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Conventional heat-engine models typically assume two heat reservoirs at fixed temperatures. In contrast, radioisotope power systems introduce a fundamentally different paradigm in which the hot sources supply heat at a constant generation…
Physical Reservoir Computing (PRC) is an unconventional computing paradigm, which exploits nonlinear dynamics of reservoir blocks to perform recognition and classification tasks. Here we show with simulations that patterned thin films…
Quantum reservoir computing (QRC) harnesses driven quantum dynamics for time-series processing, yet the mechanisms behind the differing performance levels across its many implementations remain unclear. We show that apparently unrelated…
Thermal fission energy is one of the basic parameters needed in the calculation of antineutrino flux for reactor neutrino experiments. It is useful to improve the precision of the thermal fission energy calculation for current and future…
The SNO+ Collaboration reports new results on reactor antineutrino oscillations using data acquired from May 2022 through July 2025. The spectral analysis of a flux dominated by nuclear reactors at 240, 350, and 355 kilometers yields the…
Today reactor neutrino experiments are at the cutting edge of fundamental research in particle physics. Understanding the neutrino is far from complete, but thanks to the impressive progress in this field over the last 15 years, a few…
Reactor antineutrinos have been indispensable for our understanding of neutrino mass and mixing. At the same time, discrepancies between the observed and predicted reactor $\overline{\nu}_{e}$ rate and energy spectra have grown as the…
Sensitive searches for antineutrino oscillations in atmospheric mass parameter region much discussed in recent years are based on accurate comparison of the inverse beta decay positron spectra measured in two (or more) detectors, far and…
Reservoir Computing (RC) is a time-efficient computational paradigm derived from Recurrent Neural Networks (RNNs). The Simple Cycle Reservoir (SCR) is an RC model that stands out for its minimalistic design, offering extremely low…
There is renewed interest in developing small modular reactors and micro-reactors. Innovation is necessary in both construction and operation methods of these reactors to be financially attractive. For operation, an area of interest is the…
The multipurpose nature of nuclear research reactors has been investigated. This class of reactors has gained momentum within the international community, given the multiple benefits of nuclear technology in medicine, agriculture, industry,…
The passive safety is a crucial feature of advanced nuclear reactor (Gen IV) design. During loss of power scenarios, the downcomer plays a crucial role. The fluid-flow behavior in the downcomer can involve forced to mixed to natural…
Coherent elastic neutrino-nucleus scattering (CEvNS) provides a unique probe for neutrino properties Beyond the Standard Model (BSM) physics. REactor neutrino LIquid xenon Coherent Scattering experiment (RELICS), a proposed reactor neutrino…
To achieve digital intelligence transformation and carbon neutrality, effective production planning is crucial for integrated refinery-petrochemical complexes. Modern refinery planning relies on advanced optimization techniques, whose…
We perform a systematic analysis of the nuclear dependence of two-particle-two-hole meson-exchange current contributions to inclusive lepton-nucleus scattering within the relativistic mean-field framework. We present microscopic…
As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based…
Recent progresses on the relativistic modeling of neutrino-nucleus reactions are presented and the results are compared with high precision experimental data in a wide energy range.
The main goal of the research presented in this dissertation was to develop a theoretical model for relativistic nuclear spectral functions at high missing momenta and removal energies based on the multi-nucleon short-range correlation…
We propose a new reservoir computing method for forecasting high-resolution spatiotemporal datasets. By combining multi-resolution inputs from coarser to finer layers, our architecture better captures both local and global dynamics. Applied…
The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. In a model comparison, we examine the optimal expansion of such technologies with six capacity expansion…