A coupled SPH-DEM model for fluid and solid mechanics of apple parenchyma cells during drying

A coupled SPH-DEM based two-dimensional (2-D) micro-scale single cell model is developed to predict basic cell-level shrinkage effects of apple parenchyma cells during air drying. In this newly developed drying model, Smoothed Particle Hydrodynamics (SPH) is used to model the low Reynolds Number fluid motions of the cell protoplasm, and a Discrete Element Method (DEM) is employed to simulate the polymer-like cell wall. Simulations results reasonably agree with published experimental drying results on cellular shrinkage properties such as cellular area, diameter and perimeter. These preliminary results indicate that the model is effective for the modelling and simulation of apple parenchyma cells during air drying.

A particle-based micromechanics approach to simulate structural changes of plant cells during drying

This paper is concerned with applying a particle-based approach to simulate the micro-level cellular structural changes of plant cells during drying. The objective of the investigation was to relate the micro-level structural properties such as cell area, diameter and perimeter to the change of moisture content of the cell. Model assumes a simplified cell which consists of two basic components, cell wall and cell fluid. The cell fluid is assumed to be a Newtonian fluid with higher viscosity compared to water and cell wall is assumed to be a visco-elastic solid boundary located around the cell fluid. Cell fluid is modelled with Smoothed Particle Hydrodynamics (SPH) technique and for the cell wall; a Discrete Element Method (DEM) is used. The developed model is two-dimensional, but accounts for three-dimensional physical properties of real plant cells. Drying phenomena is simulated as fluid mass reductions and the model is used to predict the above mentioned structural properties as a function of cell fluid mass. Model predictions are found to be in fairly good agreement with experimental data in literature and the particle-based approach is demonstrated to be suitable for numerical studies of drying related structural deformations. Also a sensitivity analysis is included to demonstrate the influence of key model parameters to model predictions.

Design and modeling of collaboration architecture for security

Threats against computer networks evolve very fast and require more and more complex measures. We argue that teams respectively groups with a common purpose for intrusion detection and prevention improve the measures against rapid propagating attacks similar to the concept of teams solving complex tasks known from field of work sociology. Collaboration in this sense is not easy task especially for heterarchical environments. We propose CIMD (collaborative intrusion and malware detection) as a security overlay framework to enable cooperative intrusion detection approaches. Objectives and associated interests are used to create detection groups for exchange of security-related data. In this work, we contribute a tree-oriented data model for device representation in the scope of security. We introduce an algorithm for the formation of detection groups, show realization strategies for the system and conduct vulnerability analysis. We evaluate the benefit of CIMD by simulation and probabilistic analysis.

An in-vitro 3-D cell culture model for studying pathomechanisms in AMD

Purpose To develop a novel 3-D cell culture model with the view to studying the pathomechanisms underlying the development of age-related macular degeneration (AMD). Our central hypothesis is that the silk structural protein fibroin used in conjunction with cultured human cells can be used to mimic the structural relationships between the RPE and choriocapillaris in health and disease. Methods Co-cultures of human RPE cells (ARPE-19 cells grown in Miller’s medium) and microvascular endothelial cells (HMEC-1 cells grown in endothelial culture medium) were established on opposing sides of a synthetic Bruch’s membrane (3 microns thick) constructed from B mori silk fibroin. Cell attachment was facilitated by pre-coating the fibroin membrane with vitronectin (for ARPE-19 cells) and gelatin (for HMEC-1 cells) respectively. The effects of tropoelastin on attachment of ARPE-19 cells was also examined. Barrier function was examined by measurement of trans-epithelial resistance (TER) using a voltohmmeter (EVOM-2). The phagocytic activity of the synthetic RPE was tested using vitronectin-coated microspheres (2 micron diameter FluoSpheres). In some cultures, membrane defects were created by puncturing within a 24 G needle. The architecture of the synthetic tissue before and after wounding was examined by confocal microscopy after staining for ZO-1 and F-actin. Results The RPE layer of the 3D model developed a cobblestoned morphology (validated by staining for ZO-1 and F-actin), displayed barrier function (validated by measurement of TER) and demonstrated cytoplasmic uptake of vitronectin-coated microspheres. Attachment of ARPE-19 cells to fibroin was unaffected by tropoelastin. Microvascular endothelial cells attached well to the gelatin-coated surface of the fibroin membrane and remained physically separated from the overlaying RPE layer. The fibroin membranes were amenable to puncturing without collapse thus providing the opportunity to study transmembrane migration of the endothelial cells. Conclusions Synthetic Bruch’s membranes constructed from silk fibroin, vitronectin and gelatin, support the co-cultivation of RPE cells and microvascular endothelial cells. The resulting RPE layer displays functions similar to that of native RPE and the entire tri-layered structure displays potential to be used as an in vitro model of choroidal neovascularization.

