Hydrological modelling with a dynamic neural fuzzy inference system

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. Data from Heshui catchment (2,275 km2) which is rural catchment in China, comprising daily time series of rainfall and discharge from January 1, 1990 to January 21, 2006 were analyzed. Rainfall and discharge antecedents were the inputs used for the DENFIS and ANFIS models and the output was discharge at the present time. DENFIS model results were compared with the results obtained from the physically-based University Regina Hydrologic Model (URHM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Our analysis shows that DENFIS results are better or at least comparable to URHM, but almost identical to ANFIS.

Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.

Impact of dynamic PHEVs load on renewable sources based distribution system

In this paper, charging effect of dynamic Plug in Hybrid Electric Vehicle (PHEV) is presented in a renewable energy based electricity distribution system. For planning and designing a distribution system, PHEVs are one of the most important factor as it is going to be a spinning reserve of energy, and also a major load for distribution network. A dynamic load model of PHEVs is introduced here based on third order battery model. To determine the system adequacy, it is necessary to do a micro level analysis to know the PHEVs load impact on grid. Scope of such analysis will cover the performance of wind and solar generation with dynamic PHEVs load, as well as the stability analysis of the power grid to demonstrate that it is important to consider the dynamics of PHEVs load in a renewable energy based distribution network.

Market effects on forecasting construction prices using vector error correction models

Construction price forecasting is an essential component to facilitate decision-making for construction contractors, investors and related financial institutions. Construction economists are increasingly interested in seeking a more analytical method to forecast construction prices. Although many studies have focused on construction price modelling and forecasting, few have considered the impacts of large-scale economic events and seasonality. In this study, an advanced multivariate modelling technique, namely the vector correction (VEC) model with dummy variables, was employed. The impacts of global economic events and seasonality are factored into the model to forecast the construction price in the Australian construction market. Research findings suggest that both long-run and dynamic short-term causal relationships exist among the price and levels of supply and demand in the construction market. These relationships drive the construction price and supply and demand, which interact with one another as a loop system. The reliability of forecasting models was examined by the mean absolute percentage error (MAPE) and the Theil's inequality coefficient U tests. The test results suggest that the conventional VEC model and the VEC model with dummy variable are both acceptable for forecasting the construction price, while the VEC model considering external impacts achieves higher prediction accuracy than the conventional VEC model. © 2014 © 2014 Taylor & Francis.

A factor analytical approach to the efficient futures market hypothesis

Most empirical evidence suggests that the efficient futures market hypothesis, henceforth referred to as EFMH, stating that spot and futures prices should cointegrate with a unit slope on futures prices, does not hold, a finding at odds with many theoretical models. This article argues that these results can be attributed in part to the low power of univariate tests, and that the use of panel data can generate more powerful tests. The current article can be seen as a step in this direction. In particular, a newly developed factor analytical approach is employed, which is very general and, in addition, free of the otherwise so common incidental parameters bias in the presence of fixed effects. The approach is applied to a large panel covering 17 commodities between March 1991 and August 2012. The evidence suggests that the EFMH cannot be rejected once the panel evidence has been taken into account. © 2014 Wiley Periodicals, Inc.

Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method

Ionic polymer conductive network composite (IPCNC) actuators are a class of electroactive polymer composites that exhibit some interesting electromechanical characteristics such as low voltage actuation, large displacements, and benefit from low density and elastic modulus. Thus, these emerging materials have potential applications in biomimetic and biomedical devices. Whereas significant efforts have been directed toward the development of IPMC actuators, the establishment of a proper mathematical model that could effectively predict the actuators' dynamic behavior is still a key challenge. This paper presents development of an effective modeling strategy for dynamic analysis of IPCNC actuators undergoing large bending deformations. The proposed model is composed of two parts, namely electrical and mechanical dynamic models. The electrical model describes the actuator as a resistive-capacitive (RC) transmission line, whereas the mechanical model describes the actuator as a system of rigid links connected by spring-damping elements. The proposed modeling approach is validated by experimental data, and the results are discussed. © 2014 Elsevier B.V. All rights reserved.

Condition monitoring of induction motors : A review and an application of an ensemble of hybrid intelligent models

In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e.; (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM-RF ensemble (or FMM-RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM-RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The signals are processed to produce a set of harmonic-based features for classification using the FMM-RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM-RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM-RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments. © 2014 Elsevier Ltd. All rights reserved.

A two-stage dynamic group decision making method for processing ordinal information

In group decision making (GDM) problems, ordinal data provide a convenient way of articulating preferences from decision makers (DMs). A number of GDM models have been proposed to aggregate such kind of preferences in the literature. However, most of the GDM models that handle ordinal preferences suffer from two drawbacks: (1) it is difficult for the GDM models to manage conflicting opinions, especially with a large number of DMs; and (2) the relationships between the preferences provided by the DMs are neglected, and all DMs are assumed to be of equal importance, therefore causing the aggregated collective preference not an ideal representative of the group's decision. In order to overcome these problems, a two-stage dynamic group decision making method for aggregating ordinal preferences is proposed in this paper. The method consists of two main processes: (i) a data cleansing process, which aims to reduce the influence of conflicting opinions pertaining to the collective decision prior to the aggregation process; as such an effective solution for undertaking large-scale GDM problems is formulated; and (ii) a support degree oriented consensus-reaching process, where the collective preference is aggregated by using the Power Average (PA) operator; as such, the relationships of the arguments being aggregated are taken into consideration (i.e., allowing the values being aggregated to support each other). A new support function for the PA operator to deal with ordinal information is defined based on the dominance-based rough set approach. The proposed GDM model is compared with the models presented by Herrera-Viedma et al. An application related to controlling the degradation of the hydrographic basin of a river in Brazil is evaluated. The results demonstrate the usefulness of the proposed method in handling GDM problems with ordinal information.

