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Oral English development in virtual class among Chinese learners through three ways of talking
Get PDFChinese learners are poor at oral English owing to a lack of practice. There is a high demand for opportunities to practice oral English effectively. This study investigates the use of three educationally significant ways of talking to Chinese learners' meaningful oral English development in one-to-one Computer-and Internet-Based Virtual Classes (CIBVC). It is a qualitative case study including two sets of data that contain 64 video-recorded lessons. One set of data are 32 class recordings from one participant as the primary data and the other are from another four participants as the supplementary data. Thematic analysis is employed to carry out this study and computer-assisted qualitative data analysis software is utilized to assist. The findings of this study discover effective ways of using the three ways of talking to conduct oral English teaching and learning practice. The results contribute to research into relating authentic communication in CIBVC to the discovery of effective oral English practice. Obtained practical implications are valuable references as predictors of successful teaching and learning outcomes
Deep learning based Malaysian coins recognition for visual impaired person
Get PDFCurrency recognition has been widely developed using various types of techniques and able to assist people who have a visual impairment. Machine learning is one of the methods implemented where deep learning architecture is one of them. The deep learning approach is reliable and can be used in detection and recognition of objects based on images. As currency recognition has been developed for other currencies, thus in this project, currency recognition using Malaysian coins has been developed by modeling Convolutional Neural Network (CNN) in recognizing coin images. Malaysian coins dataset was developed consist of 2400 images of four classes of coins, 5 sen, 10 sen, 20 sen, and 50 sen. In this study, pretrained CNN which are AlexNet, GoogleNet, and MobileNetV2 were formulated in recognizing such coins. Performance of each trained model was evaluated using confusion matrix and GoogleNet obtained the best performance with 99.2% testing accuracy, 99.2% precision, 99.18% recall, and 99.19% F1 score. From the trained model, it can be further developed and implemented in assisting visually impaired persons by producing a prototype using Raspberry Pi and FPGA before it can be clinically tested on the subject
Catalytic co-pyrolysis of biomass and plastic wastes over metal-modified HZSM-5: a mini critical review
No full textThe challenges faced by the fossil fuel industry has led to the exploration of biomass-derived fuel. Biomass can be converted to biofuels via pyrolysis and can be paired with plastic waste to improve biofuel yield and quality. This mini review provides concise information on recent advances in pyrolysis, focusing on using waste, specifically agricultural residues and plastic waste, as resources. Next, discussion is made on pyrolysis of biomass and plastic waste, respectively, followed by co-pyrolysis of biomass and plastic waste. Catalytic co-pyrolysis, including zeolite catalysts and metal-modified HZSM-5 is then reviewed. Finally, the future perspective of this research is discussed
Fabrication of multilayer composite hollow fiber membrane comprising NH2-MIL-125 (Ti) for CO2 removal from CH4
No full textThe presence of CO2 has created significant challenges in natural gas processing in order to meet consumer's specifications and pipeline transportation. Membrane separation is among the most effective approaches in CO2 separation from natural gas mainly to its energy efficiency. This study investigates the alternate technique for enhancing the performance of hollow fiber membrane in CO2 removal from CH4. A new type of composite hollow fiber membrane is fabricated by dip-coating of polymer solution comprising various loadings of NH2-MIL-125 (Ti) in PEBAX onto PDMS coated-polysulfone (PSf) hollow fiber. The morphology and elemental mapping analysis of the resultant membranes were characterized via scanning electron microscope (SEM) and energy dispersive X-ray (EDX), respectively. SEM images showed that no major voids or clusters were observed between the two phases of polymer and filler. Improved CO2 and CH4 gas permeance were found for composite membranes, as compared to PSf membrane coated only by PDMS and PEBAX solutions. Besides, highest improvement of CO2/CH4 ideal selectivity was obtained for composite membrane loaded with 10 wt% of filler. High porosity and high CO2 affinity of NH2-MIL- 125 (Ti) are the main reasons for the enhancement of CO2 removal from CH4. Hence, further research work is necessary to explore the performance of this membrane at various operating conditions such as temperature, flow rate, and CO2 feed concentration
Industrial application of membrane distillation technology using palm oil mill effluent in Malaysia
No full textThe palm oil industry plays a vital role in the nation's economy. Regardless of the high revenue generated, the main problem in the palm oil industry is its substantial amount of wastes including empty fruit bunches, oil palm trunks and in particular palm oil mill effluent (POME) which can be harmful to the environment if discharged without being treated. The objective of this work is to introduce advanced wastewater treatment technology, which is membrane distillation (MD) technology for the effluent treatment system. PVDF hollow fibre membranes were fabricated via wet spinning technique and characterized using Scanning Electron Microscope (SEM) and contact angle goniometer. The fabricated membranes were then tested in direct contact membrane distillation (DCMD) system using anaerobic POME as the feed solution. The effluent was analyzed before and after treatment with DCMD. The parameters included biological oxygen demand (BOD), chemical oxygen demand (COD), ammonia nitrogen (AN), nitrate-nitrogen (NN), total suspended solids (TSS), total dissolved solids (TDS), colour and turbidity. A preliminary test was carried out using distilled water before continuing with anaerobic POME as a feed solution. The average permeate flux obtained by the PVDF membrane is 2.509 kg/m2.hr with slight flux decline that is probably due to the attachment of biological compounds on the membrane pores. It was found that at least 90% rejection was obtained for almost all water quality parameters tested with the values were all lower than that of the standard set by the local authority. In a conclusion, it can be said that MD demonstrated excellent performance in treating palm oil wastewater to produce water of high quality
