Lyapunov equations and Riccati equations for descriptor systems

In this paper, two new types of Lyapunov and Riccati equations are presented for linear time-invariant descriptor systems. The two equations play key roles in asymptotic stability analysis and control synthesis for this class of systems. Fundamental properties of the two equations are investigated and interesting results are obtained.published_or_final_versio

Machinery Early Fault Detection Based on Dirichlet Process Mixture Model

© 2013 IEEE. The most commonly used single feature-based anomaly detection method for the complex machinery, such as large wind power equipment, steam turbine generator sets, and reciprocating compressors, exhibits a defect of low-alarm accuracy due to the non-stationary characteristic of the vibration signals. In order to improve the accuracy of fault detection, a novel method based on the Dirichlet process mixture model (DPMM) is proposed. First, the features of the mechanical vibration signals are used to construct the feature space of the equipment. The DPMM modeling method is then applied to self-learn the probabilistic mixture model of the feature space. The normal working condition model is used as the benchmark model. The early fault detection is realized by using a precise difference measurement method based on Kullback-Leibler divergence to calculate the difference between the real-time model and the benchmark model accurately, and by comparing the calculation result with a self-learned alarm threshold. The effectiveness and the adaptability of this novel early fault detection method are verified by comparing it to the single feature-based anomaly detection method and the Gaussian mixture model (GMM)-based early fault detection method

Income estimation based on human mobility patterns and machine learning models

Sustainable and inclusive urban development requires a thorough understanding of income distribution and poverty. Recent related research has extensively explored the use of automatically generated sensor data to proxy economic activities. Notably, human mobility patterns have been found to exhibit strong associations with socioeconomic attributes and great potential for income estimation. However, the representation of complex human mobility patterns and their effectiveness in income estimation needs further investigation. To address this, we propose three representations of human mobility: mobility indicators, activity footprints, and travel graphs. These representations feed into various models, including XGBoost, a traditional machine learning model, a convolutional neural network (CNN), and a time-series graph neural network (GCRN). By leveraging public transit data from Shenzhen, our study demonstrates that graph-based representations and deep learning models outperform other approaches in income estimation. They excel in minimising information loss and handling complex data structures. Spatial contextual attributes, such as transport accessibility, are the most influential factors, while indicators related to activity extent, temporal rhythm, and intensity contribute comparatively less. In summary, this study highlights the potential of cutting-edge artificial intelligence tools and emerging human mobility data as an alternative approach to estimating income distribution and addressing poverty-related concerns

Bounds and Inequalities Relating h-Index, g-Index, e-Index and Generalized Impact Factor

Finding relationships among different indices such as h-index, g-index, e-index, and generalized impact factor is a challenging task. In this paper, we describe some bounds and inequalities relating h-index, g-index, e-index, and generalized impact factor. We derive the bounds and inequalities relating these indexing parameters from their basic definitions and without assuming any continuous model to be followed by any of them.Comment: 17 pages, 6 figures, 5 table

Self-cleaning and antibiofouling enamel surface by slippery liquid-infused technique.

We aimed to create a slippery liquid-infused enamel surface with antibiofouling property to prevent dental biofilm/plaque formation. First, a micro/nanoporous enamel surface was obtained by 37% phosphoric acid etching. The surface was then functionalized by hydrophobic low-surface energy heptadecafluoro-1,1,2,2-tetra- hydrodecyltrichlorosilane. Subsequent infusion of fluorocarbon lubricants (Fluorinert FC-70) into the polyfluoroalkyl-silanized rough surface resulted in an enamel surface with slippery liquid-infused porous surface (SLIPS). The results of water contact angle measurement, diffuse-reflectance Fourier transform infrared spectroscopy, and atomic force microscope confirmed that the SLIPS was successfully constructed on the enamel surface. The antibiofouling property of the SLIPS was evaluated by the adsorption of salivary protein of mucin and Streptococcus mutans in vitro, as well as dental biofilm formation using a rabbit model in vivo. The results showed that the SLIPS on the enamel surface significantly inhibited mucin adhesion and S. mutans biofilm formation in vitro, and inhibited dental plaque formation in vivo.published_or_final_versio

