Applied Research and Smart Technology (ARSTech)
Aims International Journal of Applied Research and Smart Technology (ARSTech) is a peer-reviewed, biannual journal that promotes the development and application of smart technologies in various sectors, such as mechanical & materials engineering, automotive & manufacturing process, energy conversion & renewable energy, robotics, mechatronic & artificial intelligent, chemical & biomedical engineering, marine & aerospace technologies, transportations, infrastructures and environment. Smart technologies offer practical and sustainable solutions in the modern life of humankind by employing the latest technological advancements. Scope The journal presents and disseminates new developments and the latest findings in all fields of engineering and technology, especially those that contribute to the implementation of smart technologies. The topics covered by the journal include but are not limited to: autonomous systems, mechatronics and robotics, control systems in automobiles and intelligent transport systems, smart structures, materials, and metallurgy nanotechnologies and advanced materials in engineering application, sustainable and green buildings, green technology and industry 4.0, IoT-based systems, sensor network, artificial intelligence and smart grids, biomedical engineering, bioenergy technologies, design and development of automotive technologies and manufacturing process, vehicle modelling and safety, modelling and simulation (CFD) in engineering application, vehicle design and aerodynamics, applied mechanics, structure and manufacturing technology, material processing and technology for vehicles and other mechanical use, coatings technologies in engineering application, engine technologies and development for vehicles and other engineering application, hybrid and electric vehicle technologies, vehicle braking and suspension systems, thermodynamics application in engineering application, combustion and reacting flows in automotive and other engineering application, applied heat and mass transfer, fluid and thermal engineering, heating and cooling systems (HVAC) in vehicles and engineering application, fuels and lubricants in automotive engineering, development of energy conversion and conservation, new-and-renewable energy, and alternative energy in engineering application, fuel cell and solar energy, the engine technology and emission control, automotive pollution and control, vehicle motion and control systems, noise and vibrations control, pneumatic and hydraulic systems, tribology in engineering application.
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<![CDATA[The effects of diffuser profile on the performance of the liquid-gas ejector ]]>
<![CDATA[Salim, Amat Agus]]>;
<![CDATA[Sugati, Daru]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 4 No. 2 (2023): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v4i2.1456
<![CDATA[Kinetic energy originating from liquid jets at high speed can be used as an energy source for liquid-gas ejector devices. An ejector is a tool often used to support one of the processes in the industry, such as vacuum process, desalination, distillation, and refrigeration. The ejector consists of several main components: the nozzle, suction chamber, mixing chamber or throat, and diffuser. These components influence each other, so that system performance is sensitive to the performance of these components. The diffuser functions as a dynamic head converter into a static head. Its performance is affected by its dimensions, so it needs to be investigated. This study aims to determine the effect profile of a diffuser with a divergence angle of 2β 7° and a diffuser with a tiered divergence angle of 2β. This study uses an experimental method with a motive flow pressure for the primary fluid of 201.32 kPa. This study found that changes in length and the angle of divergence of the diffuser affect the value of the pressure recovery coefficient and efficiency.]]>
<![CDATA[The effects of higher bioethanol blends on greenhouse gas emissions from the UK passenger car fleet at various time horizons during the transition to net zero: A review ]]>
<![CDATA[Marchant, Denis]]>;
<![CDATA[Christensen, Jesper]]>;
<![CDATA[Davies, Huw]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 4 No. 2 (2023): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v4i2.1775
<![CDATA[There is a need to minimise the Greenhouse Gas Emissions (GHG) of petrol-powered cars during the transition to net zero. This research examines the effects on GHG from the recent adoption of E10 as the standard 95-octane petrol grade in the United Kingdom (UK). Also, it considers the potential of using higher bioethanol blends within the national car fleet and the effect of increased lifetime mileage due to the growing incidence of extended vehicle ownership. A comprehensive fleet turnover model and a separate numerical model to predict the GHG emissions for various powertrain types using different bioethanol blends were developed. Sensitivity studies that model the effects of different annual mileage using E10 and applying the proposed UK fleet composition scenarios at 10-year intervals from 2020 to 2050 were conducted. The results support the claimed percentage reduction of GHG emissions arising from the UK petrol car fleet using E10 when compared to E5 and establish that using a higher bioethanol blend such as E15 would provide still further reductions in most instances except in the case of plug-in hybrid vehicles where an increase in GHG emissions was observed at the 2030 and 2040 time horizons. An increase in annual mileage creates a linear increase in GHG emissions, although the rate of increase is not the same for each propulsion type. Such an increase can potentially disrupt the achievement of the UK's 2050 net zero target and future periodic carbon budgets.]]>
<![CDATA[Identification of stock market manipulation using a hybrid ensemble approach ]]>
<![CDATA[Quinn, Pearse]]>;
<![CDATA[Toman, Marinus]]>;
<![CDATA[Curran, Kevin]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 4 No. 2 (2023): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v4i2.2576
<![CDATA[Anomaly detection in time series data is a complex data mining issue with many useful, real-world applications. Anomalies in datasets represent deviations in the expected behaviour of a system and can indicate rare but significant events that require intervention. Market manipulation is a serious issue in financial jurisdictions worldwide, with financial regulators such as the SEC constantly trying to prevent it and prosecute those guilty of it. This paper makes use of state-of-the-art deep learning techniques as well as more classical statistical techniques in order to detect anomalies in five real-world datasets. The predictions of these models are then aggregated in two different ensemble models. The results of the individual models as well as the ensemble models, are evaluated, and F1-Score measures performance. Nine individual models, consisting of three models based on LSTM with Dynamic Thresholding, three ARIMA models and three Exponential Smoothing models, were used to generate predictions of anomalies based on daily trading volumes. The individual predictions of these models were then aggregated, with two different ensemble methods being used, namely the majority voting ensemble method and the ensemble averaging aggregation method. While both performed well, the majority voting ensemble method was seen to be the superior method in this study, with an average F1Score of 0.494, compared to an F1Score of 0.414 for the ensemble averaging aggregation method.]]>
<![CDATA[The impacts of nanoscale silica particle additives on fuel atomisation and droplet size in the internal combustion engines: A review ]]>
<![CDATA[Balikowa, Amuza]]>;
<![CDATA[Effendy, Marwan]]>;
<![CDATA[Ngafwan, Ngafwan]]>;
<![CDATA[Wandera, Catherine]]>
<![CDATA[ Applied Research and Smart Technology (ARSTech) Vol. 4 No. 2 (2023): Applied Research and Smart Technology ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/arstech.v4i2.2759
<![CDATA[The combustion process in compression ignition (CI) engines is complex and affects their efficiency and emission levels. Internal combustion engines (ICE) are being studied to find better ways to burn fuel and produce less pollution to meet the growing demand for these qualities. However, one intriguing avenue is the utilisation of nanoparticle additives, such as silica nanoparticles, to enhance fuel atomisation and droplet size. This study aimed to comprehensively review the impact of silica nanoparticle additives on fuel atomisation and droplet size in internal combustion engines. This review explores the researchers' underlying mechanisms and experimental techniques to determine nanoparticle fuel additives' overall impact on engine performance. The results achieved from the literature study indicated that incorporating these nanoparticles (following the engine design and fuel formulations) can enhance combustion efficiency and reduce exhaust emissions, thereby contributing to developing more sustainable transportation and power production systems.]]>
