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Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These global responses drive policy to achieve sustainable, resilient, clean solutions with Advanced Materials (AdMats) for industrial supply chains and economic prosperity. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance Computing through Materials Acceleration Platforms, MAPs. In this perspective, following the short paper, a broad overview about the challenges addressed, existing projects and building blocks of MAPs will be provided while concluding with a review of the remaining gaps and measures to overcome them.

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BACKGROUND: Breast cancer (BC) remains a prevalent health concern, with metastasis as the main driver of mortality. A detailed understanding of metastatic processes, particularly cell migration, is fundamental to improve therapeutic strategies. The wound healing assay, a traditional two-dimensional (2D) model, offers insights into cell migration but presents scalability issues due to data scarcity, arising from its manual and labor-intensive nature. METHOD: To overcome these limitations, this study introduces the Prediction Wound Progression Framework (PWPF), an innovative approach utilizing Deep Learning (DL) and artificial data generation. The PWPF comprises a DL model initially trained on artificial data that simulates wound healing in MCF-7 BC cell monolayers and spheres, which is subsequently fine-tuned on real-world data. RESULTS: Our results underscore the model's effectiveness in analyzing and predicting cell migration dynamics within the wound healing context, thus enhancing the usability of 2D models. The PWPF significantly contributes to a better understanding of cell migration processes in BC and expands the possibilities for research into wound healing mechanisms. CONCLUSIONS: These advancements in automated cell migration analysis hold the potential for more comprehensive and scalable studies in the future. Our dataset, models, and code are publicly available at https://github.com/frangam/wound-healing.

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BACKGROUND: Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored. OBJECTIVE: To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease. METHODS: A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions. RESULTS: The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores. CONCLUSION: The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.

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X-ray multi-projection imaging (XMPI) has the potential to provide rotation-free 3D movies of optically opaque samples. The absence of rotation enables superior imaging speed and preserves fragile sample dynamics by avoiding the centrifugal forces introduced by conventional rotary tomography. Here, we present our XMPI observations at the ID19 beamline (ESRF, France) of 3D dynamics in melted aluminum with 1000 frames per second and 8 µm resolution per projection using the full dynamical range of our detectors. Since XMPI is a method under development, we also provide different tests for the instrumentation of up to 3000 frames per second. As the high-brilliance of 4th generation light-sources becomes more available, XMPI is a promising technique for current and future X-ray imaging instruments.

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BACKGROUND: Older adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life. METHOD: A design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain. RESULTS: The results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning. CONCLUSIONS: The defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

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Allergic diseases are increasing around the world with unprecedented complexity and severity. One of the reasons is that genetically modified crops produce new potentially allergenic proteins. From this starting point, many researchers have paid attention to the development of tools to predict the allergenicity of new proteins. In this study, a novel approach is introduced for the prediction of food allergens based on Artificial Intelligence techniques: a pairwise sequence alignment with the FASTA program for feature extraction and the use of the Deep Learning technique known as Restricted Boltzmann Machines in combination with the Decision Tree method for the prediction process. The developed tool, called ALLERDET (publicly available at http://allerdet.frangam.com), overcomes the state-of-the-art methods. The performance of our method is: 98.46% sensitivity, 94.37% specificity and 97.26% accuracy), on a data set built from several publicly available sources.

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BACKGROUND AND OBJECTIVE: The assessment of dependence in older adults currently requires a manual collection of data taken from questionnaires. This process is time consuming for the clinicians and intrudes the daily life of the elderly. This paper aims to semi-automate the acquisition and analysis of health data to assess and predict the dependence in older adults while executing one instrumental activity of daily living (IADL). METHODS: In a mobile-health (m-health) scenario, we analyze whether the acquisition of data through wearables during the performance of IADLs, and with the help of machine learning techniques could replace the traditional questionnaires to evaluate dependence. To that end, we collected data from wearables, while older adults do the shopping activity. A trial supervisor (TS) labelled the different shopping stages (SS) in the collected data. We performed data pre-processing techniques over those SS and analyzed them with three machine learning algorithms: k-Nearest Neighbors (k-NN), Random Forest (RF) and Support Vector Machines (SVM). RESULTS: Our results confirm that it is possible to replace the traditional questionnaires with wearable data. In particular, the best learning algorithm we tried reported an accuracy of 97% in the assessment of dependence. We tuned the hyperparameters of this algorithm and used embedded feature selection technique to get the best performance with a subset of only 10 features out of the initial 85. This model considers only features extracted from four sensors of a single wearable: accelerometer, heart rate, electrodermal activity and temperature. Although these features are not observational, our current proposal is semi-automatic, because it needs a TS labelling the SS (with a smartphone application). In the future, this labelling process could be automatic as well. CONCLUSIONS: Our method can semi-automatically assess the dependence, without disturbing daily activities of elderly people. This method can save clinicians' time in the evaluation of dependence in older adults and reduce healthcare costs.

