Combination Of Multiple Parameters Research Articles

An approach for a reliable detection of grinding burn using the Barkhausen noise multi-parameter analysis

High loaded, case-hardened workpieces are often finished by grinding to meet required form tolerances and generate high-quality surfaces. The thermo-mechanical load during grinding influences the surface integrity and therefore the functional behavior of the workpieces. To increase productivity, grinding processes are often operated at their limit. Therefore, already even minor deviations from the desired process conditions can lead to thermo-mechanical damages, often referred to as grinding burn. Micromagnetic testing methods allow for a nondestructive analysis of the surface integrity of ground workpieces. Often, a combination of multiple parameters is used to get distinct results. In this study, the Barkhausen noise measurement was used for a nondestructive characterization of process specific influences on the surface integrity in order to further improve the grinding burn detection. Case-hardened workpieces made of AISI 4820 steel were finished by outer diameter grinding with varying specific material removal rates Q’w in a range of 0.5 mm³/(mm s) to 24 mm³/(mm s). Determining the resulting surface integrity, different levels of thermo-mechanical influence on the workpiece surface layer were detected. A detailed analysis of the effective Barkhausen noise (RMS-value), the Coercivity and the Peak Position was performed in order to evaluate the influence of the magnetization parameters on the resulting signals depending on the surface integrity. A varying sensitivity for indication of thermo-mechanical damages with different magnetization parameters was found. The presented results allow for an improvement of the reliability of the Barkhausen noise multi-parameter grinding burn analysis.

Neuronal population model of globular bushy cells covering unit-to-unit variability.

Computations of acoustic information along the central auditory pathways start in the cochlear nucleus. Bushy cells in the anteroventral cochlear nucleus, which innervate monaural and binaural stations in the superior olivary complex, process and transfer temporal cues relevant for sound localization. These cells are categorized into two groups: spherical and globular bushy cells (SBCs/GBCs). Spontaneous rates of GBCs innervated by multiple auditory nerve (AN) fibers are generally lower than those of SBCs that receive a small number of large AN synapses. In response to low-frequency tonal stimulation, both types of bushy cells show improved phase-locking and entrainment compared to AN fibers. When driven by high-frequency tones, GBCs show primary-like-with-notch or onset-L peristimulus time histograms and relatively irregular spiking. However, previous in vivo physiological studies of bushy cells also found considerable unit-to-unit variability in these response patterns. Here we present a population of models that can simulate the observed variation in GBCs. We used a simple coincidence detection model with an adaptive threshold and systematically varied its six parameters. Out of 567000 parameter combinations tested, 7520 primary-like-with-notch models and 4094 onset-L models were selected that satisfied a set of physiological criteria for a GBC unit. Analyses of the model parameters and output measures revealed that the parameters of the accepted model population are weakly correlated with each other to retain major GBC properties, and that the output spiking patterns of the model are affected by a combination of multiple parameters. Simulations of frequency-dependent temporal properties of the model GBCs showed a reasonable fit to empirical data, supporting the validity of our population modeling. The computational simplicity and efficiency of the model structure makes our approach suitable for future large-scale simulations of binaural information processing that may involve thousands of GBC units.

River flow prediction with memory-based artificial neural networks: a case study of the Dholai river basin

Prediction of hydrologic time series has been one of the most challenging tasks in water resources management due to the non-availability of adequate data. Recently, applications of artificial neural networks (ANNs) have proved quite successful in such situation in various fields. This paper demonstrates the use of memory-based ANNs to predict daily river flows. Two different networks, namely the gamma memory neural network (GMN) and genetic algorithm-gamma memory neural network (GA-GMN) have been chosen. The best network topologies for both the ANN models are achieved with Tanh transfer function and Levenberg-Marquardt learning rule after calibrations with multiple combinations of network parameters. The selected ANN models are then used to predict the daily mean flows of Dholai (Rukmi) river in Assam, India, a sub-basin of the Barak river basin. A comparative study of both networks indicates that the GA-GMN model performed better than the GMN model. The GA-GMN model gave better results for both training and testing dataset with minimum training MSE as 0.018 and minimum testing MSE as 22.97. Hence GA-GMN model is selected as an effective tool for predicting flow features of the Dholai river.

