Computer Modelling

Long-term rainfall prediction is a challenging task especially in the modern world where we are facing the major environmental problem of global warming. In general, climate and rainfall are highly non-linear phenomena in nature exhibiting what is known as the "butterfly effect". While some regions of the world are noticing a systematic decrease in annual rainfall, others notice increases in flooding and severe storms. The global nature of this phenomenon is very complicated and requires sophisticated computer modelling and simulation to predict accurately. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies [17] that underlie the conception, design and utilization of intelligent systems . In this paper, the SC methods considered are i) Evolving Fuzzy Neural Network (EFuNN) ii) Artificial Neural Network using Scaled Conjugate Gradient Algorithm (ANNSCGA) iii) Adaptive Basis Function Neural Network (ABFNN) and iv) General Regression Neural Network (GRNN). Multivariate Adaptive Regression Splines (MARS) is a regression technique that uses a specific class of basis functions as predictors in place of the original data. In this paper, we report a performance analysis for MARS [1] [16] and the SC models considered. To evaluate the prediction efficiency, we made use of 87 years of rainfall data in Kerala state, the southern part of the Indian peninsula situated at latitude-longitude pairs (8 o 29' N -76 o 57' E). The SC and MARS models were trained with 40 years of rainfall data. For performance evaluation, network predicted outputs were compared with the actual rainfall data for the remaining 47 years.

It is well known that certain fluids are birefringent and when flows are viewed in polarised light interference fringes are observed. The fringes are caused by a phase shift in the light passing through the fluid and are proportional to the integral of the maximum shear strains in the fluid. In order to understand what is happening within the flow and overcome the difficulties due to this integration of analysing three dimensional flows, additional computational or experimental information is needed. In this work, a commercially available computer code (Fluent) is used for the first time to model the flows. The flow data are then exported to a spreadsheet where the shear rates are integrated across the field and then banded for graphical output. The results from this are then compared to results generated from birefringent flow experiments and the agreement is found to be good since the modelled fringes show the same patterns as those in the experiment. This novel use of computational and experimental techniques together will allow quantitative analysis of three dimensional flows in the future. 2 Currently, there are still a lot of empirical variables involved in fitting the computational fringes to the experiment, but the results of this preliminary study show that this is a promising approach to this type of problem.

The search for the neural substrate of vertebrate action selection has focused on structures in the forebrain and midbrain, and particularly on the group of sub-cortical nuclei known as the basal ganglia. Yet, the behavioural repertoire of decerebrate and neonatal animals suggests the existence of a relatively self-contained neural substrate for action selection in the brainstem. We propose that the medial reticular formation (mRF) is the substrate's main component and review evidence showing that the mRF's inputs, outputs and intrinsic organization are consistent with the requirements of an action-selection system. The internal architecture of the mRF is composed of interconnected neuron clusters. We present an anatomical model which suggests that the mRF's intrinsic circuitry constitutes a small-world network and extend this result to show that it may have evolved to reduce axonal wiring. Potential configurations of action representation within the internal circuitry of the mRF are then assessed by computational modelling. We present new results demonstrating that each cluster's output is most likely to represent activation of a component action; thus, coactivation of a set of these clusters would lead to the coordinated behavioural response observed in the animal. Finally, we consider the potential integration of the basal ganglia and mRF substrates for selection and suggest that they may collectively form a layered/hierarchical control system.

The present study deals with the analysis of a Lotka-Volterra model describing competition between tumor and immune cells. The model consists of differential equations with piecewise constant arguments and based on metamodel constructed by Stepanova. Using the method of reduction to discrete equations, it is obtained a system of difference equations from the system of differential equations. In order to get local and global stability conditions of the positive equilibrium point of the system, we use Schur-Cohn criterion and Lyapunov function that is constructed. Moreover, it is shown that periodic solutions occur as a consequence of Neimark-Sacker bifurcation.

The construction of an excavation often means penetrating the local or regional water table. This causes inflows, which if the country rock is significantly permeable can become at best a nuisance to operations and at worst a hazard. Dry working conditions are preferable as they reduce wear and tear on machinery, reduce earth moving costs and often improve slope stability and therefore safety. Options available to mine management are dewatering, diversion, sealing or a combination of methods. To achieve the most effective, least cost method it is essential that the origin of the ground water is determined. The success of a dewatering exercise is directly linked to an understanding of the ground water regime. Mines cannot afford to use "blanket methods" when dealing with ground water. It is essential to target the actual inflows and not divert or seal off water indiscriminately.