Ethnic differences in susceptibilities to A(H1N1) flu: An epidemic parameter indicating a weak viral virulence

The current A(H1N1) flu has showed sub-population dependent susceptibility and fatality as early as April and May of 2009 in its first wave of spreading. After the pandemic outbreak spreads globally for more than seven months, the subpopulation dependence of this flu, including ethnicity, age and genderselectivity, has been recognized by several research groups. This paper attempts to discussed how to identify ethnic selectivity from the released data by WHO relevant to this ongoing flu, review some recently published papers describing the presence of ethnic differences in susceptibilities to the H1N1flu virus and further raised an argument that ethnic differences in  susceptibilities to a virus might be a piece of evidence reflecting a weak virulence of that specific virus

Inactivation kinetics of Vibrio parahaemolyticus on sand shrimp (Metapenaeus ensis) by cinnamaldehyde at 4°C

Sand shrimp (Metapenaeus ensis), shrimp shell, and shrimp meat were inoculated with a three-strain cocktail of Vibrio parahaemolyticus with or without the natural antimicrobial cinnamaldehyde (2.5 mg/ml) and were, then, stored at 4°C for up to 25 days and 18 inactivation curves were obtained. V. parahaemolyticus were inactivated down to the minimum level of detection (2.48 log CFU/g) on thiosulfate citrate bile salts sucrose agar (TCBS) plates within 7 and 10 days with low and high densities of V. parahaemolyticus inoculation, 4.5 log CFU/g and 8.2 log CFU/g, respectively. With adding cinnamaldehyde, the inactivation process of V. parahaemolyticus with low populations, 4.5 log CFU/g, lasted for only 4 days. Therefore, cinnamaldehyde inactivated cells faster as expected. However, unexpectedly, in shrimp meat cases, cells have much more persistence of over even 25 days before entering the minimum level of detection both with and without cinnamaldehyde treatment. Therefore, a hypothesis was formed that when cells kept in cold environments (4°C) after several days recovered to up to 103–104 CFU/g towards the end of the experiments and with starvation (shell and shrimp studies), cells might render a viable but nonculturable (VBNC) state

Automated tongue segmentation in hyperspectral images for medicine

Author name used in this publication: Jing-qi YanAuthor name used in this publication: David ZhangVersion of RecordPublishe

Spatio-Temporal Characteristics of Global Warming in the Tibetan Plateau during the Last 50 Years Based on a Generalised Temperature Zone - Elevation Model

Temperature is one of the primary factors influencing the climate and ecosystem, and examining its change and fluctuation could elucidate the formation of novel climate patterns and trends. In this study, we constructed a generalised temperature zone elevation model (GTEM) to assess the trends of climate change and temporal-spatial differences in the Tibetan Plateau (TP) using the annual and monthly mean temperatures from 1961-2010 at 144 meteorological stations in and near the TP. The results showed the following: (1) The TP has undergone robust warming over the study period, and the warming rate was 0.318°C/decade. The warming has accelerated during recent decades, especially in the last 20 years, and the warming has been most significant in the winter months, followed by the spring, autumn and summer seasons. (2) Spatially, the zones that became significantly smaller were the temperature zones of -6°C and -4°C, and these have decreased 499.44 and 454.26 thousand sq km from 1961 to 2010 at average rates of 25.1% and 11.7%, respectively, over every 5-year interval. These quickly shrinking zones were located in the northwestern and central TP. (3) The elevation dependency of climate warming existed in the TP during 1961-2010, but this tendency has gradually been weakening due to more rapid warming at lower elevations than in the middle and upper elevations of the TP during 1991-2010. The higher regions and some low altitude valleys of the TP were the most significantly warming regions under the same categorizing criteria. Experimental evidence shows that the GTEM is an effective method to analyse climate changes in high altitude mountainous regions