81 research outputs found
ANALYSIS OF SOUTH CAROLINA HIGH SCHOOL STUDENTS\u27 INTEREST IN SELECTED MAJORS: AGRICULTURE, BUSINESS, ECONOMICS, AND ENVIRONMENTAL AND NATURAL RESOURCES
The research presented here focuses on high school students\u27 level of interest in Business, environmental and natural resource, and agriculture as possible college majors. Data is derived from quantitative online survey that had been distributed to high school students in business and economics courses across the state of South Carolina during spring 2011, fall 2011, and spring 2012 semesters. Probit and ordered probit models are used analyze high school students\u27 strength of interest in certain majors. Relatively few students are interested in Environment and Agriculture Majors. Females are less likely interested in economics, environmental and natural resource, and agriculture majors than males, but have similar interest in business. Students who have the lowest GPA level, less than 2.0, show little interest in any of these majors, but relatively, they show higher interest in business major. If high school offers environmental classes and clubs, students are tend to be more interested in an environment major compared to other students without environmental classes and clubs in their schools. Students with parents whose jobs are involved with agriculture are more likely to be interested in environment and agriculture majors
Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions
Cardiovascular disease (CVD) is the leading cause of death worldwide, and
myocardial perfusion imaging using SPECT has been widely used in the diagnosis
of CVDs. The GE 530/570c dedicated cardiac SPECT scanners adopt a stationary
geometry to simultaneously acquire 19 projections to increase sensitivity and
achieve dynamic imaging. However, the limited amount of angular sampling
negatively affects image quality. Deep learning methods can be implemented to
produce higher-quality images from stationary data. This is essentially a
few-view imaging problem. In this work, we propose a novel 3D transformer-based
dual-domain network, called TIP-Net, for high-quality 3D cardiac SPECT image
reconstructions. Our method aims to first reconstruct 3D cardiac SPECT images
directly from projection data without the iterative reconstruction process by
proposing a customized projection-to-image domain transformer. Then, given its
reconstruction output and the original few-view reconstruction, we further
refine the reconstruction using an image-domain reconstruction network.
Validated by cardiac catheterization images, diagnostic interpretations from
nuclear cardiologists, and defect size quantified by an FDA 510(k)-cleared
clinical software, our method produced images with higher cardiac defect
contrast on human studies compared with previous baseline methods, potentially
enabling high-quality defect visualization using stationary few-view dedicated
cardiac SPECT scanners.Comment: Early accepted by MICCAI 2023 in Vancouver, Canad
Fast-MC-PET: A Novel Deep Learning-aided Motion Correction and Reconstruction Framework for Accelerated PET
Patient motion during PET is inevitable. Its long acquisition time not only
increases the motion and the associated artifacts but also the patient's
discomfort, thus PET acceleration is desirable. However, accelerating PET
acquisition will result in reconstructed images with low SNR, and the image
quality will still be degraded by motion-induced artifacts. Most of the
previous PET motion correction methods are motion type specific that require
motion modeling, thus may fail when multiple types of motion present together.
