Strategies using of Design of Experiments (DOE) techniques: In view of a review

There Design of Experiment (DOE) has developed into a valuable collection technique for statistical and mathematical processes used in modelling and analysis of problems involving multiple variables influencing the desired response. Numerous researchers and engineers use this technique in a variety of fields, including botany, pharmaceuticals, biotechnology, and other engineering disciplines. This review article summarised key points from the Design of Experiments Using Response Surface Methodology (RSM). Design of experiments (DOE) has guidelines and procedures, but the literature does not recommend a specific method for finding and selecting the best possible design from a large number of possible design

Synthesis and characterization of wound healing hydrogel using keratin protein from chicken feathers

Poultry industries produce a large amount of feather waste, which harm the environment and human health. On the other hand, chicken feathers primarily contain keratin protein, which can be exploited to produce products for biomedical applications. In the present research, keratin was extracted from chicken feathers and was applied to prepare the hydrogel films for wound healing applications. Some biopolymers were used to prepare two different hydrogel films, such as polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP) and corn starch, using the freeze-thawing technique at temperature -20°C. All biopolymers used in this study are inexpensive, non-toxic, and have been successfully applied in various biomedical applications. The first formulation, namely KS-hydrogels were prepared using keratin, polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP) and corn-starch. The second formulation, namely K-hydrogels were prepared using keratin, polyvinyl alcohol (PVA), and polyvinylpyrrolidone (PVP). The effect of keratin in hydrogel films for both samples was examined by Fourier-transform infrared spectroscopy (FTIR), confirmed the presence of keratin, scanning electron microscope (SEM) examined surface morphology, and thermogravimetric analysis (TGA) showed thermal stability was affected with different concentrations of keratin protein. The porosity of the hydrogel decreased for KS-70 and K-70 hydrogels at 33.57% and 45.22%, respectively, due to their relatively high interconnecting and low porous structure due to their low water content with high keratin content. The swelling ratio of KS70 and K70 hydrogels and 30.66% and 31.58 % after 1440 min due to its relatively increased crosslinking density with high keratin content. On the other hand, tensile strength (stress vs strain) has seen improvement with the increase of the keratin protein content into hydrogel films. Furthermore, it was found that K-hydrogel films were better than KS-hydrogel films because K-hydrogel films provided an appropriate hardness for using potential wound healing applications. Moreover, keratin release increased with increasing keratin content; the highest release was 95.72% in K70 after 96 hr on the KS-hydrogel films and K-hydrogel films release was 81% in K70 after 96 hr Higuchi square root model best predicted the keratin release behaviour. The Higuchi square root was the optimal model of keratin kinetics release for all the hydrogel films. The optimal conditions for hydrogel film synthesis were determined using response surface methodology (RSM) with four selected parameters, including (A, 30-70 v/v %), PVA/PVP ratio (B, 30-70 v/v %), freeze and thawing (C, 3-7 cycles), and mixing temperature (D, 50-70 °C). The model determined that the optimal conditions for the best formation were 50% keratin content, 50% PVA/PVP, five freeze-thaw cycles, and a mixing temperature of 60°C. ANOVA demonstrated the model is significant and has a p-value less than 0.05, with the R2 was 97.3. In vivo model on the rabbits indicated that keratin-based hydrogel film could accelerate wound healing compared with other groups after 19 days. Dependence on the results obtained in this study, the keratin hydrogel film was successfully prepared for potential wound healing applications

Dual-Directed Algorithm Design for Efficient Pure Exploration

We consider pure-exploration problems in the context of stochastic sequential adaptive experiments with a finite set of alternative options. The goal of the decision-maker is to accurately answer a query question regarding the alternatives with high confidence with minimal measurement efforts. A typical query question is to identify the alternative with the best performance, leading to ranking and selection problems, or best-arm identification in the machine learning literature. We focus on the fixed-precision setting and derive a sufficient condition for optimality in terms of a notion of strong convergence to the optimal allocation of samples. Using dual variables, we characterize the necessary and sufficient conditions for an allocation to be optimal. The use of dual variables allow us to bypass the combinatorial structure of the optimality conditions that relies solely on primal variables. Remarkably, these optimality conditions enable an extension of top-two algorithm design principle, initially proposed for best-arm identification. Furthermore, our optimality conditions give rise to a straightforward yet efficient selection rule, termed information-directed selection, which adaptively picks from a candidate set based on information gain of the candidates. We outline the broad contexts where our algorithmic approach can be implemented. We establish that, paired with information-directed selection, top-two Thompson sampling is (asymptotically) optimal for Gaussian best-arm identification, solving a glaring open problem in the pure exploration literature. Our algorithm is optimal for $\epsilon$-best-arm identification and thresholding bandit problems. Our analysis also leads to a general principle to guide adaptations of Thompson sampling for pure-exploration problems. Numerical experiments highlight the exceptional efficiency of our proposed algorithms relative to existing ones.Comment: An earlier version of this paper appeared as an extended abstract in the Proceedings of the 36th Annual Conference on Learning Theory, COLT'23, with the title "Information-Directed Selection for Top-Two Algorithms.'

Enabling the “Easy Button” for Broad, Parallel Optimization of Functions Evaluated by Simulation

Java Optimization by Simulation (JOBS) is presented: an open-source, object-oriented Java library designed to enable the study, research, and use of optimization for models evaluated by simulation. JOBS includes several novel design features that make it easy for a simulation modeler, without extensive expertise in optimization or parallel computation, to define an optimization model with deterministic and/or stochastic constraints, choose one or more metaheuristics to solve it and run, using massively parallel function evaluation to reduce wall-clock times. JOBS is supported by a new language independent, application programming interface (API) for remote simulation model evaluation and a serverless computing environment to provide massively parallel function evaluation, on demand. Dynamic loop scheduling methods are evaluated in the serverless environment with the opportunity for significant resource contention for master node computing power and network bandwidth. JOBS implements several population-based and single-solution improvement metaheuristics (solvers) for real, discrete, and mixed problems. The object-oriented design is extendible with classes that drastically reduce the amount of code required to implement a new solver and encourage re-use of solvers as building blocks for creating new multi-stage solvers or memetic algorithms