Development of Remote Sensing Assisted Water Quality Nowcasting and Forecasting Models for Coastal Beaches

A remote sensing assisted water quality modeling framework is developed in this dissertation for nowcasting and forecasting recreational water quality of Holly Beach in Louisiana, USA. The modeling framework is composed of four models/systems: (1) an Artificial Neural Network (ANN) model (Model 1) and an US EPA Virtual Beach (VB) Program-based model for predicting early morning enterococci (ENT) levels in beach waters; (2) an ANN model (Model 2) and an VB model for predicting early morning Fecal Coliform (FC) levels in beach waters; (3) a remote sensing assisted modeling system (Model 3) for predicting near real time ENT levels during daytime; and (4) a hybrid probabilistic/deterministic modeling approach (Model 4) for predicting the probability of beach water quality violation. New findings from Model 1 include (1) the identification of 7 explanatory variables and various combinations of the 7 variables responsible for the ENT level in coastal beach waters; and (2) Model 1 with Linear Correlation Coefficient (LCC) of 0.857 performs consistently better than the VB model with LCC of 0.320. A major finding from Model 2 is that a total of 6 independent environmental variables along with 8 different combinations are capable of explaining about 76% of variation in FC levels for model training data and 44% for independent data. Major new contributions made in Model 3 include (1) development of remote sensing algorithms for turbidity using Terra and Aqua satellite data; (2) development of an enhanced ANN model for predicting ENT levels at sunrise time by taking into account the cumulative effect of solar radiation on ENT inactivation; (3) development of a real-time model for predicting ENT level during the daytime by considering the turbidity effect on ENT inactivation. A novel feature of Model 4 (hybrid model) is the combination of advantages of a deterministic ANN model and a probabilistic Bayesian model. The hybrid model is capable of reproducing 86.25% of historical beach water quality advisories with 6.39% of false positive predictions and 7.36% of false negative predictions over the past 7-years. Applications of the models will improve the management of recreational beaches and the protection of public health

A novel solution for seepage problems using physics-informed neural networks

A Physics-Informed Neural Network (PINN) provides a distinct advantage by synergizing neural networks' capabilities with the problem's governing physical laws. In this study, we introduce an innovative approach for solving seepage problems by utilizing the PINN, harnessing the capabilities of Deep Neural Networks (DNNs) to approximate hydraulic head distributions in seepage analysis. To effectively train the PINN model, we introduce a comprehensive loss function comprising three components: one for evaluating differential operators, another for assessing boundary conditions, and a third for appraising initial conditions. The validation of the PINN involves solving four benchmark seepage problems. The results unequivocally demonstrate the exceptional accuracy of the PINN in solving seepage problems, surpassing the accuracy of FEM in addressing both steady-state and free-surface seepage problems. Hence, the presented approach highlights the robustness of the PINN and underscores its precision in effectively addressing a spectrum of seepage challenges. This amalgamation enables the derivation of accurate solutions, overcoming limitations inherent in conventional methods such as mesh generation and adaptability to complex geometries

Investigating the Coke Formation Mechanism of H-ZSM-5 during Methanol Dehydration Using Operando UV-Raman Spectroscopy

Methanol dehydration on solid acid catalysts is a fundamental step in many industrial chemical processes, such as methanol to dimethyl ether (MTD) and methanol to olefin (MTO). The performance of catalysts often encounters the detrimental effect of coke deposition. However, the heterogeneous distribution of feedstock and product in a fixed-bed reactor usually brings in difficulties in the study of the coking mechanism. In this work, the coking progress of H-ZSM-5 in a fixed-bed reactor under MTD conditions is investigated using operando UV-Raman spectroscopy. Methylbenzenium carbenium ions (MB+), a key precursor for coke formation, was identified by UV resonance Raman spectroscopy and isotope exchange experiments. At higher temperature (473 K), MB+ rapidly transforms into "hard coke" at the beginning of the catalyst bed. The relative intensity of the 1605 cm(-1) peak can serve as an indicator for the catalyst deactivation. Moreover, water formed during MTD can suppress the transformation of MB+ into "hard coke" at the later parts of the bed. These results provide important information for the key steps and intermediates about coke formation on solid acid catalysts during methanol conversion, and the findings will contribute to improved catalytic performance in the related catalytic reaction

