Path analysis of current status and influencing factors of nutritional literacy among community-dwelling older adults.

The aim of this study is to investigate the current status of nutritional literacy among community-dwelling elderly individuals in Hangzhou, Zhejiang Province, China, as well as the associated influencing factors and pathways. The Nutritional Literacy Assessment Scale for community-dwelling older adults and a questionnaire on factors influencing nutritional literacy were utilized. Descriptive statistics, stratified linear regression, and path analysis were employed to determine the influencing factors and pathways of nutritional literacy. The nutritional literacy rate among community-dwelling older adults in Hangzhou was moderate, with functional nutritional literacy being the highest and critical nutritional literacy being the lowest. Factors such as gender, education level, self-efficacy, healthy lifestyles, family and social networks, living environment, and policies were found to influence nutritional literacy. There is scope for improvement in the nutritional literacy level among older adults in the community in Hangzhou, particularly in terms of critical and interactive nutritional literacy.

Attention Module Magnetic Flux Leakage Linked Deep Residual Network for Pipeline In-Line Inspection.

Pipeline operational safety is the foundation of the pipeline industry. Inspection and evaluation of defects is an important means of ensuring the safe operation of pipelines. In-line inspection of Magnetic Flux Leakage (MFL) can be used to identify and analyze potential defects. For pipeline MFL identification with inspecting in long distance, there exists the issues of low identification efficiency, misjudgment and leakage judgment. To solve these problems, a pipeline MFL inspection signal identification method based on improved deep residual convolutional neural network and attention module is proposed. A improved deep residual network based on the VGG16 convolution neural network is constructed to automatically learn the features from the MFL image signals and perform the identification of pipeline features and defects. The attention modules are introduced to reduce the influence of noises and compound features on the identification results in the process of in-line inspection. The actual pipeline in-line inspection experimental results show that the proposed method can accurately classify the MFL in-line inspection image signals and effectively reduce the influence of noises on the feature identification results with an average classification accuracy of 97.7%. This method can effectively improve identification accuracy and efficiency of the pipeline MFL in-line inspection.

Status of traditional Chinese medicine healthcare services in nursing homes across China.

This study aimed to elucidate the status of traditional Chinese medicine (TCM) healthcare services provided in nursing homes across China. We investigated 484 nursing homes using self-compiled questionnaires with a convenient sampling method. Chi-squared and Wilcoxon rank-sum tests were used for univariate analysis and binary logistic regression for multi-factor analysis. Of the 443 nursing homes finally included, 215 (48.5%) provided TCM healthcare services. Nursing home leaders majored in integrated TCM and Western medicine, leaders with a better understanding of TCM and government policies, nursing homes charging over 5,000 CNY/month, and those with ≥500 beds were more likely to provide improved TCM healthcare services. Massage, moxibustion, cupping or scraping, plaster therapy, decocting pieces, and acupuncture were the most prevalent and popular TCM services. Lack of professionals, financial investment, and policy support were the most common factors limiting the provision of TCM healthcare services in Chinese nursing homes.

Compensation Method for Pipeline Centerline Measurement of in-Line Inspection during Odometer Slips Based on Multi-Sensor Fusion and LSTM Network.

The accurate measurement of pipeline centerline coordinates is of great significance to the management of oil and gas pipelines and energy transportation security. The main method for pipeline centerline measurement is in-line inspection technology based on multi-sensor data fusion, which combines the inertial measurement unit (IMU), above-ground marker, and odometer. However, the observation of velocity is not accurate because the odometer often slips in the actual inspection, which greatly affects the accuracy of centerline measurement. In this paper, we propose a new compensation method for oil and gas pipeline centerline measurement based on a long short-term memory (LSTM) network during the occurrence of odometer slip. The field test results indicated that the mean of absolute position errors reduced from 8.75 to 2.02 m. The proposed method could effectively reduce the errors and improve the accuracy of pipeline centerline measurement during odometer slips.

Literature Review: Theory and Application of In-Line Inspection Technologies for Oil and Gas Pipeline Girth Weld Defection.

