Comparing the behavior of OpenMP Implementations with various Applications on two different Fujitsu A64FX platforms
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Mixtures of ionic liquids (ILs) containing single-walled carbon nanotubes (SWCNTs) were prepared and characterized to obtain electrolytes with optimized transport properties for use in energy storage devices such as supercapacitors. Imidazolium ILs bearing cations with side chains of different functionality coupled with bis(trifluoromethylsulfonyl)amide (NTf 2- ) or bis(fluorosulfonyl)-amide (FSA-) anions, were used in mixtures containing up to 5 wt% SWCNTs. At and above 2 wt% nanotube loading, the mixtures exhibited higher conductivities than the pure ILs, in spite of the extremely high viscosities. Loadings of 5 wt% produced very high conductivities, and studies of the temperature dependence indicated a change in the charge transport mechanism between 2 and 5 wt% loading. At 5 wt% loading, the highest conductivities (up to 540 mS/cm at 25 °C) were obtained for the ILs containing the NTf 2- anion. Overall, these results can significantly contribute to the development of improved energy storage devices.
Aqueous Li-ion batteries (ALIBs) are an important class of battery chemistries owing to the intrinsic non-flammability of aqueous electrolytes. However, water is detrimental to most cathode materials and could result in rapid cell failure. Identifying the degradation mechanisms and evaluating the pros and cons of different cathode materials are crucial to guide the materials selection and maximize their electrochemical performance in ALIBs. Here, in this study, we investigate the stability of LiFePO 4 (LFP), LiMn 2 O 4 (LMO) and LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC) cathodes, without protective coating, in three different aqueous electrolytes, i.e., salt-in-water, water-in-salt, and molecular crowding electrolytes. The latter two are the widely reported "water-deficient electrolytes." LFP cycled in the molecular crowding electrolyte exhibits the best cycle life in both symmetric and full cells owing to the stable crystal structure. Mn dissolution and surface reduction accelerate the capacity decay of LMO in water-rich electrolyte. On the other hand, the bulk structural collapse leads to the degradation of NMC cathodes. LMO demonstrates better full-cell performance than NMC in water-deficient aqueous electrolytes. LFP is shown to be more promising than LMO and NMC for long-cycle-life ALIB full cells, especially in the molecular crowding electrolyte. However, none of the aqueous electrolytes studied here provide enough battery performance that can compete with conventional non-aqueous electrolytes. This work reveals the degradation mechanisms of olivine, spinel, and layered cathodes in different aqueous electrolytes and yields insights into improving electrode materials and electrolytes for ALIBs.
RT XFEL structure of CypA solved using celloluse carrier media
RT XFEL structure of CypA solved using LCP injection system
RT XFEL structure of CypA solved using MESH injection system
Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. Further, the results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.
Dual-frequency millimeter-wavelength radar observations in snowfall are analyzed in order to evaluate differences in conventional polarimetric radar variables such as differential reflectivity (ZDR) specific differential phase shift (K DP ) and linear depolarization ratio (LDR) at traditional cloud radar frequencies at Ka and W bands (~35 and ~94 GHz, correspondingly). Low radar beam elevation (~5°) measurements were performed at Oliktok Point, Alaska, with a scanning fully polarimetric radar operating in the horizontal–vertical polarization basis. This radar has the same gate spacing and very close beam widths at both frequencies, which largely alleviates uncertainties associated with spatial and temporal data matching. It is shown that observed Ka- and W-band Z DR differences are, on average, less than about 0.5 dB and do not have a pronounced trend as a function of snowfall reflectivity. The observed Z DR differences agree well with modeling results obtained using integration over nonspherical ice particle size distributions. For higher signal-to-noise ratios, KDP data derived from differential phase measurements are approximately scaled as reciprocals of corresponding radar frequencies indicating that the influence of non-Rayleigh scattering effects on this variable is rather limited. This result is also in satisfactory agreement with data obtained by modeling using realistic particle size distributions. Observed Ka- and W-band LDR differences are strongly affected by the radar hardware system polarization “leak” and are generally less than 4 dB. Smaller differences are observed for higher depolarizations, where the polarization “leak” is less pronounced. Realistic assumptions about particle canting and the system polarization isolation lead to modeling results that satisfactorily agree with observational dual-frequency LDR data.
Objective The objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta-analytic review of the area. Background Human–robot interaction (HRI) represents an increasingly important dimension of our everyday existence. Currently, the most important element of these interactions is proposed to be whether the human trusts the robot or not. We have identified three overarching categories that exert effects on the expression of trust. These consist of factors associated with (a) the human, (b) the robot, and (c) the context in which any specific HRI event occurs. Method The current body of literature was examined and all qualifying articles pertaining to trust in robots were included in the meta-analysis. A previous meta-analysis on HRI trust was used as the basis for this extended, updated, and evolving analysis. Results Multiple additional factors, which have now been demonstrated to significantly influence trust, were identified. The present results, expressed as points of difference and points of commonality between the current and previous analyses, are identified, explained, and cast in the setting of the emerging wave of HRI. Conclusion The present meta-analysis expands upon previous work and validates the overarching categories of trust antecedent (human-related, robot-related, and contextual), as well as identifying the significant individual precursors to trust within each category. A new and updated model of these complex interactions is offered. Application The identified trust factors can be used in order to promote appropriate levels of trust in robots.
Over the last several decades, designers have used digital screens to view images of real and simulated spaces and make critical design decisions. Screen technology has improved during this time, as technologies like OLED have replaced legacy displays (CRT, plasma, and LCD). These new screens provide a higher pixel resolution, luminous output and contrast ratio. Immersive head-mounted displays now allow designers to view immersive images, and recent developments in real-time rendering have encouraged the uptake of virtual reality (VR) head-mounted displays in mainstream practice and design education. This paper presents an experiment on lighting perception using a series of LED lighting conditions in a real space and a virtual representation of those conditions captured using a 360° high-dynamic-range camera and presented on an HTC Vive Pro HMD. Fifty-three participants were asked to rate each lighting condition by viewing it in a real space (n = 30) or via immersive HDR photographs displayed in a VR HMD (n = 23). The results show that ratings of visual comfort, pleasantness, evenness, contrast and glare are similar between the HTC Vive Pro HMD and our real space when evaluating well-lit scenes, but significant differences emerge in dim and highly contrasted scenes for a number of rating scales.
Chemometric regression models were developed for the quantification of praseodymium (Pr, 0–1000 µg/mL), neodymium (Nd, 0–1000 µg/mL), and nitric acid (HNO 3 , 0.1–5 M) using spectrophotometry. Designed calibration sets were composed of 20 samples each: 10 model points and 10 lack-of-fit (LOF) points. The D-optimal designs effectively minimized the number of samples required to build models, and each design resulted in similar prediction performance, suggesting that statistical design of experiments can provide a reliable framework for selecting training set samples in three-variable systems. Partial least squares regression (PLSR) models were validated against a one-factor-at-a-time validation set composed of 125 samples (three variables, five levels). The top PLS-1 models resulted in average percent root mean square error of prediction error values of 3.5%, 1.7%, and 1.2% for Pr(III), Nd(III), and HNO 3 , respectively. Power set augmentations of the model and LOF samples were investigated to optimize the number of training set samples. PLSR models built using just required model points (10) had similar predictive capabilities as models including the LOF points (20) but with fewer samples. The number of validation samples was also varied systematically to learn how many samples are needed to validate regression models. This work addresses long-standing questions in the field of chemometrics to help make this approach amenable to the near-real-time quantification of hazardous species in remote settings.
Sponges are ancient sessile metazoans, which form with their associated microbial symbionts a complex functional unit called a holobiont. Sponges are a rich source of chemical diversity; however, there is limited knowledge of which holobiont members produce certain metabolites and how they may contribute to chemical interactions. To address this issue, we applied non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) to either whole sponge tissue or fractionated microbial cells from six different, co-occurring sponge species. Several metabolites were commonly found or enriched in whole sponge tissue, supporting the notion that sponge cells produce them. These include 2-methylbutyryl-carnitine, hexanoyl-carnitine and various carbohydrates, which may be potential food sources for microorganisms, as well as the antagonistic compounds hymenialdisine and eicosatrienoic acid methyl ester. Metabolites that were mostly observed or enriched in microbial cells include the antioxidant didodecyl 3,3'-thiodipropionate, the antagonistic compounds docosatetraenoic acid, and immune-suppressor phenylethylamide. This suggests that these compounds are mainly produced by the microbial members in the sponge holobiont, and are potentially either involved in inter-microbial competitions or in defenses against intruding organisms. This study shows how different chemical functionality is compartmentalized between sponge hosts and their microbial symbionts and provides new insights into how chemical interactions underpin the function of sponge holobionts.
Abstract Context Female athletes, particularly runners, with insufficient caloric intake for their energy expenditure [low energy availability (EA) or relative energy deficiency] are at risk for impaired skeletal integrity. Data are lacking in male runners. Objective To determine whether male runners at risk for energy deficit have impaired bone mineral density (BMD), microarchitecture, and estimated strength. Design Cross-sectional. Setting Clinical research center. Participants 39 men (20 runners, 19 controls), ages 16–30 years. Main Outcome Measures Areal BMD (dual-energy x-ray absorptiometry); tibia and radius volumetric BMD and microarchitecture (high-resolution peripheral quantitative computed tomography); failure load (microfinite element analysis); serum testosterone, estradiol, leptin; energy availability. Results Mean age (24.5 ± 3.8 y), lean mass, testosterone, and estradiol levels were similar; body mass index, percent fat mass, leptin, and lumbar spine BMD Z-score (−1.4 ± 0.8 vs −0.8 ± 0.8) lower (P < .05); and calcium intake and running mileage higher (P ≤ .01) in runners vs controls. Runners with EA <median had lower lumbar spine (−1.5 ± 0.7, P = .028), while runners with EA ≥median had higher hip (0.3 ± 0.7 vs −0.4 ± 0.5, P = .002), BMD Z-scores vs controls. After adjusting for calcium intake and running mileage, runners with EA <median had lower mean tibial total and trabecular volumetric BMD, trabecular bone volume fraction, cortical porosity, and apparent modulus vs controls (P < .05). Appendicular lean mass and serum estradiol (R ≥ 0.45, P ≤ .046), but not testosterone, were positively associated with tibial failure load among runners. Conclusions Despite weight-bearing activity, skeletal integrity is impaired in male runners with lower caloric intake relative to exercise energy expenditure, which may increase bone stress injury risk. Lower estradiol and lean mass are associated with lower tibial strength in runners.