Identification of Alpine Glaciers in the Central Himalayas Using Fully Polarimetric L-Band SAR Data

To study the applicability of full polarimetric synthetic aperture radar (SAR) data to identify alpine glaciers in the central Himalayas, six polarimetric decomposition methods were used to obtain 20 polarimetric characteristic parameters based on the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band SAR (PALSAR) data. Object-oriented multiscale segmentation was performed on a Landsat 8 Operational Land Imager (OLI) image prior to classification, and the vector boundaries of different types of training samples were selected from the segmented results. We performed a support vector machine (SVM)-based classification on the characteristic parameters from each polarimetric decomposition. All 20 parameters were then screened and combined according to different requirements: the degree of separability of different types of training samples and the type of scattering mechanisms. The results show that the classification accuracy of the incoherent decomposition characteristics based on the covariance matrix is the best, reaching 87%, and it can exceed 91% after adding the local incidence angle to the suite of classifiers. Eventually, more than 93% accuracy was achieved using a combination of multiple polarimetric parameters, which reduced the misclassification between bare ice and rock. We also analyzed the use of controlling factors on the accuracy of alpine glacier identification and found that the polarimetric information and aspect of the glacier surface are the most important factors. The former is the main basis for identification but the latter will confuse the feature distributions of different categories and cause misclassification.

Assessing the performance of a physically based hydrological model using a proxy‐catchment approach in an agricultural environment

AbstractPhysically based models are useful frameworks for testing intervention strategies designed to reduce elevated sediment loads in agricultural catchments. Evaluating the success of these strategies depends on model accuracy, generally established by a calibration and evaluation process. In this contribution, the physically based SHETRAN model was assessed in two similar U.K. agricultural catchments. The model was calibrated on the Blackwater catchment (18 km2) and evaluated in the adjacent Kit Brook catchment (22 km2) using 4 years of 15 min discharge and suspended sediment flux data. Model sensitivity to changes in single and multiple combinations of parameters and sensitivity to changes in digital elevation model resolution were assessed. Model flow performance was reasonably accurate with a Nash–Sutcliffe efficiency coefficient of 0.78 in Blackwater and 0.60 in Kit Brook. In terms of event prediction, the mean of the absolute percentage of difference (μAbsdiff) between measured and simulated flow volume (Qv), peak discharge (Qp), sediment yield (Sy), and peak sediment flux (Sp) showed larger values in Kit Brook (48% [Qv], 66% [Qp], 298% [Sy], and 438% [Sp]) compared with the Blackwater catchment (30% [Qv], 41% [Qp], 106% [Sy], and 86% [Sp]). Results indicate that SHETRAN can produce reasonable flow prediction but performs less well in estimation of sediment flux, despite reasonably similar hydrosedimentary behaviour between catchments. The sensitivity index showed flow volume sensitive to saturated hydraulic conductivity and peak discharge to the Strickler coefficient; sediment yield was sensitive to the overland flow erodibility coefficient and peak sediment flux to raindrop/leaf soil erodibility coefficient. The multiparameter sensitivity analysis showed that different combinations of parameters produced similar model responses. Model sensitivity to grid resolution presented similar flow volumes for different digital elevation model resolutions, whereas event peak and duration (for both flow and sediment flux) were highly sensitive to changes in grid size.

Melt pool geometry and morphology variability for the Inconel 718 alloy in a laser powder bed fusion additive manufacturing process

Expanding on prior process mapping work by the authors, multiple melt pool cross-sections are measured at multiple process parameter combinations for the Inconel 718 alloy in a Laser Powder Bed Fusion (L-PBF) process. Collection of such data enables the study of the variability of melt pool geometry (e.g. width, depth, and cross-sectional area) across process space. Furthermore, the statistical distribution of the measured melt pool geometries is compared to that of an equivalent normal distribution and intriguing outliers are observed. The cross-sectional morphology of the melt pools are associated with defects such as keyholing porosity and balling and the variability of the defects is quantified. The final product of this work is a robust description of L-PBF In718 melt pool behavior, based on ex-situ observations, which can be linked to in-situ observations of melt pool morphology in future work.

