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A Zn2TiO4 crystalline photovoltaic glass ink was prepared by fast firing at 700 °C for 5 min by the glass crystallisation method, which effectively improved the reflectivity and acid resistance of the photovoltaic glass ink coating. The phase, morphology and properties of the samples were tested by XRD, SEM and UV-vis diffuse reflection, etc. The enhanced reflectivity mechanism was proposed. The results showed that the increase in ZnO/SiO2 ratio reduced the transition temperature (Tg) and crystallisation temperature (Tp) of the glass melt, which could promote the crystallinity of Zn2TiO4 in photovoltaic glass ink coatings and thus improve the acid resistance of photovoltaic glass inks. Significant improvement in reflectance and whiteness is due to the Zn2TiO4 crystallinity growth, which fills in the pores of the ink surface, and TiO2 fillers keep almost the same surface roughness (0.2 µm) and wetting angle (5.2°). Typical samples achieved 89.2% of the whiteness and 88.0% of the reflectance, and the weight loss in acid was 3.9 mg/cm2, which could improve the efficiency of solar power generation.

Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions.

Aiming at the problem of cable breakage of an asymmetric single-tower cable-stayed bridge by taking a single-tower cable-stayed bridge that has been in operation for more than 20 years as an engineering example, a spatial finite element model that reflected the actual stress state of the structure was established and a method for solving the time point of cable breakage was proposed. The results show that the strength of the main girder, pylon and cable could meet the stress requirements of the vibration caused by successive cable breaks, and the maximum dynamic displacement of the main girder did not exceed the stiffness limit. Although the breakage of the stay cable caused the stress in the adjacent position and the corresponding position on the opposite side of the tower to increase sharply, the stress fluctuation in other positions was small. The fluctuation of the dynamic stress of the cable tower was relatively small, but the displacement of the tower top changed greatly. The dynamic response of the cables breaking in succession was less than that of simultaneous breaking, and the structural vibration caused by cable breaking was not as severe as that caused by simultaneous breaking.

The purpose of the present study aims to develop a satisfactory model for predicting pro-social and pro-cognitive effects on azinesulfonamides of cyclic amine derivatives as potential antipsychotics. The three dimensional-quantitative structure affinity relationship (3D-QSAR) study was performed on a series of azinesulfonamides of cyclic amine derivative using comparative molecular similarity indices analysis (CoMSIA). The best statistical model of CoMSIA q2, r2, SEE and F values are 0.664, 0.973, 0.087, and 82.344, respectively. Based on the model contour maps and the highest activity structure of the 43rd compound, serial new structures were designed and the 43k1 compound was selected as the best structure. The dock results showed a good binding of 43k1 with the protein (PDB ID: 6A93). The QSAR model analysis of the contour maps can help us to provide guidelines for finding novel potential antipsychotics.

In order to improve the seismic performance of long-span double deck steel truss continuous girder bridge, taking Dao Qing Chau Bridge in Fuzhou as an engineering background, the isolation scheme of friction pendulum bearing (FPB) and friction pendulum bearing combined with viscous dampers is applied to study seismic performance. A three-dimensional dynamic model of the bridge is established using SAP2000. Taking three artificial seismic waves as seismic excitation, the seismic response of the seismic structure is calculated by nonlinear time history integration, and is then compared with the seismic response of the seismic reduction and isolation structure. The results show that the friction pendulum bearing (FPB) scheme and combined seismic dissipation and isolation (CSDI) scheme show a good seismic dissipation and isolation effect and ensure the safety of the bridge structure. However, for whole-bridge isolation, friction pendulum bearing (FPB) will produce certain residual deformations and additional stress of the bearing under the conditions of temperature and external load. For the purpose of protecting the bearing, it is recommended to use the combined seismic dissipation and isolation (CSDI) scheme.

