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These are slides for the upcoming FNUG forensics meeting with the UK. These slides have been completely taken from previously released presentation - INL-CON-23-71391. I am resubmitting to LRS for completeness.
In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can con-taminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is re-quired. Several real-time functional blocks were simu-lated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid base-line by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.
We report on the measurement of spin-density matrix elements for the K∗(892) → K+π0 photoproduction process with a recoiling Λ hyperon. The measurement used high-statistics GlueX data from photoproduction off a proton target at photon energies between 8.2 and 8.8 GeV, in a kinematic regime dominated by t-channel exchange processes. GlueX is a fixed-target experiment with a linearly polarized photon beam and a nearly 4π hermetic detector, allowing extraction of the full spin observ- ables from both the K+π0 and Λ → π−p systems. The GlueX data show clear evidence of the K∗(892) resonance in the K+π0 mass spectrum, along with additional structures at higher masses that may correspond to K∗0 or K∗2 resonances. The precise measurement of the spin-density matrix elements of the K∗(892) could serve as a standard candle for con- tinuing studies of higher-mass excited K∗ states. In addition, t-channel K∗ photoproduction differs from previous measurements of non-strange vector meson photoproduction, as it is free from Pomeron exchange and is virtually unexplored in this energy regime. Furthermore, the unprece- dented data collected by GlueX allow for investigations of correlations between the Λ polarization and the spin-density matrix elements of K∗ production, paving the way for future partial-wave analyses with full spin information involving the recoil hyperon.
Recent breakthroughs in natural language processing show that attention mech- anism in Transformer networks, trained via masked-token prediction, enables models to capture the semantic context of the tokens and internalize the grammar of language. While the application of Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol con- tribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Model- ing (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from Transformers methodology. In MSM, a subset of symbol-aligned samples is randomly masked, and a Transformer predicts the missing symbol identifiers using the surrounding “in-between” samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM’s potential by applying it to the task of demodulating sig- nals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.
Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on “omics” driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in “omics” technologies, compare the pros and cons of the different “omics” technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of “omics” technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of “omics” technologies to agricultural and food biotechnology, and review the impact of “omics” on current COVID-19 research.
As combinatorial and computational methods accelerate the identification of potentially suitable thermochemically-active oxides for use in solar thermochemical hydrogen production (STCH), the onus shifts to quickly evaluating predicted performance. Traditionally, this has required an experimental setup capable of directly carrying out a two-stage thermochemical water-splitting process. But this can be a difficult endeavor, as most off-the-shelf equipment cannot adequately deal simultaneously with the high temperatures, varying oxygen partial pressures, and high H 2 O partial pressures required; achieving sufficient temporal sensitivity to accurately quantify the kinetics is also a major challenge. However, as proposed here, a less complicated experiment can be used as a first screening for thermochemical water splitting potential. Temperature-Programmed Thermal Redox (TPTR) using thermogravimetry evaluates the thermal reduction behavior of materials. This technique does not require water splitting or CO 2 -splitting analogs but can nonetheless predict water-splitting performance. Three figures of merit are obtained from the TPTR experiment: reduction onset temperature, extent of reduction, and extent of recovery upon reoxidation. These metrics can collectively be used to determine if a material is capable of thermochemical water-splitting, and, to good approximation, predict whether the thermodynamics are favorable for use under more challenging high-conversion conditions. This paper discusses the pros and cons of using TPTR and proposes a protocol for use within the STCH community.
Targeted alpha therapy is an oncological treatment, where cytotoxic doses of alpha radiation are locally delivered to tumor cells, while the surrounding healthy tissue is minimally affected. This therapeutic strategy relies on radiopharmaceuticals made of medically relevant radionuclides chelated by ligands, and conjugated to targeting vectors, which promote the drug accumulation in tumor sites. This review discusses the state-of-the-art in the development of radiopharmaceuticals for targeted alpha therapy, breaking down their key structural components, such as radioisotope, targeting vector, and delivery formulation, and analyzing their pros and cons. Moreover, we discuss current drawbacks that are holding back targeted alpha therapy in the clinic, and identify ongoing strategies in field to overcome those issues, including radioisotope encapsulation in nanoformulations to prevent the release of the daughters. Lastly, we critically discuss potential opportunities the field holds, which may contribute to targeted alpha therapy becoming a gold standard treatment in oncology in the future.
