S - and D -wave phase shifts in isospin-2 π π scattering from lattice QCD
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Molecular crystals play a central role in a wide range of scientific fields, including pharmaceuticals and organic semiconductor devices. However, they are challenging systems to model accurately with computational approaches because of a delicate interplay of intermolecular interactions such as hydrogen bonding and Van der Waals dispersion forces. Here, by exploiting recent algorithmic developments, we report the first set of diffusion Monte Carlo lattice energies for all 23 molecular crystals in the popular and widely used X23 dataset. Comparisons with previous state-of-the-art lattice energy predictions (on a subset of the dataset) and a careful analysis of experimental sublimation enthalpies reveals that high-accuracy computational methods are now at least as reliable as (computationally derived) experiments for the lattice energies of molecular crystals. Overall, this work demonstrates the feasibility of high-level explicitly correlated electronic structure methods for broad benchmarking studies in complex condensed phase systems, and signposts a route towards closer agreement between experiment and simulation. Published by the American Physical Society 2024
We revisit the extraction of the |𝑉 𝑢𝑑 | Cabibbo-Kobayashi-Maskawa (CKM) matrix element from the superallowed transition decay rate of 26𝑚 Al → 26 Mg, focusing on finite nuclear size effects. The decay rate dependence on the 26𝑚 Al charge radius is found to be four times higher than previously believed, necessitating precise determination. However, for a short-lived isotope of an odd 𝑍 element such as 26𝑚 Al , radius extraction relies on challenging many-body atomic calculations. We performed the needed calculations, finding an excellent agreement with previous ones, which used a different methodology. This sets a new standard for the reliability of isotope shift factor calculations in many-electron systems. The ℱ𝑡 value obtained from our analysis is lower by 2.2𝜎 than the corresponding value in the previous critical survey, resulting in an increase in |𝑉 𝑢𝑑 | 2 by 0.9𝜎. Adopting |𝑉 𝑢𝑑 | from this decay alone reduces the CKM unitarity deficit by one standard deviation, irrespective of the choice of |𝑉 𝑢𝑠 |.
Microgrids are evolving as promising options to enhance reliability of the connected transmission and distribution systems. Traditional design and deployment of microgrids require significant engineering analysis. However, Microgrid Building Blocks (MBB), consisting of modular blocks that integrate seamlessly to form effective microgrids, are promising technologies to enable faster and broader adoption of microgrids. Back-to-Back converter placed at the point of common coupling of microgrid is an integral part of MBB. This paper presents applications of MBB to decouple power quality issues in grid-microgrid network serving power quality sensitive critical loads such as data centers, new grid-edge technologies such as vehicle-to-grid generation, and emergency condition loads such as electric vehicle charging loads during evacuation prior disaster events. Simulation results show that MBB effectively decouple the power quality issues across networks and allow network with low power quality to transfer high-power quality power to connected networks during emergency conditions.
This paper reviews the current state of the art in high‐speed (HS) and ultrahigh‐speed (UHS) digital image correlation (DIC) techniques, emphasizing their critical role in experimental research across various scientific domains. HS and UHS DIC have evolved significantly, driven by advancements in camera systems, image processing algorithms and experimental methodologies. These developments have opened new avenues for capturing and analysing dynamic events with unprecedented temporal and spatial detail, but not without introducing challenges such as optical distortions, motion blur and lighting issues that can affect measurement quality. This review advocates for standardized reporting practices in HS/UHS DIC methodologies to improve reproducibility and reliability across studies, drawing on guidelines from the International Digital Image Correlation Society (iDICs). Through a comprehensive analysis of over 150 articles, this review identifies key advancements in imaging technology and their application in six research domains: material characterization, test development, fracture mechanics, model validation, ballistic and explosive phenomena assessment and measurement uncertainties. Distinctions between two‐dimensional (2D) and stereo‐DIC applications are explored, offering insights into their practical implementation and trade‐offs. Good practices for HS/UHS DIC applications are proposed along with suggestions for future directions for this evolving field, highlighting the indispensable role of technological innovation in expanding the capabilities of optical metrology.