Urban development is one of the leading causes of biodiversity change. Understanding how soil microorganisms respond to urbanization is particularly important because they are crucial for the provisioning of ecosystem functions and services. Here, we collected monthly soil samples over one year across three locations representing an urbanization gradient (low-moderate-high) in the arid Southwestern USA, and we characterized their microbial communities using marker gene sequencing. Our results showed that microbial richness and community composition exhibited nonsignificant changes over time regardless of the location. Soil fungal richness was lower in moderately and highly urbanized locations, but soil bacterial/archaeal richness was not significantly different among locations. Both bacteria/archaea and fungi exhibited significant differences in community composition across locations. After inferring potential functional groups, soils in the highly urbanized location had lower proportions of arbuscular mycorrhizal fungi and soil saprotrophic fungi but had higher proportions of bacterial taxa involved in aromatic compound degradation, human pathogens, and intracellular parasites. Furthermore, ammonia-oxidizing bacteria were more abundant in the highly urbanized location, but ammonia-oxidizing archaea were more abundant in lowly and moderately urbanized locations. Together, these results highlight the significant changes in belowground microbial communities across an urbanization gradient, and these changes might have important implications for aboveground–belowground interactions, nutrient cycling, and human health.

A recently proposed product quantization method is efficient for large scale approximate nearest neighbor search, however, its performance on unstructured vectors is limited. This paper introduces residual vector quantization based approaches that are appropriate for unstructured vectors. Database vectors are quantized by residual vector quantizer. The reproductions are represented by short codes composed of their quantization indices. Euclidean distance between query vector and database vector is approximated by asymmetric distance, i.e., the distance between the query vector and the reproduction of the database vector. An efficient exhaustive search approach is proposed by fast computing the asymmetric distance. A straight forward non-exhaustive search approach is proposed for large scale search. Our approaches are compared to two state-of-the-art methods, spectral hashing and product quantization, on both structured and unstructured datasets. Results show that our approaches obtain the best results in terms of the trade-off between search quality and memory usage.

This paper focuses on online scene learning and fast camera relocalisation which are two key problems currently limiting the performance of wide area augmented reality systems. Firstly, we propose to use adaptive random trees to deal with the online scene learning problem. The algorithm can provide more accurate recognition rates than traditional methods, especially with large scale workspaces. Secondly, we use the enhanced PROSAC algorithm to obtain a fast camera relocalisation method. Compared with traditional algorithms, our method can significantly reduce the computation complexity, which facilitates to a large degree the process of online camera relocalisation. Finally, we implement our algorithms in a multithreaded manner by using a parallel-computing scheme. Camera tracking, scene mapping, scene learning and relocalisation are separated into four threads by using multi-CPU hardware architecture. While providing real-time tracking performance, the resulting system also possesses the ability to track multiple maps simultaneously. Some experiments have been conducted to demonstrate the validity of our methods.

Negative emotions of drivers may lead to some dangerous driving behaviors, which in turn lead to serious traffic accidents. However, most of the current studies on driver emotions use a single modality, such as EEG, eye trackers, and driving data. In complex situations, a single modality may not be able to fully consider a driver’s complete emotional characteristics and provides poor robustness. In recent years, some studies have used multimodal thinking to monitor single emotions such as driver fatigue and anger, but in actual driving environments, negative emotions such as sadness, anger, fear, and fatigue all have a significant impact on driving safety. However, there are very few research cases using multimodal data to accurately predict drivers’ comprehensive emotions. Therefore, based on the multi-modal idea, this paper aims to improve drivers’ comprehensive emotion recognition. By combining the three modalities of a driver’s voice, facial image, and video sequence, the six classification tasks of drivers’ emotions are performed as follows: sadness, anger, fear, fatigue, happiness, and emotional neutrality. In order to accurately identify drivers’ negative emotions to improve driving safety, this paper proposes a multi-modal fusion framework based on the CNN + Bi-LSTM + HAM to identify driver emotions. The framework fuses feature vectors of driver audio, facial expressions, and video sequences for comprehensive driver emotion recognition. Experiments have proved the effectiveness of the multi-modal data proposed in this paper for driver emotion recognition, and its recognition accuracy has reached 85.52%. At the same time, the validity of this method is verified by comparing experiments and evaluation indicators such as accuracy and F1 score.

