivory, carnivory, frugivory, nectarivory, granivory and omnivory. Since digestive enzymes mediate and limit energy and nutrient uptake, we hypothesized that genes encoding digestive enzymes have undergone adaptive evolution in birds. To test this general hypothesis, we identified 16 digestive enzyme genes (including seven carbohydrase genes (hepatic amy, pancreatic amy, salivary amy, agl, g6pc, gaa and gck), three lipase genes (cyp7a1, lipf and pnlip), two protease genes (ctrc and pgc), two lysozyme genes (lyz and lyg) and two chitinase genes (chia and chit1)) from the available genomes of 48 bird species. Among these 16 genes, three (salivary amy, lipf and chit1) were not found in all 48 avian genomes, which was further supported by our synteny analysis. Of the remaining 13 genes, eight were single-copy and five (chia, gaa, lyz, lyg and pgc) were multi-copy. Moreover, the multi-copy genes gaa, lyg and pgc were predicted to exhibit functional divergence among copies. Positively selected sites were detected in all of the analyzed digestive enzyme genes, except agl, g6pc, gaa and gck, suggesting that different diets may have favored differences in catalytic capacities of these enzymes. Furthermore, the analysis also revealed that the pancreatic amylase gene and one of the lipase genes (cyp7a1) have higher ω (the ratio of nonsynonymous to the synonymous substitution rates) values in species consuming a larger amount of seeds and meat, respectively, indicating an intense selection. In addition, the gck carbohydrase gene in species consuming a smaller amount of seeds, fruits or nectar, and a lipase gene (pnlip) in species consuming less meat were found to be under relaxed selection. Thus, gene loss, gene duplication, functional divergence, positive selection and relaxed selection have collectively shaped the evolution of digestive enzymes in birds, and the evolutionary flexibility of these enzymes may have facilitated their dietary diversification

Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state

IntroductionDiagnosing Alzheimer's disease (AD) lesions via visual examination of Electroencephalography (EEG) signals poses a considerable challenge. This has prompted the exploration of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), for AD prediction. However, the classification performance of CNN-based methods has often been deemed inadequate. This is primarily attributed to CNNs struggling with extracting meaningful lesion signals from the complex and noisy EEG data.MethodsIn contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strengths of CNNs and ViTs to compensate for their respective feature extraction limitations. Our proposed Dual-Branch Feature Fusion Network (DBN) leverages both CNN and ViT components to acquire texture features and global semantic information from EEG signals. These elements are pivotal in capturing dynamic electrical signal changes in the cerebral cortex. Additionally, we introduce Spatial Attention (SA) and Channel Attention (CA) blocks within the network architecture. These attention mechanisms bolster the model's capacity to discern abnormal EEG signal patterns from the amalgamated features. To make well-informed predictions, we employ a two-factor decision-making mechanism. Specifically, we conduct correlation analysis on predicted EEG signals from the same subject to establish consistency.ResultsThis is then combined with results from the Clinical Neuropsychological Scale (MMSE) assessment to comprehensively evaluate the subject's susceptibility to AD. Our experimental validation on the publicly available OpenNeuro database underscores the efficacy of our approach. Notably, our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) subjects.DiscussionThis outcome outperforms prevailing state-of-the-art methodologies in EEG-based AD prediction. Furthermore, our methodology enables the visualization of salient regions within pathological images, providing invaluable insights for interpreting and analyzing AD predictions

Numerical solutions of neutral stochastic functional differential equations with Markovian switching

Abstract Until now, the theories about the convergence analysis, the almost surely and mean square exponential stability of the numerical solution for neutral stochastic functional differential equations with Markovian switching (NSFDEwMSs) have been well established, but there are very few research works concentrating on the stability in distribution of numerical solution. This paper will pay attention to the stability in distribution of numerical solution of NSFDEwMSs. The strong mean square convergence analysis is also discussed

Health literacy and health behaviors among adults with prediabetes, 2016 behavioral risk factor surveillance system

Objectives: Evidence is needed for designing interventions to address health literacy–related issues among adults with prediabetes to reduce their risk of developing type 2 diabetes. This study assessed health literacy and behaviors among US adults with prediabetes and the mediating role of health literacy on health behaviors. Methods: We used data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) (N = 54 344 adults). The BRFSS health literacy module included 3 questions on levels of difficulty in obtaining information, understanding health care providers, and comprehending written information. We defined low health literacy as a response of “somewhat difficult” or “very difficult” to at least 1 of these 3 questions. Respondents self-reported their prediabetes status. We included 3 health behavior indicators available in the BRFSS survey—current smoking, physical inactivity, and inadequate sleep, all measured as binary outcomes (yes/no). We used a path analysis to examine pathways among prediabetes, health literacy, and health behaviors. Results: About 1 in 5 (19.0%) adults with prediabetes had low health literacy. The rates of physical inactivity (31.0% vs 24.6%, P <.001) and inadequate sleep (38.8% vs 33.5%, P <.001) among adults with prediabetes were significantly higher than among adults without prediabetes. The path analysis showed a significant direct effect of prediabetes and health literacy on health behaviors. The indirect effect of prediabetes through health literacy on health behaviors was also significant. Conclusion: BRFSS data from 2016 showed that rates of low health literacy and unhealthy behaviors were higher among adults with prediabetes than among adults without prediabetes. Interventions are needed to assist adults with prediabetes in comprehending, communicating about, and managing health issues to reduce the risk of type 2 diabetes. © 2020, Association of Schools and Programs of Public Health