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As the global demand for clean energy intensifies to achieve sustainability and net-zero carbon emission goals, nuclear energy stands out as a reliable solution. However, fully harnessing its potential requires overcoming key challenges, such as the high capital costs associated with nuclear power plants (NPPs). One promising strategy to mitigate these costs involves repurposing sites with existing infrastructure, including coal power plant (CPP) locations, which offer pre-built facilities and utilities. Additionally, brownfield sites - previously developed or underutilized lands often impacted by industrial activity - present another compelling alternative. These sites typically feature valuable infrastructure that can significantly reduce the costs of NPP development. This study introduces a novel multi-objective optimization methodology, leveraging combinatorial search to evaluate over 30,000 potential NPP sites in the United States. Our approach addresses gaps in the current practice of assigning pre-determined weights to each site attribute that could lead to bias in the ranking. Each site is assigned a performance-based score, derived from a detailed combinatorial analysis of its site attributes. The methodology generates a comprehensive database comprising site locations (inputs), attributes (outputs), site score (outputs), and the contribution of each attribute to the site score (outputs). We then use this database to train a machine learning neural network model, enabling rapid predictions of nuclear siting suitability across any location in the contiguous United States., Comment: 29 pages, 8 Tables, 12 figures

Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to generate initial structures and diffuse over torsion angles. In this work, we introduce Equivariant Transformer Flow (ET-Flow). We showcase that a well-designed flow matching approach with equivariance and harmonic prior alleviates the need for complex internal geometry calculations and large architectures, contrary to the prevailing methods in the field. Our approach results in a straightforward and scalable method that directly operates on all-atom coordinates with minimal assumptions. With the advantages of equivariance and flow matching, ET-Flow significantly increases the precision and physical validity of the generated conformers, while being a lighter model and faster at inference. Code is available https://github.com/shenoynikhil/ETFlow., Comment: NeurIPS 2024

The heterogeneous distribution of water-repellent materials at the soil surface causes a phenomenon known as fractional wettability. This condition frequently triggers destabilization of the wetting front during water infiltration, resulting in the formation of fingered bypass flow. However, few analytical tools exist to understand and model this behavior. Moreover, existing infiltration models fail to fit certain infiltration curves that exist in experimental data. For these reasons, we introduce a novel infiltration model to simulate water infiltration under fractional wettable conditions. We conceptualize the soil surface as a composite of two distinct portions: a water-repellent fraction, where hydrophobic effects impede water infiltration, and a wettable fraction, where capillarity and gravity are the dominant forces controlling the process. The new model was validated using a dataset comprising infiltration data from 60 field measurements. Additionally, validation was performed using 660 analytically generated infiltration curves from six synthetic soils with varying textures. This innovative approach enabled us to account for the combined influence of these two fractions and to enhance the interpretation of infiltration curves with mixed shapes, which other common methods are unable to reproduce.

We present a systematic and rigorous analytical approach, based on the transfer matrix methodology, to study the existence, evolution, and robustness of subwavelength topological interface states in practical multilayered vibroacoustic phononic lattices. These lattices, composed of membrane-air cavity unit cells, exhibit complex band structures with various bandgaps, including Bragg, band-splitting induced, local resonance, and plasma bandgaps. Focusing on the challenging low-frequency range and assuming axisymmetric modes, we show that topological interface states are confined to Bragg-like band-splitting induced bandgaps. Unlike the Su-Schrieffer-Heeger model, the vibroacoustic lattice exhibits diverse topological phase transitions across infinite bands, enabling broadband, multi-frequency vibroacoustics in the subwavelength regime. We establish three criteria for the existence of these states: the Zak phase, surface impedance, and a new reflection coefficient concept, all derived from transfer matrix components. Notably, we provide an explicit expression for the exact location of topological interface states within the band structure, offering insight for their predictive implementation. We confirm the robustness of these states against structural variations and identify delocalization as bandgaps narrow. Our work provides a complete and exact analytical characterization of topological interface states, demonstrating the effectiveness of the transfer matrix method. Beyond its analytical depth, our approach provides a useful framework and design tool for topological phononic lattices, advancing applications such as efficient sound filters, waveguides, noise control, and acoustic sensors in the subwavelength regime. Its versatility extends beyond the vibroacoustic systems, encompassing a broader range of phononic and photonic crystals with repetitive inversion-symmetric unit cells.

