Centralized H∞ Fusion Filter Design in Multi-sensor Nonlinear Fusion System

Abstract-Centralized nonlinear fusion filter design problem is to construct an asymptotically stable observer that leads to a stable estimation error process whose L 2 gain with respect to disturbance signal is less than a prescribed level for state estimation of the multi-sensor fusion system. This paper tries to resolve the problem in using of H ∞ filtering theory and LMI methods under certain conditions. Finally, a simulation example is given to illustrate the effectiveness of our methods

Neuronal aging causes mislocalization of splicing proteins and unchecked cellular stress

Aging is one of the most prominent risk factors for neurodegeneration, yet the molecular mechanisms underlying the deterioration of old neurons are mostly unknown. To efficiently study neurodegeneration in the context of aging, we transdifferentiated primary human fibroblasts from aged healthy donors directly into neurons, which retained their aging hallmarks, and we verified key findings in aged human and mouse brain tissue. Here we show that aged neurons are broadly depleted of RNA-binding proteins, especially spliceosome components. Intriguingly, splicing proteins-like the dementia- and ALS-associated protein TDP-43-mislocalize to the cytoplasm in aged neurons, which leads to widespread alternative splicing. Cytoplasmic spliceosome components are typically recruited to stress granules, but aged neurons suffer from chronic cellular stress that prevents this sequestration. We link chronic stress to the malfunctioning ubiquitylation machinery, poor HSP90α chaperone activity and the failure to respond to new stress events. Together, our data demonstrate that aging-linked deterioration of RNA biology is a key driver of poor resiliency in aged neurons

PerAF-based resting-state fMRI classifier for minimal hepatic encephalopathy

BackgroundMinimal hepatic encephalopathy (MHE) is a common cognitive impairment in patients with end-stage liver cirrhosis. However, the selection of sensitive biomarkers and the establishment of reliable diagnostic methods are currently challenging. We aimed to explore the abnormal spontaneous brain activity in patients with MHE and evaluate the clinical diagnostic value of four indicators for MHE using the support vector machine (SVM) method.MethodsA total of 45 MHE patients and 40 healthy controls were enrolled. Amplitude of low frequency fluctuation (ALFF), fractional amplitude of low frequency fluctuation (fALFF), percentage amplitude of low frequency fluctuation (PerAF), and regional homogeneity (ReHo) were used to evaluate local spontaneous brain activity. SVM analysis was used to construct the classification model and evaluate the diagnostic value.ResultsTwo-sample t-test and SVM analysis showed that, compared with the healthy control group, MHE patients had decreased ALFF values in the left angular gyrus, right inferior temporal gyrus, left postcentral gyrus, precentral gyrus, and right supplementary motor area. These regions indicated moderate classification efficacy (AUC = 0.75). Decreased ReHo metrics in the right anterior cingulate and paracingulate gyri also showed general discriminative power (AUC = 0.72). fALFF metrics, whether analyzed independently or combined with other indicators, exhibited limited classification performance (AUC < 0.70). Decreased PerAF metrics in the right superior parietal lobule, right dorsolateral prefrontal cortex, and right middle frontal gyrus achieved a good classification accuracy rate (AUC value 0.83; accuracy 81.18%; sensitivity 75.56%; specificity 87.50%), outperforming other functional metrics.ConclusionWe found that decreased mean PerAF in the right supramarginal gyrus, right dorsolateral superior frontal gyrus, and right middle frontal gyrus may serve as potential neuroimaging indicators for early identification of cognitive impairment in MHE patients, providing critical evidence for clinical screening protocols

Association of Yogurt and Dietary Supplements Containing Probiotic Consumption With All-Cause and Cause-Specific Mortality in US Adults: A Population-Based Cohort Study

