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This dataset contains survey, navigation, and platform-design data supporting instrument deployment and positioning during the 2016-2017 RODEO field operations at Block Island. It includes an acoustic localization software toolkit and its Block Island position solutions, R/V Tioga ship navigation and underway data, CTD casts, ship-track charts, and engineering designs for the deployed moorings and arrays.
Very little is known about the cost landscape for parametrized Quantum Circuits (PQCs). Nevertheless, PQCs are employed in Quantum Neural Networks and Variational Quantum Algorithms, which may allow for near-term quantum advantage. Such applications require good optimizers to train PQCs. Recent works have focused on quantum-aware optimizers specifically tailored for PQCs. However, ignorance of the cost landscape could hinder progress towards such optimizers. In this work, we analytically prove two results for PQCs: (1) We find an exponentially large symmetry in PQCs, yielding an exponentially large degeneracy of the minima in the cost landscape. Alternatively, this can be cast as an exponential reduction in the volume of relevant hyperparameter space. (2) We study the resilience of the symmetries under noise, and show that while it is conserved under unital noise, non-unital channels can break these symmetries and lift the degeneracy of minima, leading to multiple new local minima. Based on these results, we introduce an optimization method called Symmetry-based Minima Hopping (SYMH), which exploits the underlying symmetries in PQCs. Our numerical simulations show that SYMH improves the overall optimizer performance in the presence of non-unital noise at a level comparable to current hardware. Overall, this work derives large-scale circuit symmetries from local gate transformations, and uses them to construct a noise-aware optimization method.
Deployment of digital technologies within a modern shift in cyber defense systems is essential for protecting the energy production units. One of the important components of defense is cyberforensics: once an attack has been detected to locate its origin. In this paper, a review of well-known cyberattacks in nuclear facilities is provided, with the lessons learned leading to the development of a machine learning approach implementing identification of internal at- tacks in the facility's data networks. Our approach may be seen as one of the layers in a defense-in-depth strategy that identifies if the attack comes from inside, which may result in identifying faster the attacker's origin. The presented model exploits network packet examination to cast accurate predictions on detailing the origin of malicious network connections. The approach fuses multiple mathematical functions within an artificial neural network to provide a response in the form of 0/1, i. e., whether the attack is identified as internal or not. The utilization of a variety of test cases is developed to explore the relevance and validity of the predictive approach. The proposed implementation is examined with network data packet variance, and the results obtained exhibit a highly accurate detection rate.
We consider the problem of signal reconstruction for a system under sparse signal corruption by a malicious agent. The reconstruction problem follows the standard error coding problem that has been studied extensively in the literature. We include a new challenge of robust estimation of the attack support. The problem is then cast as a constrained optimization problem merging promising techniques in the area of deep learning and estimation theory. A pruning algorithm is developed to reduce the "false positive" uncertainty of data-driven attack localization results, thereby improving the probability of correct signal reconstruction. Sufficient conditions for the correct reconstruction and the associated reconstruction error bounds are obtained for both exact and inexact attack support estimation. Moreover, a simulation of a water distribution system is presented to validate the proposed techniques.
Selective Laser Melting (SLM) is a well-known additive manufacturing method based on a layer-by-layer building process that is capable of fabricating gas turbine Ni-based superalloy parts with complex and integrated cooling geometries that are typically not achievable using conventional casting methods. However, the impact of residual stress concentration on SLM component life is a concern. In this work, residual strains present at room temperature in an SLM Inconel 718 generic aerodynamic leading edge segment with transpiration cooling were investigated using neutron diffraction at Oak Ridge National Laboratory. This study reveals that areas of the part where complex cooling holes were printed exhibit residual tensile strains that can be detrimental to its integrity. The study paves the way for future studies of the design of cooling hole geometries and manufacturing parameters that achieve improved cooling while minimizing stress concentration effects.
Distributed temperature monitoring can provide useful insights into the thermal conditions in the continuous caster tundish that can affect operating stability and cast quality, such as preheat conditions, superheat uniformity, fill height, and refractory wear. In the current study, fiber optic sensors were embedded into the refractory lining of a lab-scale tundish to record the temperature profiles during dry-out, preheating, and direct molten steel exposure to demonstrate sensor performance. Additional trials were performed with fiber optic sensors embedded in an industrial tundish used in production. Furthermore, the results demonstrate that fiber optic sensors using Rayleigh technology can provide accurate distributed temperature measurements in refractory lined vessels and provide useful information about the process.
Symbiotic interactions drive species evolution, with nutritional symbioses playing vital roles across ecosystems. Chemosynthetic symbioses are globally distributed and ecologically significant, yet the lack of model systems has hindered research progress. The giant ciliate Zoothamnium niveum and its sulfur-oxidizing symbionts represent the only known chemosynthetic symbiosis with a short life span that has been transiently cultivated in the laboratory. While it is experimentally tractable and presents a promising model system, it currently lacks an open-source, simple, and standardized cultivation setup. Following the FABricated Ecosystems (EcoFABs) model, we leveraged 3D printing and polydimethylsiloxane (PDMS) casting to develop simple flow-through cultivation chambers that can be produced and adopted by any laboratory. The streamlined manufacturing process reduces production time by 86% and cuts cost by tenfold compared to the previous system. Benchmarking using previously established optimal growth conditions, the new open-source cultivation system proves stable, efficient, more autonomous, and promotes a more prolific growth of the symbiosis. For the first time, starting from single cells, we successfully cultivated the symbiosis in flow-through chambers for 20 days, spanning multiple generations of colonies that remained symbiotic. They were transferred from chamber to chamber enabling long-term cultivation and eliminating the need for continuous field sampling. The chambers, optimized for live imaging, allowed detailed observation of the synchronized growth between the host and symbiont. Highlighting the benefit of this new system, we here describe a new step in the first hours of development where the host pauses growth, expels a coat, before resuming growth, hinting at a putative symbiont selection mechanism early in the colony life cycle. With this simple, open-source, cultivation setup, Z. niveum holds promises for comparative studies, standardization of research and wide adoption by the symbiosis research community.
Significant opportunities remain for pharmacologically enhancing the clinical effectiveness of proton and carbon ion-based radiotherapies to achieve both tumor cell radiosensitization and normal tissue radioprotection. We investigated whether pretreatment with the hydroxamate-based histone deacetylase inhibitors (HDACi) SAHA (vorinostat), M344, and PTACH impacts radiation-induced DNA double-strand break (DSB) induction and repair, cell killing, and transformation (acquisition of anchorage-independent growth in soft agar) in human normal and tumor cell lines following gamma ray and light ion irradiation. Treatment of normal NFF28 primary fibroblasts and U2OS osteosarcoma, A549 lung carcinoma, and U87MG glioma cells with 5–10 µM HDACi concentrations 18 h prior to cesium-137 gamma irradiation resulted in radiosensitization measured by clonogenic survival assays and increased levels of colocalized gamma-H2AX/53BP1 foci induction. We similarly tested these HDACi following irradiation with 200 MeV protons, 290 MeV/n carbon ions, and 350 MeV/n oxygen ions delivered in the Bragg plateau region. Unlike uniform gamma ray radiosensitization, effects of HDACi pretreatment were unexpectedly cell type and ion species-dependent with C-12 and O-16 ion irradiations showing enhanced G0/G1-phase fibroblast survival (radioprotection) and in some cases reduced or absent tumor cell radiosensitization. DSB-associated foci levels were similar for proton-irradiated DMSO control and SAHA-treated fibroblast cultures, while lower levels of induced foci were observed in SAHA-pretreated C-12 ion-irradiated fibroblasts. Fibroblast transformation frequencies measured for all radiation types were generally LET-dependent and lowest following proton irradiation; however, both gamma and proton exposures showed hyperlinear transformation induction at low doses (≤25 cGy). HDACi pretreatments led to overall lower transformation frequencies at low doses for all radiation types except O-16 ions but generally led to higher transformation frequencies at higher doses (>50 cGy). The results of these in vitro studies cast doubt on the clinical efficacy of using HDACi as radiosensitizers for light ion-based hadron radiotherapy given the mixed results on their radiosensitization effectiveness and related possibility of increased second cancer induction.
