A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction

Water Resources Management - Precise and reliable monthly runoff prediction plays a vital role in the optimal management of water resources, but the nonstationarity and skewness of monthly runoff...     

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.     

Pattern design and optimization of yarn-dyed plaid fabric using isolation niche genetic algorithm and rough set theory

Under the circumstance of perceptual consumption, it is still challenging to grasp consumer's emotions and demands due to the large search space, diversified preferences, and easy fatigue of consumers. To reduce user fatigue and enlarge search space, a novel method was presented to design and optimize the pattern of yarn-dyed plaid fabric using the isolation niche genetic algorithm and rough set theory. Each pattern was encoded as a chromosome based on the real number code. The population was initialized and evolved using INGA to maintain the diversity. The rough set theory was adopted as the fitness function of isolation niche genetic algorithm to extract the consumer's demands. After multiple evolutions, a large set of practical patterns of the yarn-dyed plaid fabric are obtained. Experiments were carried out by 24 testers of different ages and genders. The results prove that the proposed method based on the isolation niche genetic algorithm and rough set theory is feasible and effective, supplying references to the designer.