Rubber‐toughened polypropylene nanocomposite: Effect of polyethylene octene copolymer on mechanical properties and phase morphology

Rubber‐toughened polypropylene (PP)/org‐Montmorillonite (org‐MMT) nanocomposite with polyethylene octene (POE) copolymer were compounded in a twin‐screw extruder at 230°C and injection‐molded. The POE used had 25 wt % 1‐octene content and the weight fraction of POE in the blend was varied in the range of 0–20 wt %. X‐ray diffraction analysis (XRD) revealed that an intercalation org‐MMT silicate layer structure was formed in rubber‐toughened polypropylene nanocomposites (RTPPNC). Izod impact measurements indicated that the addition of POE led to a significant improvement in the impact strength of the RTPPNC, from 6.2 kJ/m² in untoughened PP nanocomposites to 17.8 kJ/m² in RTPPNC containing 20 wt % POE. This shows that the POE elastomer was very effective in converting brittle PP nanocomposites into tough nanocomposites. However, the Young's modulus, tensile strength, flexural modulus, and flexural strength of the blends decreased with respect to the PP nanocomposites, as the weight fraction of POE was increased to 20 wt %. Scanning electron microscopy (SEM) was used for the investigation of the phase morphology and rubber particles size. SEM study revealed a two‐phase morphology where POE, as droplets was dispersed finely and uniformly in the PP matrix. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 99: 3441–3450, 2006     

Monitoring web services’ quality of service: a literature review

Monitoring Quality of Service (QoS) compliance is an important procedure in web service environment. It determines whether users’ expectations are met, and becomes the vital factor for them to decide whether to continue paying for the service or not. The monitoring is performed by checking the actual services performance against the QoS stated in Service Level Agreement (SLA). In relation to that, the need for monitoring vague QoS specifications in SLA has become more apparent nowadays. This paper reviews the published literature on web services QoS monitoring. A total of 60 selected articles were systematically analyzed. There were 23 of the articles selected through restrictive search criteria while the other 37 were selected based on unrestrictive search criteria. The review shows that little evidence exists on monitoring vague QoS specifications of web services. Providing ability for monitoring QoS that is specified vaguely in SLA could give new insights and implications to web services field. This paper concludes with some recommended future works to construct the theory and perform the empirical research.     

Robust Fault Diagnosis for Quadrotor UAVs Using Adaptive Thau Observer

A robust Fault Diagnosis (FD) scheme for a real quadrotor Unmanned Aerial Vehicle (UAV) is proposed in this paper. Firstly, a novel Adaptive Thau observer (ATO) is developed to estimate the quadrotor system states and build a set of offset residuals to indicate actuators’ faults. Based on these residuals, some rules of Fault Diagnosis (FD) are designed to detect and isolate the faults as well as estimate the fault offset parameters. Secondly, a synthetic robust optimization scheme is presented to improve Fault Estimation (FE) accuracies, three key issues include modeling uncertainties, and magnitude order unbalances as well as noises are addressed. Finally, a typical fault of rotors is simulated and injected into one of four rotors of the quadrotor, and experiments for the FD scheme have been carried out. Unlike former research works on the FD schemes for quadrotors, our proposed FD scheme based on the ATO can not only detect and isolate the failed actuators, but also estimate the fault severities. Regardless of roughness of the real flying data, the FD results still have sufficient FE accuracies.     

Identification of probe request attacks in WLANs using neural networks

Any sniffer can see the information sent through unprotected ‘probe request messages’ and ‘probe response messages’ in wireless local area networks (WLAN). A station (STA) can send probe requests to trigger probe responses by simply spoofing a genuine media access control (MAC) address to deceive access point (AP) controlled access list. Adversaries exploit these weaknesses to flood APs with probe requests, which can generate a denial of service (DoS) to genuine STAs. The research examines traffic of a WLAN using supervised feed-forward neural network classifier to identify genuine frames from rogue frames. The novel feature of this approach is to capture the genuine user and attacker training data separately and label them prior to training without network administrator’s intervention. The model’s performance is validated using self-consistency and fivefold cross-validation tests. The simulation is comprehensive and takes into account the real-world environment. The results show that this approach detects probe request attacks extremely well. This solution also detects an attack during an early stage of the communication, so that it can prevent any other attacks when an adversary contemplates to start breaking into the network.     