Girth weld cracking is one of the main failure modes in oil and gas pipelines; girth weld cracking inspection has great economic and social significance for the intrinsic safety of pipelines. This paper introduces the typical girth weld defects of oil and gas pipelines and the common nondestructive testing methods, and systematically generalizes the progress in the studies on technical principles, signal analysis, defect sizing method and inspection reliability, etc., of magnetic flux leakage (MFL) inspection, liquid ultrasonic inspection, electromagnetic acoustic transducer (EMAT) inspection and remote field eddy current (RFDC) inspection for oil and gas pipeline girth weld defects. Additionally, it introduces the new technologies for composite ultrasonic, laser ultrasonic, and magnetostriction inspection, and provides reference for development and application of oil and gas pipeline girth weld defect in-line inspection technology.

[Research on certified reference material of emodin in rhubarb and its alcohol extract, water extract].

The certified reference materials (CRMs) of emodin in rhubarb and its alcohol extract, water extract were developed by using quantity transfer technology from single chemical composition to the complex systems. The CRM of emodin was used for quantity transfer, and high performance liquid chromatography (HPLC) method was used to determine the contents of emodin in different matrix composition. By establishing mathematical model and calculating the parts of uncertainty, the uncertainty values were finally gotten. CRMs of emodin in rhubarb, alcohol extract and water extract were accomplished. The content values of emodin were 0.40% ±0.03%, 1.15%±0.18%, 0.16%±0.08% (k=2,P=0.95), respectively. The established method for quantity transfer has successfully solved the technical problems that the value of active ingredient of traditional Chinese medicine can't be traced to SI units. The series of CRMs are assigned as grade primary reference materials, which are useful for quality control of the emodin content, also provide the accurate and reliable CRM, materials standard and standard methods.

Path analysis of the awareness status and influencing factors of sarcopenia in older adults in the community: based on structural equation modeling.

Background: Sarcopenia is a progressive geriatric syndrome that impacts older adults' quality of life. Insufficient focus has been given to sarcopenia among Chinese residents, resulting in low level of sarcopenia awareness. This study aims to investigate awareness of sarcopenia and its influencing factors and the influencing pathways among older adults in Hangzhou. Methods: A stratified random sample of 942 community-dwelling older adults was evaluated using the SARC-CalF screening tool, along with a questionnaire based on health ecology theory to assess awareness of sarcopenia and its influencing factors and the influencing pathways. Descriptive statistics, linear regression analysis, and path analysis were conducted using SPSS 25.0 and Amos 23.0 to analyze the data. Results: The mean awareness score for sarcopenia was 60.26 ± 7.31. Self-rated physical health, daily intake of high-quality protein, exercise frequency, smoking status, self-efficacy, religious beliefs, social support, education level, occupation, participation in community free medical examinations, and awareness of nutrition policy were all factors affecting scores for sarcopenia awareness (p < 0.05). Except for negative effects observed in social support, smoking status, and self-rated physical health, all others exhibit positive effects. Conclusion: Community healthcare institutions should target populations with low awareness of sarcopenia and focus on these key factors. Diverse health education programs and multi-channel screening activities can promote awareness, guide healthy lifestyles and prevent or delay the onset of sarcopenia in the older.

Lindenane disesquiterpenoids with anti-HIV-1 activity from Chloranthus japonicus.

Five new lindenane disesquiterpenoids, chlorajaponilides A-E (1-5), together with 11 known analogues were isolated from whole plants of Chloranthus japonicus. The structure and absolute configuration of 1 was confirmed by X-ray crystallography. Compounds 1 and 2 represent the first examples of lindenane disesquiterpenoids with a C-5 hydroxy group and a C-4-C-15 double bond. Compounds 8, 9, 11, and 12 showed anti-HIV-1 replication activities in both wild-type HIV-1 and two NNRTIs-resistant strains. Shizukaol B (8) exhibited the best activity against HIV(wt), HIV(RT-K103N), and HIV(RT-K103N) with EC50 values of 0.22, 0.47, and 0.50 µM, respectively. Compounds 8, 9, 11, and 12 had significant cytotoxicities against C8166 cells with CC50 values of 0.020, 0.089, 0.047, and 0.022, respectively, and exhibited inhibitory activities against HIV-1 with EC50 values of 0.0014, 0.016, 0.0043, and 0.0033 µM, respectively.

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification.

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.