Coagent Modified Polypropylene Prepared by Reactive Extrusion: A New Look Into the Structure-Property Relations of Injection Molded Parts

Peroxide-mediated reactive extrusion of linear isotactic polypropylene (L-PP) was conducted in the presence of trimethylolpropane trimethacrylate (TMPTMA) and triallyl trimesate (TAM) coagents, using a twin screw extruder. The resulting coagent-modified polypropylenes (CM-PP) had higher viscosities and elasticities, as well as increased crystallization temperature compared to PP reacted only with peroxide (DCP-PP). Additionally, deviations from terminal flow, and strain hardening were observed in PP modified with TAM, signifying the presence of long chain branching (LCB). The CM-PP formulations retained the modulus and tensile strength of the parent L-PP, in spite of their lower molar mass and viscosities, whereas their elongation at break and the impact strength were better. This was attributed to the finer spherulitic structure of these materials, and to the disappearance of the skin-core layer in the injection molded specimens.

Method to Improve the Linearity of Active Commutating Mixers Using Dynamic Current Injection

A double balanced (DBM) CMOS mixer providing high linearity is presented in this paper. A cross-coupled pair used in the IF stage of the mixer to dynamically inject current into the to mixer provide a high linearity. The proposed DBM was fabricated using a standard 130-nm CMOS process and was tested on-wafer. The double balanced mixer delivers 10 dB conversion gain, 9.5 dBm IIP3, and input P1dB of -2.4 dBm. RF bandwidth of the proposed mixer is 6 GHz, covering 0.5 GHz to 6.5 GHz with IF bandwidth of 300 MHz. RF to IF and LO to IF isolation are also better than 59 dB in the whole frequency band. The circuit uses an area of 0.015 mm2 excluding bonding pads and draw 4.5mW from a 1.2V supply.

β-Arrestin-mediated Regulation of the Human Ether-a-go-go-Related Gene (hERG) Potassium Channel

The human ether-a-go-go-related gene (hERG) encodes the voltage-gated K+ channel, hERG (Kv11.1). This channel passes the rapidly-activating delayed rectifier K+ current (IKr), which is important for cardiac repolarization. A reduction in IKr due to loss-of-function mutations or drug interactions causes long QT syndrome (LQTS), which can lead to cardiac arrhythmias and sudden cardiac death. The density of hERG channels in the plasma membrane is a key determinant of normal physiological function, and is balanced by trafficking to and from the cell surface. Many LQTS-associated hERG mutations result in a trafficking deficiency of otherwise functional channels. Thus, elucidating mechanisms of hERG regulation at the plasma membrane is useful for the prevention and treatment of LQTS. We previously demonstrated that M3 muscarinic receptor activation increases mature hERG expression through a Gq protein-dependent protein kinase C (PKC) pathway. In addition to conventional Gq protein-coupling, M3 receptors recruit β-arrestins upon agonist binding. Traditionally known for their role in receptor desensitization and internalization, β-arrestins also act as adaptor proteins to facilitate G protein-independent signaling. In the present work, I investigated the exclusive effect of β-arrestin signaling on hERG expression by utilizing an arrestin-biased M3 designer receptor (M3D-arr) exclusively activated by clozapine-N-oxide (CNO). By expressing M3D-arr in hERG-HEK cells and treating with CNO under various conditions, I found that M3D-arr activation increased mature hERG expression and current. Within this paradigm, M3D-arr recruited β-arrestin to the plasma membrane, and promoted the PI3K-dependent activation of Akt. I further found that the activated Akt acted through phosphatidylinositol 3-phosphate 5-kinase (PIKfyve) and Rab11 to facilitate endosomal recycling of hERG channels to the plasma membrane.

Evaluation of the Genotoxic and Physiological Effects of Decabromodiphenyl Ether (BDE-209) and Dechlorane Plus (DP) Flame Retardants in Marine Mussels (Mytilus galloprovincialis)

Dechlorane Plus (DP) is a proposed alternative to the legacy flame retardant decabromodiphenyl ether (BDE-209), a major component of Deca-BDE formulations. In contrast to BDE-209, toxicity data for DP are scarce and often focused on mice. Validated dietary in vivo exposure of the marine bivalve (Mytilus galloprovincialis) to both flame retardants did not induce effects at the physiological level (algal clearance rate), but induced DNA damage, as determined by the comet assay, at all concentrations tested. Micronuclei formation was induced by both DP and BDE-209 at the highest exposure concentrations (100 and 200 mu g/L, respectively, at 18% above controls). DP caused effects similar to those by BDE-209 but at lower exposure concentrations (5.6, 56, and 100 mu g/L for DP and 56, 100, and 200 mu g/L for BDE-209). Moreover, bioaccumulation of DP was shown to be concentration dependent, in contrast to BDE-209. The results described suggest that DP poses a greater genotoxic potential than BDE-209

Evaluation of the Genotoxic and Physiological Effects of Decabromodiphenyl Ether (BDE-209) and Dechlorane Plus (DP) Flame Retardants in Marine Mussels (Mytilus galloprovincialis)

Dechlorane Plus (DP) is a proposed alternative to the legacy flame retardant decabromodiphenyl ether (BDE-209), a major component of Deca-BDE formulations. In contrast to BDE-209, toxicity data for DP are scarce and often focused on mice. Validated dietary in vivo exposure of the marine bivalve (Mytilus galloprovincialis) to both flame retardants did not induce effects at the physiological level (algal clearance rate), but induced DNA damage, as determined by the comet assay, at all concentrations tested. Micronuclei formation was induced by both DP and BDE-209 at the highest exposure concentrations (100 and 200 mu g/L, respectively, at 18% above controls). DP caused effects similar to those by BDE-209 but at lower exposure concentrations (5.6, 56, and 100 mu g/L for DP and 56, 100, and 200 mu g/L for BDE-209). Moreover, bioaccumulation of DP was shown to be concentration dependent, in contrast to BDE-209. The results described suggest that DP poses a greater genotoxic potential than BDE-209

An ether-functionalised cyclic sulfonium based ionic liquid as an electrolyte for electrochemical double layer capacitors

A novel cyclic sulfonium cation-based ionic liquid (IL) with an ether-group appendage and the bis{(trifluoromethyl)sulfonyl}imide anion was synthesised and developed for electrochemical double layer capacitor (EDLC) testing. The synthesis and chemical-physical characterisation of the ether-group containing IL is reported in parallel with a similarly sized alkyl-functionalised sulfonium IL. Results of the chemical-physical measurements demonstrate how important transport properties, i.e. viscosity and conductivity, can be promoted through the introduction of the ether-functionality without impeding thermal, chemical or electrochemical stability of the IL. Although the apparent transport properties are improved relative to the alkyl-functionalised analogue, the ether-functionalised sulfonium cation-based IL exhibits moderately high viscosity, and poorer conductivity, when compared to traditional EDLC electrolytes based on organic solvents (propylene carbonate and acetonitrile). Electrochemical testing of the ether-functionalised sulfonium IL was conducted using activated carbon composite electrodes to inspect the performance of the IL as a solvent-free electrolyte for EDLC application. Good cycling stability was achieved over the studied range and the performance was comparable to other solvent free,
IL-based EDLC systems. Nevertheless, limitations of the attainable performance are primarily the result of sluggish transport properties and a restricted operative voltage of the IL, thus highlighting key aspects of this field which require further attention.

Synthesis and Thermophysical Properties of Ether Functionalized Sulfonium Ionic Liquids as Potential Electrolytes for Electrochemical Applications

During this work, a novel series of hydrophobic room temperature ionic liquids (ILs) based on five ether functionalized sulfonium cations bearing the bis(trifluoromethyl)sulfonylimide, [NTf2]- anion were synthesized and characterized. Their physicochemical properties, such as density, viscosity and ionic conductivity, electrochemical window along with thermal properties including phase transition behavior and decomposition temperature, have been measured. All of these ILs showed large liquid range temperature, low viscosity and good conductivity. Additionally, by combining DFT calculations along with electrochemical characterization it appears that these novel ILs show good electrochemical stability windows, suitable for the potential application as electrolyte materials in electrochemical energy storage devices.

Mixing behaviour of side injection of air jets and gaseous fuel jets into the axial flow of tunnel kilns

Otto-von-Guericke-Universität Magdeburg, Fakultät für Verfahrens- und Systemtechnik, Dissertation, 2016

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.

PCA-FNN based performance prediction for water injection in oilfields.

In order to solve the problem of uncertain cycle of water injection in the oilfield, this paper proposed a numerical method based on PCA-FNN, so that it can forecast the effective cycle of water injection. PCA is used to reduce the dimension of original data, while FNN is applied to train and test the new data. The correctness of PCA-FNN model is verified by the real injection statistics data from 116 wells of an oilfield, the result shows that the average absolute error and relative error of the test are 1.97 months and 10.75% respectively. The testing accuracy has been greatly improved by PCA-FNN model compare with the FNN which has not been processed by PCA and multiple liner regression method. Therefore, PCA-FNN method is reliable to forecast the effectiveness cycle of water injection and it can be used as an decision-making reference method for the engineers.

An investigation into PL/SQL Injection.

SQL injection is a common attack method used to leverage infor-mation out of a database or to compromise a company’s network. This paper investigates four injection attacks that can be conducted against the PL/SQL engine of Oracle databases, comparing two recent releases (10g, 11g) of Oracle. The results of the experiments showed that both releases of Oracle were vulner-able to injection but that the injection technique often differed in the packages that it could be conducted in.

Numerical encoding to tame SQL injection attacks.

Recent years have seen an astronomical rise in SQL Injection Attacks (SQLIAs) used to compromise the confidentiality, authentication and integrity of organisations’ databases. Intruders becoming smarter in obfuscating web requests to evade detection combined with increasing volumes of web traffic from the Internet of Things (IoT), cloud-hosted and on-premise business applications have made it evident that the existing approaches of mostly static signature lack the ability to cope with novel signatures. A SQLIA detection and prevention solution can be achieved through exploring an alternative bio-inspired supervised learning approach that uses input of labelled dataset of numerical attributes in classifying true positives and negatives. We present in this paper a Numerical Encoding to Tame SQLIA (NETSQLIA) that implements a proof of concept for scalable numerical encoding of features to a dataset attributes with labelled class obtained from deep web traffic analysis. In the numerical attributes encoding: the model leverages proxy in the interception and decryption of web traffic. The intercepted web requests are then assembled for front-end SQL parsing and pattern matching by applying traditional Non-Deterministic Finite Automaton (NFA). This paper is intended for a technique of numerical attributes extraction of any size primed as an input dataset to an Artificial Neural Network (ANN) and statistical Machine Learning (ML) algorithms implemented using Two-Class Averaged Perceptron (TCAP) and Two-Class Logistic Regression (TCLR) respectively. This methodology then forms the subject of the empirical evaluation of the suitability of this model in the accurate classification of both legitimate web requests and SQLIA payloads.