Latest Analysis: Innovative Strategies to Improve Public Health and Prevent Chronic Diseases

This research aims to investigate the positive effect of a healthy lifestyle on the mental and bodily fitness of the community. Utilizing an interdisciplinary method, the study entails an in-intensity evaluation of the connection between wholesome ingesting styles, normal physical activity, and pressure control on the mental nicely-being and bodily condition of people. The studies method consists of a extensive survey to acquire data from diverse age businesses and backgrounds. Additionally, a complete literature assessment is employed to construct a conceptual framework supporting the studies findings. The results of this look at are predicted to provide new insights into revolutionary strategies for improving public fitness and preventing persistent diseases. In this context, the research not most effective identifies healthy practices which have the ability to increase lifespan and enhance great of existence but also offers concrete pointers for the implementation of public fitness rules that specialize in promoting a healthy lifestyle. The practical implications of those findings are predicted to significantly make contributions to the development of the healthcare machine and normal network properly-being

On the Nehari manifold for a logarithmic fractional Schrödinger equation with possibly vanishing potentials

summary:We study a class of logarithmic fractional Schrödinger equations with possibly vanishing potentials. By using the fibrering maps and the Nehari manifold we obtain the existence of at least one nontrivial solution

SOME PROPERTIES OF EF-EXTENDING RINGS

In [16], Thuyet and Wisbauer considered the extending property for the class of (essentially) finitely generated submodules. A module M is called ef-extending if every closed submodule which contains essentially a finitely generated submodule is a direct summand of M. A ring R is called right ef-extending if RR is an ef-extending module. We show that a ring R is right ef-extending and the R-dual of every simple left R-module is simple if and only if R is semiperfect right continuous with Sl = Sl &#8804;e RR. We also prove that a ring R is a QF-ring if and only if R is left Kasch and RR(&#969;) is ef-extending if and only if R is right AGP-injective satisfying DCC on right (or left) annihilators and (R &#8853; R)R is ef-extending.</p

On a viscoelastic heat equation with logarithmic nonlinearity

This work deals with the following viscoelastic heat equations with logarithmic nonlinearity ut − ∆u + Z t 0 g(t − s)∆u(s)ds = |u| p−2u ln |u|. In this paper, we show the effects of the viscoelastic term and the logarithmic nonlinearity to the asymptotic behavior of weak solutions. Our results extend the results of Peng and Zhou [Appl. Anal. 100(2021), 2804–2824] and Messaoudi [Progr. Nonlinear Differential Equations Appl. 64(2005), 351–356.]

Existence and nonexistence of global solutions for doubly nonlinear diffusion equations with logarithmic nonlinearity

In this paper, we study an initial-boundary value problem for a doubly nonlinear diffusion equation with logarithmic nonlinearity. By using the potential well method, we give some threshold results on existence or nonexistence of global weak solutions in the case of initial data with energy less than or equal to potential well depth. In addition, the asymptotic behavior of solutions is also discussed

VulCurator: A Vulnerability-Fixing Commit Detector

Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, including commit messages, code changes and issue reports for vulnerability-fixing commit classifica- tion. Our experimental results show that VulCurator outperforms the state-of-the-art baselines up to 16.1% in terms of F1-score. VulCurator tool is publicly available at https://github.com/ntgiang71096/VFDetector and https://zenodo.org/record/7034132#.Yw3MN-xBzDI, with a demo video at https://youtu.be/uMlFmWSJYOE.Comment: accepted to ESEC/FSE 2022, Tool Demos Trac

MÔĐUN BẤT BIẾN ĐẲNG CẤU TRÊN VÀNH GOLDIE PHẢI

In this paper, we study some properties of automorphism invariant modules on the right Goldie ring and state some properties related to this class of modules. In addition, we confirmed some problems related to the nonsingular automorphism invariant ring.Trong bài báo này, chúng tôi nghiên cứu một số kết quả về môđun bất biến đẳng cấu trên vành Goldie phải, đồng thời nêu một số tính chất liên quan đến lớp các môđun này. Ngoài ra, chúng tôi cũng khẳng định một số vấn đề liên quan đến lớp vành bất biến đẳng cấu không suy biến

AutoPruner: Transformer-Based Call Graph Pruning

Constructing a static call graph requires trade-offs between soundness and precision. Program analysis techniques for constructing call graphs are unfortunately usually imprecise. To address this problem, researchers have recently proposed call graph pruning empowered by machine learning to post-process call graphs constructed by static analysis. A machine learning model is built to capture information from the call graph by extracting structural features for use in a random forest classifier. It then removes edges that are predicted to be false positives. Despite the improvements shown by machine learning models, they are still limited as they do not consider the source code semantics and thus often are not able to effectively distinguish true and false positives. In this paper, we present a novel call graph pruning technique, AutoPruner, for eliminating false positives in call graphs via both statistical semantic and structural analysis. Given a call graph constructed by traditional static analysis tools, AutoPruner takes a Transformer-based approach to capture the semantic relationships between the caller and callee functions associated with each edge in the call graph. To do so, AutoPruner fine-tunes a model of code that was pre-trained on a large corpus to represent source code based on descriptions of its semantics. Next, the model is used to extract semantic features from the functions related to each edge in the call graph. AutoPruner uses these semantic features together with the structural features extracted from the call graph to classify each edge via a feed-forward neural network. Our empirical evaluation on a benchmark dataset of real-world programs shows that AutoPruner outperforms the state-of-the-art baselines, improving on F-measure by up to 13% in identifying false-positive edges in a static call graph.Comment: Accepted to ESEC/FSE 2022, Research Trac