While you can measure and chart OEE manually, today’s AI and ML-based technologies with smart sensors can help processors find the little things that can improve availability, performance and quality — leading to autonomous manufacturing.

SPI will remain a standalone brand and continue to support its existing customers.

Check out some of the latest packaging and processing solutions exhibitors plan to debut or showcase at PACK EXPO, set for Nov. 3-6 in Chicago.

This paper addresses the impact of using automated flagging devices in road construction instead of using construction workers. To examine the efficacy of automating this construction activity, a group of drivers with diverse characteristics/demographics was involved in the study. The diverse characteristics included gender, age, years of driving experience, and level of formal education (e....

For a long time Tesla used its own kind of charger plug and had its own supercharger network. That once-exclusive network is opening up to other EV manufacturers.

RFID technology does not just exist since yesterday, but especially now, it has a major impact on the profits and losses of companies, for example in retail and logistics. RFID tags that are attached to goods can be read with mobile computers in such a way that real-time results for transactions, stock levels or the order history of customers are displayed.

Cleveron, a click-and-collect automation solutions innovator, is proud to launch a modular outdoor parcel locker, Cleveron 355. The newest solution is specially engineered for DIY and home furnishing retailers, enabling the automated handover of extra-large items.

Avantra, provider of AIOps and automation solutions for SAP environments, was a prominent participant at the recent SAP Sapphire events held in Orlando and Barcelona. The company unveiled its latest AI capabilities and highlighted its pivotal role in enabling organizations to transition from manual operations to advanced automation.

[Economy] :
The minister of trade, industry and energy has met with automotive and battery industry representatives ahead of Donald Trump’s return to power in the U.S. The ministry announced on Wednesday that the meeting took place earlier in the day, with participants highlighting the importance of South Korean ...

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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.

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.

This study introduces a novel iterative Bragg peak removal with automatic intensity correction (IBR-AIC) methodology for X-ray absorption spectroscopy (XAS), specifically addressing the challenge of Bragg peak interference in the analysis of crystalline materials. The approach integrates experimental adjustments and sophisticated post-processing, including an iterative algorithm for robust calculation of the scaling factor of the absorption coefficients and efficient elimination of the Bragg peaks, a common obstacle in accurately interpreting XAS data, particularly in crystalline samples. The method was thoroughly evaluated on dilute catalysts and thin films, with fluorescence mode and large-angle rotation. The results underscore the technique's effectiveness, adaptability and substantial potential in improving the precision of XAS data analysis. While demonstrating significant promise, the method does have limitations related to signal-to-noise ratio sensitivity and the necessity for meticulous angle selection during experimentation. Overall, IBR-AIC represents a significant advancement in XAS, offering a pragmatic solution to Bragg peak contamination challenges, thereby expanding the applications of XAS in understanding complex materials under diverse experimental conditions.

Deflectometric profilometers are used to precisely measure the form of beam shaping optics of synchrotrons and X-ray free-electron lasers. They often utilize autocollimators which measure slope by evaluating the displacement of a reticle image on a detector. Based on our privileged access to the raw image data of an autocollimator, novel strategies to reduce the systematic measurement errors by using a set of overlapping images of the reticle obtained at different positions on the detector are discussed. It is demonstrated that imaging properties such as, for example, geometrical distortions and vignetting, can be extracted from this redundant set of images without recourse to external calibration facilities. This approach is based on the fact that the properties of the reticle itself do not change – all changes in the reticle image are due to the imaging process. Firstly, by combining interpolation and correlation, it is possible to determine the shift of a reticle image relative to a reference image with minimal error propagation. Secondly, the intensity of the reticle image is analysed as a function of its position on the CCD and a vignetting correction is calculated. Thirdly, the size of the reticle image is analysed as a function of its position and an imaging distortion correction is derived. It is demonstrated that, for different measurement ranges and aperture diameters of the autocollimator, reductions in the systematic errors of up to a factor of four to five can be achieved without recourse to external measurements.

