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At least 1,783 records · Page 99

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.

Ryan T Scott↗

Adaptive OFDM Waveform Design for Spatio-Temporal-Sparsity Exploited STAP Radar

In this chapter, we describe a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly moving target using an orthogonal frequency division multiplexing (OFDM) radar. The motivation of employing an OFDM signal is that it improves the target-detectability from the interfering signals by increasing the frequency diversity of the system. However, due to the addition of one extra dimension in terms of frequency, the adaptive degrees-of-freedom in an OFDM-STAP also increases. Therefore, to avoid the construction a fully adaptive OFDM-STAP, we develop a sparsity-based STAP algorithm. We observe that the interference spectrum is inherently sparse in the spatio-temporal domain, as the clutter responses occupy only a diagonal ridge on the spatio-temporal plane and the jammer signals interfere only from a few spatial directions. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data compared to the other existing STAP techniques, and produces nearly optimum STAP performance. In addition to designing the STAP filter, we optimally design the transmit OFDM signals by maximizing the output signal-to-interference-plus-noise ratio (SINR) in order to improve the STAP performance. The computation of output SINR depends on the estimated value of the interference covariance matrix, which we obtain by applying the sparse recovery algorithm. Therefore, we analytically assess the effects of the synthesized OFDM coefficients on the sparse recovery of the interference covariance matrix by computing the coherence measure of the sparse measurement matrix. Our numerical examples demonstrate the achieved STAP-performance due to sparsity-based technique and adaptive waveform design.

Sen, Satyabrata↗

An interface stabilized perovskite solar cell with high stabilized efficiency and low voltage loss

Stabilization of the crystal phase of inorganic/organic lead halide perovskites is critical for their high performance optoelectronic devices. However, due to the highly ionic nature of perovskite crystals, even phase stabilized polycrystalline perovskites can undergo undesirable phase transitions when exposed to a destabilizing environment. While various surface passivating agents have been developed to improve the device performance of perovskite solar cells, conventional deposition methods using a protic polar solvent, mainly isopropyl alcohol (IPA), results in a destabilization of the underlying perovskite layer and an undesirable degradation of device properties. We demonstrate the hidden role of IPA in surface treatments and develop a strategy in which the passivating agent is deposited without destabilizing the high quality perovskite underlayer. This strategy maximizes and stabilizes device performance by suppressing the formation of the perovskite δ-phase and amorphous phase during surface treatment, which is observed using conventional methods. Our strategy also effectively passivates surface and grain boundary defects, minimizing non-radiative recombination sites, and preventing carrier quenching at the perovskite interface. This results in an open-circuit-voltage loss of only ~340 mV, a champion device with a power conversion efficiency of 23.4% from a reverse current–voltage scan, a device with a record certified stabilized PCE of 22.6%, and enhanced operational stability. In addition, our perovskite solar cell exhibits an electroluminescence external quantum efficiency up to 8.9%.

14 SOLAR ENERGY↗

Temperature influence on luminescent coupling efficiency in concentrator MJ SCs

In the work, presented are the results of investigation of temperature dependencies of the luminescent coupling effectiveness in lattice-matched (LM) GaInP/GaAs/Ge and metamorphic (MM) GaInP/GaInAs/Ge solar cells. The “ordinary” luminescent coupling effectiveness rise has been observed with temperature decrease for GaAs-Ge, GaInP-GaInAs and GaInAs-Ge pairs of subcells, and its limiting values have been defined. A “reverse” behavior of the luminescent coupling effectiveness for the GaInP-GaAs pair has been found, determined emittance potential drop of wideband GaInP p-n junction. It is shown that the established “unusual” behavior of the LC efficiency may be determined by the presence of thermalized centers of non-radiative recombination of charge carriers for the GaInP subcell in GaInP/GaAs/Ge LM structure. Estimation of characteristic parameters for the nonradiative recombination processes in wideband GaInP p-n junction has been carried out, and values for the energy of the nonradiative center thermalization (E{sub nrad2} =79.42meV) and for the activation energy of nonradiative band-to-band recombination (E{sub A}=33.4meV) have been obtained.

