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Summary: The article examines the positional relationships and transmission/reception signal direction information within unmanned aerial vehicle (UAV) cluster formations. Considering the distinctive characteristics of various formation shapes (such as circle and cone), a study is conducted on the polar and Cartesian coordinate systems in neural network models to address position deviation issues arising in UAV formation flights. The passive receiving signal UAV positioning model is established by applying the triangular cosine theorem. Additionally, the position relationship model of the UAV is formulated using optimization theory. The article introduces a deep learning‐based deviated UAV adjustment algorithm designed to automatically adjust UAV positions. This is achieved by establishing a triangular relationship between the passive signal receiving UAV and other UAVs. The implementation involves C# programming and MATLAB visualization. The proposed method not only resolves UAV positioning and position adjustment challenges but also optimizes the adjustment strategy. This optimization leads to maximum savings in time and distance costs associated with sending and receiving signals among UAVs, thereby enhancing the overall performance of UAVs during flights. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: A monolithic column‐based mass spectrometry (MS) analysis kit was prepared for whole blood analysis with MS. The kit is disposable and can be used for purification, storage, transportation and direct analysis of whole blood. The kit mainly consists of a capillary for quantitative microsampling, a cation exchange monolithic column for purification and storage, and a syringe for loading sample. This kit is very friendly to various users that one can easily siphon the blood in the kit followed by rapid clean‐up. We established a quantitative method using the kit with a limit detection as low as 0.33 nmol/L, and achieved more than five orders of magnitude enhancement in sensitivity compared to direct nanoelectrospray ionization MS analysis. The column can avoid analyte exposure to environment, which helps the storage of the sample for laboratory analysis. The relative standard deviation of immediate blood analysis and storage blood analysis within 10 d was less than 10%. This method has been successfully applied to the quantitative analysis of procainamide hydrochloride in 2 μL rat blood. These results indicate that this disposable kit does have the potential to achieve highly sensitive quantitative MS analysis in biological samples, which is expected to become a cost‐effective and powerful tool for in vitro diagnostics. [ABSTRACT FROM AUTHOR]

Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model's parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance., Author(s): Yongyi Li (corresponding author) [1]; Ming Zhang [1]; Yixing Ding [2]; Zhenghua Zhou [3]; Lingyu Xu [1] 1. Introduction The ways to predict travel time have undergone a significant [...]

To reveal the effect pattern of expressways on regional soil moisture, in this study, using trend analysis and buffer zone analysis methods, the data of VSWI (vegetation supply water index) in central Zhejiang Province from 2005 to 2016 were extracted from landsat7 satellite data using a single window algorithm, and spatial analysis was used to investigate the law of its differentiation. The results show that the multi-year average is 0.01879, between 0.01035–0.02774, showing a gentle decreasing trend, and there are obvious regional variations in space. We found that the impact of the new expressway and interchange on the VSWI in the buffer zone lasted for more than two years, and the VSWI increased in space farther away from the road, and this trend returned to normal at 8 km. Finally, the development patterns of the VSWI in the buffer zone of the newly established expressway and the interchange are approximately the same. [ABSTRACT FROM AUTHOR]

The in‐depth study of electrochemical (EC) synthesis can require a powerful mass spectrometry (MS) analytical platform which can discover and identify fleeting intermediates in EC reactions. Here we report a floating electrolytic electrospray ionization (FE‐ESI) strategy that can perform EC processes in a floating electrolytic cell and monitor intermediates by high‐resolution MS. Compared with previous EC‐MS methods, a significant advantage of FE‐ESI‐MS is that it allows one to modulate the electrolytic and electrospray process individually, ensuring its high sensitivity in discovering intermediates and universality to investigate redox reactions in different scenarios. This powerful platform has been successfully used to investigate the EC reductive coupling of p‐tolylboronic acid and p‐nitrotoluene. A series of nitrene intermediates were discovered and identified by FE‐ESI‐MS, indicating that a hidden mechanism involving nitrene formation might play a key role in EC reductive coupling process. [ABSTRACT FROM AUTHOR]

To improve the utilization rate of parking space resources and reduce the cost of installing and maintaining sensor recognition, this paper proposed an improved computer vision-based parking space status recognition method. The overall recognition accuracy was improved by graying the video, filtering smoothing noise reduction, image enhancement pre-processing, introducing texture feature extraction method based on LBP operator, improving the background difference method, and then, we used a perceptual hash algorithm to calculate the Hamming distance between the background image and the hash string of the current frame of the video, excluding the influence of light and pedestrian on recognition accuracy. Finally, a parking space status recognition system is developed relied on the Python environment, and parking spaces are recognized in three environmental states: with direct light, without direct light, and in rain and snow. The overall average accuracy of the experimental results was 97.2%, which verifies the accuracy of the model. [ABSTRACT FROM AUTHOR]

With the continuous increase in motor vehicle ownership in recent times, traditional transportation has been unable to meet people's travel needs. Research on autonomous driving technology will help solve a series of problems associated with driving, such as traffic accidents, traffic congestion, energy consumption, and environmental pollution. In this paper, an improved artificial potential field method is proposed to complete the planning of automatic driving trajectories by adding the distance adjustment factor, dynamic road repulsive field, velocity repulsive field, and acceleration repulsive field. The invasive weed algorithm is introduced to solve the defects associated with the traditional artificial potential field method. The prediction model—for which corresponding constraint variables were set and an optimal objective function was established to build up the MPC model controller to achieve the goal of trajectory tracking—was linearized and discretized from a vehicle dynamics model. Finally, co-simulation based on MATLAB and CarSim was used to verify the practicability of the model. [ABSTRACT FROM AUTHOR]

