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We develop a method to estimate the sum of conditional means of a sequence of random variables given our access to only a subsequence by spot-checking. The method works with non-independent-and-identically-distributed (non-i.i.d.) random variables and can be applied for certifying ongoing quantum information tasks.
Calculating charge transfer (CT) excitation energies with high accuracy and low computational cost is a challenging task. Kohn-Sham density functional theory (KS-DFT), due to its efficiency and accuracy, has achieved great success in describing ground state problems. To extend to excited state problems, our group recently demonstrated an approach with good numerical results to calculate low-lying and Rydberg excitation energies of an N-electron system from a ground state KS or generalized KS calculations of an (N-1)-electron system via its orbital energies. In the present work, we explore further the same methodology to describe CT excitations. Numerical results from this work show that performance of conventional density functional approximations (DFAs) is not as good for CT excitations as for other excitations due to the delocalization error. Applying localized orbital scaling correction (LOSC) to conventional DFAs, a recently developed method in our group to effectively reduce the delocalization error, can improve the results. Overall, the performance of this methodology is better than time dependent DFT (TDDFT) with conventional DFAs. In addition, it shows that results from LOSC-DFAs in this method reach similar accuracy to other methods, such as ΔSCF, G 0 W 0 with Bethe-Salpeter equations, particle-particle random phase approximation, and even high-level wavefunction methods like CC2. Our analysis shows that the correct 1/R trend for CT excitation can be captured from LOSC-DFA calculations, stressing that the application of DFAs with the minimal delocalization error is essential within this methodology. This work provides an efficient way to calculate CT excitation energies from ground state DFT.
Purpose: To provide confirmatory evidence on the use of sulfasalazine to reduce enteritis during pelvic radiation therapy (RT), following 2 prior single-institution trials suggestive that benefit existed. Methods and Materials: A multi-institution, randomized, double-blind, placebo-controlled phase 3 trial was designed to assess the efficacy of sulfasalazine versus placebo in the treatment of RT-related enteritis during RT including the posterior pelvis (45.0-53.5 Gy) and conducted through a multicenter national cooperative research alliance. Patients received 1000 mg of sulfasalazine or placebo orally twice daily during and for 4 weeks after RT. The primary endpoint was maximum severity of diarrhea (Common Terminology Criteria for Adverse Events version 4.0). Toxicity and bowel function were assessed by providers through a self-administered bowel function questionnaire taken weekly during RT and for 6 weeks afterward. Results: Eighty-seven patients were enrolled in the trial between April 29, 2011, and May 13, 2013, with evenly distributed baseline factors. At the time of a planned interim toxicity analysis, more patients with grade ≥3 diarrhea received sulfasalazine than received placebo (29% vs 11%, P=.04). A futility analysis showed that trial continuation would be unlikely to yield a positive result, and a research board recommended halting study treatment. Final analysis of the primary endpoint showed no significant difference in maximum diarrhea severity between the sulfasalazine and placebo arms (P=.41). Conclusions: Sulfasalazine does not reduce enteritis during pelvic RT and may be associated with a higher risk of adverse events than placebo. This trial illustrates the importance of confirmatory phase 3 trials in the evaluation of symptom-control agents.
Monte Carlo (MC) numerical simulation is carried out for the intensity of multiply backscattered radiation as a function of the source–receiver distance for the Henyey–Greenstein (HG) and Rayleigh–Gans (RG) anisotropic scattering models. It is found that, in spite of the standard diffusive regime assumption on the universality of the description of scattering in terms of the extinction coefficient and the transport length, the multiple scattering intensity depends on the form of the phase function and on the degree of anisotropy even for systems with identical extinction coefficients and transport lengths. In this case, the results obtained by the RG phase function are more sensitive to the scattering ansisotropy than those obtained by the HG function.
The locally self-consistent multiple scattering (LSMS) method can perform efficient first-principles calculations of systems with a large number of atoms. Here, in this paper, we combine the Kubo-Greenwood equation with LSMS, enabling us to calculate the first-principles residual resistivity of large systems. This has been implemented in the open-source code LSMS . We apply this method to selected pure elements and binary random alloys. The results compare well with experiment, and with values obtained from a first-principles effective medium technique (the Korringa-Kohn-Rostoker coherent potential approximation). We discuss future applications of this method to complex systems where other methods are not applicable.
