Constrained Cubature Particle Filter for Vehicle Navigation.

In vehicle navigation, it is quite common that the dynamic system is subject to various constraints, which increases the difficulty in nonlinear filtering. To address this issue, this paper presents a new constrained cubature particle filter (CCPF) for vehicle navigation. Firstly, state constraints are incorporated in the importance sampling process of the traditional cubature particle filter to enhance the accuracy of the importance density function. Subsequently, the Euclidean distance is employed to optimize the resampling process by adjusting particle weights to avoid particle degradation. Further, the convergence of the proposed CCPF is also rigorously proved, showing that the posterior probability function is converged when the particle number N → ∞. Our experimental results and the results of a comparative analysis regarding GNSS/DR (Global Navigation Satellite System/Dead Reckoning)-integrated vehicle navigation demonstrate that the proposed CCPF can effectively estimate system state under constrained conditions, leading to higher estimation accuracy than the traditional particle filter and cubature particle filter.

Unravelling the Impact of Metal Dopants and Oxygen Vacancies on Syngas Conversion over Oxides: A Machine Learning-Accelerated Study of CO Activation on Cr-Doped ZnO Surfaces.

As a critical component of the OX-ZEO composite catalysts toward syngas conversion, the Cr-doped ZnO ternary system can be considered as a model system for understanding oxide catalysts. However, due to the complexity of its structures, traditional approaches, both experimental and theoretical, encounter significant challenges. Herein, we employ machine learning-accelerated methods, including grand canonical Monte Carlo and genetic algorithm, to explore the ZnO(1010) surface with various Cr and oxygen vacancy (OV) concentrations. Stable surfaces with varied Cr and OV concentrations were then systematically investigated to examine their influence on the CO activation via density functional theory calculations. We observe that Cr tends to preferentially appear on the surface of ZnO(1010) rather than in its interior regions and Cr-doped structures incline to form rectangular islands along the [0001] direction at high Cr and OV concentrations. Additionally, detailed calculations of CO reactivity unveil an inverse relationship between the reaction barrier (Ea) for C-O bond dissociation and the Cr and OV concentrations, and a linear relationship is observed between OV formation energy and Ea for CO activation. Further analyses indicate that the C-O bond dissociation is much more favored when the adjacent OVs are geometrically aligned in the [1210] direction, and Cr is doped around the reactive sites. These findings provide a deeper insight into CO activation over the Cr-doped ZnO surface and offer valuable guidance for the rational design of effective catalysts for syngas conversion.

Leveraging Interlayer Interaction in M-N-C Catalysts for Enhanced Activity in Oxygen Reduction Reactions.

Atomically dispersed metal-nitrogen-carbon (M-N-C) materials are deemed promising catalysts for the oxygen reduction reaction (ORR) in fuel cells. Yet the multilayer nature of M-N-C has been largely neglected in computational analysis. To bridge the gap, we conducted a first-principles investigation using bilayer M-N-C models (TMNx/G-TMNy/G, TM = Mn, Fe, Co, Ni, Cu, G = graphene, x, y = 3 or 4), where the TMs on the top serves as the active center. While in-plane TMN4 at the bottom has a minimal impact on the ORR, out-of-plane TMN3 substantially influences the adsorption free energy of OH through a strong interlayer bonding interaction. By leveraging interlayer interactions, we appreciably lowered the overpotential of selected TMN4 (TM = Co, Ni, Cu) and achieved a minimum of 0.40 V on CoN4/G-CuN3/G. Constant potential calculations revealed weak dependence of OH binding energy on external voltage and obtained results comparable to constant charge calculation. This study provided new physical insight into modulating naturally occurring multilayer M-N-C catalysts.

Effects of intermetal distance on the electrochemistry-induced surface coverage of M-N-C dual-atom catalysts.

