Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to overcome many of the challenges associated with their farming activities by enabling precise and timely decision making on the basis of data that are observed and subsequently converged. In this regard, Artificial Intelligence (AI) holds a key place, whereby it can assist key stakeholders in making precise decisions regarding the conditions on their farms. Machine Learning (ML), which is a branch of AI, enables systems to learn and improve from their experience without explicitly being programmed, by imitating intelligent behavior in solving tasks in a manner that requires low computational power. For the time being, ML is involved in a variety of aspects of farming, assisting ranchers in making smarter decisions on the basis of the observed data. In this study, we provide an overview of AI-driven precision farming/agriculture with related work and then propose a novel cloud-based ML-powered crop recommendation platform to assist farmers in deciding which crops need to be harvested based on a variety of known parameters. Moreover, in this paper, we compare five predictive ML algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM)—to identify the best-performing ML algorithm on which to build our recommendation platform as a cloud-based service with the intention of offering precision farming solutions that are free and open source, as will lead to the growth and adoption of precision farming solutions in the long run.

The generation and integration of photovoltaic power plants (PVPPs) into the utility grid have increased dramatically over the past two decades. In this sense, and to ensure a high quality of the PVPPs generated power as well as a contribution on the power system security and stability, some of the new power quality requirements imposed by different grid codes and standards in order to regulate the installation of PVPPs and ensure the grid stability. This study aims to investigate the recent integration requirements including voltage sag, voltage flicker, harmonics, voltage unbalance, and frequency variation. Additionally, compliance controls and methods to fulfill these requirements are developed. In line with this, a large-scale three-phase grid-connected PVPP is designed. A modified inverter controller without the use of any extra device is designed to mitigate the sage incidence and achieve the low-voltage ride-through requirement. It can efficiently operate at normal conditions and once sag or faults are detected, it can change the mode of operation and inject a reactive current based on the sag depth. A dynamic voltage regulator and its controller are also designed to control the voltage flicker, fluctuation, and unbalance at the point of common coupling between the PVPP and the grid. The voltage and current harmonics are reduced below the specified limits using proper design and a RLC filter. The obtained results show that the proposed controller fulfilled the recent standard requirements in mitigating power quality (PQ) events. Thus, this study can increase the effort towards the development of smooth PVPP integration by optimizing the design, operation and control strategies towards high PQ and green electricity.

Water is a quintessential element for the survival of mankind. Its variety of uses means that it is always in a constant state of demand. The supply of water most primarily comes from large reservoirs of water such as lakes, streams, and the ocean itself. As such, it is good practice to monitor its quality to ensure it is fit for human consumption. Current water quality monitoring is often carried out in traditional labs but is time consuming and prone to inaccuracies. Therefore, this paper aims to investigate the feasibility of implementing an Arduino-based sensor system for water quality monitoring. A simple prototype consisting of a microcontroller and multiple attached sensors was employed to conduct weekly onsite tests at multiple daily intervals. It was found that the system works reliably but is reliant on human assistance and prone to data inaccuracies. The system however, provides a solid foundation for future expansion works of the same category to elevate the system to being Internet of Things (IoT) friendly.

This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.

With the recent emphasis on environmental protection, electric vehicles (EVs) have gained popularity in regions such as Europe, America, China, and Australia; due to their `zero tail-pipe emission' and low maintenance cost. This paper aims to investigate the feasibility of introducing EVs into the Brunei market using life cycle cost analysis, as well as identify dominant factors that influence its feasibility. Although local data have been used, methodologies adopted in this paper are applicable and directly transferable for analysis of other markets. Our analysis has shown that EV is currently still expensive as compared to the Internal Combustion Engine Vehicles (ICEVs) and Hybrid Electric Vehicles (HEVs) in the market; with its acquisition cost, contributing much to its Life Cycle Cost (LCC). In order to promote EVs and make other types of vehicles less desirable, it is proposed that a direct government subsidy be introduced as well as the current gasoline price to be increased. It has been shown that the initial subsidy of USD$4100 and increasing gasoline price to USD$0.70/litre, would allow EVs to compete comfortably in the market. This subsidy can be gradually reduced with time, as EV becomes cheaper due to the expected reduction in battery price. Environmentally, however, the current greenhouse gas (GHG) emissions from EVs turn out higher than ICEVs, considering the energy chain. In this regard, cleaner renewable energy sources need to be considered and improvement in power plant efficiency needs to be made, to make EVs more environmentally competitive to conventional vehicles. In conclusion, the government needs to look into financial incentives such as subsidy and increasing gasoline price to improve the feasibility of EVs in the market, as well as to improve efficiency of the energy generation and transmission to derive the full benefit of EVs.