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Formulating effective typhoon avoidance routes is crucial for ensuring the safe navigation of ocean-going vessels. From a maritime safety perspective, this paper investigates ship route optimization under typhoon forecast uncertainty. Initially, the study calculates the probability of a ship encountering a typhoon based on the distribution of historical typhoon data within the radius of seven-level winds and the distance between the ship and the typhoon. Subsequently, the minimum safe distance is quantified, and a multi-objective ship route optimization model for typhoon avoidance is established. A three-dimensional multi-objective ant colony algorithm is designed to solve this model. Finally, a typhoon avoidance simulation experiment is conducted using Typhoon TAMRI and a classic route in the South China Sea as a case study. The experimental results demonstrate that under adverse conditions of uncertain typhoon forecasts, the proposed multi-objective typhoon avoidance route optimization model can effectively avoid high wind and wave areas of the typhoon while balancing and optimizing multiple navigation indicators. This model can serve as a reference for shipping companies in formulating typhoon avoidance strategies.

Maritime unmanned aerial vehicles (UAVs) equipped with inertial navigation systems (INS) are prone to error accumulation, which can lead to excessive positioning errors and hinder their ability to perform long distance missions. To address this issue, this study first constructs a directed graph network for a flight area based on start and end points as well as error correction points. A multi-objective route planning model is then developed for a UAV, aiming to minimize both the flight distance and the number of positioning corrections. Considering the UAV’s turning radius, a trajectory length calculation model based on 3D Dubins curves is designed. Subsequently, a forward labeling-based multi-objective path planning algorithm is proposed to develop an optimization model. Experimental results demonstrate that the proposed method can effectively constrain the UAV’s horizontal and vertical positioning errors within 2.5 m, while optimally balancing flight distance and positioning accuracy to ensure the successful execution of long-range maritime UAV missions. The comparative results demonstrate that, while satisfying the positioning error requirements, our proposed method achieves a reduction of over 1.5% in total flight distance for maritime UAVs compared to the NSGA-II algorithm.

In urban areas situated along busy waterways like the Yangtze River, the diesel engines of inland navigation ships emerge as significant contributors to air pollution. Among these vessels, certain high-emission ships exhibit considerably higher levels of nitrogen oxides (NO x ) emissions compared to others. To effectively identify such ships, this study employed a cost-effective ship emission monitoring sensor platform, comprising high-precision gas sensors, automatic identification system receiver, and sensitive meteorological sensors, along the Yangtze River in Wuhan City. By combining multi-source shore-based monitoring data, we identified ship emission signals and proposed a high-emission ship detection method using inverse modeling. Using this method, we successfully detected inland high-emission ships based on two months of monitoring data. Furthermore, the relationship between different ship types, sizes, speeds, and ship NO x emission rates were investigated. The results of this study are beneficial for strengthening the regulation of high-emission vessels in inland waterways, thereby reducing the adverse impact of ship emissions on the environment and climate. It also encourages the inland shipping industry to adopt more environmentally friendly technologies and fuels, as advocated by the International Maritime Organization.

Optical flow, inspired by biological visual systems, calculates spatial motion vectors aiming to enable robotics to excel in dynamic environments. However, current algorithms, despite human-competitive task performance on benchmark datasets, suffer from significant time delays, limiting practical deployment. Here, we introduce a neuromorphic temporal-attention hardware that emulates the interaction between the retina and the lateral geniculate nucleus (LGN) to extract temporal motion cues directly in hardware. Using a two-dimensional synaptic transistor array, the system encodes brightness changes and accumulates them in analog, non-volatile states, generating compact regions of interest (ROIs). These ROIs then act as inputs to conventional downstream optical flow and vision algorithms, enabling ultrafast motion analysis. At the hardware level, the synaptic transistor offers high-frequency response (~100 μs), non-volatility (>10,000 s), and endurance (>8,000 cycles). Compared to state-of-the-art algorithms, our approach demonstrates a 400% speedup, surpassing human-level performance while maintaining or improving accuracy through temporal priors. Human visual system relies on temporal attention to detect moving objects before high-level processing with large computational overheads. Wang et al. emulate this function in a neuromorphic hardware, showing a 400% speedup compared to algorithm-based visual perception and surpassing human capabilities.

Objective. To explore the molecular mechanism of sevoflurane-induced neurotoxicity and to determine whether lncRNA HOXA11-AS affects sevoflurane-induced neuronal apoptosis and inflammation by regulating miR-98-5p/EphA4. Methods. Morris water maze (MWM) test was used to detect the learning and memory ability of rats, HE staining was used to observe hippocampal pathology, TUNEL staining was used to detect the level of neuronal apoptosis, and RT-qPCR was used to detect the expression of HOXA11-AS, miR-98-5p, IL-6, IL-1β, and TNF-α. At the same time, the contents of IL-6, IL-1β, and TNF-α in serum were detected by ELISA. The expressions of apoptosis-related proteins EphA4, Bax, Cleaved caspase 3, and Bcl-2 were detected by Western blot. The dual-luciferase gene reporter verified the targeting relationship between HOXA11-AS and miR-98-5p and the targeting relationship between miR-98-5p and EphA4. Results. The expression of HOXA11-AS was observed in sevoflurane-treated rats or cells and promoted neuronal apoptosis and inflammation. HOXA11-AS was knocked out alone, or miR-98-5p was overexpressed which attenuates neuronal apoptosis and inflammatory inflammation after sevoflurane treatment. Furthermore, knockdown of HOXA11-AS alone was partially restored by knockdown of miR-98-5p or overexpression of EphA4. Conclusion. Inhibition of lncRNA HOXA11-AS attenuates sevoflurane-induced neuronal apoptosis and inflammatory responses via miR-98-5p/EphA4.

