Hybrid Model

The usual practices of air quality time-series forecasting are based on applying the models that deal with either the linear or nonlinear patterns. As the linear or nonlinear behavior of the time series is not known in advance, one applies the number of models and finally selects the one, which provides the most accurate results. The air pollutant concentration time series contain patterns that are not purely linear or nonlinear and applying either technique may give inadequate results. This study aims to develop a hybrid methodology that can deal with both the linear and nonlinear structure of the time series. The hybrid model is developed using the combination of autoregressive integrated moving average model, which deals with linear patterns and nonlinear dynamical model. To demonstrate the utility of the proposed technique, nitrogen dioxide concentration observed at a site in Delhi during 1999 to 2003 was utilized. The individual linear and nonlinear models were also applied in order to examine the performance of the hybrid model. The performance is compared for one-step and multi-step ahead forecasts using the error statistics such as mean absolute percentage error and relative error. It is observed that hybrid model outperforms the individual linear and nonlinear models. The exploitation of unique features of linear and nonlinear models makes it a powerful technique to predict the air pollutant concentrations.

In this paper, we review our study of excitation coefficients in rare gases and in methane, some of the excitation cross sections that were obtained, the spatial profiles of emission (with absolute calibration) and secondary electron yields. The data for excitation coefficients have been analysed to produce the cross section data in some cases. The spatial profiles of emission at the low currents were used to establish the importance of the non-hydrodynamic relaxation and the contributions of heavy particles and reflected electrons. These data were also used to get more reliable secondary electron yields for rare gases. The spatial emission profiles at higher currents have been applied to obtain field profiles and make comparisons with hybrid models. In particular, we present in this paper, the emission coefficients in krypton and we discuss the wide range of interconnected applications of excitation coefficients and spatial emission profiles.

We report on the theory and experiments of scanning micromirrors with angular vertical comb-drive (AVC) actuators. Parametric analyses of rotational vertical comb-drive actuators using a hybrid model that combines two-dimensional finite-element solutions with analytic formulations are described. The model is applied to both AVC and staggered vertical comb-drive (SVC) actuators. Detailed design tradeoffs and conditions for pull-in-free operations are discussed. Our simulation results show that the fringe fields play an important role in the estimation of maximum continuous rotation angles, particularly for combs with thin fingers, and that the maximum scan angle of the AVC is up to 60% larger than that of the SVC. Experimentally, a large dc continuous scan angle of 28.8 (optical) has been achieved with a moderate voltage (65 V) for a 1-mm-diameter scanning micromirror with AVC actuators. Excellent agreement between the experimental data and the theoretical simulations has been obtained.

An improved technique of hybrid modelling biochemical production processes is described, composed of a set of dynamical differential equations, an artificial neural network and a fuzzy expert system. The applicability of a hybrid model for state estimation, prediction, feed rate optimization, and process control is explained. Its performance is demonstrated by means of an the example of a fed-batch baker's yeast production process. The identification procedure is described in detail. Emphasis is placed on the most difficult part of it, the training of the neural network component of the hybrid model. New approaches to process control are developed based on neural networks.

BACKGROUND: A potential application of inulinase in the food industry is the production of fructooligosaccharides (FOS) through transfructosilation of sucrose. Besides their ability to increase the shelf-life and flavor of many products, FOS have many interesting functional properties. The use of an industrial medium may represent a good, cost-effective alternative to produce inulinase, since the activity of the enzyme produced may be improved or at least remain the same compared with that obtained using a synthetic medium. Thus, inulinase production for use in FOS synthesis is of considerable scientific and technological appeal, as is the development of a reliable mathematical model of the process. This paper describes a hybrid neural network approach to model inulinase production in a batch bioreactor using agroindustrial residues as substrate. The hybrid modeling makes use of a series artificial neural network to estimate the kinetic parameters of the process and the mass balance as constitutive equations. RESULTS: The proposed model was shown to be capable of describing the complex behavior of inulinase production employing agroindustrial residues as substrate, so that the mathematical framework developed is a useful tool for simulation of this process. CONCLUSION: The hybrid neural network model developed was shown to be an interesting alternative to estimate model parameters since complete elucidation of the phenomena and mechanisms involved in the fermentation is not required owing to the black-box nature of the ANN used as parameter estimator.