Solid Fueled Thorium Molten Salt Reactor (TMSR-SF1) is Chinese Fluoride-salt-cooled High Temperature Reactor (FHR) designed by the TMSR group. The construction of this test reactor is expected in the near years. Using TRISO particle based pebble fuel leads to various challenges in neutronics modeling, such as double heterogeneity effect, preserving exact number of particles, their random distributions in pebble, and etc. For the purpose of verifying the calculations, benchmark study of infinite lattice of pebble fuel within FLiBe coolant for the TMSR-SF1 design has been performed. Back-to-back comparisons have been made between TMSR and MIT researchers using different Monte-Carlo based codes. Very good agreements have been found and the results quantitatively evaluate the neutronic effect of various modeling approaches. In addition, sensitivity study has been carried out for the reference pebble-bed fuel lattice. (authors)
This thesis studies drop durability of electronic assemblies when the acceleration vector is oriented at 45° to the out-of-plane direction of the circuit card. The off-axis drop tests are accomplished with a skewed fixture and are conducted as a proxy for multiaxial drop testing. Advanced shock testing and vibration test methods have been developed over the last few decades to better represent real-world field environments during ground-based laboratory testing. However, many of these test methods require expensive and specialized equipment not available in most laboratories. An alternative approach for approximating simultaneous loading along multiple axes on conventional equipment utilizes skewed fixtures which have seen use in off-axis random vibration and drop impact testing. These methods generally rely on the conversion of a uniaxial input load from the test equipment (using a uniaxial drop tower or shaker) into a multiaxial load when resolved in the reference frame of the test article (mounted on a skewed fixture). Skewed fixture design is presented and recommendations for conducting skewed angle drop testing are introduced based on local measurements along the skewed face of the fixture to accurately monitor the impact event. Characterization tests were performed with a skewed fixture, at simultaneous acceleration loads from 500 to 3,000 g in two (in-plane and out-of-plane) directions, while meeting standard time domain tolerances. Upon experimental characterization, drop shock durability tests were conducted on a printed circuit assembly (PCA). Mean drops-to-failure were measured and quantified with Weibull statistics. Dominant solder joint failure modes were identified via failure analysis. Prior work on inclined angle impact testing is limited, and the majority of solder joint interconnect level fatigue studies are conducted considering perpendicular loading normal the circuit card. Low-cycle fatigue curves are generated based on plastic strain and plastic work density within the solder joint. A multiscale nonlinear finite element model is used to relate board-level flexure to solder joint interconnect level plastic strain. A high strain rate solder constitutive model allows for accurate modeling of solder plasticity resulting from high-impact drop shock. Fatigue parameters are computed from the Coffin-Manson relation and Palmgren-Miner damage accumulation. This work serves to apply established low-cycle fatigue methods for conventional drop shock loading (impact normal to circuit card) to non-perpendicular loading with a skewed fixture.
Posiform planting is a method for constructing QUBO instances with a unique planted solution that can be tailored to arbitrary connectivity graphs. In this study we investigate making posiform planted QUBOs computationally harder by fusing many smaller random Ising models, whose global minimum is computed classically, with posiform planted QUBOs. The unique ground state of the resulting QUBO is the concatenation of (exactly one of) the ground states of each smaller problem. Our method generates QUBO instances that have a unique solution, are native to the hardware graph, and have tunable computational hardness. We use our QUBOs to benchmark three D-Wave quantum annealing processors (with 563–5627 qubits), and compare them against simulated annealing and Gurobi. Surprisingly, we find that the D-Wave ground state sampling success rate is not dependent on the glued random QUBO size, and that some QUBO classes are solved at high success rates at short annealing times on the Zephyr processors.
Neuromorphic computing, which aims to replicate the computational structure and architecture of the brain in synthetic hardware, has typically focused on artificial intelligence applications. What is less explored is whether such brain-inspired hardware can provide value beyond cognitive tasks. Here we show that the high degree of parallelism and configurability of spiking neuromorphic architectures makes them well suited to implement random walks via discrete-time Markov chains. Overall, these random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Using IBM’s TrueNorth and Intel’s Loihi neuromorphic computing platforms, we show that our neuromorphic computing algorithm for generating random walk approximations of diffusion offers advantages in energy-efficient computation compared with conventional approaches. We also show that our neuromorphic computing algorithm can be extended to more sophisticated jump-diffusion processes that are useful in a range of applications, including financial economics, particle physics and machine learning.
