The main contribution of this paper is a new H2 gain-scheduled state-feedback synthesis condition for continuous-time linear parameter-varying (LPV) systems where the parameters have bounded rates of ...variation. The matrices of the model as well as the control gain can have arbitrary polynomial dependence on the time-varying parameters. The synthesis conditions are formulated in terms of LMI relaxations combined with a search in a bounded scalar parameter, which can be used to obtain controllers with improved performance. As an experimental validation of the results, gain-scheduled controllers are designed and applied to a control moment gyroscope modeled as an LPV system with polynomial dependence on the time-varying parameters. Comparisons with a time-invariant controller, designed with the same performance criterion, are presented to illustrate the advantages of the proposed approach.
The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 129 million confirm cases. Many health authorities around the world have ...implemented wastewater-based epidemiology as a rapid and complementary tool for the COVID-19 surveillance system and more recently for variants of concern emergence tracking. In this study, three SARS-CoV-2 target genes (N1 and N2 gene regions, and E gene) were quantified from wastewater influent samples (n = 250) obtained from the capital city and 7 other cities in various size in central Ohio from July 2020 to January 2021. To determine human-specific fecal strength in wastewater samples more accurately, two human fecal viruses (PMMoV and crAssphage) were quantified to normalize the SARS-CoV-2 gene concentrations in wastewater. To estimate the trend of new case numbers from SARS-CoV-2 gene levels, different statistical models were built and evaluated. From the longitudinal data, SARS-CoV-2 gene concentrations in wastewater strongly correlated with daily new confirmed COVID-19 cases (average Spearman's r = 0.70, p < 0.05), with the N2 gene region being the best predictor of the trend of confirmed cases. Moreover, average daily case numbers can help reduce the noise and variation from the clinical data. Among the models tested, the quadratic polynomial model performed best in correlating and predicting COVID-19 cases from the wastewater surveillance data, which can be used to track the effectiveness of vaccination in the later stage of the pandemic. Interestingly, neither of the normalization methods using PMMoV or crAssphage significantly enhanced the correlation with new case numbers, nor improved the estimation models. Viral sequencing showed that shifts in strain-defining variants of SARS-CoV-2 in wastewater samples matched those in clinical isolates from the same time periods. The findings from this study support that wastewater surveillance is effective in COVID-19 trend tracking and provide sentinel warning of variant emergence and transmission within various types of communities.
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•Wastewater SARS-CoV-2 loads strongly correlated with confirmed cases in 8 sewersheds.•Quadratic model is effective in calculating COVID-19 prevalence from wastewater data.•Normalization of SARS-CoV-2 signal by fecal indicator didn't improve the correlation.•Emerging variants of concern were identified in wastewater from various communities.
This study examines the application of nano-colloidal silica (NCS) in enhancing the mechanical properties of sandy clay soils. Consolidated undrained (CU) triaxial tests were performed on specimens ...containing varying percentages of NCS (0 %, 5 %, 10 %, and 20 %), which were then cured for different curing periods (1, 7, and 28 days) and subjected to three different confining pressures (50, 100, and 200 kPa). The findings revealed that the inclusion of 10 % NCS resulted in a significant 65 % increase in strength after 28 days compared to the untreated sample. However, higher NCS percentages, exceeding 10 %, led to a decline in strength as the excess NCS was not effectively utilized. The inclusion of NCS and increased curing time led to an increase in the brittleness of the soil and the application of confining pressure was able to reduce this brittleness.Furthermore, the use of 10% NCS and a curing period of 28 days significantly increased the stiffness and absorbed energy of the soil. Despite boosting the peak shear strength, NCS reduced the residual strength. Finally, polynomial modeling (Poly4) provided an excellent fit, enabling the characterization of the stress-strain and pore pressure-stain responses from the triaxial test.