A novel method for the sequential experimental design in order to optimize fed-batch fermentations was applied to a hyaluronidase fermentation by Streptococcus agalactiae. A ⌳-optimal design was introduced to minimize the model parameter estimation error and to maximize the performance of the fermentation process. The method employs hybrid models that contain mechanistic, fuzzy and neural network components.

Online auction, shopping, electronic billing etc. all such types of application involves problems of fraudulent transactions. Online fraud occurrence and its detection is one of the challenging fields for web development and online phantom transaction. As no-secure specification of online frauds is in research database, so the techniques to evaluate and stop them are also in study. We are providing an approach with Hidden Markov Model (HMM) and mobile implicit authentication to find whether the user interacting online is a fraud or not. We propose a model based on these approaches to counter the occurred fraud and prevent the loss of the customer. Our technique is more parameterized than traditional approaches and so,chances of detecting legitimate user as a fraud will reduce.

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. ML algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. 

A simulation model of the fouling behaviour of an arbitrary heat treatment device for milk, instancing tubular heat exchangers, is described. The final target is to examine new processing technologies in order to lengthen the processing time between two cleaning cycles whilst maintaining product quality. For this purpose a hybrid model of the fouling process in tubular heat exchangers was developed, combining deterministic differential equations with cognitive elements. The model allows the calculation of both processing and product behaviour throughout the whole heat exchanger. Pressure drop, temperature distribution and the chemical and biological effects of the heat treatment on the product can be calculated, so that the fouling behaviour and the expected product quality can be estimated. In order to validate the process model measured data from an industrial UHT plant were used. The calculated temperature profiles and pressure drops were in reasonable agreement with the experimental data. The deviation of calculated to measured values ranges between 10% and 20% for pressure, between 5% and 10% for temperature.

The sibling model is often one of the best methods for calculating preseason forecasts of adult return abundance (recruits) for populations of Pacific salmon Oncorhynchus spp. This model forecasts abundance of a given age-class for a given year based on the abundance of the previous age-class in the previous year. When sibling relations fit historical data well, the sibling model generally performs better than other forecasting methods, such as stockrecruitment models. However, when sibling relations are weak, better forecasts are obtained by other models, such as naïve models that simply use an historical average. We evaluated the performance of a hybrid model that used quantitative criteria for switching between a sibling model and a naïve model when generating forecasts for 21 stocks of chum salmon O. keta and 37 stocks of sockeye salmon O. nerka in the northeastern Pacific Ocean. Compared with the standard sibling model, the hybrid model reduced the root mean square error (RMSE) of forecasts by an average of 27% for chum salmon stocks and 28% for sockeye salmon stocks. Compared with a naïve model, the hybrid model reduced the RMSE of forecasts by an average of 16% for chum salmon stocks and 15% for sockeye salmon stocks. Our results suggest that hybrid models can improve preseason forecasts and management of these two species.

Reliable estimation of the surface energy balance from local to regional scales is crucial for many applications including weather forecasting, hydrologic modeling, irrigation scheduling, water resource management, and climate change research. Numerous models have been developed using remote sensing, which permits spatially distributed mapping of the surface energy balance over large areas. This study compares flux maps over a relatively simple agricultural landscape in central Iowa, comprised of soybean and corn fields, generated with three different remote sensing-based surface energy balance models: the Two-Source Energy Balance (TSEB) model, Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC), and the Trapezoid Interpolation Model (TIM). The three models have different levels of complexity and input requirements, but all have operational capabilities. METRIC and TIM make use of the remotely sensed surface temperature-vegetation cover relation to define key model variables linked to wet and dry hydrologic extremes, while TSEB uses these remotely sensed inputs to define component soil and canopy temperatures, aerodynamic resistances, and fluxes. The models were run using Landsat imagery collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX) in 2002 and model results were compared with observations from a network of flux towers deployed within the study area. While TSEB and METRIC yielded similar and reasonable agreement with measured heat fluxes, with root-mean-square errors (RMSE) of $50-75 W/m 2 , errors for TIM exceeded 100 W/m 2 . Despite the good agreement between TSEB and METRIC at discrete locations sampled by the flux towers, a spatial intercomparison of gridded model output (i.e., comparing output on a pixel-by-pixel basis) revealed significant discrepancies in modeled turbulent heat flux patterns that were largely correlated with vegetation density. Generally, the largest discrepancies, primarily a bias in H, between these two models occurred in areas with partial vegetation cover and a leaf area index (LAI) < 2.0. Adjustment of the minimum LE assumed for the hot/dry hydrologic extreme condition in METRIC reduced the bias in H between METRIC and TSEB, but caused a significant increase in bias in LE between the models. Spatial intercomparison of modeled flux patterns over a variety of landscapes will be required to better assess uncertainties in remote sensing surface energy balance models, and to work toward an improved hybrid modeling system.