Behavioral intention model for green information technology adoption in Nigerian manufacturing industries
No full textPurpose: Greenhouse effects and the need for cost savings necessitate that an organization's information technology (IT) managers design IT equipment acquisition and service provisioning policies to reduce carbon footprint and cost. Analyzing the influencing factors that influence stakeholders' attitudes toward adopting green information technology (Green-IT) is an important input in designing these policies. In essence, the research aims to investigate into the relationship between these factors and how they influence policy-makers' behavior in Nigerian manufacturing industries. Design/methodology/approach: The study develops a model based on the norm activation model (NAM) and the theory of planned behavior (TBP) to investigate the factors that influence decision-makers' intention in adopting Green-IT. A quantitative approach using a survey method is carried out to gather opinions of IT decision-makers using a random sampling technique. Partial least squares structural equation modeling (PLS-SEM) technique is applied to test the structural model and measurement model. Findings: The study's findings support the use of the behavior model for Green-IT adoption. The study's finding indicates that subjective norms, perceived behavior control (PBC), manager's attitude, personal norm (PN), awareness of adverse consequences and the ascription of responsibility (AR) positively influence intention to adopt Green-IT. Research limitations/implications: The development and validation of the model are the study's theoretical contributions. The study reviewed the existing literature on the utilization of Green-IT to better understand the intention to adopt Green-IT in Nigeria. It added to the literature by identifying factors that can influence it as well as theoretical underpinnings that can fit the intentions of decision-makers. The scientific community and the industrial companies would have the chance to investigate how this integrated behavioral intention model promotes the use of Green-IT. The research predictors explained about 70.20% of the variance in the behavioral intention to adopt Green-IT. Practical implications: Research offers practical implications and recommendations for top management practitioners of the manufacturing industries. Business leaders can use the results of this study to develop an effective strategic IT policy for the successful adoption of Green-IT practices for enhanced productivity. The study found that decision-makers' Green-IT attitudes had a substantial impact on their behavioral intention to adopt Green-IT. The study highlighted the importance of the top management attitude toward green products to facilitate the adoption of Green-IT practices in manufacturing industries in Nigeria. Thus, the positive and significant attitude of policy-makers is a necessary tool toward the successful adoption of Green-IT. Therefore, to foster an environmentally sustainability friendly atmosphere, Nigeria's manufacturing industries shall strive to strengthen the decision-makers' attitude toward practicing Green-IT in their respective domains. The findings showed that AR, AQ, environmental concern (EC), perceived behavior and perceived behavior are critical factors to be considered in an organization. Social implications: According to the findings, an individual's Green-IT attitude has a substantial impact on the environment as social behavior. As a result, the positive and essential attitude of the social sector is a key tool for efficient Green-IT implementation. Nigeria's social activists must try to create awareness campaigns to boost decision-makers' attitudes toward implementing Green-IT in their various regions to develop a friendlier environment. Thus, the identified factors can be of great help to the social sector in designing and implementing successful environmental-friendly policies that could support the adoption of Green-IT practices. Originality/value: The current research look at Green-IT adoption in manufacturing industries of West African countries. The study offers practical implications and recommendations for top management practitioners of the manufacturing industries, government policy-makers and organizations to enhance the use of Green-IT for mitigating environmental degradation. Recommendations for future research are stated as concluding remarks
Automated knee bone segmentation and visualisation using mask RCNN and marching cube: Data from the osteoarthritis initiative
Get PDFIn this work, an automated knee bone segmentation model is proposed. A mask region-based convolutional neural network (RCNN) algorithm is developed to segment the bone and reconstructed into 3D object by using Marching-Cube algorithm. The proposed method is divided into two stages. First, the Mask RCNN is introduced to segment subchondral knee bone from the input MRI sequence. In the second stage, the segmented output from Mask R-CNN is fed as input to the Marching cube algorithm for the 3D reconstruction of knee subchondral bone. The proposed method achieved high dice similarity scores for femur bone 95.35%, tibia bone 95.3%, and patella bone 94.40% using a Mask R-CNN with Resnet-50 as backbone architecture. Improved dice similarity scores for femur bone 97.11%, tibia bone 97.33%, and patella bone 97.05% are obtained by Mask RCNN with Resnet-101 as backbone architecture. It is noted that the Mask RCNN framework has demonstrated efficient and accurate knee subchondral bone detection as well as segmentation for input MRI sequences