The demand for powder X-ray diffraction analysis continues to increase in a variety of scientific fields, as the excellent beam quality of high-brightness synchrotron light sources enables the acquisition of high-quality measurement data with high intensity and angular resolution. Synchrotron powder diffraction has enabled the rapid measurement of many samples and various in situ/operando experiments in nonambient sample environments. To meet the demands for even higher throughput measurements using high-energy X-rays at SPring-8, a high-throughput and high-resolution powder diffraction system has been developed. This system is combined with six sets of two-dimensional (2D) CdTe detectors for high-energy X-rays, and various automation systems, including a system for automatic switching among large sample environmental equipment, have been developed in the third experimental hutch of the insertion device beamline BL13XU at SPring-8. In this diffractometer system, high-brilliance and high-energy X-rays ranging from 16 to 72 keV are available. The powder diffraction data measured under ambient and various nonambient conditions can be analysed using Rietveld refinement and the pair distribution function. Using the 2D CdTe detectors with variable sample-to-detector distance, three types of scan modes have been established: standard, single-step and high-resolution. A major feature is the ability to measure a whole powder pattern with millisecond resolution. Equally important, this system can measure powder diffraction data with high Q exceeding 30 Å−1 within several tens of seconds. This capability is expected to contribute significantly to new research avenues using machine learning and artificial intelligence by utilizing the large amount of data obtained from high-throughput measurements.

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.

The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) − F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.

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.

The success of experimental phasing in macromolecular crystallography relies primarily on the accurate locations of heavy atoms bound to the target crystal. To improve the process of substructure determination, a modified phase-retrieval algorithm built on the framework of the relaxed alternating averaged reflection (RAAR) algorithm has been developed. Importantly, the proposed algorithm features a combination of the π-half phase perturbation for weak reflections and enforces the direct-method-based tangent formula for strong reflections in reciprocal space. The proposed algorithm is extensively demonstrated on a total of 100 single-wavelength anomalous diffraction (SAD) experimental datasets, comprising both protein and nucleic acid structures of different qualities. Compared with the standard RAAR algorithm, the modified phase-retrieval algorithm exhibits significantly improved effectiveness and accuracy in SAD substructure determination, highlighting the importance of additional constraints for algorithmic performance. Furthermore, the proposed algorithm can be performed without human intervention under most conditions owing to the self-adaptive property of the input parameters, thus making it convenient to be integrated into the structural determination pipeline. In conjunction with the IPCAS software suite, we demonstrated experimentally that automatic de novo structure determination is possible on the basis of our proposed algorithm.

The title compound, C10H11N5O2S2, consists of an unexpected tautomer with a protonated nitro­gen atom in the triazine ring and a formal exocyclic double bond C=N to the sulfonamide moiety. The ring angles at the unsubstituted nitro­gen atoms are narrow, at 115.57 (12) and 115.19 (12)°, respectively, whereas the angle at the carbon atom between these N atoms is very wide, 127.97 (13)°. The inter­planar angle between the two rings is 79.56 (5)°. The mol­ecules are linked by three classical hydrogen bonds, forming a ribbon structure. There are also unusual linkages involving three short contacts (< 3 Å) from a sulfonamide oxygen atom to the C—NH—C part of a triazine ring.

In cellulo crystallization is a rare event in nature. Recent advances that have made use of heterologous overexpression can promote the intracellular formation of protein crystals, but new tools are required to detect and characterize these targets in the complex cell environment. The present work makes use of Mask R-CNN, a convolutional neural network (CNN)-based instance segmentation method, for the identification of either single or multi-shaped crystals growing in living insect cells, using conventional bright field images. The algorithm can be rapidly adapted to recognize different targets, with the aim of extracting relevant information to support a semi-automated screening pipeline, in order to aid the development of the intracellular protein crystallization approach.

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia–water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

Soft X-ray spectroscopy is an important technique for measuring the fundamental properties of materials. However, for measurements of samples in the sub-millimetre range, many experimental setups show limitations. Position drifts on the order of hundreds of micrometres during thermal stabilization of the system can last for hours of expensive beam time. To compensate for drifts, sample tracking and feedback systems must be used. However, in complex sample environments where sample access is very limited, many existing solutions cannot be applied. In this work, we apply a robust computer vision algorithm to automatically track and readjust the sample position in the dozens of micrometres range. Our approach is applied in a complex sample environment, where the sample is in an ultra-high vacuum chamber, surrounded by cooled thermal shields to reach sample temperatures down to 2.5 K and in the center of a superconducting split coil. Our implementation allows sample-position tracking and adjustment in the vertical direction since this is the dimension where drifts occur during sample temperature change in our setup. The approach can be easily extended to 2D. The algorithm enables a factor of ten improvement in the overlap of a series of X-ray absorption spectra in a sample with a vertical size down to 70 µm. This solution can be used in a variety of experimental stations, where optical access is available and sample access by other means is reduced.

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

In the country, autonomous mobility will probably mean robotic tractors rather than robotic cars, and if tractor maker Escorts has its way, they could get here sooner than thought.

Autonomous vehicles (AV) took center stage at the Chair’s Luncheon of the Transportation Research Board’s annual meeting today.

Automated research workflows — which integrate computation, laboratory automation, and tools from artificial intelligence — have the potential to increase the speed of research activities and accelerate scientific discovery. A new report recommends ways to advance their development.

To enhance and coordinate its research on autoimmune diseases, the National Institutes of Health should create an Office of Autoimmune Disease/Autoimmunity Research and a plan that spans all institutes and centers to provide an overall NIH strategy for autoimmune disease research.

Organizations that leveraged AI and automation in security prevention cut the cost of a data breach by $2.22 million compared to those that didn't deploy these technologies

A young US-returned robotic engineer is making waves with his experiments in agriculture.

Auto Contact, our range of self-service solutions, can help free up agents to handle more complex and revenue-generating calls –and at the same time improve the overall customer experience whilst reducing costs.

In this exclusive interview with ETCIO, Derek Gittoes, Vice President of Supply Chain Management Product Strategy at Oracle, shares valuable insights into the current trends shaping the future of supply chain management.

Kapil Mahajan unveils Allcargo Group's unwavering commitment to innovation. He is aiming to move 80% of operational workloads to the cloud by the end of 2025.

How to best handle supply chain shortages.

Technology replaces error-prone manual record keeping and monitors energy and utilities parameters, improving efficiency and productivity.

Snack food and wholesale bakery companies are building varying levels of automation in their warehouse shipping and receiving areas, ranging from forklifts to autonomous vehicles, to traceability software and more, all in an effort to make their operations more efficient, effective, safer, and more profitable.

Picking robots, automated storage and retrieval, and software that predicts future demand are among a host of technology, equipment and strategic changes that snack food and wholesale bakery companies are undertaking in their warehousing and logistics functions.

The line bridges automation gaps with tray filling for loose granola, nuts, and other dry ingredients.

Luxme International has supplied a SANILux Tubular Chain Conveyor with automated clean-in-place technology to nutrition bar manufacturer TruFood, enhancing food safety and increasing line efficiency at the company’s Pittsburgh-based facility.

A leader from the company discussed automated tech and ongoing labor challenges.

Bakery and snack producers are creating a wide range of products to meet consumer demand; they also are being called on to do more with less.

Recognizing that proper cooking oil management plays a critical role in achieving both objectives, Cargill has joined with Frontline International to develop the Kitchen Controller end-to-end, automated oil management system.

Companies that make belts, conveyors, and associated equipment like motors have a wide array of customers in the snack food and wholesale bakery industries, because every food production facility relies heavily on these technologies throughout their processes.

Key Technology, a member of the Duravant family of operating companies, introduces its new and improved Auto Diverter for its popular Iso-Flo vibratory conveyors.

There is nothing like a pandemic to test a company's limitations. While food producers received official designation as "essential" businesses as the COVID-19 pandemic descended on the American food industry, continuing operations during the pandemic did not equate to business as usual.

As the dark days of the COVID-19 pandemic descended upon America in early 2020, the American population began reacting to their new reality, and it was clear that snack and bakery production across the country would face a clear challenge. People were stocking up on supplies at an alarming rate, and the rush was on to keep grocery store shelves stocked.

Snack food and wholesale bakery companies in the market for machines that help prepare, store, transport, and otherwise handle ingredients are most focused on qualities like automation, ease of maintenance, and the ability to deliver higher performance in throughput and accuracy.

Automation, ease of operation, simple cleanability, hygienic design, operator safety, and the ability to process nontraditional and healthier ingredients are among the top of mind requests for snack and bakery operators looking to buy or upgrade laminators or sheeters.

CleanTech Evo is an automated handwashing station clinically proven to remove more than 99.9% of pathogens with each hand wash.

The solution is reportedly set to redefine standards in food safety and production efficiency within the food processing industry.