83 research outputs found
Two-electron Quenching of Dinuclear Ruthenium(II) Polypyridyl Complexes
A bridging ligand 5,5’-Bi- 1,10-phenanthroline, diphen, was prepared using dichlorobis(triphenylphosphine)Ni(II), Ni(PPh3)2Cl2 as catalyst with a yield of 40%. Yellow cubic crystals were able to obtain from the good purity product for single crystal analysis. The torsion angle between the planes of the subunit phenanthrolines is about 66 degrees.
A dinuclear ruthenium (II) polypyridyl complex, (phen)2Ru(diphen)Ru(phen)24+, was synthesized by using polymeric ruthenium carbonyl compound as the entry point, diphen as the bridging ligand and 1,10-phenanthroline, phen, as the terminal legand. Brown needlelike crystals were precipitated from acetonitrile that were not suitable for single crystal diffraction.
The photochemistry of the dimer was investigated in regards to the oxidation and reduction of the ruthenium centers through a series of quenching reactions excited by visible light. The analogous monomeric complexes Ru(bpy)32+ and Ru(phen)32+ were used as comparisions. In the photoinduced oxidation with peroxydisulfate, S2O82-, the dimer showed a higher Stern-Volmer quenching constant kq than Ru(phen)32+. The dimer showed faster laser flash photolysis transients than Ru(bpy)32+. In the photoinduced reduction with ascorbate, no significant difference between the dimer and Ru(phen)32+
Rigorous Derivation of Stochastic Conceptual Models for the El Ni\~no-Southern Oscillation from a Spatially-Extended Dynamical System
El Ni\~no-Southern Oscillation (ENSO) is the most predominant interannual
variability in the tropics, significantly impacting global weather and climate.
In this paper, a framework of low-order conceptual models for the ENSO is
systematically derived from a spatially-extended stochastic dynamical system
with full mathematical rigor. The spatially-extended stochastic dynamical
system has a linear, deterministic, and stable dynamical core. It also exploits
a simple stochastic process with multiplicative noise to parameterize the
intraseasonal wind burst activities. A principal component analysis based on
the eigenvalue decomposition method is applied to provide a low-order
conceptual model that succeeds in characterizing the large-scale dynamical and
non-Gaussian statistical features of the eastern Pacific El Ni\~no events.
Despite the low dimensionality, the conceptual modeling framework contains
outputs for all the atmosphere, ocean, and sea surface temperature components
with detailed spatiotemporal patterns. This contrasts with many existing
conceptual models focusing only on a small set of specified state variables.
The stochastic versions of many state-of-the-art low-order models, such as the
recharge-discharge and the delayed oscillators, become special cases within
this framework. The rigorous derivation of such low-order models provides a
unique way to connect models with different spatiotemporal complexities. The
framework also facilitates understanding the instantaneous and memory effects
of stochastic noise in contributing to the large-scale dynamics of the ENSO
A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity
Understanding ENSO dynamics has tremendously improved over the past decades.
However, one aspect still poorly understood or represented in conceptual models
is the ENSO diversity in spatial pattern, peak intensity, and temporal
evolution. In this paper, a physics-informed auto-learning framework is
developed to derive ENSO stochastic conceptual models with varying degrees of
freedom. The framework is computationally efficient and easy to apply. Once the
state vector of the target model is set, causal inference is exploited to build
the right-hand side of the equations based on a mathematical function library.
