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In computer vision, timely and accurate execution of object identification tasks is critical. However, present road damage detection approaches based on deep learning suffer from complex models and computationally time-consuming issues. To address these issues, we present a lightweight model for road damage identification by enhancing the YOLOv5s approach. The resulting algorithm, YOLO-LRDD, provides a good balance of detection precision and speed. First, we propose the novel backbone network Shuffle-ECANet by adding an ECA attention module into the lightweight model ShuffleNetV2. Second, to ensure reliable detection, we employ BiFPN rather than the original feature pyramid network since it improves the network's capacity to describe features. Moreover, in the model training phase, localization loss is modified to Focal-EIOU in order to get higher-quality anchor box. Lastly, we augment the well-known RDD2020 dataset with many samples of Chinese road scenes and compare YOLO-LRDD against several state-of-the-art object detection techniques. The smaller model of our YOLO-LRDD offers superior performance in terms of accuracy and efficiency, as determined by our experiments. Compared to YOLOv5s in particular, YOLO-LRDD improves single image recognition speed by 22.3% and reduces model size by 28.8% while maintaining comparable accuracy. In addition, it is easier to implant in mobile devices because its model is smaller and lighter than those of the other approaches.

The development of underwater high-precision navigation technology is of great significance for the application of autonomous underwater vehicles (AUVs). Traditional long baseline (LBL) positioning systems require pre-deployment and the calibration of multiple beacons, which consumes valuable time and manpower. In contrast, the range-only single-beacon (ROSB) positioning technology can help autonomous underwater vehicles (AUVs) obtain accurate position information by deploying only one beacon. This method greatly reduces the time and workload of deploying beacons, showing high application potential and cost ratio. Given the operational constraints of AUV open-ocean navigation with single-beacon weak observations and absence of valid a priori positioning data in calibration zones, a multi-sensor underwater virtual beacon localization framework was established, proposing a differential Chan–Gauss–Newton (DCGN) methodology for submerged vehicles. Based on inertial navigation, the method uses the distance measurement information from a single beacon and observations from multiple sensors, such as the Doppler velocity log (DVL) and pressure sensor, to obtain accurate position estimates by discriminating the initial position of multiple hypotheses. A simulation experiment and lake test show that the proposed method not only significantly improves the positioning accuracy and convergence speed, but also shows high reliability.

Compared with the strapdown inertial navigation system (SINS), the rotation strapdown inertial navigation system (RSINS) can effectively improve the accuracy of navigation information, but rotational modulation also leads to an increase in the oscillation frequency of attitude errors. In this paper, a dual-inertial navigation scheme that combines the strapdown inertial navigation system and the dual-axis rotation inertial navigation system is proposed, which can effectively improve the attitude error accuracy in the horizontal direction by using the high-position information of the rotation inertial navigation system and the stability characteristics of the attitude error of the strapdown inertial navigation system. Firstly, the error characteristics of the strapdown inertial navigation system and the rotation strapdown inertial navigation system are analyzed, and then the combination scheme and Kalman filter are designed according to the error characteristics, and finally, the simulation experiment shows that the pitch angle error of the dual inertial navigation system is reduced by more than 35% and the roll angle error is reduced by more than 45% compared with the rotation strapdown inertial navigation system. Therefore, the combination scheme of double inertial navigation proposed in this paper can further reduce the attitude error of the rotation strapdown inertial navigation system, and at the same time, the two sets of inertial navigation systems can also enhance the reliability of ship navigation.

The system-level calibration technology of rotational inertial navigation is one of the main methods to improve the accuracy of inertial navigation, and the design of the calibration scheme is the key to calibration technology. By the establishment of the error model of inertial navigation system, a 30-position calibration scheme is designed in this study. Based on the 30-dimensional Kalman filter, the constant errors, scale factor errors and installation error of gyroscope and accelerometer are identified. Comparing the traditional schemes and the 30-position scheme with the simulation experiment, the observability of the 30-position scheme is higher, the residual error of the estimated sensor is smaller and the navigation positioning accuracy after the estimated inertial sensor error parameter compensation is higher, which verifies the feasibility of the 30-position scheme. Finally, the measured experiment uses the 30-position scheme to estimate the error of a certain type of IMU sensor, and the calibration curve of the error parameter is well converged before the end of the calibration experiment, so it has certain practical value.

In the platform gravimeter, the stabilization accuracy of the gravimetric stabilization platform is crucial to improve the accuracy of gravity value measurements due to its uncertainties, such as mechanical friction, inter-device coupling interference, and nonlinear disturbances. These cause fluctuations in the gravimetric stabilization platform system parameters and present nonlinear characteristics. To resolve the impact of the above problems on the control performance of the stabilization platform, an improved differential evolutionary adaptive fuzzy PID control (IDEAFC) algorithm is proposed. The proposed enhanced differential evolution algorithm is used to optimize the initial control parameters of the system adaptive fuzzy PID control algorithm to achieve accurate online adjustments of the gravimetric stabilization platform’s control parameters when it is subject to external disturbances or state changes and attain a high level of stabilization accuracy. The results of simulation tests, static stability experiments, and swaying experiments on the platform under laboratory conditions, as well as on-board experiments and shipboard experiments, all show that the improved differential evolution adaptive fuzzy PID control algorithm has a higher stability accuracy compared with the conventional control PID algorithm and traditional fuzzy control algorithm, proving the superiority, availability, and effectiveness of the algorithm.

