219 research outputs found
Factors affecting academic performance of college students in China during COVID-19 pandemic: a cross-sectional analysis
IntroductionUnderstanding the factors that affected academic performance of students during the COVID-19 pandemic will help design effective interventions for improving students’ academic performance during emergency situations as well as during regular academic environment. This cross-sectional study aimed to identify the factors that explain academic performance of students in China during the pandemic.MethodsData on college students from the 2020 China Family Panel Studies were used, and the final sample consisted of 728 students. Ordered probit regression models were estimated to explain students’ relative performance in the semester when the in-person classes were suspended by using various student and household-related variables and characteristics. To compute missing values in selected variables, a multiple imputation technique was applied.ResultsThe odds of poor academic performance declined with higher Internet use for academic purposes, but Internet use for entertainment increased the probability of being in the poor academic performance. College students who spent more time studying on college work were less likely to have poor academic performance.DiscussionThis study identified the factors (Internet use and study time) associated with academic performance among Chinese college students during the COVID-19 pandemic. These results can be used to design policies to improve educational outcomes and to address educational inequalities
Intertwined dipolar and multipolar order in the triangular-lattice magnet TmMgGaO
A phase transition is often accompanied by the appearance of an order
parameter and symmetry breaking. Certain magnetic materials exhibit exotic
hidden-order phases, in which the order parameters are not directly accessible
to conventional magnetic measurements. Thus, experimental identification and
theoretical understanding of a hidden order are difficult. Here we combine
neutron scattering and thermodynamic probes to study the newly discovered
rare-earth triangular-lattice magnet TmMgGaO. Clear magnetic Bragg peaks at
K points are observed in the elastic neutron diffraction measurements. More
interesting, however, is the observation of sharp and highly dispersive spin
excitations that cannot be explained by a magnetic dipolar order, but instead
is the direct consequence of the underlying multipolar order that is "hidden"
in the neutron diffraction experiments. We demonstrate that the observed
unusual spin correlations and thermodynamics can be accurately described by a
transverse field Ising model on the triangular lattice with an intertwined
dipolar and ferro-multipolar order.Comment: Published versio
Energy-efficient MAC protocols for WBANs: Opportunities and challenges
Wireless body area networks (WBANs) are expected to play a significant role in smart healthcare systems. One of the most important attributes of WBANs is to increase network lifetime by introducing novel and low-power techniques on the energy-constrained sensor nodes. Medium access control (MAC) protocols play a significant role in determining the energy consumption in WBANs. Existing MAC protocols are unable to accommodate communication requirements in WBANs. There is a need to develop novel, scalable and reliable MAC protocols that must be able to address all these requirements in a reliable manner. In this special issue, we attracted high quality research and review papers on the recent advances in MAC protocols for WBANs
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
By offloading computation-intensive tasks of vehicles to roadside units
(RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can
relieve the onboard computation burden. However, existing model-based task
offloading methods suffer from heavy computational complexity with the increase
of vehicles and data-driven methods lack interpretability. To address these
challenges, in this paper, we propose a knowledge-driven multi-agent
reinforcement learning (KMARL) approach to reduce the latency of task
offloading in cybertwin-enabled IoV. Specifically, in the considered scenario,
the cybertwin serves as a communication agent for each vehicle to exchange
information and make offloading decisions in the virtual space. To reduce the
latency of task offloading, a KMARL approach is proposed to select the optimal
offloading option for each vehicle, where graph neural networks are employed by
leveraging domain knowledge concerning graph-structure communication topology
and permutation invariance into neural networks. Numerical results show that
our proposed KMARL yields higher rewards and demonstrates improved scalability
compared with other methods, benefitting from the integration of domain
knowledge
Asynchronous Wireless Federated Learning with Probabilistic Client Selection
Federated learning (FL) is a promising distributed learning framework where
distributed clients collaboratively train a machine learning model coordinated
by a server. To tackle the stragglers issue in asynchronous FL, we consider
that each client keeps local updates and probabilistically transmits the local
model to the server at arbitrary times. We first derive the (approximate)
expression for the convergence rate based on the probabilistic client
selection. Then, an optimization problem is formulated to trade off the
convergence rate of asynchronous FL and mobile energy consumption by joint
probabilistic client selection and bandwidth allocation. We develop an
iterative algorithm to solve the non-convex problem globally optimally.
Experiments demonstrate the superiority of the proposed approach compared with
the traditional schemes.Comment: To appear in IEEE Transactions on Wireless Communication
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