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Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mixing and Redlich-Kister models are computationally cheap but lack either accuracy or generality. To address this, a transfer learning approach using deep neural networks (DNNs) is proposed, combining Redlich-Kister models, experimental data, and ab initio properties. The approach predicts molten salt density with high accuracy ($r^{2}$ > 0.99, MAPE < 1%), outperforming the alternatives., Comment: Manuscript contains 25 pages including references and other information. Manuscript contains 4 figures and 3 tables. To be submitted to ACS Journal of Chemical Theory and Computation

This paper examines the application of ChatGPT, a large language model (LLM), for question-and-answer (Q&A) tasks in the highly specialized field of nuclear data. The primary focus is on evaluating ChatGPT's performance on a curated test dataset, comparing the outcomes of a standalone LLM with those generated through a Retrieval Augmented Generation (RAG) approach. LLMs, despite their recent advancements, are prone to generating incorrect or 'hallucinated' information, which is a significant limitation in applications requiring high accuracy and reliability. This study explores the potential of utilizing RAG in LLMs, a method that integrates external knowledge bases and sophisticated retrieval techniques to enhance the accuracy and relevance of generated outputs. In this context, the paper evaluates ChatGPT's ability to answer domain-specific questions, employing two methodologies: A) direct response from the LLM, and B) response from the LLM within a RAG framework. The effectiveness of these methods is assessed through a dual mechanism of human and LLM evaluation, scoring the responses for correctness and other metrics. The findings underscore the improvement in performance when incorporating a RAG pipeline in an LLM, particularly in generating more accurate and contextually appropriate responses for nuclear domain-specific queries. Additionally, the paper highlights alternative approaches to further refine and improve the quality of answers in such specialized domains.

This paper proposes the development of a Large Language Model (LLM) based machine learning classifier designed to categorize Station Condition Records (SCRs) at nuclear power stations into safety-related and non-safety-related categories. The primary objective is to augment the existing manual review process by enhancing the efficiency and accuracy of the safety classification process at nuclear stations. The paper discusses experiments performed to classify a labeled SCR dataset and evaluates the performance of the classifier. It explores the construction of several prompt variations and their observed effects on the LLM's decision-making process. Additionally, it introduces a numerical scoring mechanism that could offer a more nuanced and flexible approach to SCR safety classification. This method represents an innovative step in nuclear safety management, providing a scalable tool for the identification of safety events.

Understanding time-dependent blood flow dynamics in arteries is crucial for diagnosing and treating cardiovascular diseases. However, accurately predicting time-varying flow patterns requires integrating observational data with computational models in a dynamic environment. This study investigates the application of data assimilation and boundary optimization techniques to improve the accuracy of time-dependent blood flow simulations. We propose an integrated approach that combines data assimilation methods with boundary optimization strategies tailored for time-dependent cases. Our method aims to minimize the disparity between model predictions and observed data over time, thereby enhancing the fidelity of time-dependent blood flow simulations. Using synthetic time-series observational data with added noise, we validate our approach by comparing its predictions with the known exact solution, computing the L2 norm to demonstrate improved accuracy in time-dependent blood flow simulations. Our results indicate that the optimization process consistently aligns the optimized data with the exact data. In particular, velocity magnitudes showed reduced discrepancies compared to the noisy data, aligning more closely with the exact solutions. The analysis of pressure data revealed a remarkable correspondence between the optimized and exact pressure values, highlighting the potential of this methodology for accurate pressure estimation without any previous knowledge on this quantity. Furthermore, wall shear stress (WSS) analysis demonstrated the effectiveness of our optimization scheme in reducing noise and improving prediction of a relevant indicator determined at the postprocessing level. These findings suggest that our approach can significantly enhance the accuracy of blood flow simulations, ultimately contributing to better diagnostic and therapeutic strategies.

