Studies of Sodium Dodecylbenzenesulfonate-Water-Electrolyte Interactions

A surfactant selective electrode using a PVC membrane was constructed. A surfactant (sodium dodecylbenzene-sulfonate)-H2O system was studied at 15°, 19°, 25°, and 40°C with the PVC electrodes, a Na+ selective electrode, and a conductivity bridge. Pre-micelle regions at the above temparatures were observed. The critical micelle concentrations (CMCb) obtained by the PVC electrodes in salt-free systems are: 1.63x10-3 M at 15°C, 1.48x10-3 M at 19.1°C, 1.52x10-3 M at 25°C, and 1.73x10-3 M at 41.6°C. The CMC\u27c obtained by the Na+ electrode are: 1.62x10-3 M at 15°C, 1.37x10-3 M at 19.3°C, 1.47x10-3 M at 25°C, and 1.98x10-3 M at 40.5°c. The CMC obtained by the conductivity measurement is: 1.62x10-3 M at 25°C. The counterion binding was calculated and it was found that it is not constant. An equation, log[DBS-] = constant - rlog[Na+], was obtained. The r value was calculated mathematically and it was found that it is related only to the slopes of the plots of EMF vs log [NaDBS] obtained by the PVC electrode and the Na+ electrode. Hence the equality of r and the counterion binding proposed by the charged phase separation model is questionable. A decrease of the CMC by the addition of salts (NaNO3 and NaCl) was observed with the PVC electrodes. The surfactant system with Cu2+ and Na+ present was also investigated by the PVC electrodes and by a Cu2+ electrode. Complexation between Cu2+ and the micelles was observed and the solubility product of Cu(DBS)2 was calculated to be about 5x10-10

Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.Comment: accepted in ECCV 201

Modeling and Analysis of Drug-Eluting Stents With Biodegradable PLGA Coating: Consequences on Intravascular Drug Delivery

Increasing interests have been raised toward the potential applications of biodegradable poly(lactic-co-glycolic acid) (PLGA) coatings for drug-eluting stents in order to improve the drug delivery and reduce adverse outcomes in stented arteries in patients. This article presents a mathematical model to describe the integrated processes of drug release in a stent with PLGA coating and subsequent drug delivery, distribution, and drug pharmacokinetics in the arterial wall. The integrated model takes into account the PLGA degradation and erosion, anisotropic drug diffusion in the arterial wall, and reversible drug binding. The model simulations first compare the drug delivery from a biodegradable PLGA coating with that from a biodurable coating, including the drug release profiles in the coating, average arterial drug levels, and arterial drug distribution. Using the model for the PLGA stent coating, the simulations further investigate drug internalization, interstitial fluid flow in the arterial wall, and stent embedment for their impact on drug delivery. Simulation results show that these three factors, while imposing little change in the drug release profiles, can greatly change the average drug concentrations in the arterial wall. In particular, each of the factors leads to significant and yet distinguished alterations in the arterial drug distribution that can potentially influence the treatment outcomes. The detailed integrated model provides insights into the design and evaluation of biodegradable PLGA-coated drug-eluting stents for improved intravascular drug delivery.National Institutes of Health (U.S.) (NIBIB 5RO1EB005181

A mechanistic model for drug release in PLGA biodegradable stent coatings coupled with polymer degradation and erosion

Biodegradable poly(d,l-lactic-co-glycolic acid) (PLGA) coating for applications in drug-eluting stents has been receiving increasing interest as a result of its unique properties compared with biodurable polymers in delivering drug for reducing stents-related side effects. In this work, a mathematical model for describing the PLGA degradation and erosion and coupled drug release from PLGA stent coating is developed and validated. An analytical expression is derived for PLGA mass loss that predicts multiple experimental studies in the literature. An analytical model for the change of the number-average degree of polymerization [or molecular weight (MW)] is also derived. The drug transport model incorporates simultaneous drug diffusion through both the polymer solid and the liquid-filled pores in the coating, where an effective drug diffusivity model is derived taking into account factors including polymer MW change, stent coating porosity change, and drug partitioning between solid and aqueous phases. The model is used to describe in vitro sirolimus release from PLGA stent coating, and demonstrates the significance of simultaneous sirolimus release via diffusion through both polymer solid and pore space. The proposed model is compared to existing drug transport models, and the impact of model parameters, limitations and possible extensions of the model are also discussed.National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIBIB, contract grant number: 5RO1EB005181

GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images

Automatic extraction of buildings in remote sensing images is an important but challenging task and finds many applications in different fields such as urban planning, navigation and so on. This paper addresses the problem of buildings extraction in very high-spatial-resolution (VHSR) remote sensing (RS) images, whose spatial resolution is often up to half meters and provides rich information about buildings. Based on the observation that buildings in VHSR-RS images are always more distinguishable in geometry than in texture or spectral domain, this paper proposes a geometric building index (GBI) for accurate building extraction, by computing the geometric saliency from VHSR-RS images. More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images. The resulting GBI is finally measured by integrating the derived geometric saliency of buildings. Experiments on three public and commonly used datasets demonstrate that the proposed GBI achieves the state-of-the-art performance and shows impressive generalization capability. Additionally, GBI preserves both the exact position and accurate shape of single buildings compared to existing methods