On exploratory factor analysis: a review of recent evidence, an assessment of current practice, and recommendations for future use

Exploratory factor analysis (hereafter, factor analysis) is a complex statistical method that is integral to many fields of research. Using factor analysis requires researchers to make several decisions, each of which affects the solutions generated. In this paper, we focus on five major decisions that are made in conducting factor analysis: (i) establishing how large the sample needs to be, (ii) choosing between factor analysis and principal components analysis, (iii) determining the number of factors to retain, (iv) selecting a method of data extraction, and (v) deciding upon the methods of factor rotation. The purpose of this paper is threefold: (i) to review the literature with respect to these five decisions, (ii) to assess current practices in nursing research, and (iii) to offer recommendations for future use. The literature reviews illustrate that factor analysis remains a dynamic field of study, with recent research having practical implications for those who use this statistical method. The assessment was conducted on 54 factor analysis (and principal components analysis) solutions presented in the results sections of 28 papers published in the 2012 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. The main findings from the assessment were that researchers commonly used (a) participants-to-items ratios for determining sample sizes (used for 43% of solutions), (b) principal components analysis (61%) rather than factor analysis (39%), (c) the eigenvalues greater than one rule and screen tests to decide upon the numbers of factors/components to retain (61% and 46%, respectively), (d) principal components analysis and unweighted least squares as methods of data extraction (61% and 19%, respectively), and (e) the Varimax method of rotation (44%). In general, well-established, but out-dated, heuristics and practices informed decision making with respect to the performance of factor analysis in nursing studies. Based on the findings from factor analysis research, it seems likely that the use of such methods may have had a material, adverse effect on the solutions generated. We offer recommendations for future practice with respect to each of the five decisions discussed in this paper.

Application of dynamic metabolomics to examine in vivo skeletal muscle glucose metabolism in the chronically high-fat fed mouse.

RATIONALE: Defects in muscle glucose metabolism are linked to type 2 diabetes. Mechanistic studies examining these defects rely on the use of high fat-fed rodent models and typically involve the determination of muscle glucose uptake under insulin-stimulated conditions. While insightful, they do not necessarily reflect the physiology of the postprandial state. In addition, most studies do not examine aspects of glucose metabolism beyond the uptake process. Here we present an approach to study rodent muscle glucose and intermediary metabolism under the dynamic and physiologically relevant setting of the oral glucose tolerance test (OGTT). METHODS AND RESULTS: In vivo muscle glucose and intermediary metabolism was investigated following oral administration of [U-(13)C] glucose. Quadriceps muscles were collected 15 and 60 min after glucose administration and metabolite flux profiling was determined by measuring (13)C mass isotopomers in glycolytic and tricarboxylic acid (TCA) cycle intermediates via gas chromatography-mass spectrometry. While no dietary effects were noted in the glycolytic pathway, muscle from mice fed a high fat diet (HFD) exhibited a reduction in labelling in TCA intermediates. Interestingly, this appeared to be independent of alterations in flux through pyruvate dehydrogenase. In addition, our findings suggest that TCA cycle anaplerosis is negligible in muscle during an OGTT. CONCLUSIONS: Under the dynamic physiologically relevant conditions of the OGTT, skeletal muscle from HFD fed mice exhibits alterations in glucose metabolism at the level of the TCA cycle.

Predicting migratory flight altitudes by physiological migration models

Using the altitudinal profiles of wind, temperature, pressure, and humidity in three flight models, we tried to explain the altitudinal distributions of nocturnal migrants recorded by radar above a desert in southern Israel. In the simplest model, only the tailwind component was used as a predictor of the most preferred flight altitude (T model). The energy model (E model) predicted flight ranges according to mechanical power consumption in flapping flight depending on air density and wind conditions, assuming optimal adjustment of airspeed and compensation of crosswinds, and including the influence of mass loss during flight. The energy-water model (EW model) used the same assumptions and parameters as the E model but also included restrictions caused by dehydration. Because wind was by far the most important factor governing altitudinal distribution of nocturnal migrants, differences in predictions of the three models were small. In a first approach, the EW model performed slightly better than the E model, and both performed slightly better than the T model. Differences were most pronounced in spring, when migrants should fly high according to wind conditions, but when climbing and descending they must cross lower altitudes where conditions are better with respect to dehydration. A simplified energy model (Es model) that omits the effect of air density on flight costs explained the same amount of variance in flight altitude as the more complicated E and EW models. By omitting the effect of air density, the Es model predicted lower flight altitudes and thus compensated for factors that generally bias height distributions downward but are not considered in the models (i.e. climb and descent through lower air layers, cost of ascent, and decrease of oxygen partial pressure with altitude). Our results confirm that wind profiles, and thus energy rather than water limitations, govern the altitudinal distribution of nocturnal migrants, even under the extreme humidity and temperature conditions in the trade wind zone.