APP design and research based on Sichuan Intangible Cultural Heritage

The market of Sichuan intangible cultural heritage APP and mini program based on mobile terminal is lacking. The “Internet+” mode is used to expand the modern living space of intangible cultural heritage, stimulate the social and economic benefits of intangible cultural heritage resources, and realize their activation and inheritance. By showing the relevant cultural deposits and materials of the intangible cultural heritage, and including the existing intangible cultural heritage resources at all levels in Sichuan province, more people will notice the beauty of Sichuan’s intangible cultural heritage resources. This project realizes the digital inheritance of intangible cultural heritage resources through “productive protection” through the Internet platform. In accordance with the law of the market, the appropriate production, use, derived into a rich Sichuan regional characteristics of products. At the same time, the intangible cultural heritage resources of Sichuan Province as the cornerstone, continuous development and ultimately build a national intangible cultural heritage platform

Study on Laser Parameter Measurement System Based on Cone-Arranged Fibers and CCD Camera

This paper proposes a new laser parameter measuring method based on cone-arranged fibers to further improve the measurable spot size, allowable incident angle range, and spatial sampling resolution. This method takes a conical array composed of flexible fibers to sample and shrink the cross-section spot of the laser beam, facilitating low-distortion shooting with a charge-coupled diode (CCD) camera, and adopts homogenized processing and algorithm analysis to correct the spot. This method is experimentally proven to achieve high-accuracy measurements with a decimeter-level spot-receiving surface, millimeter-level resolution, and high tolerance in order to incite skew angle. Comparing the measured spot under normal incidence with the real one, the root mean square error (RMSE) of their power in the bucket (PIB) curves is less than 1%. When the incident angle change is between −8° and 8°, the RMSE is less than 2% and the measurement error of total power is less than 5% based on the premise that the fiber’s numerical aperture (NA) is 0.22. The possibility of further optimizing the measurement method by changing the fiber parameters and array design is also reported

Palladium-Catalyzed Oxidative Acetoxylation of Benzylic C–H Bond Using Bidentate Auxiliary

Pd­(OAc)₂-catalyzed oxidative acetoxylation of benzylic C–H bonds utilizing a bidentate system has been explored. A variety of picolinoyl- or quinoline-2-carbonyl-protected toluidine derivatives react with PhI­(OAc)₂ in the presence of Pd­(OAc)₂ to afford the acetoxylated products in synthetically useful yields. A broad of functionalities, such as CH₃, F, Cl, Br, I, COCH₃, CO₂Et, SO₂CH₃, and NO₂, were tolerated. This transformation provides easy access to 2-hydroxymethylaniline derivatives

Metagenomic Next-Generation Sequencing for Pathogens in Bronchoalveolar Lavage Fluid Improves the Survival of Patients with Pulmonary Complications After Allogeneic Hematopoietic Stem Cell Transplantation

Abstract Introduction Unbiased metagenomic next-generation sequencing (mNGS) has been used for infection diagnosis. In this study, we explored the clinical diagnosis value of mNGS for pulmonary complications after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods From August 2019 to June 2021, a prospective study was performed to comparatively analyze the pathogenic results of mNGS and conventional tests for bronchoalveolar lavage fluid (BALF) from 134 cases involving 101 patients with pulmonary complications after allo-HSCT. Results More pathogens were identified by mNGS than with conventional tests (226 vs 120). For bacteria, the diagnostic sensitivity (P = 0.144) and specificity (P = 0.687) were similar between the two methods. For fungus except Pneumocystis jirovecii (PJ), conventional tests had a significantly higher sensitivity (P = 0.013) with a similarly high specificity (P = 0.109). The sensitivities for bacteria and fungi could be increased with the combination of the two methods. As for PJ, both the sensitivity (100%) and specificity (99.12%) of mNGS were very high. For viruses, the sensitivity of mNGS was significantly higher (P = 0.021) and the negative predictive value (NPV) was 95.74% (84.27–99.26%). Pulmonary infection complications accounted for 90.30% and bacterium was the most common pathogen whether in single infection (63.43%) or mixed infection (81.08%). The 6-month overall survival (OS) of 88.89% in the early group (mNGS ≤ 7 days) was significantly higher than that of 65.52% (HR 0.287, 95% CI 0.101–0.819, P = 0.006) in the late group (mNGS > 7 days). Conclusions mNGS for BALF could facilitate accurate and fast diagnosis for pulmonary complications. Early mNGS could improve the prognosis of patients with pulmonary complications after allo-HSCT. Trial Registration ClinicalTrials.gov identifier, NCT 04051372