Health science popularization is an important means to improve public health literacy, promote healthy lifestyles, prevent diseases and respond to health crises, which is of great significance for improving the overall health of the people. Strengthening the medication education of patients is also one of the key factors to improve patients' medication adherence. In order to strengthen the dissemination of pharmaceutical popular science articles and give full play to the value of pharmaceutical popular science, this study takes WeChat public account as the research platform to explore effective strategies to improve pageviews of science popularization. It provides references for science popularization workers, so that science popularization can play a better role in improving the public's knowledge of medication safety. Taking the well-known pharmaceutical science popularization WeChat account \"PSM Medicine Shield Public Welfare\" as an example, we combined the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and VOSviewer visualization analysis technology to construct a hot topic analysis model for pharmaceutical science popularization articles, and analyzed the common rules and characteristics of successful hot articles. Latent Dirichlet Allocation (LDA) and The Bidirectional Encoder Representations from Transformers Topic (BERTopic) model were used to realize the construction of the topic model. The model selected the top 20% of popularization articles with the greatest reading volume between 2015 and 2023 as the database for text mining. The clustering results indicated that the public was interested in these five types of pharmaceutical science popularization themes: drug dosage, drug side effects, children's infections, the efficacy of traditional Chinese medicine and Chinese patent medicines, and the usage methods of different drug administration routes. The public's interest in topics changed from drug side effects to practical drug usage issues, as seen by the keyword time series graph. Pharmaceutical professionals may more effectively discover hot themes in the industry by combining the TF-IDF algorithm with VOSviewer visualization analysis and LDA and BERTopic in the text mining. This improves the readability of popularization articles and the impact of WeChat accounts, which may improve medication adherence and raise public awareness of medication usage.

The accuracy and interpretability of ship energy consumption prediction results are important for ship energy efficiency optimization. In order to improve the accuracy of ship energy consumption prediction and enhance the model interpretability, this paper proposes a ship energy consumption prediction method based on Stacking and SHAP. Firstly, based on Stacking theory, multiple heterogeneous and complementary base models were selected using residual correlation analysis methods to construct a fusion model. And then, to address the “black box” characteristics of the fusion model, SHAP is used to analyze the base model and energy consumption impact characteristics of the fusion model in terms of their interpretability. A large container ship is used as the research object to verify the effectiveness and interpretability of the proposed method. The experimental results show that, in terms of accuracy, compared with the best single model (RF), the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) of the Stacking fusion model are reduced by 4.1%, 16.1%, and 8.3%, respectively, and the R² is improved by 1.5%. Meanwhile, in terms of interpretability, SHAP reveals that Random Forest (RF), k-Nearest Neighbor (KNN), and Gradient Boosting (GB) models play a dominant role in the fusion model, with a total contribution value of about 67%. In addition, sailing speed, mean draft, and trim are the main factors affecting the energy consumption of a ship, and the contribution value of each influential feature can be quantitatively measured. The proposed method ensures the prediction accuracy while enhancing the model interpretability, which can provide more reliable and transparent decision support for ship energy efficiency management.

Mobility impairments from aging, injury, or medical conditions limit independence and social participation. Conventional assistive devices lack adaptability in complex environments. Recent wearable technologies integrating neural sensing, electronics, and co-design offer personalized, responsive mobility support. This perspective focuses on advances in wearable sensing and multimodal fusion for intent recognition, environmental interaction, and adaptive control in exoskeletons, prosthetics, smart wheelchairs, and navigation systems. Emphasizing human-in-the-loop and cognitive–sensorimotor integration, it outlines emerging trends and challenges, promoting intelligent, user-centered solutions to restore function and enhance autonomy, accessibility, and inclusion for individuals with mobility impairments. Mobility impairment reduces autonomy and social participation. Here, the authors discuss how wearable technologies are transforming assisted mobility towards more adaptive, intuitive, and user-centered solutions.

The indoor positioning technology of an ultra-wideband (UWB) can play an excellent supplementary role in satellite navigation and has broad application prospects. However, if strong interference exists, the measurement data based on UWB will fluctuate abnormally, which seriously affects the accuracy of positioning. In view of the above problems, based on the combination of mathematical modeling, this paper starts from the subject data, and proposes a positioning method suitable for non-interference/interference conditions and an abnormal data identification method to improve the positioning accuracy. The specific scheme includes data preprocessing model establishment and solution, positioning model establishment and solution, migration application of positioning model, interfering data identification model establishment and solution, and movement track positioning model establishment and solution.

Nanoscale silicon film electrodes in Li-ion battery undergo great deformations leading to electrochemical and mechanical failures during repeated charging-discharging cycles. In-situ experimental characterization of the stress/strain in those electrodes still faces big challenges due to remarkable complexity of stress/strain evolution while it is still hard to predict the association between the electrode cycle life and the measurable mechanical parameters. To quantificationally investigate the evolution of the mechanical parameters, we develop a new full field 3D measurement method combining digital image correlation with laser confocal profilometry and propose a strain criterion of the failure based on semi-quantitative analysis via mean strain gradient (MSG). The experimental protocol and results illustrate that the revolution of MSG correlates positively with battery capacity decay, which may inspire future studies in the field of film electrodes.