Lipid profile prediction based on artificial neural networks

Lipid profile usually includes levels of total cholesterol (TCH), low density lipoprotein (LDL), high density lipoprotein (HDL) and triglycerides (TG), all of which require a blood test. Using advances in machine learning and a relationship between lipid profile and obesity, a model that predicts lipid profile without using any laboratory results can be developed and used in clinical diagnosis. The causal relationship between lipid profile and obesity is well known—TCH, LDL and TG show an increase, while HDL is decreased in obese persons. In this paper we are using artificial neural networks (ANN) to estimate the lipid profile values using non-lab electronic health record data and some measures of obesity. The ANN inputs are gender, age, systolic and diastolic blood pressures, and a single or a combination of multiple obesity parameters, which include body mass index, saggital abdominal diameter to height ratio, waist to height ratio and body fat percentage. Study shows that the presented solution is suitable for prediction of TCH (with accuracy 81.89%), LDL (with accuracy 79.29%) and HDL (with accuracy 81.23%), while not suitable for TG prediction (with accuracy 44.48%).

Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm

Knowledge of crop phenology assists in making agricultural decisions such as appropriate irrigation and fertilization applications in order to optimize crop yield. The objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random forest algorithm applied to a multi-temporal RADARSAT-2 dataset, acquired during the Soil Moisture Active Passive (SMAP) Validation Experiment 2016 in Manitoba (SMAPVEX16-MB). The model-based and eigen-based polarimetric parameters are used to separate the vegetation and soil scattering contributions in the total radar signal. As the crop morphological shape and structure vary with phenological growth, our study assumes that the polarimetric parameters related to the volume scattering mechanism have the potential to track the crop phenology. The sensitivity of the polarimetric parameters to the ground identified crop phenology is analyzed for different crop types. For canola, a single polarimetric parameter is sufficient to characterize the crop phenology, due to the high volume scattering power and large temporal dynamic. For corn, soybean and wheat, combinations of multiple polarimetric parameters are required. For each crop type, the Random Forest algorithm trained using 60% of the data is used to retrieve the crop phenology. Performances are compared to Artificial Neural Network, Support Vector Machine Regression and k-Nearest neighborhood algorithms. The Random Forest algorithm provides the best phenology retrieval with significant (p-value < 0.01) spearman correlation coefficients (between the retrieved and ground identified phenology) of 0.93, 0.90, 0.85 and 0.91 for canola, corn, soybean and wheat, respectively. While a single polarimetric parameter demonstrates limited sensitivity to corn phenology, the retrieved phenology from the Random Forest algorithm using multiple polarimetric parameters agrees well with the ground measurements. Furthermore, the importance of different polarimetric parameters for phenology retrieval using the Random Forest algorithm is quantified for different crop types. These findings will be of interest in developing future analytical retrieval models.

The interplay between molecular flexibility and RNA chemical probing reactivities analyzed at the nucleotide level via an extensive molecular dynamics study.

Determination of the tridimensional structure of ribonucleic acid molecules is fundamental for understanding their function in the cell. A common method to investigate RNA structures of large molecules is the use of chemical probes such as SHAPE (2'-hydroxyl acylation analyzed by primer extension) reagents, DMS (dimethyl sulfate) and CMCT (1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfate), the reaction of which is dependent on the local structural properties of each nucleotide. In order to understand the interplay between local flexibility, sugar pucker, canonical pairing and chemical reactivity of the probes, we performed all-atom molecular dynamics simulations on a set of RNA molecules for which both tridimensional structure and chemical probing data are available and we analyzed the correlations between geometrical parameters and the chemical reactivity. Our study confirms that SHAPE reactivity is guided by the local flexibility of the different chemical moieties but suggests that a combination of multiple parameters is needed to better understand the implications of the reactivity at the molecular level. This is also the case for DMS and CMCT for which the reactivity appears to be more complex than commonly accepted.

β-Glycosphingolipids as Mediators of Both Inflammation and Immune Tolerance: A Manifestation of Randomness in Biological Systems.