A review on the effects of various elemental doping and nanostructuring of β-FeSi2/Si composites on the thermoelectric performance enhancement
No full textThermoelectric device is a transformative technology for renewable energy generation. It is designed in a small compact feature with a quiet mechanism and has no gas emission, enabling it to conserve energy and preserve the global environment. Iron disilicide (β-FeSi2/Si) composite materials prepared by eutectoid reaction is found promising for thermoelectric applications. The strategies to enhance the thermoelectric properties of these composites are mainly by conducting band structure engineering such as elemental doping and nanostructure engineering. This article reviews the effects of elemental doping and nanostructuring of β-FeSi2/Si composite materials. Mn and Co were found to be the most common dopants due to their contribution to carrier mobility and carrier concentration. Other dopant elements such as Al, introduced point defects on the composite structure that is effective in lowering thermal conductivity. P was also found to be effective on both electronic and lattice contribution of thermal conductivity. Furthermore, in thin films structure, flatter surface is highly recommended rather than the crystallinity of films to enhance carrier mobility and suppress thermal conductivity. Thus, the way to optimize β-FeSi2/Si composite materials were discovered by the significant number of studies on refinement of composite structure into nanoscale. From these studies, electrical conductivity value was significantly enhanced while reducing its thermal conductivity and Seebeck coefficient value. The deterioration of Seebeck coefficient is yet to be improved hence was estimated possible by introducing doping mechanisms. Therefore, this review concludes some potential strategies for improving Seebeck coefficient while simultaneously increasing electrical conductivity and decreasing thermal conductivity towards enhancing its thermoelectric performance
Impacts of sodium bicarbonate and co-amine monomers on properties of thin-film composite membrane for water treatment
Get PDFPolyamide (PA) thin-film composite (TFC) nanofiltration (NF) membranes are widely used for the treatment of water and wastewater treatment. However, the membrane surface properties could be further modified during interfacial polymerization (IP) process to achieve higher water flux and salt rejection. Herein, the effects of sodium bicarbonate (NaHCO3) and co-amine monomer—2-(2′aminoethoxy) ethylamine (AEE) on the characteristics of piperazine (PIP)-based TFC membranes were investigated for water purification and aerobically treated palm oil mill effluent (AT-POME) treatment. Characterizations based on field emission scanning electron microscopy (FESEM), Fourier transform infrared analysis (FTIR) and contact angle were carried out to provide support to the filtration results. Our findings showed that 0.5 wt% NaHCO3 was the best loading to be added to improve the membrane performance by enhancing water permeability by 37% without affecting Na2SO4 rejection. In the presence of 0.5 wt% NaHCO3, it is found that the introduction of AEE into PIP solution could further improve the Na2SO4 rejection of PIP-based membrane from 97.1 to 98.5% while producing a permeate of better quality. Further evaluation using AT-POME indicated that the AEE-modified membrane was able to enhance the separation performance of PIP-based membrane, increasing its conductivity, colour (ADMI) and COD reduction from 74.31, 92.79 and 83.4%, respectively, to 79.15, 94.26 and 89.3%. This work demonstrated the positive features of using inorganic additive and secondary amine monomer in improving characteristics of TFC membrane for water and wastewater treatment
Removal of arsenic from wastewater by using different technologies and adsorbents: a review
No full textA lot of anthropogenic activities can discharge arsenic into the ecosystem such as industrial wastes, incineration of municipal, pesticide production and wood preserving. In addition, most arsenic soluble species can enter surface waters via runoff and leach into the groundwater. Around forty million people from all over the world are affected by arsenic through drinking water above the maximum contaminant level of 0.01 mg/L. The affected by inorganic arsenic through drinking water can cause a lot of diseases especially a unique peripheral vascular disease and blackfoot disease. These diseases usually cause gangrene and end with amputation of the legs and can also cause severe systemic atherosclerosis. In addition, the wastewater treatment techniques can be divided into two groups, adsorbents and membrane separations such as electrodialysis, nanofiltration and reverse osmosis. Furthermore, most of these techniques do not function at a low level of concentration, so that moderate to high levels of concentration are required. However, the use of some of these arsenic removal approaches is costly because they require a lot of energy and reagents. Moreover, this review discusses readily adsorption technologies that have been applied to remove arsenic from wastewater along with an analysis of arsenic chemistry and contamination. This review is also focused on the removal of arsenic from wastewater using different adsorbents such as iron, aluminium, natural and biological adsorbents. Its goal is to increase our fundamental understanding of this developing research subject and to identify future research and development strategies for sustainable and cost-effective arsenic adsorption technology