Particle swarm optimization (PSO) is extensively used for real parameter optimization in diverse fields of study. This paper describes an application of PSO to the problem of designing a fractional-order proportional-integral-derivative (PI λ D δ ) controller whose parameters comprise proportionality constant, integral constant, derivative constant, integral order (λ) and derivative order (δ). The presence of five optimizable parameters makes the task of designing a PI λ D δ controller more challenging than conventional PID controller design. Our design method focuses on minimizing the Integral Time Absolute Error (ITAE) criterion. The digital realization of the deigned system utilizes the Tustin operator-based continued fraction expansion scheme. We carry out a simulation that illustrates the effectiveness of the proposed approach especially for realizing fractional-order plants. This paper also attempts to study the behavior of fractional PID controller vis-à-vis that of its integerorder counterpart and demonstrates the superiority of the former to the latter.

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System identification is a necessity in control theory. Classical control theory usually considers processes with integer order transfer functions. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme is presented for approximation of such a real world fractional order process by an ideal integral order model. A population of integral order process models is generated and updated by PSO technique, the fitness function being the sum of squared deviations from the set of observations obtained from the actual fractional order process. Results show that the proposed scheme offers a high degree of accuracy.

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Comparing the bone regeneration potential of nano whitlockite or nano bioglass in combination with FGF-18, loaded in an injectable, shear-thinning chitin/PLGA hydrogel for craniofacial bone regeneration.

BACKGROUND: Human adipose tissue is a great source of translatable biomaterials owing to its ease of availability and simple processing. Reusing discardable adipose tissue for tissue regeneration helps in mimicking the exact native microenvironment of tissue. Over the past 10 years, extraction, processing, tuning and fabrication of adipose tissue have grabbed the attention owing to their native therapeutic and regenerative potential. The present work gives the overview of next generation biomaterials derived from human adipose tissue and their development with clinical relevance. METHODS: Around 300 articles have been reviewed to widen the knowledge on the isolation, characterization techniques and medical applications of human adipose tissue and its derivatives from bench to bedside. The prospective applications of adipose tissue derivatives like autologous fat graft, stromal vascular fraction, stem cells, preadipocyte, adipokines and extracellular matrix, their behavioural mechanism, rational property of providing native bioenvironment, circumventing their translational abilities, recent advances in featuring them clinically have been reviewed extensively to reveal the dormant side of human adipose tissue. RESULTS: Basic understanding about the molecular and structural aspect of human adipose tissue is necessary to employ it constructively. This review has nailed the productive usage of human adipose tissue, in a stepwise manner from exploring the methods of extracting derivatives, concerns during processing and its formulations to turning them into functional biomaterials. Their performance as functional biomaterials for skin regeneration, wound healing, soft tissue defects, stem cell and other regenerative therapies under in vitro and in vivo conditions emphasizes the translational efficiency of adipose tissue derivatives. CONCLUSION: In the recent years, research interest has inclination towards constructive tissue engineering and regenerative therapies. Unravelling the maximum utilization of human adipose tissue derivatives paves a way for improving existing tissue regeneration and cellular based therapies and other biomedical applications.