Wind energy producers need deployable devices for wind turbines that prevent bat fatalities. Based on the speculation that bats approach turbines after visually mistaking them for trees, we tested a potential light-based deterrence method. It is likely that the affected bats see ultraviolet (UV) light at low intensities. Here, we present the results of a multi-month experiment to cast dim, flickering UV light across wind turbine surfaces at night. Our objectives were to refine and test a practical system for dimly UV-illuminating turbines while testing whether the experimental UV treatment influenced the activity of bats, birds, and insects. We mounted upward-facing UV light arrays on turbines and used thermal-imaging cameras to quantify the presence and activity of night-flying animals. The results demonstrated that the turbines can be lit to the highest reaches of the blades with “invisible” UV light, and the animal responses to such experimental treatment can be concurrently monitored. The UV treatment did not significantly change nighttime bat, insect, or bird activity at the wind turbine. Our findings show how observing flying animals with thermal cameras at night can help test emerging technologies intended to variably affect their behaviors around wind turbines.
For this study, the effects of thermal annealing and compressive strain rate on the complexity of the serration behavior in a Zr-based bulk metallic glass (BMG) was investigated. Here, as-cast and thermally-annealed (300 °C, 1 week) Zr 52.5 Cu 17.9 Ni 14.6 Al 10 Ti 5 BMG underwent room-temperature compression tests in the unconstrained condition at strain rates of 2 × 10 -5 s -1 and 2 × 10 -4 s -1 . The complexity of the serrated flow was determined, using the refined composite multiscale entropy technique. Nanoindentation testing and X-ray diffraction characterization were performed to assess the changes in the microstructure and mechanical properties of the BMG that occurred during annealing. The results indicated that the BMG did not crystallize during annealing in the prescribed heating condition. Nanoindentation tests revealed that annealing led to a significant increase in the depth-dependent nanoindentation hardness and Young’s modulus, which were attributed to the structural relaxation in the glass. Furthermore, both annealing and an increased strain rate resulted in a marked enhancement in the complexity of the serrated flow during compression. It was concluded that the increase in the sample entropy with increasing strain rate is related to an increase in the number of defect interactions during the serrated flow.
Due to structural changes in silicon during lithiation/delithiation, most Li-ion battery anodes containing silicon show rapid gravimetric capacity fade upon charge/discharge cycling. Herein, we report on a new Si powder anode in the form of electrospun fibers with only poly(acrylic acid) (PAA) binder and no electrically conductive carbon. The performance of this anode was contrasted to a fiber mat composed of Si powder, PAA binder, and a small amount of carbon powder. Fiber mat electrodes were evaluated in half-cells with a Li metal counter/reference electrode. Without the addition of conductive carbon, a stable capacity of about 1500 mAh/g (normalized to the total weight of the anode) was obtained at 1C for 50 charge/discharge cycles when the areal loading of silicon was 0.30 mgSi/cm2, whereas a capacity of 800 mAh/g was obtained when the Si loading was increased to ~1.0 mgSi/cm2. On a Si weight basis, these capacities correspond to >3500 mAh/gSi. The capacities were significantly higher than those found with a slurry-cast powdered Si anode with PAA binder. There was no change in fiber anode performance (gravimetric capacity and constant capacity with cycling) when a small amount of electrically conductive carbon was added to the electrospun fiber anodes when the Si loading was ≤1.0 mgSi/cm2.
