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At least 343 records · Page 19

State-resolved differential and integral cross sections for the Ne + H{sub 2}{sup +} (v = 0–2, j = 0) → NeH{sup +} + H reaction

State-to-state quantum dynamic calculations for the proton transfer reaction Ne + H{sub 2}{sup +} (v = 0–2, j = 0) are performed on the most accurate LZHH potential energy surface, with the product Jacobi coordinate based time-dependent wave packet method including the Coriolis coupling. The J = 0 reaction probabilities for the title reaction agree well with previous results in a wide range of collision energy of 0.2-1.2 eV. Total integral cross sections are in reasonable agreement with the available experiment data. Vibrational excitation of the reactant is much more efficient in enhancing the reaction cross sections than translational and rotational excitation. Total differential cross sections are found to be forward-backward peaked with strong oscillations, which is the indication of the complex-forming mechanism. As the collision energy increases, state-resolved differential cross section changes from forward-backward symmetric peaked to forward scattering biased. This forward bias can be attributed to the larger J partial waves, which makes the reaction like an abstraction process. Differential cross sections summed over two different sets of J partial waves for the v = 0 reaction at the collision energy of 1.2 eV are plotted to illustrate the importance of large J partial waves in the forward bias of the differential cross sections.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A differential equation for Lerch's transcendent and associated symmetric operators in Hilbert space

The function Ψ(x,y,s)=e{sup iy}Φ(−e{sup iy},s,x), where Φ(z,s,v) is Lerch's transcendent, satisfies the following two-dimensional formally self-adjoint second-order hyperbolic differential equation, where s=1/2+iλ. The corresponding differential expression determines a densely defined symmetric operator (the minimal operator) on the Hilbert space L{sub 2}(Π), where Π=(0,1)×(0,2π). We obtain a description of the domains of definition of some symmetric extensions of the minimal operator. We show that formal solutions of the eigenvalue problem for these symmetric extensions are represented by functional series whose structure resembles that of the Fourier series of Ψ(x,y,s). We discuss sufficient conditions for these formal solutions to be eigenfunctions of the resulting symmetric differential operators. We also demonstrate a close relationship between the spectral properties of these symmetric differential operators and the distribution of the zeros of some special analytic functions analogous to the Riemann zeta function. Bibliography: 15 titles.

97 MATHEMATICS AND COMPUTING↗

Efficient quantum algorithm for dissipative nonlinear differential equations

Significance Nonlinear differential equations appear in many domains and are notoriously difficult to solve. Whereas previous quantum algorithms for general nonlinear differential equations have complexity exponential in the evolution time, we give the first quantum algorithm for dissipative nonlinear differential equations that is efficient provided the dissipation is sufficiently strong relative to nonlinear and forcing terms and the solution does not decay too rapidly. We also establish a lower bound showing that differential equations with sufficiently weak dissipation have worst-case complexity exponential in time, giving an almost tight classification of the quantum complexity of simulating nonlinear dynamics. Furthermore, numerical results for the Burgers equation suggest that our algorithm may potentially address complex nonlinear phenomena even in regimes with weaker dissipation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme

Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.

97 MATHEMATICS AND COMPUTING↗

Validation and Evaluation Uses of Quasi-Differential High-Energy Scattering Data [Slides]

This presentation covers the validation and evaluation uses of quasi-differential high-energy scattering data. This lecture covers motivation behind this work. The lecture then touches on quasi-differential for evaluation, quasi-differential for covariance, and quasi-differential for validation. The presentation concludes with a summary.

97 MATHEMATICS AND COMPUTING↗

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

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.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources

Conditional probability functions are commonly used for source identification purposes in air pollution studies. CBPF (conditional bivariate probability function) categorizes the probability of high concentrations being observed at a location by wind direction/speed and investigate the directionality of local sources. PSCF (potential source contribution function), a trajectory-ensemble method, identifies the source regions most likely to be associated with high measured concentrations. However, these techniques do not allow the direct identification of areas where changes in emissions have occurred. This study presents an extension of conditional probability methods in which the differences between conditional probability values for temporally different sets of data can be used to explore changes in emissions from source locations. The differential CBPF and differential PSCF were tested using a long-term series of air quality data (12 years; 2005/2016) collected in Rochester, NY. The probability functions were computed for each of 4 periods that represent known changes in emissions. Correlation analyses were also performed on the results to find pollutants undergoing similar changes in local and regional sources. The differential probability functions permitted the identification of major changes in local and regional emission location. In Rochester, changes in local air pollution were related to the shutdown of a large coal power plant (SO 2 ) and to the abatement measures applied to road and off-road traffic (primary pollutants). The concurrent effects of these changes in local emissions were also linked to reduced concentrations of nucleation mode particles. Changes in regional source areas were related to the decreases in secondary inorganic aerosol and organic carbon. As a result, the differential probabilities for sulfate, nitrate, and organic aerosol were consistent with differences in the available National Emission Inventory annual emission values. Changes in the source areas of black carbon and PM2.5 mass concentrations were highly correlated.

54 ENVIRONMENTAL SCIENCES↗

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning.

This paper describes an efficient reverse-mode differentiation algorithm for contraction operations of tensor networks that may have arbitrary and unconventional network topologies. The approach leverages the tensor contraction tree of Evenbly and Pfeifer (2014), which provides an instruction set for the contraction sequence of a network. We show that this tree can be efficiently leveraged for differentiation of a full tensor network contraction using a recursive scheme that exploits (1) the bilinear property of contraction and (2) the property that trees have single path from root to leaves. While differentiation of tensor-tensor contraction is already possible in most automatic differentiation packages, we show that exploiting these two additional properties in the specific context of contraction sequences can improve efficiency. Following a description of the algorithm and computational complexity analysis, we investigate its utility for gradient-based supervised learning for low-rank function recovery and for fitting real-world unstructured datasets. We demonstrate improved performance over alternating least-squares optimization approaches and the capability to handle heterogeneous and arbitrary tensor network formats. When compared to alternating minimization algorithms, we find that the gradient-based approach requires a smaller oversampling ratio (number of samples compared to number model parameters) for recovery. This increased efficiency extends to fitting unstructured data of varying dimensionality and when employing a variety of tensor network formats. Here, we show improved learning using the hierarchical Tucker method over the tensor-train in high-dimensional settings on a number of benchmark problems.

97 MATHEMATICS AND COMPUTING↗

Osteogenic differentiation of human adipose derived stem cells on chemically crosslinked carbon nanomaterial coatings

Abstract Carbon nanomaterial coatings have been widely investigated for many biomedical applications including bone tissue engineering. Current methods to fabricate carbon nanomaterial coatings are limited by specific substrate requirements and the lack of strong bonds between the nanomaterials. Furthermore, few studies compare the effect of carbon nanoparticle architecture on stem cell differentiation and mineralization for osteogenic differentiation. Herein, we report a study comparing chemically crosslinked carbon nanotubes (of various diameters), graphene nanoplatelets, and graphene nanoribbons coatings for adipose derived stem cell differentiation toward an osteogenic lineage. We observed greatest autodeposition of calcium on graphene nanoribbon surfaces, while multiwalled carbon nanotubes of high diameter had the greatest influence on stem cell fate (by alkaline phosphatase activity, calcium deposition, and osteocalcin measurements). Studies indicate the cause for multiwalled carbon nanotube related stem cell differentiation, may be related to early timepoint toxicity as indicated by lactose dehydrogenase release. These results indicate suggestions for orthopedic tissue engineering applications for carbon nanomaterial coatings. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1189–1199, 2018.