14 SOLAR ENERGY↗

Photosensitivity of ZnO/CdS/Cu(In,Ga)Se{sub 2}/Mo thin-film solar cells fabricated on various substrates

The results of measuring the first spectra of relative quantum efficiency for photoconversion in thin-film ZnO/CdS/Cu(In,Ga)Se{sub 2}/Mo solar cells fabricated on rigid (glass) and flexible (polyimide) substrates are reported. The character of interband transitions has been studied and the values of the band gap for direct and indirect transitions in thin Cu(In,Ga)Se{sub 2} films are determined. It is found that a shift of the maximal photosensitivity for the obtained solar cells to shorter wavelengths is observed as rigid substrates are replaced by flexible ones. It is concluded that thin-film Cu(In,Ga)Se{sub 2} structures can be used as broad-band photoconverters of solar radiation.

36 MATERIALS SCIENCE↗

Combined chlorine dioxide–membrane distillation for the treatment of produced water

Membrane distillation (MD) is a promising technique for desalinating hypersaline brine, such as produced water (PW). To date, fouling and scaling have remained as major challenges for MD implementation. In this study, chlorine dioxide combined with induced air floatation (ClO 2 -IAF) was systematically investigated as a pretreatment prior to MD. First, the ClO 2 generation based on sodium chlorite and hydrochloric acid was optimized to maximize the on-demand ClO 2 production. The maximum production yield of 18.4% was obtained with 4wt.% NaClO 2 and 20wt.% HCl solutions, with a molar ratio of 1:1.25. Then, two real PW samples were pretreated, and removal efficiencies for total suspended solids (TSS), turbidity, iron, and total organic carbon (TOC) were comprehensively studied by varying the ClO 2 dosage between 6 and 91 mg/L. The ClO 2 -IAF pretreatment displayed TSS and turbidity removals above 90% and TOC removal close to 55%. Further, the PW constituents such as benzene, toluene, ethylbenzene, xylene (BTEX), and total petroleum hydrocarbons (TPH) were analyzed and quantified throughout the cascade of the treatments. The volatiles like BTEX were mainly removed by air floatation, while saturated hydrocarbons such as TPH were retained by the hydrophobic membrane. The MD long-term stability without any in-place cleaning was evaluated, and the membrane withstood for twenty two days without wetting, suggesting that optimizing oxidation pretreatment is critical for mitigating the fouling in MD. Results suggest that organic fouling in PW could be effectively reduced by the pretreatment, but further treatment is required to mitigate the scaling, which resulted in MD wetting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stability boundary and optimal operating parameter identification in milling using Bayesian learning

Here, this paper describes a novel Bayesian learning approach for stability boundary and optimal parameter identification in milling without the knowledge of the underlying tool dynamics or material cutting force coefficients. The paper is divided into two parts. First, a Bayesian learning method for stability lobe identification using test results is described. Each axial depth and spindle speed combination is characterized by a probability of stability which is updated using Bayes’ rule when a test result (stable or unstable) is made available. A novel likelihood function is defined which incorporates knowledge of the stability behavior. Numerical results show convergence to the analytical stability lobe diagram. Second, an adaptive experimental strategy to identify optimal operating parameters that maximize material removal rate is described. Numerical evaluation shows convergence to the optimal operating point with error less than 15 % within ten tests on average. The approach is validated using experimental results. Results show that the proposed method is an efficient and robust learning method to identify the stability lobe diagram and optimal operating parameters with a limited number of tests/data points.