Tannins are plant secondary metabolites that bind organic carbon (C) and nitrogen (N), potentially altering substrate bioavailability for enteric fermentation in ruminants. This interaction may reduce greenhouse gas (GHG) emissions and influence nitrogen partitioning. Given tannins' resistance to ruminal degradation and persistence through the gastrointestinal tract, this study investigated the effects of a tannin-based feed additive on fecal microbial diversity, fecal chemical composition, and GHG emissions. Twenty-four early- to mid-lactation dairy cows were randomized to receive either a tannin-based feed additive (TRT; containing condensed and hydrolyzable tannins from Schinopsis quebracho-colorado [Schltdl.]) or a control diet (CON) for 64 days. Cows were blocked by parity, dry matter intake, milk yield, body weight, and days in milk. Fecal samples were collected on days 0, 16, 32, and 64 and analyzed using 16S rRNA gene amplicon sequencing. Fecal C, N, and indole-3-lactate were measured, and GHG emissions (N2O, CH4, CO2) were assessed via 14-day laboratory incubation. A total of 1,538 amplicon sequence variants were identified, with Firmicutes as the dominant phylum. Fecal phylogenetic diversity showed a significant treatment × day interaction (p < 0.01), with TRT cows exhibiting reduced microbial diversity from day 16 to 64. Fecal C and N concentrations were significantly lower (p < 0.01) in TRT cows on day 16, while indole-3-lactate levels were higher on day 64 (p = 0.02). GHG emissions did not differ significantly between treatments. The tannin-based feed additive influenced fecal microbial community structure and select chemical parameters but did not significantly affect GHG emissions from feces. These findings suggest that dietary tannins may modulate gut microbial ecology with minimal impact on downstream manure-related emissions.
The increase in neutron flux at the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source (SNS), currently operating at 2.0 MW proton beam power, has created new opportunities for higher-throughput neutron scattering experiments while also increasing the importance of minimizing background scattering and optimizing sample-environment operations. To address these challenges on the Backscattering Silicon Spectrometer (BA-SIS), several upgrades were developed and evaluated, including boron carbide (B₄C) masking for flat-plate sample containers, multi-cell sample holders used with a vertically translating sample stick, and an automated helium pump and purge (HPP) system for closed-cycle refrigerators. Neutron diffraction measurements demonstrate that B₄C masks reduce background scattering by 49–67%, outperforming both borated aluminum and boron nitride masks while introducing no additional Bragg reflections within the instrument’s accessible Q-range. Commissioning tests of a double-cell flat-plate sample contain-er showed no measurable crosstalk between adjacent sample compartments and con-firmed stable thermal performance, enabling multiple samples to be measured without re-peated temperature cycling. In addition, the automated HPP system provided reproducible sample-space gas handling with approximately ±1 mbar precision while reducing the need for operator intervention and supporting remote operation. Together, these developments improve signal-to-noise performance, increase experimental throughput, and enhance operational efficiency at BASIS, supporting the instrument’s continued operation under higher neutron flux conditions.
