The metallic sphere in a uniform ac magnetic field: A simple and precise experiment for exploring eddy currents and non-destructive testing

We describe a very simple experiment that utilizes standard laboratory equipment to measure the electromagnetic response of a metallic sphere exposed to a uniform ac magnetic field. Measurements were made for a variety of non-magnetic and magnetic metals, and in all cases the results fit very well with theory over the four orders of frequency (25?Hz to 102?kHz) explored here. Precise values of magnetic permeability and electrical conductivity can be extracted from fits to the data given the sphere radius only. The same apparatus is also used to explore the effects of geometry on eddy current generation as well as to demonstrate non-destructive testing through measurements on coins of different composition."The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada, especially the Undergraduate Student Research Award for MLH."https://aapt.scitation.org/doi/10.1119/1.503435

A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images

Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It can capture detailed information about the chemical and physical properties of agricultural crops. Due to its wide spectral range, compared with multispectral- or RGB-based imaging methods, HSI can be a more effective tool for monitoring crop health and productivity. With the advent of this imaging tool in agrotechnology, researchers can more accurately address issues related to the detection of diseased and defective crops in the agriculture industry. This allows to implement the most suitable and accurate farming solutions, such as irrigation and fertilization before crops enter a damaged and difficult-to-recover phase of growth in the field. While HSI provides valuable insights into the object under investigation, the limited number of HSI datasets for crop evaluation presently poses a bottleneck. Dealing with the curse of dimensionality presents another challenge due to the abundance of spectral and spatial information in each hyperspectral cube. State-of-the-art methods based on 1D- and 2D-CNNs struggle to efficiently extract spectral and spatial information. On the other hand, 3D-CNN-based models have shown significant promise in achieving better classification and detection results by leveraging spectral and spatial features simultaneously. Despite the apparent benefits of 3D-CNN-based models, their usage for classification purposes in this area of research has remained limited. This paper seeks to address this gap by reviewing 3D-CNN-based architectures and the typical deep learning pipeline, including preprocessing and visualization of results, for the classification of hyperspectral images of diseased and defective crops. Furthermore, we discuss open research areas and challenges when utilizing 3D-CNNs with HSI data

A Variant Mimicking Hyperphosphorylated 4E-BP Inhibits Protein Synthesis in a Sea Urchin Cell-Free, Cap-Dependent Translation System

BACKGROUND: 4E-BP is a translational inhibitor that binds to eIF4E to repress cap-dependent translation initiation. This critical protein:protein interaction is regulated by the phosphorylation of 4E-BP. Hypophosphorylated 4E-BP binds to eIF4E and inhibits cap-dependent translation, whereas hyperphosphorylated forms do not. While three 4E-BP proteins exist in mammals, only one gene encoding for 4E-BP is present in the sea urchin genome. The protein product has a highly conserved core domain containing the eIF4E-binding domain motif (YxxxxLPhi) and four of the regulatory phosphorylation sites. METHODOLOGY/PRINCIPAL FINDINGS: Using a sea urchin cell-free cap-dependent translation system prepared from fertilized eggs, we provide the first direct evidence that the sea urchin 4E-BP inhibits cap-dependent translation. We show here that a sea urchin 4E-BP variant, mimicking phosphorylation on four core residues required to abrogate binding to eIF4E, surprisingly maintains physical association to eIF4E and inhibits protein synthesis. CONCLUSIONS/SIGNIFICANCE: Here, we examine the involvement of the evolutionarily conserved core domain and phosphorylation sites of sea urchin 4E-BP in the regulation of eIF4E-binding. These studies primarily demonstrate the conserved activity of the 4E-BP translational repressor and the importance of the eIF4E-binding domain in sea urchin. We also show that a variant mimicking hyperphosphorylation of the four regulatory phosphorylation sites common to sea urchin and human 4E-BP is not sufficient for release from eIF4E and translation promotion. Therefore, our results suggest that there are additional mechanisms to that of phosphorylation at the four critical sites of 4E-BP that are required to disrupt binding to eIF4E

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and plant segmentation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.Comment: 35 pages, 8 figures, Preprint submitted to PLoS On

