Magnetic projection, a novel separation method proposed recently, can separate multiple mixed materials in an efficient and low-cost way. Although promising, existing magnetic projection method cannot achieve the automatic feeding of mixed materials, which limits its applications. To address this challenge, ring magnets were used to replace conventional square magnets in this research. Specifically, a mixture of particles with different densities were fed through the hole of ring magnets and then projected to the corresponding area. Moreover, to increase the magnetic field strength, magnets were superimposed. To predict the projection process, magnetic field analysis was conducted. And from the results, an interesting trap area was found, where the separated materials may be constrained, leading to the failure of projection. The simulation of the projection process revealed that with the increase of the number of magnets (1-3 magnets), the magnetic field strength increased. However, the projection distance will not keep increasing with the increase of the magnetic field strength, which also was verified by experiments (Err within 10%). Based on this principle, an automatic feeding device with ring track and pendulum was designed and manufactured. In the separation experiment, six different plastics, that were PP, ABS, PC, PLA, PET and PVC, were used to verify the separation effect. The experimental results showed that the proposed method can automatically separate a plastic mixture with a recovery rate of over 95%. This study presents a break-through in magnetic projection, laying the foundation for the practical application of magnetic projection.
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http://dx.doi.org/10.1016/j.scitotenv.2021.147217 | DOI Listing |
Background: The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method: We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high-dimensional neuroimaging data for AD progression, incorporating age and gender as covariates.
Alzheimers Dement
December 2024
Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Background: Uniform manifold approximation and projection (UMAP) is a technique for dimension reduction and visualization of high-dimensional (HD) data. Here, we apply UMAP to represent in two dimensions, data from members of the Wake Forest School of Medicine Alzheimer's Disease Research Center (WFUSM-ADRC) clinical cohort.
Methods: We examined baseline data from 542 WFUSM-ADRC participants with mean age 70.
Background: Standardizing tau pathology quantification in vivo is challenged by differences in binding characteristics between tau-PET tracers. The HEAD study aims to generate a leading, longitudinal head-to-head dataset of MK-6240, Flortaucipir, RO948, and PI-2620 tau-PET to harmonize these tracers' outcomes and develop tools allowing for the generalization of findings across large studies and trials. Here, we present current advancements in building the HEAD study cohort and dataset.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
Background: To validate the index of diffusivity along the perivascular space (ALPS index) as a biomarker for vascular cognitive impairment and dementia (VCID).
Method: The participants and MRI data used in this study were acquired as part of the MarkVCID consortium, which consisted of seven sites. A total of 578 participants (72.
Alzheimers Dement
December 2024
Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.
Background: There is growing recognition that white matter microstructural integrity is affected in Alzheimer's disease. The goal of this study was to characterize sex, racial/ethnic, and apolipoprotein (APOE)-ε4 allele differences in white matter integrity.
Methods: This study included participants from ADNI, BLSA, ROS/MAP/MARS, and VMAP, all longitudinal cohorts of aging.
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