Predictive value of the controlling nutritional status (CONUT) score to assess long-term mortality (10 Years) in patients with hypertension.

BACKGROUND AND AIMS: Malnutrition is associated with poor outcomes in patients with chronic diseases. The aim of this study is to investigate the prevalence of malnutrition in patients with hypertension and relationship between malnutrition severity and long-term mortality in these patients. METHODS AND RESULTS: The study included 11,278 patients with hypertension from the National Health and Nutrition Examination Survey database. The degree of malnutrition was assessed using the Controlled Nutritional Status score, with patients divided into normal, mild, and moderate-to-severe groups. After 10 years of follow-up, the results showed that patients who died had higher CONUT scores, poorer nutritional status, and lower albumin, total cholesterol, and lymphocytes than those who survived (P < 0.05). The Kaplan-Meier analysis revealed that patients with poor nutritional status had a significantly higher risk of all-cause death. In the Non-Lipid Lowering Drugs group, the CONUT score (hazard ratio (HR): 1.225; 95% confidence interval (CI): 1.162-1.292; P < 0.0001), as well as mild (HR: 1.532; 95% CI 1.340-1.751; P < 0.0001) and moderate-to-severe malnutrition (HR: 2.797; 95% CI: 1.441-5.428; P = 0.0024), were independent predictors of long-term mortality. The competing risk regression models showed that cardiovascular and cerebrovascular mortality increased with increasing CONUT scores. The results were robust in both subgroup and sensitivity analyses. CONCLUSIONS: Malnutrition significantly impacts long-term mortality in hypertensive patients. The CONUT score may be a useful tool for assessing the nutritional status of patients with hypertension in the non-lipid-lowering population and for predicting their long-term mortality.

Fuzzy State-Driven Cross-Time Spatial Dependence Learning for Multivariate Time-Series Anomaly Detection.

Cross-time spatial dependence (i.e., the interaction between different variables at different time points) is indispensable for detecting anomalies in multivariate time series, as certain anomalies may have time delays in their propagation from one variable to another. However, accurately capturing cross-time spatial dependence remains a challenge. Specifically, real-world time series usually exhibits complex and incomprehensible evolutions that may be compounded by multiple temporal states (i.e., temporal patterns, such as rising, fluctuating, and peak). These temporal states mix and overlap with each other and exhibit dynamic and heterogeneous evolution laws in different time series, making the cross-time spatial dependence extremely intricate and mutable. Therefore, a cross-time spatial graph network with fuzzy embedding is proposed to disentangle latent and mixing temporal states and exploit it to meticulously learn cross-time spatial dependence. First, considering that temporal states are diversiform and their mixing modes are unknown, we introduce a fuzzy state set to uniformly characterize potential temporal states and adaptively generate corresponding membership degrees to depict how these states mix. Further, we propose a cross-time spatial graph, quantifying similarities among fuzzy states and sensing their dynamic evolutions, to flexibly learn mutable cross-time spatial dependence. Finally, we design state diversity and temporal proximity constraints to ensure the differences among fuzzy states and the evolution continuity of fuzzy states. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art models.

Towards consensual representation: Model-agnostic knowledge extraction for dual heterogeneous federated fault diagnosis.

Federated fault diagnosis has attracted increasing attention in industrial cloud-edge collaboration scenarios, where a ubiquitous assumption is that client models have the same architecture. Practically, this assumption cannot always be fulfilled due to requirements for personalized models, thereby resulting in the problem of model heterogeneity. Many approaches dealing with heterogeneous models tend to neglect the issue of representation bias, particularly in the context of non-identically and independently distributed data. In this article, to address the representation bias problem, Federated Model-Agnostic Knowledge Extraction (FedMAKE) is proposed. To bridge the information gap among clients, different from methods with public datasets, we initially develop two novel architecture-independent knowledge carriers. These carriers are derived based on the importance of process variables, without the need for additional datasets. Subsequently, we introduce a bi-directional distillation algorithm utilizing the two knowledge carriers. This algorithm facilitates the mutual transfer of knowledge embedded in carriers between a generative network and client models, thereby enabling the generation of fault data that is unbiased and well-balanced across categories. Furthermore, to mitigate the impact of statistical heterogeneity, we formulate a local objective for each client using two global knowledge carriers to guide local knowledge extraction and constrain client drift. Extensive experiments conducted on two prevalent industry datasets (TE and CWRU) illustrate that our proposed FedMAKE outperforms baseline methods. Specifically, FedMAKE enhances fault diagnosis accuracy by up to 11.7% on the TE dataset and up to 3.31% on the CWRU dataset compared to the sub-optimal method.

Research on mechanics and acoustic properties of Jute fiber composite material.

In this study, the loss of quality from the oxidative thermal decomposition of jute fiber was explored during the production of reinforced composite materials. Amino silicone oil was used to modify jute fiber, which was then subjected to thermogravimetric analysis. The modified fiber's thermal decomposition temperature was found to be 271 °C, enhancing the composite's thermal stability. The study also investigated how different jute fiber content affected the mechanical and sound absorption properties of composite materials. Results showed that jute fiber composites had better mechanical properties than pure polypropylene materials, and the average sound absorption coefficient of jute polypropylene composites increased with fiber content. Adding jute fiber to polypropylene effectively improved the sound absorption and noise reduction performance of the material. The average sound absorption coefficient of the composite material at a mass content of 20 wt% was 120 % higher than that of the polypropylene matrix material.