A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images

Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It has the ability to capture detailed information about the chemical and physical properties of agricultural crops. Due to its wide spectral range, compared with multispectral-or RGB-based imaging methods, HSI can be a more effective tool for monitoring crop health and productivity. With the advent of this imaging tool in agrotechnology, researchers can more accurately address issues related to the detection of diseased and defective crops in the agriculture industry. This allows to implement the most suitable and accurate farming solutions, such as irrigation and fertilization, before crops enter a damaged and difficult-to-recover phase of growth in the field. While HSI provides valuable insights into the object under investigation, the limited number of HSI datasets for crop evaluation presently poses a bottleneck. Dealing with the curse of dimensionality presents another challenge due to the abundance of spectral and spatial information in each hyperspectral cube. State-of-the-art methods based on 1D and 2D convolutional neural networks (CNNs) struggle to efficiently extract spectral and spatial information. On the other hand, 3D-CNN-based models have shown significant promise in achieving better classification and detection results by leveraging spectral and spatial features simultaneously. Despite the apparent benefits of 3D-CNN-based models, their usage for classification purposes in this area of research has remained limited. This paper seeks to address this gap by reviewing 3D-CNN-based architectures and the typical deep learning pipeline, including preprocessing and visualization of results, for the classification of hyperspectral images of diseased and defective crops. Furthermore, we discuss open research areas and challenges when utilizing 3D-CNNs with HSI data."This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors."https://www.sciencedirect.com/science/article/pii/S277237552300145

Magnetic diffusion, inductive shielding, and the Laplace transform

In the quasistatic limit, a time-varying magnetic field inside a conductor is governed by the diffusion equation. Despite the occurrence of this scenario in many popular physics demonstrations, the concept of magnetic diffusion appears not to have garnered much attention itself as a subject for teaching. We employ the model of a thin conducting tube in a time-varying axial field to introduce magnetic diffusion and connect it to the related phenomenon of inductive shielding. We describe a very simple apparatus utilizing a wide-band Hall-effect sensor to measure these effects with a variety of samples. Quantitative results for diffusion time constants and shielding cutoff frequencies are consistent with a single, sample-specific parameter given by the product of the tube radius, thickness, and electrical conductivity. The Laplace transform arises naturally in regard to the time and frequency domain solutions presented here, and the utility of the technique is highlighted in several places."The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada, especially the Undergraduate Student Research Awards for A.E.K. and J.J.W."https://aapt.scitation.org/doi/10.1119/10.000350

NMR Time Reversal Experiments in Highly Polarised Liquid 3He-4He Mixtures

Long-range magnetic interactions in highly magnetised liquids (laser-polarised 3He-4He dilute mixtures at 1 K in our experiment) introduce a significant non-linear and non-local contribution to the evolution of nuclear magnetisation that leads to instabilities during free precession. We recently demonstrated that a multi-echo NMR sequence, based on the magic sandwich pulse scheme developed for solid-state NMR, can be used to stabilise the magnetisation against the effect of distant dipolar fields. Here, we report investigations of echo attenuation in an applied field gradient that show the potential of this NMR sequence for spin diffusion measurements at high magnetisation densities.Comment: Accepted for publication in the Journal of Low Temperature Physic

Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification

The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop complex CNN models to achieve better performances, introducing over-parameterization and forcing the model to overfit on a training dataset. The most popular process for evaluating overfitting in a deep learning model is using accuracy and loss curves. Train and loss curves may help understand the performance of a model but do not provide guidance on how the model could be modified to attain better performance. In this article, we analyzed the relation between the features learned by a model and its capacity and showed that a model with higher representational capacity might learn many subtle features that may negatively affect its performance. Next, we showed that the shallow layers of a deep learning model learn more diverse features than the ones learned by the deeper layers. Finally, we propose SSIM cut curve, a new way to select the depth of a CNN model by using the pairwise similarity matrix between the visualization of the features learned at different depths by using Guided Backpropagation. We showed that our proposed method could potentially pave a new way to select a better CNN model.https://www.frontiersin.org/articles/10.3389/frai.2022.871162/ful

The Precision nEDM Measurement with UltraCold Neutrons at TRIUMF

The TRIUMF Ultra-Cold Advanced Neutron (TUCAN) collaboration aims at a precision neutron electric dipole moment (nEDM) measurement with an uncertainty of $10^{-27}\,e\cdot\mathrm{cm}$, which is an order-of-magnitude better than the current nEDM upper limit and enables us to test Supersymmetry. To achieve this precision, we are developing a new high-intensity ultracold neutron (UCN) source using super-thermal UCN production in superfluid helium (He-II) and a nEDM spectrometer. The current development status of them is reported in this article.Comment: Proceedings of the 24th International Spin Symposium (SPIN 2021), 18-22 October 2021, Matsue, Japa