Simulation and Fabrication of Stronger,Larger, and Faster Walking Biohybrid Machines

Advancing biologically driven soft robotics and actuators will involve employing different scaffold geometries and cellular constructs to enable a controllable emergence for increased production of force. By using hydrogel scaffolds and muscle tissue, soft biological robotic actuators that are capable of motility have been successfully engineered with varying morphologies. Having the flexibility of altering geometry while ensuring tissue viability can enable advancing functional output from these machines through the implementation of new construction concepts and fabrication approaches. This study reports a forward engineering approach to computationally design the next generation of biological machines via direct numerical simulations. This was subsequently followed by fabrication and characterization of high force producing biological machines. These biological machines show millinewton forces capable of driving locomotion at speeds above 0.5 mm s^?1. It is important to note that these results are predicted by computational simulations, ultimately showing excellent agreement of the predictive models and experimental results, further providing the ability to forward design future generations of these biological machines. This study aims to develop the building blocks and modular technologies capable of scaling force and complexity of these devices for applications toward solving real world problems in medicine, environment, and manufacturing.     

Pricing the financial Heston–Hull–White model with arbitrary correlation factors via an adaptive FDM

This paper is concerned with the pricing procedure of one of the most challenging models known as the Heston–Hull–White partial differential equation (PDE) in option pricing, at which the model is a time-dependent 3D linear PDE including three mixed derivative terms. The model comes from the fact that the price, the volatility and the interest rate are assumed to be stochastic processes. To contribute and avoid huge discretized systems, an adaptive distribution of the nodes (viz, nonuniform nodes) is taken into account with emphasis on the hot area of the solution curve. New adaptive finite difference (FD) formulas of higher orders are constructed on these meshes. Then, a set of semi-discretized equations is attained which is tackled by a time-stepping method. Several financial tests are discussed in detail to reveal the superiority of the proposed approach.     

A branch and bound algorithm for scheduling unit size jobs on parallel batching machines to minimize makespan

In this paper, we present a branch and bound algorithm for the parallel batch scheduling of jobs having different processing times, release dates and unit sizes. There are identical machines with a fixed capacity and the number of jobs in a batch cannot exceed the machine capacity. All batched jobs are processed together and the processing time of a batch is given by the greatest processing time of jobs in that batch. We compare our method to a mixed integer program as well as a method from the literature that is capable of optimally solving instances with a single machine. Computational experiments show that our method is much more efficient than the other two methods in terms of solution time for finding the optimal solution.     

Highly Hydrophobic ZIF‐8/Carbon Nitride Foam with Hierarchical Porosity for Oil Capture and Chemical Fixation of CO2

The introduction of hierarchical porosity into metal‐organic frameworks (MOFs) has been of considerable interest in gas separation and heterogeneous catalysis due to the efficient mass transfer kinetics through meso/macropores. Here, a facile, scalable approach is reported for the preparation of carbon nitride (CN) foams as structural templates with micrometer‐sized pores and high nitrogen content of 25.6 wt% by the fast carbonization of low‐cost melamine foam. The nitrogen functionalities of CN foam facilitate chemical anchoring and growth of ZIF‐8 (zeolitic imidazolate frameworks) crystals, which leads to the development of hierarchical porosity. The growth of ZIF‐8 crystals also renders CN foam, which is hydrophilic in nature, highly hydrophobic exhibiting 135° of water contact angle due to the enhanced surface roughness, thus creating a natural shield for the MOF crystals against water. The introduction of ZIF‐8 crystals onto the CN foam enables selective absorption of oils up to 58 wt% from water/oil mixtures and also facilitates the highly efficient conversion of CO₂ to chloropropene carbonate in a quantitative yield with excellent product selectivity. Importantly, this present approach could be extended to the vast number of MOF structures, including the ones suffering from water instability, for the preparation of highly functional materials for various applications.     

The production and characterization of poly(lactic acid) fibers dyeable with anionic dyes using octaammonium polyhedral oligomeric silsesquioxane nanoparticle

In this study, acid dyeable poly(lactic acid) (PLA) fiber was produced with the addition of octaammonium polyhedral oligomeric silsesquioxane (OA-POSS) nanoparticle during the melt spinning. The tensile, thermal and morphological properties of the fiber samples were characterized by tensile testing, differential scanning calorimetry, scanning electron microscopy and transmission electron microscopy. Two different anionic dyes, a disulphonated 1:2 premetallised acid dye and monosulphonated non-metallised, were used. The effects of dyeing conditions including dyeing temperature and time, OA-POSS concentration, anionic dye types and concentrations were investigated on the dyeability properties of the PLA fiber samples. It was concluded that the percent crystallinity and the tensile strength of pure PLA fiber decreased as the added amount of OA-POSS increased. According to the dyeing results, the addition of OA-POSS greatly improved the dyeability of the PLA fiber with anionic dyes by introducing ion–ion interaction between the terminal ammonium groups of POSS nanoparticle and the sulphonyl groups of dye molecules.     

A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA

Two hybrid feature selection methods (SFSP and SBSP) which are composed by combining the sequential forward selection and the sequential backward selection together with the principal component analysis developed by utilizing quadratic discriminant analysis classification algorithmic criteria so as to utilize in the diagnosis of breast cancer fast and effectively are presented in this study. The tenfold cross-validation method has been applied in the algorithm, which is utilized as criteria during the selection of the features. The dimension of the feature space for input has been decreased from 9 to 4 thanks to the selection of these two hybrid features. The Artificial Neural Networks have been used as classifier. The cross-validation method has been preferred also in the phase of this classification as in the case of the selection of the feature in order to increase the reliability of the result. The Wisconsin Breast Cancer Database obtained from the UCI has been utilized so as to determine the correctness of the system suggested. The values of the average correctness of the classification obtained by utilizing a tenfold cross-validation of the two hybrid systems developed earlier are found, respectively, as follows: for SFSP + NN, 97.57 % and for SBSP + NN, 98.57 %. SBSP + NN system has been observed that, among the studies carried out by implementing the cross-validation method for the breast cancer, the result appears to be very promising. The acquired results have revealed that this hybrid system applied by means of reducing dimension is an utilizable system in order to diagnose the diseases faster and more successfully.     

High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

This paper investigates the use of wavelet ensemble models for high performance concrete (HPC) compressive strength forecasting. More specifically, we incorporate bagging and gradient boosting methods in building artificial neural networks (ANN) ensembles (bagged artificial neural networks (BANN) and gradient boosted artificial neural networks (GBANN)), first. Coefficient of determination (R²), mean absolute error (MAE) and the root mean squared error (RMSE) statics are used for performance evaluation of proposed predictive models. Empirical results show that ensemble models (R²_BANN=0.9278, R²_GBANN=0.9270) are superior to a conventional ANN model (R²_ANN=0.9088). Then, we use the coupling of discrete wavelet transform (DWT) and ANN ensembles for enhancing the prediction accuracy. The study concludes that DWT is an effective tool for increasing the accuracy of the ANN ensembles (R²_WBANN=0.9397, R²_WGBANN=0.9528).     

Thermal and mechanical properties of polypropylene nanocomposite materials reinforced with cellulose nano whiskers

We use polypropylene (PP, an apolar polymer) and cellulose nano whiskers (CNW, a polar material) to produce nano polymer composites with enhanced mechanical and thermal properties. To improve compatibility, maleic anhydride grafted PP has also been used as a coupling agent. To enhance the uniform distribution of CNW in the composite, the matrix polymer is dissolved in toluene, and sonification and magnetic stirring are applied. Good film transparency indicates uniform CNW dispersion, but CNW domains in the composite film observed under an scanning electron microscope may indicate slight agglomeration of CNW in the composite film. The tensile strength of the composite compared with neat PP improves by 70–80% with the addition of CNW. The crystallinity has also been improved by about 50% in the CNW reinforced samples. As the content of CNW increases, the composite exhibits higher thermal degradation temperature, higher hydrophilicity, and higher thermal conductivity. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012