We propose a scheme for quantum interrogation measurements using constructive interference and post-selection to achieve single-pass high-efficiency detection for imperfect or semi-transparent absorbers. We illustrate that our method works for heralded single-photon as well as weak attenuated sources. We also study the influence of error from our equipment and show that post-selection renders robustness to our scheme against noise. We further demonstrate that with a small extension, we can quantify the transmittance of the imperfect absorber by using the process of weak value amplification (WVA), Comment: 9 pages, 6 figures

This paper proposes a methodology to solve generation expansion equilibrium problems by using a predictive model to represent the equilibrium in a simplified network constrained electricity market. The investment problem for each generation company (Genco) is a bi-level problem with the investment decision made in the upper level and market clearing condition in the lower level, which traditionally is represented as a Mathematical Program with Equilibrium Constraint (MPEC). The predictive model is trained for estimating the system-wide revenues for each technology type across energy, ancillary services and capacity markets given the amount of technology-specific installed capacity on the grid. The profit maximization investment problem for each Genco is solved using a global search algorithm, which uses the predictive model to evaluate the objective function. To solve for the strategic equilibrium, each Genco's problem is plugged into a diagonalization algorithm that is generally used in multi-leader, single-follower bi-level problems. The methodology presented here enables significant computational improvements while still capturing the desired market characteristics and dynamics of traditional equilibrium modeling approaches

In this work, we investigate the effects of H2 on the physical properties of Fe25Pd75. Broadband ferromagnetic resonance (FMR) spectroscopy revealed a significant FMR peak shift induced by H2 absorption for the FCC phased Fe25Pd75. The peak shifted towards higher applied fields, which is contrary to what was previously observed for CoPd alloys. Additionally, we conducted structural and magneto-optical Kerr ellipsometric studies on the Fe25Pd75 film and performed density functional theory calculations to explore the electronic and magnetic properties in both hydrogenated and dehydrogenated states. In the final part of this study, we deposited a Fe25Pd75 layer on top of a microscopic coplanar transmission line and investigated the FMR response of the layer while driven by a microwave current in the coplanar line. We observed a large amplitude FMR response upon hydrogen absorption, as well as desorption rates when cycling between pure N2 and a mixture of 3% H2 + 97% N2.

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

The photonic spin Hall effect of light beams reflected from the surfaces of various two-dimensional hexagonal crystalline structures, considering their associated time-reversal $\mathcal{T}$ and inversion $\mathcal{I}$ symmetries, is investigated. Employing the Haldane model with tunable parameters as a generic model, we examine the longitudinal and transverse spin-separations of the reflected beam in both topological non-trivial and trivial systems. The study reveals that the sign switching of the PSHE in these materials is attributed to the non-trivial and trivial topology. By manipulating the interplay between spin-orbit coupling and external electric fields, we demonstrate topological phase transitions in buckled Xene monolayer materials through the photonic spin Hall effect. Different behaviors of the photonic spin Hall effect are observed in various topological phases within these materials. Additionally, we explore the reflected spin and valley-polarized spatial shifts in monolayer transition metal dichalcogenides. The photonic spin Hall effect in buckled Xene monolayer materials and transition metal dichalcogenides is highly influenced by the spin and valley degrees of freedom of charge carriers, offering a promising avenue to explore spintronics and valleytronics in these hexagonal materials. We propose that the photonic spin Hall effect in Haldane materials can serve as a metrological tool for optical parameter characterization and as a promising method for determining Chern numbers and topological phase transitions through direct optical weak measurement techniques.

We present new linear relations among the masses of S-wave tetraquarks with either one flavour ($qq \bar q \bar q$) or two ($QQ\bar q \bar q$). Because the relations are sensitive to the hidden-colour, spin, and spatial degrees of freedom, comparison to experimental data can help to reveal the internal structure of tetraquarks, and discriminate among different theoretical models. Depending on the model, the relations are either exact, or valid in perturbation theory, and a thorough comparison with existing literature confirms their validity at the MeV level. Additionally, we explore the connections among tetraquark models, and show how those with effective (quark or diquark) masses are related to dynamical potential models. We also show how the spectrum of diquark models is effectively a limiting case of (more general) quark models, and in particular, that the diquark concept is most relevant in the particular combination $QQ\bar q \bar q$, where $Q$ is much heavier than $\bar q$.

