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Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots’ real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system’s performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

Innovation has been elevated beyond the traditional forces of production, by the emergence of a new wave of industrial upgrading and the technological revolution, to become a significant force in the advancement of human society. Can an enterprise group, a significant type of industrial organization, improve the effectiveness of enterprise innovation? Here, a quantitative analysis approach was used to systematically analyze the impact of group control on enterprise innovation effectiveness and its transmission path based on the logical framework of the “policy environment-influence effect-influence path”. The study found that group control significantly improves the effectiveness of enterprise innovation compared to independently listed enterprises. The impact path showed that group control can reduce financing constraints through internal capital markets. It increases the investment in innovation and thus enhances the effectiveness of enterprise innovation. Meanwhile, internal information exchange is accelerated through the internal knowledge market, improving enterprise innovation’s effectiveness. The results of this study were still valid after robustness tests, such as propensity score matching and accounting for lag effects. According to the paper’s findings, to enhance financial support for innovation, financial market reform should be intensified. The growth of manufacturing enterprise groups should also be encouraged. Additionally, the ability of businesses to innovate while improving the internal benefits of enterprise groups and their innovation paths should be strengthened.

Salinity is a major factor limiting rice productivity, and developing salt-tolerant (ST) varieties is the most efficient approach. Seventy-eight ST introgression lines (ILs), including nine promising lines with improved ST and yield potential (YP), were developed from four BC2F4 populations from inter-subspecific crosses between an elite Geng (japonica) recipient and four Xian (indica) donors at the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences. Genome-wide characterization of donor introgression identified 35 ST QTLs, 25 of which harbor 38 cloned ST genes as the most likely QTL candidates. Thirty-four are Xian-Geng differentiated ones with the donor (Xian) alleles associated with ST, suggesting differentiated responses to salt stress were one of the major phenotypic differences between the two subspecies. At least eight ST QTLs and many others affecting yield traits were identified under salt/non-stress conditions. Our results indicated that the Xian gene pool contains rich ‘hidden’ genetic variation for developing superior Geng varieties with improved ST and YP, which could be efficiently exploited by selective introgression. The developed ST ILs and their genetic information on the donor alleles for ST and yield traits would provide a useful platform for developing superior ST and high-yield Geng varieties through breeding by design in the future.

In the construction of large-scale water conservancy and hydropower transportation projects, the rock mass structural information is often used to evaluate and analyze various engineering geological problems such as high and steep slope stability, dam abutment stability, and natural rock landslide geological disasters. The complex shape and extremely irregular distribution of the structural planes make it challenging to identify and extract automatically. This study proposes a method for extracting structural planes from UAV images based on Geo-AINet ensemble learning. The UAV images of the slope are first used to generate a dense point cloud through a pipeline of SfM and PMVS; then, the multiple geological semantics, including color and texture from the image and local geological occurrence and surface roughness from the dense point cloud, are integrated with Geo-AINet for ensemble learning to obtain a set of semantic blocks; finally, the accurate extraction of structural planes is achieved through a multi-semantic hierarchical clustering strategy. Experimental results show that the structural planes extracted by the proposed method perform better integrity and edge adherence than that extracted by the AINet algorithm. In comparison with the results from the laser point cloud, the geological occurrence differences are less than three degrees, which proves the reliability of the results. This study widens the scope for surveying and mapping using remote sensing in engineering geological applications.

To solve the difficult problem of selecting initial guess values for component-level aero-engine start-up models, a novel method based on the flow-based back-calculation algorithm (FBBCA) is investigated. By exploiting the monotonic feature of low-speed aero-engine component characteristics and the principle of flow balance abided by components in the start-up process, this method traverses all the flows in each component characteristic at a given engine rotor speed. This method also limits the pressure ratios and flow rates of each component, along with the surplus power of the high-pressure rotor. Finally, a set of “fake initial values” for iterative calculation of the aero-engine start-up model can be generated and approximate true initial guess values that meet the accuracy requirement according to the Newton–Raphson iteration method. Extensive simulation verifies the low computational cost and high computational accuracy of this method as a solver for the initial guess values of the aero-engine start-up model.