Purpose: To introduce and evaluate an increment matrix approach (IMA) describing the signal statistics of energy-selective photon counting detectors including spatial–spectral correlations between energy bins of neighboring detector pixels. The importance of the occurring correlations for image-based material decomposition is studied. Methods: An IMA describing the counter increase patterns in a photon counting detector is proposed. This IMA has the potential to decrease the number of required random numbers compared to Monte Carlo simulations by pursuing an approach based on convolutions. To validate and demonstrate the IMA, an approximate semirealistic detector model is provided, simulating a photon counting detector in a simplified manner, e.g., by neglecting count rate-dependent effects. In this way, the spatial–spectral correlations on the detector level are obtained and fed into the IMA. The importance of these correlations in reconstructed energy bin images and the corresponding detector performance in image-based material decomposition is evaluated using a statistically optimal decomposition algorithm. Results: The results of IMA together with the semirealistic detector model were compared to other models and measurements using the spectral response and the energy bin sensitivity, finding a good agreement. Correlations between the different reconstructed energy bin images could be observed, and turned out to be of weak nature. These correlations were found to be not relevant in image-based material decomposition. An even simpler simulation procedure based on the energy bin sensitivity was tested instead and yielded similar results for the image-based material decomposition task, as long as the fact that one incident photon can increase multiple counters across neighboring detector pixels is taken into account. Conclusions: The IMA is computationally efficient as it required about 10{sup 2} random numbers per ray incident on a detector pixel instead of an estimated 10{sup 8} random numbers per ray as Monte Carlo approaches would need. The spatial–spectral correlations as described by IMA are not important for the studied image-based material decomposition task. Respecting the absolute photon counts and thus the multiple counter increases by a single x-ray photon, the same material decomposition performance could be obtained with a simpler detector description using the energy bin sensitivity.
Introduction: Lead exposure in shooting ranges has been under scrutiny for decades, but no information in this regard is available in respect of African settings, and in South Africa specifically. The aim of this study was to determine the blood lead levels in the users of randomly selected private shooting ranges in South Africa's Gauteng province. Methods: An analytical cross sectional study was conducted, with participants recruited from four randomly selected shooting ranges and three archery ranges as a comparator group. Results: A total of 118 (87 shooters and 31 archers) were included in the analysis. Shooters had significantly higher blood lead levels (BLL) compared to archers with 36/85 (42.4%) of shooters versus 2/34 (5.9%) of archers found to have a BLL ≥10 μg/dl (p<0.001). Conclusion: Shooting ranges may constitute an import site of elevated exposure to lead. Improved ventilation, low levels of awareness of lead hazards, poor housekeeping, and inadequate personal hygiene facilities and practices at South African shooting ranges need urgent attention. - Highlights: • This is the first study, to our knowledge, of lead exposure in shooting ranges in an African setting. • This study indicates highly elevated lead exposure amongst the users of certain private shooting ranges in South Africa. • Lead exposure may be a serious, yet under-studied, source of adult lead exposure in South Africa, and possibly elsewhere on the African continent.
During the early period of a supercritical system, the growth of fission chains shows a significant stochastic feature. In multiplying medium, it is a probability issue of fail-pass type for a single neutron to sponsor a divergent fission chain, and the probability of success is named as single neutron initiation probability. Meanwhile, in the presence of a weak neutron source, the time at which fission burst occurs also exhibits a stochastic nature, which can be observed on experiments of pulsed reactors such as GODIVA I and GODIVA II. The initiation probability and burst waiting time reflect the dynamic feature of a supercritical system, which are very important for nuclear criticality safety concerns. It is desirable that a supercritical system has an initiation probability very close to zero and releases limited energy in case of criticality accidents. The stochastic process of neutron population evolution in early age in a multiplying system is governed by Bell's equation in the form of an adjoint neutron transport equation with a non-linear fission term. In order to solve this stochastic neutron transport equation, the deterministic method discretizing the phase space was proposed long ago and has been implemented in several SN codes such as Partisn, Ardra and PANDA. However, the deterministic method is of limited usage, due to its poor ability to precisely describe real-world 3D complex geometries and continuous energy neutron spectrum. Recently, Monte Carlo methods have gradually gained attention for solving Bell's equation. Greenman from LLNL proposed an analog Monte Carlo forward-transport method for calculating initiation probability in 2007. He implemented this method by introducing the maximum age parameter, the time sub-interval and the neutron progenitor tag into Mercury in time-dependent fixed-source simulation mode. This method is only suitable for initiation probability calculations, however not for analyzing the randomness of burst waiting time. Later, GANG in 2011 developed the GSMP (Generalized Semi-Markov Process method) Monte Carlo method according to Bell's equation. While the GSMP method, which can deal with both initiation probability and burst waiting time, is flexible and powerful for zero-dimensional problems, it is not favorable when extended to 3D and continuous energy problems due to severe lack of computational efficiency. In 2015, a direct Monte Carlo simulation method for stochastic systems problems was implemented in MCATK, yet this method has not been applied specifically to the burst waiting time. In this work, a new and efficient 3D Monte Carlo direct simulation method for calculating both the initiation probability and the burst waiting time is developed. By taking random factors during evolvement of fission chains into account in dynamic Monte Carlo simulation, this new method has the potential of simulating the whole process from source neutron injection to exponential growth of the neutron population, and finally to extinction of the neutron pulse. This capability is useful in criticality accidents evaluation and pulsed reactor experiments explanation. (authors)
Purpose: Proton therapy is more sensitive to uncertainties than photon treatments due to protons’ finite range depending on the tissue density. Worst case scenario (WCS) method originally proposed by Lomax has been adopted in our institute for robustness analysis of IMPT plans. This work demonstrates that WCS method is sufficient enough to take into account of the uncertainties which could be encountered during daily clinical treatment. Methods: A fast and approximate dose calculation method is developed to calculate the dose for the IMPT plan under different setup and range uncertainties. Effects of two factors, inversed square factor and range uncertainty, are explored. WCS robustness analysis method was evaluated using this fast dose calculation method. The worst-case dose distribution was generated by shifting isocenter by 3 mm along x,y and z directions and modifying stopping power ratios by ±3.5%. 1000 randomly perturbed cases in proton range and x, yz directions were created and the corresponding dose distributions were calculated using this approximated method. DVH and dosimetric indexes of all 1000 perturbed cases were calculated and compared with the result using worst case scenario method. Results: The distributions of dosimetric indexes of 1000 perturbed cases were generated and compared with the results using worst case scenario. For D95 of CTVs, at least 97% of 1000 perturbed cases show higher values than the one of worst case scenario. For D5 of CTVs, at least 98% of perturbed cases have lower values than worst case scenario. Conclusion: By extensively calculating the dose distributions under random uncertainties, WCS method was verified to be reliable in evaluating the robustness level of MFO IMPT plans of H&N patients. The extensively sampling approach using fast approximated method could be used in evaluating the effects of different factors on the robustness level of IMPT plans in the future.
