Plug-in Selection of the Number of Frequencies in Regression Estimates of the Memory Parameter of a Long-memory Time Series

We consider the problem of selecting the number of frequencies, m , in a log-periodogram regression estimator of the memory parameter d of a Gaussian long-memory time series. It is known that under certain conditions the optimal m , minimizing the mean squared error of the corresponding estimator of d , is given by m ^(opt)= Cn ^4/5, where n is the sample size and C is a constant. In practice, C would be unknown since it depends on the properties of the spectral density near zero frequency. In this paper, we propose an estimator of C based again on a log-periodogram regression and derive its consistency. We also derive an asymptotically valid confidence interval for d when the number of frequencies used in the regression is deterministic and proportional to n ^4/5. In this case, squared bias cannot be neglected since it is of the same order as the variance. In a Monte Carlo study, we examine the performance of the plug-in estimator of d , in which m is obtained by using the estimator of C in the formula for m ^(opt) above. We also study the performance of a bias-corrected version of the plug-in estimator of d . Comparisons with the choice m = n ^1/2 frequencies, as originally suggested by Geweke and Porter-Hudak (The estimation and application of long memory time series models. Journal of Time Ser. Anal. 4 (1983), 221–37), are provided.     

Metal oxide nanoparticles for advanced energy applications

Hot-wire chemical vapor deposition (HWCVD) has been employed as an economically scalable method for the deposition of crystalline molybdenum oxide nanoparticles at high density. Under optimal synthesis conditions, only crystalline nanostructures with a smallest dimension of ~ 3-50 nm are observed with extensive transmission electron microscopy analyses. The incorporation of crystalline molybdenum oxide nanoparticles into battery electrodes has led to profound advancements in state-of-the-art negative electrodes (anodes) in lithium-ion batteries. The nanoparticle materials exhibit a high rate capability as anticipated for the reduced solid-state Li-ion diffusion length.     

Effective parameter estimation within a multi-dimensional population balance model framework

This study considers optimization problems with multi-dimensional population balance models embedded. The objective function is formulated as a least-squares problem, minimizing the difference between target data and simulated model output and the goal is to find model parameter values that best fit the data. Results show that derivative-free methods, such as the Nelder–Mead simplex method, fail to converge to an optimal solution. A similar result was obtained with gradient-based methods such as BFGS, quasi-Newton, Newton, Gauss–Newton, Levenberg–Marquardt and SQP, and with a stochastic genetic algorithm. It was hypothesized that three main issues could contribute to these convergence failures: (1) gradients were calculated based on finite differences, and as a result of improper step size determination, the numerical error could be prohibitive resulting in inaccurate derivative information, (2) the parameters may not be identifiable and (3) numerical instability could occur during the course of optimization. To circumvent these issues, this work addresses the calculation of derivative information based on automatic differentiation and sensitivity analysis to ensure increased accuracy. Issues such as parameter identifiability are also dealt with by analyzing an accurate Fisher information matrix. Given the computational burden in calculating accurate Jacobians and Hessians, compounded by the potential nonsmoothness introduced into the objective function as a result of granule nucleation, other optimization strategies may be warranted and this work addresses those accordingly. Overall, by systematically assessing the problem formulation and mechanisms, the results show that substantial improvements in convergence can be achieved by utilizing appropriate optimization techniques, thus leading to more successful and optimal parameter estimation.     

Ultrafine particles from diesel vehicle emissions at different driving cycles induce differential vascular pro-inflammatory responses: Implication of chemical components and NF-κB signaling

Background

Nanometer silicon dioxide (nano-SiO₂) has a wide variety of applications in material sciences, engineering and medicine; however, the potential cell biological and proteomic effects of nano-SiO₂ exposure and the toxic mechanisms remain far from clear.

Results

Here, we evaluated the effects of amorphous nano-SiO₂ (15-nm, 30-nm SiO₂). on cellular viability, cell cycle, apoptosis and protein expression in HaCaT cells by using biochemical and morphological analysis, two-dimensional differential gel electrophoresis (2D-DIGE) as well as mass spectrometry (MS). We found that the cellular viability of HaCaT cells was significantly decreased in a dose-dependent manner after the treatment of nano-SiO₂ and micro-sized SiO₂ particles. The IC₅₀ value (50% concentration of inhibition) was associated with the size of SiO₂ particles. Exposure to nano-SiO₂ and micro-sized SiO₂ particles also induced apoptosis in HaCaT cells in a dose-dependent manner. Furthermore, the smaller SiO₂ particle size was, the higher apoptotic rate the cells underwent. The proteomic analysis revealed that 16 differentially expressed proteins were induced by SiO₂ exposure, and that the expression levels of the differentially expressed proteins were associated with the particle size. The 16 proteins were identified by MALDI-TOF-TOF-MS analysis and could be classified into 5 categories according to their functions. They include oxidative stress-associated proteins; cytoskeleton-associated proteins; molecular chaperones; energy metabolism-associated proteins; apoptosis and tumor-associated proteins.

