Positive solutions of superlinear semipositone singular Dirichlet boundary value problems

AbstractIn this paper, we study a class of superlinear semipositone singular second order Dirichlet boundary value problem. A sufficient condition for the existence of positive solution is obtained under the more simple assumptions

Positive solutions of higher order fractional integral boundary value problem with a parameter

In this paper, we study a higher-order fractional differential equation with integral boundary conditions and a parameter. Under different conditions of nonlinearity, existence and nonexistence results for positive solutions are derived in terms of different intervals of parameter. Our approach relies on the Guo–Krasnoselskii fixed point theorem on cones

Existence and Uniqueness of Solution to Nonlinear Boundary Value Problems with Sign-Changing Green’s Function

By using the cone theory and the Banach contraction mapping principle, the existence and uniqueness results are established for nonlinear higher-order differential equation boundary value problems with sign-changing Green’s function.The theorems obtained are very general and complement previous known results

Multiple solutions for a modified quasilinear Schrödinger elliptic equation with a nonsquare diffusion term

In this paper, we establish the results of multiple solutions for a class of modified nonlinear Schrödinger equation involving the p-Laplacian. The main tools used for analysis are the critical points theorems by Ricceri and the dual approach

Emerging trends and focus on immune checkpoint inhibitors for non-small cell lung cancer treatment: visualization and bibliometric analysis

Introduction: Lung cancer is the leading cause of cancer-related deaths worldwide, and non-small cell lung carcinoma (NSCLC) accounts for approximately 80% of all cases. Immune checkpoint inhibitors (ICIs) are widely used to treat NSCLC owing to their remarkable efficacy. In this study, we analyzed the scientific collaboration network, defined the hotspots of research on the use of ICIs for NSCLC treatment, analyzed its evolution over the past few years, and forecasted the field’s future development using bibliometric analysis and a graphical study.Methods: Research articles and reviews regarding ICIs for NSCLC were retrieved and obtained from the Web of Science Core Collection on 26 September 2022. CtieSpace and VOSviewer were thereafter used to conduct the bibliometric and knowledge-map analysis.Results: We included 8,149 articles for this literature analysis. Our analysis showed that the USA had the highest number of publications and citations. We also noted that research trends in this field have changed drastically over the past 20 years, from the early development of ICIs, such as CTLA-4 inhibitors, to the development of recent ones, such as PD-1 and PD-L1 blockers. Further, the focus of research in this field has also gradually shifted from mechanisms to treatment effects and adverse events, suggesting that the field is maturing. Clinical applications are also being explored, including studies on how to enhance efficacy, reduce adverse effects, and expand to other specific cancer types.Conclusion: To the best of our knowledge, this is the first study to construct a comprehensive knowledge map on ICIs for NSCLC. It can help researchers rapidly grasp the status and focus of current research in this area, offer direction, and serve as a reference for conducting similar studies

A sufficient and necessary condition of existence of blow-up radial solutions for a k-Hessian equation with a nonlinear operator

In this paper, we establish the results of nonexistence and existence of blow-up radial solutions for a k-Hessian equation with a nonlinear operator. Under some suitable growth conditions for nonlinearity, the result of nonexistence of blow-up solutions is established, a sufficient and necessary condition on existence of blow-up solutions is given, and some further results are obtained.&nbsp

Estimating functional brain networks by incorporating a modularity prior

Functional brain network analysis has become one principled way of revealing informative organization architectures in healthy brains, and providing sensitive biomarkers for diagnosis of neurological disorders. Prior to any post hoc analysis, however, a natural issue is how to construct “ideal” brain networks given, for example, a set of functional magnetic resonance imaging (fMRI) time series associated with different brain regions. Although many methods have been developed, it is currently still an open field to estimate biologically meaningful and statistically robust brain networks due to our limited understanding of the human brain as well as complex noises in the observed data. Motivated by the fact that the brain is organized with modular structures, in this paper, we propose a novel functional brain network modeling scheme by encoding a modularity prior under a matrix-regularized network learning framework, and further formulate it as a sparse low-rank graph learning problem, which can be solved by an efficient optimization algorithm. Then, we apply the learned brain networks to identify patients with mild cognitive impairment (MCI) from normal controls. We achieved 89.01% classification accuracy even with a simple feature selection and classification pipeline, which significantly outperforms the conventional brain network construction methods. Moreover, we further explore brain network features that contributed to MCI identification, and discovered potential biomarkers for personalized diagnosis

Twin iterative solutions for a fractional differential turbulent flow model

We investigate the existence of twin iterative solutions for a fractional p-Laplacian equation with nonlocal boundary conditions. Using the monotone iterative technique, we establish a new existence result on the maximal and minimal solutions under suitable nonlinear growth conditions. We also consider some interesting particular cases and give an example to illustrate our main results