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The hydraulic fracturing process is a prominent example of fracture network evolution under stress. However, the interactions between hydraulic fractures and natural fracture networks, along with the connectivity evolution of the resultant fracture networks, require more research. This research incorporates discrete fracture networks to characterize subsurface structures and employs the Discrete Element ‐ Lattice Boltzmann Method to simulate the hydraulic fracturing process. The dynamic evolution of subsurface structures, including the initiation of hydraulic fractures and their interaction with natural fractures, is systematically investigated. Results indicate that natural fractures significantly impact fracture initiation, propagation, and connectivity evolution, which in turn affects fluid production. Fracture strength is key for the interaction, and hydraulic fractures tend to propagate along weak natural fractures with low resistance. Fracture strength variability determines the final fracture networks, with low‐strength fractures breaking due to the altered in‐situ stress and forming local clusters. High injection rates and fluid viscosity result in a large pressure buildup and exaggerate the influential region. A multi‐cluster system is thus formed during the hydraulic fracturing process, and its connectivity can be well quantified with a novel connectivity metric. In low‐permeable reservoirs, fracture clusters connected to production wells can contribute instantly, while local clusters may contribute to production from a long‐term perspective. Injection rate, fluid viscosity, fracture orientation, and clustering effects have consistent positive correlations with the total connectivity and production. Heterogeneity has a weak positive correlation with fluid production, while a moderate negative correlation with total connectivity. Plain Language Summary: Hydraulic fracturing is a widely employed technique in developing unconventional reservoirs. Ubiquitous natural fractures in the deep subsurface significantly influence the hydraulic fracturing outcomes; however, the interactions between hydraulic and natural fractures remain incompletely understood. In this work, we optimize the numerical scheme to incorporate natural fractures and explore their impact on hydraulic fracturing. We found that natural fractures significantly impact fracture initiation, propagation, and connectivity evolution, which in turn affects fluid production. Fracture strength is key for the interaction, and hydraulic fractures tend to propagate along weak natural fractures with low resistance. Fracture strength variability can significantly impact the final fracture networks. Low‐strength fractures tend to break due to the altered in‐situ stress, forming local clusters. High injection rates and fluid viscosity result in a large pressure buildup and exaggerate the influential region. Furthermore, we propose a connectivity metric to quantify the connectivity evolution of multi‐cluster fracture networks formed during the hydraulic fracturing process. Therefore, the quantitative analysis of sensitivity for influential factors on connectivity and production is available and discussed. These findings are essential for us to better understand the subsurface structures and their dynamic evolution under the hydraulic fracturing process. Key Points: Natural fractures significantly impact fracture initiation, propagation, and connectivity evolution during hydraulic fracturingLow‐strength fractures break due to altered in‐situ stress, resulting in the final fracture networks forming a multi‐cluster systemHeterogeneity is crucial in forming final fracture networks, and high injection rates and viscosity exacerbate their influence areas [ABSTRACT FROM AUTHOR] more...

Simulating vehicle trajectories at intersections is one of the challenging tasks in traffic simulation. Existing methods are often ineffective due to the complexity and diversity of lane topologies at intersections, as well as the numerous interactions affecting vehicle motion. To address this issue, we propose a deep learning based vehicle trajectory simulation method. First, we employ a vectorized representation to uniformly extract features from traffic elements such as pedestrians, vehicles, and lanes. By fusing all factors that influence vehicle motion, this representation makes our method suitable for a variety of intersections. Second, we propose a deep learning model, which has an attention network to dynamically extract features from the surrounding environment of the vehicles. To address the issue of vehicles continuously entering and exiting the simulation scene, we employ an asynchronous recurrent neural network for the extraction of temporal features. Comparative evaluations against existing rule‐based and deep learning‐based methods demonstrate our model's superior simulation accuracy. Furthermore, experimental validation on public datasets demonstrates that our model can simulate vehicle trajectories among the urban intersections with different topologies including those not present in the training dataset. [ABSTRACT FROM AUTHOR] more...

