C11H8Cl2INO, monoclinic, P2(1)/c (no. 14), a = 13.633(2) & Aring;, b = 7.8468(13) & Aring;, c = 11.5473(17) & Aring;, beta = 92.727(5)degrees, V = 1233.9(3) & Aring;(3), Z = 4, R-gt(F) = 0.0757, wR(ref)(F2) = 0.1890, T = 173 K.
Abstract C 40 H 40 Cl 4 CoO 2 P 2 , monoclinic, C 2/ c (no. 15), a = 19.0404(11) Å, b = 16.0821(11) Å, c = 14.4363(8) Å, β = 117.886(3)°, V = 3907.2(4) Å 3 , Z = 4, R gt ( F ) = 0.0341 wR ref ( F 2 ) = 0.0835, T = 293 K.
The overburdening of family carers of stroke patients is a globalised problem, and how to reduce the burden on family carers by improving their self-efficacy through family support systems deserves the attention of healthcare providers. To investigate the current status of family caregiver burden, self-efficacy, family functioning and family intimacy in first-ever ischaemic stroke patients in terms of the factors affecting family caregiver burden, and also to analyse the relationship between family caregiver burden, self-efficacy, family functioning and family intimacy, and to explore the statistically significant indirect effect of the relationship between family functioning and family intimacy in the relationship between family caregiver burden and self-efficacy. This cross-sectional study is reported in accordance with the STROBE guidelines. This questionnaire survey was conducted within four tertiary hospitals in Xianyang, Shannxi Province, China. Based on informed consent, a convenience sampling method was used to distribute questionnaires to 946 family caregivers of first-ever ischemic stroke from May 2024 to October 2024, and 915 valid questionnaires were collected. Using IBM SPSS27.0, the data were analysed to perform descriptive statistics, analysis of variance (ANOVA), multiple enter method regression analysis, Spearman’s correlation analysis and PROCESS plug-in for indirect effect analysis. (1) The median family caregiver burden score was 40 (32, 53), while the mean score of their self-efficacy was 25.188 ± 5.609, the mean score of family intimacy was 51.578 ± 8.796, and the median score of family functioning was 7 (5, 10). (2) Family caregiver burden was significantly negatively correlated with their self-efficacy, family intimacy, and family functioning, with r coefficients of -0.510, -0.465, and − 0.519, respectively. (3) Percentile bias-corrected Bootstrap method test showed significant indirect effect of family intimacy, ab =-0.251, BootstrapSE = 0.034, 95
Aiming at the challenges of data imbalance and real-time early warning in chronic disease monitoring, this paper proposes an improved big data analysis and early warning algorithm for chronic diseases based on industrial Internet of Things technology. In this paper, a fusion algorithm based on improved Stacking ensemble learning and LSTM timing prediction is proposed to solve the data imbalance and real-time warning challenges in chronic disease monitoring in the IIoT environment. By introducing the Focal Loss function and Bayesian hyperparameter optimization, the recognition ability of minority samples and the overall performance of the model are significantly improved. On the diabetes dataset, AUC reached 0.912, F1 score increased by 32%, and MSE dropped to 0.24. Experiments show that the average accuracy of the algorithm in 5-fold cross-validation is 86.2%, which is better than the existing baseline method. This study provides an effective and scalable solution for IIoT-driven intelligent management of chronic diseases.
C 19 H 14 NO 4 , monoclinic, P 2 1 / c (no. 14), a = 16.8973(12) Å, b = 5.6188(3) Å, c = 18.1088(15) Å, β = 117.145(10)°, V = 1529.9(2) Å 3 , Z = 4, R gt ( F ) = 0.0420 wR ref ( F 2 ) = 0.1050, T = 200(10) K.
