Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods

In the context of prediction science, the sources of uncertainty can be from the uncertainties of the experiments, modeling, model inputs, numerical analysis, etc. This study concentrates on quantifying the forecast uncertainty arising from the propagation of the uncertainties in the model inputs to the dynamical model. The uncertainties in the inputs include the randomness in (1) the initial conditions, (2) the forcing term (including both the external forcing and the boundary conditions), and (3) randomness in the parameters of the model. In order to quantify the uncertainties in the forecast, three uncertainty quantification (UQ) methods are studied, namely classical Monte Carlo (MC), polynomial chaos (PC) expansion and unscented transformation (UT). Using MC as the benchmark, two dynamical models are used in this study to examine the performance of PC expansion and UT. One is the low order (two components) spectral solution to the nonlinear advection equation, and the other one is the five-variable mixed-layer model which is used to describe the return flow event over the Gulf of Mexico during the cool season (between November and March) every year. The experimental results and the comparisons with MC have shown that both PC and UT can provide good estimates on the statistical information relating to the forecast, for example, the mean, variation (or standard deviation), covariance. The approach of UT utilizes a set of deterministically chosen sigma points to propagate the uncertainties contained in the inputs through the dynamical model. Only the first two moments of the forecast can be estimated by UT. Different from UT, the PC expansion represents the stochastic process in the form of a series expression (hence a surrogate approximation) in terms of the orthogonal polynomials whose type depends on the probability distribution of the random inputs. Ensemble forecast can be achieved by sampling the random variable used in the PC expansion. Furthermore, the histogram of the forecast can be constructed using the ensemble forecast, and then one can estimate the probability density function (PDF) of the forecast. What’s more, PC expansion can also give estimates on the statistics of higher order moments. The application of PC and UT in quantifying the forecast uncertainties in large scale system, the combination with data assimilation techniques and its real applications, and the ability to deal with nonGaussian distributions will be some of the topics for future study

Optimal design of line level control resonant converters in plug‐in hybrid electric vehicle battery chargers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163784/1/els2bf00015.pd

Comprehensive evaluation of high-resolution satellite-based precipitation products over China

Characterizing the errors in satellite-based precipitation estimation products is crucial for understanding their effects in hydrological applications. Six precipitation products derived from three algorithms are comprehensively evaluated against gauge data over mainland China from December 2006 to November 2010. These products include three satellite-only estimates: the Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK), the Climate Prediction Center (CPC) MORPHing (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), as well as their gauge-corrected counterparts: the GSMaP Gauge-calibrated Product (GSMaP_Gauge), bias-corrected CMORPH (CMORPH_CRT), and PERSIANN Climate Data Record (PERSIANN-CDR). Overall, the bias-correction procedures largely reduce various errors for the three groups of satellite-based precipitation products. GSMaP_Gauge produces better fractional coverage with the highest correlation (0.95) and the lowest RMSE (0.53 mm/day) but also high RB (15.77%). In general, CMORPH_CRT amounts are closer to the gauge reference. CMORPH shows better performance than GSMaP_MVK and PERSIANN with the highest CC (0.82) and the lowest RMSE (0.93 mm/day), but also presents a relatively high RB (-19.60%). In winter, all six satellite precipitation estimates have comparatively poor capability, with the IR-based PERSIANN_CDR exhibiting the closest performance to the gauge reference. Both satellite-only and gauge-corrected satellite products show poor capability in detecting occurrence of precipitation with a low POD (40%)