Developing porous sorbents represents a potential energy‐efficient way for industrial gas separation. However, a bottleneck for reducing the energy penalty is the trade‐off between dynamic adsorption capacity and selectivity. Herein, we showed this problem can be overcome by modulating the kinetic and thermodynamic separation behaviours in metal–organic frameworks for sieving 2‐butene geometric isomers, which are desired for upgrading the raffinates to higher value‐added end products. We found that the iron‐triazolate framework can realize the selective shape screening of 2‐butene isomers assisted by electrostatic interactions at the pore apertures. Further introducing uncoordinated N binding sites by ligand substitution lowered the gas diffusion barrier and greatly boosted the dynamic separation performance. In breakthrough tests under ambient conditions, trans‐2‐C4H8 can be efficiently separated from cis‐2‐C4H8 with a record capacity of 2.10 mmol g−1 with high dynamic selectivity of 2.39. [ABSTRACT FROM AUTHOR] more...

COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest X-ray images have become an important basis for screening and monitoring the disease, and deep learning has shown great potential for this task. Many studies have proposed deep learning methods for automated diagnosis of COVID-19. Although these methods have achieved excellent performance in terms of detection, most have been evaluated using limited datasets and typically use a single deep learning network to extract features. To this end, the dual asymmetric feature learning network (DAFLNet) is proposed, which is divided into two modules, DAFFM and WDFM. DAFFM mainly comprises the backbone networks EfficientNetV2 and DenseNet for feature fusion. WDFM is mainly for weighted decision-level fusion and features a new pretrained network selection algorithm (PNSA) for determination of the optimal weights. Experiments on a large dataset were conducted using two schemes, DAFLNet-1 and DAFLNet-2, and both schemes outperformed eight state-of-the-art classification techniques in terms of classification performance. DAFLNet-1 achieved an average accuracy of up to 98.56% for the triple classification of COVID-19, pneumonia, and healthy images. [ABSTRACT FROM AUTHOR] more...

There is a well‐known trade‐off between computational efficiency and computational accuracy in the field of traffic simulation. In this article, we propose a novel recurrent neural network based model with an integrated attention mechanism, called R‐CTM, to simulate heterogeneous traffic flow with multiple types of vehicles. It can effectively extract the traffic flow patterns of spatial and temporal changes from training traffic data, which can be real‐world traffic data or synthetic traffic data via microscopic simulation models. Through experiments and comparisons, we show that it can significantly outperform the state of the art methods in terms of simulation accuracy. Besides accuracy, we also demonstrate its scalability: its runtime consumption does not linearly increase with respect to the spatial extent. [ABSTRACT FROM AUTHOR] more...

Doping is fundamental to controlling the properties of bulk semiconductors. Although the antimony (SbV)‐doping strategy is widely employed in the design of practical tin oxide (SnO2) semiconductor gas sensors for higher signal‐to‐noise ratio, challenges remain to dope semiconductor nanocrystals since the diffusion of impurity atoms may be far from realized at the synthesis temperatures used. Herein, a metastable Sb‐doping strategy is proposed to overcome the serious receptor‐versus‐transducer mismatch in SnO2 quantum wires (QWs). The solvothermal synthesis of colloidal SbIII‐doped SnO2 QWs has been conducted at 180 °C, whereby the antimony amount is varied to optimize the structural and morphological properties for higher surface activity and electrical conductivity. A unique n‐type doping mechanism arising from the stable presence of SbIII on SnO2 (101) facets via SnII‐O‐SbIII is demonstrated. Further, the use of sensitive (down to 4 ppb, the lowest detection limit ever reported), fast (response and recovery time of 43 s and 96 s toward 10 ppm of H2S) gas sensors for H2S detection at near room temperature (40 °C) is showcased. The metastable Sb‐doping strategy may pave the way to ultrasensitive gas sensor possessing low power consumption and excellent integration compatibility to satisfy the increasing demand for ubiquitous and reliable gas detection. [ABSTRACT FROM AUTHOR] more...

