Once you train yourself to spot hazards, you’ll notice them all around you. They may not always be obvious or immediate concerns, but they can still pose a risk to you and your co-workers.

Washington — Managing and controlling combustible dust should be considered a unique hazard – not simply “tidying up the place,” the Chemical Safety Board says in a recently released learning review document that includes input from workers and industry stakeholders.

Download this guide from Winter Walking to identify the best traction aids for your organization.

A white paper from Lehigh explains the Compound Effect Solution to Plantar Fasciitis – how to give your employees the 1-2-3 punch, providing the best-fitting approved footwear, along with custom orthotics and medical-grade compression socks.

Toronto — To increase health care workers’ knowledge, awareness and prevention of occupational hand dermatitis, the Center for Research Expertise in Occupational Disease has launched a free e-learning module.

Washington — Chemical facilities should clearly mark pressure-retaining components of plug valves and require new valves to be designed to prevent the inadvertent removal of these components.

Oxipital AI’s 3D vision and AI offerings aim to be more convenient and effective through a different method of “training” its products.

During the Women in Food Manufacturing segment, the keynote covers how to turn an idea into a brand, which is followed by a women’s panel discussion.

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The post Guidance | Learning and Development Support Scheme for the adult social care workforce: a guide for employers was curated by information for practice.

The post Mental Health & Learning Disability Inpatient Statistics 2023/24 was curated by information for practice.

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An internship with Arm gives you exposure to real work and insight into the Arm innovations that shape extraordinary. Students who thrive at Arm take their love of learning beyond their experience of formal education and develop new ideas. This is the energy that interests us.

Internships at Arm will give you the opportunity to put theory into practice through exciting, intellectually challenging, real-world projects that enrich your personal and technical development while enhancing your future career opportunities.

This internship position is within Machine Learning Group in Arm which works on key technologies for the future of computing. Working on the cutting edge of Arm IP, this Group creates technology that powers the next generation of mobile apps, portable devices, home automation, smart cities, self-driving cars, and much more.

When applying, please make sure to include your most up to date academic transcript.

For a sneak peek what it’s like to work in Arm Lund, please have a look at the following video: http://bit.ly/2kxWMXp

The Role

You will work alongside experienced engineers within one of the IP development teams in Arm and be given real project tasks and will be supported by experienced engineers. Examples of previous project tasks are:

• Developing and trialing new processes for use by the design/verification teams.
• Investigating alternative options for existing design or verification implementations.
• Help to develop a hardware platform that can guide out customers to the best solution.
• Implement complex logic using Verilog to bridge a gap in a system.
• Develop bare metal software to exercise design functionality.
• Verify a complex design, from unit to full SoC level.
• Help to take a platform to silicon.

Arm's Machine Learning Group is seeking for a highly motivated and creative Graduate Software Engineer to join the Cambridge-based applied ML team.

From research, to proof-of-concept development, to deployment on ARM IPs, joining this team, would be a phenomenal opportunity to contribute to the full life-cycle of machine learning projects and understand how state-of-the-art machine learning is used to solve real word problems.

Working closely with field experts in a truly multi-discipline environment, you will have the chance to explore existing or build new machine learning techniques, while helping unpick the complex world of use-cases that are applied on high end mobile phones, TVs, and laptops.

About the role

Your role would be to understand, develope and implement these use case, collaborating with Arm's system architects, and working with our marketing groups to ensure multiple Arm products are molded to work well for machine learning. Also, experience deploying inference in a mobile or embedded environment would be ideal. Knowledge of the theory and concepts involved in ML is also needed, so fair comparisons of different approaches can be made.

As an in depth technical role, you will need to understand the complex applications you analyse in detail and communicate them in their simplest form to help include them in product designs, where you will be able to influence both IP and system architecture.

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Applied Psychological Measurement, Ahead of Print. Test-retest reliability is often estimated using naturally occurring data from test repeaters. In settings such as admissions testing, test takers choose if and when to retake an assessment. This self-selection can bias estimates of test-retest reliability because individuals who choose to retest are typically unrepresentative of the broader testing […]

The post Estimating Test-Retest Reliability in the Presence of Self-Selection Bias and Learning/Practice Effects was curated by information for practice.

May 10, 2013

Earlier this week, Charles Ramsey of Cleveland, Ohio rescued three women and a six year old who had been held captive by his neighbor for a decade. But it was the interview Ramsey gave to a reporter on the scene that day that made him an internet sensation. Within hours, he was trending on Twitter and the subject of numerous autotune creations.

