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Understanding urban human mobility, particularly trip purposes, is essential for optimizing traffic management, personalized recommendations, and urban planning. However, in real-world scenarios, trip purposes cannot be directly extracted from the trajectory data. To address this issue, we propose a geometry-driven neural topic model for trip purpose inference. We integrate trajectory data with nearby POI data using a geometry-driven technique to enhance the interpretability of the results. Furthermore, our model captures the semantics and relationships of the data in a high-dimensional space and identifies latent topics representing distinct trip purposes. These learned topics are analyzed using clustering algorithms to group similar trips, enabling trip purpose inference. And we evaluate our model using the trajectory data of Shenzhen and Chengdu, and compare it with baseline models. The results demonstrate that our model performs well. Furthermore, we analyze trajectory data containing trip purpose information to gain insight into human mobility patterns and the influence of trip purposes, paving the way for potential implications and future research directions. [ABSTRACT FROM AUTHOR]

Persistent organic pollutants (POPs) sourced by the forest fire release are emerging as significant contributors. Despite their increasing importance, the impact of forest fires on POPs remains inadequately explored and an unclear understanding. Herein, the research, choosing four typical forest combustibles, focuses on the relationship between typical POPs and wildfire parameters by assessing the predominant compounds and their concentration in POPs emissions from such fuels through molecular-level analysis. Experiments reveal forest combustibles thermally degrade to release products, releasing a variety of products, including acids (>7.94 %), aldehydes (>2.32 %), ketones (>3.40 %), alcohols (>7.70 %), esters (>2.33 %), ethers (>4.44 %), hydrocarbons (>6.36 %), aromatic compounds (>21.40 %), and nitrogen-bearing compounds (>11.83 %); notably, aromatic compounds, containing substantial concentrations, are also recognized as POPs. By delving into the pyrolysis (20 °C·ms−1) and burning processes (25, 35 and 50 kW/m2) of forest combustibles, we can gain a comprehensive understanding of the origin of POPs in wildfires. Moreover, Pearson correlation analysis is employed to establish connections between emitting volatiles and forest fire risk, further unveiling a significant correlation between fire hazards of forest combustibles and the presence of aromatic compounds (Correlation over 0.8). These findings are crucial for comprehending the POPs in forests and evaluating forest fire hazards at the molecular level. [Display omitted] • Exploring forest combustibles' chemical makeup, POPs up to 21.40 %, raising fire risk. • Evaluating POPs' impact on forest fire risks via correlations, coefficient over 0.8. • Molecular proof that POPs cycle from wildfires affecting forest fire risks. [ABSTRACT FROM AUTHOR]

With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing works employ Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These approaches primarily focus on modeling the pair-wise relations in LBSN to enrich the semantics and thereby relieve the data sparsity issue. However, existing approaches seldom consider the hyper-relations in LBSN, such as the mobility relation (a 3-ary relation: user-POI-time). This makes the model hard to exploit the semantics accurately. In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity.To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed to maintain and exploit the rich semantics of hyper-relations. Then we proposed a Hypergraph Neural Network to utilize the structural information of HKG in a cohesive way. In addition, a self-attention network is used to leverage sequential information and make personalized recommendations. Furthermore, side information, essential in reducing data sparsity by providing background knowledge of POIs, is not fully utilized in current methods. In light of this, we extended the current dataset with available side information to further lessen the impact of data sparsity. Results of experiments on four real-world LBSN datasets demonstrate the effectiveness of our approach compared to existing state-of-the-art methods. Our implementation is available at . [ABSTRACT FROM AUTHOR]