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The structure and constitution of opaque materials can be studied with X-ray imaging methods such as 3D tomography. To observe the dynamic evolution of their structure and the distribution of constituents, for example, during processing, heating, mechanical loading, etc., 3D imaging has to be fast enough. In this paper, the recent developments of time-resolved X-ray tomography that have led to what one now calls "tomoscopy" are briefly reviewed A novel setup is presented and applied that pushes temporal resolution down to just 1 ms, that is, 1000 tomograms per second (tps) are acquired, while maintaining spatial resolutions of micrometers and running experiments for minutes without interruption. Applications recorded at different acquisition rates ranging from 50 to 1000 tps are presented. The authors observe and quantify the immiscible hypermonotectic reaction of AlBi10 (in wt%) alloy and dendrite evolution in AlGe10 (in wt%) casting alloy during fast solidification. The combustion process and the evolution of the constituents are analyzed in a burning sparkler. Finally, the authors follow the structure and density of two metal foams over a long period of time and derive details of bubble formation and bubble ageing including quantitative analyses of bubble parameters with millisecond temporal resolution.

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Phase retrieval approaches based on deep learning (DL) provide a framework to obtain phase information from an intensity hologram or diffraction pattern in a robust manner and in real-time. However, current DL architectures applied to the phase problem rely on i) paired datasets, i. e., they are only applicable when a satisfactory solution of the phase problem has been found, and ii) the fact that most of them ignore the physics of the imaging process. Here, we present PhaseGAN, a new DL approach based on Generative Adversarial Networks, which allows the use of unpaired datasets and includes the physics of image formation. The performance of our approach is enhanced by including the image formation physics and a novel Fourier loss function, providing phase reconstructions when conventional phase retrieval algorithms fail, such as ultra-fast experiments. Thus, PhaseGAN offers the opportunity to address the phase problem in real-time when no phase reconstructions but good simulations or data from other experiments are available.

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Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to machines. Recently, some commercial low-intrusive BCI headbands have appeared, but with less electrodes than the regular BCIs. Some works have proved the ability of the headbands to detect basic motor imagery. However, all of these works have focused on the accuracy of the detection, using session sizes larger than 10 s, in order to improve the accuracy. These session sizes prevent actuators using the headbands to interact with the user within an adequate response time. In this work, we explore the reduction of time-response in a low-intrusive device with only 4 electrodes using deep learning to detect right/left hand motion imagery. The obtained model is able to lower the detection time while maintaining an acceptable accuracy in the detection. Our findings report an accuracy above 83.8% for response time of 2 s overcoming the related works with both low- and high-intrusive devices. Hence, our low-intrusive and low-cost solution could be used in an interactive system with a reduced response time of 2 s.

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An experimental technique is described for the collection of time-resolved X-ray diffraction information from a complete commercial battery cell during discharging or charging cycles. The technique uses an 80 × 80 pixel 2D energy-discriminating detector in a pinhole camera geometry which can be used with a polychromatic X-ray source. The concept was proved in a synchrotron X-ray study of commercial alkaline Zn-MnO2 AA size cells. Importantly, no modification of the cell was required. The technique enabled spatial and temporal changes to be observed with a time resolution of 20 min (5 min of data collection with a 15 min wait between scans). Chemical changes in the cell determined from diffraction information were correlated with complementary X-ray tomography scans performed on similar cells from the same batch. The clearest results were for the spatial and temporal changes in the Zn anode. Spatially, there was a sequential transformation of Zn to ZnO in the direction from the separator towards the current collector. Temporally, it was possible to track the transformation of Zn to ZnO during the discharge and follow the corresponding changes in the cathode.

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The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.

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The complex flow of liquid metal in evolving metallic foams is still poorly understood due to difficulties in studying hot and opaque systems. We apply X-ray tomoscopy -the continuous acquisition of tomographic (3D) images- to clarify key dynamic phenomena in liquid aluminium foam such as nucleation and growth, bubble rearrangements, liquid retraction, coalescence and the rupture of films. Each phenomenon takes place on a typical timescale which we cover by obtaining 208 full tomograms per second over a period of up to one minute. An additional data processing algorithm provides information on the 1 ms scale. Here we show that bubble coalescence is not only caused by gravity-induced drainage, as experiments under weightlessness show, and by stresses caused by foam growth, but also by local pressure peaks caused by the blowing agent. Moreover, details of foam expansion and phenomena such as rupture cascades and film thinning before rupture are quantified. These findings allow us to propose a way to obtain foams with smaller and more equally sized bubbles.