But Ramsey's two minute interview (and the later released call he placed to 911) grew into a larger examination of race, class and the media. The stories of the abducted women have rightfully taken center stage, but questions about Ramsey's introduction to the world media remain. This week on Basic Black, what can we learn from Charles Ramsey?


Our panel:
- Callie Crossley, host of Under The Radar, 89.7 WGBH Radio
- Peniel Joseph, professor of history, Tufts University
- Phillip Martin, senior reporter, WGBH Radio
- Kim McLarin, author, Divorce Dog: Men, Motherhood, and Midlife
- Michael Jeffries, assistant professor of American Studies, Wellesley College

One day, while hiking in the Georgia mountains, a couple finds the bones of a human body buried many years ago. The discovery prompts a search for answers: why was this person killed? Who did it? And how many more bodies are hidden in these hills?

When most of us heard about the "insect apocalypse" we were worried. When producer Jimmy Gutierrez heard it, he thought "this is great." Today he takes a journey in which he tries to learn to appreciate our many-legged companions.

Want to read a transcript or support the podcast? Check out our website.

Researchers say teaching seals to copy melodies might help inform speech therapy for humans.

The Armed Forces of Ukraine invaded Russia on August 6. What are Ukraine's goals of the attack? Why did it come as a surprise for Moscow? Pravda.Ru asked an expert opinion from military analyst and political scientist Dmitry Taran. How would you characterise the units of the Ukrainian Armed Forces that invaded the Kursk region? What weapons do they have and how many fighters are there? They are elite units that were kept in reserve and had not been used before. The story of this notorious counterattack is directly related to three events, three factors that now determine the state of affairs in the Ukrainian direction:

The English language is challenging due to complicated grammar, inconsistent sentence structure and colloquial idioms that it doesn't share with related languages. However, English is a target language that sees significantly more resources and opportunities for immersion than many other languages.

How does a hybrid car improve your gas mileage? And more importantly, does it pollute less just because it gets better gas mileage? Learn how hybrids work, plus get tips on how to drive a hybrid car for maximum efficiency.

Fall is the season of festivals and baseball here in Korea. What are some interesting festivals taking place around the country? What is the deal with the first pitch by celebrities at the baseball...

[more...]

Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data.

New software capabilities in RMCProfile allow researchers to study the structure of materials by combining machine learning interatomic potentials and reverse Monte Carlo.

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacunae, voids that house osteocytes, and submicrometer-sized canaliculi that connect bone cells. Characterization of this network in three dimensions is crucial for many bone studies. To quantify X-ray Zernike phase-contrast nanotomography data, deep learning is used to isolate and assess porosity in artifact-laden tomographies of zebrafish bones. A technical solution is proposed to overcome the halo and shade-off domains in order to reliably obtain the distribution and morphology of the LCN in the tomographic data. Convolutional neural network (CNN) models are utilized with increasing numbers of images, repeatedly validated by `error loss' and `accuracy' metrics. U-Net and Sensor3D CNN models were trained on data obtained from two different synchrotron Zernike phase-contrast transmission X-ray microscopes, the ANATOMIX beamline at SOLEIL (Paris, France) and the P05 beamline at PETRA III (Hamburg, Germany). The Sensor3D CNN model with a smaller batch size of 32 and a training data size of 70 images showed the best performance (accuracy 0.983 and error loss 0.032). The analysis procedures, validated by comparison with human-identified ground-truth images, correctly identified the voids within the bone matrix. This proposed approach may have further application to classify structures in volumetric images that contain non-linear artifacts that degrade image quality and hinder feature identification.

During beam time at a research facility, alignment and optimization of instrumentation, such as spectrometers, is a time-intensive task and often needs to be performed multiple times throughout the operation of an experiment. Despite the motorization of individual components, automated alignment solutions are not always available. In this study, a novel approach that combines optimisers with neural network surrogate models to significantly reduce the alignment overhead for a mobile soft X-ray spectrometer is proposed. Neural networks were trained exclusively using simulated ray-tracing data, and the disparity between experiment and simulation was obtained through parameter optimization. Real-time validation of this process was performed using experimental data collected at the beamline. The results demonstrate the ability to reduce alignment time from one hour to approximately five minutes. This method can also be generalized beyond spectrometers, for example, towards the alignment of optical elements at beamlines, making it applicable to a broad spectrum of research facilities.

Transition-metal nitro­gen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co–N–C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co–N–C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co–N–C catalysts, and further optimization of this class of electrocatalytic systems.

The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images.

When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.

During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form a typical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of a beamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.