Interpreting Fanger's comfort equation within the adaptive paradigm

This paper reviews the evolution of Fanger's heat balance equation in regard of adaptive opportunities. Heat balance and adaptive response are integrated into one model as two fundamental aspects of human-environment interaction that define thermal comfort perception, rather than being seen as two concepts of alternative comfort paradigms. The paper suggests to extent Fanger's model with a heat storage term in order to account for comfort perception under transient thermal conditions, and to review Fanger's modelling assumptions in order to allow for a greater variety of adaptive response options. In the presented model heat exchange is modulated through adaptation of physiological, environmental and behavioural parameters in the human-environment system defined through Fanger's heat exchange equations. A computational prototype is implemented to determine 'comfortable' values and ranges of the six comfort dimensions alternatively to Fanger's comfort indices. Thereby values of for example 'comfortable' clothing and metabolic rate are results rather than necessary input parameters, which are difficult to determine. This approach allows generating design advice for physical, organisational and social environments based on heat balance calculation in the six-dimensional opportunity space defined through Fanger's comfort equation. A starting point for the development of a dynamic adaptive comfort model is set.

Modeling of light propagation through biological tissues : a novel approach

An efficient numerical technique for modeling biological tissues using the radiative transfer equation is presented. Time dependence of the transient radiative transfer equation is approximated using Laguerre expansion. Azimuthal angle is discretized using the discrete ordinates method and the resulting set of ordinary differential equations is solved using the Runge-Kutta-Felhberg method.

An approximate numerical technique for characterizing optical pulse propagation in inhomogeneous biological tissue

An approximate numerical technique for modeling optical pulse propagation through weakly scattering biological tissue is developed by solving the photon transport equation in biological tissue that includes varying refractive index and varying scattering/absorption coefficients. The proposed technique involves first tracing the ray paths defined by the refractive index profile of the medium by solving the eikonal equation using a Runge-Kutta integration algorithm. The photon transport equation is solved only along these ray paths, minimizing the overall computational burden of the resulting algorithm. The main advantage of the current algorithm is that it enables to discretise the pulse propagation space adaptively by taking optical depth into account. Therefore, computational efficiency can be increased without compromising the accuracy of the algorithm.

Laguerre Runge-Kutta-Fehlberg method for simulating laser pulse propagation in biological tissue

An efficient algorithm for solving the transient radiative transfer equation for laser pulse propagation in biological tissue is presented. A Laguerre expansion is used to represent the time dependency of the incident short pulse. The Runge–Kutta– Fehlberg method is used to solve the intensity. The discrete ordinates method is used to discretize with respect to azimuthal and zenith angles. This method offers the advantages of representing the intensity with a high accuracy using only a few Laguerre polynomials, and straightforward extension to inhomogeneous media. Also, this formulation can be easily extended for solving the 2-D and 3-D transient radiative transfer equations.

Testing the expectations hypothesis for interest rate term structure : some Australian evidence

Many test results are found inconsistent with the expectations hypothesis of the term structure. The aim of this paper is to re-examine the expectations hypothesis of the term structure using the Australian interest rate data from 1969(7) to 1995(7). We start with the cointegration test on R_t, r_t, and S_t followed by the Granger causality test from S_t to ∇ r_t. Finally we carry out the VAR model of cross-equation restrictions test. Our findings show that there is no conclusive rejection of the expectations hypothesis of the term structure.

Recrystallization of 304SS during and after high strain rate deformation

During the hot working of austenitic stainless steels the shape of the flow curve is strongly influenced by the strain rate. Low strain rate deformation results in flow curves typical of dynamic recrystallization (DRX) but as the strain rate increases the shape changes to a ‘flat-top’ curve. This has traditionally been thought to indicate no DRX is taking place and that dynamic recovery (DRV) is the only operating softening mechanism. Examining the work-hardening behaviour and corresponding deformation microstructures showed this is not the case for austenitic stainless steel, as clear evidence of dynamic recrystallization process can be seen. The post-deformation recrystallization kinetics can be modelled using a standard Avrami equation with an Avrami exponent, n, of 1.15. With an increasing value of the Zener-Hollomon parameter it was found that the kinetics of recrystallization become less strain rate sensitive until at the highest values (highest strain rates/lowest temperatures) the recrystallization kinetics become strain rate insensitive.

