High-performance composite from epoxy and glass fibers : morphology, mechanical, dynamic mechanical, and thermal analysis

Diglycidyl ether of bisphenol-A type epoxy resin cured with diamino diphenyl sulfone was used as the matrix for fiber-reinforced composites to get improved mechanical and thermal properties for the resulting composites. E-glass fiber was used for fiber reinforcement. The morphology, tensile, flexural, impact, dynamic mechanical, and thermal properties of the composites were analyzed. The tensile, flexural, and impact properties showed dramatic improvement with the addition of glass fibers. Dynamic mechanical analysis was performed to obtain the Tg of the cured matrix as well as the composites. The improved thermal stability of the composites was clear from the thermogravimetric analysis. Scanning electron micrographs were taken to understand the interfacial adhesion between the fiber and the matrix. The values of mechanical properties were compared with modified epoxy resin composite system. Predictive models were applied using various equations to compare the mechanical data obtained theoretically and experimentally.

Multi-market industrial organizational economic models for the internationalization process by small and medium enterprise construction design service firms

Small and medium enterprises (SMEs) are critical to strategic initiatives in an economy; however, their contribution to foreign trade is not as significant. SMEs are one of the principal driving forces in economic development. One of the greatest challenges is the internationalization process for longevity rather than seeing the process as initial market entry. The internationalization process research has typically involved four key constructs: market selection, decision to enter, entry modes and factors affecting entry modes. Past research has focused on large manufacturing firms. The export of architectural, engineering and construction (AEC) firms has undergone growth, yet there is still significant opportunity for further growth. The majority of AEC firms are SMEs. Notwithstanding assistance provided through international trade missions, organized export firm support networks and information packages by a burgeoning number of government agencies, there are still perceived barriers to market entry and long-term economic sustainability for SMEs. There are a number of problems faced by SMEs acting in foreign trade. This investigation explores the successful initial internationalization process constructs and identifies unique project-oriented sector characteristics. The study identified similarities and differences between two firms that have been exporting to various localities, including Eastern Europe, Africa, Middle East, UK, Asia and South America, for more than two decades. The similarities and differences were identified within eight major constructs: purpose, firm type, market image and design philosophy, entry mode strategy, institutional arrangement, factors affecting mode of entry, market selection and firm strategy in relation to project selection. The primary reasons for internationalization were associated with the firms' motivations related to growth and financial viability. This article discusses the various internationalization processes and strategies intrinsic to each case study and establishes a detailed set of empirical observations from which to develop further a grounded theoretical model of reflexive capability for the internationalization process. This study contributes to the body of knowledge around the SME AEC design service firm's internationalization process, as a dynamic, evolving and continuously adaptable construct for project-based sectors.

Observation-switching linear dynamic systems for tracking humans through unexpected partial occlusions by scene objects

This paper focuses on the problem of tracking people through occlusions by scene objects. Rather than relying on models of the scene to predict when occlusions will occur as other researchers have done, this paper proposes a linear dynamic system that switches between two alternatives of the position measurement in order to handle occlusions as they occur. The filter automatically switches between a foot-based measure of position (assuming z = Q) to a head-based position measure (given the person's height) when an occlusion of the person's lower body occurs. No knowledge of the scene or its occluding objects is used. Unlike similar research [2, 14], the approach does not assume a fixed height for people and so is able to track humans through occlusions even when they change height during the occlusion. The approach is evaluated on three furnished scenes containing tables, chairs, desks and partitions. Occlusions range from occlusions of legs, occlusions whilst being seated and near-total occlusions where only the person's head is visible. Results show that the approach provides a significant reduction in false-positive tracks in a multi-camera environment, and more than halves the number of lost tracks in single monocular camera views.

Online context recognition in multisensor systems using Dynamic Time Warping

In this paper, we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA's benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy.

