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Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks. [ABSTRACT FROM AUTHOR]

The pivotal role of oxygen electrocatalysis in the realm of energy conversion and storage is unmistakably significant. In an endeavor to diminish the reliance on precious metals, the development of innovative catalysts exhibiting exceptional bifunctional durability and heightened activity for both the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) has garnered considerable scholarly interest. Employing a straightforward two-step methodology, we have successfully synthesized uniformly distributed MnCo2O4 and CoMn2O4 nanoparticles of diminutive size, meticulously anchored onto carbon nanotubes (CNTs). Owing to the synergistic covalent interplay between the spinel oxide nanoparticles and CNTs, these nanocomposites demonstrate ORR activity on par with, and notably superior OER activity compared to, commercially available Pt/C catalysts. The onset potential of MnCo2O4-CNTs stands at 1.03 V vs. RHE, maintaining 95.76% of its initial current density following a 10,000-s chronoamperometry test. Furthermore, MnCo2O4-CNTs outperform CoMn2O4-CNTs in OER catalysis. The outstanding performance of MnCo2O4-CNTs is attributed to the higher content of Co3+ ions, which are active for the oxygen electrocatalysis. [ABSTRACT FROM AUTHOR]

Drag-dominant tidal turbine energy holds tremendous clean energy potential but faces significant hurdles as unsuitability of the actuator disc model due to the varying swept blockage area, unaccounted bypass flow downstream interaction, and rotor parasitic drag, whereas blade element momentum theory is computably effective for majorly 3-blade lift-dominated aerofoil. This study validates a novel method to find the optimal TSR of any turbine with a cost-effective and user-friendly moment balancing algorithm to support robust tidal energy development. Performance analysis CFD study of Pinwheel and Savonius tidal turbines in a Biffis canal hydrodynamic system was carried out. Thrust and idle moment are analyzed as functions of only inlet fluid velocity and rotational speed, respectively. These relationships were verified through regression analysis, and the turbines' net moment equations were established based on these parameters. In both simulation models, rotational speed and inlet velocity were proved excellent predictor variables (R2 value ≈ 1) for idle and thrust moments, respectively. The optimal TSR values for Pinwheel and Savonius turbines were 2.537 and 0.671, respectively, within an acceptable error range for experimental validation. The optimal basin efficiency (ηopt, TSR) values for Pinwheel and Savonius in the 12% blockage channel were (29.09%, 4.0) and (25.67%, 2.87), respectively. The trade-off between TSRopt and ηopt is the key instruction concerning electricity generation and environmental impact. [ABSTRACT FROM AUTHOR]

Current performance analysis processes for drag-dominant tidal turbines are unsuitable as disk actuator theory lacks support for varying swept blockage area, bypass flow downstream interaction, and parasitic rotor drag, whereas blade element momentum theory is computably effective for three-blade lift-dominated aerofoil. This study proposes a novel technique to calculate the optimal turbine tip speed ratio (TSR) with a cost-effective and user-friendly moment balancing algorithm. A reliable dynamic TSR matrix was developed with varying rotational speeds and fluid velocities, unlike previous works simulated at a fixed fluid velocity. Thrust and idle moments are introduced as functions of inlet fluid velocity and rotational speed, respectively. The quadratic relationships are verified through regression analysis, and net moment equations are established. Rotational speed was a reliable predictor for Pinwheel's idle moment, while inlet velocity was a reliable predictor for thrust moment for both models. The optimal (Cp, TSR) values for Pinwheel and Savonius turbines were (0.223, 2.37) and (0.63, 0.29), respectively, within an acceptable error range for experimental validation. This study aims to improve prevailing industry practices by enhancing an engineer's understanding of optimal blade design by adjusting the rotor speed to suit the inlet flow case compared to 'trial and error' with cost-intensive simulations. [ABSTRACT FROM AUTHOR]