Objective: This study was undertaken to identify the risk of bleeding events and potential risk factors within 90 days in patients who carried CYP2C19 loss‐of‐function alleles and received dual antiplatelet therapy after minor stroke or transient ischemic attack. Methods: A total of 6,412 patients were enrolled from the CHANCE‐2 (Clopidogrel with Aspirin in High‐Risk Patients with Acute Non‐disabling Cerebrovascular Events II) trial. The main outcome was any bleeding within 90 days defined by the criteria from GUSTO (Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries). Results: A total of 250 (3.9%) bleeding events were reported, which occurred mainly within the 21 days of dual antiplatelet therapy (200 cases, 3.1%). Minor bleeding of the skin bruises, epistaxis, and gum bleeding were most frequent. Multivariate analysis showed that treatment with ticagrelor‐aspirin compared with clopidogrel‐aspirin was associated with increased bleeding (hazard ratio [HR] = 2.21, 95% confidence interval [CI] = 1.68–2.89, p < 0.001). Current smoking was associated with a lower risk of bleeding (HR = 0.70, 95% CI = 0.52–0.95, p = 0.02). Additionally, ticagrelor‐aspirin compared with clopidogrel‐aspirin was associated with higher risk of bleeding in patients aged <65 years (HR = 2.87, 95% CI = 1.95–4.22) and those without diabetes mellitus (HR = 2.65, 95% CI = 1.88–3.73; p for interaction = 0.04 and 0.03, respectively). Interpretation: Bleeding events mostly occurred within the 21‐day dual antiplatelet therapy stage and were generally mild. The risk of bleeding was greater in nonsmoking patients, and was associated with treatment with ticagrelor‐aspirin compared with clopidogrel‐aspirin, particularly in patients aged <65 years and nondiabetic patients. ANN NEUROL 2022;91:380–388 [ABSTRACT FROM AUTHOR] more...

Fluoromonomers are difficult to polymerize through a coordination mechanism owing to the strong chelation between fluorine and catalysts and the electron-deficient double-bond. We report herein the unprecedented polymerization of para-fluorostyrene (pFS) with excellent activity (1.12×107 g molLn−1h−1), distinguished syndioselectivity (rrrr > 99 %) and high conversion (98.4 %) using rare-earth-metal catalysts bearing a pyridyl methylene fluorenyl ligand. DFT calculations reveal polar fluorine loses overwhelming coordination priority to the active central metal ion due to the sterically bulky and electron-donating CGC-ligand, thus its power of poisoning the active species is dramatically weakened. Based on this, copolymerization of pFS and styrene (St) has been successfully achieved with high activity and controllable pFS insertion. Conversely, the unlinked half-sandwich rare-earth-metal system shows relatively lower activity, because both the transition-states and intermediates incorporate a μ1-F chelated pFS. [ABSTRACT FROM AUTHOR] more...

Two new isostructural 3D lanthanide-organic frameworks [H2N(Me)2] [Ln3(OH)(bpt)3(H2O)3] (DMF)2⋅(H2O)4 ( 1-Ln; Ln=Sm and Eu) with a 1D channel (25 Å) have been successfully assembled from the rare trinuclear [Ln3(OH)(COO)9] clusters and biphenyl-3,4′,5-tricarboxylic acid (H3bpt) and exhibit high stability towards water in the pH range 3-10. MOF 1-Eu is a promising luminescent probe for sensing Fe3+ in aqueous solution and is also selective towards rhodamine B (RhB) with a superior adsorption capacity of 735 mg g−1, which is the highest among the reported Ln-MOFs for RhB removal so far. Periodic DFT calculations further confirmed the selective adsorption of rhodamine B over other dyes. [ABSTRACT FROM AUTHOR] more...

