4D Printing of Shape Memory Materials for Textiles: Mechanism,Mathematical Modeling,and Challenges

Shape memory materials (SMMs) in 3D printing (3DP) technology garnered much attention due to their ability to respond to external stimuli, which direct this technology toward an emerging area of research, “4D printing (4DP) technology.” In contrast to classical 3D printed objects, the fourth dimension, time, allows printed objects to undergo significant changes in shape, size, or color when subjected to external stimuli. Highly precise and calibrated 4D materials, which can perform together to achieve robust 4D objects, are in great demand in various fields such as military applications, space suits, robotic systems, apparel, healthcare, sports, etc. This review, for the first time, to the best of the authors’ knowledge, focuses on recent advances in SMMs (e.g., polymers, metals, etc.) based wearable smart textiles and fashion goods. This review integrates the basic overview of 3DP technology, fabrication methods, the transition of 3DP to 4DP, the chemistry behind the fundamental working principles of 4D printed objects, materials selection for smart textiles and fashion goods. The central part summarizes the effect of major external stimuli on 4D textile materials followed by the major applications. Lastly, prospects and challenges are discussed, so that future researchers can continue the progress of this technology.     

Design of Smart Door Closer System with Image Classification over WLAN

Wireless Personal Communications - A dual purpose system is presented in this paper which serves not only as a door closer, but is equally effective for surveillance purposes. The currently...     

Engineering New Microvascular Networks On-Chip: Ingredients,Assembly, and Best Practices

Tissue engineered grafts show great potential as regenerative implants for diseased or injured tissues within the human body. However, these grafts suffer from poor nutrient perfusion and waste transport, thus decreasing their viability post-transplantation. Graft vascularization is therefore a major area of focus within tissue engineering because biologically relevant conduits for nutrient and oxygen perfusion can improve viability post-implantation. Many researchers used microphysiological systems as testing platforms for potential grafts owing to an ability to integrate vascular networks as well as biological characteristics such as fluid perfusion, 3D architecture, compartmentalization of tissue-specific materials, and biophysical and biochemical cues. Although many methods of vascularizing these systems exist, microvascular self-assembly has great potential for bench-to-clinic translation as it relies on naturally occurring physiological events. In this review, the past decade of literature is highlighted, and the most important and tunable components yielding a self-assembled vascular network on chip are critically discussed: endothelial cell source, tissue-specific supporting cells, biomaterial scaffolds, biochemical cues, and biophysical forces. This paper discusses the bioengineered systems of angiogenesis, vasculogenesis, and lymphangiogenesis and includes a brief overview of multicellular systems. It concludes with future avenues of research to guide the next generation of vascularized microfluidic models.     

Sol-Gel Preparation and Luminescence Properties of BaMgAl10O17∶Eu2+ Phosphors

The synthesis of BaMgAl10O17∶ Eu2 (BAM) phosphors using the sol-gel method and their luminescence properties were reported. The blue-light emitting BAM was synthesized using citric acid and ethylene glycol as chelating materials. Emission of blue-light was obtained from these phosphors. The luminescent intensity increases as the temperature of heat treatment is increased. This study investigated the effects of the molar ratio of ethylene glycol to citric acid (Φ value), with respect to the phase formation and luminescence properties of BAM. The variation of the Φ value resulted in the change of the sol-gel reaction mechanism and the microstructures of the resultant powders. An increase in Φ value leads to an increase in the rate of BAM phase formation. The photoluminescent intensity of the prepared phosphors increases with heating temperatures because of enhanced crystallization.     

Microarchitectures for Managing Chip Revenues under Process Variations

As transistor feature sizes continue to shrink intothe sub-90nm range and beyond, the effects of process variationson critical path delay and chip yields have amplified. A commonconcept to remedy the effects of variation is speed-binning, bywhich chips from a single batch are rated by a discrete range offrequencies and sold at different prices. In this paper, we discussstrategies to modify the number of chips in different bins andhence enhance the profits obtained from them. Particularly, wepropose a scheme that introduces a small Substitute Cacheassociated with each cache way to replicate the data elementsthat will be stored in the high latency lines. Assuming a fixedpricing model, this method increases the revenue by as much as13.8% without any impact on the performance of the chips.     

Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject’s incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.     

Prediction of the hydraulic diffusivity from pore size distribution of concrete

A model is presented in this work through which variation of hydraulic diffusivity of concrete with relative water content can be obtained from pore size distribution as an input. The specific water capacity and hydraulic conductivity of concrete are expressed in terms of pore size characteristics, considering laminar flow due to capillary suction through tortuous elliptic tubes, oriented equally in three orthogonal directions. Hydraulic diffusivity being the ratio of hydraulic conductivity and specific water capacity is thus expressed in terms of pore size characteristics. The input pore size distributions have been determined experimentally for normal strength concrete mixes through mercury intrusion porosimetry. Using the model the variation of hydraulic diffusivity with relative water content is determined for three cases viz. 1) ideal continuous wetting, 2) ideal continuous drying and 3) random access of pores by water. These results are then compared with an experimentally obtained variation.     

The C-loss function for pattern classification

This paper presents a new loss function for neural network classification, inspired by the recently proposed similarity measure called Correntropy. We show that this function essentially behaves like the conventional square loss for samples that are well within the decision boundary and have small errors, and L₀ or counting norm for samples that are outliers or are difficult to classify. Depending on the value of the kernel size parameter, the proposed loss function moves smoothly from convex to non-convex and becomes a close approximation to the misclassification loss (ideal 0–1 loss). We show that the discriminant function obtained by optimizing the proposed loss function in the neighborhood of the ideal 0–1 loss function to train a neural network is immune to overfitting, more robust to outliers, and has consistent and better generalization performance as compared to other commonly used loss functions, even after prolonged training. The results also show that it is a close competitor to the SVM. Since the proposed method is compatible with simple gradient based online learning, it is a practical way of improving the performance of neural network classifiers.     

Initial Deposition of Colloidal Particles on a Rough Nanofiltration Membrane

The initial rate of colloid deposition onto semi‐permeable membranes is largely controlled by the coupled influence of permeation drag and particle‐membrane colloidal interactions. Recent studies show that the particle‐membrane interactions are subject to immense local variations due to the inherent morphological heterogeneity (roughness) of reverse osmosis (RO) and nanofiltration (NF) membranes. This experimental investigation reports the effect of membrane roughness on the initial deposition of polystyrene latex particles on a rough NF membrane during cross flow membrane filtration under different operating pressures and solution chemistries. Atomic force microscopy was used to characterize the roughness of the membrane and observe the structure of particle deposits. At the initial stages of fouling, the AFM images show that more particles preferentially accumulate near the “peaks” than in the “valleys” of the rough NF membrane surface.