In the post-epidemic era, the e-commerce industry has become an important engine to promote the new round of growth in China’s economy. However, the frequent quality problems of products, such as shoddy goods and improper products in the market, not only violate the legitimate rights and interests of consumers and social and public interests, but also seriously restrict the steady and sound development of the e-commerce industry. This paper uses evolutionary game theory to build an evolutionary game model between the government, platform, and merchants, and it analyzes the stable evolution path of the game system and the key factors affecting product quality optimization under the situation of dual strategy set, and then it expands the game side strategy set into a continuous type and compares and explores the regulatory effects and quality output changes under the two situations. Then, it puts forward effective measures to improve the quality of e-commerce products. The findings are as follows: in the case of a binary strategy set, it is difficult for merchants to steadily evolve towards compliance management, while merchants’ violation management only has the willingness to improve their efforts when the scale of consumers is small. In the case of continuous policy set, government–enterprise cooperative supervision can realize the compliance operation of merchants, and the effort level and income of merchants are consistent with the optimal value in the case of dual policy set. The results show that the government and e-commerce platforms should adhere to the concept of dynamic regulation and adjust the regulatory strategies according to the different development stages of enterprises so as to not only give merchants sufficient development space, but also to maintain the healthy development environment of the market. At the same time, the government and e-commerce platforms should also avoid the binary choice of supervision or neglect, adopt flexible regulatory strategies, and maintain moderate flexible regulation so as to achieve the development trend of compliance, efforts, and profits of merchants.

In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers’ personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions. The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT.

Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are unavailable. The acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration capability. However, the traditional Acoustic Positioning Systems (APS) are expensive and incapable of positioning with limited acoustic observations. Two novel underwater inertial error rectification algorithms with limited acoustic observations are proposed. The first one is the single acoustic-beacon Range-only Matching Aided Navigation (RMAN) method, which is inspired by matching navigation without reference maps and presented for the first time. The second is the improved single acoustic-beacon Virtual Long Baseline (VLBL) method, which considers the impact of indicated relative position increments on virtual beacon reconstruction. Both RMAN and improved VLBL are further developed when multi acoustic-beacons are available, named mAB-RMAN and mAB-VLBL. The comprehensive simulations and field investigations were conducted. The results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline, specifically, the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90% and 98% when using single and double acoustic-beacons, respectively. These proposed techniques will provide new perspectives for underwater positioning, navigation, and timing.

Cold acclimation before winter has been shown to enhance the cold tolerance of evergreen conifers, including Pinus massoniana Lamb., a characteristic heteroblastic foliage tree in the conifer. In the initial growing season of P. massoniana, both primary needle seedlings (PNSs) and secondary needle seedlings (SNSs) are generated. While previous research has highlighted differences in the morphological structure and photosynthetic physiological functions of primary and secondary needles, their response to cold acclimation remains poorly understood. This study aimed to investigate the changes in morpho-anatomical structure, starch grain accumulation, and lignin deposition in the roots, stems, and leaves of PNSs and SNSs during cold acclimation using solid potassium iodide and hydrochloric acid phloroglucinol double-staining techniques. The results revealed that, during cold acclimation, the leaves and stems of PNSs exhibited sensitivity to low-temperature stress, resulting in noticeable shrinkage and fracture of mesophyll and cortical parenchyma cells. Furthermore, the early stages of cold acclimation promoted the accumulation of starch grains and lignin in the seedling tissues. In contrast to PNSs, the leaves and stems of SNSs exhibited a shorter cold acclimation period, attributed to the hydrolysis of starch grains in the epidermal cell walls and the transformation of xylem lignin, which supports cell structure stability and enhances cold resistance. In conclusion, these findings suggest that SNSs displayed a superior cold resistance potential compared to PNSs following cold acclimation, providing a significant theoretical basis for the further screening of cold-tolerant germplasm resources of P. massoniana and the analysis of cold resistance traits in heteroblastic foliage.

Underwater acoustic single-beacon positioning technology achieves localization by integrating vehicle motion with range measurements acquired from acoustic ranging devices, offering advantages such as system simplicity, flexible deployment, and high cost-effectiveness. However, its accuracy is limited by weak initial observability and degraded observation geometry. To address this, a sensor data correction and collaborative optimization framework is proposed. A hybrid outlier rejection strategy first suppresses acoustic multipath and sensor noise. To compensate for systematic sensor errors ignored in conventional Virtual Long Baseline methods, an affine transformation maps the true trajectory to the sensor-indicated one, reformulating error compensation as a correction to virtual beacon coordinates. To further mitigate the accuracy degradation caused by degenerated geometric configurations, this paper proposes a collaborative algorithm that integrates Chan initialization with affine transformation optimization. This approach formulates the positioning problem as an optimization task, simultaneously estimating the position information and affine transformation parameters through iterative refinement to achieve high-precision localization. The process begins with Chan’s algorithm, which provides an initial estimate from the virtual sensor array. This estimate is then refined under affine constraints to achieve high-precision localization. Experimental results show the method improves positioning accuracy by 36.30% compared to baseline algorithms, demonstrating significant performance enhancement.