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The geometry and setup for the n-C3F7I iodine laser are illustrated. The mathematical modeling of this system is described. The chemical kinetics are summarized. A sensitivity analysis was performed on the parameters occurring in the differential equations describing the chemical kinetics.
An experiment was flown on Spacelab to confirm the results of the Cytos 2 experiment on Salyut 7, which found an increase in minimal inhibitory concentration in in-flight cultures, i.e., an increase of antibiotic resistance. The 1 g centrifuge on Biorack was also used to differentiate the effects of cosmic rays and microgravity. The antibiotic sensitivity of bacteria cultivated in vitro during orbital flight was studied. The bacteria was E. coli, the antibiotic was Colistin. An increase of antibiotic resistance is observed. Three explanations are offered: stimulation of bacterial proliferation in space; a relationship between the transport of antibiotics into cells and modifications of cellular envelope permeability; and a combined effect of both phenomena.
This paper discusses the calculation of sensitivities. or derivatives, for optimization problems involving systems governed by differential equations and other state relations. The subject is examined from the point of view of nonlinear programming, beginning with the analytical structure of the first and second derivatives associated with such problems and the relation of these derivatives to implicit differentiation and equality constrained optimization. We also outline an error analysis of the analytical formulae and compare the results with similar results for finite-difference estimates of derivatives. We then attend to an investigation of the nature of the adjoint method and the adjoint equations and their relation to directions of steepest descent. We illustrate the points discussed with an optimization problem in which the variables are the coefficients in a differential operator.
We discuss the numerical computation of sensitivities via the adjoint approach in optimization problems governed by differential equations. We focus on the adjoint problem in its weak form. We show how one can avoid some of the problems with the adjoint approach, such as deriving suitable boundary conditions for the adjoint equation. We discuss the convergence of numerical approximations of the costate computed via the weak form of the adjoint problem and show the significance for the discrete adjoint problem.
Gravitropism in dark-grown hypocotyls of the wild type was compared with a starch-deficient Nicotiana sylvestris mutant (NS 458) to test the effects of starch deficiency on gravity sensing. In a time course of curvature measured using infrared video, the response of the mutant was greatly reduced compared to the wild type; 72 hours after reorientation, curvature was about 10 degrees for NS 458 and about 70 degrees for wild type. In dishes maintained in a vertical orientation, wild-type hypocotyls were predominantly vertical, whereas NS 458 hypocotyls were severely disoriented with about 5 times more orientational variability than wild type. Since the growth rates were equal for both genotypes and phototropic curvature was only slightly inhibited in NS 458, the mutation probably affects gravity perception rather than differential growth. Our data suggest that starch deficiency reduces gravitropic sensitivity more in dark-grown hypocotyls than in dark- or light-grown roots in this mutant and support the hypothesis that amyloplasts function as statoliths in shoots as well as roots.
Two thermography techniques, passive and active, are used to detect damage initiation and progression in a cyclically loaded composite cylinder. The passive thermography tracks damage progression in real time during cyclic loading. Active flash thermography, using a flash tube enclosed within the cylinder, images delaminations in a cylinder under different loads. A differential thermography processing technique eliminates normal material variations and improves sensitivity to and sizing of delaminations. The thermography results were compared to nonimmersion ultrasonic results.
The goal of the present project is to build a multidisciplinary, rapid, robust, and accurate computational tool to optimize wing-mounted propeller designs. The full Farassat’s formulation F1Afor aeroacoustic analysis is implemented in the open-source software SU2.This extension enables the prediction of far-field noise generated by moving sources. The formulation is verified, for a stationary and rotating sphere in a wind tunnel and for a tiltrotor in forward flight, by comparing the acoustic predictions ofSU2 with the predictions computed by NASA’s aeroacoustics code ANOPP2. The algorithmic differentiation capability of SU2 provides discretely consistent, adjoint-based sensitivity analysis for this formulation. The adjoint-based sensitivities are verified through comparison with complex-step sensitivities
An analysis is presented of the accuracy of infrared differential absorption lidar (DIAL) measurements of trace constituents in the atmosphere. Expressions are derived for the signal-to-noise ratios applicable in the detection of a signal from a scattering cell for the cases of single-detection heterodyne detection and direct detection. Results of computer simulations of the expectation value of the measured return for various measurement conditions are presented which illustrate the sensitivities of plume measurements for horizontal range-resolved and vertical content measurements of NH3 in heterodyne and direct detection. Advantages for long-range measurements of the use of rare isotope laser frequencies, tuning by collision broadening, and closely spaced on and off frequencies are pointed out.