Patel, Sunny C.↗

Near Activation and Differential Activation in Enzymatic Reactions

A framework that introduces the concept of near-activation complexation and differential free energy of activation is presented to extend the capabilities of the classical transition-state theory in enzymatic reactions. In our approach, reaching a near-equilibrium energy level is assumed to be necessary for complexation near the activation point, whereas an additional differential energy level is required for the near-equilibrium complex to activate and release reaction products. Integration of these energy levels within the transition-state theory explains the thermodynamic nature of the Michaelis–Menten (affinity) constant and its relationship with the rate constant under the quasi-steady-state assumption. The concepts of near-activation complexation and differential free energy of activation were tested on 57 independent experiments of NH$+\atop{4}$ and NO$-\atop{3}$ uptake by various microalgae and bacteria at temperatures ranging between 1 and 45°C. Results showed that near-activation complexation was always favored, whereas the differential energy of activation led to an apparent energy barrier consistent with earlier observations. Temperature affected all energy levels within this framework but did not alter substantially their thermodynamic features. Finally, the approach (1) mutually links the thermodynamics and kinetics of Michaelis–Menten and rate constants with a mathematical expression; (2) describes the likelihood of formation of sub-, super-, and activated complexes; and (3) shows direction and thermodynamic likelihood of each reaction branch within the transition state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Combination and interpretation of differential Higgs boson production cross sections in proton-proton collisions at $ \sqrt{s}=13 $ TeV

Precision measurements of Higgs boson differential production cross sections are a key tool to probe the properties of the Higgs boson and test the standard model. New physics can affect both Higgs boson production and decay, leading to deviations from the distributions that are expected in the standard model. In this paper, combined measurements of differential spectra in a fiducial region matching the experimental selections are performed, based on analyses of four Higgs boson decay channels (γγ, ZZ$^{(*)}$, WW$^{(*)}$, and ττ) using proton-proton collision data recorded with the CMS detector at $ \sqrt{s}=13 $ TeV, corresponding to an integrated luminosity of 138 fb$^{−1}$. The differential measurements are extrapolated to the full phase space and combined to provide the differential spectra. A measurement of the total Higgs boson production cross section is also performed using the γγ and ZZ decay channels, with a result of $ {53.4}_{-2.9}^{+2.9}{\left(\textrm{stat}\right)}_{-1.8}^{+1.9}\left(\textrm{syst}\right) $ pb, consistent with the standard model prediction of 55.6 ± 2.5 pb. The fiducial measurements are used to compute limits on Higgs boson couplings using the κ-framework and the SM effective field theory.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurement of the total and differential cross-sections of $t\bar{t}$$W$ production in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

Measurements of inclusive and differential production cross-sections of a top-quark-top-antiquark pair in association with a W boson ($t\bar{t}$$W$) are presented. They are performed by targeting final states with two same-sign or three isolated leptons (electrons or muons) and are based on $\sqrt{s}$ = 13 TeV proton-proton collision data with an integrated luminosity of 140 fb −1 , recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider. The inclusive $t\bar{t}$$W$ production cross-section is measured to be 880 ± 80 fb, compared to a reference theoretical prediction of 745 ± 50 (scale) ± 13 (2-loop approx.) ± 19 (PDF, α s ) fb. Differential cross-section measurements characterise this process in detail for the first time. Several particle-level observables are compared with a variety of theoretical predictions, which generally agree well with the normalised differential cross-section results. Additionally, the relative charge asymmetry of $t\bar{t}$$W$ + and $t\bar{t}$$W$ – is measured inclusively to be $A$$^{rel}_{C}$ = 0.33 ± 0.05, in very good agreement with the theoretical prediction of 0.322 ± 0.003 (scale) ± 0.007 (PDF), as well as differentially.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Toward multi-differential cross sections: measuring two angularities on a single jet

The analytic study of differential cross sections in QCD has typically focused on individual observables, such as mass or thrust, to great success. Here, we present a first study of double differential jet cross sections considering two recoil-free angularities measured on a single jet. By analyzing the phase space defined by the two angularities and using methods from soft-collinear effective theory, we prove that the double differential cross section factorizes at the boundaries of the phase space. We also show that the cross section in the bulk of the phase space cannot be factorized using only soft and collinear modes, excluding the possibility of a global factorization theorem in soft-collinear effective theory. Nevertheless, we are able to define a simple interpolation procedure that smoothly connects the factorization theorem at one boundary to the other. We present an explicit example of this at next-to-leading logarithmic accuracy and show that the interpolation is unique up to α s 4 order in the exponent of the cross section, under reasonable assumptions. This is evidence that the interpolation is sufficiently robust to account for all logarithms in the bulk of phase space to the accuracy of the boundary factorization theorem. We compare our analytic calculation of the double differential cross section to Monte Carlo simulation and find qualitative agreement. Because our arguments rely on general structures of the phase space, we expect that much of our analysis would be relevant for the study of phenomenologically well-motivated observables, such as N -subjettiness, energy correlation functions, and planar flow.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurements of the S-wave fraction in $B^0$ → $K^+π^-μ^+μ^-$ decays and the $B^0 → K^*(892)^0μ^+μ^-$ differential branching fraction

