Differences Of Diabetes-Related Complications And Diabetes Preventive Health Care Utilization In Asian And White Using Multiple Years National Health Survey Data

The main purpose of this study is to examine the differences of preventive management utilizations and diabetes complications in Asian Americans and Non-Hispanic whites using multiple years (2002-2013) Behavioral Risk Factor Surveillance System (BRFSS). SAS for complex survey procedures were used to perform the data analysis. Odds ratios (OR) were calculated to compare the prevalence of diabetes complications and preventive management rate in Asian with white. Compared to white, the prevalence of diabetes retinopathy in Asians were higher, while the rates of neuropathy and cardiovascular complications, pneumonia shot, personally management as well as management diabetes with doctors were lower. The prevalence of routine checkup in Asian was not significantly different from the prevalence in white. More attentions should be paid on Asians for diabetes related retinopathy

How do composite fiscal decentralization and human development promote inclusive green innovation in G7 countries?

The study analyzes the dynamic influences of composite fiscal decentralization index (CFD), human development, and research and development (R&D) expenditures on green innovations in G7 countries from 1990 to 2018. For empirical estimation, the study applies the cross-section autoregressive distributed lag method to resolve the issues of cross-section dependency and slope heterogeneity in the panel data. The results exhibit that CFD, human capital development, and R&D spending encourage green technologies in the long run. The short-run findings are also compatible with the long-run; however, their magnitude is smaller than the long-run except for CFD. In addition, the error correction term also indicates a negative and significant coefficient value, endorsing the conversion towards the long-run equilibrium position with a 25.3% annual adjustment rate in case of any shock in the short run. The robustness of the estimates is confirmed through the augmented mean group and common correlated effect mean group. These findings recommend that G7 countries should encourage human resources and R&D expenditures through education and renewable energy investment, respectively. In addition, local governments’ allocation of resources to promote green technologies must be monitored and regulated by a strong institutional framework

Review of Research on Human Trust in Artificial Intelligence

Artificial Intelligence (AI) represents today\u27s most advanced technologies that aim to imitate human intelligence. Whether AI can successfully be integrated into society depends on whether it can gain users’ trust. We conduct a comprehensive review of recent research on human trust in AI and uncover the significant role of AI’s transparency, reliability, performance, and anthropomorphism in developing trust. We also review how trust is diversely built and calibrated, and how human and environmental factors affect human trust in AI. Based on the review, the most promising future research directions are proposed

Network Group Psychological Education of College Students

Based on the perspective of psychology, this paper analyzes the causes and characteristics of college students’ network mass incidents, explores the psychological factors of college students’ network mass incidents, and puts forward the educational strategies to solve college students’ network mass incidents: No.1. Adhere to humanism and take appeals as the center; No.2. To improve the campus network public opinion guidance mechanism under the guidance of relevant social cognition theories; No.3. Strengthen communication and improve communication skills; No.4. Promote information disclosure and transparency, and eliminate uncertainty and ambiguity

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

N,N′-Bis(4-methyl­benzyl­idene)benzene-1,4-diamine

The centrosymmetric title compound, C22H20N2, crystallizes with one half-mol­ecule in the asymmetric unit. The dihedral angle between the central and outer benzene rings is 46.2 (2)°

A Comprehensive Review of Community Detection in Graphs

The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a crucial role in understanding the organization and functioning of complex systems. We begin by introducing the concept of community structure, which refers to the arrangement of vertices into clusters, with strong internal connections and weaker connections between clusters. Then, we provide a thorough exposition of various community detection methods, including a new method designed by us. Additionally, we explore real-world applications of community detection in diverse networks. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs. It serves as a valuable resource for researchers and practitioners in multiple disciplines, offering insights into the challenges, methodologies, and applications of community detection in complex networks

TBPLaS: a Tight-Binding Package for Large-scale Simulation

TBPLaS is an open-source software package for the accurate simulation of physical systems with arbitrary geometry and dimensionality utilizing the tight-binding (TB) theory. It has an intuitive object-oriented Python application interface (API) and Cython/Fortran extensions for the performance critical parts, ensuring both flexibility and efficiency. Under the hood, numerical calculations are mainly performed by both exact diagonalizatin and the tight-binding propagation method (TBPM) without diagonalization. Especially, the TBPM is based on the numerical solution of time-dependent Schr\"odinger equation, achieving linear scaling with system size in both memory and CPU costs. Consequently, TBPLaS provides a numerically cheap approach to calculate the electronic, transport and optical properties of large tight-binding models with billions of atomic orbitals. Current capabilities of TBPLaS include the calculation of band structure, density of states, local density of states, quasi-eigenstates, optical conductivity, electrical conductivity, Hall conductivity, polarization function, dielectric function, plasmon dispersion, carrier mobility and velocity, localization length and free path, Z2 topological invariant, wave-packet propagation, etc. All the properties can be obtained with only a few lines of code. Other algorithms involving tight-binding Hamiltonians can be implemented easily thanks to its extensible and modular nature. In this paper, we discuss the theoretical framework, implementation details and common workflow of TBPLaS, and give a few demonstrations of its applications.Comment: 54 pages, 16 figure