Structural Characterization of Different Asphaltenes of Kuwaiti Origin

Asphaltenes obtained by precipitation from crude Kuwaiti oils have been analyzed by proton magnetic resonance (¹H-NMR), carbon-13 nuclear magnetic resonance (¹³C-NMR) and Infrared (IR) spectral techniques. The molecular weight and elemental analysis were also determined. These combined analytical data were used for the characterization of these Kuwaiti oils. The asphaltenes molecular weights range from approximately 4200-6500 with an H/C ratio of 0.91-1.1 with an average 45-71% aromatic carbons. The average side chain length was of 4-6 carbons. It can also be concluded that the asphaltenes under investigation contain 5-9 sets of condensed aromatic rings joined together by bridges of alkyl chains or other hetero atoms and the average number of each of these sets of condensed aromatic rings is nearly 7. There are a number of alicyclic rings and condensed alicyclic rings in asphaltene. The IR spectra showed main molecular groups including OH, NH, SH, C=O and aliphatic and aromatic C-H's.     

Purification and renaturation of recombinant human lymphotoxin (tumour necrosis factor beta) expressed in Escherichia coli as inclusion bodies

High level expression of recombinant human tumour necrosis factor β (rh TNF-β) in Escherichia coli results in the formation of two portions of protein, namely soluble active protein and insoluble protein which is inactive and aggregates in the form of inclusion bodies (IBs). In this study, a procedure for purification and renaturation of rh TNF-β from inclusion bodies has been designed and verified experimentally with a product purity of more than 90% and a recovery of about 30%. The procedure includes washing of IBs with specific wash buffer (Triton X-100/EDTA/lysozyme/PMSF), their solubilization with 8 mol dm^?3 alkaline urea, purification with ion-exchange columns, refolding with renaturation buffer and finally concentration and desalination with an ultrafiltration membrane. The characteristics of the renatured protein were identical with those of purified protein from the soluble fraction as demonstrated by (1) SDS-PAGE, (2) cytotoxic activity on mouse L929 cells, (3) N-terminal amino acid sequence, and (4) gel filtration chromatography.     

New correlation for predicting the viscosity of heavy petroleum fractions

A new, simple and reliable empirical correlation for predicting the viscosities of heavy petroleum fractions is proposed, applicable to fractions with mid-boiling points from 80 to 550°C over a wide range of viscosities (0.4 to 260 mm² s⁻¹) and measurement temperatures (40 to 200°C). This correlation showed better accuracy in the prediction of heavy petroleum fraction viscosities than that of the most accurate and much more complicated predictive methods in the literature, since it gave an overall average absolute deviation of 6.5% when tested on 296 data for medium and heavy petroleum fractions.     

Stress and strain state of concrete during freezing and thawing cycles

The objective of this work is to calculate the pressures, stresses, and strains induced into moist concrete during freezing and thawing. The applied theory is based on thermodynamics and the linear theory of elasticity. If no additional salts are dissolved in the pore water the inputs needed in the theory are relative humidity and temperature measured in the sample chamber and inside concrete and evaporable water amount in the pore structure. Theoretical results were compared with the test results made with two concretes cured under water or at 96% relative humidity. One of the concretes was air entrained and in the comparison concrete no air-entraining agents were used. In the test cylinders cured under water the largest tensional stresses in freezing occurred on the surface of the test cylinders both in the axial and tangential direction. The largest tensional stress was 2.2 MPa, both in air-entrained and in non air-entrained concretes. The largest tensional stresses in the warming phase took place at the end of the thawing period when the chamber temperature was around +5 °C. Then the maximum tension occurred in the middle of the concrete cylinder in the axial direction of the cylinder. This maximum tensional stress was over 2.5 MPa in the air-entrained concrete cured in the relative humidity of 96%. The thermodynamic pumping effect at the end of the thawing phase in every cycle can increase the pore water amount remarkably if free water or moisture is available on the surface of the structure or in the environment vapor. The thermodynamic pumping effect seems to be remarkably greater and more dangerous in air-entrained concretes.     

