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Background Older people with chronic diseases in the community have a higher risk of impaired cognitive function, which seriously threatens their quality of life. Improving cognitive function is crucial for enhancing their quality of life. However, there is heterogeneity in the forms of individual cognitive impairment, and how to effectively improve their cognitive function needs to be further explored. Objective To explore the Latent class of cognitive function among older people with chronic diseases in the community, and analyze the influence factors of different categories, to provide a reference for formulating targeted cognitive function improvement strategies for older people with chronic diseases in the community. Methods Convenience sampling was used to recruit 502 older people with chronic diseases in the community who visited the Tianjin University of Traditional Chinese Medicine Affiliated Baokang Hospital from September to December 2022, using the General Information Questionnaire, Mini-mental State Examination (MMSE), Activity of Daily Living Scale (ADLS), Social Support Rating Scale (SSRS), and Cognitive Reserve Questionnaire (CR). Mplus 8.3 was used to conduct a latent class analysis of cognitive function among older people with chronic diseases in the community and establish a subgroup model. Univariate analysis and unordered multiple Logistic regression were used to analyze the influence factors of cognitive function among older people with chronic diseases in different categories. Results In this study, a total of 526 questionnaires were distributed and 502 valid questionnaires were recovered, with an effective response rate of 95.44%. The cognitive function among 502 older people with chronic diseases in the community was classified into three categories: high cognition with memory impairment group (n=253), moderate cognition with attention impairment group (n=158), and low cognition with overall impairment group (n=91). Unordered multivariate Logistic regression analysis showed that the high cognition with memory impairment group compared to the low cognition with overall impairment group, older people aged 60-74 years (OR=2.315, 95%CI=1.163-4.608, P=0.017), with two chronic diseases (OR=4.105, 95%CI=1.807-9.327, P=0.001), normal blood pressure (OR=2.763, 95%CI=1.414-5.400, P=0.003), normal blood lipids (OR=2.016, 95%CI=1.014-4.008, P=0.046), higher CR scores (OR=1.031, 95%CI=1.005-1.057, P=0.018), and higher SSRS scores (OR=1.055, 95%CI=1.007-1.105, P=0.023) were more likely to belong to the high cognition with memory impairment group, while those with higher ADLS scores (OR=0.920, 95%CI=0.885-0.957, P

The harsh environment of high dust and low lighting in the coal mine underground excavation working face results in low signal-to-noise ratio of the image, and a serious reduction in the number of effective feature points. The processed image has significant color difference and noise. When using the best seam-line algorithm for image stitching, there are problems such as fine section misalignment, unnatural transitions at the seam line, or obvious stitching traces. In order to solve the above problems, a mine image stitching method based on improved best seam-line is proposed. Firstly, the original image is subjected to HSV spatial transformation, and an improved Retinex algorithm is used for enhancement on the luminance component. Bilateral filtering is used instead of the center surround function to solve the halo problem caused by large brightness differences. The number of feature points extracted is effectively increased through the enhancement algorithm. Secondly, the SIFT algorithm is used to extract feature points, and cosine distance is used as the matching degree indicator. The method introduces pixel cosine similarity as a constraint, and uses morphological operations to improve color difference intensity, uses dynamic programming to search for the best seam-line to avoid misalignment at image stitching. Finally, combined with the gradual in and out algorithm, the image transition is smooth to achieve image fusion of the underground excavation working face. Experimental verification is conducted by simulating the actual working environment underground. The results show that the mine image stitching method based on the improved best seam-line avoids the phenomenon of misalignment stitching caused by color differences and noise compared to the traditional best seam-line algorithm. The image transition at the stitching seam is more natural, avoiding the generation of 'ghosts' and obvious stitching seams. The average gradient of the image is increased by about 2.38%, and the stitching time is increased by about 32.5%, making the fusion area smoother and more natural, improving the stitching quality.

In response to the problems of long debugging cycle, high debugging cost, high safety risk, and difficult quality evaluation in the current automatic forming and cutting control of roadways, a digital twin driven virtual debugging method for roadway automatic forming and cutting is proposed. Firstly, by using real-time appearance mapping (RTAP-MAP) technology to reconstruct the three-dimensional environment of the roadway, a control system model of the roadheader is constructed to form a virtual debugging environment. The virtual sensor technology is used to achieve precise mapping from physical space to virtual environment state. A performance evaluation method for roadway automatic forming cutting has been established to address the problem of difficulty in quantitatively evaluating the quality of section forming. Based on the recording of the section forming cutting control process in the data transmission center, the evaluation indicators of section forming precision, cutting efficiency and the number of oil cylinder switches, hard rock cutting adjustment, and over excavation and under excavation are mainly calculated. This provides precise feedback signals for the iterative optimization of deep learning algorithms. An automatic cutting control strategy that integrates reinforcement learning is proposed to improve the adaptability and precision of automated operations. To verify the effectiveness and accuracy of the virtual debugging method, an automatic control experimental platform for roadheader is built. The virtual debugging system is applied to the automatic control program for forming cutting of excavation roadway. The virtual simulation results show the following points. ① The maximum positioning errors of the X, Y, and Z axes of the debugged software at the control key points are 74.8, 72.93, 123.67 mm, respectively. It indicates that the positioning precision of the virtual debugging method meets the performance requirements. ② The trajectory of the virtual prototype is basically consistent with that of the physical prototype, indicating that this debugging method has achieved mapping to the physical space. The application result shows the following points. ① The reinforcement learning controller adapts to complex environments in virtual excavation testing, effectively converts virtual sensor inputs into precise control instructions, and verifies the feasibility of simulation reality transfer training. By processing real-time feedback on excavation precision and avoiding over excavation and under excavation, the controller learns and optimizes the strategy. ② The improved section forming cutting control performance has been improved. According to the control quantity timestamp records in the database, it takes 126 seconds, which is 8 seconds less than before the improvement. ③ After improvement, the maximum error in tracking the end trajectory of the cutting section is 6.0 mm, which is 0.3 mm lower than before. This avoids the under excavation caused by the shaking of the cutting trajectory and makes the trajectory and section smoother.

The earliest experiences of children can ensure their future success, and parenting is noted to be an influential factor (Bronfenbrenner, 1979; Lamb et al., 2002). Many researchers theorized that parental involvement could encourage children to actively engage and improve their academic achievement in schools (Epstein, 2018). However, less attention was paid to the significance of parental involvement in infants' development. This article explores the role of parental involvement in infants' development by reviewing and discussing abundant relevant studies. This study recommends that parental involvement should be considered as an affluent resource and a useful tool because it could provide rich information about infants' individual needs and positively help infant teachers to improve their instructional skills.