A measurement of the differential branching fraction of the decay $B^0 → K^*(892)^0$ $μ^+μ^-$ is presented together with a determination of the S-wave fraction of the $K^+π^-$ system in the decay $B^0 → K^+π^-μ^+μ^-$. The analysis is based on pp-collision data corresponding to an integrated luminosity of 3 fb -1 collected with the LHCb experiment. The measurements are made in bins of the invariant mass squared of the dimuon system, q 2 . Precise theoretical predictions for the differential branching fraction of $B^0 → K^*(892)^0 μ^+μ^-$ decays are available for the q 2 region 1.1 < q 2 < 6.0 GeV 2 / c 4 . In this q 2 region, for the $K^+π^-$ invariant mass range 796 < $m_{K_π}$ < 996 MeV/c 2 , the S-wave fraction of the $K^+π^-$ system in $B^0 → K^+π^-μ^+μ^-$ decays is found to be F S = 0.101 &#x00B1;<!-- ± --> 0.017 ( s t a t ) &#x00B1;<!-- ± --> 0.009 ( s y s t ) , and the differential branching fraction of $B^0 → K^*(892)^0 μ^+μ^-$ decays is determined to be d B / d q 2 = ( 0. 392 &#x2212;<!-- - --> 0.019 + 0.020 ( s t a t ) &#x00B1;<!-- ± --> 0.010 ( s y s t ) &#x00B1;<!-- ± --> 0.027 ( n o r m ) ) &#x00D7;<!-- × --> 1 0 &#x2212;<!-- - --> 7 c 4 / G e V 2 . The differential branching fraction measurements presented are the most precise to date and are found to be in agreement with Standard Model predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Nukbone® promotes proliferation and osteoblastic differentiation of mesenchymal stem cells from human amniotic membrane

Highlights: •Nukbone showed to be a good scaffold for adhesion, proliferation and differentiation of stem cells. •Nukbone induced osteoblastic differentiation of human mesenchymal stem cells. •Results showed that Nukbone offer an excellent option for bone tissue regeneration due to properties. -- Abstract: Bovine bone matrix Nukbone® (NKB) is an osseous tissue-engineering biomaterial that retains its mineral and organic phases and its natural bone topography and has been used as a xenoimplant for bone regeneration in clinics. There are not studies regarding its influence of the NKB in the behavior of cells during the repairing processes. The aim of this research is to demonstrate that NKB has an osteoinductive effect in human mesenchymal stem cells from amniotic membrane (AM-hMSCs). Results indicated that NKB favors the AM-hMSCs adhesion and proliferation up to 7 days in culture as shown by the scanning electron microscopy and proliferation measures using an alamarBlue assay. Furthermore, as demonstrated by reverse transcriptase polymerase chain reaction, it was detected that two gene expression markers of osteoblastic differentiation: the core binding factor and osteocalcin were higher for AM-hMSCs co-cultured with NKB in comparison with cultivated cells in absence of the biomaterial. As the results indicate, NKB possess the capability for inducing successfully the osteoblastic differentiation of AM-hMSC, so that, NKB is an excellent xenoimplant option for repairing bone tissue defects.

60 APPLIED LIFE SCIENCES↗

GDNF facilitates differentiation of the adult dentate gyrus-derived neural precursor cells into astrocytes via STAT3