Plasticity in biological systems is attributed to the combination of multiple parameters which determine function. These include genotypic, phenotypic, and environmental factors. While biological processes can be viewed as ordered and sequential, biological randomness was suggested to underline part of them. The present review looks into the concept of randomness in biological systems by exploring the glycosphingolipids-NKT cells example. NKT cells are a unique subset of regulatory lymphocytes which play a role in both inflammation and tolerance. Glycosphingolipids promote an immune balance by changing different arms of the immune system in opposing environments. Traditional immunology looks at skewing the immune system into different directions by different types of activation of the same cell stimulation of different cells subsets, use of different ligands, or different the effect of different immune environments. While these may explain some of the effects, the lack of consistency and opposing results under similar settings may involve randomness which may also be part of real life effects of immunomodulatory agents. It means that several of the biological processes, cannot be explained by simple linear models, and may involve more complex concepts. The application for these concepts for improving therapies to patients with Gaucher disease are discussed.SUMMARY The use of different ligands that target a variety of cell subsets in different immune environments may underlie differences in the functionality of NKT cells and their variability in response to NKT-based therapies. The novel concept of randomness in biology means that several biological processes cannot be solely explained by simple linear models and may instead involve much more complicated schemes of random disorder. These may have implications on future design of therapeutic regimens for improving the response to current treatments.

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model, while simulating double-gyre variation in Regional Ocean Modeling System (ROMS). Conditional Nonlinear Optimal Perturbation related to Parameter (CNOP-P) is an effective method of studying the parameters sensitivity, which represents a type of parameter error with maximum nonlinear development at the prediction time. Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation (CNOP). In the paper, we proposed an improved simulated annealing (SA) algorithm to solve CNOP-P to get the optimal parameters error, studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation. Specifically, we firstly found the non-period oscillation of kinetic energy time series of double gyre variation, then extracted two transition periods, which are respectively from high energy to low energy and from low energy to high energy. For every transition period, three parameters, respectively wind amplitude (WD), viscosity coefficient (VC) and linear bottom drag coefficient (RDRG), were studied by CNOP-P solved with SA algorithm. Finally, for sensitive parameters, their effect on model simulation is verified. Experiments results showed that the sensitivity order is WD>VC≫RDRG, the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.

Data-assisted physical modeling of oxygen precipitation in silicon wafers

A quantitative continuum model for oxide precipitation in silicon is presented that accounts for vacancy absorption and shape change as mechanisms of precipitate stress relief. All model parameters except one, the Si/SiO2 interface free energy, are fixed at values established in prior studies of microdefect formation. The interface free energy is described by an 8-parameter function, whose functional form and dependencies were based on an analysis of electronic structure calculations of small oxide cluster thermodynamics. The interface energy function parameters are regressed, using global optimization, to an experimental benchmark consisting of 13 wafer thermal anneals, with different temperature-time histories and resulting in widely varying measured final oxide precipitate densities. We demonstrate that the model is able to capture the benchmark features well with multiple parameter combinations and that additional constraints are required to fully specify a unique solution. We also show that a simple, single-parameter, constant interface free energy model cannot fully capture the diverse experimental benchmark, highlighting the complexity of oxide precipitation. The precipitation model is used to analyze the mechanisms responsible for several features of oxide nucleation and growth during wafer annealing.

The value of single-source dual-energy CT imaging for discriminating microsatellite instability from microsatellite stability human colorectal cancer.

