On the inhibition of mild steel corrosion by 4-amino-5-phenyl-4H-1, 2, 4-trizole-3-thiol

The corrosion inhibition of mild steel in a 2.5 M H₂SO₄ solution by 4-amino-5-phenyl-4H-1, 2, 4-trizole-3-thiol (APTT) was studied at different temperatures, utilising open circuit potential, potentiodynamic and impedance measurements. The results indicate that APTT performed as an excellent mixed-type inhibitor for mild steel corrosion in a 2.5 M H₂SO₄ solution and that the inhibition efficiencies increased with the inhibitor concentration but decreased proportionally with temperature. The kinetic and thermodynamic parameters for adsorption of APTT on the mild steel surface were calculated. A chemisorption mechanism of APTT molecules on the mild steel surface was proposed based on the thermodynamic adsorption parameters.     

A model for gene selection and classification of gene expression data

Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data which will maximize the classification accuracy. A model for gene selection and classification has been developed by using a filter approach, and an improved hybrid of the genetic algorithm and a support vector machine classifier. We show that the classification accuracy of the proposed model is useful for the cancer classification of one widely used gene expression benchmark data set.     

IoMT-Enabled Fusion-Based Model to Predict Posture for Smart Healthcare Systems

Smart healthcare applications depend on data from wearable sensors (WSs) mounted on a patient’s body for frequent monitoring information. Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures. The collection of WS data and integration of that data for diagnostic purposes is a difficult task. This paper proposes an Errorless Data Fusion (EDF) approach to increase posture recognition accuracy. The research is based on a case study in a health organization. With the rise in smart healthcare systems, WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness. As a result, it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency. Sensor breakdowns, the constant time factor, aggregation, and analysis results all cause errors, resulting in rejected or incorrect suggestions. This paper resolves this problem by using EDF, which is related to patient situational discovery through healthcare surveillance systems. Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures. This technology improves position detection accuracy, analysis duration, and error rate, regardless of user movements. Wearable devices play a critical role in the management and treatment of patients. They can ensure that patients are provided with a unique treatment for their medical needs. This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis. At first, the patients’ walking patterns are tracked at various time intervals. The characteristics are then evaluated in relation to the stored data using a random forest classifier.     

Effects of THF as cosolvent in the preparation of polydimethylsiloxane/polyethersulfone membrane for gas separation

Polydimethylsiloxane/polyethersulfone (PDMS/PES) asymmetric membranes are widely applied in gas separation. However, the effects of common cosolvent on these membranes remain unknown. In order to study the changes in membrane morphology and gas separation properties, asymmetric PDMS/PES membranes were prepared. The studied parameters were types of cosolvents, tetrahydrofuran (THF) concentration, evaporation time, and PDMS concentration. Membrane morphology was examined using scanning electron microscopy and gas separation was conducted using pure CO₂, N₂, CH₄, and Hat 25°C. The addition of cosolvent into the polymer solution decreased the dope viscosity and delayed liquid–liquid demixing during phase inversion. Macrovoids formation was observed in substructure layer after adding THF and these macrovoids elongated with the reduction in THF content. There were microvoids formed on top of macrovoids and microvoids layer became thicker due to the increasing evaporation time of solvents before coagulation in nonsolvent. The PDMS coating on the PES membrane formed a dense skin layer and exhibited higher selectivity compared to the uncoated membrane. Membrane contained THF cosolvent with 60 s evaporation time and 3 wt% coated PDMS is the optimum membrane among other membranes in this work. The CO₂/N₂ selectivity was enhanced by 73.3% with CO₂ permeance of 44.86 GPU. POLYM. ENG. SCI., 54:2177–2186, 2014. © 2013 Society of Plastics Engineers     

Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.     

Effects of protein from different sources on the characteristics of sponge cakes,rice cakes (apam), doughnuts and frying batters

Sponge cakes containing total substitution of whole egg with egg yolk had the largest volume, best texture, highest moisture, colour and flavour compared with all other cakes. Cakes made with total substitution of other proteins had smaller volumes than the control, while those made with egg white had the lowest moisture. Cakes made with whey powder had the tenderest texture. Doughnuts made with protein other than gluten had reduced oil absorption compared with the control. Doughnuts containing whey or skimmed milk had the most desirable flavour; this may be attributed to the presence of lactose. Gluten doughnuts were most disliked because of their hard texture and pale colour. Doughnuts made with total substitution of whole egg with egg white had good texture and low oil absorption. Skimmed milk powder was best for the expansion and texture of chemically leavened rice cakes (apam), while egg white produced the best expansion for yeast-leavened rice cakes, compared with the other proteins studied. The protein that produced the most desirable textures in yeast-leavened rice cakes in order of merit were skimmed milk powder, egg white and egg yolk. Whey, skimmed milk powder and gluten helped improve the crispiness of frying batters. Ovalbumin did not affect the crispiness but significantly reduced the oil absorption of the frying batters. Addition of proteins other than whey and ovalbumin, increased the oil absorption of frying batters.     

The Influences of Fibre Parameters on the Tensile and Flexural Response of Lightweight Thermoplastic Kenaf Fibre Reinforced Metal Composites

ABSTRACT

The increasing demand for high performance and lightweight materials has stimulated the development of alternative materials, namely fiber metal laminates (FMLs). FML is a sandwich structure which is formed by bonding the metallic layers with composite as core constituent using an adhesive agent. In this study, the mechanical behavior of FMLs with the core constituents of environmental friendly kenaf bast fiber reinforced polypropylene composites bonded with aluminum skin layers was investigated. The effects of fiber compositions (50, 60, and 70 wt.%), fiber lengths (30, 60, 90 mm), and alkali treatment on the mechanical responses of FML were investigated. The overall results revealed that the increase of fiber composition and fiber length reduces the mechanical strength of FML owing to the agglomeration of natural fibers when the fiber length exceeds the critical limit. However, the chemical treated kenaf bast fiber reinforced FML showed a significant enhancement of the mechanical properties in comparison to the non-treated fiber reinforced FML owing to the improved fiber-matrix adhesion level.     

Perovskite-based proton conducting membranes for hydrogen separation: A review

Hydrogen is considered a fuel of the future due to its diversified supply and zero greenhouse gas emission. The application of advanced membrane technology for hydrogen separation within the larger hydrogen production process context can substitute the use of more expensive and energy intensive cryogenic distillation and pressure swing adsorption technologies. This review overviews the basic aspects and progresses in perovskite-based proton conducting hydrogen separation membranes. Different configurations such as symmetric, asymmetric, hollow fiber, and surface modified perovskite membranes with various compositions are discussed and summarized. The challenges and future directions of such membranes are also elaborated.     

Modified 2 Satisfiability Reverse Analysis Method via Logical PermutationOperator

The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data. Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions. The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy. To address the issues, the logical permutation serves as an alternative mechanism that can enhance the probability of the 2 Satisfiability logical rule becoming true by utilizingthedefinitive finite arrangement of attributes. This work aims to examine and analyze the significant effect of logical permutation on the performance of data extraction ability of the logic mining approach incorporated with the recurrent discrete Hopfield Neural Network. Based on the theory, the effect of permutation and associate memories in recurrent Hopfield Neural Network will potentially improve the accuracy of the existing logic mining approach. To validate the impact of the logical permutation on the retrieval phase of the logic mining model, the proposed work is experimentally tested on a different class of the benchmark real datasets ranging from the multivariate and time-series datasets. The experimental results show the significant improvement in the proposed logical permutation-based logic mining according to the domains such as compatibility, accuracy, and competitiveness as opposed to the plethora of standard 2 Satisfiability Reverse Analysis methods.