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This paper is concerned with the recursive fusion estimation‐based mobile robot localization (RL) problem by employing multiple energy harvesting sensors (EHSs). In the addressed RL problem, multiple sensors with energy harvesting capacity are deployed to produce measurements used for RL. When the sensors own sufficient energy, the sensors can output measurements and then send them to the corresponding local filter. Otherwise, the sensor energy‐induced missing measurement phenomenon will occur. In order to obtain the missing measurement rate, at each time instant, the relationship between the totality of the sensor energy and its probability distribution is derived recursively. This paper aims at seeking out a practicable solution to the addressed mobile RL problem. First, in the presence of the sensor energy‐induced measurement missing phenomenon, an upper bound (UB) of the local localization error covariance is recursively acquired. Then, such a derived UB is minimized by suitably devising the desired local filter parameter. Subsequently, the covariance intersection fusion method is adopted to achieve the addressed RL problem. In the end, a simulation is conducted to verify the practicability of the developed RL scheme.

To address the limitations of the standard equilibrium optimizer (EO) in terms of insufficient optimization capability, multiple strategies are proposed to enhance its performance. These include a reverse equilibrium state pool, a non-uniform equilibrium state selection strategy, and an equilibrium state mutation strategy. The reverse equilibrium state pool is introduced to encourage candidate solutions with poorer positions to search in a wider search space, under such considerations the global search ability of the improved EO can be enhanced. The non-uniform equilibrium state selection strategy is proposed to select equilibrium state. Under the proposed selection strategy, the candidate solutions with better positions are more likely to be chosen as the equilibrium state, allowing for sufficient exploration of positions near the current optimal point. The equilibrium state mutation strategy leads to cross mutation between candidate solutions and equilibrium state, increasing the likelihood of the group exploring the global optimal solution. To verify and further analyze the performance and superiority of the improved EO, i.e., reverse equilibrium states EO (R O), 29 benchmark functions are adopted. It is verified theoretically from the experimental results that the R O is with a significant improvement in performance by comparison between the standard EO and certain frequently-used heuristic optimization algorithms. Finally, the R O is successfully applied in path planning for surface marine vehicles under the situations of both dynamic and static obstacles.

In response to issues such as incomplete contour segmentation, blurred boundaries, and small-building misclassification in remote sensing images, this paper proposes an MR-DeepLabv3+ network. The network integrates MixConv (dataset-adapted multi-scale convolutional kernels: 3 ×  3/5 × 5/7 × 7) to enhance multi-scale feature capture and a segmentation-optimized R-Drop Loss (decoder-level channel-wise masking with dynamic KL divergence weight) to reinforce noise robustness. Experiments are conducted on three distinct building datasets: Self-building (1270 images, 10–50 pixel slender buildings), WHU (8170 images, 0.075 m resolution dense small buildings), and Massachusetts (151 images, 340 km 2 large urban clusters). The experimental results show that the MR-DeepLabv3 + achieves Acc, MIoU, and FWIoU of 98.34%, 88.93%, 96.88% (Self-building), 98.22%, 88.56%, 97.18% (WHU), and 88.32%, 79.33%, 86.53% (Massachusetts), outperforming the baseline DeepLabv3+ and 4 recent transformer models. MR-DeepLabv3 + balances model compactness and inference efficiency, making it well-suited for remote sensing image segmentation tasks, especially in scenarios with computational resource constraints. Ultimately, it is proven that the method effectively improves building segmentation accuracy and addresses small-building missing issues, with practical value for UAV-based mapping.

This paper is concerned with the measurement outlier (MO)-resistant mobile robot localization (MRL) problem. For the purpose of mitigating the effect of the MOs, a time-varying state estimator is constructed containing a saturation function with variable saturation level. The purpose of this paper is mainly to seek an effective solution to the addressed MRL problem by devising the desired time-varying state estimator which ensures that, over a finite horizon, the estimation error dynamics satisfies the H ∞ performance constraint. By constructing an appropriate Lyapunov function, the existing condition of the estimator is first obtained. Then, the desired state estimator gain is given through the solution to a set of certain matrix inequalities and the MO-resistant MRL algorithm is presented. Finally, an example is conducted to testify the usefulness of the MRL algorithm proposed.

Underwater sonar target recognition is crucial in fields such as national defense, navigation, and environmental monitoring. However, it faces issues such as the complex characteristics of ship-radiated noise, imbalanced data distribution, non-stationarity, and bottlenecks of existing technologies. This paper proposes the MultiFuseNet-AID network, aiming to address these challenges. The network includes the TriFusion block module, the novel lightweight attention residual network (NLARN), the long- and short-term attention (LSTA) module, and the Mamba module. Through the TriFusion block module, the original, differential, and cumulative signals are processed in parallel, and features such as MFCC, CQT, and Fbank are fused to achieve deep multi-domain feature fusion, thereby enhancing the signal representation ability. The NLARN was optimized based on the ResNet architecture, with the SE attention mechanism embedded. Combined with the long- and short-term attention (LSTA) and the Mamba module, it could capture long-sequence dependencies with an O(N) complexity, completing the optimization of lightweight long sequence modeling. At the same time, with the help of feature fusion, and layer normalization and residual connections of the Mamba module, the adaptability of the model in complex scenarios with imbalanced data and strong noise was enhanced. On the DeepShip and ShipsEar datasets, the recognition rates of this model reached 98.39% and 99.77%, respectively. The number of parameters and the number of floating point operations were significantly lower than those of classical models, and it showed good stability and generalization ability under different sample label ratios. The research shows that the MultiFuseNet-AID network effectively broke through the bottlenecks of existing technologies. However, there is still room for improvement in terms of adaptability to extreme underwater environments, training efficiency, and adaptability to ultra-small devices. It provides a new direction for the development of underwater sonar target recognition technology.