A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression of EO task, which results in the absence of task process management. The current study proposed an EO task representation model based on meta-object facility to address this problem. The model not only describes the static information of a task, but it also defines the dynamics of an observation task by introducing a functional metamodel. This metamodel describes the full life cycle of a task; it comprises five process methods: birth, separation, combination, updating, and extinction. An earth observation task modeling and management prototype system (EO-TMMS) for conducting a remote sensing satellite sensor observation task representation experiment on flooding was developed. In accordance with the results, the proposed model can describe various EO tasks demands and the full life cycle process of an EO task. Compared with other typical observation task information models, the proposed model satisfies the dynamic and fine-grain process representation of EO tasks, which can improve the efficiency of EO sensor utilization. [ABSTRACT FROM AUTHOR]

Liquid ammonia is an ideal zero-carbon fuel for internal combustion engines. High-pressure injection is a key technology in organizing ammonia combustion. Characteristics of high-pressure liquid ammonia injection is lack of research. Spray behaviors are likely to change when a high-pressure diesel injector uses liquid ammonia as its fuel. This study uses high-speed imaging with a DBI method to investigate the liquid penetration, width, and spray tip velocity of high-pressure liquid ammonia injection up to 100 MPa. Non-flash and flash boiling conditions were included in the experimental conditions. Simulation was also used to evaluate the results. In non-flash boiling conditions, the Hiroyasu model provided better accuracy than the Siebers model. In flash boiling conditions, a phenomenon was found that liquid penetration and spray tip velocity were strongly suppressed in the initial stage of the injection process, this being the "spray resistance phenomenon". The "spray resistance phenomenon" was observed when ambient pressure was below 0.7 MPa during 0–0.05 ms ASOI and was highly related to the superheated degree. The shape of near-nozzle sprays abruptly changed at 0.05 ms ASOI, indicating that strong cavitation inside the nozzle caused by needle lift effects is the key reason for the "spray resistance phenomenon". [ABSTRACT FROM AUTHOR]

Solanum nigrum is a noxious weed in agricultural ecosystem that limits many crops' production. The aim of the present study was to understand the level of genetic diversity and population structure of S. nigrum in China. A specific-locus amplified fragment (SLAF) sequencing method was conducted to detect single-nucleotide polymorphisms (SNPs) in the genomes of S. nigrum from 66 populations in China. A total of 616,533 high-quality SNPs were identified from 189,840 SLAFs, with an average sequencing depth of 10.59× fold and a Q30 value of 93% and a GC content of 42.78%. It showed a considerable amount of genetic diversity and genetic variability of S. nigrum among samples. The genetic differentiation of S. nigrum indicated that there was a low level of genetic differentiation (Fst < 0.1000) among geographical populations. A cluster analysis showed that populations of S. nigrum were divided into two subgroups, with some samples from adjacent position roughly clustered together, which showed some correlation between geographic origins. A population structure analysis suggested the 66 S. nigrum samples could have originated from three different genetic clusters. The Xinjiang site was the only location where all genetic clusters were found, which suggested these populations were genetically diverse. These results showed that there was a high degree of genetic diversity and low difference among the different geographical populations of S. nigrum. The results from the genetic structure of the SNP markers indicated that wide genetic variability exists among the population of S. nigrum in China, which may contribute to the adaptation and infestation of this weed species. [ABSTRACT FROM AUTHOR]

Stone cells are a distinctive characteristic of pears and their formation negatively affects the quality of the fruit. To evaluate the stone cell content (SCC) of Korla fragrant pears, we developed a Vis/NIR spectroscopy system that allowed for the adjustment of the illuminating angle. The successive projective algorithm (SPA) and the Monte Carlo uninformative variable elimination (MCUVE) based on the sampling algorithm were used to select characteristic wavelengths. The particle swarm optimization (PSO) algorithm was used to optimize the combination of penalty factor C and kernel function parameter g. Support vector regression (SVR) was used to construct the evaluation model of the SCC. The SCC of the calibration set ranged from 0.240% to 0.657% and that of the validation set ranged from 0.315% to 0.652%. The SPA and MCUVE were used to optimize 57 and 83 characteristic wavelengths, respectively. The combinations of C and g were (6.2561, 0.2643) and (2.5133, 0.1128), respectively, when different characteristic wavelengths were used as inputs of SVR, indicating that the first combination had good generalization ability. The correlation coefficients of the SPA-SVR model after pre-processing the standardized normal variate (SNV) for both sets were 0.966 and 0.951, respectively. These results show that the SNV-SPA-SVR model satisfied the requirements of intelligent evaluation of SCC in Korla fragrant pears. [ABSTRACT FROM AUTHOR]

