Predication Model Research Articles

Development of photodegradable gelatin hydrogels and image analysis technique for automatic optical cell separation system based on the cellular morphology in embedding culture

Event Abstract Back to Event Development of photodegradable gelatin hydrogels and image analysis technique for automatic optical cell separation system based on the cellular morphology in embedding culture Masato Tamura1, Shinji Sugiura1, Taku Satoh1, Toshiyuki Kanamori1, Shibuta Mayu2, Kei Kanie2, Ryuji Kato2, Hirofumi Matsui3 and Masumi Yanagisawa4 1 National Institute of Advanced Industrial Science and Technology (AIST), Department of Life Science and Biotechnology, Japan 2 Nagoya University, Graduate School of Pharmaceutical Sciences, Japan 3 University of Tsukuba, Faculty of Medicine, Japan 4 Engineering System Co., Ltd., Japan This paper reports novel cell separation strategy and automated cell separation system based on the cellular morphology under 3D culture environment in the photodegradable hydrogel. Recently, we developed gelatin-based photodegradable hydrogels by NHS-activated-ester reaction [1], and applied this hydrogels to optical cell separation [2]. More recently, we developed “click-crosslinkable and photodegradable gelatin hydrogels” [3]. The present study applied this hydrogels to the most promising application, cell separation based on the cellular morphology. The present study includes proof concept of morphology-based cell separation and development of automatic cell separation system. 3D culture environment with a specific extracellular matrix regulates cellular function and phenotype. In addition, cancer cell morphology is alternated depending on its malignancy in 3D culture environment [4]. Recently we developed the predication model of stem cell differentiation by image analysis [5]. In this paper, we introduced this image analysis technique and the features of click-crosslinkable and photodegradable gelatin hydrogels to the automated morphology-based cell separation system in 3D culture environment in the above mentioned photodegradable gelatin hydrogels. Figure 1 shows the schematic procedure for optical cell separation based on the cellular morphology under 3D culture environment in the photodegradable hydrogel. Suspension of cells including heterogeneous population is mixed with pregel solutions and cells are encapsulated in the gelatin-based photodegradable hydrogels (Figure 1a). After the culture in 3D environment (Figure 1b), microscopic images of the cells are captured. The captured images are analyzed to distinguish the target cells from the other cells by using the image analysis algorithm, which we previously developed for analyzing stem cells (Figure 2) [5]. The hydrogels around the target area is irradiated with UV light (Figure 1c). The cells in the irradiated area are collected by automated pipetting system (Figure 1d). We developed automated system for this optical cell separation procedure, including cultivation, image acquisition, image analysis, light irradiation, and pipetting for cell collection (Figure 3). We are currently developing an automated image analysis algorithm to distinguish cancer cells from normal cells under 3D environment. The automated optical cell separation system with image analysis algorithm will be applied to the establishment of novel cancer-cell lines from clinical samples such as biopsy tissue. KAKENHI (14J07186); NEDO (1009004)

On Preventing Location Attacks for Urban Vehicular Networks

The prevalence of global positioning system (GPS) equipped in vehicular networks exposes users’ location information to the location-based services. We argue that such data contains rich informative cues on drivers’ private behaviors and preferences, which will lead to the location privacy attacks. In this paper, we proposed a sophisticated prediction model to predict driver’s next location by using ak-order Markov chain-based third-rank tensor representing the partially observed transfer information of vehicles. Then Bayesian Personalized Ranking (BPR) is used to learn the unobserved transitions within the tensor for transition predication. Experimental results manifest the efficacy of the proposed model in terms of location predication accuracy, compared with several state-of-the-art predication methods. We also point out that the precision achieved by such advanced predication model is restricted to the order of the Markov chaink. Accordingly, we propose a schema to decrease the risks of such attacks by preventing the conformation of higher order Markov chain. Experimental results obtained based on the real-world vehicular network data demonstrated the effectiveness of our proposed schema.