A novel and efficient immunoaffinity column (IAC) based on bispecific monoclonal antibody (BsMAb) recognizing aflatoxin B1 (AFB1) and ochratoxin A (OTA) was prepared and applied in simultaneous extraction of AFB1 and OTA from food samples and detection of AFB1/OTA combined with ic-ELISA (indirect competitive ELISA). Two deficient cell lines, hypoxanthine guanine phosphoribosyl-transferase (HGPRT) deficient anti-AFB1 hybridoma cell line and thymidine kinase (TK) deficient anti-OTA hybridoma cell line, were fused to generate a hybrid-hybridoma producing BsMAb against AFB1 and OTA. The subtype of the BsMAb was IgG1 via mouse antibody isotyping kit test. The purity and molecular weight of BsMAb were confirmed by SDS-PAGE method. The cross-reaction rate with AFB2 was 37%, with AFG1 15%, with AFM1 48%, with AFM2 10%, and with OTB 36%. Negligible cross-reaction was observed with other tested compounds. The affinity constant (Ka) was determined by ELISA. The Ka (AFB1) and Ka (OTA) was 2.43 × 108 L/mol and 1.57 × 108 L/mol, respectively. Then the anti-AFB1/OTA BsMAb was coupled with CNBr-Sepharose, and an AFB1/OTA IAC was prepared. The coupling time and elution conditions of IAC were optimized. The coupling time was 1 h with 90% coupling rate, the eluent was methanol–water (60:40, v:v, pH 2.3) containing 1 mol/L NaCl, and the eluent volume was 4 mL. The column capacities of AFB1 and OTA were 165.0 ng and 171.3 ng, respectively. After seven times of repeated use, the preservation rates of column capacity for AFB1 and OTA were 69.3% and 68.0%, respectively. The ic-ELISA for AFB1 and OTA were applied combined with IAC. The IC50 (50% inhibiting concentration) of AFB1 was 0.027 ng/mL, the limit of detection (LOD) was 0.004 ng/mL (0.032 µg/kg), and the linear range was 0.006 ng/mL~0.119 ng/mL. The IC50 of OTA was 0.878 ng/mL, the LOD was 0.126 ng/mL (1.008 µg/kg), and the linear range was 0.259 ng/mL~6.178 ng/mL. Under optimum conditions, corn and wheat samples were pretreated with AFB1-OTA IAC. The recovery rates of AFB1 and OTA were 95.4%~105.0% with ic-ELISA, and the correlations between the detection results and LC-MS were above 0.9. The developed IAC combined with ic-ELISA is reliable and could be applied to the detection of AFB1 and OTA in grains.

In this study, forchlorfenuron (CPPU) was coupled with succinic anhydride to yield a CPPU hapten (CPPU-COOH), and a high-affinity monoclonal antibody (mAb) that can specifically recognize CPPU was produced. Using this mAb as a recognition reagent, a sensitive indirect competitive enzyme-linked immunosorbent assay (icELISA) for CPPU was optimized, which exhibits an IC50 of 1.04 ng/mL, a limit of detection of 0.16 ng/mL, and a linear range of 0.31–3.43 ng/mL for CPPU. Cross-reactivity percentages with six analogues were all below 6%. The average recovery rates for cucumber and orange samples were from 85.23% to 119.14%. The analysis results of this icELISA showed good consistency with those from liquid chromatography mass spectrometry. These results suggest that the proposed icELISA provides a sensitive, specific, and reliable strategy for CPPU detection in food samples.

Magnesium-based bulk metallic glass matrix composites (BMGMCs) have better plasticity than the corresponding bulk metallic glasses (BMGs); however, their strength and density are often compromised due to the fact that the effective reinforcement phase is mostly plastic heavy metal. For lightweight SiC-particle reinforced BMGMCs, interface wettability and the sharpness of the particles often reduce the strengthening effect. In this work, SiC particles were coated with a thin Cu coating by electroless plating, and added to Mg54Cu26.5Ag8.5Gd11 melt in an amount of 5 wt % to prepare a BMGMC. The microstructure of the interface, mechanical behavior and fracture morphology of the BMGMC were studied by scanning electron microscopy and quasi-static compression testing. The results showed that the Cu coating improved the wettability between SiC and the matrix alloy without obvious interfacial reactions, leading to the dispersion of SiC particles in the matrix. The addition of Cu-coated SiC particles improved the plastic deformation ability of Mg54Cu26.5Ag8.5Gd11 BMG, proving that electroless plating was an effective method for controlling the interface microstructure and mechanical behavior of BMGMCs.