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This paper presents a unified and efficient method of solving the inverse kinematics for 6 d.o.f. serial connections of two 3 d.o.f. serial and parallel manipulators.
The Reservoir Temperature Estimator (RTEst) is a multicomponent geothermometry tool for estimating reservoir geochemical parameters including reservoir temperature, CO 2 fugacity, mass of water lost or gained, and a reaction factor. It estimates these parameters and their associated uncertainties by minimizing an objective function that is the weighted sum of squares of the saturation indices of a user-selected set of minerals believed to be equilibrated with the reservoir fluid. RTEst accomplishes these estimates by combining the geochemical modeling capabilities of The Geochemist’s Workbench® with the optimization/parameter estimation resources of PEST®. An included interface aids the user in selecting plausible mineral phases to comprise the objective function and calculates their weighting factors. The working principles of RTEst are described and its efficacy is illustrated by presenting results of its application to various geothermal fields with known conditions. These examples show RTEst can account for the alteration of ascending reservoir fluid by mineral (calcite) re-equilibration with changes in temperature, reconstruct waters with CO 2 loss, correct for the deficit of water and other volatiles (CO 2,gas , H 2 S gas ) from boiling, and determine the amount of mixing of thermal and non-thermal waters. RTEst can use data with basis species below detection limit, missing, or unreliable either by assuming equilibrium with a controlling mineral (fixed-analyte method) or by treating the analyte concentration as an optimization parameter. The inverse of variance weighting method included in RTEst provides more representative results than either the normalization or unit weighting methods. Finally, the ability of RTEst to calculate reservoir temperatures, gas fugacity, and mixing fractions demonstrates its usefulness as a tool for evaluating geothermal systems.
Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here in this paper, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.
I 2 -II-IV-VI 4 and I-III-VI 2 semiconductor nanocrystals have found applications in photovoltaics and other optoelectronic technologies because of their low toxicity and efficient light absorption into the near-infrared. Herein, we report the discovery of a metastable wurtzite-like polymorph of Cu 2 FeSnSe 4 , a member of the I 2 -II-IV-VI 4 family of semi-conductors containing only Earth-abundant metals. Density functional theory calculations on this metastable poly-morph of Cu 2 FeSnSe 4 indicate that it may be a superior semiconductor for solar energy and optoelectronics applications compared to the thermodynamically preferred stannite polymorph, since the former displays a sharper dispersion of energy levels near the conduction band minimum that can enhance electron mobility and suppress hot electron cooling. The experimental optical band gap was measured by the inverse logarithmic derivative method to be direct, in agreement with theory, and in the range of 1.48-1.59 eV. Mechanistic studies reveal that this metastable phase de-rives from intermediate Cu 3 Se 2 nanocrystals that serve as a structural template for the final hexagonal wurtzite-like product. We compare the chemistry of wurtzite-like Cu 2 FeSnSe 4 to the related CuFeSe 2 material system. Our experimental and computational comparisons between Cu 2 FeSnSe 4 and CuFeSe 2 help explain both the crystal chemistry of CuFeSe 2 that prevents it from forming wurtzite-like polymorphs and the essential role of Sn in stabilizing the metastable structure of Cu 2 FeSnSe 4 . Furthermore, this work provides insight into the importance of elemental composition when designing syntheses for metastable materials.
Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range of mechanical properties. Here, we show that the range of time dependent behaviour which can reliably be resolved from the typical method of FD inversion (fitting constitutive FD relations to FD data) is inherently restricted by the experimental parameters: sampling frequency, experiment length, and strain rate. Specifically, we demonstrate that violating these restrictions can result in errors in the values of the parameters of the complex modulus. In the case of complex materials, such as cells, whose behaviour is not specifically understood a priori, the physical sensibility of these parameters cannot be assessed and may lead to falsely attributing a physical phenomenon to an artifact of the violation of these restrictions. We use arguments from information theory to understand the nature of these inconsistencies as well as devise limits on the range of mechanical parameters which can be reliably obtained from FD experiments. The results further demonstrate that the nature of these restrictions depends on the domain (time or frequency) used in the inversion process, with the time domain being far more restrictive than the frequency domain. Lastly, we demonstrate how to use these restrictions to better design FD experiments to target specific timescales of a material's behaviour through our analysis of a polydimethylsiloxane (PDMS) polymer sample.