Solar power has innate issues with weather, grid demand and time of day, which can be mitigated through use of thermal energy storage for concentrating solar power (CSP). Nuclear reactors, including lead-cooled fast reactors (LFRs), can adjust power output according to demand; but with high fixed costs and low operating costs, there may not be sufficient economic incentive to make this worthwhile. We investigate potential synergies through coupling CSP and LFR together in a single supercritical CO 2 Brayton cycle and/or using the same thermal energy storage. Combining these cycles allows for the LFR to thermally charge the salt storage in the CSP cycle during low-demand periods to be dispatched when grid demand increases. The LFR/CSP coupling into one cycle is modeled to find the preferred location of the LFR heat exchanger, CSP heat exchanger, sCO 2 -to-salt heat exchanger (C2S), turbines, and recuperators within the supercritical CO 2 Brayton cycle. Three cycle configurations have been studied: two-cycle configuration, which uses CSP and LFR heat for dedicated turbocompressors, has the highest efficiencies but with less component synergies; a combined cycle with CSP and LFR heat sources in parallel is the simplest with the lowest efficiencies; and a combined cycle with separate high-temperature recuperators for both the CSP and LFR is a compromise between efficiency and component synergies. Additionally, four thermal energy storage charging techniques are studied: the turbine positioned before C2S, requiring a high LFR outlet temperature for viability; the turbine after the C2S, reducing turbine inlet temperature and therefore power; the turbine parallel to the C2S producing moderate efficiency; and a dedicated circulator loop. While all configurations have pros and cons, use of a single cycle offers component synergies with limited efficiency penalty. Using a turbine in parallel with the C2S heat exchanger is feasible but results in a low charging efficiency, while a dedicated circulator loop offers flexibility and near-perfect heat storage efficiency but increasing cost with additional cycle components.
A holistic approach to decision-making in modern energy systems is vital due to their increase in complexity and interconnectedness. However, decision makers often rely on narrowly-focused strategies, such as economic assessments, for energy system strategy selection. The approach in this paper helps considers various factors such as economic viability, technological feasibility, environmental impact, and social acceptance. By integrating these diverse elements, decision makers can identify more economically feasible, sustainable, and resilient energy strategies. While existing focused approaches are valuable since they provide clear metrics of a potential solution (e.g., an economic measure of profitability), they do not offer the much needed system-as-a-whole understanding. This lack of understanding often leads to selecting suboptimal or unfeasible solutions, which is often discovered much later in the process when a change may not be possible. This paper presents a novel evaluation framework to support holistic decision-making in energy systems. The framework is based on a systems thinking approach, applied through systems engineering principles and model-based systems engineering tools, coupled with a multicriteria decision analysis approach. The systems engineering approach guides the development of feasible solutions for novel energy systems, and the multicriteria decision analysis is used for a systematic evaluation of available strategies and objective selection of the best solution. The proposed framework enables holistic, multidisciplinary, and objective evaluations of solutions and strategies for energy systems, clearly demonstrates the pros and cons of available options, and supports knowledge collection and retention to be used for a different scenario or context. The framework is demonstrated in case study evaluation solutions for a novel energy system of clean hydrogen generation.
Abstract Yellowknife Bay (Great Slave Lake, Northwest Territories, Canada) is a water body valued by surrounding communities for its subsistence, recreational, and cultural use. Located directly downstream of the former Giant Mine and Con Mine, Yellowknife Bay has received inputs from mine waste streams enriched in arsenic (As), antimony (Sb), and metals since the late 1930s. Lake sediments in Yellowknife Bay provide a record of metal(loid) contamination from aerially deposited roaster stack emissions, mine effluent, and Giant Mine tailings. A sediment sampling program was conducted in Yellowknife Bay to characterize As and Sb mineralogy using scanning electron microscopy-mineral liberation analysis. Mineralogical analysis of As- and Sb-hosted minerals in nine sediment cores suggests that arsenic trioxide (As2O3), originally deposited during the period of peak-mining emissions, has since been transformed into authigenic sulfides (interpreted to be realgar) down core from peak-mining emissions. Arsenic has also been attenuated by iron (Fe)-oxyhydroxides and roaster-generated iron oxides up-core from peak-mining emissions, near the sediment–water interface. The Sb-bearing minerals appear to be stable in Yellowknife Bay sediments, with no conclusive evidence of post-depositional mobility having been identified. The observed prevalence of arsenic trioxide in surface sediments proximal to Giant Mine suggests that As and Sb contamination is ongoing, likely from terrestrial weathering of contaminated soils and shoreline outcrops. Arsenic-bearing oxide minerals prevalent in surface sediments may become unstable should redox conditions in the hypolimnion change; prolonged anoxia could destabilize the As hosting minerals and release As to bottom waters. Therefore, long-term monitoring of the water column, including hypolimnion conditions, in Yellowknife Bay is recommended.