High concentrations of fine particulate matter (PM2.5) are well known to reduce environmental quality, visibility, atmospheric radiation, and damage the human respiratory system. Satellite-based aerosol retrievals are widely used to estimate surface PM2.5 levels because satellite remote sensing can break through the spatial limitations caused by sparse observation stations. In this work, a spatiotemporal weighted bagged-tree remote sensing (STBT) model that simultaneously considers the effects of aerosol optical depth, meteorological parameters, and topographic factors was proposed to map PM2.5 concentrations across China that occurred in 2018. The proposed model shows superior performance with the determination coefficient (R2) of 0.84, mean-absolute error (MAE) of 8.77 μg/m3 and root-mean-squared error (RMSE) of 15.14 μg/m3 when compared with the traditional multiple linear regression (R2 = 0.38, MAE = 18.15 μg/m3, RMSE = 29.06 μg/m3) and linear mixed-effect (R2 = 0.52, MAE = 15.43 μg/m3, RMSE = 25.41 μg/m3) models by the 10-fold cross-validation method. The results collectively demonstrate the superiority of the STBT model to other models for PM2.5 concentration monitoring. Thus, this method may provide important data support for atmospheric environmental monitoring and epidemiological research. [ABSTRACT FROM AUTHOR]

Structural monitoring provides valuable information on the state of structural health, which is helpful for structural damage detection and structural state assessment. However, when the sensors are exposed to harsh environmental conditions, various anomalies caused by sensor failure or damage lead to abnormalities of the monitoring data. It is inefficient to remove abnormal data by manual elimination because of the massive number of data obtained by monitoring systems. In this paper, a data anomaly detection method based on structural vibration signals and a convolutional neural network (CNN) is proposed, which can automatically identify and eliminate abnormal data. First, the anomaly detection problem is modeled as a time series classification problem. Data preprocessing and data augmentation, including data expansion and down-sampling to construct new samples, are employed to process the original time series. For a small number of samples in the data set, randomly increase outliers, symmetrical flipping, and noise addition methods are used for data expansion, and samples with the same label are added without increasing the original samples. The down-sampling method of symmetrically extracting the maximum value and the minimum value at the same time can effectively reduce the dimensionality of the input sample, while retaining the characteristics of the data to the greatest extent. Using hyperparameter tuning of the classification weights, CNN is more effective in dealing with unbalanced training sets. Finally, the effectiveness of the proposed method is proved by the anomaly detection of acceleration data on a long-span bridge. For the anomaly detection problem modeled as a time series classification problem, the proposed method can effectively identify various abnormal patterns. [ABSTRACT FROM AUTHOR]

Immunoglobulin (Igκ) has been reported to be expressed in sorted liver epithelial cells of μMT mice, and the sequence characteristics of hepatocyte-derived Igκ were different from those of classical B-cell-derived Igκ. However, the physiological function of hepatocyte-derived Igκ is still unclear. The expression of Igκ was firstly identified in primary hepatocytes and normal liver cell line (NCTC1469), and hepatocyte-derived Igκ expression was elevated and displayed unique localization in hepatocytes of concanavalin A (ConA)-induced hepatitis model. Moreover, Igκ knockout mice were more sensitive to ConA-induced hepatitis and had higher serum aspartate aminotransferase (AST) levels, more severe histological injury and a greater number of terminal deoxynucleotide transferase-mediated deoxyuridine triphosphate nick end-labeling (TUNEL)-positive cells as compared with littermate controls. Furthermore, knockdown of Igκ in primary hepatocytes and NCTC1469 cells led to accelerated activation of the mitochondrial death pathway and caspase-3 cleavage in vitro, which might be related to inhibition of NF-κB signaling pathway and activation of JNK via the cytoskeleton dynamics. Taken together, these results indicate that hepatocyte-derived Igκ mediates cellular resistance to ConA-induced liver injury by inhibiting activation of caspase-3 and the mitochondrial death pathway, suggesting that Igκ plays an important role in hepatocyte survival and exerts a protective effect against ConA-induced liver injury in mice. [ABSTRACT FROM AUTHOR]

