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With the rapid advancement of computer vision technology, traditional manual methods of reading meters are increasingly being replaced by automated water meter reading technologies based on image recognition. This technology can precisely locate and recognize the readings on captured images of water meter dials, laying a solid technical foundation for the implementation of remote automatic meter reading systems. However, in practical applications, the recognition of water meter readings still faces challenges due to interference from factors such as shooting angles and changes in environmental lighting. To address these challenges, this paper proposes an innovative method based on deep learning. Firstly, the ResNet-based Feature Pyramid Network (FPN) is used to detect the reading area of the water meter to ensure the accuracy of the detection. For the problem of digit character detection, the character detection attention mechanism is introduced to improve the performance of digit detection and reduce the interference of background noise while ensuring high accuracy. For numerical character recognition, the improved LeNet-5 network can better identify water meter readings in natural scenes. Additionally, the integration of a global average pooling layer within the network effectively alleviates the issue of overfitting. To verify the effectiveness of our method, we conducted experiments on the CCF real-world water meter reading automatic identification dataset. The experimental results show that by scaling the water meter reading area and introducing the character attention mechanism to assist in numerical character detection, the recognition accuracy of individual digits improved by 8.8% and 5.5%, respectively, and the overall recognition accuracy of the final water meter reading also increased by 7.0% and 2.2%. These significant improvements demonstrate the superiority and effectiveness of our method in practical applications.

The precise detection of effluent biological oxygen demand (BOD) is crucial for the stable operation of wastewater treatment plants (WWTPs). However, existing detection methods struggle to meet the evolving drainage standards and management requirements. To address this issue, this paper proposed a multivariable probability density-based auto-reconstruction bidirectional long short-term memory (MPDAR-Bi-LSTM) soft sensor for predicting effluent BOD, enhancing the prediction accuracy and efficiency. Firstly, the selection of appropriate auxiliary variables for soft-sensor modeling is determined through the calculation of k-nearest-neighbor mutual information (KNN-MI) values between the global process variables and effluent BOD. Subsequently, considering the existence of strong interactions among different reaction tanks, a Bi-LSTM neural network prediction model is constructed with historical data. Then, a multivariate probability density-based auto-reconstruction (MPDAR) strategy is developed for adaptive updating of the prediction model, thereby enhancing its robustness. Finally, the effectiveness of the proposed soft sensor is demonstrated through experiments using the dataset from Benchmark Simulation Model No.1 (BSM1). The experimental results indicate that the proposed soft sensor not only outperforms some traditional models in terms of prediction performance but also excels in avoiding ineffective model reconstructions in scenarios involving complex dynamic wastewater treatment conditions.

Enhancing generalization capabilities and robustness in scenarios with limited sample sizes, while simultaneously decreasing reliance on extensive and high-quality datasets, represents a significant area of inquiry within the domain of radar target recognition. This study introduces a few-shot learning framework that leverages multimodal feature fusion. We develop a cross-modal representation optimization mechanism tailored for the target recognition task by incorporating natural resonance frequency features that elucidate the target’s scattering characteristics. Furthermore, we establish a multimodal fusion classification network that integrates bi-directional long short-term memory and residual neural network architectures, facilitating deep bimodal fusion through an encoding-decoding framework augmented by an energy embedding strategy. To optimize the model, we propose a cross-modal equilibrium loss function that amalgamates similarity metrics from diverse features with cross-entropy loss, thereby guiding the optimization process towards enhancing metric spatial discrimination and balancing classification performance. Empirical results derived from simulated datasets indicate that the proposed methodology achieves a recognition accuracy of 95.36% in the 5-way 1-shot task, surpassing traditional unimodal image and concatenation fusion feature approaches by 2.26% and 8.73%, respectively. Additionally, the inter-class feature separation is improved by 18.37%, thereby substantiating the efficacy of the proposed method.

Background and aims Changes in snow cover can influence root growth and distribution of herbaceous species in water limiting desert ecosystems. However, how the growth of root systems of herbaceous species responds to snow cover changes remains unclear. Thus, the present study was aimed to examine the influence of snow cover changes on root growth of herbaceous species in a temperate desert of central Asia. Methods Plots with four snow cover depth treatments in winter were investigated in the Gurbantunggut Desert. The four treatments were snow removal (− S), ambient snow, double snow (+ S), and triple snow (+ 2S). We examined the root growth of two typical herbaceous species: one ephemeral species, Erodium oxyrhinchum , and one annual species, Ceratocarpus arenarius . Result The root length of the annual plant was significantly reduced by snow removal compared with the ambient treatment. The specific root length and specific surface area of the ephemeral plants increased with increasing snow depth, whereas the annual plants showed the opposite trends. Snow removal significantly increased the root–shoot ratio of the annual plants, with no effects found in the ephemeral plants. The individual root biomass and total underground biomass of the two species had similar responses to the snow depth treatments, with the highest values found with the ambient treatment. Conclusions These results can contribute to explaining to changing winter snow cover depth can alter plant growth, community structure, and ecosystem function in the growing season in temperate desert ecosystems.