Bayesian machine learning↗

Roadmap to control HBV and HDV epidemics in China

Hepatitis B virus (HBV) is endemic in China. Almost 10% of HBV infected individuals are also infected with hepatitis D virus (HDV) which has a 5–10 times higher mortality rate than HBV mono-infection. The aim of this manuscript is to devise strategies that can not only control HBV infections but also HDV infections in China under the current health care budget in an optimal manner. Furthermore, using a mathematical model, an annual budget of 10 billion dollars was optimally allocated among five interventions namely, testing and HBV adult vaccination, treatment for mono-infected and dually-infected individuals, second line treatment for HBV mono-infections, and awareness programs. As a result, we determine that the optimal strategy is to test and treat both infections as early as possible while applying awareness programs at full intensity. Under this strategy, an additional 19.8 million HBV, 1.9 million HDV infections and 0.25 million lives will be saved over the next 10 years at a cost-savings of 79 billion dollars than performing no intervention. Introduction of second line treatment does not add a significant economic burden yet prevents 1.4 million new HBV infections and 15,000 new HDV infections. In conclusion, test and treatment programs are highly efficient in reducing HBV and HDV prevalence in the population. Under the current health budget in China, not only test and treat programs but awareness programs and second line treatment can also be implemented that minimizes prevalence and mortality, and maximizes economic benefits.

60 APPLIED LIFE SCIENCES↗

Comparing probabilistic and descriptive analyses of time–dose–toxicity relationship for determining no-observed-adverse-effect level in drug development

The no-observed-adverse-effect level (NOAEL) of a drug defined from animal studies is important for inferring a maximal safe dose in human. However, several issues are associated with its concept, determination and application. It is confined to the actual doses used in the study; becomes lower with increasing sample size or dose levels; and reflects the risk level seen in the experiment rather than what may be relevant for human. We explored a pharmacometric approach in an attempt to address these issues. We first used simulation to examine the behaviour of the NOAEL values as determined by current common practice; and then fitted the probability of toxicity as a function of treatment duration and dose to data collected from all applicable toxicology studies of a test compound. Our investigation was in the context of an irreversible toxicity that is detected at the end of the study. Simulations illustrated NOAEL's dependency on experimental factors such as dose and sample size, as well as the underlying uncertainty. Modelling the probability as a continuous function of treatment duration and dose simultaneously to data from multiple studies allowed the estimation of the dose, along with its confidence interval, for a maximal risk level that might be deemed as acceptable for human. The model-based data integration also reconciled between-study inconsistency and explicitly provided maximised estimation confidence. Such alternative NOAEL determination method should be explored for its more efficient data use, more quantifiable insight to toxic doses, and the potential for more relevant animal-to-human translation. - Highlights: • Simulations revealed issues with NOAEL concept, determination and application. • Probabilistic modelling was used to address these issues. • The model integrated time-dose-toxicity data from multiple studies. • The approach uses data efficiently and may allow more meaningful human translation.

60 APPLIED LIFE SCIENCES↗

Extracting quasi-steady Lagrangian transport patterns from the ocean circulation: An application to the Gulf of Mexico

We construct a climatology of Lagrangian coherent structures (LCSs)—the concealed skeleton that shapes transport—with a twelve-year-long data-assimilative simulation of the sea-surface circulation in the Gulf of Mexico (GoM). Computed as time-mean Cauchy–Green strain tensorlines of the climatological velocity, the climatological LCSs (cLCSs) unveil recurrent Lagrangian circulation patterns. The cLCSs strongly constrain the ensemble-mean Lagrangian circulation of the instantaneous model velocity, showing that a climatological velocity can preserve meaningful transport information. The quasi-steady transport patterns revealed by the cLCSs agree well with aspects of the GoM circulation described in several previous observational and numerical studies. For example, the cLCSs identify regions of persistent isolation, and suggest that coastal regions previously identified as high-risk for pollution impact are regions of maximal attraction. We also show that cLCSs are remarkably accurate at identifying transport patterns observed during the Deepwater Horizon and Ixtoc oil spills, and during the Grand LAgrangian Deployment (GLAD) experiment. Thus it is shown that computing cLCSs is an efficient and meaningful way of synthesizing vast amounts of Lagrangian information. The cLCS method confirms previous GoM studies, and contributes to our understanding by revealing the persistent nature of the dynamics and kinematics treated therein.