Sulfidization xanthate flotation remains the most promising method for the beneficiation of malachite. In this study, L-arginine (LA) was first used to modify the malachite surface and improve the efficiency of sulfidization flotation. The performance of LA was evaluated by the flotation experiments. The mechanism of interaction between LA and the malachite surface was investigated by adsorption experiments, zeta potential measurements, scanning electron microscopy (SEM-EDS) and X-ray photoelectron spectroscopy (XPS) analysis. Flotation experiments showed that LA had a significantly promoting effect on malachite sulfidization flotation. Adsorption experiments and SEM-EDS results indicated that LA improved the adsorption of S (II) species into the malachite surface and promoted the formation of sulfides. This finding was further confirmed by the XPS analysis. The XPS measurements results determined that S (II) species reacted with Cu (II) on the malachite surface and form polysulfides, adding LA promoted the reaction. The zeta potential measurements showed that LA increased the positive electrical properties of the mineral surface, which was conducive to S (II) species adsorption and the sulfidization reaction. This work sheds new light on the development of sulfidization activation.

Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology, and climate. Synthetic aperture radar (SAR) microwave remote sensing is an important means of farmland surface soil moisture (SSM) measurement. The inversion of farmland SSM by microwave remote sensing is greatly affected by vegetation cover. To address this problem, a multisource remote sensing inversion method of farmland SSM based on feature optimization and machine learning is proposed in this paper. Six typical machine learning algorithms suitable for small sample training, including random forest, radial basis function neural network, generalized regression neural network, support vector regression, genetic algorithm–back propagation neural network, and extreme learning machine, were selected in this paper. The features extracted from Sentinel-1/2 and Radarsat-2 remote sensing data were analyzed by Pearson correlation, and those with high correlation coefficients were selected to form the optimal feature subset as the input for the subsequent machine learning models. Then, the SSM collaborative inversion models under different machine learning algorithms were constructed, and comparative experiments were set up to select the optimal prediction model. The models’ accuracy under different feature parameters were studied, and the difference in the performance between the dual-polarization SAR data and the quad-polarization SAR data in SSM inversion was explored. The experimental results showed that among the six models, the random forest model had a higher inversion accuracy, with a coefficient of determination of 0.6395 and a root mean square error of 0.0264 cm3/cm3. Meanwhile, the inversion accuracy could be greatly improved after feature optimization, and the inversion accuracy of the quad-polarization SAR data combined with optical remote sensing data, was better than that of the dual-polarization SAR data combined with optical remote sensing data.

The underground energy geostructure represented by the energy pile is one of the key paths for the cooperative development of underground space and geothermal energy. Because of its advantages of low cost, high efficiency and no extra occupation of underground space, it has become a feasible alternative to the borehole heat exchanger. The change in the temperature field of the energy pile and its surrounding ground not only affects the geological environment but also influences the thermomechanical performance and the durability of the structure. However, the temporal and spatial unsteady-state temperature distribution of piles and surrounding rock under typical intermittent and unbalanced thermal load conditions is still unclear. In this paper, a finite element model was applied to analyze the unsteady-state temperature distribution, and the thermomechanical behavior of the energy pile group was developed and verified. The temperature field distribution of pile and surrounding rock under typical intermittent working and unbalanced thermal load conditions were determined. Moreover, the thermomechanical behavior characteristics of the energy pile group were investigated. Finally, the influences of pile layout on the thermomechanical behavior of the energy pile group were identified by designing six different scenarios. The results indicate that under typical intermittent operation conditions, the temperature of the energy pile and surrounding ground near the heat exchange pipe varies periodically. For areas with unbalanced cooling and heating loads, long-term operation of energy piles leads to thermal accumulation, and the maximum temperature of energy piles occurs in the first daily cycle. In summer/winter working conditions, the increase/decrease in pile temperature induces axial compression/tensile stress. When the pile group is partially used as the energy pile, the non-energy pile acts as the “anchor pile”, and it generates the added tensile stress.

The traction motor (TM) is an essential part of the high-speed train, the health condition of which determines the quality and safety of the vehicle. Hence, this study proposed a novel approach to diagnosing and predicting the TM stator interturn short-circuit fault (SISCF). Based on the Park vector (PV) of the stator current, this method could overcome the interference of current sensor errors, null shift, and motor frequency fluctuations in the actual conditions. More specifically, Park’s transformation was used to obtain the PV of the stator current. Then, the PV was fitted to obtain the elliptical trajectory and its parameters from which the negative sequence component of the stator current could be calculated. Finally, the SISCF diagnosis and prediction method were realized by the magnitude and trend of the negative current as well as the inclination of the trajectory ellipse. Furthermore, the performance of the proposed method was validated by a simulation model and a series of experiments. The simulation results were consistent with the experimental results, supporting the validity and correctness of the method proposed in this study.