Also, those methods are customized for standard long acquisition and could not
be directly applied to accelerated PET. To this end, modeling-free universal
motion correction reconstruction for accelerated PET is still highly
under-explored. In this work, we propose a novel deep learning-aided motion
correction and reconstruction framework for accelerated PET, called
Fast-MC-PET. Our framework consists of a universal motion correction (UMC) and
a short-to-long acquisition reconstruction (SL-Reon) module. The UMC enables
modeling-free motion correction by estimating quasi-continuous motion from
ultra-short frame reconstructions and using this information for
motion-compensated reconstruction. Then, the SL-Recon converts the accelerated
UMC image with low counts to a high-quality image with high counts for our
final reconstruction output. Our experimental results on human studies show
that our Fast-MC-PET can enable 7-fold acceleration and use only 2 minutes
acquisition to generate high-quality reconstruction images that
outperform/match previous motion correction reconstruction methods using
standard 15 minutes long acquisition data.Comment: Accepted at Information Processing in Medical Imaging (IPMI 2023
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
Low-count PET is an efficient way to reduce radiation exposure and
acquisition time, but the reconstructed images often suffer from low
signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream
tasks. Recent advances in deep learning have shown great potential in improving
low-count PET image quality, but acquiring a large, centralized, and diverse
dataset from multiple institutions for training a robust model is difficult due
to privacy and security concerns of patient data. Moreover, low-count PET data
at different institutions may have different data distribution, thus requiring
personalized models. While previous federated learning (FL) algorithms enable
multi-institution collaborative training without the need of aggregating local
data, addressing the large domain shift in the application of
multi-institutional low-count PET denoising remains a challenge and is still
highly under-explored. In this work, we propose FedFTN, a personalized
federated learning strategy that addresses these challenges. FedFTN uses a
local deep feature transformation network (FTN) to modulate the feature outputs
of a globally shared denoising network, enabling personalized low-count PET
denoising for each institution. During the federated learning process, only the
denoising network's weights are communicated and aggregated, while the FTN
remains at the local institutions for feature transformation. We evaluated our
method using a large-scale dataset of multi-institutional low-count PET imaging
data from three medical centers located across three continents, and showed
that FedFTN provides high-quality low-count PET images, outperforming previous
baseline FL reconstruction methods across all low-count levels at all three
institutions.Comment: 13 pages, 6 figures, Accepted at Medical Image Analysis Journal
(MedIA
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging
As PET imaging is accompanied by substantial radiation exposure and cancer
risk, reducing radiation dose in PET scans is an important topic. Recently,
diffusion models have emerged as the new state-of-the-art generative model to
generate high-quality samples and have demonstrated strong potential for
various tasks in medical imaging. However, it is difficult to extend diffusion
models for 3D image reconstructions due to the memory burden. Directly stacking
2D slices together to create 3D image volumes would results in severe
inconsistencies between slices. Previous works tried to either apply a penalty
term along the z-axis to remove inconsistencies or reconstruct the 3D image
volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless,
these previous methods failed to produce satisfactory results in challenging
cases for PET image denoising. In addition to administered dose, the noise
levels in PET images are affected by several other factors in clinical
settings, e.g. scan time, medical history, patient size, and weight, etc.
Therefore, a method to simultaneously denoise PET images with different
noise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3D
low-dose PET imaging (DDPET-3D) to address these challenges. We extensively
evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of
600 testing studies), and demonstrated superior performance over previous
diffusion models for 3D imaging problems as well as previous noise-aware
medical image denoising models. The code is available at:
https://github.com/xxx/xxx.Comment: Paper under review. 16 pages, 11 figures, 4 table
Characterization and modelling the mechanical behaviour of poly (l-lactic acid) for the manufacture of bioresorbable vascular scaffolds by stretch blow moulding
Bioresorbable Vascular Scaffolds (BVS) manufactured from poly (l-lactic acid) (PLLA) offer an alternative to metal scaffolds for the treatment of coronary heart disease. One of the key steps in the manufacture of these scaffolds is the stretch blow moulding process where the PLLA is biaxially stretched above glass transition temperature (Tg), inducing biaxial orientation and thus increasing ductility, strength and stiffness. To optimise the manufacture and performance of these scaffolds it is important to understand the influence of temperature and strain rate on the constitutive behaviour of PLLA in the blow moulding process. Experiments have been performed on samples of PLLA on a custom built biaxial stretch testing machine to replicate conditions typically experienced during blow moulding i.e. in a temperature range from 70 °C to 100 °C and at strain rates of 1 s−1, 4 s−1 and 16 s−1 respectively. The data is subsequently used to calibrate a nonlinear viscoelastic material model to represent the deformation behaviour of PLLA in the blow moulding process. The results highlight the significance of temperature and strain rate on the yielding and strain hardening behaviour of PLLA and the ability of the selected model to capture it
