Removal of phenols from aqueous solutions by emulsion liquid membranes

The present study deals with the extraction of phenols from aqueous solutions by using the emulsion liquid membranes technique. Besides phenol, two derivatives of phenol, i.e., tyrosol (2-(4-hydroxyphenyl)ethanol) and p-coumaric acid (4-hydroxycinnamic acid), which are typical components of the effluents produced in olive oil plants, were selected as the target solutes. The effect of the composition of the organic phase on the removal of solutes was examined. The influence of pH of feed phase on the extraction of tyrosol and p-coumaric was tested for the membrane with Cyanex 923 as an extractant. The use of 2% Cyanex 923 allowed obtaining a very high extraction of phenols (97-99%) in 5-6 min of contact time for either single solute solutions or for their mixtures. The removal efficiency of phenol and p-coumaric acid attained equivalent values by using the system with 2% isodecanol, but the removal rate of tyrosol was found greatly reduced. The extraction of tyrosol and p-coumaric acid from their binary mixture was also analysed for different operating conditions like the volume ratio of feed phase to stripping phase (sodium hydroxide), the temperature and the initial concentration of solute in the feed phase.     

An Integrated Software Environment For Powertrain Feasibility Assessment Using Optimization And Optimal Control

With the increase in automotive powertrain complexity, an upfront assessment of powertrain capability in meeting its design targets is important early on in the development programs. The optimization of control policy based on powertrain simulation models can facilitate this assessment and establish limits of achievable performance for a given powertrain configuration and parameters. The paper discusses several computational optimization and user interface solutions for deploying a numerical optimal control approach in a user‐friendly software environment.     

Universal bio-molecular signal transduction-based nano-electronic bio-detection system

The development of rapid and ultra-sensitive detection technologies is a long-standing goal for researchers in the bio-detection fields. Nanowire field-effect transistor (nano-FET) devices have shown great promise in label-free and ultra sensitive detection of biological agents. However, critical application problems in using this technology have not been addressed, particularly the difficulties of FET sensing surface modification for various targets and lower detection specificity in real biological samples. A novel molecular signal transduction system reported herein overcomes such problems. With this system, various complicated bio-molecular interactions are “translated” into simple signal molecules with universal sequences. These signal molecules are captured on the sensing surface of nano-FET devices through sequence-specific recognition and generate detectable electronic signals. Using this system, nano-FET devices become universal for detecting almost any bio-agents. Staphylococcus aureus was used as the target to demonstrate the detection of DNA, RNA and protein on the same nano-FET device. Detection sensitivity was achieved at 25 fM levels in pure sample.     

Development of electrically conductive hybrid nanofibers based on CNT‐polyurethane nanocomposite for cardiac tissue engineering

Conductive nanofibers have been considered as one of the most interesting and promising candidate scaffolds for cardiac patch applications with capability to improve cell–cell communication. Here, we successfully fabricated electroconductive nanofibrous patches by simultaneous electrospray of multiwalled carbon nanotubes (MWCNTs) on polyurethane nanofibers. A series of CNT/PU nanocomposites with different weight ratios (2:10, 3:10, and 6:10wt%) were obtained. Scanning electron microscopy, conductivity analysis, water contact angle measurements, and tensile tests were used to characterize the scaffolds. FESEM showed that CNTs were adhered on PU nanofibers and created an interconnected web‐like structures. The SEM images also revealed that the diameters of nanofibers were decreased by increasing CNTs. The electrical conductivity, tensile strength, Young's modulus, and hydrophilicity of CNT/PU nanocomposites also enhanced after adding CNTs. The scaffolds revealed suitable cytocompatibility for H9c2 cells and human umbilical vein endothelial cells (HUVECs). This study indicated that simultaneous electrospinning and electrospray can be used to fabricate conductive CNT/PUnanofibers, resulting in better cytocompatibility and improved interactions between the scaffold and cardiomyoblasts.     

A modelling framework to support design of complex engineering systems in early design stages

Production, assembly or logistic systems exist in widespread domains. It is agreed that more than 50% of life-cycle performance, costs and environmental impacts of such systems are due to those decisions that are made in their early design stages (Reich, Res Eng Design 28(4):411–419, https://doi.org/10.1007/s00163-017-0270-7, 2017). However, the large scale and multi-disciplinary essence of such systems make their design considerably challenging. Most of the design approaches follow a sequential approach such that the design in each lower level is finalized/frozen before proceeding to the next level. However, such approaches do not properly address the interaction between different design disciplines which may later lead to design inconsistencies. Therefore, this paper aimed to propose a modelling framework that allows having an integrated approach in the early design stages of such systems. To this end, first the framework prescribed developing an executable meta-architecture that can embody all the design requirements. Second, the framework describes the interconnections between the meta-architecture with certain supporting algorithms and optimization models. This allows generating and simulating different design alternatives and observing the impact of different design decisions on system integrated performance. Therefore, the proposed framework with its providing outcomes can be used to support the decision making in early design stages of such systems. The framework is applied in a real case study from the warehousing domain, which serves to show the practical application of the proposed framework.     

Deep-Learning Based DOA Estimation in the Presence of Multiplicative Noise

Wireless Personal Communications - The paper addresses the narrowband direction of arrival estimation problem in the presence of multiplicative noise, namely, the local scatterers affect the...     

Cooling Performance and Indoor Air Quality Characteristics of an Earth Air Tunnel Cooled Building

Most of the modern air-conditioned buildings have a low fresh air supply to reduce energy consumption. This results in a poor indoor air quality (IAQ) and a 30–200% higher sick building syndrome than that of a naturally ventilated building. Alternatively, energy-efficient and eco-friendly earth air tunnel system (EATS) provides a good IAQ because of the sufficient fresh air supply. This paper presents the cooling performance and IAQ characteristics of a sparsely occupied building supported with an EATS. The building was monitored for the concentration of fine and coarse particulate matter (PM), carbon dioxide (CO₂) and carbon monoxide (CO), temperature distribution and relative humidity (RH) in September and October 2012. The average PM₁₀, PM_2.5 and PM₁ concentrations were 6.77, 6.11 and 3.17 μg/m³ respectively when the EATS was operated. These are marginally higher compared to that when the EATS was not operated. The average indoor CO₂ level, air temperature and RH were 418 ppm, 26.5 °C and 58.2% respectively when the EATS was operated. The diurnal indoor CO₂ trend relates well with photosynthetic and anthropogenic activities in and around the building. The CO₂ and PM concentrations correlate well with indoor air temperature and RH with a time lag.     

Robust and efficient memory management in Apache AsterixDB

Traditional relational database systems handle data by dividing their memory into sections such as a buffer cache and working memory, assigning a memory budget to each section to efficiently manage a limited amount of overall memory. They also assign memory budgets to memory-intensive operators such as sorts and joins and control the allocation of memory to these operators; each memory-intensive operator attempts to maximize its memory usage to reduce disk I/O cost. Implementing such memory-intensive operators requires a careful design and application of appropriate algorithms that properly utilize memory. Today's Big Data management systems need the ability to handle large amounts of data similarly, as it is unrealistic to assume that truly big data will fit into memory. In this article, we share our memory management experiences in Apache AsterixDB, an open-source Big Data management software platform that scales out horizontally on shared-nothing commodity computing clusters. We describe the implementation of AsterixDB's memory-intensive operators and their designs related to memory management. We also discuss memory management at the global (cluster) level. We conducted an experimental study using several synthetic and real datasets to explore the impact of this work. We believe that future Big Data management system builders can benefit from these experiences.