Measurements of surface radiation in China are too sparse to meet demand for scientific research and engineering applications. Moreover, the radiation data often include erroneous and questionable values though preliminary quality-check has been done before the data release. Therefore, quality control of radiation data is often a prerequisite for using these data. In this study, a set of quality-check procedures were implemented to control the quality of the solar radiation measurements at 97 stations in China. A hybrid model for estimating global solar radiation was then evaluated against the controlled data. The results show that the model can estimate the global radiation with accuracy of MBE less than 1.5 MJ m À2 and RMSE less than 2.8 MJ m À2 for daily radiation and RMSE less than 2.0 MJ m À2 for monthly-mean daily radiation at individual stations over most of China except at a few stations where unsatisfactory estimates were possibly caused by severe air pollution or too dense clouds. The MBE averaged over all stations are about 0.7 MJ m À2 and RMSE about 2.0 MJ m À2 for daily radiation and RMSE about 1.3 MJ m À2 for monthly-mean daily radiation. Finally, this model was used to fill data gaps and to expand solar radiation data set using routine meteorological station data in China. This data set would substantially contribute to some radiation-related scientific studies and engineering applications in China.

This paper presents an approach for the Opinion Finding task at TREC 2008 Blog Track. For the Ad-hoc Retrieval subtask, we adopt language model to retrieve relevant documents. For the Opinion Retrieval subtask, we propose a hybrid model of lexicon-based approach and machine learning approach for estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity and linear combination technique for ranking polar documents. The hybrid model which utilize both lexicon-based approach and machine learning approach to predict and rank opinionated documents are the focuses of our participation this year. Regarding the hybrid method for opinion retrieval subtask, our submitted runs yield 15% improvement over baseline.

This paper is a comprehensive review of spatial forest-planning initiative that has been carried out in forest-management planning over the last 2 decades. It describes, first of all, the conceptual framework of spatial forest planning by identifying spatial and non-spatial forest-planning concepts. The paper focuses on the spatial considerations or problems of spatial configuration of patches including their size (opening size) and distribution, shape, adjacency or green-up delay, connectivity, proximity, and core area that make the conventional forest-management planning "spatial" powered by geographical information systems (GIS). Then, the paper explains various management approaches to conceptualize the spatial forest planning and analytical decision-making techniques, such as simulation, mathematical optimization and meta-heuristic techniques to solve the spatial forest management problem. It also provides information to measure the performance of various meta-heuristic techniques, when solving large-scale spatial problems. Also, some apparent new requirements, important needs for spatial forest planning, and important unresolved problems in spatial forest planning are identified. In conclusion, development of a hybrid-modeling technique, identification of target landscape structure, characterization of various forest values, such as biodiversity, water production, recreation, visual quality, erosion control, and an enthusiasm to implement the approach are identified important future development to spatial forest modeling.

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).

It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific face. This paper addresses a novel decision strategy that efficiently uses both holistic and facial component (eye, nose and mouth) feature analysis to recognize faces. The proposed algorithm first performs a holistic search using the whole image features and selects probable candidates for further processing. Then the facial features of these probable candidates are compared and classified with the stored patterns in the train set. Finally the classification results are fused with a weighted majority voting to form the final decision. Simulation studies justify the superior performance of the proposed method as compared to that of Eigenface method.