Background Preoperative assessment of lymphovascular invasion(LVI) of rectal cancer has very important clinical significance. However, accurate preoperative imaging evaluation of LVI is highly challenging because the resolution of MRI is still limited. Relatively few studies have focused on prediction of LVI of rectal cancer with the tool of radiomics, especially in patients with negative statue of MRI-based extramural vascular invasion (mrEMVI).The purpose of this study was to explore the preoperative predictive value of biparametric MRI-based radiomics features for LVI of rectal cancer in patients with the negative statue of mrEMVI. Methods The data of 146 cases of rectal adenocarcinoma confirmed by postoperative pathology were retrospectively collected. In the cases, 38 had positive status of LVI. All patients were examined by MRI before the operation. The biparametric MRI protocols included T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). We used whole-volume three-dimensional method and two feature selection methods, minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO), to extract and select the features. Logistics regression was used to construct models. The area under the receiver operating characteristic curve (AUC) and DeLong’s test were used to evaluate the diagnostic performance of the radiomics based on T2WI and DWI and the combined models. Results Radiomics models based on T2WI and DWI had good predictive performance for LVI of rectal cancer in both the training cohort and the validation cohort. The AUCs of the T2WI model were 0.87 and 0.87, and the AUCs of the DWI model were 0.94 and 0.92. The combined model was better than the T2WI model, with AUCs of 0.97 and 0.95. The predictive performance of the DWI model was comparable to that of the combined model. Conclusions The radiomics model based on biparametric MRI, especially DWI, had good predictive value for LVI of rectal cancer. This model has the potential to facilitate the clinical recognition of LVI in rectal cancer preoperatively.

Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus .

Studies of cultured embryos have provided insights into human peri-implantation development. However, detailed knowledge of peri-implantation lineage development as well as underlying mechanisms remains obscure. Using 3D-cultured human embryos, herein we report a complete cell atlas of the early post-implantation lineages and decipher cellular composition and gene signatures of the epiblast and hypoblast derivatives. In addition, we develop an embryo-like assembloid (E-assembloid) by assembling naive hESCs and extraembryonic cells. Using human embryos and E-assembloids, we reveal that WNT, BMP and Nodal signaling pathways synergistically, but functionally differently, orchestrate human peri-implantation lineage development. Specially, we dissect mechanisms underlying extraembryonic mesoderm and extraembryonic endoderm specifications. Finally, an improved E-assembloid is developed to recapitulate the epiblast and hypoblast development and tissue architectures in the pre-gastrulation human embryo. Our findings provide insights into human peri-implantation development, and the E-assembloid offers a useful model to disentangle cellular behaviors and signaling interactions that drive human embryogenesis.

To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective study included TNBC patients from the First Affiliated Hospital of Soochow University and Jiangsu Province Hospital (2016–2023). We analyzed clinical characteristics and radiomic features from T2-weighted MRI. Using LASSO regression for feature selection, we applied Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) to build prediction models. A total of 163 patients, with a median age of 53 years (range: 24–73), were divided into a training group ( n  = 115) and a validation group ( n  = 48). Among them, 54 (33.13%) had ALN metastasis, and 109 (66.87%) were non-metastasis. Nottingham grade ( P  = 0.005), tumor size ( P  = 0.016) were significant difference between non-metastasis cases and metastasis cases. In the validation set, the LR-based combined model achieved the highest AUC (0.828, 95%CI: 0.706–0.950) with excellent sensitivity (0.813) and accuracy (0.812). Although the RF-based model had the highest AUC in the training set and the highest specificity (0.906) in the validation set, its performance was less consistent compared to the LR model. MRI-T2WI radiomic features predict ALN metastasis in TNBC, with integration into clinical models enhancing preoperative predictions and personalizing management.

Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system’s bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system’s resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems.

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNNs) in the frequency domain is often constrained by memory limitations and the stringent real-time requirements of embedded systems. To surmount these obstacles, we introduce the Split_ Composite method, an innovative convolution acceleration technique grounded in Fast Fourier Transform (FFT). This method employs input block decomposition and a composite zero-padding approach to streamline memory bandwidth and computational complexity via optimized frequency-domain convolution and image reconstruction. By capitalizing on FFT’s inherent periodicity to augment frequency resolution, Split_ Composite facilitates weight sharing, curtailing both memory access and computational demands. Our experiments, conducted using the OpenSARShip-4 dataset, confirm that the Split_ Composite method upholds high recognition precision while markedly enhancing inference velocity, especially in the realm of large-scale data processing, thereby exhibiting exceptional scalability and efficiency. When juxtaposed with state-of-the-art convolution optimization technologies such as Winograd and TensorRT, Split_ Composite has demonstrated a significant lead in inference speed without compromising the precision of recognition.