To carry out the estimation and reliability research of post-earthquake traffic travel time, which has the great influence for efficient allocation of relief materials. By analyzing the relationship among floating vehicle trajectory, target path and road network path, the intermediate parameters of converting floating vehicle trajectory data into target path travel time were defined and improved. In addition, the road damage identification method relying on lane detection is applied for evaluating the damage of road after the earthquake through the image information. Then, Bayesian average adaptive kernel density estimation method was used to estimate the distribution of post-earthquake road travel time, and a new formula for calculating the reliability of road travel time after earthquake was proposed. According to the example simulation and analysis, the proposed post-earthquake road travel time distribution estimation and its reliability are verified. The results show that when the threshold value is determined, the travel time of the path before the earthquake is the most dependable, and with the increase in the earthquake damage index, the travel time of this road section becomes increasingly unreliable. However, after the earthquake, the peak probability density of road travel time distribution weakens, and the overall probability shifts to the direction of long time. [ABSTRACT FROM AUTHOR]

In this study, the phase-structure and rheological response-behavior of multi-walled carbon nanotube (MWCNTs) modified asphalt-binder (MWCNTs-MA) were measured and quantified in the laboratory. The changes in the molecular dynamics due to MWCNTs modification were simulated and quantified based on the intermolecular interaction energy computations, electrostatic potential surface analyses and phase-structure modeling of the asphalt-binder matrix. The rheological properties such as the asphalt-binder viscosity and complex modulus, of both the base and modified asphalt-binders, were determined using the standard Brookfield viscometer (BV) and dynamic shear rheology (DSR) test devices, respectively. In comparison to the base asphalt-binder, the corresponding BV-DSR test results exhibited higher viscosity and complex modulus for the MWCNTs modified asphalt-binder, with reduced sensitivity and susceptibility to temperature variations. From the study results, it was observed that MWCNTs significantly improved the rheological properties and high-temperature performance of the asphalt-binder. Overall, the study has demonstrated that MWCNT modified asphalt-binder has great promising potential for application and usage as a road-pavement material, particularly with respect to mitigating the high temperature related distresses such as rutting. [ABSTRACT FROM AUTHOR]

Summary: Zero watermark has no intervention on the carrier image, which is invisible in nature, and completely solves the problem of mutual constraint between robustness and invisibility in the traditional digital watermark technology. In order to improve the robustness of the zero watermark, a zero watermark algorithm based on Gabor transform and discrete cosine transform (DCT) is proposed in this paper. The algorithm performs Gabor transformation on the carrier image to obtain the directional characteristics of the image. Then it performs DCT and blocks, and performs singular value decomposition (SVD) on each subblock. The feature matrix is constructed using the largest eigenvalue of the subblock, and then XORed with the encrypted watermark to form a zero watermark. The algorithm utilizes the advantages of Gabor transform in the adjustment of scale and direction and selects the optimized parameters to extract image features. At the same time, the information concentration and decorrelation of DCT and SVD are utilized to improve the robustness and antiattack capability of the algorithm. The experimental results show that the proposed algorithm is robust to filtering, JPEG compression, noise, shearing, rotation, and other attacks, especially effective in antishear attack and rotation attack. [ABSTRACT FROM AUTHOR]

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain. [ABSTRACT FROM AUTHOR]

Artificial neural network algorithm has strong nonlinear mapping function. BP neural network algorithm is the representative of artificial neural network algorithm. Due to the relatively small scale of small, medium, and micro enterprises and lack of mortgage assets, the biggest market risk they usually face is credit risk. In order to avoid huge economic losses for banks, the impact of risk analysis on credit decision-making is the key. Therefore, the establishment of a scientific and complete credit risk assessment system has become the primary task. This paper mainly uses BP neural network algorithm to analyze the risk of small and medium-sized enterprises and solve the problem of credit decision-making. First, analyze the various influencing factors that cause credit risk, and layer the factors according to the degree of risk; secondly, use the fuzzy comprehensive evaluation method in mathematical modeling to rank each factor; then, use the fuzzy analytic hierarchy process to calculate the weight of each indicator factor as calculated by establishing the risk value function, using the function expression of the annual interest rate and the total credit formula to calculate the total credit, which solves the problem of the loan line of each enterprise, and use the optimal linear programming to give priority to the selection of the line and the interest rate. Enterprises: finally, for enterprises that do not have a credit rating, reasonable adjustments are made to the quota by establishing lending qualification expression, taking into account the impact of unexpected external factors, establishing an improved annual interest rate function, and achieving an optimized credit strategy through the establishment and solution of the model. The goal of optimizing credit strategy has been achieved. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Twenty‐two new limonoids, mufolinoids A—V (1—22), including six rings A, B‐seco limonoids (1—6), twelve ring A‐seco limonoids (7—18), four ring‐intact limonoids (19—22), together with thirteen known compounds (23—35) were isolated from Munronia unifoliolata. Their structures including absolute configurations were elucidated by combination of NMR, HR‐MS, single‐crystal X‐ray diffraction and calculations of ECD and NMR technologies. Compounds 24, 25, 33, 34 could be significantly reversed the multidrug resistance of MCF‐7/doxorubicin (DOX) cells, and the reversal fold (RF) was much higher than that of positive drug Verapamil. Compounds 24, 28, and 29 exhibited significant anti‐inflammatory activity with the IC50 values in the range of 17.7—39.4 μmol/L. Furthermore, compound 29 could markedly inhibit the release of IL‐1β by inhibiting the initiation and assembly of NLRP3 inflammasome, which demonstrates the great potential of limonoids as an anti‐inflammatory agent. [ABSTRACT FROM AUTHOR]