Towards a paradigm shift in the modeling of soil organic carbon decomposition for earth system models

Soils are the largest terrestrial carbon pools and contain approximately 2200 Pg of carbon. Thus, the dynamics of soil carbon plays an important role in the global carbon cycle and climate system. Earth System Models are used to project future interactions between terrestrial ecosystem carbon dynamics and climate. However, these models often predict a wide range of soil carbon responses and their formulations have lagged behind recent soil science advances, omitting key biogeochemical mechanisms. In contrast, recent mechanistically-based biogeochemical models that explicitly account for microbial biomass pools and enzyme kinetics that catalyze soil carbon decomposition produce notably different results and provide a closer match to recent observations. However, a systematic evaluation of the advantages and disadvantages of the microbial models and how they differ from empirical, first-order formulations in soil decomposition models for soil organic carbon is still needed. This dissertation consists of a series of model sensitivity and uncertainty analyses and identifies dominant decomposition processes in determining soil organic carbon dynamics. Poorly constrained processes or parameters that require more experimental data integration are also identified. This dissertation also demonstrates the critical role of microbial life-history traits (e.g. microbial dormancy) in the modeling of microbial activity in soil organic matter decomposition models. Finally, this study surveys and synthesizes a number of recently published microbial models and provides suggestions for future microbial model developments

New Broadband Common-Mode Filtering Structures Embedded in Differential Coplanar Waveguides for DC to 40 GHz Signal Transmission

Coplanar waveguides (CPWs) provide effective transmission with low dispersion into the millimeter-wave frequencies. For high-speed signaling, differential transmission lines display an enhanced immunity to outside interference and are less likely to interfere with other signals, when compared to single-ended transmission lines. Common-mode (CM) conversion from the differential-mode (DM) signal energy can produce unintentional radiation as well as degraded board-level electromagnetic compatibility (EMC) and signal integrity SI environments. Due to the negative effects of CM signals, filtering structures are often used to suppress the propagation of these signals. The filtering structures introduced in this project all implement the same CM filter design concept. While the concept itself is not new, the physical design of the filter combined with broadside differential CPWs had not been explored at the time of writing this thesis. The CM filtering structures described herein demonstrated to offer broadband CM filtering together with effective DM transmission into millimeter-wave frequencies

USING THE AUTOMATED RANDOM FOREST APPROACH FOR OBTAINING THE COMPRESSIVE STRENGTH PREDICTION OF RCA

The intricate relationships and cohesiveness among numerous components make the task of designing mixture proportions for high-performance concrete (HPC) a challenging endeavour. Machine learning (ML) algorithms are indeed efficacious in mitigating this predicament. However, their lack of an explicit correlation between mixture proportions and compressive strength renders them opaque black box models. To surpass this constraint, the present research puts forward a semi-empirical methodology that involves the utilization of tactics such as non-dimensionalization and optimization. The methodology proposed exhibits a remarkable level of accuracy in predicting compressive strength across various datasets, exemplifying its all-encompassing applicability to diverse datasets.Furthermore, the exact association furnished by semi-empirical equations is a valuable asset for engineers and researchers operating in this domain, especially concerning their prognostic capabilities. The compressive strength of concrete holds significant importance in designing high-performance concrete, and achieving an optimal mixture proportion necessitates a comprehensive comprehension of the complex interplay among diverse factors, including the type and proportion of cement, water-cement ratio, size and type of aggregate, curing conditions, and admixtures. The semi-empirical approach put forth in this study presents a potential remedy to the intricate undertaking by establishing a more unequivocal correlation between mixture ratios and compressive strength

Kernel Density Metric Learning

This paper introduces a supervised metric learning algorithm, called kernel density metric learning (KDML), which is easy to use and provides nonlinear, probability-based distance measures. KDML constructs a direct nonlinear mapping from the original input space into a feature space based on kernel density estimation. The nonlinear mapping in KDML embodies established distance measures between probability density functions, and leads to correct classification on datasets for which linear metric learning methods would fail. Existing metric learning algorithms, such as large margin nearest neighbors (LMNN), can then be applied to the KDML features to learn a Mahalanobis distance. We also propose an integrated optimization algorithm that learns not only the Mahalanobis matrix but also kernel bandwidths, the only hyper-parameters in the nonlinear mapping. KDML can naturally handle not only numerical features, but also categorical ones, which is rarely found in previous metric learning algorithms. Extensive experimental results on various benchmark datasets show that KDML significantly improves existing metric learning algorithms in terms of kNN classification accuracy