Perovskite solar cells (PSC) have become a growing research interest due to their flexibility, attractive properties, and low production cost. However, the thin-film structure of PSC often results in a not fully absorbed incident light by the active layer, which is crucial to determine PSC efficiency. Thus, the fabrication of an active layer with unique nanostructures is often used to enhance light absorption and general PSC efficiency. Using the theoretical simulation based-on Finite-Difference Time-Domain (FDTD) technique, this work demonstrates the successful improvement of light absorption by embedding corrugated void-like structure and perovskite thickness modification. The investigation of a corrugated void-type anti-reflection layer effect on light absorption is done by modifying the radius (r) and lattice constant (a) to obtain the optimum geometry. In addition, the MAPbI3 perovskite layer thickness is also adjusted to examine the optimum light absorption within the visible length to nearinfrared. The theoretical calculations show that the optimum r=692 nmand a=776 nm. Meanwhile, the optimum absorber layer thickness is 750 nm. Compared to flat PSC, our proposed PSC absorbed more light, especially in the near-infrared region. Our result shows demonstrates the successful enhancement of light absorption by embedding corrugated void-like structure and modifying the perovskite thickness using a theoretical simulation based on theFDTD technique.

Graphene-silicon Schottky junction (GSJ) which has the potential for large-scale manufacturing and integration can bring new opportunities to Schottky solar cells for photovoltaic (PV) power conversion. However, the essential power conversion limitation for these devices lies in the small open-circuit voltage ($V_{oc}$), which depends on the Schottky barrier height (SBH). In this study, we introduce an electromechanical method based on the flexoelectric effect to enhance the PV efficiency in GSJ. By atomic force microscope (AFM) tip-based indentation and in situ current measurement, the current-voltage (I-V) responses under flexoelectric strain gradient are obtained. The $V_{oc}$ is observed to increase for up to 20$\%$, leading to an evident improvement of the power conversion efficiency. Our studies suggest that strain gradient may offer unprecedented opportunities for the development of GSJ based flexo-photovoltaic applications., Comment: 7 pages, 4 figures. The following article has been accepted by Applied Physics Letters

Driven by the global decarbonization effort, the rapid integration of renewable energy into the conventional electricity grid presents new challenges and opportunities for the battery energy storage system (BESS) participating in the energy market. Energy arbitrage can be a significant source of revenue for the BESS due to the increasing price volatility in the spot market caused by the mismatch between renewable generation and electricity demand. In addition, the Frequency Control Ancillary Services (FCAS) markets established to stabilize the grid can offer higher returns for the BESS due to their capability to respond within milliseconds. Therefore, it is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions. This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit. Then, Proximal Policy Optimization, a model-free deep reinforcement learning algorithm, is employed to learn the optimal bidding strategy from the dynamic environment of the energy market under a continuous bidding scale. The proposed model is trained and validated using real-world historical data of the Australian National Electricity Market. The results demonstrate that our developed joint bidding strategy in both markets is significantly profitable compared to individual markets.

The performance of nanoscale electronic devices based on a two-three dimensional (2D-3D) interface is significantly affected by the electrical contacts that interconnect these materials with external circuitry. This work investigates charge transport effects at the 2D-3D ohmic contact coupled with the thermionic injection model for graphene/Si Schottky junction. Here, w e focus on the intrinsic properties of graphene-metal contacts, paying particular attention to the nature of the contact failure mechanism under high electrical stress. According to our findings, severe current crowding (CC) effects in highly conductive electrical contact significantly affect device failure that can be reduced by spatially varying the contact properties and geometry. The impact of electrical breakdown on material degradation is systematically analyzed by atomic force, Raman, scanning electron, and energy dispersive X-ray spectroscopies. Our devices withstand high electrostatic discharge spikes over a longer period, manifesting high robustness and operational stability. This research paves the way towards a highly robust and reliable graphene/Si heterostructure in futuristic on-chip integration in dynamic switching. The methods we employed here can be extended for other nanoscale electronic devices based on 2D-3D interfaces