The often-overlooked electrocatalytic bridge-site poisoning of the emerging dual-atom catalysts (DACs) has aroused broad concerns very recently. Herein, based on surface Pourbaix analysis, we identified a significant change in the electrochemistry-induced surface coverages of DACs upon changing the intermetal distance. We found a pronounced effect of the intermetal distance on the electrochemical potential window and the type of pre-covered adsorbate, suggesting an interesting avenue to tune the electrocatalytic function of DACs.

Computation-based regulation of excitonic effects in donor-acceptor covalent organic frameworks for enhanced photocatalysis.

The strong excitonic effects widely exist in polymer-semiconductors and the large exciton binding energy (Eb) seriously limits their photocatalysis. Herein, density functional theory (DFT) calculations are conducted to assess band alignment and charge transfer feature of potential donor-acceptor (D-A) covalent organic frameworks (COFs), using 1,3,5-tris(4-aminophenyl)triazine (TAPT) or 1,3,5-tris(4-aminophenyl)benzene (TAPB) as acceptors and tereph-thaldehydes functionalized diverse groups as donors. Given the discernable D-A interaction strengths in the D-A pairs, their Eb can be systematically regulated with minimum Eb in TAPT-OMe. Guided by these results, the corresponding D-A COFs are synthesized, where TAPT-OMe-COF possesses the best activity in photocatalytic H2 production and the activity trend of other COFs is associated with that of calculated Eb for the D-A pairs. In addition, further alkyne cycloaddition for the imine linkage in the COFs greatly improves the stability and the resulting TAPT-OMe-alkyne-COF with a substantially smaller Eb exhibits ~20 times higher activity than the parent COF.

Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H2 Activation Using Machine Learning-Accelerated First-Principles Simulations.

Oxygen vacancies (OVs) play important roles on any oxide catalysts. In this work, using an investigation of the OV effects on ZnO(101̅0) for CO and H2 activation as an example, we demonstrate, via machine learning potentials (MLPs), genetic algorithm (GA)-based global optimization, and density functional theory (DFT) validations, that the ZnO(101̅0) surface with 0.33 ML OVs is the most likely surface configuration under experimental conditions (673 K and 2.5 MPa syngas (H2:CO = 1.5)). It is found that a surface reconstruction from the wurtzite structure to a body-centered-tetragonal one would occur in the presence of OVs. We show that the OVs create a Zn3 cluster site, allowing H2 homolysis and C-O bond cleavage to occur. Furthermore, the activity of intrinsic sites (Zn3c and O3c sites) is almost invariable, while the activity of the generated OV sites is strongly dependent on the concentration of the OVs. It is also found that OV distributions on the surface can considerably affect the reactions; the barrier of C-O bond dissociation is significantly reduced when the OVs are aligned along the [12̅10] direction. These findings may be general in the systems with metal oxides in heterogeneous catalysis and may have significant impacts on the field of catalyst design by regulating the concentration and distribution of the OVs.

Interface engineering of Ni/NiO heterostructures with abundant catalytic active sites for enhanced methanol oxidation electrocatalysis.

Designing efficient and stable non-noble metal electrocatalysts with good performance in reaction kinetics is desirable yet challenging for the study of methanol oxidation reaction (MOR). Herein, we have reported well-defined nanoscale nickel/nickel oxide (Ni/NiO) heterostructures supported by a three-dimensional (3D) porous graphene network (RG) via a delicate interface engineering technique. The as-prepared 3D Ni/NiO/RG composites achieve outstanding catalytic activity (79.5 mA cm-2/1262.1 mA mg-1) for MOR in alkaline solution, outperforming most reported non-precious catalysts. A combined experimental and computational investigation shows that such a good performance benefits from the specific Ni/NiO interface, which not only bears abundant accessible active sites but also improves the energetics of MOR. Moreover, this interface contributes to favorable kinetic and improved structural stability during electrocatalysis, ensuring superior catalytic performance after 1000 consecutive cyclic voltammetry tests for MOR. Our work demonstrates the potential of interface engineering in the rational design of efficient precious-metal-free electrocatalysts.