•A series of CU tests were conducted to investigate the strength behavior of sandy clay soil with NCS additive.•10 % was the optimal content of NCS to modify sandy clay soil, which can improve strength.•The addition of NCS to the sandy clay soil enhances the strength and reduces the failure strain.•Empirical models were developed to predict and satisfactorily simulate q-ɛ and u- ε curves.
There have been no reports about predicting survival of patients with advanced cancer constructed entirely with objective variables. We aimed to develop a prognostic model based on laboratory ...findings and vital signs using a fractional polynomial (FP) model.
A multicentre prospective cohort study was conducted at 58 specialist palliative care services in Japan from September 2012 to April 2014. Eligible patients were older than 20 years and had advanced cancer. We developed models for predicting 7-day, 14-day, 30-day, 56-day and 90-day survival by using the FP modelling method.
Data from 1039 patients were analysed to develop each prognostic model (Objective Prognostic Index for advanced cancer OPI-AC). All models included the heart rate, urea and albumin, while some models included the respiratory rate, creatinine, C-reactive protein, lymphocyte count, neutrophil count, total bilirubin, lactate dehydrogenase and platelet/lymphocyte ratio. The area under the curve was 0.77, 0.81, 0.90, 0.90 and 0.92 for the 7-day, 14-day, 30-day, 56-day and 90-day model, respectively. The accuracy of the OPI-AC predicting 30-day, 56-day and 90-day survival was significantly higher than that of the Palliative Prognostic Score or the Prognosis in Palliative Care Study model, which are based on a combination of symptoms and physician estimation.
We developed highly accurate prognostic indexes for predicting the survival of patients with advanced cancer from objective variables alone, which may be useful for end-of-life management. The FP modelling method could be promising for developing other prognostic models in future research.
•We developed a prognostic index that required only laboratory data and vital signs.•Survival of advanced cancer patients can be predicted without a physician's assessments.•Our prognostic index was more accurate than existing predictive tools.•Our prognostic index could minimize attrition rates of clinical trials.•Fractional polynomials model is a promising way to develop prognostic indexes.
•PAN/PANI blend nanofibers were prepared by electrospinning and polymerization.•PAN/PANI blend sensor was found to be sensitive for NH3 gas.•The highest sensitivity was found to be nearly at room ...temperature.•Bessel’s polynomial model is applicable for gas sensing.
In the present paper, we report the fabrication of electrospun polyacrylonitrile/polyaniline (PAN/PANI) blend nanofibers by electrospinning and polymerization and Bessel’s polynomial model applied for its ammonia sensing characteristics. As-fabricated PAN/PANI blend nanofibers were characterized by scanning electron microscopy and Fourier transform infrared spectroscopy for the confirmation of fibers with nanoscale and blends of PAN and PANI. The semiconducting behavior of the PAN/PANI blend nanofibers was found to respond quickly towards ammonia gas. Sensitivity of the blend was obtained at near room temperature for different concentrations of ammonia. Bessel’s polynomial function was found to be well fitted with the experimental data for the response towards ammonia gas.
•Develop a novel framework for simulating non-stationary non-Gaussian processes.•Transform non-stationary non-Gaussian ACF to non-stationary Gaussian ACF.•Translate underlying Gaussian processes into ...non-stationary non-Gaussian process using L-moments-based HPM.•Incompatibilities between L-moments and ACF are effectively resolved.•Demonstrate the efficiency and accuracy of the proposed method.
A novel and efficient method is proposed for simulating strongly non-Gaussian and non-stationary processes by combining Karhunen–Loève expansion with Linear-moments-based (L-moments-based) Hermite polynomial model (HPM). In this method, the complete transformation from non-stationary non-Gaussian auto-correlation function (ACF) to non-stationary Gaussian ACF is realized using L-moments-based HPM. Then, the underlying Gaussian processes is represented by Karhunen–Loève expansion and further transformed into target non-stationary non-Gaussian processes by L-moments-based HPM. Moreover, a novel approach is proposed to deal with the two kinds of incompatibilities that may occur in strongly non-Gaussian processes, including that non-stationary non-Gaussian ACF falls outside of its applicable range and non-stationary Gaussian ACF is non-positive semi-definite. It can be found from some representative numerical examples that the precision and efficiency of the proposed method are considerable.
RESUMO Os objetivos desse trabalho foram: a) determinar o padrão de variação longitudinal da densidade básica; b) avaliar os principais métodos de amostragem longitudinal; e c) propor novas ...alternativas amostrais longitudinais representativas da densidade básica média. Foram retirados discos da base, 1,30 m (DAP) e, a partir deste ponto, de metro em metro até a altura comercial. Foram ajustados modelos lineares explicativos da variação longitudinal da densidade básica. O modelo polinomial de 5º ordem foi o que melhor explicou o padrão de variação longitudinal da densidade básica. Esse modelo foi utilizado na predição da densidade básica nas alturas relativas de 1 a 100% da altura comercial. A posição de 79% da altura comercial foi a mais precisa dentre as avaliadas. As alternativas amostrais A (5, 25, 27, 78 e 79%), B (5, 26, 27, 78 e 79%) e C (5, 26, 27, 78 e 80%) foram mais adequadas às amostragens comumente utilizadas.
The beam-oriented digital predistortion (BO-DPD) is not sufficient to linearize the output from a subarray of power amplifiers (PAs) in different directions except the desired direction. Therefore, ...subsequent to the BO-DPD operation, we perform a post-weighting (PW) processing to minimize the nonlinear radiations in the wide range of directions under crosstalk. Here, the optimized PW coefficients are multiplied by the polynomial terms of the BO-DPD, then, the resultant signals are distributed to the PAs to compensate the nonlinear radiations. In this work, first, we propose fully-featured post-weighting (FF-PW) scheme, then, we derive a low-complexity post-weighting (LC-PW) scheme.
Emissions from transport account for 20%–25% of global carbon dioxide emissions, with more than 70% coming from road transport, making it an extremely important topic in the context of ...decarbonization. The aim of the article is to analyze the trend of CO2 generated from road transport, taking into account the its various sources, and also to examine the manner in which reduced mobility during the pandemic affected the emissions at the time.
For this purpose, a time series containing observations up to the pandemic outbreak and a time series containing additional observations from the pandemic period were analyzed. For each time series, a trend was determined and described by a polynomial and then verified to see if the pandemic phenomenon significantly affects a parameter of the proposed model, using appropriate statistical tests.
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•Data on chemical-enzymatic pretreatment of lignocellulosic biomass compiled.•Yield of reducing sugar is modelled with nine depnedendent variables.•Reduced interaction model offered ...the best-fit R2: 0.891, Adj. R2: 0.849.•Acid concentration and severity remain the least important predictors.•Genetic algorithm optimized artificial neural network offered excellent fit.
Reducing sugar generation from lignocellulosic biomass (LCB) is closely linked with biomass characteristics, pretreatment and enzymatic hydrolysis conditions. In this study curated experimental data from literature was used to develop multivariate regression and artificial neural network (ANN) model considering nine predictors (i.e., cellulose, hemicellulose, lignin content, cellulose-lignin ratio, acid concentration, temperature, time, pretreatment severity, and enzyme concentration). Selected reduced polynomial model (R2: 0.891, Adj. R2: 0.849) suggests positive influence of acid and enzyme, while negative influence of treatment severity, temperature and time on reducing sugar generation. Genetic algorithm-optimized ANN model offered excellent fitness for LCB hydrolysis on training (R2: 0.997), validation (R2: 0.984), and test sets (R2: 0.967). Sensitivity analysis of the ANN predictors suggests lignin and to some extent hemicellulose contents can be inhibitory. Though polynomial models can have simple interpretation, use of optimized ANN offers better predictability in dataset with diverse biomass compositions.
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