Entanglement suppression, enhanced symmetry, and a standard-model-like Higgs boson
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Although quantum machine learning has shown great promise, the practical application of quantum computers remains constrained in the noisy intermediate-scale quantum era. To take advantage of quantum machine learning, we investigate the underlying mathematical principles of these quantum models and find that the quantum neural network with amplitude encoding is equivalent to a weight-constrained neural network. Motivated by this discovery, we develop a classical weight-constrained neural network. We find that this approach can reduce the number of variables in a classical neural network by a factor of 135 while preserving its accuracy. In addition, we develop a dropout method to enhance the robustness of quantum machine learning models, which are highly susceptible to adversarial attacks. This technique can also be applied to improve the adversarial robustness of the classical weight-constrained neural network, which is essential for industry applications, such as self-driving vehicles. Our work offers an approach to reduce the complexity of large classical neural networks, addressing a critical challenge in machine learning.
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The growth in load and generation sources at the edge of the grid is driving the innovation in power electronic (PE) grid interfaces at the consumer end to improve the grid resiliency, reliability, security, and cost of the future infrastructure. Novel PE technologies are key enablers for the future grid infrastructure to handle the issues that arise because of the projected growth. This paper introduces a novel fundamental building block (FBB) architecture with plug-and play features. An example framework that enables co-ordination of multiple FBBs hierarchically to provide various grid functions is also presented. The FBBs are designed to be equipped with advanced features like online health monitoring, embedded intelligence, and decision-making capability for enhancing the metrics of consumer end plug and play interfaces. The paper elaborates on the proposed architecture and the developed framework i.e., the controls, communication, protection, and the corresponding timing requirements for the building blocks. The architecture and the framework have been validated through simulations and the communication framework has been validated with a control hardware in the loop platform.
This is a conference paper about open source tools for circuit simulation and model compilers.
Salt hydrates are promising candidates for phase change materials (PCMs) because of their low cost and high energy storage capacity. However, practical applications of salt hydrate based PCMs are limited due to their poor thermal cycling performance caused by phase separation and supercooling. Various techniques, including differential scanning calorimetry (DSC) and transient plane source (TPS) have been used to characterize salt hydrate based PCMs but may not provide full understanding of the degradation mechanism. In this manuscript, salt hydrate and graphite composites are prepared. Temperature history (T-History) method and X-ray diffraction (XRD) are utilized for measuring melting temperature, energy storage, and latent heat degradation of the PCM composites. Highlighting the importance of each technique and how each technique plays an important role in analyzing salt hydrates performance accurately. Temperature history, and X-ray diffraction are also used in conjunction to precisely predict the cause of degradation in salt hydrate PCMs with thermal cycling, as in most literatures the cause of degradation in salt hydrate are attributed to phase segregation, supercooling, and change in stichometry water concentration without any scientific validation.
At the IEEE/ACM International Conference for High-Performance Computing, Networking, Storage, and Analysis (SC23), held in Denver, experts discussed the convergence of high-performance computing and cloud computing. Experts explored how this integration could address current scientific computing limitations, enhance computational capabilities, and foster global collaboration while focusing on economic, security, technical, and community challenges and opportunities.
The traditional approach to planning the distribution grid has focused on reliability in the context of gradual and reasonably predictable load growth. Forecasts of load growth, combined with asset management practices, were used by system planners to identify upgrades to the system to maintain or improve reliability. The decisions, typically based within load flow analysis tools, included considerations about contingency scenarios and corporate forecasts (i.e., top-down predictions at a summary level of what would happen in a particular area that could impact load growth and behavior). As a result, today, this traditional approach no longer fits all purposes.
For new experimental setups, the initial testing and calibrations can be expensive and time consuming without prior optimization. However, these issues can be mitigated using realistic modeling and simulation studies of the proposed system a priori. Specifically, an experiment can be performed virtually using realistic simulations and the expected results evaluated to inform adjustments of the experimental setup to achieve optimum results. At the Penn State Breazeale Reactor, a new pneumatic transfer system has been developed for the detection and characterization of short-lived fission fragments to enhance and complement existing nuclear data. The system accomplishes this task by transporting samples cyclically between an assortment of gamma-ray/neutron detectors and the reactor-core vicinity with sub-second transit times. The measured neutron flux paired with simulations of detector response using Geant4 and a custom module for estimating cascade summing effects and corrections were used to select sample-material masses for system characterization. With the optimized sample masses, an experimental counting plan for characterizing the repeatability of the newly developed pneumatic system was developed. Finally, the predictions made with these simulations allowed for optimization of the irradiation sample characteristics and gamma-ray detection system.
The deployment of high-power dc equipment is increasing in solar photovoltaic (PV) plants, but very few studies have quantified dc arc-flash risks. Currently, PV plant owners and operators rely on theoretical, simplified models, such as those in NFPA-70E and other publications, for the assessment of risk associated with dc arc-flash. Here, this article presents an overview of arc-flash risks in a PV system based on a series of field experiments based on IEEE-1584 in two large-scale ground-mounted PV plants. The experiments include various high-power dc equipment of a PV plant, such as central inverters, combiner boxes, recombiner boxes, string inverters, and multiple configurations of electrodes in a 20-in calibration cube. The study reveals that none of the available dc arc-flash models are applicable for a PV plant. This work is an important first step toward developing an improved model that more accurately assesses dc arc-flash risk in a PV plant.
Predicting creep crack growth (CCG) of flaws found during operation in high-temperature alloy components is essential for assessing the remaining lifetime of those components. While defect assessment procedures are available for this purpose in design codes, these are limited in their range of applicability. This study assesses the application of a local damage-based finite-element methodology as a more general technique for the prediction of CCG at high temperatures on a variety of structural configurations. Numerical results for stainless steel 316H, which are validated against experimental data, show the promise of this approach. This integration of continuum damage mechanics (CDM) based methodologies, together with adequate inelastic models, into assessment procedures can therefore inform the characterization of CCG under complex operating conditions, while avoiding excessive conservatism. This article shows that such modeling frameworks can be calibrated to experimental data and used to demonstrate that the degree of tri-axiality ahead of a growing creep crack affects its rate of growth. The framework is also successfully employed in characterizing CCG in a realistic reactor pressure vessel geometry under an arbitrary loading condition. These results are particularly relevant to the nuclear power industry for defect assessment and inspections as part of codified practices of structural components with flaws in high-temperature reactors.
In the original HTML version of this article, one affiliation of the author V. O. Tikhomirov was missing. He is affiliated to the following two affiliations: P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia and National Research Nuclear University MEPhI, Moscow, Russia. The original article has been corrected.
For many decades, the main source of information on the top-left corner element of the Cabibbo–Kobayashi–Maskawa quark mixing matrix, V ud , was superallowed nuclear β decays with an impressive 0.01% precision. This precision, apart from experimental data, relies on theoretical calculations in which nuclear structure–dependent effects and uncertainties play a prime role. This review is dedicated to a thorough reassessment of all ingredients that enter the extraction of the value of V ud from experimental data. We try to keep balance between historical retrospect and new developments, many of which occurred in just the past 5 years. They have not yet been reviewed in a complete manner, not least because new results are forthcoming. This review aims to fill this gap and offers an in-depth yet accessible summary of all recent developments.
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