Now, most existing dynamic RGB-D SLAM methods are based on deep learning or mathematical models. Abundant training sample data is necessary for deep learning, and the selection diversity of semantic samples and camera motion modes are closely related to the robust detection of moving targets. Furthermore, the mathematical models are implemented at the feature-level of segmentation, which is likely to cause sub or over-segmentation of dynamic features. To address this problem, different from most feature-level dynamic segmentation based on mathematical models, a non-prior semantic dynamic segmentation based on a particle filter is proposed in this paper, which aims to attain the motion object segmentation. Firstly, GMS and optical flow are used to calculate an inter-frame difference image, which is considered an observation measurement of posterior estimation. Then, a motion equation of a particle filter is established using Gaussian distribution. Finally, our proposed segmentation method is integrated into the front end of visual SLAM and establishes a new dynamic SLAM, PFD-SLAM. Extensive experiments on the public TUM datasets and real dynamic scenes are conducted to verify location accuracy and practical performances of PFD-SLAM. Furthermore, we also compare experimental results with several state-of-the-art dynamic SLAM methods in terms of two evaluation indexes, RPE and ATE. Still, we provide visual comparisons between the camera estimation trajectories and ground truth. The comprehensive verification and testing experiments demonstrate that our PFD-SLAM can achieve better dynamic segmentation results and robust performances.

Honeycomb materials have low density, high specific strength and stiffness, impact resistance, and good sound insulation effect, and therefore are widely used in aerospace, automobile, and ship field applications. In this paper, we study the in-plane impact response of a second-order hierarchical honeycomb (SHH) material. Its main structure is a hexagonal honeycomb, and the substructure is composed of an augmented double arrow honeycomb (ADAH) negative Poisson’s ratio unit. Through a finite element simulation, the failure stress of an hierarchical honeycomb in two directions of quasi-static crushing and dynamic crushing was analyzed, the failure stress of the hierarchical honeycomb under different densities, different speeds, and different substructures was discussed, and the theoretical failure stress was verified. The numerical analysis results show that a second-order hierarchical honeycomb (SHH) has better collapse stress than a first-order regular hexagonal honeycomb (FHH) and an augmented double arrow honeycomb (ADAH).

Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps.

Sulfuration flotation is the most widely used technology in malachite beneficiation. However, the inhomogeneity of malachite surfaces usually results in a non-uniform sulfuration surface. The motivation of this work is attempt to adopt different functional combination collectors to enhance the sulfuration flotation of malachite. Accordingly, the flotation behaviors and adsorption mechanisms of benzohydroxamic acid (BHA) and sodium butyl-xanthate (SBX) on the surface of malachite were systematically investigated using flotation tests, zeta-potential measurements, Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and first-principle calculations. The test results of vulcanization flotation showed that the combined collectors of SBX with BHA possessed a higher recovery than only using SBH by 20%, indicating that there may be a synergistic effect between BHA and SBX. The IR and Raman spectroscopy demonstrated that both BHA and SBX could chemically adsorb onto the malachite surface. The density functional theory (DFT) calculation results further indicated that the combined adsorption energy of BHA and SBX was much lower than that of only BHA or SBX, which confirmed the synergistic effects of BHA and SBX on the malachite surface. This work may shed new light on the design and development of more efficient combined flotation reagents.

In this study, salicylhydroxamic acid (SHA), which exhibits superior flotation performance to conventional collector benzohydroxamic acid (BHA), was first introduced in ilmenite flotation. The addition of lead(II) can significantly increase the recovery of ilmenite using SHA as collector. Thus, the adsorption mechanism of SHA on lead(II)-activated ilmenite surface was systematically studied using micro-flotation tests, adsorption analysis, zeta potential measurements, Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). Micro-flotation results revealed that SHA had stronger collecting ability than BHA, and ilmenite floatability could be activated by lead ions with either SHA or BHA as collector. Ilmenite showed good floatability at pH 6–8 (over 90% recovery) in the presence of Pb2+ and SHA. In such conditions, the main lead species of Pb(OH)+ and Pb2+ acted as active sites and caused positive surface potential shifts, thereby increasing the adsorbed amounts of negatively charged SHA on the surface of the mineral. FTIR and XPS analyses suggested that the lead species was chemically adsorbed on the surface of ilmenite to form active sites chelated by SHA. Moreover, the free lead ions in solution might form the Pb–SHA complexes to adsorb on the mineral surface, thereby increasing the floatability of ilmenite.

