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In order to achieve impact load localization of complex structures such as ships, this paper proposes a multi-scale feature fusion convolutional neural network (MSFF-CNN) method for impact load localization. An end-to-end machine learning model is used, where the raw vibration signals of impact loads are directly fed into the network model to avoid the process of feature extraction. Automatic feature learning and feature concatenation of the signal are achieved through four independent convolutional layers, each using a different size of convolutional kernel. Data normalization and L2 regularization techniques are introduced to enhance the data and prevent overfitting. Classification and localization of impact loads are accomplished using a softmax classification layer. Validation experiments are carried out using a ship's stern compartment model. Our results show that the classification and localization accuracy of the impact load sample group of MSFF-CNN reaches 94.29% compared with a traditional CNN. The method further improves the ability of the network to extract state features, takes local perception and global vision into account, effectively improves the classification ability of the model, and has good prospects for engineering applications. [ABSTRACT FROM AUTHOR]

Inherited retinal degenerations (IRDs) are a group of genetic disorders characterized by the progressive degeneration of retinal cells, leading to irreversible vision loss. SLC4A7 has emerged as a candidate gene associated with IRDs, yet its mechanisms remain largely unknown. This study aims to investigate the role of slc4a7 in retinal development and its associated molecular pathogenesis in zebrafish. Morpholino oligonucleotide knockdown, CRISPR/Cas9 genome editing, quantitative RT-PCR, eye morphometric measurements, immunofluorescent staining, TUNEL assays, visual motor responses, optokinetic responses, rescue experiments, and bulk RNA sequencing were used to assess the impact of slc4a7 deficiency on retinal development. Our results demonstrated that the knockdown of slc4a7 resulted in a dose-dependent reduction in eye axial length, ocular area, and eye-to-body-length ratio. The fluorescence observations showed a significant decrease in immunofluorescence signals from photoreceptors and in mCherry fluorescence from RPE in slc4a7-silenced morphants. TUNEL staining uncovered the extensive apoptosis of retinal cells induced by slc4a7 knockdown. Visual behaviors were significantly impaired in the slc4a7-deficient larvae. GO and KEGG pathway analyses reveal that differentially expressed genes are predominantly linked to aspects of vision, ion channels, and phototransduction. This study demonstrates that the loss of slc4a7 in larvae led to profound visual impairments, providing additional insights into the genetic mechanisms predisposing individuals to IRDs caused by SLC4A7 deficiency. [ABSTRACT FROM AUTHOR]

In addressing the challenging issue of impact source localization for large-scale anisotropic stiffened compartmental cylindrical shell structures, this paper presents a novel impact localization method. The method is based on a time-reversal virtual focusing triangulation approach and does not rely on prior knowledge of the structure or specific measurements of wave velocity. By employing energy power filtering to select key sensors, wavelet packet decomposition is utilized to extract narrowband Lamb wave signals, which are then synthesized. Further enhancement of signal recognition is achieved through time-reversal amplification techniques. Experimental results demonstrate that under non-motorized operating conditions, this method achieves an average error of 0.89 m. Under motorized operating conditions, the average error is 1.12 m. Although the presence of background noise leads to an increase in error, the overall localization performance is superior to traditional triangulation methods. Additionally, selecting the top three sensors in terms of energy power ranking can more accurately record impact response. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate the risk factors of postmenopausal special uterine leiomyoma pathological types or leiomyosarcoma and to develop a nomogram for clinical risk assessment, ultimately to reduce unnecessary surgical interventions and corresponding economic expenses. Methods: A total of 707 patients with complete information were enrolled from 1 August 2012 to 1 August 2022. Univariate and multivariate logistic regression models were used to analyse the association between variables and special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. A nomogram for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients was developed and validated by bootstrap resampling. The calibration curve was used to assess the accuracy of the model and receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were compared with the clinical experience model. Results: The increasing trend after menopause, the diameter of the largest uterine fibroid, serum carcinoembryonic antigen 125 concentration, Serum neutrophil to lymphocyte ratio, and Serum phosphorus ion concentration were independent risk factors for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. We developed a user-friendly nomogram which showed good diagnostic performance (AUC=0.724). The model was consistent and the calibration curve of our cohort was close to the ideal diagonal line. DCA indicated that the model has potential value for clinical application. Furthermore, our model was superior to the previous clinical experience model in terms of ROC and DCA. Conclusion: We have developed a prediction nomogram for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. This nomogram could serve as an important warning signal and evaluation method for special uterine leiomyoma pathological types or leiomyosarcoma in postmenopausal patients. [ABSTRACT FROM AUTHOR]

In order to comprehensively evaluate and analyze the effectiveness of various heuristic intelligent optimization algorithms, this research employed particle swarm optimization, wind driven optimization, grey wolf optimization, and one-to-one-based optimizer as the basis. It applied 22 benchmark test functions to conduct a comparison and analysis of performance for these algorithms, considering descriptive statistics such as convergence speed, accuracy, and stability. Additionally, time and space complexity calculations were employed, alongside the nonparametric Friedman test, to further assess the algorithms. Furthermore, an investigation into the impact of control parameters on the algorithms' output was conducted to compare and analyze the test results under different algorithms. The experimental findings demonstrate the efficacy of the aforementioned approaches in comprehensively analyzing and comparing the performance on different types of intelligent optimization algorithms. These results illustrate that algorithm performance can vary across different test functions. The one-to-one-based optimizer algorithm exhibited superior accuracy, stability, and relatively lower complexity. [ABSTRACT FROM AUTHOR]