Experimental Investigation of Oblique Impact Behavior of Adhesively Bonded Composite Single-Lap Joints

Applied Composite Materials - Determining the impact behavior of adhesive joints allows the designing of high-strength joints. Therefore, the dynamic behavior of adhesive joints has recently become...     

Effect of high hydrostatic pressure on physicochemical and biochemical properties of milk

Interest in high hydrostatic pressure (HHP) applications on milk and dairy products has recently increased as HHP offers a new technology for food preservation to the food industry. Although HHP-induced microbial destruction, rennet or acid coagulation of milk and increase in cheese yield has been reported, the actual effect of HHP application on milk constituents still remains to be unexplained. Therefore, we have analyzed the effect of HHP on physicochemical and biochemical properties such as turbidity, pH and especially protein micelle surface hydrophobicity of milk proteins. To serve for this purpose, milk samples with different fat contents were pressurized from 110 to 440 MPa at 25 °C for 10 and 20 min. Turbidity decreased with pressure increase and there was a slight change in pH. In order to measure the extent of exposure of hydrophobic groups of proteins to HHP, the method described by Bonomi et al. [1], based on use of a fluorescent probe, was utilized. In the light of the results obtained, it can be concluded that HHP has an effect on non-covalent interactions and especially hydrophobic bonds in milk. As the pressure is increased from 110 to 440 MPa, the micelles possibly decompose into sub-micelles and the embedded hydrophobic areas inside these micelles re-position in such a way that they can readily interfere with the fluorescent marker, ANS. These results may lead to practical applications of HHP treatment in the dairy industry to produce microbiologically safe, minimally processed products with high nutritional and sensory quality and novel texture.     

Novel hybrid DNN approaches for speaker verification in emotional and stressful talking environments

Neural Computing and Applications - In this work, we conducted an empirical comparative study of the performance of text-independent speaker verification in emotional and stressful environments....     

Effect of high temperature heat treatment on intergranular corrosion of AlMgSi(Cu) model alloy

Copper containing 6000-series aluminium alloys may become susceptible to intergranular corrosion (IGC) as a result of improper thermomechanical processing. Effect of cooling rate after solution heat treatment on the corrosion behaviour of a model AlMgSi(Cu) alloy of nominal composition (wt%) 0.6 Mg, 0.6 Si, 0.2 Fe, 0.2 Mn and 0.1 Cu was investigated. Slow cooling rates were simulated by isothermal treatment for predetermined times in lower temperature baths immediately after solution heat treatment. Treatment for 10-100 s at temperatures below 400 °C introduced susceptibility to IGC. Longer heat treatment at the same temperatures introduced susceptibility to pitting. A corrosion resistant time zone was found between the zones of IGC and pitting at temperatures lower than 350 °C. Quenching in water after solution heat treatment prevented IGC. IGC was related to microgalvanic coupling between the noble Q-phase (Al₄Mg₈Si₇Cu₂) grain boundary precipitates and the adjacent depleted zone. Pitting was attributed to coarse particles in the matrix. Possible mechanisms causing the corrosion resistant intermediate zone are discussed. The results indicate possible methods for obtaining increased corrosion resistance of similar alloys by proper thermal processing.     

Lead recovery from aqueous environment by using porous carbon of citrus fruits waste: equilibrium,kinetics and thermodynamic studies

ABSTRACT

The mixture of citrus fruits (orange, grapefruit, mandarin, and lemon) wastes was utilized to obtain high surface area activated carbon (AC) by H₃PO₄ activation. The production conditions were optimized and the optimum conditions were determined. The optimal-activated carbon (CFWAC) was characterized by various physicochemical techniques. CFWAC was also used as a sorbent for Pb (II) ions from water. Batch experiments were performed to explore the adsorption capacity and mechanism. The Langmuir isotherm and pseudo-second-order kinetic model showed good fitness to the experimental data. The maximum Pb (II) adsorption capacity of CFWAC was found to be 163.93 mg/g.     

Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams

This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associated with this approach include choosing the model representation for each agent and how to effectively share these representations under limited communication. A decentralized extension of the model learning scheme based on the Incremental Feature Dependency Discovery (Dec-iFDD) is presented to address the distributed learning problem. The representation selection problem is solved by leveraging iFDD’s property of adjusting the model complexity based on the observed data. The model sharing problem is addressed by having each agent rank the features of their representation based on the model reduction error and broadcast the most relevant features to their teammates. The algorithm is tested on the multi-agent block building and the persistent search and track missions. The results show that the proposed distributed learning scheme is particularly useful in heterogeneous learning setting, where each agent learns significantly different models. We show through large-scale planning under uncertainty simulations and flight experiments with state-dependent actuator and fuel-burn- rate uncertainty that our planning approach can outperform planners that do not account for heterogeneity between agents.     

An adaptive support vector regressor controller for nonlinear systems

In this study, a novel online support vector regressor (SVR) controller based on system model estimated by a separate online SVR is proposed. The main idea is to obtain an SVR controller based on an estimated model of the system by optimizing the margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, then the parameters of the SVR controller are optimized so as to optimize the closed-loop margin and minimize the tracking error. In order to construct the closed-loop margin, the model of the system estimated by an online SVR is utilized. The parameters of the SVR controller are adjusted via the SVR model of system. The stability of the closed-loop system has also been analyzed. The performance of the proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR) and a bioreactor, and the results show that SVR model and SVR controller attain good modeling and control performances.