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High-speed X-ray imaging in two dimensions (radioscopy) and three dimensions (tomography) is combined with fast X-ray diffraction in a new experimental setup at the synchrotron radiation source BESSY II. It allows for in situ studies of time-dependent phenomena in complex systems. As a first application, the foaming process of an aluminium alloy was studied in three different experiments. Radioscopy, optical expansion measurements and diffraction were used to correlate the change of foam morphology to the various phases formed during heating of an AlMg15Cu10 alloy to 620°C in the first experiment. Radioscopy was then replaced by tomography. Acquiring tomograms and diffraction data at 2 Hz allows even more details of foam evolution to be captured, for example, bubble size distribution. In a third experiment, 4 Hz tomography yields dynamic insights into fast phenomena in evolving metal foam.

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An experimental setup has been developed that allows for capturing up to 25 tomograms s-1 using the white X-ray beam at the experimental station EDDI of BESSY II, Berlin, Germany. The key points are the use of a newly developed, precise and fast rotation stage, a very efficient scintillator and a fast CMOS camera. As a first application, the foaming of aluminium alloy granules at 923 K was investigated in situ. Formation and growth of bubbles in the liquid material were observed and found to be influenced by the limited thermal conductivity in the bulk granules. Changes that took place between two tomographic frames separated in time by 39 ms could be detected and analysed quantitatively.

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Our understanding of the structural features of foams and emulsions has advanced significantly over the last 20 years. However, with a search for "super-stable" liquid dispersions, foam and emulsion science employs increasingly complex formulations which create solid-like visco-elastic layers at the bubble/drop surfaces. These lead to elastic, adhesive and frictional forces between bubbles/drops, impacting strongly how they pack and deform against each other, asking for an adaptation of the currently available structural description. The possibility to modify systematically the interfacial properties makes these dispersions ideal systems for the exploration of soft granular materials with complex interactions. We present here a first systematic analysis of the structural features of such a system using a model silicone emulsion containing millimetre-sized polyethylene glycol drops (PEG). Solid-like drop surfaces are obtained by polymeric cross-linking reactions at the PEG-silicone interface. Using a novel droplet-micromanipulator, we highlight the presence of elastic, adhesive and frictional interactions between two drops. We then provide for the first time a full tomographic analysis of the structural features of these emulsions. An in-depth analysis of the angle of repose, local volume fraction distributions, pair correlation functions and the drop deformations for different skin formulations allow us to put in evidence the striking difference with "ordinary" emulsions having fluid-like drop surfaces. While strong analogies with frictional hard-sphere systems can be drawn, these systems display a set of unique features due to the high deformability of the drops which await systematic exploration.

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This work gives an overview of the production, properties and industrial applications of metal foams. First, it classifies the most relevant manufacturing routes and methods. Then, it reviews the most important properties, with special interest in the mechanical and functional aspects, but also taking into account costs and feasibility considerations. These properties are the motivation and basis of related applications. Finally, a summary of the most relevant applications showing a large number of actual examples is presented. Concluding, we can forecast a slow, but continuous growth of this industrial sector.

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The evolution of a three-dimensional monodisperse foam was investigated using X-ray tomography over the course of seven days. The coarsening of the sample was inhibited through the use of perfluorohexane gas. The internal configuration of bubbles is seen to change markedly, evolving from a disordered arrangement towards a more ordered state. We chart this ordering process through the use of the coordination number, the bond orientational order parameter (BOOP) and the translational order parameter.

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Pushing synchrotron x-ray radiography to increasingly higher image-acquisition rates (currently up to 100,000 fps) while maintaining spatial resolutions in the micrometer range implies drastically reduced fields of view. As a consequence, either imaging a small subregion of the sample with high spatial resolution or only the complete specimen with moderate resolution is applicable. We introduce a concept to overcome this limitation by making use of a semi-transparent x-ray detector positioned close to the investigated sample. The hard x-rays that pass through the sample either create an image on the first detector or keep on propagating until they are captured by a second x-ray detector located further downstream. In this way, a process can be imaged simultaneously in a hierarchical manner within a single exposure and a projection of the complete object with moderate resolution as well as a subregion with high resolution are obtained. As a proof-of-concept experiment, image sequences of an evolving liquid-metal foam are shown, employing frame rates of 1000 images/s (1.2 µm pixel size) and 15,000 images/s (18.1 µm pixel size) for the first and second detector, respectively.

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