Regulation of granulocyte colony-stimulating factor and its receptor in skeletal muscle is dependent upon the type of inflammatory stimulus

The cytokine granulocyte colony-stimulating factor (G-CSF) binds to its receptor (G-CSFR) to stimulate hematopoietic stem cell mobilization, myelopoiesis, and the production and activation of neutrophils. In response to exercise-induced muscle damage, G-CSF is increased in circulation and G-CSFR has recently been identified in skeletal muscle cells. While G-CSF/G-CSFR activation mediates pro- and anti-inflammatory responses, our understanding of the role and regulation in the muscle is limited. The aim of this study was to investigate, in vitro and in vivo, the role and regulation of G-CSF and G-CSFR in skeletal muscle under conditions of muscle inflammation and damage. First, C2C12 myotubes were treated with lipopolysaccharide (LPS) with and without G-CSF to determine if G-CSF modulates the inflammatory response. Second, the regulation of G-CSF and its receptor was measured following eccentric exercise-induced muscle damage and the expression levels we investigated for redox sensitivity by administering the antioxidant N-acetylcysteine (NAC). LPS stimulation of C2C12 myotubes resulted in increases in G-CSF, interleukin (IL)-6, monocyte chemoattractant protein-1 (MCP-1), and tumor necrosis factor-α (TNFα) messenger RNA (mRNA) and an increase in G-CSF, IL-6, and MCP-1 release from C2C12 myotubes. The addition of G-CSF following LPS stimulation of C2C12 myotubes increased IL-6 mRNA and cytokine release into the media, however it did not affect MCP-1 or TNFα. Following eccentric exercise-induced muscle damage in humans, G-CSF levels were either marginally increased in circulation or remain unaltered in skeletal muscle. Similarly, G-CSFR levels remained unchanged in response to damaging exercise and G-CSF/G-CSFR did not change in response to NAC. Collectively, these findings suggest that G-CSF may cooperate with IL-6 and potentially promote muscle regeneration in vitro, whereas in vivo aseptic inflammation induced by exercise did not change G-CSF and G-CSFR responses. These observations suggest that different models of inflammation produce a different G-CSF response.

Nonlinear large deformation dynamic analysis of electroactive polymer actuators

Electroactive polymers have attracted considerable attention in recent years due to their sensing and actuating properties which make them a material of choice for a wide range of applications including sensors, biomimetic robots, and biomedical micro devices. This paper presents an effective modeling strategy for nonlinear large deformation (small strains and moderate rotations) dynamic analysis of polymer actuators. Considering that the complicated electro-chemo-mechanical dynamics of these actuators is a drawback for their application in functional devices, establishing a mathematical model which can effectively predict the actuator's dynamic behavior can be of paramount importance. To effectively predict the actuator's dynamic behavior, a comprehensive mathematical model is proposed correlating the input voltage and the output bending displacement of polymer actuators. The proposed model, which is based on the rigid finite element (RFE) method, consists of two parts, namely electrical and mechanical models. The former is comprised of a ladder network of discrete resistive-capacitive components similar to the network used to model transmission lines, while the latter describes the actuator as a system of rigid links connected by spring-damping elements (sdes). Both electrical and mechanical components are validated through experimental results.

Implementing novel models of posttreatment care for cancer survivors: Enablers, challenges and recommendations

AIM: The American Society of Clinical Oncology and US Institute of Medicine emphasize the need to trial novel models of posttreatment care, and disseminate findings. In 2011, the Victorian State Government (Australia) established the Victorian Cancer Survivorship Program (VCSP), funding six 2-year demonstration projects, targeting end of initial cancer treatment. Projects considered various models, enrolling people of differing cancer types, age and residential areas. We sought to determine common enablers of success, as well as challenges/barriers. METHODS: Throughout the duration of the projects, a formal "community of practice" met regularly to share experiences. Projects provided regular formal progress reports. An analysis framework was developed to synthesize key themes and identify critical enablers and challenges. Two external reviewers examined final project reports. Discussion with project teams clarified content. RESULTS: Survivors reported interventions to be acceptable, appropriate and effective. Strong clinical leadership was identified as a critical success factor. Workforce education was recognized as important. Partnerships with consumers, primary care and community organizations; risk stratified pathways with rapid re-access to specialist care; and early preparation for survivorship, self-management and shared care models supported positive project outcomes. Tailoring care to individual needs and predicted risks was supported. Challenges included: lack of valid assessment and prediction tools; limited evidence to support novel care models; workforce redesign; and effective engagement with community-based care and issues around survivorship terminology. CONCLUSION: The VCSP project outcomes have added to growing evidence around posttreatment care. Future projects should consider the identified enablers and challenges when designing and implementing survivorship care.