An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling

Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct and diffuse components, which provide the necessary key meteorological inputs for most land surface models. We developed a simple satellite-based computing scheme to enable fast and reliable estimation of these variables. The global Moderate Resolution Imaging Spectroradiometer (MODIS) products at 1° spatial resolution for the period 2003–2011 were used as the forcing data. Evaluations at Baseline Surface Radiation Network (BSRN) sites show good agreement between the estimated radiation and ground-based observations. At all the 48 BSRN sites, the RMSE between the observations and estimations are 34.59, 41.98 and 28.06 W∙m−2 for total, direct and diffuse solar radiation, respectively. Our estimations tend to slightly overestimate the total and diffuse but underestimate the direct solar radiation. The errors may be related to the simple model structure and error of the input data. Our estimation is also comparable to the Clouds and Earth’s Radiant Energy System (CERES) data while shows notable improvement over the widely used National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. Using our MODIS-based datasets of total solar radiation and its partitioned components to drive land surface models should improve simulations of global dynamics of water, carbon and climate

Spectrum-effect relationship between HPLC fingerprints and inhibitory activity in MUC5AC mucin of Pinelliae Rhizoma Praeparatum Cum Alumine

Purpose: To investigate the spectrum-effect relationship between HPLC fingerprints and the inhibitory effect on MUC5AC mucin of Pinelliae Rhizoma Praeparatum Cum Alumine (PRPCA).Methods: The fingerprints of 20 PRPCA batches were established using HPLC and their similarities or differences were analyzed using hierarchical cluster analysis (HCA) and principal component analysis (PCA). The inhibitory effects of MUC5AC mucin were evaluated in LPS-treated NCI-H292 cells. The spectrum-effect relationship between common chromatographic peaks and MUC5AC inhibition was established using a partial least squares-discriminant analysis (PLS-DA).Results: Fifteen common chromatographic peaks were identified by analyzing HPLC fingerprints, with uridine, tyrosine, uracil, and inosine found as possible markers to distinguish the PRPCA from different sources. Spectrum-effect relationship analysis showed that the chromatographic peaks 5, 6, 10 (vernine), 12 (5-hydroxymethylfurfural), 14 (tryptophan) and 15 (adenosine) were closely associated with the inhibitory effect on MUC5AC mucin.Conclusion: The spectrum-effect relationship between HPLC fingerprints and the inhibitory effect on MUC5AC mucin of PRPCA was successfully established in the present study. Our findings further reveal the material basis of PRPCA and provide an effective method for its quality control

Current Status of the Open Abdomen Treatment for Intra-Abdominal Infection

The open abdomen has become an important approach for critically ill patients who require emergent abdominal surgical interventions. This treatment, originating from the concept of damage control surgery, was first applied in severe traumatic patients. The ultimate goal is to achieve formal abdominal fascial closure by several attempts and adjuvant therapies (fluid management, nutritional support, skin grafting, etc.). Up to the present, open abdomen therapy becomes matured and is multistage-approached in the management of patients with severe trauma. However, its application in patients with intra-abdominal infection still presents great challenges due to critical complications and poor clinical outcomes. This review focuses on the specific use of the open abdomen in such populations and detailedly introduces current concerns and advanced progress about this therapy

Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy

Automated label-free quantitative imaging of biological samples can greatly benefit high throughput diseases diagnosis. Digital holographic microscopy (DHM) is a powerful quantitative label-free imaging tool that retrieves structural details of cellular samples non-invasively. In off-axis DHM, a proper spatial filtering window in Fourier space is crucial to the quality of reconstructed phase image. Here we describe a region-recognition approach that combines shape recognition with an iterative thresholding to extracts the optimal shape of frequency components. The region recognition technique offers fully automated adaptive filtering that can operate with a variety of samples and imaging conditions. When imaging through optically scattering biological hydrogel matrix, the technique surpasses previous histogram thresholding techniques without requiring any manual intervention. Finally, we automate the extraction of the statistical difference of optical height between malaria parasite infected and uninfected red blood cells. The method described here pave way to greater autonomy in automated DHM imaging for imaging live cell in thick cell cultures