PurposeTo compare clinical and urodynamic results of transurethral resection of the prostate (TURP) to original and PErFecTED prostate artery embolization (PAE) methods for benign prostatic hyperplasia.MethodsWe prospectively randomized 30 patients to receive TURP or original PAE (oPAE) and compared them to a cohort of patients treated by PErFecTED PAE, with a minimum of 1-year follow-up. Patients were assessed for urodynamic parameters, prostate volume, international prostate symptom score (IPSS), and quality of life (QoL).ResultsAll groups were comparable for all pre-treatment parameters except bladder contractility and peak urine flow rate (Q{sub max}), both of which were significantly better in the TURP group, and IIEF score, which was significantly higher among PErFecTED PAE patients than TURP patients. All groups experienced significant improvement in IPSS, QoL, prostate volume, and Q{sub max}. TURP and PErFecTED PAE both resulted in significantly lower IPSS than oPAE but were not significantly different from one another. TURP resulted in significantly higher Q{sub max} and significantly smaller prostate volume than either original or PErFecTED PAE but required spinal anesthesia and hospitalization. Two patients in the oPAE group with hypocontractile bladders experienced recurrence of symptoms and were treated with TURP. In the TURP group, urinary incontinence occurred in 4/15 patients (26.7 %), rupture of the prostatic capsule in 1/15 (6.7 %), retrograde ejaculation in all patients (100 %), and one patient was readmitted for temporary bladder irrigation due to hematuria.ConclusionsTURP and PAE are both safe and effective treatments. TURP and PErFecTED PAE yield similar symptom improvement, but TURP is associated with both better urodynamic results and more adverse events.
In this work, a parameter-free electronic structure approach is applied to the study of stability and chemical order in the 15 substitutional body-centered cubic (bcc)-based alloys made of the six transition metals of groups 5 (V, Nb, Ta) and 6 (Cr, Mo, W) of the periodic table. The method is based on a Green’s function description of the electronic structure of the random alloys. Configurational order is treated within the generalized perturbation method, and temperature effects are examined with a generalized mean-field approach. In contrast to the results summarized in the assessed phase diagrams, stability and ordering trends are predicted in a broad range of alloy composition for at least seven alloys, and explanation is found in their electronic structure properties. Short-range order results, thermodynamics analysis, and bcc-based phase diagrams are also presented.
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Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.
We report strengthening in complex multicomponent systems such as solid solution alloys is controlled primarily by the dynamic interactions between dislocation lines and heterogeneously distributed solute species. Modeling of extended defect length scales in such multicomponent systems becomes prohibitively expensive, motivating the development of reduced order approaches. This work explores the application of the Concurrent Atomistic-Continuum (CAC) method to model dislocation mobility in random alloys at extended length scales. By employing recently developed average-atom interatomic potentials, the average “bulk” material response in coarse-grained regions interacts with true random solute species in the atomistic-scale domain. We demonstrate that spurious stresses in domain resolution transition regions are eliminated entirely due to the CAC formulation. Simultaneously, the key details of local stress fluctuation due to randomness in the dislocation core region are captured, and fluctuating stress smoothly decays to the long-range dislocation stress field response. Dislocation mobility calculations, for line lengths over 400 nm, are computed as a function of alloy composition in the model FeNiCr system and compared to full molecular dynamics (MD). The results capture the composition-dependent trends, while reducing degrees of freedom by nearly 40%. This approach can be readily extended to any system described by an EAM potential and facilitates the study of large-scale defect dynamics in complex solute environments to support computational alloy design.