Towards SAR Tomographic Inversion via Sparse Bayesian Learning

Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion of the SAR imaging model, which are often computationally expensive. Previous study showed perspective of using data-driven methods like KPCA to decompose the signal and reduce the computational complexity. This paper gives a preliminary demonstration of a new data-driven method based on sparse Bayesian learning. Experiments on simulated data show that the proposed method significantly outperforms KPCA methods in estimating the steering vectors of the scatterers. This gives a perspective of data-drive approach or combining it with model-driven approach for high precision tomographic inversion of large areas.Comment: accepted in preliminary version for EUSAR2020 conferenc

The Moore-Penrose inverse of 2 x 2 matrices over a certain *-regular ring

In this paper, we study representations of the Moore-Penrose inverse of a 2 x 2 matrix M over a *-regular ring with two term star-cancellation. As applications, some necessary and sufficient conditions for the Moore-Penrose inverse of M to have different types are given.This research is supported by the National Natural Science Foundation of China (11201063) and (11371089), the Specialized Research Fund for the Doctoral Program of Higher Education (20120092110020), the Foundation of Graduate Innovation Program of Jiangsu Province(CXLX13-072) and the Fundamental Research Funds for the Central Universities (22420135011), `FEDER Funds through "Programa Operacional Factores de Competitividade-COMPETE' and the Portuguese Funds through FCT-`Fundação para a Ciência e a Tecnologia', within the project PEst-OE/MAT/UI0013/2014

Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery

This work has been accepted by IEEE TGRS for publication. The majority of optical observations acquired via spaceborne earth imagery are affected by clouds. While there is numerous prior work on reconstructing cloud-covered information, previous studies are oftentimes confined to narrowly-defined regions of interest, raising the question of whether an approach can generalize to a diverse set of observations acquired at variable cloud coverage or in different regions and seasons. We target the challenge of generalization by curating a large novel data set for training new cloud removal approaches and evaluate on two recently proposed performance metrics of image quality and diversity. Our data set is the first publically available to contain a global sample of co-registered radar and optical observations, cloudy as well as cloud-free. Based on the observation that cloud coverage varies widely between clear skies and absolute coverage, we propose a novel model that can deal with either extremes and evaluate its performance on our proposed data set. Finally, we demonstrate the superiority of training models on real over synthetic data, underlining the need for a carefully curated data set of real observations. To facilitate future research, our data set is made available onlineComment: This work has been accepted by IEEE TGRS for publicatio

Change Detection Meets Visual Question Answering

The Earth's surface is continually changing, and identifying changes plays an important role in urban planning and sustainability. Although change detection techniques have been successfully developed for many years, these techniques are still limited to experts and facilitators in related fields. In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multi-temporal aerial images. In particular, multi-temporal images can be queried to obtain high level change-based information according to content changes between two input images. We first build a CDVQA dataset including multi-temporal image-question-answer triplets using an automatic question-answer generation method. Then, a baseline CDVQA framework is devised in this work, and it contains four parts: multi-temporal feature encoding, multi-temporal fusion, multi-modal fusion, and answer prediction. In addition, we also introduce a change enhancing module to multi-temporal feature encoding, aiming at incorporating more change-related information. Finally, effects of different backbones and multi-temporal fusion strategies are studied on the performance of CDVQA task. The experimental results provide useful insights for developing better CDVQA models, which are important for future research on this task. We will make our dataset and code publicly available

Biomass Estimation from Tree Heights on Individual-Level with Gaussian Process Regressor

To monitor the forests and estimate the above-ground biomass in national to global scale, remote sensing data have been widely used. However, due to their coarse resolution (hundreds of trees present within one pixel), it’s costly to collect the ground reference data. Thus, an automatic biomass estimation method on individual tree level using high-resolution remote sensing data (such as Lidar data) is of great importance. In this paper, we explored to estimate tree’s biomass from single parameter - the tree height – using Gaussian process regressor. We collected a dataset of 8342 records, in which individual tree’s height (in m), diameter (in cm), and the biomass (in Kg) are measured. Besides, Jucker data with crown diameter measurement are also used. The datasets coverage eight dominant biomes. Using the data, we compared five candidate biomass estimation models, including three single-parameter biomass-height models (proposed Gaussian process regressor, random forest, and linear model in log-log scale) and two two-parameter models (biomass-height-crown diameter model, and biomass height-diameter model). Results showed a high correlation between biomass and height as well as diameter, and the biomass-height-diameter model has low biases of 0.08 and 0.11, and high R-square scores of 0.95 and 0.78 when using the two datasets respectively. The biomass-height-crown diameter has a median performance with R-square score of 0.66, bias of 0.26, and root mean square error of 1.11Mg. Although the biomass-height models are less accurate, the proposed Gaussian regressor has a better performance over linear log-log model and random forest (R-square: 0.66, RMSE: 4.95 Mg; bias: 0.34). Besides, the results also suggest that non-linear models have an advantage over linear model on reducing the uncertainty either when the tree has a large (> 1 Mg) or small (< 10 kg) biomass