Self-reinforcing mechanisms for cellularisation: a longitudinal case study

Firms that adopt cellular manufacturing (CM) often need to restructure their products, personnel, equipment, factory management and organisation culture. Despite voluminous literature on CM, only a few case studies related to cellularisation exist. Therefore, to enhance understanding in this area, this paper presents a longitudinal case study on cellularisation in an electronic assembly plant. Six years of data were collected from the observations and discussions of an industrialist and an academician. Events instrumental to the persistence of CM were highlighted, with reference to path dependence analysis. Cellularisation is not entirely evolutionary or teleological but is an interplay of the two; it is significantly influenced by the positive feedback of self-reinforcing mechanisms after departing from critical junctures. The observed self-reinforcing mechanisms were expounded and divided into six categories.     

Leakage mitigation in NW FET using negative Schottky junction drain and its process variation analysis

Journal of Computational Electronics - In this work, a Schottky junction on the drain side employing low workfunction (WF) metal is proposed as a method to suppress the OFF-state leakage in...     

Removal of chromium (VI) from aqueous solution using treated oil palm fibre

This study proposed an oil palm by-product as a low-cost adsorbent for the removal of hexavalent chromium [Cr (VI)] from aqueous solution. Adsorption of Cr (VI) by sulphuric acid and heat-treated oil palm fibre was conducted using batch tests. The influence of pH, contact time, initial chromium concentration and adsorbent dosage on the removal of Cr (VI) from the solutions was investigated. The optimum initial pH for maximum uptake of Cr (VI) from aqueous solution was found to be 1.5. The removal efficiency was found to correlate with the initial Cr (VI) concentration, adsorbent dosage as well as the contact time between Cr (VI) and the adsorbent. The adsorption kinetics tested with pseudo first order and pseudo second order models yielded high R(2) values from 0.9254 to 0.9870 and from 0.9936 to 0.9998, respectively. The analysis of variance (ANOVA) showed significant difference between the R(2) values of the two models at 99% confidence level. The Freundlich isotherm (R(2)=0.8778) described Cr (VI) adsorption slightly better than the Langmuir isotherm (R(2)=0.8715). Difficulty in desorption of Cr (VI) suggests the suitability of treated oil palm fibre as a single-use adsorbent for Cr (VI) removal from aqueous solution.     

Miscibility of oligomeric poly(dimethyl siloxanes) with long-chain unbranched alkanes: 2. Unsymmetrical cloud point curves

Previously obtained cloud point curves for mixtures of oligomeric poly(dimethyl siloxanes) (weight fraction W₁) with oligomeric polyethylenes have been supplemented by the use of samples of higher molecular weight. Initial flat regions at low-medium W₁ as well as a slight shoulder at extremely high W₁ are now considered to be due to crystallization, since the temperatures at which they occur are the melting points of the oligoethylenes. The actual cloud point curves were of an unsymmetrical dome shape, the form of which could not be simulated by using a concentration-independent interaction parameter g in the Flory-Huggins equation. However, spinodals calculated by assuming a quadratic dependence of g on composition were of the same shape as the experimental cloud point curves. The critical compositions calculated on this basis were also close to the experimental ones.     

a-Si:H/SiNW shell/core for SiNW solar cell applications

Vertically aligned silicon nanowires have been synthesized by the chemical etching of silicon wafers. The influence of a hydrogenated amorphous silicon (a-Si:H) layer (shell) on top of a silicon nanowire (SiNW) solar cell has been investigated. The optical properties of a-Si:H/SiNWs and SiNWs are examined in terms of optical reflection and absorption properties. In the presence of the a-Si:H shell, 5.2% reflection ratio in the spectral range (250 to 1,000 nm) is achieved with a superior absorption property with an average over 87% of the incident light. In addition, the characteristics of the solar cell have been significantly improved, which exhibits higher open-circuit voltage, short-circuit current, and efficiency by more than 15%, 12%, and 37%, respectively, compared with planar SiNW solar cells. Based on the current–voltage measurements and morphology results, we show that the a-Si:H shell can passivate the defects generated by wet etching processes.     

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.     

Generative regular-freeform surface recognition for generating material removal volume from stock model

The present paper illustrates the development of generative surface recognition for regular and freeform. To obtain the final form of product, material removal volume generation from a stock model is also discussed. Only a few studies integrated the regular and freeform surfaces to provide a comprehensive definition of surface recognition as well as for the volumetric estimation of removal material in finishing and roughing operations. The current research deploys a comprehensive surface recognition approach that can recognise both regular and freeform surfaces based on the geometry as well as loop entity of a face. In contrast to the regular surface that can be categorised into a particular group of geometrical shape, such as cylindrical shape, the proposed approach enables the recognition of a freeform surface that cannot be defined as a generic geometrical shape. In addition, the new method also simplifies the existing surface recognition for regular surfaces. The material removal volumes created consist of machining volumes for finishing and roughing operations needed to be machined to obtain the final form of the product. The present research provides a unique user customisation feature that enables user to specify the volumetric thickness for material removal volume in the finishing operation as well as the size for the stock model. These estimated volumes are prepared for subsequent manufacturing applications, such as sequencing of machining operation.     

Effect of steel fiber on the mechanical properties of oil palm shell lightweight concrete

This paper reports the results of a study conducted to investigate the effect of low volume content of steel fiber on the slump, density, compressive strength under different curing conditions, splitting tensile strength, flexural strength and modulus of elasticity of a grade 35 oil palm shell (OPS) lightweight concrete mixture. The results indicate that an increase in steel fiber decreased the workability and increased the density. All the mechanical properties except the modulus of elasticity (E) improved significantly. The 28 day compressive strength of steel fiber OPS lightweight concrete in continuously moist curing was in the range of 41–45 MPa. The splitting tensile/compressive and the flexural/compressive strength ratio for plain OPS concrete are comparable with artificial lightweight aggregate. The (E) value measured in this study was about 15.5 GPa on average for all mixes, which is higher than previous studies and is in the range of normal weight concrete. Steel fiber can be used as an alternative material to reduce the sensitivity of OPS concrete in poor curing environments.     

Selective formation of tungsten nanowires

We report on a process for fabricating self-aligned tungsten (W) nanowires with polycrystalline silicon core. Tungsten nanowires as thin as 10 nm were formed by utilizing polysilicon sidewall transfer technology followed by selective deposition of tungsten by chemical vapor deposition (CVD) using WF6 as the precursor. With selective CVD, the process is self-limiting whereby the tungsten formation is confined to the polysilicon regions; hence, the nanowires are formed without the need for lithography or for additional processing. The fabricated tungsten nanowires were observed to be perfectly aligned, showing 100% selectivity to polysilicon and can be made to be electrically isolated from one another. The electrical conductivity of the nanowires was characterized to determine the effect of its physical dimensions. The conductivity for the tungsten nanowires were found to be 40% higher when compared to doped polysilicon nanowires of similar dimensions.