Integration of opinion into customer analysis model

As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. In order to enhance customer satisfaction and their shopping experiences, it has become important to analysis customers reviews to extract opinions on the products that they buy. Thus, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes.

Chitosan-collagen scaffolds with nano/microfibrous architecture for skin tissue engineering

In this study, a hierarchical nano/microfibrous chitosan/collagen scaffold that approximates structural and functional attributes of native extracellular matrix (ECM), has been developed for applicability in skin tissue engineering. Scaffolds were produced by electrospinning of chitosan followed by imbibing of collagen solution, freeze-drying and subsequent cross-linking of two polymers. Scanning electron microscopy showed formation of layered scaffolds with nano/microfibrous architechture. Physico-chemical properties of scaffolds including tensile strength, swelling behavior and biodegradability were found satisfactory for intended application. 3T3 fibroblasts and HaCaT keratinocytes showed good in vitro cellular response on scaffolds thereby indicating the matrices′ cytocompatible nature. Scaffolds tested in an ex vivo human skin equivalent (HSE) wound model, as a preliminary alternative to animal testing, showed keratinocyte migration and wound re-epithelization — a pre-requisite for healing and regeneration. Taken together, the herein proposed chitosan/collagen scaffold, shows good potential for skin tissue engineering.

Towards a model of spray-canopy interactions : interception, shatter, bounce and retention of droplets on horizontal leaves

Pesticides used in agricultural systems must be applied in economically viable and environmentally sensitive ways, and this often requires expensive field trials on spray deposition and retention by plant foliage. Computational models to describe whether a spray droplet sticks (adheres), bounces or shatters on impact, and if any rebounding parent or shatter daughter droplets are recaptured, would provide an estimate of spray retention and thereby act as a useful guide prior to any field trials. Parameter-driven interactive software has been implemented to enable the end-user to study and visualise droplet interception and impaction on a single, horizontal leaf. Living chenopodium, wheat and cotton leaves have been scanned to capture the surface topography and realistic virtual leaf surface models have been generated. Individual leaf models have then been subjected to virtual spray droplets and predictions made of droplet interception with the virtual plant leaf. Thereafter, the impaction behaviour of the droplets and the subsequent behaviour of any daughter droplets, up until re-capture, are simulated to give the predicted total spray retention by the leaf. A series of critical thresholds for the stick, bounce, and shatter elements in the impaction process have been developed for different combinations of formulation, droplet size and velocity, and leaf surface characteristics to provide this output. The results show that droplet properties, spray formulations and leaf surface characteristics all influence the predicted amount of spray retained on a horizontal leaf surface. Overall the predicted spray retention increases as formulation surface tension, static contact angle, droplet size and velocity decreases. Predicted retention on cotton is much higher than on chenopodium. The average predicted retention on a single horizontal leaf across all droplet size, velocity and formulations scenarios tested, is 18, 30 and 85% for chenopodium, wheat and cotton, respectively.

A novel characterisation model for microvasulature changes following closed soft tissue trauma using micro-CT imaging

INTRODUCTION There is evidence that the reduction of blood perfusion caused by closed soft tissue trauma (CSTT) delays the healing of the affected soft tissues and bone [1]. We hypothesise that the characterisation of vascular morphology changes (VMC) following injury allows us to determine the effect of the injury on tissue perfusion and thereby the severity of the injury. This research therefore aims to assess the VMC following CSTT in a rat model using contrast-enhanced micro-CT imaging. METHODOLOGY A reproducible CSTT was created on the left leg of anaesthetized rats (male, 12 weeks) with an impact device. After euthanizing the animals at 6 and 24 hours following trauma, the vasculature was perfused with a contrast agent (Microfil, Flowtech, USA). Both hind-limbs were dissected and imaged using micro-CT for qualitative comparison of the vascular morphology and quantification of the total vascular volume (VV). In addition, biopsy samples were taken from the CSTT region and scanned to compare morphological parameters of the vasculature between the injured and control limbs. RESULTS AND DISCUSSION While the visual observation of the hindlimb scans showed consistent perfusion of the microvasculature with microfil, enabling the identification of all major blood vessels, no clear differences in the vascular architecture were observed between injured and control limbs. However, overall VV within the region of interest (ROI)was  measured to be higher for the injured limbs after 24h. Also, scans of biopsy samples demonstrated that vessel diameter and density were higher in the injured legs 24h after impact. CONCLUSION We believe these results will contribute to the development of objective diagnostic methods for CSTT based on changes to the microvascular morphology as well as aiding in the validation of future non-invasive clinical assessment modalities.