Reliable super-resolution methods are crucial for applications like remote sensing, grid resilience and disaster impact analysis, and standoff biometrics. These methods infuse additional high-frequency information into reconstructions, allowing for better contextualization and image intelligence. However, super-resolution models can also introduce hallucinations or other unseen vulnerabilities that could be exploited by an adversary. This is further compounded by the prominence of deep learning in these models, as models are often blindly applied on out-of-distribution images. In this work, we implement adversarial attacks in common open-source super-resolution models and examine their impact on reconstructions and downstream classification tasks. We find that an adversarially trained super-resolution model can produce high-quality reconstructions that degrade downstream classifications. Moreover, these attacks do not require access to low-resolution imagery or class labels at inference time. These results demonstrate the vulnerability of super-resolution methods to malicious actors and motivates the development of a detector for super-resolution adversarial attacks. Further exploration of adversarial attacks in this domain is required to ensure trustworthiness and robustness of super-resolution models for national security applications.
We used a data set of archival Hubble Space Telescope images obtained through the F555W, F814W, and F656N filters to perform a complete search for objects showing Hα emission in the globular cluster NGC 6397. As a photometric diagnostic, we used the (V−Hα){sub 0} color excess in the (V−Hα){sub 0}–(V−I){sub 0} color–color diagram. In the analyzed field of view, we identified 53 Hα emitters. In particular, we confirmed the optical counterpart to 20 X-ray sources (7 cataclysmic variables, 2 millisecond pulsars and 11 active binaries) and identified 33 previously unknown sources, thus significantly enlarging the population of known active binaries in this cluster. We report the main characteristics for each class of objects. Photometric estimates of the equivalent width of the Hα emission line were derived from the (V−Hα){sub 0}-excess and, for the first time, compared to the spectroscopic measurements obtained from the analysis of MUSE spectra. The very good agreement between the spectroscopic and photometric measures fully confirmed the reliability of the proposed approach to measure the Hα emission. The search demonstrated the efficiency of this novel approach to pinpoint and measure Hα-emitters, thus offering a powerful tool to conduct a complete census of objects with formations and evolutions that can be strongly affected by dynamical interactions in star clusters.
The Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) is a unique, water-cooled, high-flux test reactor capable of performing tests prototypical of PWR operating conditions. The Radiation Measurements Laboratory (RML) was founded in the 1960s to support reactor operations and to conduct independent scientific research. For nearly six decades RML has performed four primary functions: monitoring of radioactivity by gamma-ray spectroscopy of routine reactor samples, fluence rate determinations for irradiation cycles, fission-rate measurements for the ATR-Critical (ATR-C) Facility, and independent research and development of radiation detection systems and applications. Through the decades, RML has seen technological advancements that have been integrated into each of the critical functions of the laboratory. However, many of the measurement and analysis systems employed to this day can be improved by modernization. Recent progess at RML are improving reliability and accuracy of the radiation measurements performed in support of nuclear energy research for the U.S.A. Department of Energy. The control and data collection systems supporting ATR-C have been upgraded. New High-Purity Germanium (HPGe) spectrometers have been procured with liquid nitrogen recycling capabilities to improve up-time and reduce measurement uncertainties. Fluence-rate measurement techniques are also being improved to ensure accuracy, avoid systemic errors, and identify biases. In a parallel effort, new scientific research avenues are being explored which will provide an opportunity to further enhance the utilization of ATR and ensure the sustainability of the RML as nuclear research continues to evolve. The RML is improving the effectiveness of irradiation services provided by ATR while ensuring a sustainable future for nuclear energy research.
The Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) is a unique, water-cooled, high-flux test reactor capable of performing tests prototypical of PWR operating conditions. The Radiation Measurements Laboratory (RML) was founded in the 1960s to support reactor operations and to conduct independent scientific research. For nearly six decades RML has performed four primary functions: monitoring of radioactivity by gamma-ray spectroscopy of routine reactor samples, fluence rate determinations for irradiation cycles, fission-rate measurements for the ATR-Critical (ATR-C) Facility, and independent research and development of radiation detection systems and applications. Through the decades, RML has seen technological advancements that have been integrated into each of the critical functions of the laboratory. However, many of the measurement and analysis systems employed to this day can be improved by modernization. Recent progress at RML is improving reliability and accuracy of the radiation measurements performed in support of nuclear energy research for the U.S.A. Department of Energy. The control and data collection systems supporting ATR-C have been upgraded. New High-Purity Germanium (HPGe) spectrometers have been procured with liquid nitrogen recycling capabilities to improve up-time and reduce measurement uncertainties. Fluence-rate measurement techniques are also being improved to ensure accuracy, avoid systemic errors, and identify biases. In a parallel effort, new scientific research avenues are being explored which will provide an opportunity to further enhance the utilization of ATR and ensure the sustainability of the RML as nuclear research continues to evolve. The RML is improving the effectiveness of irradiation services provided by ATR while ensuring a sustainable future for nuclear energy research.