Using models in teaching and learning science

Models can be excellent tools to help explain abstract scientific concepts and for students to better understand these abstract concepts. A model could be a copy or replica, but it can also be a representation that is not like the real thing but can provide insight about a scientific concept. Models come in a variety of forms, such as three dimensional and concrete, two dimensional and pictorial, and digital forms. The features of models often depend on their purpose: for example, they can be visual, to show what something might look like, dynamic to show how something might work, and or interactive to show how something might respond to changes. One model is often not an accurate representation of a concept, so multiple models may be used.
Students’ modelling ability has been shown to improve through instruction and with practice of mapping the model to the real thing, highlighting the similarities and differences. The characteristics of a model that can be used in this assessment include accuracy and purpose. Models are commonly used by science teachers to describe, and explain scientific concepts, however, pedagogical approaches that include students using models to make predictions and test ideas about scientific concepts encourages students to use models for higher order thinking processes. This approach relates the use of models to the way scientists work, reflecting the nature of science and the development of scientific ideas. This chapter will focus on the way models are used in teaching: identifying pedagogical processes to raise students’ awareness of characteristics of models. In this way, the strengths and limitations of any model are assessed in relation to the real thing so that the accuracy and merit of the model and its explanatory power can be determined.

In vitro strain measurements in cerebral aneurysm models for cyber-physical diagnosis

Background: The development of new diagnostic technologies for cerebrovascular diseases requires an understanding of the mechanism behind the growth and rupture of cerebral aneurysms. To provide a comprehensive diagnosis and prognosis of this disease, it is desirable to evaluate wall shear stress, pressure, deformation and strain in the aneurysm region, based on information provided by medical imaging technologies. Methods: In this research, we propose a new cyber-physical system composed of in vitro dynamic strain experimental measurements and computational fluid dynamics (CFD) simulation for the diagnosis of cerebral aneurysms. A CFD simulation and a scaled-up membranous silicone model of a cerebral aneurysm were completed, based on patient-specific data recorded in August 2008. In vitro blood flow simulation was realized with the use of a specialized pump. A vision system was also developed to measure the strain at different regions on the model by way of pulsating blood flow circulating inside the model. Results: Experimental results show that distance and area strain maxima were larger near the aneurysm neck (0.042 and 0.052), followed by the aneurysm dome (0.023 and 0.04) and finally the main blood vessel section (0.01 and 0.014). These results were complemented by a CFD simulation for the addition of wall shear stress, oscillatory shear index and aneurysm formation index. Diagnosis results using imaging obtained in August 2008 are consistent with the monitored aneurysm growth in 2011. Conclusion: The presented study demonstrates a new experimental platform for measuring dynamic strain within cerebral aneurysms. This platform is also complemented by a CFD simulation for advanced diagnosis and prediction of the growth tendency of an aneurysm in endovascular surgery.

A sequence based dynamic SOM model for text clustering

Text clustering can be considered as a four step process consisting of feature extraction, text representation, document clustering and cluster interpretation. Most text clustering models consider text as an unordered collection of words. However the semantics of text would be better captured if word sequences are taken into account.

In this paper we propose a sequence based text clustering model where four novel sequence based components are introduced in each of the four steps in the text clustering process.

Experiments conducted on the Reuters dataset and Sydney Morning Herald (SMH) news archives demonstrate the advantage of the proposed sequence based model, in terms of capturing context with semantics, accuracy and speed, compared to clustering of documents based on single words and n-gram based models.

The effects of training on performance and performance-related states in individual elite athletes : a dynamic approach

There are difficulties undertaking controlled training studies with elite athletes. Thus, data from non-elite performers are often presented in scientific journals and subsequently used to guide general training principles. This information may not be transferable or specific enough to inform training practices in an individual elite athlete. However, the nature of athletic participation at elite levels provides the opportunity to collect training data, performance-related variables, and performance data of elite athletes over long periods. In this paper, we describe how dynamic linear models provide an opportunity to use these data to inform training. Data from an elite female triathlete collected over a 111-day training period were used to model the relationship between training and self-reported fatigue. The dynamic linear model analysis showed the independent effects of the three modes of triathlon training on fatigue, how these can change across time, and the possible influence of other unmeasured variables. This paper shows the potential for the use of dynamic linear models as an aid to planning training in elite athletes.

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.

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.

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.

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.