The Adaptive Safety Analysis and Monitoring (ASAM) system is a hybrid model-based software tool for assisting intelligence analysts to identify terrorist threats, to predict possible evolution of the terrorist activities, and to suggest strategies for countering terrorism. The ASAM system provides a distributed processing structure for gathering, sharing, understanding, and using information to assess and predict terrorist network states. In combination with counter-terrorist network models, it can also suggest feasible actions to inhibit potential terrorist threats. In this paper, we will introduce the architecture of the ASAM system, and discuss the hybrid modeling approach embedded in it, viz., Hidden Markov Models (HMMs) to detect and provide soft evidence on the states of terrorist network nodes based on partial and imperfect observations, and Bayesian networks (BNs) to integrate soft evidence from multiple HMMs. The functionality of the ASAM system is illustrated by way of application to the Indian Airlines Hijacking, as modeled from open sources.

Abstract: MIX is an ESPRIT project aimed at developing strategies and tools for integrating symbolic and neural methods in hybrid systems. The project arose from the observation that current hybrid systems are generally small-scale experimental systems which couple one symbolic and ...

Life cycle assessment is a technique to assess environmental aspects associated with a product or process by identifying energy, materials, and emissions over its life cycle. The energy analysis includes four stages of a life cycle: material production phase, manufacturing phase, use phase, and end-of-life phase. In this study, the life cycle energy of fiber-reinforced composites manufactured by using the pultrusion process was analyzed. For more widespread use of composites, it is critical to estimate how much energy is consumed during the lifetime of the composites compared to other materials. In particular, we evaluated a potential for composite materials to save energy in automotive applications. A hybrid model, which combines process analysis with economic input-output analysis, was used to capture both direct and indirect energy consumption of the pultrusion process in the material production and manufacturing stages.

A model-based optimization of an industrial fed- batch sugar crystallisation process is considered in this paper. The objective is to define the optimal profiles of the manipulated process inputs, the feeding rate of liquor/syrup and the steam supply rate, such that the crystal content and the crystal size distribution (CSD) measures at the end of the batch cycle reach the

The diffusion of innovations model (DIM) and complex adaptive systems theory (CAS) can be employed together in the construction of predictive or applied hybrid models of induced change in population behavior. In such interventions, differentiated heterogeneous zones may act as catalysts for the adoption of innovation. The present study explores the actual and potential hybridization of these two systems theories, relying on illustrations from historical practical applications of DIM, particularly the STOP AIDS communication campaign in San Francisco.

The past emphasis in this conference series has been on the best ways to deregulate regulated public transport markets. This workshop reverses this process by examining the best ways to regulate deregulated public transport markets. A hierarchy of regulatory needs is identified and three hybrid models examined, based loosely on experience from Great Britain, New Zealand and Sweden. It is argued that deregulated public transport markets are a global phenomenon but regulatory measures should reflect local requirements. The resultant process of glocalisation might result in regulatory measures that focus on the rules of law and their enforcement in emerging public transport markets (such as urban transport in Sub Saharan Africa and for the soon to be competitive inter urban market in Germany) but that focus on guidance for network integration and incentivisation for welfare maximisation in more mature public transport markets (as in Great Britain, New Zealand and Sweden).

Objective. The aim of this study was to determine the stiffness characteristics of the standard and hybrid Ilizarov fixators. Design. Five different frame models (one standard and four hybrid Ilizarov) were designed. Four full rings were used in the standard Ilizarov frame. Two rings were placed proximal and two rings were placed distal to the osteotomy line with two wires at 90°to each other on each ring. The distal tibial fixation of all the hybrid configurations and standard Ilizarov fixator were the same, and only the proximal fixations were different. In hybrid models, different numbers of 90°femoral arches (1-3) were fixed to the proximal segment by using the half-pins with different numbers (2-4) and different angles to each other (45°and 90°).

