Electric field effects on CNTs/vinyl ester suspensions and the resulting electrical and thermal composite properties

In this study, electrical conductivity of a vinyl ester based composite containing low content (0.05, 0.1 and 0.3 wt.%) of double and multi-walled carbon nanotubes with and without amine functional groups (DWCNTs, MWCNTs, DWCNT-NH₂ and MWCNT-NH₂) was investigated. The composite with pristine MWCNTs was found to exhibit the highest electrical conductivity. Experiments aimed to induce an aligned conductive network with application of an alternating current (AC) electric field during cure were carried out on the resin suspensions with MWCNTs. Formation of electric anisotropy within the composite was verified. Light microscopy (LM), scanning electron (SEM) and transmission electron microscopy (TEM) were conducted to visualize dispersion state and the extent of alignment of MWCNTs within the polymer cured with and without application of the electric field. To gain a better understanding of electric field induced effects, glass transition temperature (T_g) of the composites was measured via Differential Scanning Calorimetry (DSC). It was determined that at 0.05 wt.% loading rate of MWCNTs, the composites, cured with application of the AC electric field, possessed a higher T_g than the composites cured without application of the AC electric field.     

Scaled 3-D model studies of immiscible CO2 flooding using horizontal wells

In this study, a comprehensive laboratory investigation was conducted for the recovery of heavy oil from a scaled three-dimensional (3-D) physical model, packed with 18° API gravity crude oil, brine and crushed limestone. A total of 20 experiments were conducted using the scaled 3-D physical model with 30×30×6 cm³ dimensions. Basically, four different immiscible CO₂–water displacement processes were used for recovering heavy oil: (i) continuous CO₂ injection, (ii) waterflooding, (iii) simultaneous injection of CO₂ and water, and (iv) water alternating gas (WAG) process. Three groups of well configurations were mainly used: (1) vertical injection and vertical production wells, (2) vertical injection and horizontal production wells, and (3) horizontal injection and horizontal production wells. Base experiments were run with water only and carbon dioxide alone and optimum rates for WAG and simultaneous water–CO₂ injection were determined. In continuous CO₂ injection, highest recovery was obtained by vertical injection–horizontal production (VI–HP), followed by vertical injection–vertical production (VI–VP) and the least by horizontal injection–horizontal production (HI–HP). In VI–HP configuration, the best recovery was obtained as 15.1% OOIP. Higher oil recovery was obtained with a VI–HP wells than with a pair of vertical wells and horizontal wells. The WAG 1:5 ratio yielded a final recovery of 34.5% OOIP with VI–VP well configuration and 17.0% OOIP of additional recovery over waterflooding. In turn, the WAG 1:10 ratio was the best with a final recovery of 20.9% of OOIP with VI–HP well configuration. Oil production from WAG injection is higher than that obtained from the injection of continuous CO₂ or waterflooding alone.     

Modeling of current transport and 1/f noise in heterojunction bipolar transistors

An analytic model is proposed to determine the effect of band offsets at heteroemitter interface on the current transport and 1/f noise in heterojunction bipolar transistors (HBTs). The proposed model uses the modified form of drift-diffusion formalism, which requires that the net recombination rates be proportional to the densities of other type carriers across the heterointerface. The numerical analysis of the current–voltage and 1/f noise characteristics of Npn AlGaAs/GaAs HBT and npn GaAs BJT demonstrates that the role of band offsets at heteroemitter interface in the overall current transport and 1/f noise is very important in HBTs at low forward biases. The junction resistance due to diffusing minority electrons is much stronger (weaker) at small (high) forward biases than that due to recombined electrons and holes across the heteroemitted space charge region in both Npn AlGaAs/GaAs HBTs and npn GaAs BJTs.     