We present a comprehensive photometric and spectroscopic analysis of the short-period (∼5.32 h) and low-mass eclipsing binary TMTSJ0803 discovered by Tsinghua-Ma Huateng Telescope for Survey (TMTS). By fitting the light curves and radial velocity data with the Wilson–Devinney code, we find that the binary is composed of two late spotted active M dwarfs below the fully convective boundary. This is supported by the discovery of a significant Balmer emission lines in the LAMOST spectrum and prominent coronal X-ray emission. In comparison with the typical luminosity of rapidly rotating fully convective stars, the much brighter X-ray luminosity (L X/ L bol = 0.0159 ± 0.0059) suggests the stellar magnetic activity of fully convective stars could be enhanced in such a close binary system. Given the metallicity of [M/H] = − 0.35 dex as inferred from the LAMOST spectrum, we measure the masses and radii of both stars to be M 1 = 0.169 ± 0.010 M⊙, M 2 = 0.162 ± 0.016 M⊙, R 1 = 0.170 ± 0.006 R⊙, and R 2 = 0.156 ± 0.006 R⊙, respectively. Based on the luminosity ratio from the light-curve modelling, the effective temperatures of two components are also estimated. In comparison with the stellar evolution models, the radii, and effective temperatures of two components are all below the isochrones. The radius deflation might be mainly biased by a small radial velocity (RV) data or (and) a simple correction on RVs, while the discrepancy in effective temperature might be due to the enhanced magnetic activity in this binary. [ABSTRACT FROM AUTHOR]

The electrolytes for lithium metal batteries (LMBs) are plagued by a low Li+ transference number (T+) of conventional lithium salts and inability to form a stable solid electrolyte interphase (SEI). Here, we synthesized a self‐folded lithium salt, lithium 2‐[2‐(2‐methoxy ethoxy)ethoxy]ethanesulfonyl(trifluoromethanesulfonyl) imide (LiETFSI), and comparatively studied with its structure analogue, lithium 1,1,1‐trifluoro‐N‐[2‐[2‐(2‐methoxyethoxy)ethoxy)]ethyl]methanesulfonamide (LiFEA). The special anion chemistry imparts the following new characteristics: i) In both LiFEA and LiETFSI, the ethylene oxide moiety efficiently captures Li+, resulting in a self‐folded structure and high T+ around 0.8. ii) For LiFEA, a Li−N bond (2.069 Å) is revealed by single crystal X‐ray diffraction, indicating that the FEA anion possesses a high donor number (DN) and thus an intensive interphase "self‐cleaning" function for an ultra‐thin and compact SEI. iii) Starting from LiFEA, an electron‐withdrawing sulfone group is introduced near the N atom. The distance of Li−N is tuned from 2.069 Å in LiFEA to 4.367 Å in LiETFSI. This alteration enhances ionic separation, achieves a more balanced DN, and tunes the self‐cleaning intensity for a reinforced SEI. Consequently, the fast charging/discharging capability of LMBs is progressively improved. This rationally tuned anion chemistry reshapes the interactions among Li+, anions, and solvents, presenting new prospects for advanced LMBs. [ABSTRACT FROM AUTHOR]

The electrolytes for lithium metal batteries (LMBs) are plagued by a low Li+ transference number (T+) of conventional lithium salts and inability to form a stable solid electrolyte interphase (SEI). Here, we synthesized a self‐folded lithium salt, lithium 2‐[2‐(2‐methoxy ethoxy)ethoxy]ethanesulfonyl(trifluoromethanesulfonyl) imide (LiETFSI), and comparatively studied with its structure analogue, lithium 1,1,1‐trifluoro‐N‐[2‐[2‐(2‐methoxyethoxy)ethoxy)]ethyl]methanesulfonamide (LiFEA). The special anion chemistry imparts the following new characteristics: i) In both LiFEA and LiETFSI, the ethylene oxide moiety efficiently captures Li+, resulting in a self‐folded structure and high T+ around 0.8. ii) For LiFEA, a Li−N bond (2.069 Å) is revealed by single crystal X‐ray diffraction, indicating that the FEA anion possesses a high donor number (DN) and thus an intensive interphase "self‐cleaning" function for an ultra‐thin and compact SEI. iii) Starting from LiFEA, an electron‐withdrawing sulfone group is introduced near the N atom. The distance of Li−N is tuned from 2.069 Å in LiFEA to 4.367 Å in LiETFSI. This alteration enhances ionic separation, achieves a more balanced DN, and tunes the self‐cleaning intensity for a reinforced SEI. Consequently, the fast charging/discharging capability of LMBs is progressively improved. This rationally tuned anion chemistry reshapes the interactions among Li+, anions, and solvents, presenting new prospects for advanced LMBs. [ABSTRACT FROM AUTHOR]