Fuel cell (FC) technologies are expected to become a viable solution for vehicular applications because they use alternative fuel converters and are environmentally friendly. However, a stand alone FC system may not be sufficient to satisfy the load demands, especially during cold start, peak demand periods or transient events, for vehicular applications. In addition, the FC system is not capable of being reversed for regenerative energy. An ultra-capacitor (UC) bank can supply a large burst of power but cannot store much energy. By operating the FC and UC in parallel, both steady state and peak power demands can be satisfied. Use of a FC/UC hybrid model provides a potential solution for better energy efficiency while reducing the cost of FC power technology. This paper describes a new modeling and design methodology for FC/UC hybrid vehicular power systems. A feasible design and a dynamic model have been presented for the proposed technique. Simulation results are presented, using the MATLAB Ò , Simulink Ò and SimPowerSystems Ò environments, based on the mathematical and electrical models of the proposed system.

After a survey of some approaches for modeling hybrid systems by means of Petri nets (t-time Petri nets, colored Petri nets, hybrid Petri nets and differential predicate transition nets), this paper illustrates how these formal models can be used for simulation and validation. It is shown how it was possible to use aggregated qualitative models concurrently with detailed ones.

Theories of fuzzy sets and rough sets are powerful mathematical tools for modelling various types of uncertainty. Dubois and Prade investigated the problem of combining fuzzy sets with rough sets. Soft set theory was proposed by Molodtsov as a general framework for reasoning about vague concepts. The present paper is devoted to a possible fusion of these distinct but closely related soft computing approaches. Based on a Pawlak approximation space, the approximation of a soft set is proposed to obtain a hybrid model called rough soft sets. Alternatively, a soft set instead of an equivalence relation can be used to granulate the universe. This leads to a deviation of Pawlak approximation space called a soft approximation space, in which soft rough approximations and soft rough sets can be introduced accordingly. Furthermore, we also consider approximation of a fuzzy set in a soft approximation space, and initiate a concept called soft-rough fuzzy sets, which extends Dubois and Prade's rough fuzzy sets. Further research will be needed to establish whether the notions put forth in this paper may lead to a fruitful theory.

The present study examined the extent to which scores on the Dispositional Flow Scale-2 (DFS-2) could differentiate individuals who frequently experience flow characteristics in physical activity from those who do not. A total of 993 participants completed the Japanese version of the DFS-2. Latent class factor analysis (LCFA), which combines the strengths of both latent class analysis and factor analysis, was conducted on the DFS-2 responses. Six classes were identified through a series of LCFAs and the patterns of the item-average scores for the nine flow attributes were found to be parallel among these classes. The top two and bottom two classes (19.3% and 13.4% of the whole sample) were considered the groups who experience flow characteristics frequently and seldom, respectively. These results indicated that individuals who often experience flow attributes in physical activity could be differentiated from those who do not based on their DFS-2 scores. Note. The article published i...

We introduce the MIR task of segmenting melodies into phrases, summarise the musicological and psychological background to the task and review existing computational methods before presenting a new model, IDyOM, for melodic segmentation based on statistical learning and information-dynamic analysis. The performance of the model is compared to several existing algorithms in predicting the annotated phrase boundaries in a large corpus of folk music. The results indicate that four algorithms produce acceptable results: one of these is the IDyOM model which performs much better than naive statistical models and approaches the performance of the best-performing rule-based models. Further slight performance improvement can be obtained by combining the output of the four algorithms in a hybrid model, although the performance of this model is moderate at best, leaving a great deal of room for improvement on this task.

This paper presents a survey of ocean simulation and rendering methods in computer graphics. To model and animate the ocean's surface, these methods mainly rely on two main approaches: on the one hand, those which approximate ocean dynamics with parametric, spectral or hybrid models and use empirical laws from oceanographic research. We will see that this type of methods essentially allows the simulation of ocean scenes in the deep water domain, without breaking waves. On the other hand, physically-based methods use Navier-Stokes Equations (NSE) to represent breaking waves and more generally ocean surface near the shore. We also describe ocean rendering methods in computer graphics, with a special interest in the simulation of phenomena such as foam and spray, and light's interaction with the ocean surface.