Aiming at inertial and viscous parameter identification for the Stewart manipulator regardless of the influence of Coulomb friction, a simple and effective dynamical parameter identification method based on wavelet transform and joint velocity analysis is proposed in this paper. Compared with previously known identification methods, the advantages of the new approach are that (1) the excitation trajectory is easy to design, and (2) it can not only identify the inertial matrix, but also the viscous matrix accurately regardless of the influence of Coulomb friction. Comparison is made among the identification method proposed in this paper, another identification method proposed previously, and the true value calculated with a formula. The errors from results of different identification methods demonstrate that the method proposed in this paper shows great adaptability and accuracy.

Improving local farmers&prime, climate change adaptive capacity is an important policy issue in rural China. This study investigates farmers&prime, risk cognition, risk preferences and climate change adaptive behavior. Based on unique data from a survey and a paired lottery experiment completed by 240 rural farmers in Chongqing City of China, this paper finds that farmers have a pessimistic risk cognition towards climate change and the typical farmers are risk-averse and loss-averse. Risk cognition and adaptation cognition have significantly positive influences on climate change adaptive behavior, and loss aversion has a significantly positive influence on farmers&prime, adaptation decisions. Loss aversion exerts a positive impact on risk cognition and adaptation cognition, and risk aversion has a positive impact on adaptation cognition. This paper contributes to the emerging literature that relates risk preference in experiments and risk cognition to farmers&prime, climate change adaptive behavior.

K+ modified hydrotalcites and its activity as a solid base catalyst for ultrasonic wave- assisted biodiesel conversion was investigated. The solid alkaline catalysts of the MgAl-hydrotalcites (HT) was prepared by co-precipitation and modified with K+ by impregnation. The influence of K+ incorporation on the performance of Mg-Al hydrotalcites catalysts was investigated by SmartLab X-ray powder diffractometer (XRD), infrared spectrum (IR), thermogravimetric-differential thermal analysis (TG&ndash, DTA), CO2 temperature programmed desorption (CO2-TPD), scanning electron microscopy (SEM), and N2 adsorption&ndash, desorption isotherm (BET). The research discovered that K+ modified of double layered structure of MgAl-hydrotalcite resulted in a significant increase in catalytic activity in transesterification of rapeseed oil. It exhibited high catalytic activity that achieved a biodiesel yield of 99% when the reaction was conducted with 2 wt% catalysts, K+/HT load ratio of 6.25%, a methanol/rapeseed oil molar ratio of 12:1, and reaction at 60 &deg, C over 1 h. The result showed that the K+ modified HT as a transesterification catalyst had the potency for biodiesel conversion. In addition, under the above reaction conditions, the biodiesel yield was up to 99.9% in only five minutes with ultrasonic aid.

A scientific and rigorous study on the adsorption behavior and molecular mechanism of collector sodium oleate (NaOL) on a Ca2+-activated hydroxylated &alpha, quartz surface was performed through experiments and density functional theory (DFT) simulations. The rarely reported hydroxylation behaviors of water molecules on the &alpha, quartz (101) surface were first innovatively and systematically studied by DFT calculations. Both experimental and computational results consistently demonstrated that the adsorbed calcium species onto the hydroxylated structure can significantly enhance the adsorption of oleate ions, resulting in a higher quartz recovery. The calculated adsorption energies confirmed that the adsorbed hydrated Ca2+ in the form of Ca(H2O)3(OH)+ can greatly promote the adsorption of OL&minus, on hydroxylated quartz (101). In addition, Mulliken population analysis together with electron density difference analysis intuitively illustrated the process of electron transfer and the Ca-bridge phenomenon between the hydroxylated surface and OL&minus, ions. This work may offer new insights into the interaction mechanisms existing among oxidized minerals, aqueous medium, and flotation reagents.