Forecasting the Jordanian stock index: modelling asymmetric volatility and distribution effects within a GARCH framework

The modelling of market returns can be especially problematical in emerging and frontier financial markets given the propensity of their returns to exhibit significant non-normality and volatility asymmetries. This paper attempts to identify which representations within the GARCH family of models can most efficiently deal with these issues. A number of different distributions (normal, Student t, GED and skewed Student) and different volatility of returns asymmetry representations (EGARCH and GJR- -GARCH) are examined. Our data set consists of daily Jordanian stock market returns over the period January 2000 – November 2014. Using both the Superior Predicative Ability (SPA) and Model Confidence Set (MCS) testing frameworks it is found that using GJR-GARCH with a skewed Student distribution most accurately and efficiently forecasts Jordanian market movements. Our findings are consistent with similar research undertaken in respect to developed markets.

Fuzzy time series-based cross-layer optimization scheme for cooperative communication in wireless network

The cross-layered concept of wireless network breaks the traditional network layered concept to realize interaction between layers. In addition, the broadcasting nature of a wireless environment makes cooperative communications possible. In this paper, by utilizing the cross-layer optimization scheme, we introduce the fuzzy time series predication model that has linear computational complexity to forecast network performance. When we establish a fuzzy time series model, the sent queue length of the link layer is taken into consideration for the network layer's throughput prediction. In addition, we propose a fuzzy time series-based congestion control framework that is combined with a cooperative communication mechanism, and we apply the fuzzy time series prediction results in the preprocessing of network congestion. Through simulation, we demonstrate that the proposed cross-layer cooperative strategies achieve significant network throughput improvement and average packet delivery ratio.

Application of response surface methodology for ultrasound-assisted rapid ‎adsorption of meso-tetrakis(4-sulfonatophenyl) porphyrin by copper ‎nanowire-loaded in activated carbon: Characterization, equilibrium and ‎kinetic modeling

<div> <p>The combined ultrasonic assisted/nanoparticle based procedure was described for an economical and rapid removal of meso-tetrakis (4-sulfonatophenyl) porphyrin (TSPP) by copper nanowires loaded on activated carbon (Cu-NW-AC). The synthesized Cu-NW-AC was investigated by scanning electron microscopy (SEM) and X-ray diffraction (XRD). Response surface methodology (RSM) combined with central composite design (CCD) give useful information about the individual contribution and interaction amoung variables correspound to above adsorption. In CCD, the effects of variables in the following range, pH (X<sub>1</sub>: 5.0-7.0), adsorbent dosage (X<sub>2</sub>: 0.021-0.051 g), initial TSPP concentration (X<sub>3</sub>: 3-15 mg l<sup>-1</sup>) and ultrasound time (X<sub>4</sub>: 2-10 min.) was investigated to obtain maximum adsorption effeciency. The experimental data were subsequently fitted to a second-order polynomial equation using multiple regression analysis by appropriate statistical methods. According to the results, the optimum adsorption conditions were found to be Cu-NW-AC = 0.04 g, TSPP = 6 mg l<sup>-1</sup>, pH = 5.5 and ultrasound time = 8.0 min. The experimental extraction yield under optimal conditions was found to be 97.60% which confirmed by three replicate at optimum conditions leading to removal percentage of 98.16%. The adsorption equilibrium isotherm and kinetic models investigation revealed the suitability of Langmuir isotherm and pseudo-second-order model for best predication of experimental data. Maximum monolayer capacity (Q<sub>m</sub>) calculated from Langmuir model was found to be 26.385 mg g<sup>-1</sup>.</p> </div> <p> </p>

TBARS and microbial growth predicative models of pork sausage stored at different temperatures

Kinetic models were developed for quality changes of emulsified pork sausages to predict the TBARS (2-thiobarbituric acid reactive substance) and TVC (total viable counts) changes at different temperatures during storage. Kinetic models of TBARS changes with respect to the temperature during storage were developed based on Arrhenius equation. The regression coefficients (R2 > 0.86) indicated the acceptability of the zero-order reaction and Arrhenius model for predicting TBARS changes. The activation energy (Ea) of TBARS was 109 kJ/mol and the corresponding rate constant (k0) was 2.6979 × 1018. External model validation was performed for the sausage stored at 12°C. A high correlation between the observed and modelled TBARS values as well as low RMSE level were obtained. The Baranyi model was fitted to the growth curves. The polynomial model predicted more accurately the influence of temperature on the growth rate, reaching the high adjusted determination coefficient (0.98). Therefore, the established models could effectively predict TBARS content and TVC growth in the emulsified pork sausages.

