For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.

The purpose of this study was to compare the incidence and longitudinal propagation of dentin defects after gutta-percha removal with hand and rotary instruments using microcomputed tomography. Twenty mandibular incisors were prepared using the balanced-force technique and scanned in a 19.9 μm resolution. Following filling with the lateral compaction technique, gutta-percha was removed with ProTaper Universal Retreatment (PTUR) or hand instruments. After rescanning, a total of 24,120 cross-sectional images were analyzed. The numbers, types, and longitudinal length changes of defects were recorded. Defects were observed in 36.90% of the cross sections. A total of 73 defects were comprised of 87.67% craze lines, 2.73% partial cracks, and 9.58% fractures. No significant difference in terms of new defect formation was detected between the retreatment groups. The apical and middle portions of the roots had more dentin defects than the coronal portions. Defects in three roots of the PTUR instrument group increased in length. Under the conditions of this in vitro study, gutta-percha removal seemed to not increase the incidence of dentin defect formation, but the longitudinal defect propagation finding suggests possible cumulative dentinal damage due to additional endodontic procedures. Hand and rotary instrumentation techniques caused similar dentin defect formation during root canal retreatment.

Biomineralization of the extracellular matrix (ECM) plays a crucial role in bone formation. Functional and structural biomimetic native bone ECM components can therefore be used to change the fate of stem cells and induce bone regeneration and mineralization. Glycosaminoglycan (GAG) mimetic peptide nanofibers can interact with several growth factors. These nanostructures are capable of enhancing the osteogenic activity and mineral deposition of osteoblastic cells, which is indicative of their potential application in bone tissue regeneration. In this study, we investigated the potential of GAG-mimetic peptide nanofibers to promote the osteogenic differentiation of rat mesenchymal stem cells (rMSCs) in vitro and enhance the bone regeneration and biomineralization process in vivo in a rabbit tibial bone defect model. Alkaline phosphatase (ALP) activity and Alizarin red staining results suggested that osteogenic differentiation is enhanced when rMSCs are cultured on GAG-mimetic peptide nanofibers. Moreover, osteogenic marker genes were shown to be upregulated in the presence of the peptide nanofiber system. Histological and micro-computed tomography (Micro-CT) observations of regenerated bone defects in rabbit tibia bone also suggested that the injection of a GAG-mimetic nanofiber gel supports cortical bone deposition by enhancing the secretion of an inorganic mineral matrix. The volume of the repaired cortical bone was higher in GAG-PA gel injected animals. The overall results indicate that GAG-mimetic peptide nanofibers can be utilized effectively as a new bioactive platform for bone regeneration.

Engineers, mathematicians, and scientists were always interested in numerical solutions of real-world problems. The ultimate objective within nearly all engineering projects is to reach a functional design without violating any of the performance, cost, time, and safety constraints while optimizing the design with respect to one of these metrics. A good mathematical model is at the heart of each powerful engineering simulation being a key component in the design process. In this chapter, we review role of simulation in the engineering process, the historical developments of different approaches, in particular simulation of machinery and continuum problems which refers basically to the numerical solution of a set of differential equations with different initial/boundary conditions. Then, an overview of well-known methods to conduct continuum based simulations within solid mechanics, fluid mechanics and electromagnetic is given. These methods include FEM, FDM, FVM, BEM, and meshless methods. Also, a summary of multi-scale and multi-physics-based approaches are given with various examples. With constantly increasing demands of the modern age challenging the engineering development process, the future of simulations in the field hold great promise possibly with the inclusion of topics from other emerging fields. As technology matures and the quest for multi-functional systems with much higher performance increases, the complexity of problems that demand numerical methods also increases. As a result, large-scale effective computing continues to evolve allowing for efficient and practical performance evaluation and novel designs, hence the enhancement of our thorough understanding of the physics within highly complex systems.

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