Altered gut microbiota in temporal lobe epilepsy with anxiety disorders

IntroductionPatients with epilepsy are particularly vulnerable to the negative effects of anxiety disorders. In particular, temporal lobe epilepsy with anxiety disorders (TLEA) has attracted more attention in epilepsy research. The link between intestinal dysbiosis and TLEA has not been established yet. To gain deeper insight into the link between gut microbiota dysbiosis and factors affecting TLEA, the composition of the gut microbiome, including bacteria and fungi, has been examined.MethodsThe gut microbiota from 51 temporal lobe epilepsy patients has been subjected to sequencing targeting 16S rDNA (Illumina MiSeq) and from 45 temporal lobe epilepsy patients targeting the ITS-1 region (through pyrosequencing). A differential analysis has been conducted on the gut microbiota from the phylum to the genus level.ResultsTLEA patients' gut bacteria and fungal microbiota exhibited distinct characteristics and diversity as evidenced by high-throughput sequencing (HTS). TLEA patients showed higher abundances of Escherichia-Shigella (genus), Enterobacterales (order), Enterobacteriaceae (family), Proteobacteria (phylum), Gammaproteobacteria (class), and lower abundances of Clostridia (class), Firmicutes, Lachnospiraceae (family), Lachnospirales (order), and Ruminococcus (genus). Among fungi, Saccharomycetales fam. incertae sedis (family), Saccharomycetales (order), Saccharomycetes (class), and Ascomycota (phylum) were significantly more abundant in TLEA patients than in patients with temporal lobe epilepsy but without anxiety. Adoption and perception of seizure control significantly affected TLEA bacterial community structure, while yearly hospitalization frequency affected fungal community structures in TLEA patients.ConclusionHere, our study validated the gut microbiota dysbiosis of TLEA. Moreover, the pioneering study of bacterial and fungal microbiota profiles will help in understanding the course of TLEA and drive us toward preventing TLEA gut microbiota dysbiosis

Preparation of water-soluble multi-walled carbon nanotubes by Ce(IV)-induced redox radical polymerization

Abstract Poly(acrylic acid), poly(N-isopropylacrylamide) and polyacrylamide functionalized MWNTs were prepared by Ce(IV)-induced redox radical polymerization. The reaction can be conducted in aqueous media at room temperature, and the polymer graft ratio increased with the increase of monomer feed ratio. MWNTs anchored with PAA on the surface are pH sensitive and exhibit a reversible assembly-deassembly response in aqueous solution, whereas those coated with PNIPAM are thermally sensitive. All the polymer-functionalized MWNTs are highly soluble in water to give robust stable black solutions. Such water-soluble MWNTs are promising for biological and biomedical applications

The clinical predictive value of geriatric nutritional risk index in elderly rectal cancer patients received surgical treatment after neoadjuvant therapy

ObjectiveThe assessment of nutritional status has been recognized as crucial in the treatment of geriatric cancer patients. The objective of this study is to determine the clinical predictive value of the geriatric nutritional risk index (GNRI) in predicting the short-term and long-term prognosis of elderly rectal cancer (RC) patients who undergo surgical treatment after neoadjuvant therapy.MethodsBetween January 2014 and December 2020, the clinical materials of 639 RC patients aged ≥70 years who underwent surgical treatment after neoadjuvant therapy were retrospectively analysed. Propensity score matching was performed to adjust for baseline potential confounders. Logistic regression analysis and competing risk analysis were conducted to evaluate the correlation between the GNRI and the risk of postoperative major complications and cumulative incidence of cancer-specific survival (CSS). Nomograms were then constructed for postoperative major complications and CSS. Additionally, 203 elderly RC patients were enrolled between January 2021 and December 2022 as an external validation cohort.ResultsMultivariate logistic regression analysis showed that GNRI [odds ratio = 1.903, 95% confidence intervals (CI): 1.120–3.233, p = 0.017] was an independent risk factor for postoperative major complications. In competing risk analysis, the GNRI was also identified as an independent prognostic factor for CSS (subdistribution hazard ratio = 3.90, 95% CI: 2.46–6.19, p < 0.001). The postoperative major complication nomogram showed excellent performance internally and externally in the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analysis (DCA). When compared with other models, the competing risk prognosis nomogram incorporating the GNRI achieved the highest outcomes in terms of the C-index, AUC, calibration plots, and DCA.ConclusionThe GNRI is a simple and effective tool for predicting the risk of postoperative major complications and the long-term prognosis of elderly RC patients who undergo surgical treatment after neoadjuvant therapy