Synthesis and characterization of photopolymerizable hydrogels based on poly (ethylene glycol) for biomedical applications

Hydrogels are polymeric materials widely used in medicine due to their similarity with the biological components of the body. Hydrogels are biocompatible materials that have the potential to promote cell proliferation and tissue support because of their hydrophilic nature, porous structure, and elastic mechanical properties. In this work, we demonstrate the microwave-assisted synthesis of three molecular weight varieties of poly(ethylene glycol) dimethacrylate (PEGDMA) with different mechanical and thermal properties and the rapid photo of them using 1-hydroxy-cyclohexyl-phenyl-ketone (Irgacure 184) as UV photoinitiator. The effects of the poly(ethylene glycol) molecular weight and degree of acrylation on swelling, mechanical, and rheological properties of hydrogels were investigated. The biodegradability of the PEGDMA hydrogels, as well as the ability to grow and proliferate cells, was examined for its viability as a scaffold in tissue engineering. Altogether, the biomaterial hydrogel properties open the way for applications in the field of regenerative medicine for functional scaffolds and tissues.     

A disease network-based recommender system framework for predictive risk modelling of chronic diseases and their comorbidities

The prediction of chronic diseases and their comorbidities is an essential task in healthcare, aiming to predict patients’ future disease risk based on their previous medical records. The accumulation of administrative data has laid a solid foundation for applying deep learning approaches in healthcare. Existing studies focused on the patients’ characteristics such as gender, age and location to predict the risk of the different diseases. However, there are high dimensional, incomplete and noisy problems in the administrative data. In this research, using administrative health data, we implemented graph theory and content-based recommender system approaches to analyse and predict chronic diseases and their comorbidities. Firstly, we used bipartite graphs to represent the relationships between patients and diseases. Then, we projected this graph to a one-mode graph, namely ‘disease network’. After that, six recommender system models with patient features and network features were trained. The outputs of these models are the severity levels of diseases and the predicted diseases with rank. Finally, we evaluated the performance of these models against the same models without network features. The results demonstrated that the models with network features have lower prediction error and better performances for predicting chronic diseases and their latent comorbidities on large administrative data. Among these models, the graph convolution matrix completion model reveals the least amount of error and the best performance for prediction. Further, using a case study of a specific patient, we demonstrated the application of these models in predictive disease risk analysis. Thus, this study showed the potential application of the recommender system approaches to the health sector utilising administrative claim data, which could significantly contribute to healthcare services and stakeholders.

     

Royal Crown Shaped Polarization Insensitive Perfect Metamaterial Absorber for C-, X-, and Ku-Band Applications

This study proposed a new royal crown-shaped polarisation insensitive double negative triple band microwave range electromagnetic metamaterial absorber (MA). The primary purpose of this study is to utilise the exotic characteristics of this perfect metamaterial absorber (PMA) for microwave wireless communications. The fundamental unit cell of the proposed MA consists of two pentagonal-shaped resonators and two inverse C-shaped metallic components surrounded by a split ring resonator (SRR). The bottom thin copper deposit and upper metallic resonator surface are disjoined by an FR-4 dielectric substrate with 1.6 mm thickness. The CST MW studio, a high-frequency electromagnetic simulator has been deployed for numerical simulation of the unit cell in the frequency range of 4 to 14 GHz. In the TE mode, the offered MA structure demonstrated three different absorption peaks at 6.85 GHz (C-band), 8.87 GHz (X-band), and 12.03 GHz (Ku-band), with 96.82%, 99.24%, and 99.43% absorptivity, respectively. The electric field, magnetic field, and surface current distribution were analysed using Maxwell’s-Curl equations, whereas the angle sensitivity was investigated to comprehend the absorption mechanism of the proposed absorber. The numerical results were verified using the Ansys HFSS (high-frequency structure simulator) and ADS (advanced design system) for equivalent circuit models. Moreover, the proposed MA is polarisation and incident angle independent. Hence, the application of this MA can be extended to a great extent, including airborne radar applications, defence, and stealth-coating technology.     

Estimation of the surface velocity of debris flow with computer-based spatial filtering

A computer-based spatial-filtering velocimeter to measure the surface velocity of natural debris flow is described. This is a simple and interesting technique implemented with a spatial filter constructed as a software program that processes the video image of debris flow instead of a hardware implementation. The surface velocity of the debris flow at the Mt. Yakedake Volcano, Japan, was estimated by this computer-based spatial-filtering method, and the results were compared with those obtained by a hardware-based spatial-filtering method. Computer-based spatial filtering has the important advantage of a capability for tuning the spatial-filter parameters to the target flow.     

A recursive state estimator in the presence of state inequality constraints

This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness of the proposed estimator, several simulation results are presented and discussed.     

Development and implementation of a hybrid intelligent controller for interior permanent-magnet synchronous motor drives

A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an interior permanent-magnet synchronous motor (IPMSM) drive is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition a genetic algorithm is used to optimize the PI controller parameters in a closed-loop vector control scheme. In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN-based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1-hp IPMSM. The effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications in an IPMSM drive.