Conclusions

These results showed that nano-SiO₂ exposure exerted toxic effects and altered protein expression in HaCaT cells. The data indicated the alterations of the proteins, such as the proteins associated with oxidative stress and apoptosis, could be involved in the toxic mechanisms of nano-SiO₂ exposure.     

A Light Harvesting,Self‐Powered Monolith Tactile Sensor Based on Electric Field Induced Effects in MAPbI3 Perovskite

Organolead trihalide perovskite MAPbI₃ shows a distinctive combination of properties such as being ferroelectric and semiconducting, with ion migration effects under poling by electric fields. The combination of its ferroelectric and semiconducting nature is used to make a light harvesting, self‐powered tactile sensor. This sensor interfaces ZnO nanosheets as a pressure‐sensitive drain on the MAPbI₃ film and once poled is operational for at least 72 h with just light illumination. The sensor is monolithic in structure, has linear response till 76 kPa, and is able to operate continuously as the energy harvesting mechanism is decoupled from its pressure sensing mechanism. It has a sensitivity of 0.57 kPa^?1, which can be modulated by the strength of the poling field. The understanding of these effects in perovskite materials and their application in power source free devices are of significance to a wide array of fields where these materials are being researched and applied.     

An evaluation of fuelwood properties of some Aravally mountain tree and shrub species of Western India

The study analyses the fuelwood characteristics of 26 trees including shrub species from the dry deciduous forest in Aravally region, Rajasthan, Western India was carried out to explore trees with potential for fuelwood production. Fuelwood value index (FVI) based on the properties of calorific value, wood density and ash. Calorific value was ranged between 18.54 ± 0.04 and 27.44 ± 0.09 KJ g⁻¹ in Jatropha curcus and Wrightia tinctoria respectively. Wood density varied from 0.538 ± 0.01 to 0.966 ± 0.07 g/cm³ in J. curcus and Acacia nilotica. Same way ash and moisture content was highest in J. curcus (3.38 ± 0.19%) and Sterculia urens (70.28 ± 7.52%) and lowest in Miliusa tomentosa (0.85 ± 0.06%) and Azadirachta indica (30.7 ± 10.02%) respectively. On the basis, of the 26 species analyzed, M. tomentosa has the highest FVI, followed by Lannea coromandelica, Acacia leucophloea, Madhuca indica, A. nilotica, W. tinctoria, Butea monosperma, Zizyphus nummularia, S. urens, Boswellia serrata, A. indica, Grewia tenax, Syzygium cuminii, Tectona grandis and Dalbergia sissoo were shown to have promising fuelwood production.     

Nutritional components as mitigators of cellular senescence in organismal aging: a comprehensive review

Food Science and Biotechnology - The process of cellular senescence is rapidly emerging as a modulator of organismal aging and disease. Targeting the development and removal of senescent cells is...     

Low thermal conductivity in porous SiC–SiO2–Al2O3–TiO2 ceramics induced by multiphase thermal resistance

The introduction of multiple heterogeneous interfaces in a ceramic is an efficient way to increase its thermal resistance. Novel porous SiC–SiO₂–Al₂O₃–TiO₂ (SSAT) ceramics were fabricated to achieve multiple heterogeneous interfaces by sintering equal volumes of SiC, SiO₂, Al₂O₃, and TiO₂ compacted powders with polysiloxane as a bonding phase and carbon as a template at 600 °C in air. The porosity could be controlled between 66% and 74% by adjusting the amounts of polysiloxane and the carbon template. The lowest thermal conductivity (0.059 W/(m·K) at 74% porosity) obtained in this study is an order of magnitude lower than those (0.2–1.3 W/(m·K)) of porous monolithic SiC, SiO₂, Al₂O₃, and TiO₂ ceramics at an equivalent porosity. The typical specific compressive strength value of the porous SSAT ceramics at 74% porosity was 3.2 MPa cm³/g.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.