In this paper, we define a new type of M(G)-group action , called M(G)-group soft union(SU) action and M(G)-ideal soft union(SU) action on a soft set. This new concept illustrates how a soft set effects on an M(G)-group in the mean of union and inclusion of sets and its function as bridge among soft set theory, set theory and M(G)-group theory. We also obtain some analog of classical M(G)-group theoretic concepts for M(G)-group SU-action. Finally, we give the application of SU-actions on M(G)-group to M(G)-group theory.

5th International Conference on Applied Mathematics and Sciences (AMA 2021)will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Mathematics and Sciences. The conference documents practical and theoretical results which make a fundamental contribution for the development of Mathematics and Sciences. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on Mathematics and Sciences advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Mathematics.

C4I (command, control, communications, computer and intelligence) system is a complex system of systems that enables the military commander to achieve decision superiority by affecting adversary's information and information based processes while protecting one`s own information systems. Owing to the sensitivity of military information the security threats to C4I systems are real and growing therefore security is a major challenge in order to maintain integrity, confidentiality, availability and accountability. Different Models and architectures frameworks e.g. DODAF, CDSA have been proposed which decompose the detail structure of C4I systems however these models and frameworks do not provide mechanism to capture associated security risks and issues. Threat modeling is the process to understand different ways system can be attacked which helps towards secure system design and implement countermeasures. This paper suggests that using threat modeling could help to eradicate or reduce the effect of advancing threats to C4I systems. In this paper we also identify how security can be modeled in top down manner e.g. from architecture level to lower level of modeling so that countermeasures can be incorporated on system level rather than on bit by bit basis.

Information security is a serious issue especially in present age because a solo attack may cause a big harm in computer and network systems. Several intrusion detection approaches exist to tackle this critical issue but the problem is which one is more suitable in the field of intrusion. Further, these approaches are used in intrusion detection systems. Therefore, in this paper, we evaluated them so that a suitable approach may be advised to intrusion detection systems. This work describes the concepts, tool and methodology being used for evaluation analysis of different intrusion detection approaches using multi-criteria decision making technique. Moreover, conclusion on results is made and direction for future works is presented.

The possibility of using a nodal method allowing irregular distribution of nodes in a natural way is one of the main advantages of the generalized finite difference method (GFDM) with regard to the classical finite difference method. Moreover, this feature has made it one of the most-promising meshless methods because it also allows us to reduce the time-consuming task of mesh generation and the numerical solution of integrals. This characteristic allows us to shape geological features easily whilst maintaining accuracy in the results, which can be a source of great interest when dealing with this kind of problems. Two widespread geophysical investigation methods in civil engineering are the cross-hole method and the seismic refraction method. This paper shows the use of the GFDM to model the aforementioned geophysical investigation tests showing precision in the obtained results when comparing them with experimental data.

Finally, I would like to express m y deepest thanks and love to m y Parents, m y Wife and children. Their moral support during the difficult period of m y study, their encouragement, and m y children's patience contributed to the success of this thesis. I a m proud of them. JmA UJWVRJ i& amknkAj to-Jmam KUMOMI rna/ituAy ol m/mmri a/nxl hMkjb.

Climate and rainfall are highly non-linear and complicated phenomena, which require sophisticated computer modelling and simulation for accurate prediction. An artificial intelligence technology allows knowledge processing and can be used .as forecasting tool. For example, the application of Artificial Neural Networks (ANN), to predict the behaviors of nonlinear systems has become an attractive alternative to traditional statistical methods. In this paper, we present tools for modeling and predicting the behavioral pattern in rainfall phenomena based on past observations. The paper introduces two fundamentally different approaches for designing a model, the statistical method based on autoregressive integrated moving average (ARIMA) and the emerging computationally powerful techniques based on ANN. In order to evaluate the prediction efficiency, we made use of 104 years of mean annual rainfall data from year 1901 to 2003 of Hyderabad region (India). The models were trained with 93 years of mean annual rainfall data. The ANN and the ARIMA approaches are applied to the data to derive the weights and the regression coefficients respectively. The performance of the model was evaluated by using remaining 10 years of data. The study reveals that ANN model can be used as an appropriate forecasting tool to predict the rainfall, which out performs the ARIMA model.