To demonstrate the value of single-source dual-energy computed tomography (ssDECT) imaging for discriminating microsatellite instability (MSI) from microsatellite stability (MSS) colorectal cancer (CRC). Thirty-eight and seventy-six patients with pathologically proven MSI and MSS CRC, respectively, were retrospectively selected and compared. These patients underwent contrast-enhanced abdominal ssDECT scans before any anti-cancer treatment. Effective atomic number (Eff-Z) in precontrast phase, slope k of spectral HU curve in precontrast (k-P), arterial (k-A), venous (k-V), and delayed phase (k-D), normalized iodine concentration in arterial (NIC-A), venous (NIC-V), and delayed phase (NIC-D), of tumors in two groups were measured by two reviewers. Consistency of measurements was tested by intra-class correlation coefficients (ICC). Mann-Whitney U test or Student's t test was used to compare above values between MSI and MSS. Multivariate logistic regression was used to analyze multiple parameters. Receiver operating characteristic curves were calculated to assess diagnostic efficacies. Interobserver agreement was excellent (ICC > 0.80). MSI CRC had significantly lower values in all measurements (NIC-A, V, D; k-P, A, V, D; Eff-Z) than MSS CRC. For discriminating MSI from MSS CRC, the area under curve (AUC) using k-A was the highest (AUC, 0.803; sensitivity, 72.4%; specificity, 76.3%). The multivariate logistic regression (selection method, Enter) with combined ssDECT parameters (NIC-A, NIC-V, NIC-D, Eff-Z, k-P, k-A, k-V, k-D) significantly improved diagnostic capability with AUC of 0.886 (sensitivity, 81.6%; specificity, 81.6%). The combination of multiple parameters in ssDECT imaging by multivariate logistic regression provides relatively high diagnostic accuracy for discriminating MSI from MSS CRC. • ssDECT generates multiple parameters for discriminating CRC with MSI from MSS. • ssDECT measurements for MSI CRC were significantly lower than MSS CRC. • Combination of ssDECT parameters further improves diagnostic capability for differentiation.

A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties.

In current rice breeding programs, morphological parameters such as plant height, leaf length and width, leaf angle, panicle architecture, and tiller number during the grain filling stage are used as major selection targets. However, so far, there is no robust approach to quantitatively define the optimal combinations of parameters that can lead to increased canopy radiation use efficiency (RUE). Here we report the development of a three-dimensional canopy photosynthesis model (3dCAP), which effectively combines three-dimensional canopy architecture, canopy vertical nitrogen distribution, a ray-tracing algorithm, and a leaf photosynthesis model. Concurrently, we developed an efficient workflow for the parameterization of 3dCAP. 3dCAP predicted daily canopy RUE for different nitrogen treatments of a given rice cultivar under different weather conditions. Using 3dCAP, we explored the influence of three canopy architectural parameters-tiller number, tiller angle and leaf angle-on canopy RUE. Under different weather conditions and different nitrogen treatments, canopy architecture optimized by manipulating these parameters can increase daily net canopy photosynthetic CO2 uptake by 10-52%. Generally, a smaller tiller angle was predicted for most elite rice canopy architectures, especially under scattered light conditions. Results further show that similar canopy RUE can be obtained by multiple different parameter combinations; these combinations share two common features of high light absorption by leaves in the canopy and a high level of coordination between the nitrogen concentration and the light absorbed by each leaf within the canopy. Overall, this new model has potential to be used in rice ideotype design for improved canopy RUE.

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective.

For severely paralyzed people, Brain‐Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain‐based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in‐depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.

Investigating the Mechanical Characteristics of Bone-Metal Implant Interface Using in situ Synchrotron Tomographic Imaging.

Long-term stability of endosseous implants depends on successful bone formation, ingrowth and adaptation to the implant. Specifically, it will define the mechanical properties of the newly formed bone-implant interface. 3D imaging during mechanical loading tests (in situ loading) can improve the understanding of the local processes leading to bone damage and failure. In this study, titanium screws were implanted into rat tibiae and were allowed to integrate for 4 weeks with or without the addition of the growth factor Bone Morphogenetic Protein and the bisphosphonate Zoledronic Acid. Samples were subjected to in situ pullout using high-resolution synchrotron x-ray tomography at the Tomcat beamline (SLS, PSI, Switzerland) at 30 keV with 25 ms exposure time, resulting in a total acquisition time of 45 s per scan, with a 3.6 μm isotropic voxel size. Using a custom-made loading device positioned inside the beamline, screws were pulled out with 0.05 mm increment, acquiring multiple scans until rupture of the sample. The in situ loading protocol was adapted to ensure short imaging time, which enabled multiple samples to be tested with short loading steps, while keeping the total testing time low and reducing dose deposition. Higher trabecular bone content was quantified in the surrounding of the screw in the treated groups, which correlated with increased mechanical strength and stiffness. Differences in screw implantation, such as contact between threads and cortex as well as minor tilt of the screw were also correlated to the mechanical parameters. In situ loading enabled the investigation of crack propagation during the pullout, highlighting the mechanical behavior of the interface. Three typical crack types were observed: (1) rupture at the interface of trabecular and cortical bone tissues, close to the screw, (2) large crack inside the cortex connected to the implant, and (3) first failure away from the screw with cracks propagating toward the screw-bone interface. Mechanical properties of in vivo integrated bone-metal screws rely on a combination of multiple parameters that are difficult to identify and separate one from the other.