54 ENVIRONMENTAL SCIENCES↗

An orthogonal metabolic framework for one-carbon utilization

Metabolic engineering often entails concurrent engineering of substrate utilization, central metabolism, and product synthesis pathways to maximize the conversion of a carbon substrate into desired product(s). Here, we report an alternative approach using synthetic pathways for C1 bioconversion that are orthogonal to the host metabolic network, thus minimizing interdependency on native metabolism and enabling more efficient biocatalysts. Here, the engineered pathways are based on formyl-CoA elongation (FORCE) reactions catalyzed by the enzyme 2-hydroxyacyl-CoA lyase (HACL) and generate multi-carbon products directly from C1 elongation units in the form of formyl-CoA. Herein, we use thermodynamic and stoichiometric analyses to evaluate different FORCE pathway variants, including aldose elongation, α-reduction, and aldehyde elongation. Promising variants were further prototyped using cell-free systems (purified enzymes and cell extracts) as well as resting and growing cultures of non-methylotrophic bacterium Escherichia coli. We demonstrate that C1 substrates formate, formaldehyde, and methanol can be used as inputs for FORCE pathways and that FORCE reactions can serve as a platform for varied product synthesis including glycolate, ethylene glycol, ethanol, and glycerate. Furthermore, the orthogonal FORCE pathways have the potential to be integrated with host metabolism for synthetic methylotrophy by the production of native growth substrates as demonstrated in a two-strain culture system.

59 BASIC BIOLOGICAL SCIENCES↗

Ultrathin electron and proton-conducting membranes for nanoscale integrated artificial photosystems

Reducing the thickness of separation membranes without compromising their selectivity and robustness is the most effective way of maximizing the areal conductivity. This is especially important for the integration of visible light-driven water oxidation and carbon dioxide (or proton) reduction into a complete artificial photosystem on the shortest possible length scale – the nanoscale – because of the efficiency advantages over macroscale photosystems. In addition to their excellent separation property, ultrathin membranes of 10 nm thickness or less need to exhibit sufficient electrical and proton conductivity in order for the photocatalytic rates to keep up with the photon flux at maximum solar intensity. Furher, two materials, graphene and amorphous silica with embedded molecular wires, have emerged as promising ultrathin membranes for the development of nanoscale integrated solar-fuel systems. Moreover, electrically conducting metal–organic or covalent–organic frameworks can be used to fabricate high surface area-supports that enable the use of molecular catalysts and/or light absorbers at an adequate areal density for nanoscale integration with graphene membranes. Following an overview of the electron and proton conductivity of these ultrathin materials and recent examples of photoelectrocatalytic applications that take advantage of some but not all the properties that constitute a complete functional membrane, the status and future opportunities for complete nanoscale integrated photosystems featuring an ultrathin membrane are discussed.

59 BASIC BIOLOGICAL SCIENCES↗

Recent Experience with the CMS Data Management System

The CMS[1] experiment manages a large-scale data infrastructure, currently handling over 200 PB of disk and 500 PB of tape storage and transferring more than 1 PB of data per day on average between various WLCG[2] sites. Utilizing Rucio[3] for high-level data management, FTS[4] for data transfers, and a variety of storage and network technologies at the sites, CMS confronts inevitable challenges due to the system’s growing scale and evolving nature. Key challenges include managing transfer and storage failures, optimizing data distribution across different storages based on production and analysis needs, implementing necessary technology upgrades and migrations, and efficiently handling user requests. The data management team has established comprehensive monitoring to supervise this system and has successfully addressed many of these challenges. The team’s efforts aim to ensure data availability and protection, minimize failures and manual interventions, maximize transfer throughput and resource utilization, and provide reliable user support. This paper details the operational experience of CMS with its data management system in recent years, focusing on the encountered challenges, the effective strategies employed to overcome them and the ongoing challenges as we prepare for future demands.

Öztürk, Hasan [CERN]↗

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Deterministic spin-wave interferometer based on the Rydberg blockade

The spin-wave (SW) N-particle path-entangled |N,0>+|0,N> (NOON) state is an N-particle Fock state with two atomic spin-wave modes maximally entangled. Attributed to the property that the phase is sensitive to collective atomic motion, the SW NOON state can be utilized as an atomic interferometer and has promising application in quantum enhanced measurement. In this paper we propose an efficient protocol to deterministically produce the atomic SW NOON state by employing the Rydberg blockade. Possible errors in practical manipulations are analyzed. A feasible experimental scheme is suggested. Our scheme is far more efficient than the recent experimentally demonstrated one, which only creates a heralded second-order SW NOON state.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Efficient and accurate numerical integration of exchange-correlation density functionals

We present a method for efficient calculation of exchange correlation in a density-functional theory calculation with Gaussian basis functions. It seamlessly combines the treatments of smooth (low-energy cutoff) and nonsmooth parts of the electron density such that computational performance can be maximized without approximation. Benchmark calculations show that the scheme can speed up the calculation of the exchange-correlation by as much as ten times or more without error.