A joint European Canadian Chinese development of a supercritical water-cooled small modular reactor (SCW-SMR) technology is in progress since September 2020 in the framework of a Horizon 2020 project called ECC-SMART. As a main purpose of the project, proper estimates of energy deposition and its spatial distribution are prerequisites for the accurate analysis of safety related parameters of the SCW-SMR concept under development. A supercritical water reactor fuel computational benchmark model, provided by Canadian Nuclear Laboratories, was applied for detailed comparison of different energy deposition calculation options available in the Serpent 2 Monte Carlo code. The effect of energy deposition options on the normalization of the results as well as on the spatial distribution of the energy deposition are discussed. Consistent energy deposition calculation methods are presented between three Monte Carlo codes, viz., Serpent 2, MCNP6 and OpenMC. Although resource-intensive, the use of the coupled neutron-photon transport mode of Serpent 2 is recommended for accurate spatial and quantitative characterization of energy deposition in the SCW-SMR fuel assemblies, accounting for both neutron and photon heating of all the materials. (authors)
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Previously, we had demonstrated that Escherichia coli O157:H7 (O157) strain 86–24 expresses proteins involved in survival rather than virulence in vitro in rumen fluid from dairy cattle limit fed a maintenance diet. Here, we verified if this observation would be true for different O157 strains grown in vitro in rumen fluid from, and in vivo in the rumen of, animals on contrasting maintenance (high fiber) and lactation (high energy-protein) diets usually limit fed to dairy cattle. For the in vivo studies, an economical, novel, reusable and non-terminal rumen-fistulated animal model permitting simultaneous evaluation of multiple bacterial strains in the bovine rumen was developed. All experiments were conducted in duplicate using different animals to account for host-related variations. The O157 strains included, 86–24, EDL933 and the super shed SS-17. E. coli Nal R (#5735), derived from a bovine intestinal commensal E. coli, was included as a control. As expected, diet influenced ruminal pH and volatile fatty acid (VFA) composition. The pH ranged from 6.2–7.0 and total VFA concentrations from 109–141 μM/ml, in animals fed the maintenance diet. In comparison, animals fed the lactation diet had a ruminal pH ranging between 5.18–6.0, and total VFA of 125–219 μM/ml. Strain dependent differences in O157 recovery from the rumen fluid of cattle fed either diet was observed, both in vitro and in vivo, with O157 strains 86–24 and EDL933 demonstrating similar survival patterns. Analysis of the O157 proteomes expressed in the rumen fluid/rumen verified previous observations of adaptive responses. Any difference in the adaptive response was mainly influenced by the animal’s diet and growth conditions (in vitro and in vivo) and not the O157 strain. These new insights into the O157 responses could help formulate modalities to control O157 across strains in cattle at all stages of husbandry.
Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations.
This data was generated to answer the research question: After the end of a 30-year nitrogen (N) fertilization experiment in an Eastern temperate forest, to what extent do plant microbial interactions and carbon cycling in arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) dominated stands follow different recovery trajectories?The soil and the roots used in this dataset were sampled from two watersheds (N-fertilized watershed 3 and Reference watershed 7) with 6 AM and 6 ECM dominated stands each, over the course of three months (June, July, August) in 2021 and 2022. The "All_Roots.csv" data includes fine root biomass (in g) scaled to sampled soil (m2) (Scaled_Rt_Bm_gm2), and AM and ECM root colonization (%) in two soil fractions: the organic horizon (O) and the mineral horizon (B). The "All_Enzymes.csv" data includes extracellular soil enzyme activity (N-acetyl-glucosaminidase (NAG), acid phosphatase (AP), β-glucosidase (BG), phenol oxidase and peroxidase), and the ratios of BG to AP (BG:AP) and BG to NAG (BG:NAG) in three soil fractions: the organic horizon (O), the bulk soil (B), and the rhizosphere (R).The "All_Nmin" data includes inorganic N (NO3- and NH4+) pools (unit: μg of N per g of dry soil) measured pre- (I_Nitrate_ugNgsoil and I_Ammonia_ugNgsoil) and post-incubation (F_Nitrate_ugNgsoil and F_Ammonia_ugNgsoil), as well as calculated nitrification (Nitrification_day) and nitrogen mineralization (Mineralization_day) rates per day, in three soil fractions: the organic horizon (O), the bulk soil (B), and the rhizosphere (R).