Energy-Efficiency based Clustering and Data Aggregation for Wireless Sensor Networks

wireless sensor network is maximizing the lifetime of the network. All sensor nodes directly send information to the base station so the energy requirement is very high. The data collection mechanism comes at the cost of power consumption and packet collision. We introduce automatic time series modeling based data aggregation scheme in wireless sensor networks. The decrease in the number of transmitting data values between sensor node and aggregation by using the time series predication model. We proposed in the paper energy-efficient cluster for wireless sensor network which is better suited periodical data gathering from the application. We proposed Energy -Efficiency Clustering and Data aggregation for sensor Network. There are four phases: cluster head, cluster head selection, data aggregation and maintenance. Important issued in wireless sensor network is minimize the total energy consumption requires to collect data. Keywordssensor network, data aggregation, clustering, energy

Abstract 319: Development and Validation of a Mortality Prediction Model for Hospitalized Heart Failure Patients

Background: Clinical predication models for hospitalized patients with heart failure have good performance characteristics but require medical record review, which is both costly and time-consuming. In contrast, administrative claims data are readily available for research but have limited clinical detail. Methods: We used highly detailed billing data to derive a multivariable in-hospital mortality prediction model for patients with heart failure. We validated the model in a separate multi-hospital dataset derived from electronic health record (EHR) data. For the derivation, we included patients aged ≥18 years admitted to one of 433 hospitals that participate in the Premier, Inc. Data Warehouse (PDW) between January 2009 and June 2011. Patients with a principal ICD-9-CM diagnosis of heart failure or principal diagnosis of respiratory failure with secondary diagnosis of heart failure were included. After dividing the dataset into derivation and validation sets, we used generalized estimating equations to estimate the parameters of a generalized linear model that adjusted for clustering within hospitals. The model included selected patient demographics, comorbidities, history of prior heart failure admissions, and initial medications and therapies (e.g., inotropes, mechanical ventilation) administered in the first 2 hospital days. We applied the models to the validation set. Finally, we applied the coefficients from the model to a novel multi-hospital dataset, HealthFacts (Cerner Corp), which contains information about hospitalizations and is derived from the electronic health record of 76 participating sites (years 2010-2012). Results: In PDW, we identified 200,832 patients ≥18 years old with a diagnosis of heart failure. Of these, 80% (160,839) were randomly assigned to the derivation cohort and the remaining 20% (39,993) were assigned to the validation cohort. Mortality was 4.0%. In the derivation cohort, the model showed a c-statistic of 0.79 Factors that were most strongly associated with mortality included age over 80 years, early inotropes, early vasopressors, and diagnosis of acute kidney injury that was present on admission. In the validation cohort, the c-statistic was 0.79. We then validated the model in the HealthFacts dataset, which had a higher percentage of African American patients (28% vs. 17%). In this cohort, the c-statistic was 0.82. Conclusions: A heart failure mortality model based on detailed administrative data available in the first 2 hospital days had excellent performance characteristics in a derivation and an external, EHR-based validation dataset. When clinical data are not available, this model may be useful for severity adjustment in comparative effectiveness studies of heart failure patients.