This paper focuses on developing a novel hybrid-haptic (nHH) device with a remote center of rotation with 4 DOFs (degrees of freedom) intendant to be used as a haptic device. The new architecture is composed of two chains handling each one a part of the motions. It has the advantages of a parallel robot as high stiffness and accuracy, and the large workspace of the serial robots. The optimal synthesis of the nHH was performed using real-coded genetic algorithms. The optimization criteria and constraints were established and successively formulated and solved using a mono-objective function. A validation and comparison study were performed between the spherical parallel manipulator and the nHH. The obtained results are promising since the nHH is compared to other similar task devices, such as spherical parallel manipulator, and presents a suitable kinematic performance with a task workspace free singularity inside.

Beyond classical Bragg diffraction, we report on new sub-Bragg phenomena to achieve enhanced sound wave tunability in the low-frequency range in a multilayered acoustic metamaterial system. Remarkably, we reveal and study the formation of genuinely sub-wavelength Bragg-like band-splitting induced bandgaps, generalizing the band-folding induced bandgaps in the literature. Additionally, we propose a methodology to widen sub-wavelength local resonance bandgaps by simultaneously hosting two local resonances within the same bandgap. These sub-Bragg bandgaps are realized in an axisymmetric vibroacoustic phononic metamaterial consisting of repetitive multilayered unit cells, each composed of two layers of membrane-cavity resonators. The resulting coupled sound-structure interaction system is analytically solved in closed form. Furthermore, the studied multilayered vibroacoustic metamaterial exhibits sub-wavelength acoustical transparency, akin to electromagnetically induced transparency, and an acoustic analogue of low-frequency plasma oscillations, resulting in a plasma bandgap where wave propagation is entirely prohibited below the plasma frequency. These sub-wavelength phenomena, achieved predictively and well below the ubiquitous first-order Bragg diffraction range, provide broadband attenuation and superior wave manipulation capabilities for low-frequency sound. This highlights the potential of utilizing sound-structure interaction across diverse physical applications. Moreover, our findings can be extrapolated to wave propagation in broader classes of physical systems where sub-wavelength phenomena are crucial, including phononic and photonic crystals of more general configurations.

This study investigates a vibroacoustic phononic metamaterial system composed of repeated monolayered membrane-air cavity unit-cells to assess its efficacy in controlling sound waves. Assuming low-frequency axisymmetric modes, the coupled membrane-cavity vibroacoustic system for a representative unit-cell is solved entirely analytically. Unlike previous research that relied on an infinite series of eigenfunctions, our analysis offers a single-term exact solution for the membrane's displacement field, fully accounting for coupling with the acoustic cavities. Utilizing the transfer matrix method and the Bloch-Floquet theorem, we offer a comprehensive analytical characterization of the band structure, including closed-form analytical expressions for determining the bounding frequencies of the bandgaps and the dispersion branches. Interaction between Bragg and local resonance bandgaps is examined by adjusting Bragg bandgap positions, with detailed mathematical descriptions provided for their overlapping and transition. Additionally, a "plasma bandgap" analogous to metallic plasma oscillations is identified, with a derived analytical expression for its frequency. First two passbands remain robust against cavity depth variations, limiting wave manipulation capabilities. Analysis of the finite phononic system involves constructing the global transfer matrix to study natural frequencies and scattering coefficients. Interaction between Bragg and local resonance bandgaps in finite systems results in ultra-narrow passbands, creating transparency windows analogous to electromagnetically induced transparency by quantum interference. This theoretical framework enables precise characterization and engineering of bandgaps in the monolayered vibroacoustic phononic metamaterial, highlighting its potential for controlling low-frequency sound wave propagation across multiple frequencies.

Reducing operation and maintenance costs is a key objective for advanced reactors in general and microreactors in particular. To achieve this reduction, developing robust autonomous control algorithms is essential to ensure safe and autonomous reactor operation. Recently, artificial intelligence and machine learning algorithms, specifically reinforcement learning (RL) algorithms, have seen rapid increased application to control problems, such as plasma control in fusion tokamaks and building energy management. In this work, we introduce the use of RL for intelligent control in nuclear microreactors. The RL agent is trained using proximal policy optimization (PPO) and advantage actor-critic (A2C), cutting-edge deep RL techniques, based on a high-fidelity simulation of a microreactor design inspired by the Westinghouse eVinci\textsuperscript{TM} design. We utilized a Serpent model to generate data on drum positions, core criticality, and core power distribution for training a feedforward neural network surrogate model. This surrogate model was then used to guide a PPO and A2C control policies in determining the optimal drum position across various reactor burnup states, ensuring critical core conditions and symmetrical power distribution across all six core portions. The results demonstrate the excellent performance of PPO in identifying optimal drum positions, achieving a hextant power tilt ratio of approximately 1.002 (within the limit of $<$ 1.02) and maintaining criticality within a 10 pcm range. A2C did not provide as competitive of a performance as PPO in terms of performance metrics for all burnup steps considered in the cycle. Additionally, the results highlight the capability of well-trained RL control policies to quickly identify control actions, suggesting a promising approach for enabling real-time autonomous control through digital twins., Comment: 15 pages, 3 figures, and 2 tables