Central vascular access (CVA) may be critical for trauma care and stabilizing the casualty. However, it requires skilled personnel, often unavailable during remote medical situations and combat casualty care scenarios. Automated CVA medical devices have the potential to make life-saving therapeutics available in these resource-limited scenarios, but they must be properly designed. Unfortunately, currently available tissue phantoms are inadequate for this use, resulting in delayed product development. Here, we present a tissue phantom that is modular in design, allowing for adjustable flow rate, circulating fluid pressure, vessel diameter, and vessel positions. The phantom consists of a gelatin cast using a 3D-printed mold with inserts representing vessels and bone locations. These removable inserts allow for tubing insertion which can mimic normal and hypovolemic flow, as well as pressure and vessel diameters. Trauma to the vessel wall is assessed using quantification of leak rates from the tubing after removal from the model. Lastly, the phantom can be adjusted to swine or human anatomy, including modeling the entire neurovascular bundle. Overall, this model can better recreate severe hypovolemic trauma cases and subject variability than commercial CVA trainers and may potentially accelerate automated CVA device development.
Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.
Proton exchange membrane (PEM) capable of working over a broad operating condition window is critical for successful adoption of PEM-based electrochemical devices. In this work, phosphoric acid (PA)-doped biphenyl-backbone ion-pair coordinated PEMs were prepared by quaternization of BPBr-100, a precursor polymer, with three different tertiary amines including trimethylamine, 1-methylpiperidine, and 1,2-dimethylimidazole followed by membrane casting, ion exchange reaction to hydroxide ion, and doping with PA. The resulting PA-doped ion-pair PEMs were characterized in terms of PA doping level, proton conductivity, relative humidity (RH) tolerance, thermal stability, and mechanical properties. PA doping levels were between six and eight according to acid-base titration. The size and structure of the cation group of ion-pair polymers were found to affect the PA doping level and water uptake. Proton conductivity was studied as a function of RH over a wide range of 5% to 95% RH. Stable conductivity at 80 °C was observed up to 70% RH for 10 h. Mechanical property characterization indicates that the PA doping process resulted in more ductile membranes with significantly increased elongation at break due to the plasticization effect of PA. A combination of high proton conductivity at low RH conditions, and good humidity tolerance makes this new class of PEMs great potential candidates for use in electrochemical devices such as proton exchange membrane fuel cells and electrochemical hydrogen compressors.
Nanofluid electrodes with high loading of active solid materials have significant potential as high energy density flow battery electrolytes; however, two key criteria need to be met: they must have a manageable viscosity for pumping and simultaneously exhibit good electrochemical activity. A typical dispersion of nickel hydroxide nanoparticles (~100 nm) is limited to 5–10 wt.% of solids, above which it has a paste-like consistency, incompatible with flow applications. We report on the successful formulation of stable dispersions of a nano-scale nickel hydroxide cathode (β-Ni(OH) 2 ) with up to 60 wt.% of solids and low viscosity (32 cP at 25 °C), utilizing a surface graft of small organic molecules. The fraction of grafting moiety is less than 3 wt.% of the nanoparticle weight, and its presence is crucial for the colloidal stability and low viscosity of suspensions. Electrochemical testing of the pristine and modified β-Ni(OH) 2 nanoparticles in the form of solid casted electrodes were found to be comparable with the latter exhibiting a maximum discharge capacity of ~237 mAh/g over 50 consecutive charge–discharge cycles, close to the theoretical capacity of 289 mAh/g.
Development of a defensible source-term model (STM), usually a thermodynamical model for radionuclide solubility calculations, is critical to a performance assessment (PA) of a geologic repository for nuclear waste disposal. Such a model is generally subjected to rigorous regulatory scrutiny. In this article, we highlight key guiding principles for STM model development and validation in nuclear waste management. We illustrate these principles by closely examining three recently developed thermodynamic models with the Pitzer formulism for aqueous H + —Nd 3+ —NO 3 - (—oxalate) systems in a reverse alphabetical order of the authors: the XW model developed by Xiong and Wang, the OWC model developed by Oakes et al., and the GLC model developed by Guignot et al., among which the XW model deals with trace activity coefficients for Nd(III), while the OWC and GLC models are for concentrated Nd(NO 3 ) 3 electrolyte solutions. The principles highlighted include the following: (1) Principle 1. Validation against independent experimental data: A model should be validated against experimental data or field observations that have not been used in the original model parameterization. We tested the XW model against multiple independent experimental data sets including electromotive force (EMF), solubility, water vapor, and water activity measurements. The results show that the XW model is accurate and valid for its intended use for predicting trace activity coefficients and therefore Nd solubility in repository environments. (2) Principle 2. Testing for relevant and sensitive variables: Solution pH is such a variable for an STM and easily acquirable. All three models are checked for their ability to predict pH conditions in Nd(NO 3 ) 3 electrolyte solutions. The OWC model fails to provide a reasonable estimate for solution pH conditions, thus casting serious doubt on its validity for a source-term calculation. In contrast, both the XW and GLC models predict close-to-neutral pH values, in agreement with experimental measurements. (3) Principle 3. Honoring physical constraints: Upon close examination, it is found that the Nd(III)-NO 3 association schema in the OWC model suffers from two shortcomings. Firstly, its second stepwise stability constant for Nd(NO 3 ) 2+ (log K 2 ) is much higher than the first stepwise stability constant for NdNO 3 2+ (log K 1 ), thus violating the general rule of (log K 2 –log K 1 ) < 0, or $\frac{K1}{K2}$>1. Secondly, the OWC model predicts abnormally high activity coefficients for Nd(NO 3 ) 2 + (up to ~900) as the concentration increases. (4) Principle 4. Minimizing degrees of freedom for model fitting: The OWC model with nine fitted parameters is compared with the GLC model with five fitted parameters, as both models apply to the concentrated region for Nd(NO 3 ) 3 electrolyte solutions. The latter appears superior to the former because the latter can fit osmotic coefficient data equally well with fewer model parameters. The work presented here thus illustrates the salient points of geochemical model development, selection, and validation in nuclear waste management.
One of the main challenges of using a high fiber volume content in a cement composite is the narrow margin of fiber volume content beyond which fibers can cause an adverse effect on the mechanical properties. In this paper, the significance of fiber size distribution and fiber volume content of different proportions of chopped and milled carbon microfibers are investigated. The mixes’ flowability showed improvement with altering the fiber size distribution despite having a high fiber content. Uniaxial compression cylinders and unnotched and notched beams were cast and then tested at 7 and 28 days of age. It was found that the compressive strength is significantly affected by fiber size distribution more than fiber volume content. On the other hand, the modulus of rupture and fracture toughness are proportional to the fiber volume content with little effect of fiber size distribution. Finally, neither high fiber volume content nor altered fiber size distribution significantly affected the elastic modulus of the fiber cement composites.
Styrene acrylic emulsions (SAEs) have emerged as a promising material for water-based coatings. However, they are still limited by their own defects in practical applications, poor weatherability, and degradation of performance at lower or higher temperatures. Here, we introduce a facile approach to producing fluorescent carbon quantum dots (CQDs) from wood processing residues and fabricating fluorescent CQD/SAE coating films via emulsion-casting. The addition of the fluorescent CQDs enhanced the optical performance of the CQD/SAE coating films. The fluorescent CQDs were prepared via a hydrothermal approach and were obtained after heating at 180 °C for 6 h at a reaction concentration of 50 mg/mL. The synthesized CQDs resulted in a high fluorescence, and the CQDs had an average size of 1.63 nm. Various concentrations of the fluorescent CQDs were doped into the SAE coating film, which improved its optical properties. We also characterized and discussed the products and then explored their optical properties. This study presents the potential of fluorescent CQD/SAE coating films for applications in anti-counterfeiting coatings, fluorescent adhesives, and papermaking.