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.     

Abnormal selection of antibody repertoires in retrovirus-induced murine AIDS

The mature B cell repertoire in the course of murine AIDS (MAIDS) was investigated. The polymerase chain reaction (PCR) was used to amplify a large diversity of rearranged Ig H chain genes in normal or infected mice, 2 and 8 wk after virus inoculation. Libraries were constructed from the polymerase chain reaction products. By sequencing V-D-J clones in these libraries and analyzing the respective complementary determining region 3 (CDR3), we have shown at 8 wk the emergence of a population of B cells with significantly less N diversity, some sequences lacking any N addition, a typical feature of fetal repertoires known for degeneracy, and autoreactivities. This decreased N diversity was not present 2 wk after inoculation and could not be related to a defect in terminal deoxytransferase expression because the steady-state levels of terminal deoxytransferase mRNA were found normal in MAIDS bone marrow 8 wk after inoculation. FACS analyses revealed a decreased number of bone marrow B cells (B220+, sIgM+) in MAIDS already present at 2 wk, suggesting an alteration in the pathway of B cell differentiation and resulting in a decrease of peripheral B cells renewal. A relative enrichment of spleen cells in long lived B cells as a consequence of this blockade may participate in the abnormal antibody repertoire selection occurring in MAIDS. These data suggest in the MAIDS pathogeny the relationship between an abnormal repertoire selection and the pathologic process.     

Application, design, and evolution of DWDM in Pan-European Transport Networks

This article is a follow-up to our March 2000 article, "Application, design, and evolution of WDM in GTS's pan-European transport network" (Struyve et al. 2000). Since then, many events have led toward dismantling the GTS pan-European transport network. In this article we choose to reiterate the main statements of our previous article that are still valid today, and give a general overview of the current status and possible evolution trends of DWDM-based transport networks.     

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.     

Thermal analysis of polymer–water interactions and their relation to gas hydrate inhibition

Gas hydrates formed in oil production pipelines are crystalline solids where hydrocarbon gas molecules such as methane, propane, and their mixtures are trapped in a cagelike structure by hydrogen‐bonded water molecules to form undesirable plugs. Methanol and glycol are currently used to prevent these plugs via thermodynamic inhibition. Small amounts of water‐soluble polymers may provide an alternate approach for preventing gas hydrates. In this study, we expand the fundamental understanding of water–polymer systems with differential scanning calorimetry. Nonfreezable bound water was used to quantify polymer–water interactions and relate them to the chemical structure for a series of polymers, including acrylamides, cyclic lactams, and n‐vinyl amides. For good interactions, the water structure needs to be stabilized through hydrophobic interactions. An increased hydrophobicity of the pendant group also appears to favor polymer performance as a gas hydrate inhibitor. Good inhibitors, such as poly(diethyl acrylamide) and poly(N‐vinyl caprolactam), also show higher heat capacities, which indicate higher hydrophobicity, than poor performers such as polyzwitterions, in which hydrophilicity dominated. The phase behavior and thermodynamic properties of dilute polymer solutions were also evaluated through measurements of the heat of demixing and lower critical solution temperature. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 2642–2653, 2007     

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.     

A method for automatic classification of gender based on text- independent handwriting

Handwriting recognition is used for the prediction of various demographic traits such as age, gender, nationality, etc. Out of all the applications gender prediction is mainly admired topic among researchers. The relation between gender and handwriting can be seen from the physical appearance of the handwriting. This research work predicts gender from handwriting using the landmarks of differences between the two genders. We use the shape or visual appearance of the handwriting for extracting features of the handwriting such as slanteness (direction), area (no of pixels occupied by text), perimeter (length of edges), etc. Classification is carried out using the Support Vector Machine (SVM) as a classifier which transforms the nonlinear problem into linear using its kernel trick, logistic regression, KNN and at the end to enhance the classification rates we use Majority Voting. The experimental results obtained on a dataset of 282 writers with 2 samples per writer shows that the proposed method attains appealing performance on writer detection and text-independent environment.