Enhancing conversion of polysulfides via porous carbon nanofiber interlayer with dual-active sites for lithium-sulfur batteries.

Lithium-sulfur (Li-S) batteries are promising candidates for next-generation energy storage. However, the notorious lithium polysulfides (LiPSs) shuttle effect and torpid redox kinetics hinder their practical application. Enhancing phase conversion efficiency and limiting the dissolution of LiPSs are critical for stabilizing Li-S batteries. Herein, sulfiphilic defective TiO2 nanoparticles (D-TiO2) were integrated into the lithiophilic N-doped porous carbon nanofiber membrane (D-TiO2@NPCNF) to construct interlayer for catalyzing the conversion of LiPSs. The D-TiO2@NPCNF provides hierarchical porous structure and large specific surface area, and the formed 3D conductive network accelerates the transport of electrons and ions. The dual-active sites (N and D-TiO2) enhance the interface conversion and chemisorption ability of LiPSs via forming "Li-N and Ti-S" bonds. Due to the structural advantage of the D-TiO2@NPCNF, the Li-S batteries exhibit excellent cycling stability (only 0.049% decay per cycle in 800cycles at 1.0C) and impressive specific capacity (608 mAh g-1 at 3.0C). This work is expected to deepen the comprehension of complex interphase conversion processes of LiPSs and provide novel ideas for the design of new interlayer materials.

Differences in Power Acquisition Between Only and Non-only Children: The Effects of Cooperative Orientation, Competitive Orientation, and Dependency on Parents.

Drawing upon a developmental perspective, we investigated the differences in power acquisition (i.e., rank at work and leader role occupancy in university) between only and non-only children as well as the mediating role of cooperative and competitive orientations and the moderating role of dependency on parents. To test our hypotheses, we conducted two field studies in 155 part-time Master of Business Administration (MBA) students (Study 1) and 375 senior students (Study 2). Results showed that: (1) non-only children were more likely to achieve higher rank at work than only children; (2) only children were less likely than non-only children to acquire power in organizations because they scored lower in cooperative orientation; however, the mediating effect of competitive orientation was not significant; (3) the difference in cooperative orientation between only and non-only children was smaller when dependency on parents was high, whereas it became larger when dependency on parents was low. Our research contributes to the understanding of how family structure influences individual power acquisition.

Photocatalytic Molecular Oxygen Activation by Regulating Excitonic Effects in Covalent Organic Frameworks.

Excitonic effects caused by Coulomb interactions between electrons and holes play subtle and significant roles on photocatalysis, yet have been long ignored. Herein, porphyrinic covalent organic frameworks (COFs, specifically DhaTph-M), in the absence or presence of different metals in porphyrin centers, have been shown as ideal models to regulate excitonic effects. Remarkably, the incorporation of Zn2+ in the COF facilitates the conversion of singlet to triplet excitons, whereas the Ni2+ introduction promotes the dissociation of excitons to hot carriers under photoexcitation. Accordingly, the discriminative excitonic behavior of DhaTph-Zn and DhaTph-Ni enables the activation of O2 to 1O2 and O2•-, respectively, under visible light irradiation, resulting in distinctly different activity and selectivity in photocatalytic terpinene oxidation. Benefiting from these results, DhaTph-Ni exhibits excellent photocatalytic activity in O2•--engaged hydroxylation of boronic acid, while DhaTph-Zn possesses superior performance in 1O2-mediated selective oxidation of organic sulfides. This work provides in-depth insights into molecular oxygen activation and opens an avenue to the regulation of excitonic effects based on COFs.

Cooperative Nitrogen Activation and Ammonia Synthesis on Densely Monodispersed Mo-N-C Sites.

The production of ammonia from nitrogen reduction reaction (NRR) under mild conditions is one of the most challenging issues in modern chemistry. The linear scaling relationship between the adsorption energies of -N2H and -NH2 on a single active site is a well-established bottleneck. By investigating a series of densely monodispersed Mo-N-C sites embedded in graphene using first-principles calculations, we found that previously underappreciated neighboring effects between adjacent active sites may help break the limit: they not only improve the energetics of potential determining steps of NRR but also promote an alternative associative mechanism based on a cooperative bridge-on adsorption of N2 by two Mo-N-C sites of ∼6 Šapart. Further, a barrier of 0.71 eV for N-N bond dissociation is achieved by proper ratio of coordinated C/N atoms of Mo. Our work suggests the cooperation of two adjacent active sites may offer an alternative strategy of nitrogen fixation.

Impact of Active Site Density on Oxygen Reduction Reactions Using Monodispersed Fe-N-C Single-Atom Catalysts.

Exploring the impact of active site density on catalytic reactions is crucial for reaching a more comprehensive understanding of how single-atom catalysts work. Utilizing density functional theory calculations, we have systematically investigated the neighboring effects between two adjacent Fe-N-C sites of monodispersed Fe-N-C single-atom catalysts on oxygen reduction reaction (ORR). While the thermodynamic limiting potential (UL) is strongly dependent on the intersite distance and the nature of adjacent active sites in FeN3, it is almost invariable in FeN4 until two FeN4 sites are ∼4 Šapart. Further, under certain conditions, an otherwise unfavorable physisorbed-O2-initiated 2e- pathway becomes feasible due to charge transfer between reactive species and graphene support. Our results cast new insight into the rational design of high-density single-atom catalysts and may create an alternative route to manipulate their catalytic activities.

Cobalt-Salen-Based Porous Ionic Polymer: The Role of Valence on Cooperative Conversion of CO2 to Cyclic Carbonate.

Cobalt-salen-based porous ionic polymers, which are composed of cobalt and halogen anions decorated on the framework, effectively catalyze the CO2 cycloaddition reaction of epoxides to cyclic carbonates under ambient conditions. The cooperative effect of bifunctional active sites of cobalt as the Lewis acidic site and the halogen anion as the nucleophile responds to the high catalytic performance. Moreover, density functional theory results indicate that the cobalt valence state and the corresponding coordination group influence the rate-determining step of the CO2 cycloaddition reaction and the nucleophilicity of halogen anions.

Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter.

The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.

Dictionary learning based noisy image super-resolution via distance penalty weight model.

In this study, we address the problem of noisy image super-resolution. Noisy low resolution (LR) image is always obtained in applications, while most of the existing algorithms assume that the LR image is noise-free. As to this situation, we present an algorithm for noisy image super-resolution which can achieve simultaneously image super-resolution and denoising. And in the training stage of our method, LR example images are noise-free. For different input LR images, even if the noise variance varies, the dictionary pair does not need to be retrained. For the input LR image patch, the corresponding high resolution (HR) image patch is reconstructed through weighted average of similar HR example patches. To reduce computational cost, we use the atoms of learned sparse dictionary as the examples instead of original example patches. We proposed a distance penalty model for calculating the weight, which can complete a second selection on similar atoms at the same time. Moreover, LR example patches removed mean pixel value are also used to learn dictionary rather than just their gradient features. Based on this, we can reconstruct initial estimated HR image and denoised LR image. Combined with iterative back projection, the two reconstructed images are applied to obtain final estimated HR image. We validate our algorithm on natural images and compared with the previously reported algorithms. Experimental results show that our proposed method performs better noise robustness.

Aspirin regulates SNARE protein expression and phagocytosis in dendritic cells.

