Emergency Management System for Sudden Water Pollution Accidents

The emergency management system for sudden water pollution accidents of the main canal is the integrated application of the aforesaid three key technologies and is the key to verify the effect of practical application of these technologies. The emergency management system is formed by integrating basic information, measured data, and professional models through the communication mode of network transmission. The system can provide support for emergency response in case of emergency conditions including sudden water pollution accidents and technical support for security operations of the MRP

Traceability Technology for Sudden Water Pollution Accidents in Rivers

The traceability technology for sudden water pollution accidents can be used for fast, accurate identification of a pollution source in the river. A correlation optimization model with the pollution source position and release time as its parameters is established based on hydrodynamic calculation and on the coupling relationship between forward concentration probability density and backward position probability density; and the solution of the model is realized by using a differential evolution algorithm (DEA). A coupled probability density method is to convert the traceability of a sudden water pollution accident into the optimization of two minimum values. This method is simple in principle and easy in solution, realizing the decoupling of parameter of the pollution source. The concept of gradient is introduced to the differential evolution algorithm, improving the efficiency of searching process. The proposed method of traceability was applied to the emergency demonstration project of the SNWDMRP. The results indicate that the model has good efficiency of traceability and high simulation precision and that traceability results have a certain guiding significance to the emergent regulation and control of sudden water pollution events in a river

Enable High-resolution, Real-time Ensemble Simulation and Data Assimilation of Flood Inundation using Distributed GPU Parallelization

Numerical modeling of the intensity and evolution of flood events are affected by multiple sources of uncertainty such as precipitation and land surface conditions. To quantify and curb these uncertainties, an ensemble-based simulation and data assimilation model for pluvial flood inundation is constructed. The shallow water equation is decoupled in the x and y directions, and the inertial form of the Saint-Venant equation is chosen to realize fast computation. The probability distribution of the input and output factors is described using Monte Carlo samples. Subsequently, a particle filter is incorporated to enable the assimilation of hydrological observations and improve prediction accuracy. To achieve high-resolution, real-time ensemble simulation, heterogeneous computing technologies based on CUDA (compute unified device architecture) and a distributed storage multi-GPU (graphics processing unit) system are used. Multiple optimization skills are employed to ensure the parallel efficiency and scalability of the simulation program. Taking an urban area of Fuzhou, China as an example, a model with a 3-m spatial resolution and 4.0 million units is constructed, and 8 Tesla P100 GPUs are used for the parallel calculation of 96 model instances. Under these settings, the ensemble simulation of a 1-hour hydraulic process takes 2.0 minutes, which achieves a 2680 estimated speedup compared with a single-thread run on CPU. The calculation results indicate that the particle filter method effectively constrains simulation uncertainty while providing the confidence intervals of key hydrological elements such as streamflow, submerged area, and submerged water depth. The presented approaches show promising capabilities in handling the uncertainties in flood modeling as well as enhancing prediction efficiency

SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection

Genome-wide association study identifies single-nucleotide polymorphism in KCNB1 associated with left ventricular mass in humans: The HyperGEN Study

<p>Abstract</p> <p>Background</p> <p>We conducted a genome-wide association study (GWAS) and validation study for left ventricular (LV) mass in the Family Blood Pressure Program – HyperGEN population. LV mass is a sensitive predictor of cardiovascular mortality and morbidity in all genders, races, and ages. Polymorphisms of candidate genes in diverse pathways have been associated with LV mass. However, subsequent studies have often failed to replicate these associations. Genome-wide association studies have unprecedented power to identify potential genes with modest effects on left LV mass. We describe here a GWAS for LV mass in Caucasians using the Affymetrix GeneChip Human Mapping 100 k Set. Cases (N = 101) and controls (N = 101) were selected from extreme tails of the LV mass index distribution from 906 individuals in the HyperGEN study. Eleven of 12 promising (<it>Q </it>< 0.8) single-nucleotide polymorphisms (SNPs) from the genome-wide study were successfully genotyped using quantitative real time PCR in a validation study.</p> <p>Results</p> <p>Despite the relatively small sample, we identified 12 promising SNPs in the GWAS. Eleven SNPs were successfully genotyped in the validation study of 704 Caucasians and 1467 African Americans; 5 SNPs on chromosomes 5, 12, and 20 were significantly (<it>P </it>≤ 0.05) associated with LV mass after correction for multiple testing. One SNP (rs756529) is intragenic within <it>KCNB1</it>, which is dephosphorylated by calcineurin, a previously reported candidate gene for LV hypertrophy within this population.</p> <p>Conclusion</p> <p>These findings suggest <it>KCNB1 </it>may be involved in the development of LV hypertrophy in humans.</p

Hydrological Simulation for Karst Mountain Areas: A Case Study of Central Guizhou Province

A groundwater model is needed to describe the complex groundwater confluence process of the groundwater system in karst areas. This is because surface water flows through dolines, grikes, and by other means and is directly exchanged with the groundwater. In this study, using the Xin&#8217;anjiang model, the conversion of surface water into groundwater and the influence of multiple series-parallel underground reservoirs on groundwater confluence through the generalization of dolines in karst areas were simulated. The water cycle process in the Sancha River Basin was simulated with measured data using multiobjective particle swarm optimization. Then, model parameters were validated with measured runoff data and compared with simulation results obtained using the traditional Xin&#8217;anjiang model based on its optimal parameters. The results showed that the determination coefficients of all hydrological stations over the study period were &gt;0.76, and the Nash efficiency coefficient was &gt;0.76, which were better than those for the improved Xin&#8217;anjiang model. Next, the simulation accuracy of the flood period in the karst area was analyzed. The model achieved a high fitting rate for the main flood peaks in a year, and the passing rate for the worst hydrological stations was 53%. Finally, the influence of karst development on the runoff was examined. The results indicate that different karst development stages and the heterogeneity of the karst in the basin have different effects on runoff

A decision theoretic model for stress recognition and user assitance

We present a general unified probabilistic decisiontheoretic model based on Influence Diagrams for simultaneously modeling both user stress recognition and user assistance. Stress recognition is achieved through dynamic probabilistic inference from the available sensory data from multiple-modality sources. User assistance is automatically achieved by balancing the benefits of improving user performance and the costs of performing user assistance. In addition, a non-invasive real-time system is built to validate the proposed framework. Utilizing the evidences from four modalities (physical appearance features, physiological measures, user performance and behavioral data), the system can successfully recognize human stress and provide timely and appropriate assistance in a task-specific environment