Kidney has a hierarchy of nephrons on the vascular arteriole tree consisting of nephron groups that interact with neighbourhoods. The kidney modeling approach for the pressure dynamics is complex in mathematical and simulation models for paired and impaired nephrons. CA rules have been developed for multi nephron kidney model from individual nephron CA rules using cumulative algorithm based on additive cellular automata and modular arithmetic. The global emergent properties obtained from CA rules depict the pressure dynamics of the multi-scaled nephron network mode up to ten lakh nephrons, clearly illustrating the nephron’s behaviour that has been captured from the experimental analysis. Regular oscillation as normotensive behaviour and irregular oscillations as hypertensive behaviour have been observed in 30000 and 1000000 nephron network models for different cases. The chaotic oscillations have been observed in the 30000-nephron kidney which leads to chronic kidney disease. Studying the causes and effects of renal diseases due to impairment of nephrons using CA kidney models leads to improvement in the early diagnosis and treatment.

Fluid flow simulation through a natural fracture is one of the most important and complex problem in Geomechanics. In general, various analytical and numerical methods are used to model fluid flow in fractures. Cellular automata method has been known as a powerful tool for simulation of complex phenomena such as fluid flow, fault movement and fracture production and propagation in a media. As a result, it can have predominant role on simulation of fluid flow in rock fractures.In this study, the modeling of fluid flow in ideal fracture has been carried out employing cellular automata method. For this purpose, a computer program has been developed and used in Fortran Power Station Domain. In this paper, the cellular automata method has been introduced and its application in fluid flow modeling described. The method of fluid flow simulation has also been presented and the results compared with available analytical solution.

This paper presents a new mixed integer nonlinear mathematical model to design dynamic cellular manufacturing systems (DCMSs). In the dynamic environment, the entire planning horizon is divided into multiple smaller periods, and each period has different product mix and part demand. In reality, production quantity may not be equal to the demand as it may be satisfied from inventory or backorders. Thus production quantity should be determined through PP decisions in order to determine the number and type of machines to be installed in manufacturing cells. Consequently, the manufactured cells should be reconfigured in each period. Design and implementation of an effective CMS involves many issues such as cell formation (CF), production planning (PP), layout design, and scheduling. The proposed model is concurrently making the CF and PP decisions and incorporates several design features including multi-period production planning, alternate routings, system reconfiguration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material flow between machines. Machine capacity is also considered. Capacity requirement and the number of required machines in each cell are defined as decision variables and for the first time, calculated based on flow shop perspective. The model forms independent cells and determines all related decision variables during each period of the time horizon according to the availability of the machines at the beginning of the first period. The objective is to minimize the total costs of intra-cell material handling, machine operating, maintenance and setup, cell reconfiguration and forming, inventory holding and backorder, and production. Linearization techniques are used to transform the suggested non-linear programming model into a linearized formulation. Using flow shop perspective to calculate capacity requirement is considered in this paper for the first time and so to verify the performance of the proposed model, a numerical example with randomly generated data is solved by a branch-and-bound (B&B) method under the Lingo 12.0 software.

Candidiasis is an opportunistic fungal disease with several predisposing factors such as leukemia and lymphoma. Since immune system can play a major role in prevention of this disease, part of the relation between cellular immunity and candidiasis in leukemic and Iymphomatic patients was investigated in this study. The case group .consisted of 38 candidiasis patients which divided to 3 subgroups based on their suffering from one of the following diseases, myeloblastic or lymphonblastic leukemia and lymphoma. The control group consisted of 20 patients, suffering from one of the leukemic diseases or lymphoma, but not from candidiasis. Blood samples were taken from both groups and number as well as type of Iymphocytes was examined using the flow cytometric method. Statistical comparison was performed using Z and t-test. The results showed that, the averages of T cells and CD4 lymphocytes were significantly (P<0.05) lower in myeloblastic patients compared to those of control group. It seems that the lower counts of T and CD4 lymphocytes in case group, are the predisposing factors for candidiasis, since these lymphocytes play a major role in prevention of candidiasis.