Speckle-tracking X-ray imaging is an attractive candidate for dynamic X-ray imaging owing to its flexible setup and simultaneous yields of phase, transmission and scattering images. However, traditional speckle-tracking imaging methods suffer from phase distortion at locations with abrupt changes in density, which is always the case for real samples, limiting the applications of the speckle-tracking X-ray imaging method. In this paper, we report a deep-learning based method which can achieve dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval. The calibration results of a phantom show that the profile of the retrieved phase is highly consistent with the theoretical one. Experiments of polyurethane foaming demonstrated that the proposed method revealed the evolution of the complicated microstructure of the bubbles accurately. The proposed method is a promising solution for dynamic X-ray imaging with high-accuracy phase retrieval, and has extensive applications in metrology and quantitative analysis of dynamics in material science, physics, chemistry and biomedicine.

This paper uses deep learning to present a proof-of-concept for data-driven chemistry in single-molecule magnets (SMMs). Previous discussions within SMM research have proposed links between molecular structures (crystal structures) and single-molecule magnetic properties; however, these have only interpreted the results. Therefore, this study introduces a data-driven approach to predict the properties of SMM structures using deep learning. The deep-learning model learns the structural features of the SMM molecules by extracting the single-molecule magnetic properties from the 3D coordinates presented in this paper. The model accurately determined whether a molecule was a single-molecule magnet, with an accuracy rate of approximately 70% in predicting the SMM properties. The deep-learning model found SMMs from 20 000 metal complexes extracted from the Cambridge Structural Database. Using deep-learning models for predicting SMM properties and guiding the design of novel molecules is promising.

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. A novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold2 has previously been established. It is particularly well suited to the detection of register errors. Importantly, this new approach is orthogonal to traditional methods based on stereochemistry or map–model agreement, and is resolution independent. Here, thousands of likely register errors are identified by scanning 3–5 Å resolution structures in the PDB. Unlike most methods, the application of this approach yields suggested corrections to the register of affected regions, which it is shown, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterized, but this approach is expected to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping to ensure the accuracy of future depositions.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

A deep-learning algorithm is proposed for the inpainting of Bragg coherent diffraction imaging (BCDI) patterns affected by detector gaps. These regions of missing intensity can compromise the accuracy of reconstruction algorithms, inducing artefacts in the final result. It is thus desirable to restore the intensity in these regions in order to ensure more reliable reconstructions. The key aspect of the method lies in the choice of training the neural network with cropped sections of diffraction data and subsequently patching the predictions generated by the model along the gap, thus completing the full diffraction peak. This approach enables access to a greater amount of experimental data for training and offers the ability to average overlapping sections during patching. As a result, it produces robust and dependable predictions for experimental data arrays of any size. It is shown that the method is able to remove gap-induced artefacts on the reconstructed objects for both simulated and experimental data, which becomes essential in the case of high-resolution BCDI experiments.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Ptychography is a powerful computational imaging technique with microscopic imaging capability and adaptability to various specimens. To obtain an imaging result, it requires a phase-retrieval algorithm whose performance directly determines the imaging quality. Recently, deep neural network (DNN)-based phase retrieval has been proposed to improve the imaging quality from the ordinary model-based iterative algorithms. However, the DNN-based methods have some limitations because of the sensitivity to changes in experimental conditions and the difficulty of collecting enough measured specimen images for training the DNN. To overcome these limitations, a ptychographic phase-retrieval algorithm that combines model-based and DNN-based approaches is proposed. This method exploits a DNN-based denoiser to assist an iterative algorithm like ePIE in finding better reconstruction images. This combination of DNN and iterative algorithms allows the measurement model to be explicitly incorporated into the DNN-based approach, improving its robustness to changes in experimental conditions. Furthermore, to circumvent the difficulty of collecting the training data, it is proposed that the DNN-based denoiser be trained without using actual measured specimen images but using a formula-driven supervised approach that systemically generates synthetic images. In experiments using simulation based on a hard X-ray ptychographic measurement system, the imaging capability of the proposed method was evaluated by comparing it with ePIE and rPIE. These results demonstrated that the proposed method was able to reconstruct higher-spatial-resolution images with half the number of iterations required by ePIE and rPIE, even for data with low illumination intensity. Also, the proposed method was shown to be robust to its hyperparameters. In addition, the proposed method was applied to ptychographic datasets of a Simens star chart and ink toner particles measured at SPring-8 BL24XU, which confirmed that it can successfully reconstruct images from measurement scans with a lower overlap ratio of the illumination regions than is required by ePIE and rPIE.

Approximately 100 community leaders learned about two programs designed to protect area children at the Children's Advocacy and Protection Center's second annual Children's Breakfast.

Many public school students receive little or no exposure to the theory of evolution, the most important concept in understanding biology, says a new guidebook from the National Academy of Sciences (NAS).

American students progress toward proficiency in mathematics requires major changes in instruction, curricula, and assessment in the nations schools, says a new report from the National Research Council of the National Academies.

Each year, tens of millions of Americans, young and old, choose to learn about science in informal ways -- by visiting museums and aquariums, attending after-school programs, pursuing personal hobbies, and watching TV documentaries, for example.