Three-dimensional Reduced-Complexity Simulation of Fluvio-Deltaic Clastic Stratigraphy

Abstract A novel reduced-complexity approach to 3D forward modeling of siliciclastic stratigraphy is presented for the simulation of erosion, transport, and sedimentation in continental, transitional, and marine depositional domains. The numerical model is based on defining centerlines that connect sediment input points to the shoreline. For each centerline, erosional and depositional surfaces bound depositional domains, and sand and mud proportions are assigned to each domain. The position of each depositional surface follows a set of geologic rules and ensures mass balance with sediment input. The numerical model is tested by simulating the basin-fill architecture of the XES02 laboratory experiment run at the University of Minnesota, which generates stratigraphy mimicking a passive-margin basin fill. Automatic calibration is used to test multiple combinations of uncertain model input parameters to find those that produce scenarios consistent with the experimental stratigraphy. Calibrated models accurately reproduce shoreline and mass-balance centroid migration, marine sediment proportions, and shoreline trajectories measured in the XES02 experiment. The models also provide reasonable approximations of sand distribution. Of interest, falling shoreline trajectories in the experiment and the calibrated models develop coeval to topset aggradation or topset incision depending on rate of base-level fall. The results reported herein validate the numerical approach for simulating sand distribution in an experimental basin, representing a first step towards the application of the numerical model in fluvio-deltaic settings. For field applications, analogue data can be used to independently constrain the geometry of surfaces bounding depositional domains and their composition. The simplicity of the numerical model then enables multiple realizations by quickly varying uncertain boundary conditions, allowing probabilistic predictions in settings with limited data constraints.

Ensemble Learning Enhanced Stepwise Cluster Analysis for River Ice Breakup Date Forecasting

Frequently occurring ice jams often cause concern in northern regions. Breakup timing is directly related to emergency responses preparation and thus its early accurate forecasting is beneficial to ice-related flooding management. The stepwise cluster analysis (SCA) is a non-parameter regression method, which generates a classification tree in the sense of probability through cutting or merging operations according to certain statistic criteria. To enhance SCA’s predictive performance, a SCA ensemble (SCAE) method is developed and applied to forecasting of annual river ice breakup dates (BDs). In detail, the SCA is employed as a base model at the lower level while the simple average method is selected as combining models at the upper level. The SCA base models are selected according to different performance selection criteria and searched for further combination. A site on a representative river prone to river ice flooding in Alberta, Canada is selected to demonstrate the effectiveness of the proposed SCAE. The results mainly show that: the SCA base models with multiple combinations of inputs and internal parameters are able to predict the BDs with good performances (the highest average of correlation coefficients for training can be 0.958); the optimal SCA base model has three inputs, which indicates that the temperatures before breakup and just after freeze-up as well as the maximum of water flow in March are relatively important indicators of BD. The optimal SCAE, including base models from different performance selection criteria, has the lowest average of root mean squared error, which improves upon the optimal SCA base model by 25.3%. It indicates the different model selection criteria do improve the diversity and thus further help to improve the performance of ensemble models. This first application of the SCAE to river ice forecasting highlights the possibility of using the ensemble learning paradigm to enhance the SCA. The potential applications of the SCAE to other forecasting problems are expected.