Objective. This study aimed to examine the potential role of memory T follicular helper (Tfh) cells in patients with neuromyelitis optica/neuromyelitis optica spectrum disorders (NMO/NMOSD). Methods. The percentages of different subsets of circulating memory Tfh cells in 25 NMO/NMOSD patients before and after treatment as well as in 17 healthy controls were examined by flow cytometry. The levels of IL-21 and AQP4 Ab in plasma and CSF were measured by ELISA. Results. The percentages and numbers of circulating memory Tfh cells, ICOS+, CCR7−, CCR7−ICOS+, CCR7+, CCR7+ICOS+ memory Tfh cells, and the levels of IL-21 in plasma and CSF were significantly increased in NMO/NMOSD patients. The percentages of CCR7− and CCR7−ICOS+ memory Tfh cells were positively correlated with ARR, plasma IL-21, and AQP4 Ab levels. The percentages of CCR7+ and CCR7+ICOS+ memory Tfh cells were positively correlated with CSF white blood cell counts, proteins, and IL-21 levels. Treatment with corticosteroids significantly reduced the numbers of CCR7−ICOS+ and CCR7+ICOS+ memory Tfh cells as well as plasma IL-21 levels in patients with partial remission. Conclusions. Our findings indicate that circulating memory Tfh cells may participate in the relapse and development of NMO/NMOSD and may serve as a new therapeutic target. [ABSTRACT FROM AUTHOR] more...

The mechanism for the reaction of radical FOO with NO was investigated theoretically. The electronic structure information for the singlet potential energy surfaces (PES) was obtained at the MP2/6-311+G(2df) level of theory, and the single-point energies were refined by the CCSD(T)/6-311+G(2df) level. The rate constants as well as the pressure and temperature dependence of various product channels for the reaction are predicted. The calculated results show that starting from the energy-rich intermediate FOONOtp (IM1), at room temperature 298 K and at 1 Torr, FNO is the exclusive product which is consistent with the experimental results. The present results will be useful to gain a deep insight into the reaction mechanism of FOO + NO reaction. [ABSTRACT FROM AUTHOR] more...

In Ag2O-mediated benzylation, the addition of a catalytic amount of KI can greatly improve reactivity. This is usually attributed to the formation of a more reactive iodo-substituted electrophile. However, our studies show this to be due to the enhanced basicity of Ag2O through coordination of soft iodide anions to the silver atom, and show KI to be an initiator. A catalytic amount of Ag2O and NaBr can catalyze transesterification reactions, indicating the enhanced basicity of Ag2O by bromide. We believe that this is a general effect for metal oxides and soft anions, applicable to a wider range of organic reaction systems. [ABSTRACT FROM AUTHOR] more...

The first four-coordinate methanediide/alkyl lutetium complex (BODDI)Lu2(CH2SiMe3)2(μ2pt>-CHSiMe3)(THF)2 (BODDI=ArNC(Me)CHCOCHC(Me)NAr, Ar=2,6- iPr2C6H3) ( 1) was synthesized by a thermolysis methodology through α-H abstraction from a Lu-CH2SiMe3 group. Complex 1 reacted with equimolar 2,6- iPrC6H3NH2 and Ph2CO to give the corresponding lutetium bridging imido and oxo complexes (BODDI)Lu2(CH2SiMe3)2(μ2- N-2,6- iPr2C6H3)(THF)2 ( 2) and (BODDI)Lu2(CH2SiMe3)2(μ2-O)(THF)2 ( 3). Treatment of 3 with Ph2CO (4 equiv) caused a rare insertion of Lu-μ2-O bond into the CO group to afford a diphenylmethyl diolate complex 4. Reaction of 1 with PhN=CO (2 equiv) led to the migration of SiMe3 to the amido nitrogen atom to give complex (BODDI)Lu2(CH2SiMe3)2-μ-{PhNC(O)CHC(O)NPh(SiMe3)-κ3N,O,O}(THF) ( 5). Reaction of 1 with tBuNC formed an unprecedented product (BODDI)Lu2(CH2SiMe3){μ2-[η2:η2- tBuNC(=CH2)SiMe2CHCN tBu-κ1N]}( tBuNC)2 ( 6) through a cascade reaction of NC bond insertion, sequential cyclometalative γ-(sp3)-H activation, CC bond formation, and rearrangement of the newly formed carbene intermediate. The possible mechanistic pathways between 1, PhNCO, and tBuNC were elucidated by DFT calculations. [ABSTRACT FROM AUTHOR] more...