There is strong astrophysical interest in the structure of 19 F states near the α-decay threshold, as their properties are important for understanding the development of α clustering in the 20 Ne region. The emergence of clustered states, and more generally of states that couple strongly to reaction channels near their decay thresholds, is also a topic of current interest in theoretical nuclear physics. In this work, we determine the parameters of broad low-spin states in 19 F near the α-decay threshold and present a theoretical study of these states. The analysis is restricted to ℓ = 0 and 1 resonances in the α + 15 N system close to the α threshold in 19 F. Excitation functions for 15 N(α,α) elastic scattering were measured using the Thick Target Inverse Kinematics (TTIK) method, and these new data, together with older high–energy-resolution measurements, were analyzed within the R-matrix framework. The nuclear structure of 19 F was calculated using configuration-interaction methods with a recently developed effective interaction Hamiltonian. As a result, we identify a sequence of α-cluster resonances in 19 F and map the distribution of clustering strength, which is relevant for astrophysical reaction modeling and for the theoretical understanding of many-body dynamics and the emergence of clustering in loosely bound or unstable nuclei. Furthermore, the work advances theoretical insight into the origins of clustering and highlights open questions for future theoretical and experimental studies.
This work investigates the nuclear structure of 19 F and 19 Ne, which is important for understanding α clustering in the A = 20 mass region and for astrophysical applications. The only high-resolution, broad angular- and energy-range study of the 19 F resonance structure in α + 15 N scattering was published over 60 years ago, when a detailed analysis of complex excitation functions with overlapping resonances and multiple decay channels was not feasible. We have performed a modern R-matrix analysis of these data to assign spins and determine resonance parameters for levels in 19 F up to an excitation energy of 8.2 MeV. Our R-matrix parameters were successfully tested by fitting recent α + 15 N data obtained with the Thick Target Inverse Kinematics (TTIK) method at 180°. The new 19 F parameters were then used to fit TTIK data for α + 15 O, the mirror resonant reaction. In conclusion, comparison of these isobaric mirror reactions provides valuable insight into the underlying nuclear structure.
Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Nevertheless, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential that dictates the particle number distribution of each protein subunit from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulated an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multicomponent mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of succinate dehydrogenase with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.
Due to the high penetrating power of cosmic-ray muons, they can be used to probe very thick and dense objects. As muons are charged particles, they can be tracked by ionization detectors, determining the position and direction of the muons. With detectors on either side of an object to measure particle direction change, scattering information within the object can be found. This can be used to produce a scattering-intensity image within the object related to density and atomic number. Such imaging is typically performed with a single detector-object orientation, taking advantage of the more intense downward flux of muons, producing planar imaging with some depth-of-field information in the third dimension. Several simulation studies were published with multiorientation tomography, which can form a three-dimensional representation faster than a single-orientation view. In this study, experimental muon-scatter-based tomography was performed using a concrete filled steel drum with several different metal wedges inside, with the drum between detector planes. Data were collected from different detector-object orientations by rotating the steel drum. The data collected from each orientation were combined using two different tomographic methods. A traditional inverse Radon transform approach used for computed tomography and a combination of multiple depth-of-field reconstructions were applied to the data. As cosmic-ray muon flux imaging is rate limited, the imaging techniques were compared for sparse data. Using the combined depth-of-field reconstruction technique, fewer detector-object orientations were needed to reconstruct images that could be used to differentiate the metal wedges.
Unitary matrix representing generalized inverses, proving weak method of steepest descent for least squares solution of equations
FORTRAN subprograms for the solution of systems of linear algebraic equations are described, listed, and evaluated in this report. Procedures considered are direct solution, iteration, and matrix inversion. Both incore methods and those which utilize auxiliary data storage devices are considered. Some of the subroutines evaluated require the entire coefficient matrix to be in core, whereas others account for banding or sparceness of the system. General recommendations relative to equation solving are made, and on the basis of tests, specific subprograms are recommended.
