Nonlinear Least Squares in ℝ N

Recent research and new paradigms in mathematics, engineering, and science assume nonlinear signal models of the form ?=∪ _i∈I V _i consisting of a union of subspaces V _i instead of a single subspace ?=V. These models have been used in sampling and reconstruction of signals with finite rate of innovation, the Generalized Principle Component Analysis and the subspace segmentation problem in computer vision, and problems related to sparsity, compressed sensing, and dictionary design. In this paper, we develop an algorithm that searches for the best nonlinear model of the form ?=∪ _i=1 ^l V _i ?? ^N that is optimally compatible with a set of observations ?={f ₁,…,f _m }?? ^N . When l=1 this becomes the classical least squares optimization. Thus, this problem is a nonlinear version of the least squares problem. We test our algorithm on synthetic data as well as images.     

Glycerol‐lactic acid star‐shaped oligomers as efficient biobased surface modifiers for improving superabsorbent polymer hydrogels

The glycerol‐lactic acid‐based star‐shaped modifier (SM) were synthesized and employed for surface modification of hygienic superabsorbent polymer (SAP) hydrogels for the first time. Surface crosslinking was carried out to increase the saline‐absorbency under load (AUL) and the swollen gel strength. The surface treatment process was analyzed employing free absorbency and AUL tests, salt sensitivity, attenuated total reflectance‐Fourier‐transform infrared spectroscopy (ATR‐FTIR), oscillatory rheometry, as well as scanning electron microscopy analysis. The effect of the branched architecture and the contents of SM on the properties of the modified SAPs were studied. The implementation of surface treatment leads to increase up to a 28% in the AUL of the modified SAP. Moreover, the loss modulus was surprisingly increased, while the storage modulus was enhanced (possibly due to the star architecture of oligomers). M_c and crosslink density values have been estimated based on modified rubber elasticity theory. Salt sensitivity factor (f) was calculated; the modified samples showed lower salt sensitivity in NaCl (f = 0.7) and CaCl₂ (f = 0.93–0.95) compared with the intact SAP (f = 0.84 for NaCl, and f = 0.95–0.97 for CaCl₂).     

Baryogenesis in f(R,T) Gravity

We study gravitational baryogenesis in the context of f(R, T) gravity where the gravitational Lagrangian is given by a generic function of the Ricci scalar R and the trace of the stress-energy tensor T. We explore how this type of modified gravity is capable to shed light on the issue of baryon asymmetry in a successful manner. We consider various forms of baryogenesis interaction and discuss the effect of these interaction terms on the baryon to entropy ratio in this setup. We show that baryon asymmetry during the radiation era of the expanding universe can be non-zero in this framework. Then, we calculate the baryon to entropy ratio for some specific f(R, T) models and by using the observational data, we give some constraints on the parameter spaces of these models.     

Stimulus-frequency otoacoustic emission: measurements in humans and simulations with an active cochlear model

An efficient method for measuring stimulus-frequency otoacoustic emissions (SFOAEs) was developed incorporating (1) stimulus with swept frequency or level and (2) the digital heterodyne analysis. SFOAEs were measured for 550-1450 Hz and stimulus levels of 32-62 dB sound pressure level in eight normal human adults. The mean level, number of peaks, frequency spacing between peaks, phase change, and energy-weighted group delays of SFOAEs were determined. Salient features of the human SFOAEs were stimulated with an active cochlear model containing spatially low-pass filtered irregularity in the impedance. An objective fitting procedure yielded an optimal set of model parameters where, with decreasing stimulus level, the amount of cochlear amplification and the base amplitude of the irregularity increased while the spatial low-pass cutoff and the slope of the spatial low-pass filter decreased. The characteristics of the human cochlea were inferred with the model. In the model, an SFOAE consisted of a long-delay component originating from irregularity in a traveling-wave peak region and a short-delay component originating from irregularity in regions remote from the peak. The results of this study should be useful both for understanding cochlear function and for developing a clinical method of assessing cochlear status.     

Late-time acceleration and phantom divide line crossing with?non-minimal coupling and Lorentz-invariance violation

We consider two alternative dark-energy models: a Lorentz-invariance preserving model with a non-minimally coupled scalar field and a Lorentz-invariance violating model with a minimally coupled scalar field. We study accelerated expansion and the dynamics of the equation of state parameter in these scenarios. While a minimally coupled scalar field does not have the capability to be a successful dark-energy candidate with line crossing of the cosmological constant, a non-minimally coupled scalar field in the presence of Lorentz invariance or a minimally coupled scalar field with Lorentz-invariance violation have this capability. In the latter case, accelerated expansion and phantom divide line crossing are the results of the interactive nature of this Lorentz-violating scenario.     

Automated segmentation and classification of high throughput yeast assay spots

Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here, an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally, an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved, respectively, for scoring the X-Gal and growth assay spots.     

Fine tuning of SAP properties via epoxy‐silane surface modification

Using 3‐[(2,3‐epoxypropoxy)‐propyl]‐trimethoxysilane as a surface modifier, superabsorbent polymers with improved gel strength in their swollen state and saline absorbency under load are synthesized. The products are characterized using attenuated total reflectance–Fourier transform infrared spectroscopy (ATR–FTIR), rheometry, scanning electron microscopy–energy dispersive X‐ray analysis, contact angle, thermogravimetric analysis, water absorbency and gel content. The temperature and the duration effect of the surface‐treatment process on residual monomer content are also investigated by high performance liquid chromatography. The gel strength (as shown by storage modulus) and absorbency under load are improved up to 3500–4000 Pa, and 30–40 g/g, respectively. It is suggested that the surface of the superabsorbent polymer particles has been modified by two mechanisms: i.e. interpenetrating polymer network and cross‐linking. Moreover, the surface modification has enhanced thermo‐stability and prohibited undesirable gel blockage. Depending on the post‐treatment method used, the wetting behavior of particles is also altered. Copyright © 2017 John Wiley & Sons, Ltd.     

Generating an adaptive multiresolution image analysis with compact cupolets

We present an efficient control scheme for stabilizing unstable periodic orbits of chaotic systems. The resulting orbits are called cupolets and have been proven to be useful in the representation of oscillatory or quasi periodic signals such as appear in music and image compression (Short et al., AES 118th Convention preprint 6446, May 2005; Short et al., AES 119th Convention preprint 6588, October 2005). In this paper we show that these cupolets can be used effectively to produce an adaptive basis for the space of real-valued functions of a discrete variable. From this basis, we construct a multiresolution analysis which allows for the approximation of signals at different resolution levels and apply it to image compression. This adaptive multiresolution analysis provides an interesting continuum between Fourier analysis and wavelet analysis.     

Direct Determination of Pyrethroids in Aqueous Samples by a Metal Organic Framework “MIL-101(Cr)” Sorbent for Solid-Phase Extraction and Thermal Desorption Coupled with GC-FID

Journal of Analytical Chemistry - In this study, an online solid-phase extraction-thermal desorption method coupled with gas chromatography-flame ionization detection was used to extract seven...     

Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts

Similar to advancements gained from big data in genomics, security, internet of things, and e-commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self-learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer-aided materials design together with advances in data mining, machine learning, and predictive analytics are expected to provide inexpensive and accelerated pathways towards tailor-made functionally optimized energy materials. Fundamental research in the field of electrochemical energy materials focuses primarily on complex interfacial phenomena and kinetic electrocatalytic processes. This perspective article critically assesses AI-driven modeling and computational approaches that are currently applied to those objects. An application-driven materials intelligence platform is introduced, and its functionalities are scrutinized considering the development of electrocatalyst materials for CO₂ conversion as a use case.