Educational and business leaders want todays students both to master school subjects and to excel in areas such as problem solving, critical thinking, and communication

As researchers’ and teachers’ understanding of how best to learn and teach science evolves and curricula are redesigned, many teachers are left without the experience needed to enhance the science and engineering courses they teach, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Despite their potential, many English learners (ELs) -- who account for more than 9 percent of K-12 enrollment in the U.S. -- lag behind their English-speaking monolingual peers in educational achievement, in part because schools do not provide adequate instruction and social-emotional support to acquire English proficiency or access to academic subjects at the appropriate grade level, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Policymakers, employers, and educational institutions should take steps to strengthen the nation’s skilled technical workforce, says a new report from the National Academies of Sciences, Engineering, and Medicine.

A new report from the National Academies of Sciences, Engineering, and Medicine highlights the dynamic process of learning throughout the life span and identifies frontiers in which more research is needed to pursue an even deeper understanding of human learning.

A shift is needed in how science, technology, engineering, and mathematics (STEM) subjects are taught to students in grades K-12 who are learning English, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Scientific research that involves nonscientists contributing to research processes – also known as ‘citizen science’ – supports participants’ learning, engages the public in science, contributes to community scientific literacy, and can serve as a valuable tool to facilitate larger scale research, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Centering science instruction around investigation and design can improve learning in middle and high schools and help students make sense of phenomena in the world around them.

Between one-third and one-half of U.S. clinicians experience burnout and addressing the epidemic requires systemic changes by health care organizations, educational institutions, and all levels of government, says a new report from the National Academy of Medicine.

A new report from the National Academies of Sciences, Engineering, and Medicine recommends ways to make supply chains -- the systems that provide populations with critical goods and services, such as food and water, gasoline, and pharmaceuticals and medical supplies – more resilient in the face of hurricanes and other disasters, drawing upon lessons learned from the 2017 hurricanes Harvey, Irma, and Maria.

Colleges and universities should embrace sustainability education as a vital field that requires tailored educational experiences delivered through courses, majors, minors, and research and graduate degrees, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Learning experiences in computing that are designed with attention to K-12 students’ interests, identities, and backgrounds may attract underrepresented groups to computing better than learning experiences that mimic current professional computing practices and culture do, says a new report from the National Academies of Sciences, Engineering, and Medicine.

As COVID-19 vaccination programs across the country transition from meeting urgent demand to reaching people who are less eager to get the shot, leaders are looking for new vaccine communications strategies.

Organizations, including those that weren’t struck by the CrowdStrike incident, should resist the temptation to attribute the IT meltdown to exceptional circumstances

Jiro's Pick this week is AstroVolley Courseware by Paul Huxel.Back in my undergraduate studies (many, many years ago), I took a Pascal programming course, and it was the first official programming... read more >>

Google's head of research, Yossi Matias, emphasizes the enduring importance of coding skills in an AI-driven world. While acknowledging AI's growing role in software development, Matias argues that basic coding knowledge is crucial for understanding and leveraging AI's potential. He compares coding to math, suggesting that both are fundamental for navigating an increasingly tech-reliant society.

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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When roboticists create behaviors for teams of robots, they first build algorithms that focus on the intended task. Then they wrap safety behaviors around those primary algorithms to keep the machines from running into each other. Each robot is essentially given an invisible bubble that other robots must stay away from. As long as nothing touches the bubble, the robots move around without any issues. But that's where the problems begin.

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Since joining the cereal division of Kerry Inc., Beloit, WI, seven months ago as a research and development technologist, I’ve learned about an array of technologies that I had limited exposure to previously. 

Peanuts go through many different phases before ending up at their final destination on grocery store shelves.

As controller, my job essentially boils down to two things: support our branches and ensure that Leap Partners has the cash flow to fund further acquisitions.  I want to share with you the three fundamental lessons I’ve learned to achieve these goals.

The partnership between Interplay and Peterman comes at a pivotal time as Peterman continues to expand its operations and scale its technical training efforts.

Educational sessions will offer continuing education units (CEUs).

Regardless of how long you’ve been in the trades, I’m sure you know how important learning is to your journey. Let’s take a deeper look.

Included in Interplay’s career development platform, SAM provides technicians with immediate guidance to help them gain skills faster and learn more efficiently in a more engaging manner. S

Heating contractors attended the event to learn about high-efficiency heating equipment, including the Ambient Air-to-Water Heat Pump, Citadel commercial condensing boiler, Alta self-adaptive boiler, and the Ambient electric boiler.  

My technical boiler books all use the “lessons learned” theme, and the following are the lessons I have learned about boiler water treatment.

I’ll admit it. I was skeptical that distance learning could be a worthwhile experience for service contractors. 

I spotted a story in the newspaper last year that made me smile with a memory that was bittersweet. 

Deloitte (2014) describes the modern learner in its infographic, “Meet the Modern Learner.” The infographic shows multiple constraints employees face when developing necessary skills. Many writers and training professionals interpret this to say that people today learn differently. Learning has evolved with the office.

If there’s one thing the global business community has learned from the COVID-19 pandemic that continues to ebb, flow and unfold on the daily, wreaking having on bottom lines in every corner of the world in its wake, it’s the outright imperative for companies to be agile “from top to bottom.”

How to use psychological and behavioral knowledge to improve workplace safety. As a safety leader, it’s important to recognize moments when people are looking to you as an example and ensure that your behavior aligns with the values you’re working to instill in others.

The Institute is a collective of in-field and C-suite environmental, health & safety expertise.

Henry Halladay, Ph.D, PE is premiering today the latest installment of his celebrated podumentary, Learn Learn Learn, along with an exclusive Q&A bonus show and confirmation that season three is slated for 2022.

Ushering in the era of AI with Microsoft Copilot courses tailored for business professionals.