Managing Complexity in Object-Oriented Analysis

The current approaches in Object-Oriented Analysis have limitations on modeling complex real world systems because they require priori knowledge about objects and their interactions before applying them. This may be practical in small systems and systems with clear domain knowledge, but not in large real world systems with unclear domain knowledge. Our approach uses a stepwise refinement technique in a top-down manner to the Object-Oriented Analysis stage with the application of use cases. This approach is especially good for new areas where we do not know all the information in advance. We present the approach with an example of its application to the B-ISDN service modeling and distributed systems.     

Epitaxial growth of silicon from SiH4 in the temperature range 800–1150°C

Results on the deposition of silicon from SiH₄ are reported for a temperature range 800–1150°C. Deposition was carried out at atmospheric pressure and at reduced pressures in the range 0·2-1 Torr where it was possible to maintain an h.f. glow discharge in the reactor tube. Using the h.f. discharge good quality epitaxial growth was achieved at 800°C for both undoped and heavily doped n-type layers. The main advantage of using the discharge is thought to be the marked cleanup it gives to the substrate prior to deposition taking place.     

Protein kinase B/Akt mediates effects of insulin on hepatic insulin-like growth factor-binding protein-1 gene expression through a conserved insulin response sequence

Insulin regulates the expression of multiple hepatic genes through a conserved insulin response sequence (IRS) (CAAAAC/TAA) by an as yet undetermined mechanism. Protein kinase B/Akt (PKB/Akt), a member of the PKA/PKC serine/threonine kinase family, functions downstream from phosphatidylinositol 3'-kinase (PI3K) in mediating effects of insulin on glucose transport and glycogen synthesis. We asked whether PKB/Akt mediates sequence-specific effects of insulin on hepatic gene expression using the model of the insulin-like growth factor binding protein-1 (IGFBP-1) promoter. Insulin lowers IGFBP-1 mRNA levels, inhibits IGFBP-1 promoter activity, and activates PKB/Akt in HepG2 hepatoma cells through a PI3K-dependent, rapamycin-insensitive mechanism. Constitutively active PI3K and PKB/Akt are each sufficient to mediate effects of insulin on the IGFBP-1 promoter in a nonadditive fashion. Dominant negative K179 PKB/Akt disrupts the ability of insulin and PI3K to activate PKB/Akt and to inhibit promoter activity. The IGFBP-1 promoter contains two IRSs each of which is sufficient to mediate sequence-specific effects of insulin, PI3K, and PKB/Akt on promoter activity. Highly related IRSs from the phosphoenolpyruvate carboxykinase and apolipoprotein CIII genes also are effective in this setting. These results indicate that PKB/Akt functions downstream from PI3K in mediating sequence-specific effects of insulin on the expression of IGFBP-1 and perhaps multiple hepatic genes through a conserved IRS.     

Aerobic Oxidation of Benzylic Alcohols in Water by 2,2,6,6‐Tetramethylpiperidine‐1‐oxyl (TEMPO)/Copper(II) 2‐N‐Arylpyrrolecarbaldimino Complexes

Novel copper(II) 2‐N‐arylpyrrolecarbaldimine‐based catalysts for the aerobic oxidation of benzylic alcohols mediated by the 2,2,6,6‐tetramethylpiperidine‐1‐oxyl (TEMPO) radical are reported. The catalytic activity for both synthesized and in situ made complexes in alkaline water solutions was studied revealing high efficiency and selectivity (according to GC selectivity always >99%) for both of these catalytic systems. For example, quantitative conversion of benzyl alcohol to benzaldehyde can be achieved with the in situ prepared bis[2‐N‐(4‐fluorophenyl)‐pyrrolylcarbaldimide]copper(II) catalysts in 2 h with atmospheric pressure of O₂ at 80 °C. Interestingly, these catalysts can utilize dioxygen as well as air or hydrogen peroxide as the end oxidants, producing water as the only by‐product.     

Magnetohydrodynamic bio‐nanoconvective Naiver slip flow of micropolar fluid in a stretchable horizontal channel

The purpose of this paper is to formulate and analyze a nano‐bio transport model for magnetohydrodynamic convective flow, heat, and mass diffusion of micropolar fluid containing gyrotactic microorganisms through a horizontal channel. Both the walls are considered to be stretched, and the Navier slip boundary condition is taken into account. The governing bio‐nano transport partial differential equations are rendered to ordinary differential equations using similarity variables. The resulting normalized self‐similar boundary value problem is solved computationally with the Matlab bvp4c function. The effect of the controlling parameters on the nondimensional velocity, temperature, nanoparticle concentration, and motile microorganism density functions, and their gradients at the wall are visualized graphically and in a tabular form and expounded at length. Validation with a previous simpler model is included. All physical quantities, except the local Nusselt number, increases with an increase in the velocity slip and magnetic parameters. The present problem finds applications in industries related to pharmaceutical, nanofluidic devices, microbial enhanced oil recovery, modeling oil, and gas‐bearing sedimentary basins.     

Enhanced Hydrogen Generation from Empty Fruit Bunches by Charcoal Addition into a Downdraft Gasifier

Hydrogen production by co-gasification of empty fruit bunches of palm oil could be enhanced by adding charcoal. Physiochemical characterization of raw feedstocks was performed to determine their exergy potentiality. The raw feedstocks, gasified charcoal, and the end product of produced gas were analyzed by different techniques. Gasification experiments were performed using a pilot-scale downdraft gasifier. The heating value, composition of product gas, yield of hydrogen, and exergy efficiency were used to verify the improvement of hydrogen production during the co-gasification process. Charcoal with empty fruit bunches of palm oil leads to a much higher yield of hydrogen than lower charcoal ratios or solely empty fruit bunches. This enhanced hydrogen fuel can contribute to future energy demand.     

A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus

Applied Intelligence - In recent years, the prevalence of chronic diseases such as type 2 diabetes mellitus (T2DM) has increased, bringing a heavy burden to healthcare systems. While regular...     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

Race Classification Using Deep Learning

Race classification is a long-standing challenge in the field of face image analysis. The investigation of salient facial features is an important task to avoid processing all face parts. Face segmentation strongly benefits several face analysis tasks, including ethnicity and race classification. We propose a race-classification algorithm using a prior face segmentation framework. A deep convolutional neural network (DCNN) was used to construct a face segmentation model. For training the DCNN, we label face images according to seven different classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN model developed in the first phase was used to create segmentation results. The probabilistic classification method is used, and probability maps (PMs) are created for each semantic class. We investigated five salient facial features from among seven that help in race classification. Features are extracted from the PMs of five classes, and a new model is trained based on the DCNN. We assessed the performance of the proposed race classification method on four standard face datasets, reporting superior results compared with previous studies.