The usage of Unmanned Aerial Vehicles (UAV) in infrastructure projects has inclined drastic recently due to its benefits. However, there is a slowness in transition from conventional construction approach to new innovation. Besides, there are lots of issues in current practices such as cost overrun, dangerous, delay of time completion and lack of an overall comprehensive scientific system. Furthermore, although there was high level of awareness on the advantages of using UAVs, the Malaysian construction industry are still reluctant for the changes. In response, to overcome issues that the construction industry faces, such as infrastructure projects thus, this study aims to develop a conceptual framework for promoting the applications of UAV in Malaysia infrastructure projects. The methods adopted include explanatory sequential research methods. For instance, questionnaire surveys and interviews are used to collect quantitative and qualitative data, respectively. Besides that, a refined conceptual framework of the challenges and barriers of UAV adoption in Malaysia infrastructure projects were mainly contributed by the restrictive regulatory environment and industry point of view. For example, the privacy issues and guidelines on autonomous UAV and the industry lack of confidence in the adoption of UAV and education on the adoption of the UAV. In addition, it offers some solutions for the issues that have been revealed in the preliminary conceptual framework for the challenges on UAV adoption in Malaysia infrastructure projects. For instance, amaze the contractor with the final data from the UAV. Lastly, the identify solution served as a reference for other Malaysian construction stakeholders that have not used UAVs before. The recommendations for future scholarly research include the roles of UAV technology with other technologies such as Artificial Intelligent (AI) or integration of data with other software.

The COVID-19 pandemic has caused significant global suffering and mortality, and effective control measures have been elusive. This study aims to develop an accurate and reliable prediction model using deep neural networks (DNN) to estimate the epidemic size and trends of COVID-19 cases, as well as the effective reproduction number, R(t). The efficacy of various control measures for COVID-19 has been questioned, and an efficient prediction model can aid in decision-making and planning. Overfitting is a common issue in neural networks, which can limit their accuracy and reliability. A modified dropout regularization technique and particle swarm optimization (PSO) are employed to enhance the accuracy of the DNN. The proposed model outperforms conventional neural networks and previous studies in terms of accuracy and reliability. The estimated R(t) values are consistent with measured values, which demonstrates the usefulness of this model in analyzing the situation and informing effective intervention strategies. The developed dropout-DNN-PSO model is an accurate and reliable predictor of COVID-19 trends and R(t) values. It can aid decision-makers in planning and implementing effective control measures. The proposed model can be extended to other countries to analyze and predict the trends of COVID-19 cases.

The State Electricity Company supports 59% of the national electricity supply in Indonesia. The electricity supply from hydro energy is only 8.17%, so this value must be increased to reduce the supply of power plants sourced from fossil fuels. A water turbine is one of the main components in converting hydropower to electrical energy. A cross-flow type water turbine is one of the popular rotors in research in recent years. Several methods are used in turbine development research, including computational fluid dynamics and experimental methods. This research has been done using two methods: the experimental and CFD methods. The study was conducted on variations in the number of blades on the rotor, with variations in blades 3, 4, 6, 8, 10, and 12. This study aims to determine the effect of the number of blades on turbine performance. This study was carried out for five repetitions for each rotor. CFD analysis was performed using the Ansys Student version with the CFX solver. The meshing method is tetrahedral, with a speed limit of 4.91 m/s and outlet pressure according to room pressure. This study uses CFD to know the pressure contours and velocity streamlines. The results of this study were analyzed using a single-factor DOE. The most optimal number of blades is 3-blades producing 9.38 Watt of power, and the Coefficient of power is 0.0748. Analysis of Variance for the Single-Factor shows that the number of blades significantly affects the energy produced. Cross-flow rotor design with 3-blades is a good design alternative for hydro turbines.