As the dominant manganese oxide mineral phase in terrestrial and aquatic environments, birnessite plays an important role in many biogeochemical processes. The coexistence of birnessite with aqueous Mn2+ is commonly found in the subsurface environments undergoing Mn redox cycling. This study investigates the change in Mn average oxidation state (AOS) of birnessite after reaction with 0.1–0.4 mM Mn2+ at pH 4.5–6.5, under conditions in which phase transformation of birnessite by Mn2+ was not detectable. The amount of Mn2+ uptake by birnessite and the equilibrium concentration of Mn(III) proportionally increased with the initial concentration of Mn2+. The Mn AOS of birnessite particles became 3.87, 3.75, 3.64, and 3.53, respectively, after reaction with 0.1, 0.2, 0.3, and 0.4 mM Mn2+ at pH 5.5. Oxidation potentials (Eh) of birnessite with different AOS values were estimated using the equilibrium concentrations of hydroquinone oxidized by the birnessite samples, indicating that Eh was linearly proportional to AOS. The oxidation kinetics of bisphenol A (BPA), a model organic pollutant, by birnessite suggest that the logarithms of surface area-normalized pseudo-first-order initial rate constants (log kSA) for BPA degradation by birnessite were linearly correlated with the Eh or AOS values of birnessite with AOS greater than 3.64. [ABSTRACT FROM AUTHOR]

We compare the electron dynamics at monocrystalline Cu(111), Au(100) and Pd(111) precursor substrates with vicinal nanosteps. The unoccupied bands of a surface superlattice are populated via the resonant charge transfer (RCT) between the surface and a H − ion that flies by at grazing angles. A quantum mechanical wave packet propagation approach is used to simulate the motion of the active electron, and time-evolved wave packet densities are used to visualize the dynamics through the superlattice. The survived ion fraction in the reflected beam generally exhibits modulations as a function of the vicinal terrace size and shows peaks at those energies that access the image state subband dispersions. Differences in magnitudes of the ion-survival as a function of the particular substrate selection and the ion-surface interaction time, based on the choice of two ion-trajectories, are examined. A square well model, producing standing waves between the steps on the surface, explains the energies of the maxima in the ion survival probability for all the metals considered. This indicates that the primary process of confinement induced subband formation is robust. The work may motivate measurements and applications of shallow-angle ion-scattering spectroscopy to access electronic substructures in periodically nanostructured surfaces. [ABSTRACT FROM AUTHOR]

With the development of agriculture and the widespread application of agrichemicals in Tibet, herbicide residues have become a threat to the ecological safety of Tibetan water bodies. Algae, as the producers in the food chain in water bodies, play an important role in aquatic ecosystems. Therefore, the impact of herbicides on Tibetan algae is of great significance for evaluating ecological health and the protection of Tibetan water ecosystems. In this study, we investigated the inhibitory effect of glyphosate, a herbicide, on the photosynthetic system of Chlorella vulgaris , Tibetan algae, by determining chlorophyll fluorescence and the activity of an antioxidant system. The results revealed that glyphosate at low concentration did not affect the photosynthetic activity of C. vulgaris ; however, glyphosate at a high concentration significantly inhibited photosynthetic activity and reduced pigment content. Moreover, high levels of glyphosate also decreased photochemical efficiency and electron transport rate and resulted in ROS accumulation, high SOD activity, and lipid peroxidation. These results suggested that glyphosate could decrease the primary production of aquatic ecosystems and influence their performance. Therefore, reducing the herbicide levels could protect the Tibetan aquatic environment and maintain the health of ecosystems.