An ecofriendly coaxial antibacterial and anticorrosion nanofiber pullulan-ethyl cellulose embedded with carvacrol coating for protection against marine corrosion.

Coaxial electrospun coatings with antibacterial and anticorrosion properties have a marked potential to protect against corrosion in marine environments. Ethyl cellulose is a promising biopolymer for corrosion caused by microorganisms owing to its high mechanical strength, nontoxicity, and biodegradability. In this study, a coaxial electrospun coating loaded with antibacterial carvacrol (CV) in the core and anticorrosion pullulan (Pu) and ethyl cellulose (EC) as a shell layer was successfully fabricated. The formation of core-shell structure was confirmed using transmission electron microscopy. Pu-EC@CV coaxial nanofiber had small diameters, uniform distribution, smooth surface, strong hydrophobicity, and no fractures. Electrochemical impedance spectroscopy was used to analyze corrosion of the electrospun coating surface in a medium containing bacterial solution. The results indicated significant corrosion resistance of the coating surface. In addition, the antibacterial activity and mechanism of coaxial electrospun were studied. The Pu-EC@CV nanofiber coating exhibited excellent antibacterial properties by effectively increasing the permeability of cell membranes and killing bacteria, as determined by plate counts, scanning electron microscopy, cell membrane permeability, and the activity of alkaline phosphatase. In summary, the coaxial electrospun pullulan-ethyl cellulose embedded with CV coating can be used as antibacterial and anticorrosion materials and may have potential applications in the field of marine corrosion.

Efficient Hardware Accelerator Design of Non-Linear Optimization Correlative Scan Matching Algorithm in 2D LiDAR SLAM for Mobile Robots.

Simultaneous localization and mapping (SLAM) is the major solution for constructing or updating a map of an unknown environment while simultaneously keeping track of a mobile robot's location. Correlative Scan Matching (CSM) is a scan matching algorithm for obtaining the posterior distribution probability for the robot's pose in SLAM. This paper combines the non-linear optimization algorithm and CSM algorithm into an NLO-CSM (Non-linear Optimization CSM) algorithm for reducing the computation resources and the amount of computation while ensuring high calculation accuracy, and it presents an efficient hardware accelerator design of the NLO-CSM algorithm for the scan matching in 2D LiDAR SLAM. The proposed NLO-CSM hardware accelerator utilizes pipeline processing and module reusing techniques to achieve low hardware overhead, fast matching, and high energy efficiency. FPGA implementation results show that, at 100 MHz clock, the power consumption of the proposed hardware accelerator is as low as 0.79 W, while it performs a scan match at 8.98 ms and 7.15 mJ per frame. The proposed design outperforms the ARM-A9 dual-core CPU implementation with a 92.74% increase and 90.71% saving in computing speed and energy consumption, respectively. It has also achieved 80.3% LUTs, 84.13% FFs, and 20.83% DSPs saving, as well as an 8.17× increase in frame rate and 96.22% improvement in energy efficiency over a state-of-the-art hardware accelerator design in the literature. ASIC implementation in 65 nm can further reduce the computing time and energy consumption per scan to 5.94 ms and 0.06 mJ, respectively, which shows that the proposed NLO-CSM hardware accelerator design is suitable for resource-limited and energy-constrained mobile and micro robot applications.

Mixture proportional hazards cure model with latent variables.

A mixture proportional hazards cure model with latent variables is proposed. The proposed model assesses the effects of the observed and latent risk factors on the hazards of uncured subjects and the cure rate through a proportional hazards model and a logistic model, respectively. Factor analysis is employed to measure the latent variables through correlated multiple indicators. Maximum likelihood estimation is performed through a Gaussian quadratic technique that approximates the integration over the latent variables. A piecewise constant function is used for the unspecified baseline hazard of uncured subjects. The proposed method can be conveniently implemented by using SAS Proc NLMIXED. Simulation studies are conducted to evaluate the performance of the proposed approach. An application to a study concerning the risk factors of chronic kidney disease for type 2 diabetic patients is provided.

Variable selection for a mark-specific additive hazards model using the adaptive LASSO.

In HIV vaccine efficacy trials, mark-specific hazards models have important applications and can be used to evaluate the strain-specific vaccine efficacy. Additive hazards models have been widely used in practice, especially when continuous covariates are present. In this article, we conduct variable selection for a mark-specific additive hazards model. The proposed method is based on an estimating equation with the first derivative of the adaptive LASSO penalty function. The asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a dataset from the first HIV vaccine efficacy trial is provided.

Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping.

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.

Variable selection in joint frailty models of recurrent and terminal events.

Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the "minimum approximated information criterion" method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.

A novel model for the X-chromosome inactivation association on survival data.

The X-linked genetic association is overlooked in most of the genetic studies because of the complexity of X-chromosome inactivation process. In fact, the biological process of the gene at the same locus can vary across different subjects. Besides, the skewness of X-chromosome inactivation is inherently subject-specific (even tissue-specific within subjects) and cannot be accurately represented by a population-level parameter. To tackle this issue, a new model is proposed to incorporate the X-linked genetic association into right-censored survival data. The novel model can present that the X-linked genes on different subjects may go through different biological processes via a mixed distribution. Further, a random effect is employed to describe the uncertainty of the biological process for every subject. The proposed method can derive the probability for the escape of X-chromosome inactivation and derive the unbiased estimates of the model parameters. The Legendre-Gauss Quadrature method is used to approximate the integration over the random effect. Finite sample performance of this method is examined via extensive simulation studies. An application is illustrated with the implementation on a cancer genetic study with right-censored survival data.

Variable selection for random effects two-part models.

Random effects two-part models have been applied to longitudinal studies for zero-inflated (or semi-continuous) data, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include monthly medical costs, daily alcohol drinks, relative abundance of microbiome, etc. With the advance of information technology for data collection and storage, the number of variables available to researchers can be rather large in such studies. To avoid curse of dimensionality and facilitate decision making, it is critically important to select covariates that are truly related to the outcome. However, owing to its intricate nature, there is not yet a satisfactory variable selection method available for such sophisticated models. In this paper, we seek a feasible way of conducting variable selection for random effects two-part models on the basis of the recently proposed "minimum information criterion" (MIC) method. We demonstrate that the MIC formulation leads to a reasonable formulation of sparse estimation, which can be conveniently solved with SAS Proc NLMIXED. The performance of our approach is evaluated through simulation, and an application to a longitudinal alcohol dependence study is provided.

Mark-specific additive hazards regression with continuous marks.

For survival data, mark variables are only observed at uncensored failure times, and it is of interest to investigate whether there is any relationship between the failure time and the mark variable. The additive hazards model, focusing on hazard differences rather than hazard ratios, has been widely used in practice. In this article, we propose a mark-specific additive hazards model in which both the regression coefficient functions and the baseline hazard function depend nonparametrically on a continuous mark. An estimating equation approach is developed to estimate the regression functions, and the asymptotic properties of the resulting estimators are established. In addition, some formal hypothesis tests are constructed for various hypotheses concerning the mark-specific treatment effects. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a data set from the first HIV vaccine efficacy trial is provided.

Thermal properties of carbon black aqueous nanofluids for solar absorption.

In this article, carbon black nanofluids were prepared by dispersing the pretreated carbon black powder into distilled water. The size and morphology of the nanoparticles were explored. The photothermal properties, optical properties, rheological behaviors, and thermal conductivities of the nanofluids were also investigated. The results showed that the nanofluids of high-volume fraction had better photothermal properties. Both carbon black powder and nanofluids had good absorption in the whole wavelength ranging from 200 to 2,500 nm. The nanofluids exhibited a shear thinning behavior. The shear viscosity increased with the increasing volume fraction and decreased with the increasing temperature at the same shear rate. The thermal conductivity of carbon black nanofluids increased with the increase of volume fraction and temperature. Carbon black nanofluids had good absorption ability of solar energy and can effectively enhance the solar absorption efficiency.

Preparation and thermal conductivity of CuO nanofluid via a wet chemical method.

In this article, a wet chemical method was developed to prepare stable CuO nanofluids. The influences of synthesis parameters, such as kinds and amounts of copper salts, reaction time, were studied. The thermal conductivities of CuO nanofluids were also investigated. The results showed that different copper salts resulted in different particle morphology. The concentration of copper acetate and reaction time affected the size and shape of clusters of primary nanoparticles. Nanofluids with different microstructures could be obtained by changing the synthesis parameters. The thermal conductivities of CuO nanofluids increased with the increase of particle loading.