An accelerated particle swarm optimization based levenberg marquardt back propagation algorithm

The Levenberg Marquardt (LM) algorithm is one of the most effective algorithms in speeding up the convergence rate of the Artificial Neural Networks (ANN) with Multilayer Perceptron (MLP) architectures. However, the LM algorithm suffers the problem of local minimum entrapment. Therefore, we introduce several improvements to the Levenberg Marquardt algorithm by training the ANNs with meta-heuristic nature inspired algorithm. This paper proposes a hybrid technique Accelerated Particle Swarm Optimization using Levenberg Marquardt (APSO_LM) to achieve faster convergence rate and to avoid local minima problem. These techniques are chosen since they provide faster training for solving pattern recognition problems using the numerical optimization technique.The performances of the proposed algorithm is evaluated using some bench mark of classification’s datasets. The results are compared with Artificial Bee Colony (ABC) Algorithm using Back Propagation Neural Network (BPNN) algorithm and other hybrid variants. Based on the experimental result, the proposed algorithms APSO_LM successfully demonstrated better performance as compared to other existing algorithms in terms of convergence speed and Mean Squared Error (MSE) by introducing the error and accuracy in network convergence.

Investigation of hydraulic fracture propagation using a post-peak control system coupled with acoustic emission

This study investigates the fracture mechanism of fluid coupled with a solid resulting from hydraulic fracture. A new loading machine was designed to improve upon conventional laboratory hydraulic fracture testing and to provide a means of better understanding fracture behavior of solid media. Test specimens were made of cement mortar. An extensometer and acoustic emission (AE) monitoring system recorded the circumferential deformation and crack growth location/number during the test. To control the crack growth at the post-peak stage the input fluid rate can be adjusted automatically according to feedback from the extensometer. The complete stress-deformation curve, including pre- and post-peak stages, was therefore obtained. The crack extension/growth developed intensively after the applied stress reached the breakdown pressure. The number of cracks recorded by the AE monitoring system was in good agreement with the amount of deformation (expansion) recorded by the extensometer. The results obtained in this paper provide a better understanding of the hydraulic fracture mechanism which is useful for underground injection projects. © 2014 Springer-Verlag Wien.

Modeling and analysis on the propagation dynamics of modern email malware

Due to the critical security threats imposed by email-based malware in recent years, modeling the propagation dynamics of email malware becomes a fundamental technique for predicting its potential damages and developing effective countermeasures. Compared to earlier versions of email malware, modern email malware exhibits two new features, reinfection and self-start. Reinfection refers to the malware behavior that modern email malware sends out malware copies whenever any healthy or infected recipients open the malicious attachment. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. In the literature, several models are proposed for email malware propagation, but they did not take into account the above two features and cannot accurately model the propagation dynamics of modern email malware. To address this problem, we derive a novel difference equation based analytical model by introducing a new concept of virtual infected user. The proposed model can precisely present the repetitious spreading process caused by reinfection and self-start and effectively overcome the associated computational challenges. We perform comprehensive empirical and theoretical study to validate the proposed analytical model. The results show our model greatly outperforms previous models in terms of estimation accuracy. © 2013 IEEE.

Can panel data really improve the predictability of the monetary exchange rate model?

A common explanation for the inability of the monetary model to beat the random walk in forecasting future exchange rates is that conventional time series tests may have low power, and that panel data should generate more powerful tests. This paper provides an extensive evaluation of this power argument to the use of panel data in the forecasting context. In particular, by using simulations it is shown that although pooling of the individual prediction tests can lead to substantial power gains, pooling only the parameters of the forecasting equation, as has been suggested in the previous literature, does not seem to generate more powerful tests. The simulation results are illustrated through an empirical application. Copyright © 2007 John Wiley & Sons, Ltd.

Reliability evaluation for clustered WSNs under malware propagation

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node's MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN.