1,709 research outputs found
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
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