On the issue of transparency and reproducibility in nanomedicine
Below we report short extracts highlighting the main messages of the correspondences we received.
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Below we report short extracts highlighting the main messages of the correspondences we received.
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In recent years we have witnessed the rise of commercialization efforts for soft robotic technology, including soft grippers (Soft Robotics, Inc.), stretchable sensors (StretchSense, Inc. and Lightlace, Inc.), and platforms for human-robot interaction (Festo Inc., Meta Reality Labs, Toyota Research Institute, and Disney Research). However, commercialization as a whole lags the trends enjoyed by other robotic technology at equivalent points in their respective lifecycles.
The capability to replicate the predictions by machine learning (ML) or artificial intelligence (AI) models and the results in scientific workflows that incorporate such ML/AI predictions is driven by a variety of factors.
As the field of superconducting quantum computing approaches maturity, optimization of single-device performance is proving to be a promising avenue towards large-scale quantum computers. However, this optimization is possible only if performance metrics can be accurately compared among measurements, devices, and laboratories. Currently such comparisons are inaccurate or impossible due to understudied errors from a plethora of sources. In this Perspective, we outline the current state of error analysis for qubits and resonators in superconducting quantum circuits, and discuss what future investigations are required before superconducting quantum device optimization can be realized.
Real-time time-dependent density functional theory (TDDFT) is widely considered to be the most accurate available method for calculating electronic stopping powers from first principles, but there have been relatively few assessments of the consistency of its predictions across different implementations. This problem is particularly acute in the warm dense regime, where computational costs are high and experimental validation is rare and resource intensive. We report a comprehensive cross-verification of stopping power calculations in conditions relevant to inertial confinement fusion conducted using four different TDDFT implementations. We find excellent agreement among both the post-processed stopping powers and relevant time-resolved quantities for alpha particles in warm dense hydrogen. We also analyze sensitivities to a wide range of methodological details, including the exchange-correlation model, pseudopotentials, initial conditions, observable from which the stopping power is extracted, averaging procedures, projectile trajectory, and finite-size effects. We show that among these details, pseudopotentials, trajectory-dependence, and finite-size effects have the strongest influence, and we discuss different strategies for controlling the latter two considerations.
In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro–Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density–temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub.
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Spin-echo small-angle neutron scattering (SESANS) is a unique method to measure structures of materials in real space with length scales from ∼ 30 μm to ∼20 µm. As shown in Figure 1, the accessible length scale of SESANS is given by its ability to encode the momentum transfer into the Larmor phase, namely Φ = $\vec{𝛿}$ ⋅ $\vec{𝑄}$, where $\vec{𝑄}$ is the momentum transfer and $\vec{𝛿}$ is the encoding vector of the setup and its projection along Q (δQ) is called spin-echo length (SEL). The spin echo length, which is synonymous with the spatial correlation distance probed, is defined as the following 𝛿𝑄 ∝ 𝜆 2 𝐵𝐿cot𝜃 where 𝜆 is neutron wavelength, B is magnetic field, L is length of the parallelogram magnetic field region, and 𝜃 is the angle between the inclined magnetic field boundary and the beam direction, as shown in Figure 1. The result of the SESANS experiment is a Hankel transformation of the SANS scattering function I(Q), which yields the correlation function of the sample in real space.
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Abstract Background Many organizations face challenges in managing and analyzing data, especially when relevant datasets arise from multiple sources and methods. Analyzing heterogeneous datasets and additional derived data requires rigorous tracking of their interrelationships and provenance. This task has long been a Grand Challenge of data science and has more recently been formalized in the FAIR principles: that all data objects be Findable, Accessible, Interoperable, and Reusable, both for machines and for people. Adherence to these principles is necessary for proper stewardship of information, for testing regulatory compliance, for measuring the efficiency of processes, and for facilitating reuse of data-analytical frameworks. Findings We present the Contextual Ontology-based Repository Analysis Library (CORAL), a platform that greatly facilitates adherence to all 4 of the FAIR principles, including the especially difficult challenge of making heterogeneous datasets Interoperable and Reusable across all parts of a large, long-lasting organization. To achieve this, CORAL's data model requires that data generators extensively document the context for all data, and our tools maintain that context throughout the entire analysis pipeline. CORAL also features a web interface for data generators to upload and explore data, as well as a Jupyter notebook interface for data analysts, both backed by a common API. Conclusions CORAL enables organizations to build FAIR data types on the fly as they are needed, avoiding the expense of bespoke data modeling. CORAL provides a uniquely powerful platform to enable integrative cross-dataset analyses, generating deeper insights than are possible using traditional analysis tools.
Optically stimulated luminescent dosimeters (OSLDs) are devices that, when stimulated with light, emit light in proportion to the integrated ionizing radiation dose. The stimulation of OSL material results in the loss of a small fraction of signal stored within the dosimetric traps. Previous studies have investigated the signal loss due to readout stimulation, and the optical annealing of OSLDs. This study builds on former research by examining the behavior of OSL signal after annealing, exploring the functionality of a previously developed signal loss model, and comparing uncertainties for dosimeters reused with or without annealing. For a completely annealed dosimeter, the minimum signal level was 56 ± 8 counts, and readings followed a Gaussian distribution. For dosimeters above this signal level, the fractional signal loss due to the reading process has a linear relationship with the calculated signal. At low signal levels (below 20,000 counts), in this OSLD system,calculated signal percent errors increase significantly but otherwise are on average 0.72 ± 0.27%, 0.40 ± 0.19%, 0.33±0.12%, and 0.24±0.07% for 30, 75, 150, and 300 readings. Theoretical calculations of uncertainties showed that annealing before reusing dosimeters allows for dose errors below 1% with as few as 30 readings. Reusing dosimeters multiple times increases the dose errors especially with low numbers of readouts, so theoretically around 300 readings would be necessary to achieve errors around 1% or below in most scenarios. Note that these dose errors do not include the error associated with the signal-to-dose conversion factor.
The 'Framing' (local discoloration along cell edges) was induced by a simple sequential accelerated stress test (consisting of hygrothermal- and UV-stressors) applied to the PV modules with high OTR (oxygen transmission rate) backsheet, irrespective of the inclusion of UV-absorber in poly(ethylene-co-vinyl acetate) (EVA) encapsulant. UV-fluorescence imaging of the PV modules suggests that the spatially-inhomogeneous degradation of EVA material under UV-irradiating conditions is correlated to this 'Framing' indicating an underlying common mechanism. These findings would contribute to the development of test procedures to broadly mimic the actual failures observed in fielded PV.
In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real- world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5- based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.
The use of inconsistent and incorrect terminology in scientific publications contributes to misunderstanding, confusion, and erroneous results in the scientific literature. This issue is of particular importance in x-ray photoelectron spectroscopy (XPS) that is in widespread use for many different purposes by scientists with diverse backgrounds. A set of surface analysis terminology, approved through consensus by international experts, has been developed by International Organization for Standardization Technical Committee TC201 on Surface Chemical Analysis. To encourage wide use, the terminology is accessible at several web sites free of charge. This short overview provides examples to highlight the importance of agreed terminology in eliminating confusion between similar terms. Examples are provided of terms that are commonly misused or confused in the literature. Other examples highlight terminology that provides a common basis for comparing instrument parameters and performance. As science advances, it is important to clarify terminology for describing evolving concepts and developments important to XPS.