The purpose of this study was to enhance the adhesion strength between the epoxy resin (EP) substrate and the electroless copper plating layer by utilizing a MnO 2 –H 2 SO 4 –H 2 O–Na 4 P 2 O 7 system for surface modification. The effect of the volume ratio of V H2SO4 :V H2O and the duration of surface modification on various surface characteristics and adhesion strength was examined. The results provide valuable insights into the relationship between these factors and their impact on the properties of the surface. Optimal results were achieved with a volume ratio of V H2SO4 :V H2O at 3:1, a surface modification time of 20 min, Na 4 P 2 O 7 content of 30 g L−1, and a modification temperature of 50 °C. Following the surface modification treatment, the EP substrate surface exhibited many deep micro-holes, significantly improving surface roughness. Due to the enhanced roughness on the surface and the application of suitable modifications, the contact angle on the EP substrate surface exhibited a significant decrease from 94.6° to 19.5°. This alteration caused the surface to transition from hydrophobic to hydrophilic. The adhesion strength between the EP substrate and the electroless copper plating layer was significantly increased to 0.74 kN m−1. This improvement was attributed to the combined effects of surface roughness and hydrophilicity of the substrate surface. [ABSTRACT FROM AUTHOR]

The macrovascular complications of diabetes cause high mortality and disability in patients with type 2 diabetes mellitus (T2DM). The inflammatory response of vascular smooth muscle cell (VSMC) runs through its pathophysiological process. Salvianolic acid B (Sal B) exhibits beneficial effects on the cardiovascular system. However, its role and mechanism in diabetic vascular inflammatory response remain unclear. In this study, we found that Sal B reduced vascular inflammation in diabetic mice and high glucose- (HG-) induced VSMC inflammation. Subsequently, we found that Sal B reduced HG-induced VSMC inflammation by downregulating FOXO1. Furthermore, miR-486a-5p expression was obviously reduced in HG-treated VSMC. Sal B attenuated HG-induced VSMC inflammation by upregulating miR-486a-5p. Loss- and gain-of-function experiments had proven that the transfection of the miR-486a-5p mimic inhibited HG-induced VSMC inflammation whereas that of the miR-486a-5p inhibitor promoted HG-induced VSMC inflammation, thereby leading to the amelioration of vascular inflammation in the diabetic mice. Furthermore, studies had shown that miR-486a-5p inhibited FOXO1 expression by directly targeting its 3′-UTR. In conclusion, Sal B alleviates the inflammatory response of VSMC by upregulating miR-486a-5p and aggravating its inhibition of FOXO1 expression. Sal B exerts a significant anti-inflammatory effect in HG-induced VSMC inflammation by modulating the miR-486a-5p/FOXO1 axis. [ABSTRACT FROM AUTHOR]

Background: Zirconia is a widely used material in the dental industry due to its excellent mechanical and aesthetic properties. Recently, a new 3D printing process called suspension-enclosing projection stereolithography (SEPS) was introduced to fabricate zirconia dental restorations. However, the effect of the sintering time and temperature on the properties of zirconia produced via SEPS has not been fully investigated. Methods: Zirconia slurries were prepared with varying percentages of zirconia powders and 3D printing resins, and 5Y-TZP (5 mol% yttria-stabilized zirconia) (n = 40) and 3Y-TZP (3 mol% yttria-stabilized zirconia) (n = 40) bar specimens were fabricated via SEPS manufacturing. The specimens were sintered at different temperatures and dwell times, and their flexural strength, density, and phase composition were measured. The viscosity of the slurries was also measured. Statistical analysis was performed using Welch's ANOVA and Kruskal–Wallis tests to evaluate the impact of the sintering conditions. Results: Significant differences in flexural strength (p < 0.01) were observed between the 5Y-TZP samples, with those sintered at 1530 °C for 120 min showing an average strength of 268.34 ± 44.66 MPa, compared to 174.16 ± 42.29 MPa for those sintered at 1450 °C for 120 min. In terms of density, significant differences (p < 0.01) were noted for the 3Y-TZP specimens, with an average density of 6.66 ± 0.49 g/cm3 for samples sintered at 1530 °C for 120 min, versus 5.75 ± 0.55 g/cm3 for those sintered at 1530 °C for 10 min. X-ray diffraction confirmed the presence of a predominantly tetragonal phase in both materials. Conclusions: Zirconia printed via SEPS manufacturing can be sintered at a higher temperature with shorter dwell times, thereby producing high density samples. Different sintering conditions can be used to fully sinter 3D-printed zirconia for potential dental applications. [ABSTRACT FROM AUTHOR]