Hengnian Qi’s research while affiliated with Huzhou University and other places

Background Older adults with Mild Cognitive Impairment (MCI) show decreased flexibility, speed, and fluency in writing, suggesting the feasibility of using quantitative handwriting analysis for MCI detection. Given task variability and the complexities of Chinese versus English characters, investigating Chinese handwriting characteristics is crucial for refining MCI screening approach. Method 259 participants (109 with MCI, 150 controls) were engaged to completed six handwriting tasks using a dot‐matrix digital pen, encompassing four Chinese characters tasks and two graphical drawing tasks. Kinematic handwriting parameters were collected and analyzed using Light GBM, XGBoost, RF, and SVM machine learning models to assess their effectiveness in MCI recognition. Result A total of 48 handwriting characteristics were extracted, of which 12 characteristics derived from graphical drawing tasks and 18 from Chinese characters writing tasks exhibited significant differences between the two groups (P<0.05). Compared to the control group, the MCI group demonstrated a significantly longer mean lag time (Z = ‐2.991, P = 0.003 for graphical drawing tasks; Z = ‐6.441, P<0.001 for Chinese characters tasks), lower average writing velocity for graphical drawing tasks (Z = ‐3.263, P = 0.001), shorter single stroke lengths (Z = ‐2.352, P = 0.019), and decreased writing correctness scores (Z = ‐9.388, P<0.001 for graphical drawing tasks; Z = ‐5.949, P<0.001 for Chinese characters tasks). The accuracy of XGBoost, Light GBM, RF, and SVM machine learning models constructed based on handwriting characteristics were 81.35%, 83.28%, 81.03%, and 63.02%, respectively, with their Area Under the Curve (AUC) values being 0.888, 0.896, 0.895, and 0.570. Conclusion Handwriting characteristics in Chinese writing tasks showed promise for MCI screening in older adults, with the Light GBM model offering improved predictive accuracy, potentially enhancing MCI detection among community‐dwelling older adults. Trial registration: The trial was registered at Chinese Clinical Trials Registry on 05/02/2022 (www.chictr.org.cn; Number Registry: ChiCTR2200059499). Funding: National Natural Science Foundation of China (NO.72174061; NO.71704053), China Scholarship Council Foundation (NO. 202308330251).

Detecting ancient Chinese characters in various media, including stone inscriptions, calligraphy, and couplets, is challenging due to the complex backgrounds and diverse styles. This study proposes an advanced deep-learning framework for detecting ancient Chinese characters in complex scenes to improve detection accuracy. First, the framework introduces an Ancient Character Haar Wavelet Transform downsampling block (ACHaar), effectively reducing feature maps’ spatial resolution while preserving key ancient character features. Second, a Glyph Focus Module (GFM) is introduced, utilizing attention mechanisms to enhance the processing of deep semantic information and generating ancient character feature maps that emphasize horizontal and vertical features through a four-path parallel strategy. Third, a Character Contour Refinement Layer (CCRL) is incorporated to sharpen the edges of characters. Additionally, to train and validate the model, a dedicated dataset was constructed, named Huzhou University-Ancient Chinese Character Dataset for Complex Scenes (HUSAM-SinoCDCS), comprising images of stone inscriptions, calligraphy, and couplets. Experimental results demonstrated that the proposed method outperforms previous text detection methods on the HUSAM-SinoCDCS dataset, with accuracy improved by 1.36–92.84%, recall improved by 2.24–85.61%, and F1 score improved by 1.84–89.08%. This research contributes to digitizing ancient Chinese character artifacts and literature, promoting the inheritance and dissemination of traditional Chinese character culture. The source code and the HUSAM-SinoCDCS dataset can be accessed at https://github.com/youngbbi/AncientGlyphNet and https://github.com/youngbbi/HUSAM-SinoCDCS.