Preparation and characterization of polyethyleneglycolmethacrylate (PEGMA)-co-vinylimidazole (VI) microspheres to use in heavy metal removal

Polyethyleneglycolmethacrylate (PEGMA) and vinylimidazole (VI) were used in order to obtain microspheres of PEGMA-VI copolymers that can be used in heavy metal removal applications. The obtained copolymers were characterized and their use as sorbents in heavy metal removal was investigated. In the first part of the study, PEGMA-VI microspheres were prepared by suspension polymerization method. The obtained swellable microspheres with 10-50 microm average diameter did not have permanent porosity according to the morphological and physicochemical determinations. The sizes of microspheres became smaller with increasing VI and cross-linker ethyleneglycoldimethacrylate (EGDMA) contents and increasing agitation rate. The VI content, EGDMA ratio, pH and ionic strength were determined as the effective parameters on the swelling behavior of PEGMA-VI microspheres. In the second part of the study, Cu(II) ions were used as a model species in order to investigate the usability of the obtained PEGMA-VI microspheres in heavy metal removal. Adsorption capacities under optimum conditions were determined. The Cu(II) ion adsorption capacity increased by increasing the initial Cu(II) ion concentration, and it reached the maximum value (i.e., 30 mg Cu(II)/g PEGMA-VI microspheres) at 400 mg Cu(II)/L initial Cu(II) ion concentration under the determined optimum conditions. Microspheres were found to be reusable after desorption for several times.     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

Harmonic waves in a prestressed thin elastic tube filled with a viscous fluid

In this work a theoretical analysis is presented for wave propagation ina thin-walled prestressed elastic tube filled with a viscous fluid. Thefluid is assumed to be incompressible and Newtonian, whereas the tubematerial is considered to be incompressible, isotropic and elastic.Considering the physiological conditions that the arteries experience, sucha tube is initially subjected to a mean pressure P_i and anaxial stretch _z. If it is assumed that in the course ofblood flow small incremental disturbances are superimposed on this initialfield, then the governing equations of this incremental motion are obtainedfor the fluid and the elastic tube. A harmonic-wave type of solution issought for these field equations and the dispersion relation is obtained.Some special cases, as well as the general case, are discussed and thepresent formulation is compared with some previous works on the samesubject.     

Coreless Fiber‐Based Whispering‐Gallery‐Mode Assisted Lasing from Colloidal Quantum Well Solids

Whispering gallery mode (WGM) resonators are shown to hold great promise to achieve high‐performance lasing using colloidal semiconductor nanocrystals (NCs) in solution phase. However, the low packing density of such colloidal gain media in the solution phase results in increased lasing thresholds and poor lasing stability in these WGM lasers. To address these issues, here optical gain in colloidal quantum wells (CQWs) is proposed and shown in the form of high‐density close‐packed solid films constructed around a coreless fiber incorporating the resulting whispering gallery modes to induce gain and waveguiding modes of the fiber to funnel and collect light. In this work, a practical method is presented to produce the first CQW‐WGM laser using an optical fiber as the WGM cavity platform operating at low thresholds of ≈188 µJ cm^?2 and ≈1.39 mJ cm^?2 under one‐ and two‐photon absorption pumped, respectively, accompanied with a record low waveguide loss coefficient of ≈7 cm^?1 and a high net modal gain coefficient of ≈485 cm^?1. The spectral characteristics of the proposed CQW‐WGM resonator are supported with a numerical model of full electromagnetic solution. This unique CQW‐WGM cavity architecture offers new opportunities to achieve simple high‐performance optical resonators for colloidal lasers.     

Forecasting Bank Failure: Base Learners,Ensembles and Hybrid Ensembles

The prediction of bankruptcy for financial companies, especially banks, has been extensively researched area and creditors, auditors, stockholders and senior managers are all interested in bank bankruptcy prediction. In this paper, three common machine learning models namely Logistic, J48 and Voted Perceptron are used as the base learners. In addition, an attribute-base ensemble learning method namely Random Subspaces and two instance-base ensemble learning methods namely Bagging and Multi-Boosting are employed to enhance the prediction accuracy of conventional machine learning models for bank failure prediction. The models are grouped in the following families of approaches: (i) conventional machine learning models, (ii) ensemble learning models and (iii) hybrid ensemble learning models. Experimental results indicate a clear outperformance of hybrid ensemble machine learning models over conventional base and ensemble models. These results indicate that hybrid ensemble learning models can be used as a reliable predicting model for bank failures.     

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.