Quantum detectors of single photons are an essential component for quantum information processing across computing, communication and networking. Today's quantum detection system, which consists of single photon detectors, timing electronics, control and data processing software, is primarily used for counting the number of single photon detection events. However, it is largely incapable of extracting other rich physical characteristics of the detected photons, such as their wavelengths, polarization states, photon numbers, or temporal waveforms. This work, for the first time, demonstrates a smart quantum detection system, SQuaD, which integrates a field programmable gate array (FPGA) with a neural network model, and is designed to recognize the features of photons and to eliminate detector dark-count. The SQuaD is a fully integrated quantum system with high timing-resolution data acquisition, onboard multi-scale data analysis, intelligent feature recognition and extraction, and feedback-driven system control. Our \name experimentally demonstrates 1) reliable photon counting on par with the state-of-the art commercial systems; 2) high-throughput data processing for each individual detection events; 3) efficient dark count recognition and elimination; 4) up to 100% accurate feature recognition of photon wavelength and polarization. Additionally, we deploy the SQuaD to an atomic (erbium ion) photon emitter source to realize noise-free control and readout of a spin qubit in the telecom band, enabling critical advances in quantum networks and distributed quantum information processing.
The high-luminosity program has seen numerous extrapolations of its needed computing resources that each indicate the need for substantial changes if the desired HL-LHC physics program is to be supported within the current level of computing resource budgets. Drivers include large increases in event complexity (leading to increased processing time and analysis data size) and trigger rates needed (5-10 fold increases) for the HL-LHC program. The CMS experiment has recently undertaken an effort to merge the ideas behind short-term and long-term resource models in order to make easier and more reliable extrapolations to future needs. Near term computing resource estimation requirements depend on numerous parameters: LHC uptime and beam intensities; detector and online trigger performance; software performance; analysis data requirements; data access, management, and retention policies; site characteristics; and network performance. Longer term modeling is affected by the same characteristics, but with much larger uncertainties that must be considered to understand the most interesting handles for increasing the "physics per computing dollar" of the HL-LHC. In this presentation, we discuss the current status of long term modeling of the CMS computing resource needs for HL-LHC with emphasis on techniques for extrapolations, uncertainty quantification, and model results. We illustrate potential ways that high-luminosity CMS could accomplish its desired physics program within today's computing budgets.
Recently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing criteria. Experimental particle physicists have been aware of the precursors of non-replicable research for many decades and have many safeguards to ensure that the published results are as reliable as possible. The experiments require large investments of time and effort to design, construct, and operate. Large collaborations produce and check the results, and many papers are signed by more than three thousand authors. This paper gives an introduction to what experimental particle physics is and to some of the tools that are used to analyze the data. It describes the procedures used to ensure that results can be computationally reproduced, both by collaborators and by non-collaborators. It describes the status of publicly available data sets and analysis tools that aid in reproduction and recasting of experimental results. It also describes methods particle physicists use to maximize the reliability of the results, which increases the probability that they can be replicated by other collaborations or even the same collaborations with more data and new personnel. Examples of results that were later found to be false are given, both with failed replication attempts and one with alarmingly successful replications. While some of the characteristics of particle physics experiments are unique, many of the procedures and techniques can be and are used in other fields.
The goal of the proposed research is to establish a computational and experimental platform that will allow reliable and efficient power and structural performance evaluation of FOWT. A secondary goal of the research is to establish an educational FOWT setup to promote renewable energy research among undergraduate and high-school students.
Highlights: • Compares industry standard and {sup 14}C methods for determining bioenergy content of MSW. • Differences quantified through study at an operational energy from waste plant. • Manual sort and selective dissolution are unreliable measures of feedstock bioenergy. • {sup 14}C methods (esp. AMS) improve precision and reliability of bioenergy determination. • Implications for electricity generators and regulators for award of bio-incentives. - Abstract: {sup 14}C analysis of flue gas by accelerator mass spectrometry (AMS) and liquid scintillation counting (LSC) were used to determine the biomass fraction of mixed waste at an operational energy-from-waste (EfW) plant. Results were converted to bioenergy (% total) using mathematical algorithms and assessed against existing industry methodologies which involve manual sorting and selective dissolution (SD) of feedstock. Simultaneous determinations using flue gas showed excellent agreement: 44.8 ± 2.7% for AMS and 44.6 ± 12.3% for LSC. Comparable bioenergy results were obtained using a feedstock manual sort procedure (41.4%), whilst a procedure based on selective dissolution of representative waste material is reported as 75.5% (no errors quoted). {sup 14}C techniques present significant advantages in data acquisition, precision and reliability for both electricity generator and industry regulator.