In the domain of assistive robotics, the significance of effective modeling is well acknowledged. Prior research has primarily focused on enhancing model accuracy or involved the collection of extensive, often impractical amounts of data. While improving individual model accuracy is beneficial, it necessitates constant remodeling for each new task and user interaction. In this paper, we investigate the generalizability of different modeling methods. We focus on constructing the dynamic model of an assistive exoskeleton using six data-driven regression algorithms. Six tasks are considered in our experiments, including horizontal, vertical, diagonal from left leg to the right eye and the opposite, as well as eating and pushing. We constructed thirty-six unique models applying different regression methods to data gathered from each task. Each trained model's performance was evaluated in a cross-validation scenario, utilizing five folds for each dataset. These trained models are then tested on the other tasks that the model is not trained with. Finally the models in our study are assessed in terms of generalizability. Results show the superior generalizability of the task model performed along the horizontal plane, and decision tree based algorithms., Comment: Accepted to 2024 21st International Conference on Ubiquitous Robots (UR)

Excited atomic nitrogen atoms play an important role in plasma formation in hypersonic shock-waves, as happens during spacecraft reentry and other high velocity vehicle applications. In this study, we have thoroughly studied collision induced excitation (CIE) associated with two colliding nitrogen atoms in the N(4S), N(2D) and N(2P) states at collisions energies up to 6 eV, using time-independent scattering calculations to determine cross sections and temperature-dependent rate coefficients. The calculations are based on potential curves and couplings determined in earlier MRCI calculations with large basis sets, and the results are in good agreement with experiment where comparisons are possible. To properly consider the spin-orbit coupling matrix, we have developed a scaling method for treating transitions between different fine-structure components with calculations that only require calculations with two coupled states, and with this we define accurate degeneracy factors for determining cross sections and rate coefficients that include all states. The results indicate that both spin-orbit and derivative coupling effects can play important roles in collisional excitation and quenching, and that although derivative coupling is always much stronger than spin-orbit, there are many transitions where only spin-orbit can contribute. As part of this, we identify two distinct pathways associated with N(2D) relaxation, including one Auger-like mechanism leading to 2N(2D) that could be important at high temperature.

It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumption that the strategic bidders have prior knowledge of rival bids, either perfectly or with some uncertainty. This is not necessarily a realistic assumption. This paper takes a different approach by relaxing such an assumption and exploits a no-regret learning algorithm for repeated games. In particular, by using the \emph{a posteriori} information about rivals' bids, a learner can implement a no-regret algorithm to optimize her/his decision making. Given this information, we utilize a multiplicative weight-update algorithm, adapting bidding strategies over multiple rounds of an auction to minimize her/his regret. Our numerical results show that when the proposed learning approach is used the social cost and the market-clearing prices can be higher than those corresponding to the classical game-theoretic approaches. The takeaway for market regulators is that electricity markets might be exposed to greater market power of suppliers than what classical analysis shows.

There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to financial-specific events and establishes Twitter as a relevant feeder for taking decisions regarding the financial market and event fraudulent activities in that market. However, the provenance of contributions, their different levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specific financial action on the 27th January 2017:{~ }the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange (LSE), to create the UK's Leading Food Business. The experiment attempts to answer five key research questions which aim to characterize the features of Twitter permeability to the financial market. The experimental results confirm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that despite, Twitter not being a specific financial forum, it is permeable to financial events.

In this work, we propose and assess the potential of generative artificial intelligence (AI) to generate public engagement around potential clean energy sources. Such an application could increase energy literacy -- an awareness of low-carbon energy sources among the public therefore leading to increased participation in decision-making about the future of energy systems. We explore the use of generative AI to communicate technical information about low-carbon energy sources to the general public, specifically in the realm of nuclear energy. We explored 20 AI-powered text-to-image generators and compared their individual performances on general and scientific nuclear-related prompts. Of these models, DALL-E, DreamStudio, and Craiyon demonstrated promising performance in generating relevant images from general-level text related to nuclear topics. However, these models fall short in three crucial ways: (1) they fail to accurately represent technical details of energy systems; (2) they reproduce existing biases surrounding gender and work in the energy sector; and (3) they fail to accurately represent indigenous landscapes -- which have historically been sites of resource extraction and waste deposition for energy industries. This work is performed to motivate the development of specialized generative tools and their captions to improve energy literacy and effectively engage the public with low-carbon energy sources., Comment: 26 pages, 11 figures, 9 tables, submitted to review