Since being introduced globally as Aspirin in 1899, acetylsalicylic acid (ASA) has been widely used as an analgesic, immune-regulatory, anti-pyretic and anti-thrombotic drug. ASA and its metabolite, salicylate, were also reported to be able to modulate antigen presenting functions of dendritic cells (DC). However, the intracellular targets of ASA in DC are still poorly understood. Since phagocytosis is the initial step taken by antigen-presenting cells in the uptake of antigens for processing and presentation, ASA might exerts its immune-regulatory effects by regulating phagocytosis. Here we show that ASA inhibits phagocytosis and modulates expression of endosomal SNAREs, such as Vti1a, Vti1b, VAMP-3, VAMP-8 and Syn-8 (but not syn-6 and syn-16) in DC. We further show that the phagocytic inhibitory effect of ASA is dependent on the expression of Vti1a and Vti1b. Consistently, Vti1a and Vti1b localize to the phagosomes and up-regulation of Vti1a and Vti1b inhibits phagocytosis in DC. Our results suggest that ASA modulates phagocytosis in part through the control of endosomal SNARE protein expression and localization in DC. All experiments were performed using either a murine DC line (DC2.4) or primary DC derived from murine bone marrow cells.

[Change of voltage-gate potassium channel in pulmonary arterial smooth muscle cells of pulmonary hypertension induced by left-to-right shunt in rats].

OBJECTIVE: To investigate the electrophysiological changes of voltage-gate potassium channel (Kv) of pulmonary arterial smooth muscle cells of pulmonary arterial hypertension in rats induced by left to right shunt, and to analyze the role of Kv during the progress of pulmonary arterial hypertension. METHODS: Forty male SD rats were randomly divided into three groups, group A (control, n = 10), group B (sham operated only group, n = 10), and group C (PAH model group, n = 20). Mean pulmonary artery pressure (mPAP) and right ventricular hypertrophy index (RVHI) of each rat were measured, single pulmonary artery smooth muscle cell (PASMC) was obtained by acute enzyme separation method (collagenase I plus papain) and the conventional whole-cell patch clamp technique was used to record resting membrane potential (Em), potassium ion current of voltage-gated potassium channel, the I-V curve between each 2 groups was compared, and correlation of each parameter was analyzed. RESULT: (1) The mPAP and RVHI of group C were significantly higher than those of group A and group B (P < 0.01, respectively). (2) The Em of group C [(-33.00 ± 4.09) mV] was significantly higher than that of group A [(-48.10 ± 4.54) mV] and group B [(-51.11 ± 3.66) mV], P < 0.01. (3) The peak current at +50 mV of voltage-gated potassium channel: in group C [(64.80 ± 8.40) pA/pF], it was significantly lower than that of group A [(120.85 ± 11.66) pA/pF] and group B [(118.03 ± 10.18) pA/pF] (P < 0.01, respectively). None of the parameters showed any significant difference between group A and group B (P > 0.05 for all comparisons). (4) Compared with group A and group B, the I-V curve of group C significantly downward shifted (P < 0.01, respectively). The difference in I-V curve between group A and group B was not significant, P > 0.05. (5) The correlation of resting membrane potential and mPAP and RVHI had significantly positive correlation (P < 0.001, respectively); but the correlation of membrane current, membrane current density and mPAP, RVHI and resting membrane potential had significantly negative correlation (P < 0.001, respectively). CONCLUSION: During the formation process of left-to-right shunt induced pulmonary arterial hypertension, function of Kv channel was inhibited, suggesting that Kv channel may be the mechanism of pulmonary arterial hypertension induced by left-to-right shunting.

Transactive memory system links work team characteristics and performance.

Teamwork and coordination of expertise among team members with different backgrounds are increasingly recognized as important for team effectiveness. Recently, researchers have examined how team members rely on transactive memory system (TMS; D. M. Wegner, 1987) to share their distributed knowledge and expertise. To establish the ecological validity and generality of TMS research findings, this study sampled 104 work teams from a variety of organizational settings in China and examined the relationships between team characteristics, TMS, and team performance. The results suggest that task interdependence, cooperative goal interdependence, and support for innovation are positively related to work teams' TMS and that TMS is related to team performance; moreover, structural equation analysis indicates that TMS mediates the team characteristics-performance links. Findings have implications both for team leaders to manage their work teams effectively and for team members to improve their team performance.