We seek to address criticisms of the concept of moral panics by offering a hybrid model of moral panics (MPs) that synthesizes theory and practice of MPs research. A review of the literature on MPs from sociology, media studies and related fields shows a wide variety of usage and lack of conceptual clarity of the term moral panic. Yet there

Applying quantitative models for forecasting and assisting investment decision making has become more indispensable in business practices than ever before. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In the literature, several hybrid techniques have been proposed by combining different time series models together, in order to overcome the deficiencies of single models and yield hybrid models that are more accurate. In this paper, in contrast of the traditional hybrid models, a new methodology is proposed in order to construct a new class of hybrid models using a time series model as basis model and a classifier. As classifiers cannot be lonely applied as forecasting model for continuous problems, in the first stage of the proposed model, a forecasting model is used as basis model. Then, the estimated values of the basis model are modified in the second stage, based on the distinguished trend of the residuals of the basis model and the optimum step length, which are respectively calculated by a classifier model and a mathematical programming model. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way in order to construct a more accurate hybrid model than its basis time series model. Therefore, it can be used as an appropriate alternative model for forecasting tasks, especially when higher forecasting accuracy is needed.

In this paper we use First–Order Hybrid Petri nets (FOHPN), an hybrid model that combines fluid and discrete event dynamics, to model the concurrent activ-ities of manufacturing systems. In particular we consider an existing mineral water bottling plant and we show how the ...

Numerical simulations of subsurface fluid and heat flow are commonly controlled manually via input files or from graphical user interfaces (GUIs). Manual editing of input files is often tedious and errorprone, while GUIs typically limit the full capability of the simulator. Neither approach lends itself to automation, which is desirable for more complex simulations.

The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting third party claim amount for different categories of vehicles based on the chosen characteristics of the data. In actuarial research, predicting the insurance claim amount for different vehicle categories is a challenging task, and minimal empirical research studies were done to forecast the claims. In the present study, the annual time series historical data were collected for a period of 34 years. We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively. In this context, we exhibited the feasibility of using a statistical machine learning approach such as Linear regression Model, the Exponential Smoothing Model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict the various categories of vehicles claim amount. The data were analyzed, compared, and the empirical analysis showed that Artificial Neural Network is a better predictive model among the other time series models based on performance evaluation metrics RMSE and MAPE with lesser variance. Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement and management for different categories of vehicles.

This review paper focuses on modelling of wastewater treatment plants (WWTP). White-box modelling is widely applied in this field, with learning, design and process optimisation as the main applications. The introduction of the ASM model family by the IWA task group was of great importance, providing researchers and practitioners with a standardised set of basis models. This paper introduces the nowadays most frequently used white-box models for description of biological nitrogen and phosphorus removal activated sludge processes. These models are mainly applicable to municipal wastewater systems, but can be adapted easily to specific situations such as the presence of industrial wastewater. Some of the main model assumptions are highlighted, and their implications for practical model application are discussed. A step-wise procedure leads from the model purpose definition to a calibrated WWTP model. Important steps in the procedure are: model purpose definition, model selection, data collection, data reconciliation, calibration of the model parameters and model unfalsification. The model purpose, defined at the beginning of the procedure, influences the model selection, the data collection and the model calibration. In the model calibration a process engineering approach, i.e. based on understanding of the process and the model structure, is needed. A calibrated WWTP model, the result of an iterative procedure, can usually be obtained by only modifying few model parameters, using the default parameter sets as a starting point. Black-box, stochastic grey-box and hybrid models are useful in WWTP applications for prediction of the influent load, for estimation of biomass activities and effluent quality parameters. These modelling methodologies thus complement the process knowledge included in white-box models with predictions based on data in areas where the white-box model assumptions are not valid or where white-box models do not provide accurate predictions. Artificial intelligence (AI) covers a large spectrum of methods, and many of them have been applied in applications related to WWTPs. AI methodologies and white-box models can interact in many ways; supervisory control systems for WWTPs are one evident application. Modular agent-based systems combining several AI and modelling methods provide a great potential. In these systems, AI methods on one hand can maximise the knowledge extracted from data and operator experience, and subsequently apply this knowledge to improve WWTP control. White-box models on the other hand allow evaluating scenarios based on the available process knowledge about the WWTP. A white-box model calibration tool, an AI based WWTP design tool and a knowledge representation tool in the WWTP domain are other potential applications where fruitful interactions between AI methods and white-box models could be developed. 