PCA-SVM Model Building and Application for Financial Distress Predication of Listed Companies

Principal Component Analysis (PCA) is a technique to simplify data collection. Support Vector Machine (SVM) has advantages of suitability of dealing with small sample problem, high dimension, and strong performance of generalization, etc. Therefore, this paper combines the two methods, and proposes a flnancial crisis predication model by integration of principal component analysis into support vector machine. Firstly, complete data pre-processing through extracting principal components of sample set, compress efiectively dimension of sample set, simplify input vectors, and eliminate col-linearity between variables; secondly, train SVM through training set, and on this basis make use of flnancial. data of listed companies to conduct empirical analysis of flnancial predication. Simulation results show that flnancial crisis prediction model built based on PCA-SVM algorithm has superior learning ability and generalization ability, can efiectively reduce the dimensions of sample set, and further improve accuracy of the flnancial prediction, with good feasibility and practical signiflcance.

Cold-formed steel pallet rack connection: an experimental study

Industrial pallet rack systems are made up of three-dimensional structural arrangement using cold-formed steel members. The rack columns (upright) have perforations at prescribed intervals to facilitate the assemblage of beams with end connections. The tabs are used as connections which are engaged into the perforations and are in particular, highly semi-rigid in nature. Due to the diversity of rack systems, connecting member’s stiffness and strength, it is almost impossible to develop a generalised model for analytical predication of the connection stiffness. This paper describes the beam–column connection tests carried out on a commercially available pallet rack system by adopting single cantilever test set-up. Thirty-five sets of combinations are identified based on the variation in upright profile and thickness, depth of beam and the connector to study the connection stiffness. Three tests were performed for each set to bring in uniformity in the result taking the total number of tests to 105. A full range parametric study is carried out to understand the influence of above said parameters on moment–rotation behaviour and the joint stiffness. The experimental results showed that an improved performance of the joint connection is achieved using connectors with more number of tabs, greater thickness and improved profile of the upright and larger depth of the beam.

An Experimental Investigation on Addition of Bentonite on Tannery Wastewater Characteristics

In this study, wastewater generated from Africa tannery in Khartoum North was investigated for removing of chromium from a wastewater by utilizing Bentonite. Experiments were carried out in order to obtain the optimum dose of Bentonite to be applied for the best removal of chromium and other wastewater parameters which include chemical oxygen demand, Oil and grease, total suspended solid, and chloride. For a 15mg/L dose of Bentonite removal rates of 99%, 44%, 94%, 75% and 53% of chromium(Cr), chemical oxygen demand (C.O.D), total suspended solid (T.S.S) and chloride (Cl) were respectively obtained. The Removal time for the above dose 15 mg/L was 6 hours. The results of the test were statistically analyzed by regression technique and a predication model was obtained after three trends of testing (linear, Exponential and polynomial). The exponential trend was the best (R2=0.9368). The prediction model was validated by comparing predicted and measured data and good fit of data were obtained. Therefore, the use of Bentonite to remove chromium is considered as a very successful method, and it can be a solution to the problem of industrial waste resulting from the tanning industry.

Modeling and Optimization of Hybrid Systems

In this paper a predicate transition model for discrete event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model a hybrid Petri net, including explicit differential equations and shared variables, is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30% reduction in energy consumption.

Determination of nuclear magnetic resonance T2 cutoff value based on multifractal theory — An application in sandstone with complex pore structure

The cutoff value of nuclear magnetic resonance (NMR) transversal relaxation time [Formula: see text] is vital for pore structure characterization, permeability prediction, and irreducible water saturation calculation. Conventional default values often lead to inaccurate results for rocks with complex pore structure. Based on NMR experiments and multifractal theory, we have developed an effective statistical method to predict [Formula: see text] cutoff values without other petrophysical information. The method is based on multifractal theory to analyze the NMR [Formula: see text] spectrum with the assumption that the [Formula: see text] spectrum is an indicator of pore size distribution. Multifractal parameters, such as multifractal dimension, singularity strength, and mass exponent, are calculated to investigate the multifractal behavior of [Formula: see text] spectrum via NMR experiments and a dyadic scaling-down algorithm. To obtain the optimal [Formula: see text] cutoff value, the rotation speed and time of centrifugation are enlarged increasingly to optimal centrifugal state. A predicating model for [Formula: see text] cutoff value based on multiple linear regressions of multifractal parameters was proposed after studying the influential factors. On the basis of the multifractal analysis of NMR [Formula: see text] spectrum, a reasonable predication model for [Formula: see text] cutoff value was rendered. Upon testing, the predicted results were highly consistent with the experimental results.

TRAINING SYSTEM OF FUTURE SPECIALISTS: QUALITY CONTROL

The aim of the investigation is development of innovative strategy of quality control training of engineers and skilled workers (hereinafter – future specialists) in educational professional organizations on the principles of social partnership. Methods. Theoretic: theoretic and methodological analysis, polytheoretic synthesis, modeling. Empirical: research and generalization of the system, process and competence – based approaches experience, experiment, observation, surveys, expert evaluation, SWOT-analysis as a method of strategic planning which is to identify the internal and external factors (socio-cultural) of the organization surrounding. Results. The strategy of the development of the process of quality control training in educational professional organizations and a predictive model of the system of quality control training for future engineers and workers have been created on the analysis and synthesis of a quantitative specification of the quality, the obtained experience and success in control training of future specialists in educational professional organizations in recent economic and educational conditions. Scientific novelty. There has been built a predicative model of quality control training of future specialists to meet modern standards and the principles of social partnership; the control algorithm of the learning process, developed in accordance with the standards (international) of quality ISO in the implementation of the quality control systems of the process approach (matrix-based responsibility, competence and remit of those responsible for the education process in the educational organization, the «problem» terms and diagnostic tools for assessing the quality of professional training of future specialists). The perspective directions of innovation in the control of the quality of future professionals training have been determined; the parameters of a comprehensive analysis of the state of the system to ensure the quality of their training on the basis of procedures for self-examination and questioning have been supported; the improving system technology to ensure the quality of vocational training have been developed; the implementation mechanism of the system of quality training control training of future specialists in the professional organization have been specified. Practical significance. The obtained results exploitation in educational practice can improve the effectiveness of the innovative development of the quality control training system of future specialists in educational organizations.

Dynamic Analysis of Competitive Technology Innovation Diffusion Model

Technological innovation diffusion theory and models play an important position in the forecasting field. Many researchers use these models to predict in different areas. Although the basic Bass model easy to use, but there are many limitations, the effects of Bass diffusion model for innovation diffusion factors (such as price, advertising, income, and technology supply) cannot measure. In order to find the principles of the relationships between innovations and the importance of the affection on the active of an innovation, this paper reviews the application of innovation diffusion models in the field of demand forecasting, through summarizing and analyzing a variety of predication models. Competitive innovation diffusion model include of monopolistic competition in a technology diffusion models and the evolution of the coexistence of two technologies competing diffusion evolution model. Under the aim of this paper, it uses mathematical methods in-depth analysis of competition, technological innovation diffusion models. This mathematical method includes nonlinear equilibrium point, stability analysis. Through the asymmetric evolutionary stability analysis, the paper derived the long-term evolutionary stable equilibrium of the innovation. Finally, the paper gives some suggestions on how to strength the competitive technology innovation in diffusion model.

A Data Predication Model for Integrating Wireless Sensor Networks and Cloud Computing

Cloud computing has been proved to be a promising solution for managing and processing big data by providing a data center centric and efficient algorithms for managing and organizing the data. One of the cloud system's data sources is Wireless Sensor Networks (WSNs). WSNs present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which a fine-detailed data streams lead to exhaustive energy consumption. In this paper, we propose a data prediction model that is built within the sensor nodes and used by the cloud system to generate the data. The purpose of the proposed model is to exempt the sensor nodes from sending a large amount of data and thus reduces the energy consumption of the sensor's battery. We manage to formulate the prediction model as a line equation through two n-dimensional vectors in n-space. Initial results showed that the proposed model will be capable to achieve a better error rate as compared to traditional data prediction techniques.

A Socioecological Predication Model of Posttraumatic Stress Disorder in Low-Income, High-Risk Prenatal Native Hawaiian/Pacific Islander Women.

Only recently has perinatal posttraumatic stress disorder (PTSD) been researched in any depth; however, the causes and consequences of this serious illness remain unclear. Most commonly, childbirth trauma and interpersonal violence have been reported as contributing factors. However, not all Native Hawaiian/Pacific Islander (NHPI) women who experience these events experience PTSD. The factors affecting PTSD are many and complex, intertwining individual, family, and community contexts. Using a socioecological framework, 3 levels of contextual variables were incorporated in this study (individual, family, and social/community). The purpose of this study was to determine the socioecological predictors associated with prenatal PTSD among NHPI. A case-control design was used to collect retrospective data about socioecological variables from medical record data. The sample was low-income, high-risk NHPI women receiving perinatal health care at a rural community health center in Hawaii who screened positive (n = 55) or negative (n = 91) for PTSD. Hierarchical logistic regression was conducted to determine socioecological predictors of positive PTSD screening. Although the majority of women (66.4%) experienced some form of interpersonal violence, a constellation of significant predictor variables from all 3 levels of the model were identified: depression (individual level), lack of family support and family stress (family level), and violence (social/community level). Each of the predictor variables has been identified by other researchers as significantly affecting perinatal PTSD. However, it is because these variables occur together that a more complex picture emerges, suggesting the importance of considering multiple variables in context when identifying and caring for these women. Although additional research is needed, it is possible that the significant predictor variables could be useful in identifying women who are at higher risk for PTSD in other similar populations.

Towards the development of a predictive model of the formulation-dependent mechanical behaviour of edible oil-based ethylcellulose oleogels

The functionality of the recently discovered ethylcellulose (EC) oleogel systems are highly dependent on the mechanical strength of these gels. This mechanical strength depends strongly on a variety of different compositional parameters. Here we report on a predicative model relating the mechanical strength of EC oleogels as a function of both gelator and surfactant concentration for a variety of oils, surfactants, and EC molecular weights. Predictive modelling was based on a goodness-of-fit approach achieved in a step-wise fashion. Two-dimensional fits were performed on the experimental data for gel strength as a function of both mass fraction ethylcellulose (0.07–0.15) and surfactant (fixed to three EC/surfactant ratios) to obtain candidate equations for the eventual three-dimensional model. It was discovered that gel strength increases with mass fraction of EC in a power law fashion for all conditions tested. Comparison of mechanical strength determined by back extrusion and texture profile analysis (TPA) showed similar trends in scaling behaviour. In addition, the behaviour of the elastic constant extracted from TPA was also found to follow a power law with increasing gelator concentration. Finally, the validity of the predictive model was investigated through interpolation.

Predictive Models for Pre-Operative Diagnosis of Rotator Cuff Tear: A Comparison Study of Two Methods between Logistic Regression and Artificial Neural Network

Rotator cuff tears are the most common disorder of the shoulders.agnetic resonance Image (MRI) is the diagnostic gold standard of rotator cuff tears. However, there are some dilemmas in the rotator cuff tears treatment. Clinically, surgical results of rotator cuff tears are sometimes different from MRI results of rotator cuff tears. The main purpose of this study is to build up predicative models for pre-operative diagnosis of rotator cuff tears There are two models of this study are proposed: logistic regression model and artificial neural network model. Patients are divided into two sets: Set1 is patients with full thickness rotators cuff tears. Set 2 is patients with partial thickness rotators cuff tears. The charts of 158 patients are completely reviewed and the collected data were analyzed. The results showed that the predictive accuracy of artificial neural networks model is higher than the predictive accuracy of logistic model. The application of this study can assist doctors to increase the accuracy rate of pre-operative diagnosis and to decrease the legal problems.

Research on Vehicle Rollover Warning System

In this paper, a TTR vehicle rollover predication algorithm is calculated. A vehicle rollover predication model is built. Also, a vehicle rollover predication warning system is designed by using TMS320LF2407 DSP, and the warning predication theory is introduced. The results show that the rollover prediction model can predicate rollover occur time effectively and the TTR rollover predication method can be used to give direction to the driver.