Partial differential equation (PDE)-governed inverse problems are fundamental across various scientific and engineering applications; yet they face significant challenges due to nonlinearity, ill-posedness, and sensitivity to noise. Here, we introduce a computational framework, regularization by denoising using diffusion models for partial differential equations (RED-DiffEq), by integrating physics-driven inversion and data-driven learning. RED-DiffEq leverages pretrained diffusion models as a regularization mechanism for PDE-governed inverse problems. We apply RED-DiffEq to solve the full waveform inversion problem in geophysics, a challenging seismic imaging technique that seeks to reconstruct high-resolution subsurface velocity models from seismic measurement data. Our method shows enhanced accuracy and robustness compared to benchmark methods. Additionally, it exhibits strong generalization and domain decomposition capacity, enabling the inversion of more complex velocity models with larger domains than those used in training the diffusion model. Our framework can also be directly applied to diverse PDE-governed inverse problems.
Well-motivated models of dark matter often result in a population of electrons and positrons within galaxies produced through dark matter annihilation — usually in association with gamma rays. As they diffuse through galactic magnetic fields, these e ± produce synchrotron radio emission. The intensity and morphology of this signal depends on the properties of the interstellar medium through which the e ± propagate. Using observations of the Andromeda Galaxy (M31) to construct a model of the gas, magnetic fields, and starlight, we set constraints on dark matter annihilation to b$\overline{b}$ using the morphology of 3.6 cm radio emission. As the emission signal at the center of M31 is very sensitive to the diffusion coefficient and dark matter profile, we base our limits on the differential flux in the region between 0.9 – 6.9 kpc from the center. We exclude annihilation cross sections ≳ 3 × 10 −25 cm 3 /s in the mass range 10 – 500 GeV, with a maximum sensitivity of 7 × 10 −26 cm 3 /s at 20 – 40 GeV. Though these limits are weaker than those found in previous studies of M31, they are robust to variations of the diffusion coefficient.
Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.
Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.
Unweighting, but not denervation, of muscle reportedly "spares" insulin receptors, increasing insulin sensitivity. Unweighting also increases beta-adrenergic responses of carbohydrate metabolism. These differential characteristics were studied further by comparing cyclic adenosine monophosphate (cAMP) accumulation and beta-adrenergic binding in normal and 3-day unweighted or denervated soleus muscle. Submaximal amounts of isoproterenol, a p-agonist, increased cAMP accumulation in vitro and in vivo (by intramuscular (IM) injection) to a greater degree (P less than .05) in unweighted muscles. Forskolin or maximal isoproterenol had similar in vitro effects in all muscles, suggesting increased beta-adrenergic sensitivity following unweighting. Increased sensitivity was confirmed by a greater receptor density (B(sub max)) for iodo-125(-)-pindolol in particulate preparations of unweighted (420 x 10(exp -18) mol/mg muscle) than of control or denervated muscles (285 x 10(exp-18) mol/mg muscle). The three dissociation constant (Kd) values were similar (20.3 to 25.8 pmol/L). Total binding capacity (11.4 fmol/muscle) did not change during 3 days of unweighting, but diminished by 30% with denervation. This result illustrates the "sparing" and loss of receptors, respectively, in these two atrophy models. In diabetic animals, IM injection of insulin diminished CAMP accumulation in the presence of theophylline in unweighted muscle (-66% +/- 2%) more than in controls (-42% +'- 6%, P less than .001). These results show that insulin affects CAMP formation in muscle, and support a greater in vivo insulin response following unweighting atrophy. These various data support a role for lysosomal proteolysis in denervation, but not in unweighting, atrophy.
Rock, concrete, and other engineered materials are often composed of several minerals that change volumetrically in response to variations in the moisture content of the local environment. Such differential shrinkage is caused by varying shrinkage rates between mineral compositions during dehydration. Using both 3D X-ray imaging of geo-architected samples and peridynamic (PD) numerical simulations, we show that the spatial distribution of the clay affects the crack network geometry with distributed clay particles yielding the most complex crack networks and percent damage (99.56%), along with a 60% reduction in material strength. We also demonstrate that crack formation, growth, coalescence, and distribution during dehydration, are controlled by the differential shrinkage rates between a highly shrinkable clay and a homogeneous mortar matrix. Sensitivity tests performed with the PD models show a clay shrinkage parameter of 0.4 yields considerable damage, and reductions in the parameter can result in a significant reduction in fracturing and an increase in material strength. Additionally, isolated clay inclusions induced localized fracturing predominantly due to debonding between the clay and matrix. These insights indicate differential shrinkage is a source of potential failure in natural and engineered barriers used to sequester anthropogenic waste.
The aim of this study is to find a reliable numerical algorithm to calculate thermal design sensitivities of a transient problem with discontinuous derivatives. The thermal system of interest is a transient heat conduction problem related to the curing process of a composite laminate. A logical function which can smoothly approximate the discontinuity is introduced to modify the system equation. Two commonly used methods, the adjoint variable method and the direct differentiation method, are then applied to find the design derivatives of the modified system. The comparisons of numerical results obtained by these two methods demonstrate that the direct differentiation method is a better choice to be used in calculating thermal design sensitivity.
In developing the automated methodologies for simulation-based optimal shape designs, their accuracy, efficiency and practicality are the defining factors to their success. To that end, four recent improvements to the building blocks of such a methodology, intended for more practical design optimization, have been reported. First, in addition to a polynomial-based parameterization, a partial differential equation (PDE) based parameterization was shown to be a practical tool for a number of reasons. Second, an alternative has been incorporated to one of the tedious phases of developing such a methodology, namely, the automatic differentiation of the computer code for the flow analysis in order to generate the sensitivities. Third, by extending the methodology for the thin-layer Navier-Stokes (TLNS) based flow simulations, the more accurate flow physics was made available. However, the computer storage requirement for a shape optimization of a practical configuration with the -fidelity simulations (TLNS and dense-grid based simulations), required substantial computational resources. Therefore, the final improvement reported herein responded to this point by including the alternating-direct-implicit (ADI) based system solver as an alternative to the preconditioned biconjugate (PbCG) and other direct solvers.
Differential muscle responses can be simulated by hypokinetic/hypodynamic (H/H) suspension of rats with complete unloading of the hindlimb muscles. Since mechanism(s) underlying these atrophic effects were not clearly elucidated, experiments were initiated to investigate a possible role for glucocorticoids in the physiological and biochemical responses to H/H. The principal objective was to assess the potential for alterations in peripheral responsiveness to glucocorticoids in response to H/H. Studies have initially focused on the determination of tissue levels of glucocorticoid receptors as one index of hormonal sensitivity at the cellular level. Four hindlimb muscles (soleus, gastrocnemius, plantaris and EDL), previously demonstrated to exhibit differential responses to H/H, were investigated. Receptor levels in other glucocorticoid sensitive tissues (heart, liver, and kidney) were determined. Male rats (180-200g) were suspended for 7 or 14 days, sacrificed by cervical dislocation, and the tissues excised.
Passive thermometry is critically important because most fuels and materials irradiation experiments are not instrumented, and it is necessary to understand the irradiation temperature to properly interpret any post-irradiation examination data, including evolving properties and/or microstructures. The standard passive thermometry approach uses continuous dilatometry to evaluate changes in the instantaneous coefficient of thermal expansion during post-irradiation thermal annealing. This approach has limitations in terms of sample size (minimum length requirements) and the maximum irradiation temperature that can be accurately determined, which is limited by the reduced swelling (and therefore recovery) following higher temperature irradiation and limitations on the furnaces used with push-rod dilatometers. This work evaluates two new proposed techniques for post-irradiation evaluation of passive SiC temperature monitors: differential scanning calorimetry (DSC) and Raman spectroscopy. DSC is an extremely sensitive technique that can be used for any specimen geometry and is capable of higher temperature operation. Raman spectroscopy is similar in that it is a surface technique capable of examining extremely small samples (submillimeter), can be used with a heated stage up to 1,500°C (planned for future work), and is capable of mapping local irradiation temperatures throughout a sample. Existing SiC samples that were previously irradiated over a wide range of temperatures were cut into multiple pieces to allow for annealing studies using multiple different techniques: dilatometry, DSC, and Raman spectroscopy. This approach mitigates the concern that samples analyzed using one technique may have a slightly different irradiation history than those analyzed using a different technique. Recovery was clearly observed during annealing using both DSC and dilatometry. In some cases, a direct comparison could not be made due to some of the DSC runs accidentally including material from multiple specimens and issues with using an alternative DSC sample holder for the highest temperature annealing studies. Nevertheless, one trend was clear: the DSC runs resulted in higher irradiation temperatures compared to those of the dilatometry runs. Part of this could be attributed to the higher temperature ramp rates used during the DSC runs, which are often preferred to reduce noise in the measurements. By comparison, dilatometry has previously been shown to produce better data at lower ramp rates. Future work should further investigate the ideal ramp rate for both techniques to produce consistent results. Additional work should evaluate the best holder material to use for DSC runs exceeding 1,000°C to provide reliable data while preventing interactions between SiC and the holder.