Highlights: •GDNF has no effect on ADP proliferation and apoptosis. •GDNF increases ADP differentiation into astrocyte. •A specific inhibitor of STAT3 decreases the astrogliogenic effect of GDNF. •STAT3 knockdown by lentiviral shRNA vector also decreases the astrogliogenic effect of GDNF. •GDNF increases the phosphorylation of STAT3. -- Abstract: While the pro-neurogenic actions of antidepressants in the adult hippocampal dentate gyrus (DG) are thought to be one of the mechanisms through which antidepressants exert their therapeutic actions, antidepressants do not increase proliferation of neural precursor cells derived from the adult DG. Because previous studies showed that antidepressants increase the expression and secretion of glial cell line-derived neurotrophic factor (GDNF) in C6 glioma cells derived from rat astrocytes and GDNF increases neurogenesis in adult DG in vivo, we investigated the effects of GDNF on the proliferation, differentiation and apoptosis of cultured neural precursor cells derived from the adult DG. Data showed that GDNF facilitated the differentiation of neural precursor cells into astrocytes but had no effect on their proliferation or apoptosis. Moreover, GDNF increased the phosphorylation of STAT3, and both a specific inhibitor of STAT3 and lentiviral shRNA for STAT3 decreased their differentiation into astrocytes. Taken together, our findings suggest that GDNF facilitates astrogliogenesis from neural precursor cells in adult DG through activating STAT3 and that this action might indirectly affect neurogenesis.

60 APPLIED LIFE SCIENCES↗

NGF induces adult stem Leydig cells to proliferate and differentiate during Leydig cell regeneration

Highlights: •Nerve growth factor has shown significant changes on mRNA levels during Adult Leydig cells regeneration. •We established the organ culture model of rat seminiferous tubules with ethane dimethyl sulphonate (EDS) treatment. •Nerve growth factor has shown proliferation and differentiation-promoting effects on Adult stem Leydig cells. •Nerve growth factor induces progenitor Leydig cells to proliferate and differentiate and immature Leydig cells to proliferate. -- Abstract: Nerve growth factor (NGF) has been reported to be involved in male reproductive physiology. However, few reports have described the activity of NGF during Leydig cell development. The objective of the present study was to examine the role of NGF during stem-Leydig-cell (SLC) regeneration. We investigated the effects of NGF on Leydig-cell (LC) regeneration by measuring mRNA levels in the adult rat testis after ethane dimethanesulfonate (EDS) treatment. Furthermore, we used the established organ culture model of rat seminiferous tubules to examine the regulation of NGF during SLC proliferation and differentiation using EdU staining, real-time PCR and western blotting. Progenitor Leydig cells (PLCs) and immature Leydig cells (ILCs) were also used to investigate the effects of NGF on LCs at different developmental stages. NGF mRNA levels changed significantly during Leydig-cell regeneration in vivo. In vitro, NGF significantly promoted the proliferation of stem Leydig cells and also induced steroidogenic enzyme gene expression and 3β-HSD protein expression. The data from PLCs and ILCs showed that NGF could increase Cyclin D1 and Hsd 17b3 mRNA levels in PLCs and Cyclin D1 mRNA levels in ILCs. These results indicate that NGF may play an important role during LC regeneration by regulating the proliferation and differentiation of LCs at different developmental stages, from SLCs to PLCs and from PLCs to ILCs. The discovery of this effect of NGF on Leydig cells will provide useful information for developing new potential therapies for PADAM (Partial Androgen Deficiency in the Aging Male)

60 APPLIED LIFE SCIENCES↗

Actin-associated protein palladin is required for migration behavior and differentiation potential of C2C12 myoblast cells

Highlights: • Palladin is involved in myogenesis in vitro. • Palladin knockdown by siRNA increases myoblast proliferation, viability and differentiation. • Palladin knockdown decreases C2C12 myoblast migration ability. - Abstract: The actin-associated protein palladin has been shown to be involved in differentiation processes in non-muscle tissues. However, but its function in skeletal muscle has rarely been studied. Palladin plays important roles in the regulation of diverse actin-related signaling in a number of cell types. Since intact actin-cytoskeletal remodeling is necessary for myogenesis, in the present study, we pursue to investigate the role of actin-associated palladin in skeletal muscle differentiation. Palladin in C2C12 myoblasts is knocked-down using specific small interfering RNA (siRNA). The results show that down-regulation of palladin decreased migratory activity of mouse skeletal muscle C2C12 myoblasts. Furthermore, the depletion of palladin enhances C2C12 vitality and proliferation. Of note, the loss of palladin promotes C2C12 to express the myosin heavy chain, suggesting that palladin has a role in the modulation of C2C12 differentiation. It is thus proposed that palladin is required for normal C2C12 myogenesis in vitro.

60 APPLIED LIFE SCIENCES↗