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure&ndash, activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

Diabetic nephropathy (DN) is a leading cause of end-stage renal failure, and no effective treatment is available. Notoginsenoside R1 (NGR1) is a novel saponin that is derived from Panax notoginseng, and our previous studies showed the cardioprotective and neuroprotective effects of NGR1. However, its role in protecting against DN remains unexplored. Herein, we established an experimental model in db/db mice and HK-2 cells exposed to advanced glycation end products (AGEs). The in vivo investigation showed that NGR1 treatment increased serum lipid, β2-microglobulin, serum creatinine, and blood urea nitrogen levels of db/db mice. NGR1 attenuated histological abnormalities of kidney, as evidenced by reducing the glomerular volume and fibrosis in diabetic kidneys. In vitro, NGR1 treatment was further found to decrease AGE-induced mitochondria injury, limit an increase in reactive oxygen species (ROS), and reduce apoptosis in HK-2 cells. Mechanistically, NGR1 promoted nucleus nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) expressions to eliminate ROS that induced apoptosis and transforming growth factor beta (TGF-β) signaling. In summary, these observations demonstrate that NGR1 exerts renoprotective effects against DN through the inhibition of apoptosis and renal fibrosis caused by oxidative stress. NGR1 might be a potential therapeutic medicine for the treatment of DN.

The body dimension measurement of large animals plays a significant role in quality improvement and genetic breeding, and the non-contact measurements by computer vision-based remote sensing could represent great progress in the case of dangerous stress responses and time-costing manual measurements. This paper presents a novel approach for three-dimensional digital modeling of live adult Qinchuan cattle for body size measurement. On the basis of capturing the original point data series of live cattle by a Light Detection and Ranging (LiDAR) sensor, the conditional, statistical outliers and voxel grid filtering methods are fused to cancel the background and outliers. After the segmentation of K-means clustering extraction and the RANdom SAmple Consensus (RANSAC) algorithm, the Fast Point Feature Histogram (FPFH) is put forward to get the cattle data automatically. The cattle surface is reconstructed to get the 3D cattle model using fast Iterative Closest Point (ICP) matching with Bi-directional Random K-D Trees and a Greedy Projection Triangulation (GPT) reconstruction method by which the feature points of cattle silhouettes could be clicked and calculated. Finally, the five body parameters (withers height, chest depth, back height, body length, and waist height) are measured in the field and verified within an accuracy of 2 mm and an error close to 2%. The experimental results show that this approach could be considered as a new feasible method towards the non-contact body measurement for large physique livestock.

Novel collector lead(II)-benzohydroxamic acid (Pb(II)&ndash, BHA) complexes in aqueous solution were characterized by using experimental approaches, including Ultraviolet-visible (UV-Vis) spectroscopy and electrospray ionization-mass spectrometry (ESI-MS), as well as first-principle density functional theory (DFT) calculations with consideration for solvation effects. The Job plot delineated that a single coordinated Pb(BHA)+ should be formed first, and that the higher coordination number complexes can be formed subsequently. Moreover, the Pb(II)&ndash, BHA species can aggregate with each other to form complicated structures, such as Pb(BHA)2 or highly complicated complexes. ESI-MS results validated the existence of Pb-(BHA)n=1,2 under different solution pH values. Further, the first-principles calculations suggested that Pb(BHA)+ should be the most stable structure, and the Pb atom in Pb(BHA)+ will act as an active site to attack nucleophiles. These findings are meaningful to further illustrate the adsorption mechanism of Pb(II)&ndash, BHA complexes, and are helpful for developing new reagents in mineral processing.

In this work, density functional theory (DFT) calculations, X-ray photoelectron spectroscopy (XPS) and electrochemistry analysis were combined to analyze the electrochemical dissolution process of bornite during bioleaching. DFT calculations showed that bornite was a conductor with metallic conductivity. The formula of bornite may be (Cu+)5Fe3+(S2−)4 and the surface reconstruction of (111)-S surface was discussed. Electrochemistry and XPS analysis showed that bornite tended to be directly oxidized with high conductivity when the potential was higher than 0.3 V vs. Ag/AgCl. Elemental sulfur (S0), FeOOH and CuS were the main intermediate species on the bornite surface during the oxidation process. The production of S0 and FeOOH on bornite surface can be significantly accelerated with increased redox potential, but no insoluble sulfate (SO42−) formed on bornite surface in 0.3–0.65 V vs. Ag/AgCl. The oxidative dissolution of bornite was significantly accelerated with increasing redox potential, which was one important reason why mixed culture was more effective than single strains of A. caldus and L. ferriphilum in bornite bioleaching. The insoluble SO42− was formed mainly through the chemical reactions in solution and then covered the bornite surface in bioleaching. Based on the obtained results, a model for interpreting the dissolution process of bornite in bioleaching was proposed.