A hybrid RANS/LES model is set up by blending a k-\varepsilon model and the Smagorinsky model according to the LNS idea [1], i.e. using the minimum value among the eddy viscosities given by the RANS and the LES closures. The Navier-Stokes equations are discretized by a mixed finite-volume/finite-element formulation applicable to unstrucured grid. A low diffusion MUSCL scheme, designed for LES, is adopted [2]. Both explicit (Runge-Kutta) and implicit (second-order accurate) time advancing can be used. The hybrid model is applied to the simulation of the flows around a square cylinder and around a sphere. Results are compared with those obtained with RANS and LES and with experimental data. REFERENCES: [1] P.Batten.LNS-An approach towards embedded LES. In phAIAA Paper 2002-0427. American Institute of Aeronautics and Astronautics, 2002. [2] S.Camarri,M.V.Salvetti,B.Koobus and A.Dervieux. A low-diffusion MUSCL scheme for LES on unstructured grids. phComputers and Fluids 2003; 33:1101-1129.

A flexible and powerful dynamic simulation approach to grinding circuit simulation has recently been developed in CSIRO Minerals. The MATLAB/SIMULINK graphical programming environment has been used to construct a library of dynamic mathematical models of a number of key grinding and separation devices and to link them into various complex dynamic grinding circuits. True real-time dynamic simulation and visualisation of interlinked unit process operations in grinding circuits of arbitrary complexity can readily be achieved.

Throughout the world, the distinctive organizational forms adopted by social enterprises depend on the existing legal frameworks, on the political economy of welfare provision and on the cultural and historical traditions of non-profit development in each country. Consequently, the social enterprise sector globally encompasses both new typologies of organizations and traditional third sector organizations, re-imagined through the lens of sustainable income-generation.In this thought leadership article (TLA), we consider the landscape of the existing research on social enterprises, early forms of social enterprises in Bangladesh, evolution of the sector in the 1980s and 1990s, and intersections between businesses and social entrepreneurship. We identify key sectoral trends, assess the policy environment and envision a roadmap for future development of the social enterprise space.

being developed by Boeing Helicopters. The DAFCS will incorporate modern control algorithms in the low-speed/hover regime to improve handling qualities for operations in a Degraded Visual Environment (DVE). In support of the modern control law development, the US Army required a complete closed-loop analysis model in SIMULINK ® . The central element of this analysis model was an accurate state-space representation of the bareairframe response. CIFER ® was used to perform the system identification at three gross weights and two airspeeds having a common quasi-steady model structure. The quasi-steady model identification had a restricted frequency range due to rotor-on-rotor and inflow dynamics. Subsequently, the quasi-steady model was extended to higher-frequencies by incorporating higher-order rotor dynamics. This paper covers the identification of the longitudinal/heave-axis bare-airframe dynamics for the Heavy Gross Weight (HGW) hover configuration, and provides a comparison of results for the quasi-steady and hybrid models. F = stability derivative matrix g = acceleration due to gravity, (32.174 ft/sec 2 ) G = control derivative matrix h = vertical distance from aircraft center-of-gravity to center of rotor hub, (ft) H, H = hub in-plane force; or output matrix when bold I = aircraft moment of inertia about y-axis, (slug-ft 2 ) yy J = output control matrix K = control gain converting differential collective pitch inputs to effective blade root pitch angle, (rad/in) DCP K θ = control gain converting collective pitch inputs to effective blade root pitch angle, (rad/in) = longitudinal distance from aircraft center-of-gravity to center of rotor hub, (ft) l m = mass of aircraft, (slug) M, M = derivative for specific pitching moment; or hub moment; or modal matrix when bold M = derivative for specific higher-order pitching moment Mf = derivative for pitch axis flapping moment 0 1 2 ) (LON COL τ = equivalent time delay for collective (or longitudinal) axis, (sec) υ = rotor dynamic inflow state = rotor speed, (rad/sec) Ω n ω = natural frequency, (rad/sec)