Fundamentally different from standard nonlinear regression, the auto-learning
framework provides a parsimonious model by retaining only terms that improve
the dynamical consistency with observations. It can also identify crucial
latent variables and provide physical explanations. Exploiting a realistic
six-dimensional reference recharge oscillator-based ENSO model, a hierarchy of
three- to six-dimensional models is derived using the auto-learning framework
and is systematically validated by a unified set of validation criteria
assessing the dynamical and statistical features of the ENSO diversity. It is
shown that the minimum model characterizing ENSO diversity is four-dimensional,
with three interannual variables describing the western Pacific thermocline
depth, the eastern and central Pacific sea surface temperatures (SSTs), and one
intraseasonal variable for westerly wind events. Without the intraseasonal
variable, the resulting three-dimensional model underestimates extreme events
and is too regular. The limited number of weak nonlinearities in the model are
essential in reproducing the observed extreme El Ni\~nos and nonlinear
relationship between the eastern and western Pacific SSTs
The design and simulation of an autonomous system for aircraft maintenance scheduling
International audienceOperational support is a key issue for aircraft maintenance, which aims to improve operational efficiency and reduce operating costs under the premise of ensuring flight safety. Although many works have emerged to achieve this aim, they mostly address the concept of maintenance systems, the relationship between stakeholders and the loop of maintenance information separately. Hence, the cooperation between stakeholders could be impeded especially when urgent decisions should be made, relying on historical data and real-time data. In this paper, we propose an innovative design of an autonomous system supporting the automatic decision-making for maintenance scheduling. The design starts from the proposition of the analysis framework, to concept formulation of the system, to information transitional level interface, and ends with an instance of system module interactions. The underlying architecture illustrates the high-level fusion of technical and business drives; optimizes strategies and plans with regard to maintenance costs, service level and reliability. An agent-based simulation system is developed as a proof to illustrate the feasibility of the system principle and algorithms. Furthermore, the simulation experiment analyzing the impact of maintenance sequence strategies on maintenance costs and service level has demonstrated the algorithm functionality and the feasibility of the proposed approach
Mapping the tail fiber as the receptor binding protein responsible for differential host specificity of Pseudomonas aeruginosa bacteriophages PaP1 and JG004.
The first step in bacteriophage infection is recognition and binding to the host receptor, which is mediated by the phage receptor binding protein (RBP). Different RBPs can lead to differential host specificity. In many bacteriophages, such as Escherichia coli and Lactococcal phages, RBPs have been identified as the tail fiber or protruding baseplate proteins. However, the tail fiber-dependent host specificity in Pseudomonas aeruginosa phages has not been well studied. This study aimed to identify and investigate the binding specificity of the RBP of P. aeruginosa phages PaP1 and JG004. These two phages share high DNA sequence homology but exhibit different host specificities. A spontaneous mutant phage was isolated and exhibited broader host range compared with the parental phage JG004. Sequencing of its putative tail fiber and baseplate region indicated a single point mutation in ORF84 (a putative tail fiber gene), which resulted in the replacement of a positively charged lysine (K) by an uncharged asparagine (N). We further demonstrated that the replacement of the tail fiber gene (ORF69) of PaP1 with the corresponding gene from phage JG004 resulted in a recombinant phage that displayed altered host specificity. Our study revealed the tail fiber-dependent host specificity in P. aeruginosa phages and provided an effective tool for its alteration. These contributions may have potential value in phage therapy
Data-Driven Statistical Reduced-Order Modeling and Quantification of Polycrystal Mechanics Leading to Porosity-Based Ductile Damage
Predicting the process of porosity-based ductile damage in polycrystalline
metallic materials is an essential practical topic. Ductile damage and its
precursors are represented by extreme values in stress and material state
quantities, the spatial PDF of which are highly non-Gaussian with strong fat
tails. Traditional deterministic forecasts using physical models often fail to
capture the statistics of structural evolution during material deformation.