The relaxation method for the inverse solution of the radiative transfer equation is applied in a dual-frequency scheme for the determination of complete vertical temperature profiles in cloudy atmospheres from radiance observations alone, without any additional information related to the expected solutions. The dual-frequency principle employs to advantage a property in the Planck function of the dependence of intensity on frequency. This property leads to the formulation of a new convergence criterion for the selection of cloud-sounding frequencies to be used for reconstructing the clear column radiance from observations made in the presence of a broken cloud layer in all fields of view. The principle is applied to the case of observations in two adjacent or partially overlapping fields of view and to the case of observations in a single field of view. The solutions are illustrated by numerical examples in the dual-frequency ranges of the 4.3 and 15-micron CO2 bands of the terrestrial atmosphere.
An analytic model approach is applied to several simple atmospheric inversion problems. This method gives a sharp determination of aerosol size distribution parameters. It is shown that this analytic approach, together with ground level point sampling data measurements, can be used to infer information on the tropospheric ozone profile.
A method is presented that performs a rapid, reasonably accurate preliminary pitchline optimization of axial gas turbine annular flowpath geometry, as well as an initial estimate of blade profile shapes, given only a minimum of thermodynamic cycle requirements. No geometric parameters need be specified. The following preliminary design data are determined: (1) the optimum flowpath geometry, within mechanical stress limits; (2) initial estimates of cascade blade shapes; (3) predictions of expected turbine performance. The method uses an inverse calculation technique whereby blade profiles are generated by designing channels to yield a specified velocity distribution on the two walls. Velocity distributions are then used to calculate the cascade loss parameters. Calculated blade shapes are used primarily to determine whether the assumed velocity loadings are physically realistic. Model verification is accomplished by comparison of predicted turbine geometry and performance with four existing single stage turbines.
Microwave remote sensing of agricultural crops and forested regions is studied. Long term goals of the research involve modeling vegetation so that radar signatures can be used to infer the parameters which characterize the vegetation and underlying ground. Vegetation is modeled by discrete scatterers viz, leaves, stems, branches and trunks. These are replaced by glossy dielectric discs and cylinders. Rough surfaces are represented by their mean and spectral characteristics. Average scattered power is then calculated by employing discrete random media methodology such as the distorted Born approximation or transport theory. Both coherent and incoherent multiple scattering techniques are explored. Once direct methods are developed, inversion techniques can be investigated.
Recent and potential applications of spectral method computation to incompressible, dissipative magnetohydrodynamics are surveyed. Linear stability problems for one dimensional, quasi-equilibria are approachable through a close analogue of the Orr-Sommerfeld equation. It is likely that for Reynolds-like numbers above certain as-yet-undetermined thresholds, all magnetofluids are turbulent. Four recent effects in MHD turbulence are remarked upon, as they have displayed themselves in spectral method computations: (1) inverse cascades; (2) small-scale intermittent dissipative structures; (3) selective decays of ideal global invariants relative to each other; and (4) anisotropy induced by a mean dc magnetic field. Two more conjectured applications are suggested. All the turbulent processes discussed are sometimes involved in current carrying confined fusion magnetoplasmas and in space plasmas.
SOLVE II program combines any number of sets of normal equations and obtains solution vector and related statistics. Normal equations of square, nonnegative definite matrix form. Program utilizes only upper symmetric portion of matrix. Program uses partitioned Cholesky decomposition method for matrix inversion to accommodate large parameter systems.
Models of the geomagnetic field are, in general, produced from a least-squares fit of the coefficients in a truncated spherical harmonic expansion to the available data. Downward continuation of such models to the core-mantle boundary (CMB) is an unstable process: the results are found to be critically dependent on the choice of truncation level. Modern techniques allow this fundamental difficulty to be circumvented. The method of stochastic inversion is applied to modeling the geomagnetic field. Prior information is introduced by requiring that the spectrum of spherical harmonic coefficients to fall-off in a particular manner which is consistent with the Ohmic heating in the core having a finite lower bound. This results in models with finite errors in the radial field at the CMB. Curves of zero radial field can then be determined and integrals of the radial field over patches on the CMB bounded by these null-flux curves calculated. With the assumption of negligible magnetic diffusion in the core; frozen-flux hypothesis, these integrals are time-invariant.