536 research outputs found
Bone in vivo: Surface mapping technique
Bone surface mapping technique is proposed on the bases of two kinds of
uniqueness of bone in vivo, (i) magnitude of the principal moments of inertia,
(ii) the direction cosines of principal axes of inertia relative to inertia
reference frame. We choose the principal axes of inertia as the bone coordinate
system axes. The geographical marks such as the prime meridian of the bone in
vivo are defined and methods such as tomographic reconstruction and boundary
development are employed so that the surface of bone in vivo can be mapped.
Experimental results show that the surface mapping technique can both reflect
the shape and help study the surface changes of bone in vivo. The prospect of
such research into the surface shape and changing laws of organ, tissue or cell
will be promising.Comment: 9 pages, 6 figure
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Pulmonary diseases induced by ambient ultrafine and engineered nanoparticles in twenty-first century.
Air pollution is a severe threat to public health globally, affecting everyone in developed and developing countries alike. Among different air pollutants, particulate matter (PM), particularly combustion-produced fine PM (PM2.5) has been shown to play a major role in inducing various adverse health effects. Strong associations have been demonstrated by epidemiological and toxicological studies between increases in PM2.5 concentrations and premature mortality, cardiopulmonary diseases, asthma and allergic sensitization, and lung cancer. The mechanisms of PM-induced toxicological effects are related to their size, chemical composition, lung clearance and retention, cellular oxidative stress responses and pro-inflammatory effects locally and systemically. Particles in the ultrafine range (<100 nm), although they have the highest number counts, surface area and organic chemical content, are often overlooked due to insufficient monitoring and risk assessment. Yet, ample studies have demonstrated that ambient ultrafine particles have higher toxic potential compared with PM2.5. In addition, the rapid development of nanotechnology, bringing ever-increasing production of nanomaterials, has raised concerns about the potential human exposure and health impacts. All these add to the complexity of PM-induced health effects that largely remains to be determined, and mechanistic understanding on the toxicological effects of ambient ultrafine particles and nanomaterials will be the focus of studies in the near future
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Applying Deep Learning in Street View Imagery for Environmental Health Research
Urban green space is associated with multiple physical and mental health outcomes. Several benefits of green space, such as stress reduction and attention restoration, are dependent on visual perception of green space exposures. However, traditional green space exposure measures do not capture street-level exposures. In this project, we apply deep learning models to measure green space in street view imagery. We train Faster R-CNN model on PasadenaUrbanTree dataset and equip it with the ability to detect trees, which is then used to count the number of trees in the test images. We also employ a PSPNet model that pretrained on ade20k dataset to do semantic segmentation on street view images and compute the portion of green space. Combining the outcomes from object detection and semantic segmentation, the green space in street view imagery can be measured quantitatively
Spectroscopic study of light scattering in linear alkylbenzene for liquid scintillator neutrino detectors
We has set up a light scattering spectrometer to study the depolarization of
light scattering in linear alkylbenzene. From the scattering spectra it can be
unambiguously shown that the depolarized part of light scattering belongs to
Rayleigh scattering. The additional depolarized Rayleigh scattering can make
the effective transparency of linear alkylbenzene much better than it was
expected. Therefore sufficient scintillation photons can transmit through the
large liquid scintillator detector of JUNO. Our study is crucial to achieving
the unprecedented energy resolution 3\%/ for JUNO
experiment to determine the neutrino mass hierarchy. The spectroscopic method
can also be used to judge the attribution of the depolarization of other
organic solvents used in neutrino experiments.Comment: 6 pages, 5 figure
Characterization and Modeling of Silicon-on-Insulator Lateral Bipolar Junction Transistors at Liquid Helium Temperature
Conventional silicon bipolars are not suitable for low-temperature operation
due to the deterioration of current gain (). In this paper, we
characterize lateral bipolar junction transistors (LBJTs) fabricated on
silicon-on-insulator (SOI) wafers down to liquid helium temperature (4 K). The
positive SOI substrate bias could greatly increase the collector current and
have a negligible effect on the base current, which significantly alleviates
degradation at low temperatures. We present a physical-based compact
LBJT model for 4 K simulation, in which the collector current
() consists of the tunneling current and the additional
current component near the buried oxide (BOX)/silicon interface caused by the
substrate modulation effect. This model is able to fit the Gummel
characteristics of LBJTs very well and has promising applications in amplifier
circuits simulation for silicon-based qubits signals
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
Graph classification is a crucial task in many real-world multimedia
applications, where graphs can represent various multimedia data types such as
images, videos, and social networks. Previous efforts have applied graph neural
networks (GNNs) in balanced situations where the class distribution is
balanced. However, real-world data typically exhibit long-tailed class
distributions, resulting in a bias towards the head classes when using GNNs and
limited generalization ability over the tail classes. Recent approaches mainly
focus on re-balancing different classes during model training, which fails to
explicitly introduce new knowledge and sacrifices the performance of the head
classes. To address these drawbacks, we propose a novel framework called
Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature
extractor and an unbiased classifier in a decoupled manner. In the feature
extractor training stage, we develop a graph retrieval module to search for
relevant graphs that directly enrich the intra-class diversity for the tail
classes. Moreover, we innovatively optimize a category-centered supervised
contrastive loss to obtain discriminative representations, which is more
suitable for long-tailed scenarios. In the classifier fine-tuning stage, we
balance the classifier weights with two weight regularization techniques, i.e.,
Max-norm and weight decay. Experiments on various popular benchmarks verify the
superiority of the proposed method against state-of-the-art approaches.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202
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