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"Ma, Jian"
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عقيدتي والآخر : \حكايات مسلمي هونغ كونغ\ : نثر
يتخذ هذا الكتاب مسلمي هونغ كونغ كخط أساسيا، حيث يصف مساجدهم ومنازلهم وأعيادهم وحياتهم اليومية وأسلوب معيشتهم، كما يناقش أيضا بعض الاختلافات بين مسلمي هونغ كونغ وسكان البر الرئيس للصين، ويلقي الضوء على عدد من مشاكلهم التي تسببها الاختلافات الثقافية والعرقية، وذلك عن طريق بعض الخواطر النثرية لهذا الكاتب الذي قضى بضع سنوات من حياته في هونغ كونغ.
Book
Revealing Hi-C subcompartments by imputing inter-chromosomal chromatin interactions
Higher-order genome organization and its variation in different cellular conditions remain poorly understood. Recent high-coverage genome-wide chromatin interaction mapping using Hi-C has revealed spatial segregation of chromosomes in the human genome into distinct subcompartments. However, subcompartment annotation, which requires Hi-C data with high sequencing coverage, is currently only available in the GM12878 cell line, making it impractical to compare subcompartment patterns across cell types. Here we develop a computational approach, SNIPER (Subcompartment iNference using Imputed Probabilistic ExpRessions), based on denoising autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. SNIPER accurately reveals subcompartments using moderate coverage Hi-C datasets and outperforms an existing method that uses epigenomic features in GM12878. We apply SNIPER to eight additional cell lines and find that chromosomal regions with conserved and cell-type specific subcompartment annotations have different patterns of functional genomic features. SNIPER enables the identification of subcompartments without high-coverage Hi-C data and provides insights into the function and mechanisms of spatial genome organization variation across cell types.
Genome-wide mapping of chromatin interactions reveals various levels of 3D genome organization. Here, the authors develop SNIPER, a computational method for identifying subcompartments using Hi-C data with moderate coverage.
Journal Article
China dream : a novel
\"Ma Daode is feeling pleased with himself. He has just been appointed Director of the China Dream Bureau, tasked with overwriting people's private dreams with President Xi's great China Dream of national rejuvenation. He has an impressive office, three properties and a bevy of mistresses texting him night and day. But just as Ma Daode is putting the finishing touches to his plan for a mass golden wedding anniversary celebration, things take an uneasy turn. Suddenly plagued by flashbacks of the Cultural Revolution, Ma Daode's nightmares from the past threaten to undo his dream of a glorious future\"-- Provided by publisher.
Book
Research on object detection and recognition in remote sensing images based on YOLOv11
This study applies the YOLOv11 model to train and detect ground object targets in high-resolution remote sensing images, aiming to evaluate its potential in enhancing detection accuracy and efficiency. The model was trained on 70,389 samples across 20 target categories. After 496 training epochs, the loss functions (Box_Loss, Cls_Loss, and DFL_Loss) demonstrated rapid convergence, indicating effective optimization in target localization, classification, and detail refinement. The evaluation metrics yielded a precision of 0.8861, a recall of 0.8563, a map
50
of 0.8920, a map
50–95
of 0.8646, and an F1 score of 0.8709, highlighting the model’s high accuracy and robustness in addressing complex detection tasks. Furthermore, 80% of the test samples achieved confidence scores exceeding 85%, confirming the reliability of YOLOv11 in multiclass and multiobject detection scenarios. These findings suggest that YOLOv11 holds significant promise for remote sensing image target detection, demonstrating exceptional detection performance while offering robust technical support for intelligent remote sensing image analysis. Future studies will focus on expanding the dataset, refining the model architecture, and improving its performance in detecting small targets and processing complex scenes, paving the way for its broader applications in environmental protection, urban planning, and multiobject detection.
Journal Article
A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network
Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.
Journal Article
Perturbative calculations of gravitational form factors at large momentum transfer
A
bstract
We perform a perturbative QCD analysis of the gravitational form factors (GFFs) of nucleon at large momentum transfer. We derive the explicit factorization formula of the GFFs in terms of twist-3 and twist-4 light-cone distribution amplitudes of nucleon. Power behaviors for these GFFs are obtained from the leading order calculations. Numeric results of the quark and gluon contributions to various GFFs are presented with model assumptions for the distribution amplitudes in the literature. We also present the perturbative calculations of the scalar form factor 〈
P
′
|
F
2
|
P
〉 for pion and proton at large momentum transfer.
Journal Article
Prevalence and burden of hepatitis D virus infection in the global population: a systematic review and meta-analysis
ObjectiveHepatitis D virus (HDV) is a defective virus that completes its life cycle only with hepatitis B virus (HBV). The HBV with HDV super-infection has been considered as one of the most severe forms of the chronic viral hepatitis. However, there is a scarcity of data on the global burden of HDV infection.DesignWe searched PubMed, Embase, Cochrane Library and China Knowledge Resource Integrated databases from 1 January 1977 to 31 December 2016. We included studies with a minimum sample size of 50 patients. Our study analysed data from a total of 40 million individuals to estimate the prevalence of HDV by using Der-Simonian Laird random-effects model. The data were further categorised according to risk factors.ResultsFrom a total of 2717 initially identified studies, only 182 articles from 61 countries and regions met the final inclusion criteria. The overall prevalence of HDV was 0.98% (95% CI 0.61 to 1.42). In HBsAg-positive population, HDV pooled prevalence was 14.57% (95% CI 12.93 to 16.27): Seroprevalence was 10.58% (95% CI 9.14 to 12.11) in mixed population without risk factors of intravenous drug use (IVDU) and high-risk sexual behaviour (HRSB). It was 37.57% (95% CI 29.30 to 46.20) in the IVDU population and 17.01% (95% CI 10.69 to 24.34) in HRSB population.ConclusionWe found that approximately 10.58% HBsAg carriers (without IVDU and HRSB) were coinfected with HDV, which is twofold of what has been estimated before. We also noted a substantially higher HDV prevalence in the IVDU and HRSB population. Our study highlights the need for increased focus on the routine HDV screening and rigorous implementation of HBV vaccine programme.
Journal Article