We use HST/STIS optical spectroscopy of 10 M dwarfs in five closely separated binary systems to test models of M-dwarf structure and evolution. Individual dynamical masses ranging from 0.083 to 0.405 M {sub ⊙} for all stars are known from previous work. We first derive temperature, radius, luminosity, surface gravity, and metallicity by fitting the BT-Settl atmospheric models. We verify that our methodology agrees with empirical results from long-baseline optical interferometry for stars of similar spectral types. We then test whether or not evolutionary models can predict those quantities given the stars’ known dynamical masses and the conditions of coevality and equal metallicity within each binary system. We apply this test to five different evolutionary model sets: the Dartmouth models, the MESA/MIST models, the models of Baraffe et al., the PARSEC models, and the YaPSI models. We find marginal agreement between evolutionary model predictions and observations, with few cases where the models respect the condition of coevality in a self-consistent manner. We discuss the pros and cons of each family of models and compare their predictive power.
Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.
Combi heat pump systems, also referred to multifunctional variable refrigerant flow heat recovery (MF-VRFHR) systems, are specifically designed for residential applications to manage both space conditioning and domestic hot water (DHW). They have attracted attention due to their potential for energy conservation through heat recovery. The incorporation of a hot water tank introduces various system configurations, each characterized by distinct pros and cons related to energy efficiency, system stability, and maintenance. Despite this, a critical gap exists as the specific energy performance remains unquantified under diverse operational modes (e.g., heating mode and heat recovery mode). This paper aims to bridge this gap by conducting a comprehensive comparative analysis of two prevalent system configurations while considering feasible proposed control logics. Configuration 1 integrates a separate hot water tank and a refrigerant-to-water heat exchanger (HEX), also known as a Hydro Kit while Configuration 2 incorporates a refrigerant-wrapped hot water tank. To facilitate this analysis, we developed high-fidelity system models for both configurations in Modelica, capturing system dynamics and detailed control sequences effectively. These system models were built upon the TIL library for HVAC equipment components and the Buildings library for residential building thermal load calculations. The validation of the simulation testbed utilized data from experiments conducted in the PNNL lab home for Configuration 1. To establish the simulation testbed for Configuration 2, we extended the modeling setup derived from Configuration 1. This extension specifically involved substituting the separate hot water tank and Hydro Kit with a refrigerant-wrapped hot water tank of similar sizing sourced from an actual product. The simulation analysis of heating-only and heat recovery modes reveals that Configuration 2 not only saves energy and maintains warmer tank temperatures but also demonstrates faster water heating capabilities. This is attributed to decreased energy loss and improved heat transfer. The study encompasses a wide range of scenarios, considering diverse thermal loads and water usage patterns across heating and heat recovery modes. Overall, the comprehensive results indicate that Configuration 2 achieves energy savings ranging from 3.5% to 12.2% compared to Configuration 1, depending on factors such as water usage patterns, thermal loads, and operational modes.
Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportu-nities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially con-cerning chemical reagent usage. Feed concentrations and product demand variability require flex-ible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, thus informing materials optimization with process and infrastructure scale performance targets. Finally, scale-up must be accelerated and de-risked across the materials-to-process levels to fully realize the opportunity presented by membranes, thereby fostering the development of a circular economy for critical minerals. Tackling these challenges requires integrating efforts across diverse disciplines. We advocate for a holistic molecular-to-systems perspective for fully realizing PI with membranes to address sustainability challenges in critical mineral processing. The opportunities for PI with membranes are excellent applications for emerging research in machine learning, data science, automation, and optimization.
