q-Generalizations of a family of harmonic number identities

AbstractPaule and Schneider (2003) [3], and Chu (Chu and Donno) (2005) [1] gave a family of wonderful harmonic number identities. Their generalized versions associated with q-harmonic numbers will be established by applying a derivative operator to Watson's q-Whipple transformation

Finite element analysis of gear of full-rotating propeller steering assembly

In order to ensure that the gear of the steering component of the full-rotation propeller meets the limit of bearing capacity and normal use under the action of moment load, this paper takes a steering propeller device as the research object, establishes the finite element model of the gear of the steering component under normal and braking conditions, carries out stress bending strength analysis, and carries out modal analysis. It is verified that the gear of the steering assembly meets the safety requirements, which provides a reference for the design of the steering device

Trends and profiles of acute poisoning cases: a retrospective analysis

Acute poisoning is a significant public health concern. This retrospective study investigates trends in acute poisoning cases and explores the clinical and sociodemographic profiles associated with this condition. Medical data from 859 hospitalized patients diagnosed with acute poisoning between January 2017 and December 2022 were comprehensively analyzed. The descriptive statistical analysis revealed that 360 patients had underlying diseases, with depression being the most prevalent among them. Furthermore, urban areas accounted for 87.2% of the acute poisoning cases, indicating a higher incidence compared to rural areas. The substances implicated in acute poisoning incidents varied, with drugs of abuse being the most common (53.2%), followed by pesticides (22.2%), carbon monoxide (11.8%), and alcohol (5.4%). Suicide attempt/suicide emerged as the leading cause of acute poisoning incidents, accounting for 75.9% of cases, while poisoning accidents predominantly occurred within the home setting. Through chi-square tests, it was determined that risk factors for suicide attempt/suicide included female gender and underlying medical conditions. Temporal analysis showed that the total number of acute poisoning cases increased from 2017 to 2019 and decreased from 2019 to 2022. Notably, suicide-related cases exhibited an upward trend, with suicide attempt/suicide accounting for over 80% of all acute poisoning cases after 2020. This study contributes valuable insights into the trends, profiles, and risk factors associated with acute poisoning cases

A \u3cem\u3eLIN28B\u3c/em\u3e Tumor-Specific Transcript in Cancer

The diversity and complexity of the cancer transcriptome may contain transcripts unique to the tumor environment. Here, we report a LIN28B variant, LIN28B-TST, which is specifically expressed in hepatocellular carcinoma (HCC) and many other cancer types. Expression of LIN28B-TST is associated with significantly poor prognosis in HCC patients. LIN28B-TST initiates from a de novo alternative transcription initiation site that harbors a strong promoter regulated by NFYA but not c-Myc. Demethylation of the LIN28B-TST promoter might be a prerequisite for its transcription and transcriptional regulation. LIN28B-TST encodes a protein isoform with additional N-terminal amino acids and is critical for cancer cell proliferation and tumorigenesis. Our findings reveal a mechanism of LIN28B activation in cancer and the potential utility of LIN28B-TST for clinical purposes

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Exploring the Chinese public's affective attitudes toward digital transformation in agriculture: A social media-based analysis

Used for storing social media data on agricultural digital transformation, as well as data on agricultural total output value and agricultural technology investment

Semantic Segmentation of Polarimetric SAR Image Based on Dual-Channel Multi-Size Fully Connected Convolutional Conditional Random Field

The traditional fully connected convolutional conditional random field has a proven robust performance in post-processing semantic segmentation of SAR images. However, the current challenge is how to improve the richness of image features, thereby improving the accuracy of image segmentation. This paper proposes a polarization SAR image semantic segmentation method based on a dual-channel multi-size fully connected convolutional conditional random field. Firstly, the full-polarization SAR image and the corresponding optical image are input into the model at the same time, which can increase the richness of feature information. Secondly, multi-size input integrates image information of different sizes and models images of various sizes. Finally, the importance of features is introduced to determine the weights of polarized SAR images and optical images, and CRF is improved into a potential function so that the model can adaptively adjust the degree of influence of different image features on the segmentation effect. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method

An Improved SAR Image Semantic Segmentation Deeplabv3+ Network Based on the Feature Post-Processing Module

Synthetic Aperture Radar (SAR) can provide rich feature information under all-weather and day-night conditions because it is not affected by climatic conditions. However, multiplicative speckle noise exists in SAR images, which makes it difficult to accurately identify some fuzzy targets in SAR images, such as roads and rivers, during semantic segmentation. This paper proposes an improved Deeplabv3+ network that can be effectively applied to the semantic segmentation task of SAR images. Firstly, this paper added the attention mechanism and, combined with the idea of an image pyramid, proposed the Feature Post-Processing Module (FPPM) to post-process the network output feature map, obtain better fine image features, and solve the problem of fuzzy texture and spectral features of SAR images. Compared to the original Deeplabv3+ network, the segmentation accuracy has been improved by 3.64% and mIoU improved by 1.09%. Secondly, to solve the problems of limited SAR image data and an unbalanced sample, this paper used the focal loss function to improve the backbone function of the network, which increased the mIoU by 1.01%. Finally, the Atrous Spatial Pyramid Pooling (ASPP) module was improved and the 3 × 3 void convolution in ASPP was decomposed into 2D, which can maintain the void ratio and effectively reduce the calculation amount of the module, shorten the training time by 19 ms and improve the semantic segmentation effect

Semantic Segmentation of Polarimetric SAR Image Based on Dual-Channel Multi-Size Fully Connected Convolutional Conditional Random Field

The traditional fully connected convolutional conditional random field has a proven robust performance in post-processing semantic segmentation of SAR images. However, the current challenge is how to improve the richness of image features, thereby improving the accuracy of image segmentation. This paper proposes a polarization SAR image semantic segmentation method based on a dual-channel multi-size fully connected convolutional conditional random field. Firstly, the full-polarization SAR image and the corresponding optical image are input into the model at the same time, which can increase the richness of feature information. Secondly, multi-size input integrates image information of different sizes and models images of various sizes. Finally, the importance of features is introduced to determine the weights of polarized SAR images and optical images, and CRF is improved into a potential function so that the model can adaptively adjust the degree of influence of different image features on the segmentation effect. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method

Design Method for a Higher Order Extended Kalman Filter Based on Maximum Correlation Entropy and a Taylor Network System

This paper proposes one new design method for a higher order extended Kalman filter based on combining maximum correlation entropy with a Taylor network system to create a nonlinear random dynamic system with modeling errors and unknown statistical properties. Firstly, the transfer function and measurement function are transformed into a nonlinear random dynamic model with a polynomial form via system identification through the multidimensional Taylor network. Secondly, the higher order polynomials in the transformed state model and measurement model are defined as implicit variables of the system. At the same time, the state model and the measurement model are equivalent to the pseudolinear model based on the combination of the original variable and the hidden variable. Thirdly, higher order hidden variables are treated as additive parameters of the system; then, we establish an extended dimensional linear state model and a measurement model combining state and parameters via the previously used random dynamic model. Finally, as we only know the results of the limited sampling of the random modeling error, we use the combination of the maximum correlation estimator and the Kalman filter to establish a new higher order extended Kalman filter. The effectiveness of the new filter is verified by digital simulation