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This paper proposes a Hexagonal Circular Photonic Crystal Fiber (HC-PCF) with uniform tiny air holes around the core, obtaining ultra-high negative dispersion, high Birefringence and low confinement loss. The modal properties of the HC-PCF are investigated using the full-vectorial-Finite Element Method (FEM) with a perfectly matched layer as the boundary condition. The numerical investigations show ultra-high negative dispersion of −25503 ps/nm.km, a birefringence of 1.36×10-2, a nonlinearity of 38.34w-1km-1 and a confinement loss of 4.95×10-3 dB km-1 at the wavelength of 1550 nm. Moreover, the proposed HC-PCF exhibits a high negative dispersion and multiple Zero Dispersion within the 800 to 2000 nm wavelengths range. Specifically, the high negative dispersion, low optical Confinement Loss, and multiple Zero Dispersion Wavelengths (ZDW) give the proposed HC-PCF high Power Spectral Density (PSD). These characteristics make the proposed fibre suitable for long-range telecommunication networks and optical sensing.

This paper presents the design and numerical analysis of a circular photonic crystal fiber (PCF) biosensor based on surface plasmon resonance (SPR) for aqueous environments. The proposed biosensor has a geometrical design that uses the cladding at the fiber's exterior to simplify the gold and analyte deposition process. The sensing characteristics and performances of the proposed biosensor are fully investigated with the finite element method. The investigation indicates that the plasmonic material's thickness affects the biosensor's performance. The results of the proposed biosensor demonstrate a wide refractive index range of 1.30 to 1.44, with the highest wavelength or spectral sensitivity as 21,000 nm/RIU and the highest resolution as 9.09×10−6 RIU. The proposed biosensor has a straightforward geometric profile. The proposed biosensor is stable and suited for aqueous surroundings and wide-range detection applications.

Electricity ranks among the world’s most plundered commodities. The fraudulent act of acquiring electrical power without paying for it is termed electricity theft. Electricity theft is captured in power distribution systems as non-technical losses (NTL), representing a major loss in revenue for power utility companies. Electricity theft has far-reaching financial consequences owing to unrealised revenue, and this has a knock-on effect on both developed and developing countries because electricity represents a major part of a country’s GDP and facilitates other industries. AMI-based smart energy meters (SM) gather large amounts of electricity consumption (EC) data that power utilities can utilise to monitor and detect fraudulent customers. This EC data is fed to a machine learning (ML) based electricity theft detection model to learn the behaviour of fraudulent customers. However, existing ML-based electricity theft detection (ETD) models do not produce the best outcomes because of; consecutive missing values in EC datasets, data class imbalance problems, inappropriate hyperparameter tuning of ML models, etc. This research introduces an ETD model using an extremely randomised trees classifier to detect electricity theft in smart grids efficiently. SMOTE Tomek sampling is used to deal with the data class imbalance, and the grid search optimisation technique is employed to optimise the hyperparameters of the proposed model. The proposed model shows its capacity to detect electricity theft by obtaining 98%, 95.06%, 98%, 97%, 98%, and 99.65% accuracy, Matthew’s correlation coefficient, detection rate, Precision, F1-score, and area under the curve receiver operating characteristic, respectively.

In this work, we report for the first time, a simplex index guiding circular PCF (CPCF) structure operating from 1350 nm to 1650 nm wavelength, which exhibits a highly negative dispersion of −13686.5 ps/(nm-km) at the telecommunication wavelength of 1.55 µm. The proposed CPCF exhibits a birefringence of 2.26×10-2, with a low confinement loss of 4.7×10-5 dB/km, which is suitable for ultra-high speed broadband dispersion compensation in long-distance transmission as well as supercontinuum generation, polarization maintenance, and sensing applications.

A novel method for estimating the passenger densities of minivan taxis popularly known as Trotros in Ghana is proposed. A smartphone is used to collect time, location and velocity data from groups of passengers travelling in parts of the Kumasi Metropolitan Assembly, Ghana. Passengers are clustered by four different combinations of their location, time and average acceleration data using the agglomerative hierarchical clustering algorithm. A classification method was then used to externally validate the results by comparing the cluster labels to an initial class labelling which had been assigned during data collection called the group code. The count of the group code represented the estimated number of passengers aboard the vehicle. Results from the various clustering combinations performed indicated that using the time and location variables only for classification gave the highest accuracy of about 89.2% as compared to the other combinations. The proposed method of counting passengers in moving vehicles is particularly useful in the Ghanaian context due to the fact that trotros do not have to be retro-fitted with expensive devices for data collection and thus can be implemented without financially burdening the privately-owned trotro industry. Also, counting passengers in trotros adds to the growing pool of trotro research data which is beneficial for improvements in the trotro industry and also for future research.

In this paper, the random departure of intercity public transport buses in Ghana which is influenced by the arrival patterns of passengers is shaped into a deterministic one with regular departure intervals based on the dynamic rate leaky bucket algorithm. This is essential because a regulated bus departure system is capable of providing transit information to travellers thereby reducing passenger waiting times. The arrival patterns of passengers were simulated and grouped into three blocks based on the frequency of arrival of passengers. The three blocks represented peak seasons, moderate seasons and off-peak seasons. Departure intervals were then calculated to suit each block. The recommended fleet size required to maintain the departure consistency was also determined based on the fixed departure interval and the round-trip time. The second phase involved developing a bus schedule based on the determined fixed departure intervals and recommended fleet size. The results as depicted with graphs showed that the fixed departure schedule reduced passenger waiting times. The minimum fleet size could also give transit operators an idea of how to manage their fleet to efficiently use resources. The KNUST Campus shuttle was used as a test case for the model. A fixed departure schedule was developed based on the proposed model. Simulation results showed a significant reduction in passenger waiting times for various passenger arrival patterns and also proposed a recommended fleet size and bus capacity.