The purpose of this study was to produce cellulase enzyme from a native strain Trichoderma harzianum, isolated from crop soil. Cellulase production was carried out by two different methods of fermentation, submerging and solid state fermentation. Mandels mineral medium was used as a source of nitrogen and ammonium sulfate. Dry and ground wheat straw was used in fermentation as a carbon source. Cellulase activity was determined using the 3,5-dinitrosalicylic acid (DNS) method. The results showed that wheat straw has the potential to be used as a substrate in the production of cellulases. Trichoderma harzianum showed enzymatic activity of 0.125 FPU / ml (filter paper units) and 1. 35 U / ml CMC (carboxymethyl cellulose units) by solid state fermentation (FSS) after 96 hours of fermentation. The optimum pH for the two substrates was determined by testing with citrate buffers (pH 4 to 6), phosphates (pH 6 to 7) and tris-HCL (pH 8 to 9). To determine the optimum temperature the range of 30 ° C to 80 ° C was investigated. Storage tests were performed every seven days over a period of 6 weeks. The optimum activity pH on paper and CMC was found to be 6. The optimum activity temperature is 70 ° C for PF and 80 ° C for CMC. El propósito de este estudio fue producir enzima celulasa a partir de una cepa nativa Trichoderma harzianum, aislada de suelo de cultivo. La producción de celulasa se realizó por dos métodos diferentes de fermentación, sumergida y fermentación en estado sólido. Se utilizó medio mineral Mandels y como fuentes de nitrógeno urea y sulfato de amonio. Se utilizó paja de trigo seca y molida en las fermentaciones como fuente de carbono. La actividad de la celulasa se determinó por medición de azúcares reductores al papel filtro y a la carboximetilcelulosa usando el método del ácido 3,5- dinitrosalicílico (DNS). Los resultados mostraron que la paja de trigo tiene el potencial para usarse como sustrato en la producción de celulasas. Trichoderma harzianum mostró actividad enzimática de 0.125 FPU/ml (unidades de papel filtro) y 1.35 U/ml a la CMC (unidades de carboximetilcelulosa) por fermentación en estado sólido (FSS) después de 96 horas de fermentación. El pH óptimo para los dos sustratos se determinó realizando pruebas con buffers de citratos (pH 4 a 6), de fosfatos (pH 6 a 7) y tris- HCL (pH 8 a 9). Para la determinación de la temperatura óptima se manejó el rango de 30°C a 80°C. Las pruebas de almacenamiento se realizaron cada siete días en un periodo de 6 semanas. Se encontró que el pH óptimo de actividad en papel y CMC es de 6. La temperatura óptima de actividad es de 70°C para PF y de 80°C para CMC.
As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at the edge of scientific discovery in the next several decades. Often, it is necessary to simplify or precondition solvers to accelerate the study of large systems of linear equations commonly seen in a number of scientific fields. Preconditioning a problem to increase efficiency is often seen as the best approach; yet, preconditioners which are fast, smart, and efficient do not always exist. Following the progress of [1], we present a new preconditioner for symmetric diagonally dominant (SDD) systems of linear equations. These systems are common in certain PDEs, network science, and supervised learning among others. Based on spectral support graph theory, this new preconditioner builds off of the work of [2], computing and applying a V-cycle chain of approximate inverse matrices. This preconditioner approach is both algebraic in nature as well as hierarchically-constrained depending on the condition number of the system to be solved. Due to its generation of an Approximate Inverse Chain of matrices, we refer to this as the AIC preconditioner. We further accelerate the AIC preconditioner by utilizing precomputations to simplify setup and multiplications in the con-text of an iterative Krylov-subspace solver. While these iterative solvers can greatly reduce solution time, the number of iterations can grow large quickly in the absence of good preconditioners. Initial results for the AIC preconditioner have shown a very large reduction in iteration counts for SDD systems as compared to standard preconditioners such as Incomplete Cholesky (ICC) and Multigrid (MG). We further show significant reduction in iteration counts against the more advanced Combinatorial Multigrid (CMG) preconditioner. We have further developed no-fill sparsification techniques to ensure that the computational cost of applying the AIC preconditioner does not grow prohibitively large as the depth of the V-cycle grows for systems with larger condition numbers. Our numerical results have shown that these sparsifiers maintain the sparsity structure of our system while also displaying significant reductions in iteration counts.1 2