74 ATOMIC AND MOLECULAR PHYSICS↗

Projecting Future Energy Production from Operating Wind Farms in North America. Part III: Variability

Abstract Daily expected wind power production from operating wind farms across North America are used to evaluate capacity factors (CF) computed using simulation output from the Weather Research and Forecasting (WRF) Model and to condition statistical models linking atmospheric conditions to electricity production. In Parts I and II of this work, we focus on making projections of annual energy production and the occurrence of electrical production drought. Here, we extend evaluation of the CF projections for sites in the Northeast, Midwest, southern Great Plains (SGP), and southwest U.S. coast (SWC) using statewide wind-generated electricity supply to the grid. We then quantify changes in the time scales of CF variability and the seasonality. Currently, wind-generated electricity is lowest in summer in each region except SWC, which causes a substantial mismatch with electricity demand. While electricity of residential heating may shift demand, research presented here suggests that summertime CF are likely to decline, potentially exacerbating the offset between seasonal peak power production and current load. The reduction in summertime CF is manifest for all regions except the SGP and appears to be linked to a reduction in synoptic-scale variability. Using fulfillment of 50% and 90% of annual energy production to quantify interannual variability, it is shown that wind power production exhibits higher (earlier fulfillment) or lower (later fulfillment) production for periods of over 10–30 years as a result of the action of internal climate modes. Significance Statement Electrical power system reassessment and redesign may be needed to aid efficient increased use of variable renewables in the generation of electricity. Currently wind-generated electricity in many regions of North America exhibits a minimum in summertime and hence is not well synchronized with electricity demand, which tends to be maximized in summer. Future projections indicate evidence of reductions in wind power during summer that would amplify this offset. However, electrification of heating may lead to increased wintertime demand, which would lead to greater synchronization.

Coburn, Jacob↗

Radiation Source Mapping with Bayesian Inverse Methods

In this work, we present a method to map the spectral and spatial distributions of radioactive sources using a limited number of detectors. Locating and identifying radioactive materials is important for border monitoring, in accounting for special nuclear material in processing facilities, and in cleanup operations following a radioactive material spill. Most methods to analyze these types of problems make restrictive assumptions about the distribution of the source. In contrast, the source mapping method presented here allows an arbitrary three-dimensional distribution in space and a gamma peak distribution in energy. To apply the method, the problem is cast as an inverse problem where the system’s geometry and material composition are known and fixed, while the radiation source distribution is sought. A probabilistic Bayesian approach is used to solve the resulting inverse problem since the system of equations is ill-posed. The posterior is maximized with a Newton optimization method. The probabilistic approach also provides estimates of the confidence in the final source map prediction. A set of adjoint, discrete ordinates flux solutions, obtained in this work by the Denovo code, is required to efficiently compute detector responses from a candidate source distribution. These adjoint fluxes form the linear mapping from the state space to the response space. The test of the method’s success is simultaneously locating a set of 137 Cs and 60 Co gamma sources in a room. This test problem is solved using experimental measurements that we collected for this purpose. Because of the weak sources available for use in the experiment, some of the expected photopeaks were not distinguishable from the Compton continuum. However, by supplanting 14 flawed measurements (out of a total of 69) with synthetic responses computed by MCNP, the proof-of-principle source mapping was successful. The locations of the sources were predicted within 25 cm for two of the sources and 90 cm for the third, in a room with an ~4-x 4-m floor plan. Finally, the predicted source intensities were within a factor of ten of their true value.

07 ISOTOPE AND RADIATION SOURCES↗