The explosive popularity of small-cell and Internet of Everything devices has tremendously increased traffic loads. This increase has revolutionised the current network into 5G technology, which demands increased capacity, high data rate and ultra-low latency. Two of the research focus areas for meeting these demands are exploring the spectrum resource and maximising the utilisation of its bands. However, the scarcity of the spectrum resource creates a serious challenge in achieving an efficient management scheme. This work aims to conduct an in-depth survey on recent spectrum sharing (SS) technologies towards 5G development and recent 5G-enabling technologies. SS techniques are classified, and SS surveys and related studies on SS techniques relevant to 5G networks are reviewed. The surveys and studies are categorised into one of the main SS techniques on the basis of network architecture, spectrum allocation behaviour and spectrum access method. Moreover, a detailed survey on cognitive radio (CR) technology in SS related to 5G implementation is performed. For a complete survey, discussions are conducted on the issues and challenges in the current implementation of SS and CR, and the means to support efficient 5G advancement are provided.

Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of software-defined networks and 5G.

Monitoring the condition of transformer oil is considered to be one of the preventive maintenance measures and it is very critical in ensuring the safety as well as optimal performance of the equipment. Various oil properties and contents in oil can be monitored such as acidity, furanic compounds and color. The current method is used to determine the color index (CI) of transformer oil produces an error of 0.5 in measurement, has high risk of human handling error, additional expense such as sampling and transportations, and limited samples can be measured per day due to safety and health reasons. Therefore, this work proposes the determination of CI of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. Results show a good correlation between the CI of transformer oil and the absorbance spectral responses of oils from 300 nm to 700 nm. Modeled equations were developed to relate the CI of the oil with the cutoff wavelength and absorbance, and with the area under the curve from 360 nm to 600 nm. These equations were verified with another set of oil samples. The equation that describes the relationship between cutoff wavelength, absorbance and CI of the oil shows higher accuracy with root mean square error (RMSE) of 0.1961.

The metal-assisted chemical etching (MACE) technique is commonly employed for texturing the wafer surfaces when fabricating black silicon (BSi) solar cells and is considered to be a potential technique to improve the efficiency of traditional Si-based solar cells. This article aims to review the MACE technique along with its mechanism for Ag-, Cu- and Ni-assisted etching. Primarily, several essential aspects of the fabrication of BSi are discussed, including chemical reaction, etching direction, mass transfer, and the overall etching process of the MACE method. Thereafter, three metal catalysts (Ag, Cu, and Ni) are critically analyzed to identify their roles in producing cost-effective and sustainable BSi solar cells with higher quality and efficiency. The conducted study revealed that Ag-etched BSi wafers are more suitable for the growth of higher quality and efficiency Si solar cells compared to Cu- and Ni-etched BSi wafers. However, both Cu and Ni seem to be more cost-effective and more appropriate for the mass production of BSi solar cells than Ag-etched wafers. Meanwhile, the Ni-assisted chemical etching process takes a longer time than Cu but the Ni-etched BSi solar cells possess enhanced light absorption capacity and lower activity in terms of the dissolution and oxidation process than Cu-etched BSi solar cells.

Multiprotocol label switching (MPLS) networks are packet‐based networks that offer considerable advantages, including improved network utilisation, reduced network latency, and the ability to meet the quality of service and strict level agreement requirements of any incoming traffic. A vast number of applications are now migrating to packet‐based conditions that cause increased pressure on network providers to change their systems. Innovations and improvements on MPLS are still on‐going to ensure that such networks can cater to the ever‐increasing bandwidth demand whenever required. This study provides a review of MPLS networks and their promising technologies, such as traffic engineering, protection and restoration, differentiated services, and MPLS‐transport profile (MPLS‐TP) and its applications. This work also reviews recent issues on MPLS networks and discusses the implementation of MPLS‐TP networks in the power grid. A review of recent literature shows that researchers should be careful in proposing new protocols or designs for MPLS to ensure that it achieves the most efficient and optimal performance. Furthermore, it can be concluded that although MPLS is a promising technology for future networks, there are challenges to overcome with regards to security and network flexibility, especially as far as migration to MPLS‐TP is concerned.