A critical realist perspective of enterprise architecture evolution : preliminary findings

This paper investigates how Enterprise Architecture (EA) evolves due to emerging trends. It specifically explores how EA integrates the Service-oriented Architecture (SOA). Archer’s Morphogenetic theory is used as an analytical approach to distinguish the architectural conditions under which SOA is introduced, to study the relationships between these conditions and SOA introduction, and to reflect on EA evolution (elaborations) that then take place. The paper focuses on reasons for why EA evolution could take place, or not and what architectural changes could happen due to SOA integration. The research builds on sound theoretical foundations to discuss EA evolution in a field that often lacks a solid theoretical groundwork. Specifically, it proposes that critical realism, using the morphogenetic theory, can provide a useful theoretical foundation to study enterprise architecture (EA) evolution. The initial results of a literature review (a-priori model) were extended using explorative interviews. The findings of this study are threefold. First, there are five different levels of EA-SOA integration outcomes. Second, a mature EA, flexible and well-defined EA framework and comprehensive objectives of EA improve the integration outcomes. Third, the analytical separation using Archer’s theory is helpful in order to understand how these different integration outcomes are generated.

Effects of scaffold architecture on mechanical characteristics and osteoblast response to static and perfusion bioreactor cultures

Tissue engineering focuses on the repair and regeneration of tissues through the use of biodegradable scaffold systems that structurally support regions of injury whilst recruiting and/or stimulating cell populations to rebuild the target tissue. Within bone tissue engineering, the effects of scaffold architecture on cellular response have not been conclusively characterized in a controlled-density environment. We present a theoretical and practical assessment of the effects of polycaprolactone (PCL) scaffold architectural modifications on mechanical and flow characteristics as well as MC3T3-E1 preosteoblast cellular response in an in vitro static plate and custom-designed perfusion bioreactor model. Four scaffold architectures were contrasted, which varied in inter-layer lay-down angle and offset between layers, whilst maintaining a structural porosity of 60 ± 5%. We established that as layer angle was decreased (90° vs. 60°) and offset was introduced (0 vs. 0.5 between layers), structural stiffness, yield stress, strength, pore size and permeability decreased, whilst computational fluid dynamics-modeled wall shear stress was increased. Most significant effects were noted with layer offset. Seeding efficiencies in static culture were also dramatically increased due to offset (~45% to ~86%), with static culture exhibiting a much higher seeding efficiency than perfusion culture. Scaffold architecture had minimal effect on cell response in static culture. However, architecture influenced osteogenic differentiation in perfusion culture, likely by modifying the microfluidic environment.

Model-predictive control and closed-loop stability considerations for nonlinear plants described by local ARX-type models

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Simulation of plant cell shrinkage during drying : A SPH–DEM approach

Plant based dried food products are popular commodities in global market where much research is focused to improve the products and processing techniques. In this regard, numerical modelling is highly applicable and in this work, a coupled meshfree particle-based two-dimensional (2-D) model was developed to simulate micro-scale deformations of plant cells during drying. Smoothed Particle Hydrodynamics (SPH) was used to model the viscous cell protoplasm (cell fluid) by approximating it to an incompressible Newtonian fluid. The visco-elastic characteristic of the cell wall was approximated to a Neo-Hookean solid material augmented with a viscous term and modelled with a Discrete Element Method (DEM). Compared to a previous work [H. C. P. Karunasena, W. Senadeera, Y. T. Gu and R. J. Brown, Appl. Math. Model., 2014], this study proposes three model improvements: linearly decreasing positive cell turgor pressure during drying, cell wall contraction forces and cell wall drying. The improvements made the model more comparable with experimental findings on dried cell morphology and geometric properties such as cell area, diameter, perimeter, roundness, elongation and compactness. This single cell model could be used as a building block for advanced tissue models which are highly applicable for product and process optimizations in Food Engineering.

Model-driven service engineering in home telecare

Health care systems are highly dynamic not just due to developments and innovations in diagnosis and treatments, but also by virtue of emerging management techniques supported by modern information and communication technology. A multitude of stakeholders such as patients, nurses, general practitioners or social carers can be integrated by modeling complex interactions necessary for managing the provision and consumption of health care services. Furthermore, it is the availability of Service-oriented Architecture (SOA) that supports those integration efforts by enabling the flexible and reusable composition of autonomous, loosely-coupled and web-enabled software components. However, there is still the gap between SOA and predominantly business-oriented perspectives (e.g. business process models). The alignment of both views is crucial not just for the guided development of SOA but also for the sustainable evolution of holistic enterprise architectures. In this paper, we combine the Semantic Object Model (SOM) and the Business Process Modelling Notation (BPMN) towards a model-driven approach to service engineering. By addressing a business system in Home Telecare and deriving a business process model, which can eventually be controlled and executed by machines; in particular by composed web services, the full potential of a process-centric SOA is exploited.

Non-linear model predictive control for UAVs with slung/swung load

Suspended loads on UAVs can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present software and flight system architecture to test controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions.