Reaction mechanism and kinetics for CO₂ reduction on nickel single atom catalysts from quantum mechanics

Experiments have shown that graphene-supported Ni-single atom catalysts (Ni-SACs) provide a promising strategy for the electrochemical reduction of CO₂ to CO, but the nature of the Ni sites (Ni-N₂C₂, Ni-N₃C₁, Ni-N₄) in Ni-SACs has not been determined experimentally. Here, we apply the recently developed grand canonical potential kinetics (GCP-K) formulation of quantum mechanics to predict the kinetics as a function of applied potential (U) to determine faradic efficiency, turn over frequency, and Tafel slope for CO and H₂ production for all three sites. We predict an onset potential (at 10 mA cm⁻²) U_(onset) = −0.84 V (vs. RHE) for Ni-N₂C₂ site and U_(onset) = −0.92 V for Ni-N₃C₁ site in agreement with experiments, and U_(onset) = −1.03 V for Ni-N₄. We predict that the highest current is for Ni-N₄, leading to 700 mA cm⁻² at U = −1.12 V. To help determine the actual sites in the experiments, we predict the XPS binding energy shift and CO vibrational frequency for each site

Contextual Modeling for 3D Dense Captioning on Point Clouds

3D dense captioning, as an emerging vision-language task, aims to identify and locate each object from a set of point clouds and generate a distinctive natural language sentence for describing each located object. However, the existing methods mainly focus on mining inter-object relationship, while ignoring contextual information, especially the non-object details and background environment within the point clouds, thus leading to low-quality descriptions, such as inaccurate relative position information. In this paper, we make the first attempt to utilize the point clouds clustering features as the contextual information to supply the non-object details and background environment of the point clouds and incorporate them into the 3D dense captioning task. We propose two separate modules, namely the Global Context Modeling (GCM) and Local Context Modeling (LCM), in a coarse-to-fine manner to perform the contextual modeling of the point clouds. Specifically, the GCM module captures the inter-object relationship among all objects with global contextual information to obtain more complete scene information of the whole point clouds. The LCM module exploits the influence of the neighboring objects of the target object and local contextual information to enrich the object representations. With such global and local contextual modeling strategies, our proposed model can effectively characterize the object representations and contextual information and thereby generate comprehensive and detailed descriptions of the located objects. Extensive experiments on the ScanRefer and Nr3D datasets demonstrate that our proposed method sets a new record on the 3D dense captioning task, and verify the effectiveness of our raised contextual modeling of point clouds

Multi-scale modelling of the human left ventricle

In this paper, a multi-scale computational frame is proposed to simulate dynamics of the human left ventricle. First of all, a modified Level Set method is used to segment the cardiac magnetic resonance imaging and then reconstruct the 3D computational domain of the left ventricle. The Holzapfel-Ogden's nonlinear and anisotropic model is imposed to calculate the passive elastic response. The Fenton-Karma model with stimulus current is optimized to produce the reasonable membrane potential and intracellular calcium concentration. Based on the obtained calcium concentration, the active tension is calculated. Finally, the passive elastic response and the active tension of the left ventricle are coupled with the blood and the obtained fluid structure interaction is solved by the immersed boundary method. Our numerical results at end-diastole and end-systole are generally in good agreement with the clinical measurement and the earlier studies, which verifies the efficiency of the method

HPC-driven computational reproducibility

Reproducibility of results is a cornerstone of the scientific method. Scientific computing encounters two challenges when aiming for this goal. Firstly, reproducibility should not depend on details of the runtime environment, such as the compiler version or computing environment, so results are verifiable by third-parties. Secondly, different versions of software code executed in the same runtime environment should produce consistent numerical results for physical quantities. In this manuscript, we test the feasibility of reproducing scientific results obtained using the IllinoisGRMHD code that is part of an open-source community software for simulation in relativistic astrophysics, the Einstein Toolkit. We verify that numerical results of simulating a single isolated neutron star with IllinoisGRMHD can be reproduced, and compare them to results reported by the code authors in 2015. We use two different supercomputers: Expanse at SDSC, and Stampede2 at TACC. By compiling the source code archived along with the paper on both Expanse and Stampede2, we find that IllinoisGRMHD reproduces results published in its announcement paper up to errors comparable to round-off level changes in initial data parameters. We also verify that a current version of IlliinoisGRMHD reproduces these results once we account for bug fixes which has occurred since the original publicationComment: 22 pages, 6 figures, submitted to Classical and Quantum Gravit

Document-oriented heterogeneous business process integration through collaborative e-marketplace

In this paper, we studied the semantic consistency maintenance issue between heterogeneous contexts, that is, how a firm ‟ busi-ness process of one e-marketplace can be transformed to another firm‟s business process of another e-marketplace in a semantically consistent way. The proposed solution of this paper uses XML Product Map (XPM) of collaborative concept to represent semantically consistent business processes, and adopts common action concept pool and XPM documents to design heterogeneous business processes that are suitable for heterogeneous business process transformation. We motivated the approach with a real-world PVC poncho trade problem and explained it in architecture of collaborative process design and automatic service provision. We reported the implementation specification within a hybrid human-agent framework, where four layers of system modules are specified. The approach is evaluated based on a new semantic impact chain method particularly for evaluating concept consistency in meaning representation between heterogeneous contexts of business processes

Genetic Diversity and Population Structure of a Camelina sativa Spring Panel

There is a need to explore renewable alternatives (e.g., biofuels) that can produce energy sources to help reduce the reliance on fossil oils. In addition, the consumption of fossil oils adversely affects the environment and human health via the generation of waste water, greenhouse gases, and waste solids. Camelina sativa, originated from southeastern Europe and southwestern Asia, is being re-embraced as an industrial oilseed crop due to its high seed oil content (36–47%) and high unsaturated fatty acid composition (\u3e90%), which are suitable for jet fuel, biodiesel, high-value lubricants and animal feed. C. sativa’s agronomic advantages include short time to maturation, low water and nutrient requirements, adaptability to adverse environmental conditions and resistance to common pests and pathogens. These characteristics make it an ideal crop for sustainable agricultural systems and regions of marginal land. However, the lack of genetic and genomic resources has slowed the enhancement of this emerging oilseed crop and exploration of its full agronomic and breeding potential. Here, a core of 213 spring C. sativa accessions was collected and genotyped. The genotypic data was used to characterize genetic diversity and population structure to infer how natural selection and plant breeding may have affected the formation and differentiation within the C. sativa natural populations, and how the genetic diversity of this species can be used in future breeding efforts. A total of 6,192 high-quality single nucleotide polymorphisms (SNPs) were identified using genotypingby- sequencing (GBS) technology. The average polymorphism information content (PIC) value of 0.29 indicate moderate genetic diversity for the C. sativa spring panel evaluated in this report. Population structure and principal coordinates analyses (PCoA) based on SNPs revealed two distinct subpopulations. Sub-population 1 (POP1) contains accessions that mainly originated from Germany while the majority of POP2 accessions (\u3e75%) were collected from Eastern Europe. Analysis of molecular variance (AMOVA) identified 4% variance among and 96% variance within subpopulations, indicating a hig