State Estimation for Discrete-Time Fuzzy Cellular Neural Networks with Mixed Time Delays

This paper is concerned with the exponential state estimation problem for a class of discrete-time fuzzy cellular neural networks with mixed time delays. The main purpose is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable. By constructing a novel Lyapunov-Krasovskii functional which contains a triple summation term, some sufficient conditions are derived to guarantee the existence of the state estimator. The linear matrix inequality approach is employed for the first time to deal with the fuzzy cellular neural networks in the discrete-time case. Compared with the present conditions in the form of M-matrix, the results obtained in this paper are less conservative and can be checked readily by the MATLAB toolbox. Finally, some numerical examples are given to demonstrate the effectiveness of the proposed results

A Novel Black Box Process Quality Optimization Approach based on Hit Rate

Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models

A Simple yet Effective Self-Debiasing Framework for Transformer Models

Current Transformer-based natural language understanding (NLU) models heavily rely on dataset biases, while failing to handle real-world out-of-distribution (OOD) instances. Many methods have been proposed to deal with this issue, but they ignore the fact that the features learned in different layers of Transformer-based NLU models are different. In this paper, we first conduct preliminary studies to obtain two conclusions: 1) both low- and high-layer sentence representations encode common biased features during training; 2) the low-layer sentence representations encode fewer unbiased features than the highlayer ones. Based on these conclusions, we propose a simple yet effective self-debiasing framework for Transformer-based NLU models. Concretely, we first stack a classifier on a selected low layer. Then, we introduce a residual connection that feeds the low-layer sentence representation to the top-layer classifier. In this way, the top-layer sentence representation will be trained to ignore the common biased features encoded by the low-layer sentence representation and focus on task-relevant unbiased features. During inference, we remove the residual connection and directly use the top-layer sentence representation to make predictions. Extensive experiments and indepth analyses on NLU tasks show that our framework performs better than several competitive baselines, achieving a new SOTA on all OOD test sets

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

[EN] The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m-1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Ben Dor, E.; Szabó, B.... (2020). An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water. 12(5):1-36. https://doi.org/10.3390/w12051495S13612

Analysis of risk factors and short-term prognostic factors of arrhythmia in patients infected with mild/moderate SARS-CoV-2 Omicron variant

BackgroundComplications, including arrhythmia, following severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection continue to be of concern. Omicron is the mainstream SARS-CoV-2 mutant circulating in mainland China. At present, there are few epidemiological studies concerning the relationship between arrhythmia and Omicron variant infection in mainland China.ObjectivesTo investigate the risk factors of arrhythmia in patients infected with the SARS-CoV-2 Omicron variant and the factors influencing prognosis.MethodsData from 192 Omicron infected patients with symptoms of arrhythmia (AH group) and 100 Omicron infected patients without arrhythmia (Control group) were collected. Patients in the AH group were divided into the good and poor prognosis groups, according to the follow-up results 4–6 weeks after infection. The general and clinical data between the AH and Control groups, and between the good and poor prognosis groups were compared. The variables with differences between the groups were included in the multivariate logistic regression analysis, and the quantitative variables were analyzed by receiver operating characteristic curve to obtain their cut-off values.ResultsCompared with the control group, the body mass index (BMI), proportion of patients with a history of arrhythmia, proportion of antibiotics taken, heart rate, moderate disease severity, white blood cell (WBC) count, and the aspartate aminotransferase, creatine kinase (CK), CK isoenzyme (CK-MB), myoglobin (Mb), high-sensitive troponin I (hs-cTnI), lymphocyte ratio and high sensitivity C-reactive protein (hs-CRP) levels in the AH group were significantly higher (p < 0.05). In addition, obesity (BMI ≥24 kg/m2), fast heart rate (≥100 times/min), moderate disease severity, and WBC, CK-MB and hs-cTnI levels were independent risk factors of arrhythmia for patients with Omicron infection (p < 0.05), and hs-CRP was a protective factor (p < 0.05). Compared with the good prognosis group, the age, proportion of patients with a history of arrhythmia, heart rate, proportion of moderate disease severity, and hs-CRP, CK, Mb and hs-cTnI levels were significantly higher in the poor prognosis group, while the proportion of vaccination was lower in the poor prognosis group (p < 0.05). Advanced age (≥65 years old), proportion of history of arrhythmia, moderate disease severity, vaccination, and hs-CRP, Mb and cTnI levels were independent factors for poor prognosis of patients with arrhythmia (p < 0.05).ConclusionThe factors that affect arrhythmia and the prognosis of patients infected with Omicron include obesity, high heart rate, severity of the disease, age. history of arrhythmia, WBC, hs-CRP, and myocardial injury indexes, which could be used to evaluate and prevent arrhythmia complications in patients in the future

Exosomes Derived From Bone Mesenchymal Stem Cells Ameliorate Early Inflammatory Responses Following Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of mortality and disability worldwide. Although treatment guidelines have been developed, no best treatment option or medicine for this condition exists. Recently, mesenchymal stem cells (MSCs)-derived exosomes have shown lots of promise for the treatment of brain disorders, with some results highlighting the neuroprotective effects through neurogenesis and angiogenesis after TBI. However, studies focusing on the role of exosomes in the early stages of neuroinflammation post-TBI are not sufficient. In this study, we investigated the role of bone mesenchymal stem cells (BMSCs)-exosomes in attenuating neuroinflammation at an early stage post-TBI and explored the potential regulatory neuroprotective mechanism. We administered 30 μg protein of BMSCs-exosomes or an equal volume of phosphate-buffered saline (PBS) via the retro-orbital route into C57BL/6 male mice 15 min after controlled cortical impact (CCI)-induced TBI. The results showed that the administration of BMSCs-exosomes reduced the lesion size and improved the neurobehavioral performance assessed by modified Neurological Severity Score (mNSS) and rotarod test. In addition, BMSCs-exosomes inhibited the expression of proapoptosis protein Bcl-2-associated X protein (BAX) and proinflammation cytokines, tumor necrosis factor-α (TNF-α) and interleukin (IL)-1β, while enhancing the expression of the anti-apoptosis protein B-cell lymphoma 2 (BCL-2). Furthermore, BMSCs-exosomes modulated microglia/macrophage polarization by downregulating the expression of inducible nitric oxide synthase (INOS) and upregulating the expression of clusters of differentiation 206 (CD206) and arginase-1 (Arg1). In summary, our result shows that BMSCs-exosomes serve a neuroprotective function by inhibiting early neuroinflammation in TBI mice through modulating the polarization of microglia/macrophages. Further research into this may serve as a potential therapeutic strategy for the future treatment of TBI

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead