A problem in generalized magneto-thermoelasticity for an infinitely long annular cylinder

The problem of an infinitely long annular cylinder whose inner and outer surfaces are subjected to known surrounding temperatures and are traction-free is considered in the presence of an axial uniform magnetic field. The problem is in the context of generalized magneto-thermoelasticity theory with one relaxation time. The Laplace transform with respect to time is used. A numerical method based on a Fourier-series expansion is used for the inversion process. Numerical computations for the temperature, displacement and stress distributions as well as for the induced magnetic and electric fields are carried out and represented graphically.     

Assessment of formulation parameters needed for successful vitamin C entrapped polycaprolactone nanoparticles

Vitamin C (Vit.C)-entrapped polycaprolactone (PCL) nanoparticles (Vit.C–PCNs) were prepared by encapsulation of Vit.C into PCL-based nanoparticles (PCNs) which were prepared using double emulsion method with two steps. First, the inner aqueous phase (W₁) was added to dichloromethane solution containing PCL with homogenization to form primary emulsion (W₁/O) which was emulsified with the outer aqueous phase (W₂) containing polyvinyl alcohol as stabilizer to attain the double emulsion (W₁/O/W₂). Versatile parameters were investigated to reach to the most successful formulation for Vit.C–PCNs, such as time, effect of speed of homogenization on drug encapsulation efficiency, etc.     

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

Contemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real-time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye-view of their characteristics, functions, and applications. The review also presents the state-of-the-art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI-assisted support systems relating to clinical decisions are presented and discussed.     

Mu-based trajectory tracking control for a quad-rotor UAV

In this paper, the design and application of a robust mu-synthesis-based controller for quad-rotor trajectory tracking are presented. The proposed design approach guarantees robust performance over a weakly nonlinear range of operation of the quad-rotor, which is a practical range that suits various applications. The controller considers different structured and unstructured uncertainties, such as unmodeled dynamics and perturbation in the parameters. The controller also provides robustness against external disturbances such as wind gusts and wind turbulence. The proposed controller is fixed and linear; therefore, it has a very low computational cost. Moreover, the controller meets all design specifications without tuning. To validate this control strategy, the proposed approach is compared to a linear quadratic regulator (LQR) controller using a high- fidelity quad-rotor simulation environment. In addition, the experimental results presented show the validity of the proposed control strategy.     

Reduced-gate convolutional long short-term memory using predictive coding for spatiotemporal prediction

Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional LSTM (convLSTM) modules. We introduce a novel rgcLSTM architecture that requires a significantly lower parameter budget than a comparable convLSTM. By using a single multifunction gate, our reduced-gate model achieves equal or better next-frame(s) prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time and memory requirements. We tested our reduced gate modules within a predictive coding architecture on the moving MNIST and KITTI datasets. We found that our reduced-gate model has a significant reduction of approximately 40% of the total number of training parameters and a 25% reduction in elapsed training time in comparison with the standard convolutional LSTM model. The performance accuracy of the new model was also improved. This makes our model more attractive for hardware implementation, especially on small devices. We also explored a space of 20 different gated architectures to get insight into how our rgcLSTM fits into that space.     

Thermoelectric spherical shell with fractional order heat transfer

Microsystem Technologies - The model of fractional magneto-thermoelasticity is applied to one-dimensional problems of a thermoelectric spherical shell subjected to an arbitrary thermal loading of...     

γ‐Radiation‐treated starch/poly(vinyl alcohol) composites for cotton fabric sizing

Films of different composites based essentially on maize starch (MS)/poly(vinyl alcohol) (PVA) blends were prepared by the solution‐casting technique and subjected to various doses (20–100 kGy) of γ‐radiation. The MS/PVA blends were modified by the addition of glycerol (GY) and a graft copolymer (GP) of MS with acrylamide separately or together with the polymer blend solutions before casting. The γ‐treated composites were evaluated in terms of the apparent viscosity and their suitability as sizing materials for cotton fabrics. The sizeability of these composites for cotton fabrics was assessed in terms of the size removal percentage at different temperatures and the effect on the tensile properties and water absorption. The change in the apparent viscosity with the shear rate showed that γ‐irradiation improved the behavior of MS/PVA blends and their composites with GY or GP as a sizing material for cotton fabrics. Moreover, the improvement in the tensile mechanical properties of the sized cotton fabrics with these composites gave further support to this finding. The results for the size removal percentage and water adsorption indicated that these composites could be removed by washing at 70°C for 10 min. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91: 3818–3826, 2004     

Quantifying connectivity in wireless sensor networks with grid-based deployments

Measuring network connectivity under grid-based deployment in 3D space is a challenging problem in wireless sensor networks (WSNs). Solving such a problem becomes an even more intricate task with realistic deployment factors such as placement uncertainty and hindrances to wireless communication channels. While much work has been published on optimizing connectivity, only a few papers have addressed such realistic aspects which cause severe connectivity problems in practice. In this paper, we introduce a novel grid-based deployment metric, called Average Connectivity Percentage in order to characterize the deployed network connectivity when sensor placements are subject to random errors around their corresponding grid locations. A generic approach is proposed to assess and evaluate the proposed metric. This generic approach is independent of the grid-shape, random error distributions, and different environment-based channel characteristics. We apply the generic approach in two practical deployment scenarios: the grid-based deployment with bounded uniform errors and with unbounded normal errors. In both cases, the average connectivity percentage is computed numerically and verified via extensive simulations. Based on the numerical results, quantified effects of positioning errors and grid edge length on the average connectivity percentage are outlined.