Carbon fiber (CF)-reinforced epoxy composites are widely used in vehicle applications, where early damage detection is crucial for reliability and safety. To address this need, we developed a self-sensing epoxy/CF composite by embedding a PVDF-MoS 2 nanosensor via an embedded 3D printing method. By harnessing the intrinsic curing kinetics of epoxy, we tailored its rheological properties to optimize the embedded printing process, enabling precise and reliable support for sensor filaments without compromising the composite’s structural and functional integrity. Through comprehensive rheological and kinetic analysis, we established a quantitative relationship among curing temperature, conversion rate, and resulting yield modulus─defining a narrow processing window essential for successful sensor integration. Specifically, we identified that an epoxy yield modulus range of 180–294 Pa and a conversion rate below 10% are critical to support the PVDF-MoS 2 filament architecture. Here, this embedded 3D printing method produces complex and multimaterial PVDF-MoS 2 sensors within an epoxy matrix with minimal deformation and reduced postprocessing, which is scalable and adaptable for industrial applications. Under cyclic loading, the embedded sensors exhibited stable signals under constant loads and increased voltage signals in response to crack formation (17–35% higher) and catastrophic failure (1 order of magnitude higher), effectively capturing structural changes in real time. This study demonstrates the potential of PVDF-MoS 2 nanocomposite sensor materials for real-time structural health monitoring in epoxy–CF composite systems, enabling early detection of defects and stress anomalies, significantly reducing the risk of unexpected failures, and enhancing structural reliability.
The monitoring of enrichment operations may require innovative analysis to allow for imperfect or missing data. The presence of minor isotopes may help or hurt - they can complicate a calculation or provide additional data to corroborate a calculation. However, they must be considered in a rigorous analysis, especially in cases involving reuse. This study considers matched-abundanceratio cascades that involve at least three isotopes and allows generalized input that does not require all feed assays or the enrichment factor to be specified. Calculations are based on the equations developed for the MSTAR code but are generalized to allow input of various combinations of assays, flows, and other cascade properties. Traditional cascade models have required specification of the enrichment factor, all feed assays, and the product and waste assays of the primary enriched component. The calculation would then produce the numbers of stages in the enriching and stripping sections and the remaining assays in waste and product streams. In cases where the enrichment factor or feed assays were not known, analysis was difficult or impossible. However, if other quantities are known (e.g., additional assays in waste or product streams), a reliable calculation is still possible with the new code, but such nonstandard input may introduce additional numerical difficulties into the calculation. Thus, the minimum input requirements for a stable solution are discussed, and a sample problem with a non-unique solution is described. Both heuristic and mathematically required guidelines are given to assist the application of cascade modeling to situations involving such non-standard input. As a result, this work provides both a calculational tool and specific guidance for evaluation of enrichment cascades in which traditional input data are either flawed or unknown. It is useful for cases involving minor isotopes, especially if the minor isotope assays are desired (or required) to be important contributors to the overall analysis.
Dramatic reductions in solar, wind, and battery storage costs create new opportunities to reduce emissions and costs in China’s electricity sector, beyond current policy goals. This study examines the cost, reliability, emissions, public health, and employment implications of increasing the share of non-fossil fuel (“carbon free”) electricity generation in China to 80% by 2035. The analysis uses state-of-the-art modeling with high resolution load, wind, and solar inputs. The study finds that achieving an 80% carbon free electricity system in China by 2035 could reduce wholesale electricity costs, relative to a current policy baseline, while maintaining high levels of reliability, reducing deaths from air pollution, and increasing employment. In our 80% scenario, wind and solar generation capacity reach 3 TW and battery storage capacity reaches 0.4 TW by 2035, implying a rapid scale up in these resources that will require changes in policy targets, markets and regulation, and land use policies.
This paper introduces a framework of online oscillation monitoring systems and forced oscillation detection & source location tools that Peak Reliability ('PEAK') has implemented for the Reliability Coordinator (RC) function of the Western Interconnection. The framework consists of four main components: (1) Montana Tech's Modal Analysis Software (MAS) engine; (2) Washington State University's Oscillation Monitor System (OMS) software; (3) Forced Oscillation Detection and Source Location Algorithms (FODSL); and (4) PEAK in-house visualization tool and alarming logic built in PI Processbook. The framework has been validated and applied for real system oscillation studies successfully. Implementation experience and lessons learned will be discussed in the paper.