The use of solar PV as an alternative to fulfill household-scale electricity needs has begun to be widely developed. However, the problem of investment costs and the location of solar PV placement for household scale is still a challenge in its implementation. The construction model of rooftop solar PV can affect the investment cost and performance of solar PV. In this paper, the triangle model of rooftop solar PV on grid with PLN (PT. Perusahaan Listrik Negara) electricity network is studied in terms of technology and economics to determine the feasibility of implementing 900 VA household-scale power plants. Testing the application of solar PV technology under solar radiation conditions in the city of Surabaya, Indonesia as a case study. Calculation of electricity production, energy savings, energy sales, and energy purchases to determine technological feasibility as well Net Present Value (NPV), Benefit Cost Ratio (BCR), and Payback Period (PP) to determine the level of economic feasibility. The results of the research of 1,5 KWP (kilowatt peak) solar PV technology on a household scale are able to meet energy needs and reduce PLN electricity purchases to 0% and can sell electrical energy by 13.96% / year of the total electrical energy produced. In addition, the NPV, BCR with a value greater than zero, and PP of 8.6 is less than 15 years which is the service life of solar PV, so solar PV/ PLN on grid is feasible to be implemented for household scale power generation models.

Limited funding in meeting the financing needs for the provision of toll road infrastructure makes the government need to look for alternative financing so that the project can still be carried out. Public Private Partnership (PPP) is an alternative option to overcome this problem. However, infrastructure projects with PPP schemes require large investments and a relatively long concession period, thus allowing risk uncertainty to arise as a result of decisions made during the project. Risk identification in a construction project is important at the beginning of the project. This study was aimed at identifying critical risks, determining risk allocation and risk mitigation strategies, and analyzing the interrelationship between critical risks. The case study was conducted on the Trans Sumatra toll road project in South Sumatra. Quantitative data (questionnaire survey) and qualitative data (interview) were collected from the toll-road private sector. The results of the study identified five critical risks in the construction phase (CP) of the toll road project along with the allocation of risks, namely the risks of geographical conditions (shared), delays in work progress (private sector), design errors (private sector), force majeure (shared) and weather conditions (shared). The mitigation strategy for the risks of geographical conditions and design errors was to redesign the construction structure by adjusting with the land conditions of the toll road. Force majeure (Covid-19) causing delays in work progress required rescheduling and recalculation of construction costs. Weather conditions (heavy rain) occurring on swampy land caused puddles/floods; this condition caused delays in construction work. The mitigation strategy that could be carried out was by coordinating with the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) in the region and preparing disaster management Standard Operating Procedures (SOPs). The cause and effect of each risk showed that the critical risks in the construction phase of the toll road project were interrelated to each other which the risks would finally contribute to the increase in the construction costs of the toll road project.

Vulnerability analysis has always been an integral part of disaster risk assessment. This study aims to assess the physical vulnerability of public school buildings against flooding. It discusses the vulnerable elements or attributes, the extent of damages incurred during flood events, the damage response, and the future steps that must be taken to increase flood resiliency. The study used empirical data collection using semi-structured interviews to characterize the common types of public schools and to collate the damage responses. Quantity surveying was performed to measure the amount of damages related to different flood depths. Structural damages to public school buildings are not expected for flood heights ranging from ground level up to ten (10) m. However, damages are incurred by building finishes and fixtures. Floors, walls, and septic system are cosmetically damaged. Vulnerable components include wooden elements like doors, cabinets, blackboards and ceiling, and electrical fixtures such as wiring, lighting, outlets, switches, and fire alarm system. Comprehensive vulnerability description of public school buildings were represented as curves of flood depth vs. damage index. The damage ratio decreases as the number of floors increase. Maximum damage to one-story building is 23.6% while at 15.02% for four-story school building. This study is an attempt to promote further research of the subject matter in developing countries towards flood resiliency in the built environment.