Optimized scheduling study of user side energy storage in cloud energy storage model
Abstract With the new round of power system reform, energy storage, as a part of power system frequency regulation and peaking, is an indispensable part of the reform. Among them, user-side small energy storage devices have the advantages of small size, flexible use and convenient application, but present decentralized characteristics in space. Therefore, the optimal allocation of small energy storage resources and the reduction of operating costs are urgent problems to be solved. In this study, the author introduced the concept of cloud energy storage and proposed a system architecture and operational model based on the deployment characteristics of user-side energy storage devices. Additionally, a cluster scheduling matching strategy was designed for small energy storage devices in cloud energy storage mode, utilizing dynamic information of power demand, real-time quotations, and supply at the load side. Subsequently, numerical analysis was conducted to verify that the proposed operational mode and optimal scheduling scheme ensured the maximum absorption of renewable energy, improved the utilization rate of energy storage resources at the user side, and contributed to peak shaving and load leveling in the power grid. The model put forward in this study represents a valuable exploration for new scenarios in energy storage application
Low Computing Leakage, Wide-Swing Output Compensation Circuit for Linearity Improvement in SRAM Multi-Row Read Computing-in-Memory
To increase the throughput of computing-in-memory (CIM) designs, multi-row read methods have been adopted to increase computation in the analog region. However, the nonlinearity created by doing so degrades the precision of the results obtained. The results of CIM computation need to be precise in order for CIM designs to be used in machine learning circumstances involving complex algorithms and big data sets. In this study, a low computing leakage, wide-swing output compensation circuit is proposed for linearity improvement in such circumstances. The proposed compensation circuit is composed of a current competition circuit (as dynamic feedback of the bitline discharge current), a current mirror (to separate the result capacitor and provide charge current), and an additional pull-down circuit (for better precision in high voltage results). Measurements show that by applying our method, an almost full-swing output with 51.2% nonlinearity decrement compared with no compensation can be achieved. Power consumption is reduced by 36% per round on average and the computing leakage current, after wordlines are deactivated for 1 ns, is reduced to 55% of that when using conventional methods. A figure of merit (FOM) is proposed for analog computing module evaluation, presenting a comprehensive indicator for the computation precision of such designs
Characterization and verification of CAF-relevant prognostic gene signature to aid therapy in bladder cancer
Cancer-associated fibroblasts (CAFs) are significantly involved in determining the patient's prognosis and response to bladder cancer (BLCA) therapy. CAFs can induce epithelial-mesenchymal transformation (EMT) as well as complex interaction with immune cells. Hence, it is imperative to identify potential markers for enhancing our understanding of CAFs in BLCA progression and immune regulation. A variety of algorithms and analyses were employed in the study, leading to the development of a novel prognostic feature for CAFs-Stromal-EMT (CSE)-prognostic feature. This feature was constructed based on the genes MFAP5, PCOLCE2, and JUN. Furthermore, we revealed that patients with higher CSE risk scores responded to immunotherapy better compared to those with lower. Finally, we verified two CSE-related genes using in vitro experiments. Our results suggested that the CSE-prognostic feature could predict the prognosis and evaluate the response of patients to immune and chemotherapies. This would aid clinicians in designing treatment strategies for patients with BLCA
B4SDC: A Blockchain System for Security Data Collection in MANETs
Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). A detection node (i.e., collector) should discover available routes to a collection node for data collection and collect security-related data during route discovery for determining reliable routes. However, few studies provide incentives for security-related data collection in MANETs. In this paper, we propose B4SDC, a blockchain system for security-related data collection in MANETs. Through controlling the scale of Route REQuest (RREQ) forwarding in route discovery, the collector can constrain its payment and simultaneously make each forwarder of control information (namely RREQs and Route REPlies, in short RREPs) obtain rewards as much as possible to ensure fairness. At the same time, B4SDC avoids collusion attacks with cooperative receipt reporting, and spoofing attacks by adopting a secure digital signature. Based on a novel Proof-of-Stake consensus mechanism by accumulating stakes through message forwarding, B4SDC not only provides incentives for all participating nodes, but also avoids forking and ensures high efficiency and real decentralization. We analyze B4SDC in terms of incentives and security, and evaluate its performance through simulations. The thorough analysis and experimental results show the efficacy and effectiveness of B4SDC.Peer reviewe
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