Multi-temporal Nonlinear Regression Method for Landsat Image Simulation

Optical remote sensing is limited in its potential for acquiring time-series images due to the restricted weather conditions in which it may be used. The proposed method simulates a Landsat image at a specific time and applies a multiple nonlinear regression equation that provides a higher degree of correlation with the observed data distribution than the commonly used multiple linear regression equation. In this study, Multivariate Adaptive Regression Splines (MARS) and Gaussian Process Regression (GPR) were considered as methods of multiple nonlinear regression. In addition to weather, environmental parameters such as temperature and humidity were added to analyze the input parameters in the regression process. Here, the GPR method of nonlinear regression results show significant improvement in Landsat image simulation. Furthermore, regardless of the season, simulation results using multiple parameter combinations showed the highest correlation with the reference images when temperature (ground), humidity, precipitation, visibility distance, Normalized Difference Vegetation Index (NDVI), and three types of radiation were applied. It was confirmed that introduction of Moderate Resolution Imaging Spectroradiometer (MODIS) products had little positive effects on the results. Thus, the GPR method defined here provides the best simulation results by employing multiple parameters in the calculation.

Phenotipic Heterogeneity Studied By Spade Algorithm in Relapsed/Refractory Acute Myeloid Leukemia

Background.Acute myeloid leukemia (AML) is a heterogeneous disease with respect to cell phenotypes and clinical outcomes.Recently, detailed genetic studies have highlighted the mutational spectrum of clonal variability and increased our knowledge of biologic features of this disease. Nonetheless, characterization of AML cell heterogeneity by traditional Flow Cytometry (FC) is still ineffective because the hierarchical two-dimensional gating analysis of FC data is unable to distinguish all different sub-populations, as defined by the combination of multiple cell parameters. Here, we applied the SPADE algorithm (Ref. Qiu P., Nat Biotechnol, 2011), a new computational approach for dimensional reduction of FC data, in order to represent the intratumoral heterogeneity of AML at single-cell resolution in leukemias at the diagnosis vs. relapsed/refractory AMLs. The purpose of our study was to investigate the clonal dissimilarities of cell subsets as identified by SPADE analysis between diagnosis and relapse/refractory disease status in AML and to define the cell markers mainly associated to the clonal variation of tumor before and after standard therapies.Methods. 30AML patients were investigated at diagnosis and at relapse/ refractory (R/R) disease status by FC analysis.The SPADE algorithm was applied using Cytosplore software (Ref. Hollt. T, 2016) in order to depict the immunophenotype of clonal structures in AML before and after treatment. Each subpopulation was represented by colored circles (nodes) and described the substantial differences between and within individual leukemia subsets. At least a threshold of 7% of events was used to define significant clusters (node) and up to 50 target nodes were used to define the complexity of cell population. All nodes were hierarchically separated by relatedness into a “minimal spanning tree” in relation to the expression levels of 8 Ab markers used for this experiment in the FC panel. The readouts of spanning-tree progression analysis of normalized events (SPADE) reflected the cell heterogeneity underlying the fluorescence intensity of markers used in the FC analysis and defined the relationships among hierarchical cell subsets of leukemia before and after treatment. T-tests and Pearson's coefficient were used to compare antigenic distribution between diagnosis and R/R status. Results.Based on CD45 gating strategy, leukemic population of AML patients at diagnosis showed a median number of nodes significantly lower than that found at R/R disease status (n=4 vs. n= 6 nodes, p=0.0129)(Figure). These data defined an increase of different subpopulations in the R/R subset. Following antigens resulted mainly espressed on the entire study population: CD117, CD34, CD33, CD13, HLADR. The distribution of these markers were further investigated in order to define which cell parameters were able to discriminate the different subpopulations by CD45 expression. Indeed, a comparison of the number of nodes with highest antigenic expression (>90% of intensity value) between AML samples at diagnosis and R/R status was showed. CD117 and CD34 distribution showed a significant difference (p=0.0041 vs p=0.0139, respectively)(Figure) while no statistically significant values were observed among the others. However, only CD34+ nodes showed a significant linear growth between diagnosis and R/R status (p=0.0227).Conclusions. SPADE analysis allowed us to find an increase of subpopulations in R/R AML when compared to samples at diagnosis. CD34 and CD117 antigens mainly characterized these subpopulations, thus defining the cell markers mainly associated to the clonal variation. CD34 antigen was the widest distributed antigen among the new subpopulations, denoting a great instability of more immature clones. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.