BackgroundAlthough probiotic intake had beneficial effects on several specific disorders, limited evidence was available about the benefits of probiotic intake in the general population. This study aimed to evaluate the relationship between yogurt (as a natural probiotic source) and dietary supplements containing probiotic consumption and mortality in US adults.MethodsWe conducted an observational cohort study comprised of a nationally representative sample of adults who were enrolled in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2014. Individuals were linked to the US National Death Index.ResultsWe included 32,625 adults in our study. Of the study cohort, 3,539 participants had yogurt consumption, 213 had dietary supplements containing probiotic consumption, and the remaining participants (28,873) did not have yogurt and/or dietary supplements containing probiotic consumption. During 266,432 person-years of follow-up, 3,881 deaths from any cause were ascertained, of which 651 were due to cardiovascular disorders and 863 were due to cancer. Weighted Cox proportional hazards models suggested that yogurt consumption was inversely associated with all-cause mortality (adjusted hazard ratio (HR), 0.83 [95% confidence interval (CI), 0.71–0.98]) but not cardiovascular mortality (adjusted HR, 0.68 [95%CI, 0.43–1.08]) and cancer mortality (adjusted HR, 1.00 [95%CI, 0.72–1.38]). However, dietary supplements containing probiotic were not associated with decreased all-cause and cause-specific mortality.ConclusionsThe present study suggested that yogurt consumption was associated with a lower risk of all-cause mortality among U.S. adults. Yogurt consumption in diet might be a sensible strategy for reducing the risk of death.</jats:sec

Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks

This study introduces a novel approach for predicting the decay levels of potato by integrating an electronic nose system combined with feature-optimized deep learning models. The electronic nose system was utilized to collect volatile gas data from potatoes at different decay stages, offering a non-invasive method to classify decay levels. To mitigate data scarcity and improve training model robustness, a Gaussian Mixture Embedded Generative Adversarial Network (GMEGAN) was used to generate synthetic data, resulting in augmented datasets that increased diversity and improved model performance. Several machine learning and deep learning models, including traditional classifiers (SVM, LR, RF, ANN) and advanced neural networks (CNN, ECA-CNN, CAM-CNN, Conditional CNN), were trained and evaluated. Models incorporating feature-optimized channel attention modules (f-CAM, f-ECA) achieved a classification accuracy of up to 90.28%, significantly outperforming traditional machine learning models (72&ndash;77%) and standard CNN models (83.33%). The inclusion of GMEGAN-generated datasets further enhanced classification performance, especially for feature-optimized Conditional CNN models, with an observed increase in accuracy of up to 5.55%. A comprehensive evaluation of the GMEGAN-generated data, including feature mapping consistency, data distribution similarity, and quality metrics, demonstrated that the generated data closely resembled real data, thereby effectively enhancing dataset diversity. The proposed approach shows significant potential in improving classification accuracy and robustness for agricultural quality assessment, particularly in predicting the decay levels of potatoes

Recent advances on electrocatalytic fixation of nitrogen under ambient conditions

This review summarizes and discusses recent efforts paid to the design and synthesis of electrocatalysts for nitrogen fixation under ambient conditions.</p

Analysis of Water Resources and Water Environmental Carrying Capacity of Animal Husbandry in China&mdash;Based on Water Footprint Theory

Water shortage and water pollution have become the key factors restricting the sustainable development of animal husbandry in China. In this study, the water footprint model was used to analyze the water resource carrying capacity and water environment bearing pressure of animal husbandry in 31 provinces of China from 2001 to 2019. The findings indicate that: (1) The development of animal husbandry has exacerbated the regional water deficiency problem. Shandong, Henan, Hebei, and Liaoning have become the most serious water deficit areas of animal husbandry in China. The decreasing water resource carrying capacity indicates that water resources are difficult in supporting the growth of animal husbandry; (2) the change of animal feeding structures has led to the decrease of gray water footprint and the alleviation of the water environment bearing pressure; however, the water environment of animal husbandry in northern China and the northwest is still overburdened, which poses a major challenge to the control of agricultural non-point source pollution; (3) furthermore, according to the spatial and temporal characteristics of the water resource carrying capacity and water environment bearing pressure, the main livestock-producing areas in the north are facing a profound &ldquo;water-livestock&rdquo; contradiction and showing an increasing trend. The research results will help decision-makers to adjust the development mode of animal husbandry, optimize resource allocation, and promote the sustainable development of resource-saving and environment-friendly animal husbandry