The objective of this study is to examine the use of ICT by autism care centres (ACC) in Malaysia during the pandemic as well as to know the ACC teachers' view on Virtual Teaching and Learning (VTL) platform. A preliminary study through site visits to four (4) selected care centres were carried out to get visual insight of ICT facilities used by them. An online survey was conducted to get feedback from the teachers on the perceived usefulness and their willingness to adopt the VTL. The site observation results found that all the care centres have basic ICT requirements that can help the teachers to access the internet and do e-filing records, but lack of ICT resources limits the VTL. The survey results reveal that more than 81% of the responding teachers are interested and support the implementation of VTL platform. They are confident that the platform able to aid them in VTL especially during pandemic. It is concluded that the objectives of the preliminary study are achieved and the VTL is very well accepted. Finally, further study on the digital model of teaching-learning that support the development of VTL platform is essential to move forward towards VTL implementation.
<span>In oil and gas industry, productivity is very important as the industry involves high cost and can be considered as a large-scale industry. Therefore, time and budget should be kept minimal to avoid loss to the oil and gas company. An example of lack of productivity in the industry is there are many complaints in the oil and gas industry that welders do not perform their job on time. Therefore, this project discussed about a system that can be used to monitor these welding stations. This system is important because it can help supervisors track the welding works from afar or anywhere using internet of things (IoT). To achieve that, a system must consist of hardware and software that are capable of connecting to the internet and monitor the welding works. In this project, the hardware chosen were Arduino Uno for data processing, ESP8266 to connect the microcontroller to the internet, voltage sensor to detect the voltage of the welding machine and a website to show the data taken. Other than that, this system was able to warn the welder of overvoltage of the welding machine. Thus, the system solved the problem of welders not performing their job on time. Supervisors were also able to monitor the job of welders to ensure maximum productivity. Based on the testing done on the system, the prototype was able to work as intended. The welding station monitoring system was able to detect welding usage, measure voltage values of welding and send the data to IoT for monitoring.</span>
<p class="0abstract">The use of lights are the command sources in our life, anytime and everywhere. On trending of demands, the alternative sources are important to increase the satisfaction on user requirement. While, saving energy is another issue to be considered on the light system designation. Therefore, this research provides the details on the implementation of the smart lamp with controlled light system based on Arduino and mobile application usage to save the energy. Smart lamp is a finest way to minimize and preserve light with the remote system in order to monitor and control the brightness of light. This research is created to design the energy saving smart light system via mobile application and devices. It is a minor prototype that is fully automated and controlled by Arduino board. The designation begin with identification of input of LDR sensor, PIR sensor and circuit setting. Next, the integration of hardware and software is implemented for Arduino program, mobile application and the devices. Lastly, the testing process is executed and the data is collected and analysed. The overall system has been experimentally validated with the scenario as setup. Through monitoring and controlling the light in such a way is always accurately matched to the actual need that allows to save on the energy usage and costs, as well as to improve the human comfort and efficiency.</p>
<span>Business Intelligence (BI) offered many advantages to organizations adopting the system such as improved decision making and boost organization’s performance. The lack of research on the continuous usage of BI in manufacturing motivates the initiative in this study to have an understanding of the determinants that influenced it. The study proposed a model of individual-related determinants that lead to the continuous usage of BI in manufacturing. A model integrating Unified Theory of Acceptance and Use of Technology (UTAUT) and Information System Continuance Model (ISCM) will be developed. The model will portray 20 hypotheses and 11 determinants leading continuance usage of BI. Data will be collected through survey questionnaires instrument and validated using Structural Equation Modelling (SEM). The result is hoping to show significant relationships between the determinants towards the continuous usage of BI in manufacturing. The study can potentially be used to guide manufacturers and practitioners for considerations in implementing BI in the manufacturing industry. </span>
<span>Manufacturing organizations implemented Business Intelligence (BI) due to many advantages offered by it. The lack of research on the acceptance of BI in manufacturing motivates the initiative in this study to have an understanding of the factors that influence the acceptance of BI in manufacturing sector. Therefore, the research proposes a model which indicates the acceptance factors of BI in manufacturing. An integrated model consisting of underlying models of Technology Acceptance Model (TAM), Expectation Confirmation Theory (ECT) and Task-Technology Fit (TTF) will be developed. The new model will formulate 19 hypotheses and 11 factors contributing to the continuance and acceptance of BI. The model will be tested using quantitative and qualitative survey conducted to Malaysian manufacturing companies and validated using Structural Equation Modelling (SEM) to investigate the causal and mediating relationships between the factors. The expected result is hoping to suggest that selected factors in the model are positively related towards the acceptance of BI in manufacturing. The results are also hoping to guide future initiatives by industrial practitioners to develop and distribute BI to the manufacturing market.</span>
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