Seed quality is of great importance for agricultural cultivation. High-throughput phenotyping techniques can collect magnificent seed information in a rapid and non-destructive manner. Emerging deep learning technology brings new opportunities for effectively processing massive and diverse data from seeds and evaluating their quality. This article comprehensively reviews the principle of several high-throughput phenotyping techniques for non-destructively collection of seed information. In addition, recent research studies on the application of deep learning-based approaches for seed quality inspection are reviewed and summarized, including variety classification and grading, seed damage detection, components prediction, seed cleanliness, vitality assessment, etc. This review illustrates that the combination of deep learning and high-throughput phenotyping techniques can be a promising tool for collection of various phenotype information of seeds, which can be used for effective evaluation of seed quality in industrial practical applications, such as seed breeding, seed quality inspection and management, and seed selection as a food source.

Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the fruit for firmness) are both subjective and destructive, which tend to diminish the integrity of fruit samples, consequently undermining their market value. Compared to traditional detection methods, nondestructive technology offers efficient and noninvasive solutions for rapidly and accurately assessing internal external quality of peaches. This can significantly enhance product classification and quality assurance while reducing the need for extensive human resources and minimizing potential physical damage to peaches. This review provided a comprehensive overview of nondestructive techniques for peach quality evaluation, including visible/near‐infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric and optical properties, fluorescence spectroscopy, electronic nose/tongue, and acoustic vibration methods. It also evaluates the effectiveness of each technique in assessing internal quality, maturity, and disease detection of peaches. The advantages and limitations of each method were also summarized. This study focuses specifically on peaches and encompasses all existing nondestructive testing methods, providing valuable insights and references for future studies in the field of peach quality analysis using nondestructive testing methods.

BACKGROUND Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotype, resulting in the difficulties of establishing a universal model. In this study, hyperspectral imaging was used to detect the Soil and Plant Analyzer Development (SPAD) values and water content of fresh rice leaves of three rice varieties (Jiahua 1, Xiushui 121 and Xiushui 134). RESULTS Both partial least squares regression and convolutional neural networks were used to establish single‐task and multi‐task models. Transfer component analysis (TCA) was used as transfer learning to learn the common features to achieve an approximate identical distribution between any two varieties. Single‐task and multi‐task models were also built using the features of the source domain, and these models were applied to the target domain. These results indicated that for models of each rice variety the prediction accuracy of most multi‐task models was close to that of single‐task models. As for TCA, the results showed that the single‐task model achieved good performance for all transfer learning tasks. CONCLUSION Compared with the original model, good and differentiated results were obtained for the models using features learned by TCA for both the source domain and target domain. The multi‐task models could be constructed to predict SPAD values and water content simultaneously and then transferred to another rice variety, which could improve the efficiency of model construction and realize rapid detection of rice growth indicators. © 2024 Society of Chemical Industry.

Background Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is not easily detected in the early stage. Handwriting and walking have been shown to be potential indicators of cognitive decline and are often affected by AD. Objective This study proposes an assisted screening framework for AD based on multimodal analysis of handwriting and gait and explores whether using a combination of multiple modalities can improve the accuracy of single modality classification. Methods We recruited 90 participants (38 AD patients and 52 healthy controls). The handwriting data was collected under four handwriting tasks using dot-matrix digital pens, and the gait data was collected using an electronic trail. The two kinds of features were fused as inputs for several different machine learning models (Logistic Regression, SVM, XGBoost, Adaboost, LightGBM), and the model performance was compared. Results The accuracy of each model ranged from 71.95% to 96.17%. Among them, the model constructed by LightGBM had the best performance, with an accuracy of 96.17%, sensitivity of 95.32%, specificity of 96.78%, PPV of 95.94%, NPV of 96.74%, and AUC of 0.991. However, the highest accuracy of a single modality was 93.53%, which was achieved by XGBoost in gait features. Conclusions The research results show that the combination of handwriting features and gait features can achieve better classification results than a single modality. In addition, the assisted screening model proposed in this study can achieve effective classification of AD, which has development and application prospects.