Bacterial cellulose (BC) is a fermentative product of Acetobacter xylinum characterized by high purity and crystallinity of up to 80%. Due to its excellent physical and mechanical properties, bacterial cellulose is increasingly interested in research, especially in the study of applying BC in different fields. Although it is a potential direction, the large-scale production of BC still has certain limitations, mainly the fermentation medium's high cost. Therefore, this study used pineapple waste as a carbon source for BC fermentation. After investigating the influence of fermentation factors on BC yield, this study focused on evaluating the crystallinity of BC under different fermentation conditions. The X-ray diffraction technique was used to determine the crystallinity, while Scanning Electron Microscopy was used to assess the differentiation of the BC structure. The study results showed that, at different fermentation conditions of temperature (25–35°C), time (5–10 days), and bacterial concentration (5–15%), the bacterial cellulose crystallinity was significantly different and in the range of 40.6 % to 83.4 %. The optimum crystallinity of BC was recorded when the experiment was set up at the fermentation temperature of 30°C, 13 days of fermentation time, and bacterial concentration of 14%, with the BC crystallinity being 82.2%.

Along with the development of the economy and society and the speedy increasing number of personal vehicles, especially motorcycles, many cities in developing countries like Hanoi (Vietnam) are facing many transportation problems, notably traffic accidents involving motorcyclists. Motorcycle accidents are caused by main human-error factors, and could be considered as the typical grey system with the features of complexity and imperfect information. In this study, a grey relational analysis (GRA) model of the human-error factors is presented to quickly explore the main human-error factors contributing to traffic accidents caused by motorcycles. In this model, the grey relational degree and the grey relational order of each human-error factor also are determined to rank the main contributing human-error factors. Taking the data of road traffic accidents caused by motorcycles in Hanoi, the capital of Vietnam between 2015 and 2017 as experimental data, the grey relational degrees of human-error factors have been analyzed quantitatively through the GRA model. The experimental results show the first three human-error factors include wrong lane shifting, poor road observation, and speeding, respectively. The results also help to conduct effective countermeasures for controlling human errors and reducing road traffic accidents.

The perseverance of dental implant system in restoration of occlusion is highly dependent on biomechanical overloading factors such as implant macro geometries, parafunctional oral habits, and material. Different implant materials could affect the load transfer at the bone-implant interface differently which is related to stress shielding phenomenon. To date, the role of various implant materials on the surrounding tissues as well as implant stability is still debatable and unclear especially when the implant failure is of concern. Through this study, implant body with different materials or stiffnesses that are zirconia, Ti-6Al-4V, cpTi, TiZr, and PEEK were investigated via 3-D FEA. The bone tissues were modelled based on CT image datasets and subsequently be processed in SolidWorks software. All geometries were converted into finite element models and analysed in ANSYS software. The bone and implant models were assigned with anisotropic and isotropic properties, respectively. A dynamic occlusal loading of 300 N and pretension of 20 N were applied on the implant body and screw, respectively. The results showed that the less stiff implant increased the bone stress and decreased the implant body stress values compared to the stiffer implant. Moreover, the implant with lower stiffness exhibited lower bone strain and higher implant deformation than the implant with higher stiffness. Of all implant materials analysed, PEEK is observed to be the most satisfactory. However, further modifications on PEEK would be necessary to improve inherent bioactivity and osseointegration.

The majority of research on biogas adsorption does not use biogas gas but by mixing CO2 and CH4 gas or (binary gas) or with additional other gases, such as N2 or H2S. In real application, the adsorption method of biogas purification is conducted by using its complex mixture of biogas to obtain the purer methane gas. Accordingly, in case of biogas purification, adsorption capacity of the adsorbent towards both CH4 and CO2 gases from a complex gas mixture of biogas is indispensable to be studied thoroughly for improving the existing purification method, such as the pressure swing adsorption (PSA) method. The objective of this experiment is to compare the adsorption of carbon-dioxide and methane from complex mixture of biogas by Zeolite 13X under pressurized condition using experimental data. The experiment was conducted using a specially designed experimental setup, and the data obtained was evaluated and analyzed using the Freundlich Isotherm Model. The results show that experimental study on the adsorption of CO2 and CH4 gas from complex mixture of biogas in Zeolite 13X was distinguishable by their values of Freundlich coefficient. CO2 and CH4 gas adsorption in zeolite vary directly with pressure raised to the power 1/n, where its value for CH4 adsorption was higher than CO2. However, within the range of applicable pressure, the equilibrium adsorption of CO2 in Zeolite 13X was still higher than that of CH4.