As illustrated by the approaches presented during the 5 th FSPM workshop (Prusinkiewicz 16 and Hanan 2007, and this issue), the development of functional-structural plant models 17 requires an increasing amount of computer modeling. All these models are developed by 18 different teams in various contexts and with different goals. Efficient and flexible 19 computational frameworks are required to augment the interaction between these models, 20 their reusability, and the possibility to compare them on identical datasets. 21

Infants (from Latin infans, speechless) are human beings who cannot speak. It took most of us the whole first year of our lives to overcome this infancy and to produce our first few meaningful words, but we were not idle as infants. We worked, rather independently, on two basic ingredients of word production. On the one hand, we established our primary notions of agency, interactancy, the temporal and causal structures of events, object permanence and location. This provided us with a matrix for the creation of our first lexical concepts, concepts flagged by way of a verbal label. Initially, these word labels were exclusively auditory patterns, picked up from the environment. On the other hand, we created a repertoire of babbles, a set of syllabic articulatory gestures. These motor patterns normally spring up around the seventh month. The child carefully attends to their acoustic manifestations, leading to elaborate exercises in the repetition and concatenation of these syllabic patterns. In addition, these audiomotor patterns start resonating with real speech input, becoming more and more tuned to the mother tongue (De Boysson-Bardies & Vihman 1991; Elbers 1982). These exercises provided us with a protosyllabary, a core repository of speech motor patterns, which were, however, completely meaningless.

The Vetiver System (VS) is now well accepted and used worldwide for numerous applications. Amongst these, environmental protection applications are the most popular due to its effectiveness, simplicity and low cost. Earlier research conducted to understand the role of the extraordinary physiological and morphological attributes of vetiver grass in soil and water conservation, discovered that vetiver grass also possesses some unique attributes highly suitable for treating polluted wastewater from industries as well as domestic discharges and contaminated lands from industries and mining. • VS can reduce the volume or dispose unwanted wastewater by: seepage control, land irrigation and wetland. Successful applications include treatment of: - domestic and municipal sewage effluent and landfill leachate - wastewater from intensive animal farms - industrial wastewater recycling and disposal - industrial and mining seepage. • VS can improve wastewater quality by: trapping debris, sediment...

During the re-configuration of their grate-kiln induration system for the firing of iron ore pellets at the Whyalla Steelworks, caused by a change from an earthy hematite to a magnetite ore with considerably less flux, OneSteel commissioned a detailed thermal flow analysis of the design options on the existing cooler in support of the process change. A key objective of this investigation was to decide if and where one might locate baffle walls within the cooler to improve the process and to control the off-gas flows to earlier stages within the process.

There are some methods for solving Multiple Criteria Decision-Making problems, of which one is the TOPSIS method. When data is nondeterministic like interval data, the method must be modified to show the correct result. In this research we present a new TOPSIS method for ranking DMUs with interval data yielding the interval score for each alternative, and in the end we show that when data is deterministic, our new method is the same as the conventional one.

A screen for TBX1 gene mutations identified two mutations in patients with some features compatible with the 22q11.2-deletion syndrome but with no deletions. One is a de novo missense mutation and the other is a 5 0 untranslated region (5 0 UTR) C4T change that affects a nucleotide with a remarkable trans-species conservation. Computer modelling shows that the 5 0 UTR change is likely to affect the mRNA structure and in vitro translation experiments demonstrate that it produces a twofold increase in translation efficiency. Recently, duplications in the 22q11.2 region were reported in patients referred for fragile-X determination because of cognitive and behavioural problems. Because the 5 0 UTR nucleotide change may be a functional equivalent of a duplication of the TBX1 gene, we decided to screen 200 patients who had been referred for fragile-X determination and 400 healthy control individuals. As a result, we found the 5 0 UTR mutation to be present in three patients with mental retardation or behavioural problems and absent in control individuals of the same ethnic background. This observation suggests that it may be reasonable to screen for such mutation among patients with unspecific cognitive deficits and we provide an easy and quick way to do it with an amplification refractory mutation system (ARMS) approach. To our knowledge, this is the first human mutation showing that TBX1 is a candidate causing mental retardation associated with the 22q11.2 duplication syndrome.

