Equipment Using a Predictive Health Model

Abstract—In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based on the health state, the failure rate of the equipment can be estimated. We propose to use this predictive health model to predict the effects of different maintenance actions. The effects of maintenance actions over a future time window are evaluated by a cost function. The maintenance actions are optimized using this cost function. The proposed framework is applied in the optimization of the loading of transformers based on the thermal degradation of the paper insulation

MCMFIT: A Program to fit a generalized non-linear advection- dispersion model to experimental data

The problem of estimating model parameters is encountered frequently in practice. There are several packages available for estimating the parameters for linear advection-dispersion equations (ADE) for which there are exact solutions (e.g., CXTFIT, TFMFIT, etc.). For non- linear transport equations, the use of standard numerical solutions (e.g., Crank-Nicolson) to estimate parameters is very time consuming and hence inefficient. On the other hand, mixing-cell solutions are very efficient by comparison. In particular, the solution obtained from the improved mixing cell model has been found to agree very well with the results of a numerical Crank-Nicolson solution while being much more efficient. Thus, an improved mixing cell model has been used hereto estimate model parameters for a variety of transport models. The code, MCMFIT, makes use of nonlinear least-squares fitting to find optimal parameter values by matching improved mixing cell model predictions with measured experimental data. The experimental data can be either in the form of a breakthrough curve or concentrations within a soil profile. The program can handle linear, Freundlich, Langmuir, and S- curve adsorption isotherms in conjunction with the transport equation. Both equilibrium and non- equilibrium (fully kinetic and two site adsorption) cases can be dealt with along with first- and third-type surface boundary conditions. Use of the program is demonstrated with a number of examples. Both synthetic data as well as data from yield and laboratory experiments have been used in the illustrative examples

Analysis of one-dimensional multispecies transport experiments in laboratory soil columns

A numerical scheme based on the mixing cell concept is used to analyse multispecies solute transport under the condition of steady flow. Explicit examples are presented for the case of two chemical species that are reacting during transport. Also, unlike the standard mixing cell approach, the improved method used here maintains second- order accuracy. The mixing cell scheme is applied to various data sets from multispecies transport experiments. First, the scheme is used to analyse the snow-plow effect as observed in a laboratory experiment. This phenomenon results from large concentration differences in the solution and solid phases within the soil profile. The analysis revealed that the solute exchange process is time dependent, rather than being in equilibrium. In another case, the scheme was used to analyse the observed snow- plow peaks of Ca resulting from ion exchange reactions between Ca and Mg in a soil profile. The analysis revealed that the selectivity coefficient changes with total cation concentration in the system. The results demonstrate that nonlinear reactions in multispecies transport can be analysed efficiently, accurately, and quickly using the improved mixing cell model

Direct experimental evidence for quadruplex–quadruplex interaction within the human ILPR

Here we report the analysis of dual G-quadruplexes formed in the four repeats of the consensus sequence from the insulin-linked polymorphic region (ACAGGGGTGTGGGG; ILPRn=4). Mobilities of ILPRn=4 in nondenaturing gel and circular dichroism (CD) studies confirmed the formation of two intramolecular G-quadruplexes in the sequence. Both CD and single molecule studies using optical tweezers showed that the two quadruplexes in the ILPRn=4 most likely adopt a hybrid G-quadruplex structure that was entirely different from the mixture of parallel and antiparallel conformers previously observed in the single G-quadruplex forming sequence (ILPRn=2). These results indicate that the structural knowledge of a single G-quadruplex cannot be automatically extrapolated to predict the conformation of multiple quadruplexes in tandem. Furthermore, mechanical pulling of the ILPRn=4 at the single molecule level suggests that the two quadruplexes are unfolded cooperatively, perhaps due to a quadruplex–quadruplex interaction (QQI) between them. Additional evidence for the QQI was provided by DMS footprinting on the ILPRn=4 that identified specific guanines only protected in the presence of a neighboring G-quadruplex. There have been very few experimental reports on multiple G-quadruplex-forming sequences and this report provides direct experimental evidence for the existence of a QQI between two contiguous G-quadruplexes in the ILPR

SUSTAINABLE DIVERSIFIED AGRICULTURE AND LAND MANAGEMENT IN THE HIMALAYA: IMPLICATIONS FOR CLIMATE CHANGE ADAPTATION AND MITIGATION

The soil and land resources play a vital role in sustaining the local livelihoods of rural communities in the Himalaya. Most of the arable land has already been brought under cultivation, hence the ever-increasing demand for food and fiber has left farmers with no choice but to intensify agriculture. However, producing more crops and greater quantities of food, fiber and other materials on the same parcel of land can to soil fertility and productivity decline with overall degradation of land quality. Therefore, ways and means to intensify agriculture to enhance productivity without degrading the soil and land resource base have become imperative. Agro-forestry, agro-slivi-pastoral systems, and the adoption of a variety of crop, soil and water management and conservation practices offer potential to deliver multiple benefits without sacrificing the very resource upon which the human population depends. Presented herein are findings on approaches to sustainable intensification of agriculture and land management related to soil OM management and C sequestration for multiple benefits, and, agro-forestry as a crop diversification strategy with both livelihood, and climate change adaptation/mitigation benefits. The results indicate that sustainable soil management practices could lead to significant SOC accumulations (4-8 t/ha over 6 yrs). SOC and soil C stocks tend to increase with elevation due to cooler climate and slow decomposition rates. Carbon stocks for the 3 LU types was in the order CF>AF/LH>AG, suggesting that diversified cropping practices including agro-forestry have good potential sequester C while providing livelihood opportunities and climate adaptive capacity for local farming communities. Biochar amendment increased growth of both coffee plants and radish with mixed grass/weed biochar being most effective. Biochar application also significantly decreased emission of GHGs, especially N2O

A Large-Scale Synthesis and Characterization of Quaternary CuIn\u3csub\u3e\u3cem\u3ex\u3c/em\u3e\u3c/sub\u3eGa\u3csub\u3e1−\u3cem\u3ex\u3c/em\u3e\u3c/sub\u3eS\u3csub\u3e2\u3c/sub\u3e Chalcopyrite Nanoparticles via Microwave Batch Reactions

Various quaternary CuInxGa1−xS2 (0 ≤ x ≤ 1) chalcopyrite nanoparticles have been prepared from molecular single-source precursors via microwave decomposition. We were able to control the nanoparticle size, phase, stoichiometry, and solubility. Depending on the choice of surface modifiers used, we were able to tune the solubility of the resulting nanoparticles. This method has been used to generate up to 5 g of nanoparticles and up to 150 g from multiple batch reactions with excellent reproducibility. Data from UV-Vis, photoluminescence, X-ray diffraction, TEM, DSC/TGA-MS, and ICP-OES analyses have shown high reproducibility in nanoparticle size, composition, and bandgap

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

BACKGROUND: Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). OBJECTIVE: This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient\u27s mortality using their longitudinal EHR data. METHODS: We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient\u27s encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians\u27 input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. RESULTS: Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models ( \u3c 0.86). In addition, physicians\u27 agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model. CONCLUSIONS: Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality