Spacetime symmetric qubit regularization of the asymptotically free two-dimensional O ( 4 ) model
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Solar photovoltaic (PV) systems are common and continue to gain popularity. This article helps electrical workers and firefighters understand basic operating principles and hazards of PV dc arrays. The effects of solar radiation and temperature on power output are discussed including; PV-module testing standards; methods for calculating the number of modules in a PV string; some National Electrical Code (NEC) guidelines that are aimed toward improving array safety; dc-array commissioning, periodic maintenance, and testing; arc-flash-hazard potential ways firefighters can work safely around PV arrays; potential risks posed by moonlight and other light sources; some unique dc-system hazards. We also present statistics on PV dc-array electrical incidents and injuries.
We model black hole microstates and quantum tunneling transitions between them with networks and simulate their time evolution using well-established tools in network theory. In particular, we consider two models based on Bena-Warner three-charge multi-centered microstates and one model based on the D1-D5 system; we use network theory methods to determine how many centers (or D1-D5 string strands) we expect to see in a typical late-time state. We find three distinct possible phases in parameter space for the late-time behaviour of these networks, which we call ergodic, trapped, and amplified, depending on the relative importance and connectedness of microstates. We analyze in detail how these different phases of late-time behavior are related to the underlying physics of the black hole microstates. Our results indicate that the expected properties of microstates at late times cannot always be determined simply by entropic arguments; typicality is instead a highly non-trivial, emergent property of the full Hilbert space of microstates.
Modern software systems, which often are concurrent and manipulate complex data structures must be extremely reliable. We present a novel framework based on symbolic execution, for automated checking of such systems. We provide a two-fold generalization of traditional symbolic execution based approaches: one, we define a program instrumentation, which enables standard model checkers to perform symbolic execution; two, we give a novel symbolic execution algorithm that handles dynamically allocated structures (e.g., lists and trees), method preconditions (e.g., acyclicity of lists), data (e.g., integers and strings) and concurrency. The program instrumentation enables a model checker to automatically explore program heap configurations (using a systematic treatment of aliasing) and manipulate logical formulae on program data values (using a decision procedure). We illustrate two applications of our framework: checking correctness of multi-threaded programs that take inputs from unbounded domains with complex structure and generation of non-isomorphic test inputs that satisfy a testing criterion. Our implementation for Java uses the Java PathFinder model checker.
It has long been expected that the 3d Ising model can be thought of as a string theory, where one interprets the domain walls that separate up spins from down spins as two-dimensional string worldsheets. The usual Ising Hamiltonian measures the area of these domain walls. This theory has string coupling of unit magnitude. We add new local terms to the Ising Hamiltonian that further weight each spin configuration by a factor depending on the genus of the corresponding domain wall, resulting in a new 3d Ising model that has a tunable bare string coupling g_s g s . We use a combination of analytical and numerical methods to analyze the phase structure of this model as g_s g s is varied. We study statistical properties of the topology of worldsheets and discuss the prospects of using this new deformation at weak string coupling to find a worldsheet description of the 3d Ising transition.
A system and method of using one or more DC-DC/DC-AC converters and/or alternative devices allows strings of multiple module technologies to coexist within the same PV power plant. A computing (optimization) framework estimates the percentage allocation of PV power plant capacity to selected PV module technologies. The framework and its supporting components considers irradiation, temperature, spectral profiles, cost and other practical constraints to achieve the lowest levelized cost of electricity, maximum output and minimum system cost. The system and method can function using any device enabling distributed maximum power point tracking at the module, string or combiner level.
Photovoltaic (PV) modules consist of a string of electrically interconnected silicon solar cells capable of producing practical quantities of electrical power when exposed to sunlight. To insure high reliability and long term performance, the functional components of the solar cell module must be adequately protected from the environment by some encapsulation technique. The encapsulation system must provide mechanical support for the cells and corrosion protection for the electrical components. The goal of the program is to identify and develop encapsulation systems consistent with the PV module operating requirements of 30 year life and a target cost of $0.70 per peak watt ($70 per square meter) (1980 dollars). Assuming a module efficiency of ten percent, which is equivalent to a power output of 100 watts per square meter in midday sunlight, the capital cost of the modules may be calculated to be $70.00 per square meter. Out of this cost goal, only 20 percent is available for encapsulation due to the high cost of the cells, interconnects, and other related components. The encapsulation cost allocation may then be stated as $14.00 per square meter, included all coatings, pottant and mechanical supports for the cells.
We study the free energy of a quark–antiquark pair near the deconfinement temperature by particle mesh Ewald’s method for noninteracting dyon ensemble. We show that the free energy of the quark–antiquark pair increases linearly by increasing the distance between them. The string tension decreases by increasing the temperature, as expected. - Highlights: • A noninteracting dyon ensemble is studied by particle mesh Ewald’s method. • Free energy of quark-antiquark pair increases linearly by increasing the distance. • Particle mesh Ewald’s method is less time consuming compared with Ewald’s method.
Readout errors on near-term quantum computers can introduce significant error to the empirical probability distribution sampled from the output of a quantum circuit. These errors can be mitigated by classical postprocessing given the access of an experimental response matrix that describes the error associated with the measurement of each computational basis state. However, the resources required to characterize a complete response matrix and to compute the corrected probability distribution scale exponentially with the number of qubits, n . In this work, we modify standard matrix inversion techniques using perturbative approximations with significantly reduced complexity and bounded error when the likelihood of high-order bit-flip events is strongly suppressed. Given a characteristic error rate q , we discuss a method to recover the probability of the all-zeros bit string p 0 by sampling only a small subspace of the response matrix before inverting readout error, resulting in a relative speedup of poly [ 2 n / ( n w ) ] , which we motivate using a simplified error model for which the approximation incurs only O ( q w ) error for some integer w . We then provide a generalized technique to efficiently recover full output distributions with O ( q w ) error in the perturbative limit. These approximate techniques for readout-error correction may greatly accelerate near-term quantum computing applications.
We evaluate one-loop open-string amplitudes at finite \alpha' α ′ for the first time. Our method involves a deformation of the integration contour over the modular parameter \tau τ to a fractal contour introduced by Rademacher in the context of analytic number theory. This procedure leads to explicit and practical formulas for the one-loop four-point amplitudes in type-I superstring theory, amenable to numerical evaluation. We plot the amplitudes as a function of the Mandelstam invariants s s and t t and directly verify long-standing conjectures about their behaviour at high energies.
We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.
The intensity of narrow muon groups observed in Kolar Gold Field (KGF) at the depth of 3375 m.w.e. was calculated in terms of quark-gluon strings model for high energy hadron - air nuclei interactions by the method of direct modeling of nuclear cascade in the air and muon propagation in the ground for normal primary cosmic ray composition. The calculated intensity has been found to be approx. 10 to the 4 times less than one observed experimentally.
An apparatus for detecting potential kicks in a well is provided. In one embodiment, the apparatus includes a drill string positioned in a well, a mud circulation system coupled to supply drilling mud to the drill string, and a mud analyzer. The mud analyzer includes a sensor that is positioned along a drilling mud return path closer to a wellhead assembly installed at the well than to a mud tank of the mud circulation system, and the mud analyzer enables identification of a potential formation kick based on data acquired by the sensor. Additional systems, devices, and methods are also disclosed.
The dynamic analysis of a grid structure hung from the ceiling by two steel wires is described. The method of approach uses the discrete mass model system. The concept of the bifilar pendulum is used in writing the equation of motion. Assumptions are made with regard to the bar stiffnesses in the vertical and horizontal planes. The discrete mases are assumed to be connected by inextensible massless strings that do not provide any torsion or bending. The modal frequencies obtained by this method are compared with those obtained from the finite element model.
Given physics requirements, the mechanical fabrication and assembly tolerances for storage ring magnets can be calculated using analytical methods [1, 2]. However, this method is not easy for complicated magnet designs [1]. In this paper, a novel method is proposed to determine fabrication and assembly tolerances consistent with physics requirements, through a combination of magnetic and mechanical tolerance analyses. In this study, finite element analysis using OPERA is conducted to estimate the effect of fabrication and assembly errors on the magnetic field of a quadrupole magnet and to determine the allowable tolerances to achieve the specified magnetic performances. Based on the study, allowable fabrication and assembly tolerances for the quadrupole assembly are specified for the mechanical design of the quadrupole magnet. Next, to achieve the required assembly level tolerances, mechanical tolerance stackup analyses using a 3D tolerance analysis package are carried out to determine the part and subassembly level fabrication tolerances. This method can be used to determine the tolerances for design of other individual magnets and of magnet strings.
Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.
Abstract The expectation value of a smooth conformal line defect in a CFT is a conformal invariant functional of its path in space-time. For example, in largeNholographic theories, these fundamental observables are dual to the open-string partition function in AdS. In this paper, we develop a bootstrap method for studying them and apply it to conformal line defects in Chern-Simons matter theories. In these cases, the line bootstrap is based on three minimal assumptions — conformal invariance of the line defect, largeNfactorization, and the spectrum of the two lowest-lying operators at the end of the line. On the basis of these assumptions, we solve the one-dimensional CFT on the line and systematically compute the defect expectation value in an expansion around the straight line. We find that the conformal symmetry of a straight defect is insufficient to fix the answer. Instead, imposing the conformal symmetry of the defect along an arbitrary curved line leads to a functional bootstrap constraint. The solution to this constraint is found to be unique.
The empirical valence bond technique allows classical force fields to model reactive processes. However, parametrization from experimental data or quantum mechanical calculations is required for each reaction present in the simulation. We show that the parameters present in the empirical valence bond method can be predicted using a neural network model and the SMILES strings describing a reaction. This removes the need for quantum calculations in the parametrization of the empirical valence bond technique. In doing so, we have taken the first steps toward defining a new procedure for enabling reactive atomistic simulations. This procedure would allow researchers to use existing classical force fields for reactive simulations, without performing additional quantum mechanical calculations.