This study proposes a data-driven statistical reduced-order modeling framework
to provide a probabilistic forecast of the deformation process leading to
porosity-based ductile damage, with uncertainty quantification. The framework
starts with computing the time evolution of the leading moments of specific
state variables from full-field polycrystal simulations. Then a sparse model
identification algorithm based on causation entropy, including essential
physical constraints, is used to discover the governing equations of these
moments. An approximate solution of the time evolution of the PDF is obtained
from the predicted moments exploiting the maximum entropy principle. Numerical
experiments based on polycrystal realizations show that the model can
characterize the time evolution of the non-Gaussian PDF of the von Mises stress
and quantify the probability of extreme events. The learning process also
reveals that the mean stress interacts with higher-order moments and extreme
events in a strongly nonlinear and multiplicative fashion. In addition, the
calibrated moment equations provide a reasonably accurate forecast when applied
to the realizations outside the training data set, indicating the robustness of
the model and the skill for extrapolation. Finally, an information-based
measurement shows that the leading four moments are sufficient to characterize
the crucial non-Gaussian features throughout the entire deformation history
Increasing the Difference in Decision Making for Oneself and for Others by Stimulating the Right Temporoparietal Junction
The right temporoparietal junction (rTPJ) has been thought to be associated with the difference in self-other decision making. In the present study, using noninvasive transcranial direct current stimulation (tDCS), we examined whether stimulating the rTPJ could modulate the self-other decision-making difference. We found that after receiving anodal stimulation of the rTPJ, participants were more likely to choose a high-value item for others than for themselves in the situations where the win probability of the high-value item was equal to or greater than that of a low-value item, indicating that elevating the cortical excitability of the rTPJ might increase the self-other decision-making difference in certain decision contexts. Our results suggest that decision making for others depends on neural activity in the rTPJ and regulation of the excitability of the rTPJ can influence the self-other decision-making difference
Network analysis of affect, emotion regulation, psychological capital, and resilience among Chinese males during the late stage of the COVID-19 pandemic
BackgroundPrevious studies have confirmed that both affect and emotion regulation strategies are closely associated with psychological capital (PsyCap) and resilience. These factors are assumed to buffer the effect of the COVID-19 pandemic on mental health, especially among males. However, these interactions have not been closely examined to date. To fill this gap, this paper explores the dimension-level relationships of these psychological constructs among Chinese males during the late stage of the COVID-19 pandemic and identified critical bridge dimensions using network analysis.MethodsA total of 1,490 Chinese males aged 21–51 years completed self-report scales assessing emotion regulation strategies, affect, PsyCap, and psychological resilience. Two regularized partial correlation networks, namely the affect and emotion regulation-PsyCap network and the affect and emotion regulation-psychological resilience network, were then constructed to examine links between the dimensions of these constructs. The bridge expected influence (BEI) index was also calculated for each node to identify important bridge nodes.ResultsPositive affect, negative affect, cognitive reappraisal, and expressive suppression showed distinct and complex links to various dimensions of PsyCap or psychological resilience. In both networks, positive affect, cognitive reappraisal, and negative affect were identified as critical bridge nodes, with the first two having positive BEI values and the third having a negative value.ConclusionThe findings elucidate the specific role of the dimensions of emotion regulation or affect in relation to PsyCap and psychological resilience, which facilitates further understanding of the mechanisms underlying these interrelationships. These findings also provide implications for developing effective intervention strategies to increase PsyCap and psychological resilience
Unlocking the mystery of the hard-to-sequence phage genome: PaP1 methylome and bacterial immunity
BACKGROUND: Whole-genome sequencing is an important method to understand the genetic information, gene function, biological characteristics and survival mechanisms of organisms. Sequencing large genomes is very simple at present. However, we encountered a hard-to-sequence genome of Pseudomonas aeruginosa phage PaP1. Shotgun sequencing method failed to complete the sequence of this genome. RESULTS: After persevering for 10 years and going over three generations of sequencing techniques, we successfully completed the sequence of the PaP1 genome with a length of 91,715 bp. Single-molecule real-time sequencing results revealed that this genome contains 51 N-6-methyladenines and 152 N-4-methylcytosines. Three significant modified sequence motifs were predicted, but not all of the sites found in the genome were methylated in these motifs. Further investigations revealed a novel immune mechanism of bacteria, in which host bacteria can recognise and repel modified bases containing inserts in a large scale. This mechanism could be accounted for the failure of the shotgun method in PaP1 genome sequencing. This problem was resolved using the nfi(-) mutant of Escherichia coli DH5α as a host bacterium to construct a shotgun library. CONCLUSIONS: This work provided insights into the hard-to-sequence phage PaP1 genome and discovered a new mechanism of bacterial immunity. The methylome of phage PaP1 is responsible for the failure of shotgun sequencing and for bacterial immunity mediated by enzyme Endo V activity; this methylome also provides a valuable resource for future studies on PaP1 genome replication and modification, as well as on gene regulation and host interaction. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-803) contains supplementary material, which is available to authorized users
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