Computer simulations are used to represent the essential features of a real system so that analyzers can test their design skills in a convenient and safe environment. Organizations throughout the world are quickly moving to adopt process modelling and simulation as an integral part of their business decision-making and continuous improvement initiatives, due to their significant role in the design of a cost-effective system. ARENA fulfils these needs by mapping processes and simulating discrete and continuous systems. The properties of ARENA software engine involved in its ease of use and rapid mastery, power of complex system modelling, and open architecture to support user applications. This paper outlines how ARENA is applied to model, simulate and analyze the basic production processes. The case study is the bakery's production processes, at which the model creation is explained in detail and the simulation results are analyzed. This study gives a real-life example of how to use ARENA to model a complete production line in an efficient way.

a b s t r a c t In 2007, Huang and Zhang [L.G. Huang, X. Zhang, Cone metric spaces and fixed point theorems of contractive mappings, J. Math. Anal. Appl. 332 (2007) 1468-1476] rediscovered normal cone metric spaces and obtained the Banach contraction principle for this setting. Later on, Rezapour and Hamlbarani [Sh. Rezapour, R. Hamlbarani, Some notes on the paper: Cone metric spaces and fixed point theorems of contractive mappings, J. Math. Anal. Appl. 345 (2008) 719-724] showed that there are non-normal cones and that the assumption of normality is redundant.

Irradiation by ion beams, used alone or combined with electron beams/X-rays, is in the basis of most vacuum techniques for surface analysis of materials. The ion bombardment effects in solids are however still hard to predict and consequently the ion beam techniques are less quantitative than others. Lack of proper description of some mechanisms and the large number of processes that take place concurrently during the ion beam irradiation lead to difficulties in the interpretation of the experimental data that are collected in the measurements. Computer simulations are considered a promising approach to understand the main physical and chemical phenomena, and a valuable tool in processing and interpreting the analytical data.

While there has been significant progress in the treatment of ischemic heart failure, it remains a significant health and economic problem worldwide. In this paper, we present the challenges of modelling ischemic heart failure and introduce a user-friendly software system that will be a sub-set of the Virtual Pathological Heart environment which is currently being developed under the FP7 VPH2

The intention of this paper is to demonstrate the feasibility of a performance-based solution for providing adequate life safety levels for the occupants of an industrial warehouse. Focus was given to the evaluation of the performance of a smoke venting system and the exit distribution in an industrial warehouse. Quantitative analyses of the available safe egress time and the required safe egress time were carried out using both a simple calculation and a computer modelling technique. The study included a sensitivity analysis to investigate multiple fire scenarios involving variations in design fires, ventilation conditions and number of exits.

Cognitive neuroscience has made considerable progress in understanding the involvement of the medial temporal and frontal lobes in long-term memory. Whereas the medial temporal lobe has traditionally been associated with the encoding, storage and retrieval of long-term memories, the prefrontal cortex has been linked with cognitive control processes such as selection, engagement, monitoring and inhibition. However, there has been little attempt to understand how these regions might interact during encoding and retrieval, and little consideration of the anatomical connections between them. Recent advances in functional neuroimaging, neurophysiology, crossed-lesion neuropsychology and computational modelling highlight the importance of understanding how the medial temporal and frontal lobes interact to allow successful remembering, and provide an opportunity to explore these interactions.

In a laboratory activity students study the behaviour of a bouncing ball. With the help of a high-speed camera they can study the motion in detail. Computer modelling enables them to relate the measurement results to the theory. They experience that reality ...

Jarosite [KFe 3 (SO 4 ) 2 (OH) 6 ] is a mineral that is common in acidic, sulphate-rich environments, such as acid sulphate soils derived from pyrite-bearing sediments, weathering zones of sulphide ore deposits and acid mine or acid rock drainage (ARD/AMD) sites. ...

Delamination in composite structures is best modelled at a mesoscopic level. In this approach, the plies are modelled as continua, which can either be assumed to behave linearly elastically or to degrade according to a damage law. Delamination in the interfaces between them is modelled using a discrete relation between interface tractions and relative displacements. Key in this type of modelling is the presence of a work of separation or fracture energy, which governs the growth of the delamination. This cohesive-surface approach has traditionally been implemented numerically using special interface elements which connect the continuum elements that are used to model the plies. Exploiting the partition-of-unity property of finite element shape functions to model the interface separation process offers some advantages, since interfaces can be inserted at the onset of delamination and not a priori, as in the conventional approach. As a consequence, elastic compliance of the interface prior to onset of delamination, spurious traction oscillations ahead of the delamination front and spurious wave reflections because of the presence of a high stiffness value are avoided. Moreover, unlike the conventional approach, unstructured meshes can be employed.

An approach to real-time control of a network of signalized intersections is proposed based on a discrete time, stationary, Markov control model (also known as Markov decision process or Markov dynamic programming). The approach incorporates microscopic simulation of actuated controller output signals in response to probabilistic forecasts of individual vehicle actuations at downstream inductance loop detectors derived from a macroscopic link transfer function. An Artificial Neural Network representation of vehicle delay estimations is proposed and tested for approximate real-time evaluation of potential traffic signal transitions at three-second evaluation intervals. A series of off-line tests of the developed procedures are applied to a simplified network of five intersections: these tests provide promising indications of this approach.