Effect of Dyebath Additives on Decolorization Efficiency in the Ozonation of Dyehouse Effluent

It is necessary to study the effect of dyebath additives on decolorization efficiency in order to optimize ozone-based decolorization processes as the consumption of ozone can be reduced through selecting ozone favorable additives. The effect of 5 dyebath additives viz. electrolytes (sodium chloride and sodium sulfate), chelating agent (ethylene diamine tetra acetic acid or EDTA), reducing agent (sodium dithionite), optical brightener (Uvitex BHT), and dispersing agent (Zetex DNVL) was investigated. All of the additives showed synergistic effect as addition of sodium chloride, sodium dithionite and Zetex DN-VL markedly improved decolorization efficiency, but EDTA and optical brightener showed negative effect. Sodium sulfate did not show any positive or negative effect on decolorization efficiency.     

Using a virtual environment to assess cognition in the elderly

Early diagnosis of Alzheimer’s disease (AD) is essential if treatments are to be administered at an earlier point in time before neurons degenerate to a stage beyond repair. In order for early detection to occur tools used to detect the disorder must be sensitive to the earliest of cognitive impairments. Virtual reality technology offers opportunities to provide products which attempt to mimic daily life situations, as much as is possible, within the computational environment. This may be useful for the detection of cognitive difficulties. We develop a virtual simulation designed to assess visuospatial memory in order to investigate cognitive function in a group of healthy elderly participants and those with a mild cognitive impairment (MCI). Participants were required to guide themselves along a virtual path to reach a virtual destination which they were required to remember. The preliminary results indicate that this virtual simulation has the potential to be used for detection of early AD since significant correlations of scores on the virtual environment with existing neuropsychological tests were found. Furthermore, the test discriminated between healthy elderly participants and those with a MCI.     

Decolourisation of aqueous dyes by sequential oxidation treatment with ozone and Fenton's reagent

Combined oxidation with ozone and Fenton's reagent (‘Fentozone’ process) for decolourisation of aqueous dyes was studied and compared with traditional Fenton's reagent. Although the ‘Fentozone’ process was found to be effective at a wide range of pH values, the maximum colour removal was achieved at pH 4. The effect of pre‐ozonation on colour removal efficiency of aqueous dyestuffs in the subsequent treatment with Fenton's reagent was investigated. The reaction kinetics using water‐soluble acid and reactive dyes were also studied. Our experimental results show that pre‐ozonation can considerably accelerate decomposition of dyestuffs in the subsequent treatment with Fenton's reagent. Different concentrations of ferrous sulfate were used to investigate their influences on the removal of colour. The rate of reaction increased with increasing doses of ferrous sulfate. © 2002 Society of Chemical Industry     

Buoyancy effects on gaseous slip flow in a vertical rectangular microchannel

Consideration is given to the buoyancy effects on the fully developed gaseous slip flow in a vertical rectangular microduct. Two different cases of the thermal boundary conditions are considered, namely uniform temperature at two facing duct walls with different temperatures and adiabatic other walls (case A) and uniform heat flux at two walls and uniform temperature at other walls (case B). The rarefaction effects are treated using the first-order slip boundary conditions. By means of finite Fourier transform method, analytical solutions are obtained for the velocity and temperature distributions as well as the Poiseuille number. Furthermore, the threshold value of the mixed convection parameter to start the flow reversal is evaluated. The results show that the Poiseuille number of case A is an increasing function of the mixed convection parameter and a decreasing function of the channel aspect ratio, whereas its functionality on the Knudsen number is not monotonic. For case B, the Poiseuille number is decreased by increasing each of the mixed convection parameter, the Knudsen number, and the channel aspect ratio.     

Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach

Diagnosis, detection and classification of tumors, in the brain MRI images, are important because misdiagnosis can lead to death. This paper proposes a method that can diagnose brain tumors in the MRI images and classify them into 5 categories using a Convolutional Neural Network (CNN). The proposed network uses a Convolutional Auto-Encoder Neural Network (CANN) to extract and learn deep features of input images. Extracted deep features from each level are combined to make desirable features and improve results. To classify brain tumor into three categories (Meningioma, Glioma, and Pituitary) the proposed method was applied on Cheng dataset and has reached a considerable performance accuracy of 99.3%. To diagnosis and grading Glioma tumors, the proposed method was applied on IXI and BraTS 2017 datasets, and to classify brain images into six classes including Meningioma, Pituitary, Astrocytoma, High-Grade Glioma, Low-Grade Glioma and Normal images (No tumor), the all datasets including IXI, BraTS2017, Cheng and Hazrat-e-Rassol, was used by the proposed network, and it has reached desirable performance accuracy of 99.1% and 98.5%, respectively.

     

TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.