Object detection in underwater environments presents significant challenges due to the inherent limitations of sonar imaging, such as noise, low resolution, lack of texture, and color information. This paper introduces AquaYOLO, an enhanced YOLOv8 version specifically designed to improve object detection accuracy in underwater sonar images. AquaYOLO replaces traditional convolutional layers with a residual block in the backbone network to enhance feature extraction. In addition, we introduce Dynamic Selection Aggregation Module (DSAM) and Context-Aware Feature Selection (CAFS) in the neck network. These modifications allow AquaYOLO to capture intricate details better and reduce feature redundancy, leading to improved performance in underwater object detection tasks. The model is evaluated on two standard underwater sonar datasets, UATD and Marine Debris, demonstrating superior accuracy and robustness compared to baseline models.

Background Endometrial carcinoma (EC) is the most prevalent gynecological cancer. Transcription factor (TF) regulates a large number of downstream target genes and is a key determinant of all physiological activities, including cell proliferation, differentiation, apoptosis, and cell cycle. The transcription factor E2F1 shows prominent roles in EC. BMI1 is a member of Polycomb suppressor Complex 1 (PRC1) and has been shown to be associated with EC invasiveness. It is currently unclear whether E2F1 can participate in the proliferation, migration, and invasion processes of EC cells by regulating BMI1 transcription. Objective We investigated whether E2F1 could participate in the proliferation, migration, and invasion processes of EC cells by regulating BMI1 transcription, in order to further clarify the pathogenesis and etiology of EC, and provide reference for identifying potential therapeutic targets and developing effective prevention and treatment strategies for this disease. Methods Human endometrial epithelial cells (hEECs) and human EC cell lines were selected. E2F1 expression was assessed by Western blot. E2F1 was silenced in AN3CA or overexpressed in HEC-1 by transfections, or E2F1 was silenced and BMI1 was overexpressed in AN3CA by cotransfection. Cell proliferation, migration, and invasion were detected by MTT, wound healing, and Transwell assays. The binding sites between E2F1 and BMI1 promoters were predicted through JASPAR website, and the targeted binding was verified by dual-luciferase report and ChIP assays. Results E2F1 was up-regulated in human EC cell lines, with its expression highest in AN3CA, and lowest in HEC-1. AN3CA invasion, migration, and proliferation were repressed by E2F1 knockdown, while those of HEC-1 cells were promoted by E2F1 overexpression. E2F1 overexpression increased the activity of wild type BMI1 reporter vector promoter, while this promotion was weakened after mutation of the predicted binding site in the BMI1 promoter. In the precipitated E2F1, BMI1 promoter site level was higher than that of IgG immunoprecipitant. BMI1 silencing suppressed AN3CA cell growth. BMI1 overexpression partially abrogated E2F1 silencing-inhibited EC cell growth. Conclusion E2F1 promoted EC cell proliferation, invasion, and migration by promoting the transcription of BMI1.

In this study, the effect of the emergence angle of a source array on acoustic transmission in a typical shallow sea is simulated and analyzed. The formula we derived for the received signal based on the Normal Mode indicates that the signal is determined by the beamform on the modes of all sources and the samplings of all modes at the receiving depth. Two characteristics of the optimal emergence angle (OEA) are obtained and explained utilizing the aforementioned derived formula. The observed distributions of transmission loss (TL) for different sources and receivers are consistent with the obtained characteristics. The results of this study are valuable for the development and design of active sonar detection.

This paper analyzes the English teaching model’s structure, function, and characteristics and constructs the basic structure of the classroom innovation teaching model. Then, the English teaching model is constructed, and the English teaching evaluation index system is established based on the algorithms of structural equation model, multigroup structural equation and multigroup parameter estimation. Finally, the quantitative analysis of index weights and satisfaction was conducted with the example of college A. The results showed that the weights of the primary indicators were 0.2873, 0.1294, 0.2546, and 0.3287 for personalized needs, perceived quality, perceived value, and satisfaction, respectively, which indicated that the multigroup structural model could help universities to realize the innovation of English teaching mode and to adopt diversified teaching methods to meet student’s diverse needs in the teaching process. Diversified the needs of students and improved their satisfaction.

Fine particulate matter (PM2.5) exposure, especially to its organic components, induces adverse health effects on the respiratory system. However, the molecular mechanisms have still not been fully elucidated. Long non-coding RNA (lncRNA) is involved in various physio-pathological processes. In this study, the roles of lncRNA were investigated to reveal the toxicology of PM2.5. Organic extracts of PM2.5 from Nanjing and Shanghai cities were adopted to treat human bronchial epithelial cell lines (BEAS-2B and A549). RNA sequencing showed that the lncRNA functioned as antisense RNA, intergenic RNA and pre-miRNA. The mRNA profiles were also altered after exposure. PM2.5 from Nanjing showed a more serious impact than that from Shanghai. In detail, higher expression of n405968 was positively related to the elevated mRNA levels of inflammatory factors (IL-6 and IL-8). Increasing levels of metastasis associated lung adenocarcinoma transcript 1 (MALAT1) were positively associated with the induced epithelial-mesenchymal transition (EMT) process. Similar response was observed between both cell lines. The higher content of polycyclic aromatic hydrocarbons (PAHs) is likely to contribute to higher toxicity of PM2.5 from Nanjing than that from Shanghai. Antagonism of aryl hydrocarbon receptor (AHR) or inhibition of CYP1A1 diminished the effects stimulated by PM2.5. Our results indicated that lncRNAs could be involved in the toxicology of PM2.5 through regulating the inflammation and EMT process.