According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.

In this paper, computational modelling of the exhaust system of two types of large-bore, multicylinder, two-stroke engine is performed. The airflow performance of a four-cylinder V-bank Cooper GMV-4TF engine and a six-cylinder in-line Clark TLA engine is simulated. The simulation includes the computation of pressure wave propagation in the exhaust manifold. Using a modified method of the steepest ascent numerical technique, tuned exhaust manifolds are designed for each engine with the objective of reduced NO emissions. The NO reduction is accomplished by increasing the trapped cylinder mass and correspondingly reducing the peak combustion temperature. The simulations predict NO reductions in the range 10-30 per cent as a result of exhaust tuning.

There are many factors which influence the incorporation of particulate in metal matrix composites (MMCs). This paper presents work which examines the effect of viscosity during Al–SiC MMC production. Processing periods (up to 65 min), stirring speeds (50–500 rpm), and re-inforcement sizes (13–100 μm) for two different viscosity levels (1 and 300 mPa s) were investigated. Computer simulations, room temperature analogue fluid simulations, and MMC castings were performed. Volume fraction results of SiC at different locations within the fluids were assessed by each of these methods and compared. From these tests, a stirring speed of 200 rpm for the lower viscosity fluid and 300 rpm for the higher viscosity fluid were found to be best in order to produce uniform distributions of SiC. In order to obtain a uniform re-inforcement distribution in the lower viscosity system, stirring periods were found to range from 14 to 170 s and for the higher viscosity system from 540 to more than 3920 s. Fully uniform suspensions remained for just a couple of seconds in the lower viscosity system compared to about an hour for the higher viscosity system. The modelling approach chosen was found to be useful in predicting settling behaviour in the semi-solid metal.

The numerical simulation of transitional flow around a two-dimensional stationary circular cylinder is presented using two groups of turbulence models in a sub-critical flow regime. In the first group, enhanced two-equation turbulence models based on the eddy viscosity concept are used. They include the non-linear k -m model with extended models, such as renormalization group (RNG) and the anisotropic model. In the second group, flow simulation is carried out using the large eddy simulation (LES) method, which is based on a standard sub-grid scale (SGS) model with a near-wall approach. This near-wall model, without using the 'law of wall', is achieved in a finite element code. The numerical results extracted from these simulations are compared with each other and with the experimental data in order to determine the relative performance of these turbulence models and to find the best model for the flow of interest. Although most of the LES simulations have been previously carried out using finite volume methods, results from using the present model show that the finite element method (FEM) can also be used with confidence.

The fluid-particle interaction and the impact of different heat transfer conditions on pyrolysis of biomass inside a 150 g/h fluidised bed reactor are modelled. Two different size biomass particles (350 μm and 550 μm in diameter) are injected into the fluidised bed. The different biomass particle sizes result in different heat transfer conditions. This is due to the fact that the 350 μm diameter particle is smaller than the sand particles of the reactor (440 μm), while the 550 μm one is larger. The bed-to-particle heat transfer for both cases is calculated according to the literature. Conductive heat transfer is assumed for the larger biomass particle (550 μm) inside the bed, while biomass-sand contacts for the smaller biomass particle (350 μm) were considered unimportant. The Eulerian approach is used to model the bubbling behaviour of the sand, which is treated as a continuum. Biomass reaction kinetics is modelled according to the literature using a two-stage, semi-global model which takes into account secondary reactions. The particle motion inside the reactor is computed using drag laws, dependent on the local volume fraction of each phase. FLUENT 6.2 has been used as the modelling framework of the simulations with the whole pyrolysis model incorporated in the form of User Defined Function (UDF).

A computer model that simulates the population dynamics and epidemiology of three major species of parasitic nematodes of sheep found in the UK (Telodorsagia [Ostertagia] spp., Haemonchus spp. and Trichostrongylus spp.) is described. The model has been developed as a tool for veterinarians and advisors to aid in the implementation of integrated parasite control strategies designed to optimise anthelmintic usage and delay the development of resistance on UK farms. The model represents the parasite life cycle, flock dynamics and the response of individuals with different susceptible and resistant genotypes to the major broad-spectrum classes of anthelmintic available in the UK. Where possible, UK data have been used for the model parameters. The model allows worm control simulations on individual UK farms. Inputs include environmental and farm management variables which impact on the epidemiology of the disease, e.g. regional weather data; flock stocking rates; initial pasture larval ...

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.