The Hitting Times of A Stochastic Epidemic Model

In this paper, we focus on the hitting times of a stochastic epidemic model presented by \cite{Gray}. Under the help of the auxiliary stopping times, we investigate the asymptotic limits of the hitting times by the variations of calculus and the large deviation inequalities when the noise is sufficiently small. It can be shown that the relative position between the initial state and the hitting state determines the scope of the hitting times greatly

Comparison theorem of one-dimensional stochastic hybrid delay systems

The comparison theorem of stochastic differential equations has been investigated by many authors. However, little research is available on the comparison theorem of stochastic hybrid systems, which is the topic of this paper. The systems discussed is stochastic delay differential equations with Markovian switching. It is an important class of hybrid systems

ICT Stages and Moderating Effect of Technological Uncertainty

The impact of ICT in the supply chain has been given much attention in recent literature. Although ICT generally leads to performance gains, it is still unclear which specific aspects of ICT affect which specific aspects of supply chain performance. Therefore, this paper investigates the role of three subsequent ICT stages an organization can employ. It was expected that higher stages yield more benefits than lower stages. Moreover, the concept of technological uncertainty was expected to moderate these relationships. Industries with high uncertainty would benefit from the highest stage of ICT by attaining a competitive advantage, whilst firms performing in low technological uncertainty should employ lower stages. An empirical survey-based research was conducted amongst Chinese manufacturers. A supplier perspective was used and questions were related to the key buyer of the firm. Therefore, performance was measured on a dyadic level. The results show that all ICT stages lead to increased service performance, whilst no effect was found for cost performance. Additionally, a moderating effect was found between the highest stage of ICT and both types of performance. These findings confirm the positive impact of ICT and imply it has different effects on different types of performance. Moreover, ICT capability should be employed when the technological uncertainty is high, as it does not pay off in industries with low uncertainty

Stability analysis for continuous-time switched systems with stochastic switching signals

This paper is concerned with the stability problem of randomly switched systems. By using the probability analysis method, the almost surely globally asymptotical stability and almost surely exponential stability are investigated for switched systems with semi-Markovian switching, Markovian switching and renewal process switching signals, respectively. Two examples are presented to demonstrate the effectiveness of the proposed results, in which an example of consensus of multi-agent systems with nonlinear dynamics is taken into account

Strong convergence and asymptotic stability of explicit numerical schemes for nonlinear stochastic differential equations

In this article we introduce a number of explicit schemes, which are amenable to Khasminski’s technique and are particularly suitable for highly nonlinear stochastic differential equations (SDEs). We show that without additional restrictions to those which guarantee the exact solutions possess their boundedness in expectation with respect to certain Lyapunov-type functions, the numerical solutions converge strongly to the exact solutions in finite-time. Moreover, based on the convergence theorem of nonnegative semimartingales, positive results about the ability of the explicit numerical scheme proposed to reproduce the well-known LaSalle-type theorem of SDEs are proved here, from which we deduce the asymptotic stability of numerical solutions. Some examples are discussed to demonstrate the validity of the new numerical schemes and computer simulations are performed to support the theoretical results

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimagingbased AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95%CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROBwas particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95%CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95%CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2%(95%CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9%(95%CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application

Modeling habitat suitability for Yunnan Snub-nosed monkeys in Laojun Mountain National Park

We provide new information on Yunnan snub-nosed monkey (Rhinopithecus bieti) behavioral ecology, contributing to future conservation efforts within the Laojun Mountain National Park. Habitat evaluation procedures are used to quantify the value of land as a habitat for a species. We analyzed environmental variables hypothesized to influence habitat suitability for Yunnan snub-nosed monkeys, and mapped the distribution of suitable habitats across the study area and adjacent areas. Spatial analysis with GPS data was conducted to investigate home-range change of these monkeys. Predictor variables were generated using ArcMap and R programming language. We prepared 34 environmental variables at 30-m spatial resolution. Maxent was used to analyze environmental variables that contributed to suitability. Using satellite remote sensing and GIS, we modeled the distribution of suitable habitat for Yunnan snub-nosed monkeys in the Jinsichang area of the Laojun Mountains in China. This study did not describe the frequency or intensity of habitat use. Habitat suitability was affected by several variables, the most influential, as determined by permutation importance, being mean diurnal temperature range (31.6%), precipitation during the wettest quarter of the year (30.4%), average annual precipitation (17%), normalized difference vegetation index (5%), wetness (4.6%), and aspect (4.5%). This habitat suitability model provides information about the current distribution of Yunnan snub-nosed monkeys, which is important for appropriate implementation of conservation actions

The impact of gene polymorphism and hepatic insufficiency on voriconazole dose adjustment in invasive fungal infection individuals

Voriconazole (VRZ) is a broad-spectrum antifungal medication widely used to treat invasive fungal infections (IFI). The administration dosage and blood concentration of VRZ are influenced by various factors, posing challenges for standardization and individualization of dose adjustments. On the one hand, VRZ is primarily metabolized by the liver, predominantly mediated by the cytochrome P450 (CYP) 2C19 enzyme. The genetic polymorphism of CYP2C19 significantly impacts the blood concentration of VRZ, particularly the trough concentration (Ctrough), thereby influencing the drug’s efficacy and potentially causing adverse drug reactions (ADRs). Recent research has demonstrated that pharmacogenomics-based VRZ dose adjustments offer more accurate and individualized treatment strategies for individuals with hepatic insufficiency, with the possibility to enhance therapeutic outcomes and reduce ADRs. On the other hand, the security, pharmacokinetics, and dosing of VRZ in individuals with hepatic insufficiency remain unclear, making it challenging to attain optimal Ctrough in individuals with both hepatic insufficiency and IFI, resulting in suboptimal drug efficacy and severe ADRs. Therefore, when using VRZ to treat IFI, drug dosage adjustment based on individuals’ genotypes and hepatic function is necessary. This review summarizes the research progress on the impact of genetic polymorphisms and hepatic insufficiency on VRZ dosage in IFI individuals, compares current international guidelines, elucidates the current application status of VRZ in individuals with hepatic insufficiency, and discusses the influence of CYP2C19, CYP3A4, CYP2C9, and ABCB1 genetic polymorphisms on VRZ dose adjustments and Ctrough at the pharmacogenomic level. Additionally, a comprehensive summary and analysis of existing studies’ recommendations on VRZ dose adjustments based on CYP2C19 genetic polymorphisms and hepatic insufficiency are provided, offering a more comprehensive reference for dose selection and adjustments of VRZ in this patient population