The physicochemical and fire extinguishing performance of aqueous film-forming foams based on a class of short-chain fluorinated surfactants

Aqueous film-forming foams (AFFFs) are an important for fire extinguishing, and their key ingredient is fluorinated surfactant. In recent years, traditional long-chain fluorinated surfactants have been banned by most countries because of their persistence, bio-accumulation and toxicity. Therefore, increased attention has been paid to the research and development of short-chain fluorinated surfactants. As is well known, the introduction of hydrophilic or hydrophobic groups in a surfactant affects its surface activity, and therefore, the fire extinguishing performance of AFFFs. In this work, a series of short-chain fluorosurfactant-based AFFFs with different hydrophobic chain lengths were prepared. The physicochemical performance of mixed systems (fluorinated surfactant plus sodium hexanesulfonate), including surface activity, spreading ability, foam expansion, drainage time, and the fire extinguishing and burn-back performance of AFFFs were studied. The results show that the critical micelle concentration (CMC) and the surface tension (γ_CMC) at the CMC of mixed systems at 25°C are lower than 7.68 mmol/L and 16.51 mN/m, respectively. For mixed systems, the average spreading rate is more than 1.09 cm/s, the foam expansion is over 7.1, and the drainage time is greater than 3.28 min. The fire extinguishing time of AFFFs on fuels is less than 51 s while the burn-back time is more than 15.18 min. The results imply a potential application prospect of the short-chain fluorinated surfactants in AFFFs.     

Protein from Hylocereus polyrhizus protects MRC-5 cells against hydrogen peroxide (H2O2)-induced damage

Food Science and Biotechnology - The cytoprotective and potential molecular mechanisms of Hylocereus polyrhizus protein (RFPP) were investigated on the hydrogen peroxide (H2O2)-triggered damage in...     

Substrate temperature-controlled precursor reaction mechanism of PEALD-deposited MoOx thin films

Journal of Materials Science - Molybdenum oxide (MoOx) films had been grown by using plasma-enhanced atomic layer deposition (PEALD) with Mo(CO)6 precursor and O2 plasma reactant in a substrate...     

Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Wireless Networks - Unmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of...     

Correction to: Impact of Size Distribution of Cell Model on the Effective Thermal Conductivity of Saturated Porous Media

International Journal of Thermophysics - The addition of nanoparticles to base fuel may bring about remarkable changes in a thermodynamic process such as evaporation, due to the intensified...     

Identification and U-control of a state-space system with time-delay

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.     

Visual privacy attacks and defenses in deep learning: a survey

The concerns on visual privacy have been increasingly raised along with the dramatic growth in image and video capture and sharing. Meanwhile, with the recent breakthrough in deep learning technologies, visual data can now be easily gathered and processed to infer sensitive information. Therefore, visual privacy in the context of deep learning is now an important and challenging topic. However, there has been no systematic study on this topic to date. In this survey, we discuss algorithms of visual privacy attacks and the corresponding defense mechanisms in deep learning. We analyze the privacy issues in both visual data and visual deep learning systems. We show that deep learning can be used as a powerful privacy attack tool as well as preservation techniques with great potential. We also point out the possible direction and suggestions for future work. By thoroughly investigating the relationship of visual privacy and deep learning, this article sheds insights on incorporating privacy requirements in the deep learning era.