This paper presents the implementation of natural gas vehicles (NGVs) in Tanzania’s road transportation sector. The peculiarity of this analysis is the evaluation of the technical and economic performance of the converted gasoline and diesel engines to use compressed natural gas (CNG) as the cleanest-burning hydrocarbon. The technical performance involved vehicle mileage (MiCNG), fuel consumption (Fcons), speed drop, engine fuel enhancement (Fenh), and fuel saving, while the economic performance involved conversion cost (Cc), fuel cost saving (FCsaving), and payback (PB). Considering the conversion of gasoline vehicles, the MiCNG could reach an average of 100 to 500 km per filling, depending on the CNG cylinder size. The Fenh and fuel saving were ranging between 1.9 and 3.9 and 71 and 78%. With a proportion of 30:70 diesel-CNG fuel, the heavy-duty truck with 180 kg of CNG could reach 1300 km, saving about 440 L, which is 78.6% per roundtrip, while the medium passenger car with 15 kg of CNG could reach 350 km, presenting a fuel saving of about 75%. From an economic point of view, gasoline retrofitted NGVs cost about 50 to 200 TZS/km, yielding a fuel cost saving of up to 79% and starting to pay off between 2 and 7 months or 10,000 and 40,000 km, depending on the engine capacity. Considering dual fuel, the heavy-duty truck consumes about 496 TZS/km, saving about 62.3% of diesel fuel and starting to pay off after 2.5 months or 29,304 km. To conclude, NGV technologies have been successfully implemented in Tanzania’s road transportation sector, presenting significant fuel savings and reducing reliance on imported oil. While taking measures, this study paves a way for Tanzania and other sub-Saharan countries to promote NGV growth.

The presence of Poisson noise in medical X-ray images leads to degradation of the image quality. The obscured information is required for accurate diagnosis. During X-ray image acquisition process, weak light results into limited number of available photons, which leads into the Poisson noise commonly known as X-ray noise. Currently, the available X-ray noise removal methods have not yet obtained satisfying total denoising results to remove noise from the medical X-ray images. The available techniques tend to show good performance when the image model corresponds to the algorithm’s assumptions used but in general, the denoising algorithms fail to do complete denoise. X-ray image quality could be improved by increasing the X-ray dose value (beyond the maximum medically permissible dose) but the process could be lethal to patients’ health since higher X-ray energy may kill cells due to the effects of higher dose values. In this study, the hybrid model that combines the Poisson Principal Component Analysis (Poisson PCA) with the nonlocal (NL) means denoising algorithm is developed to reduce noise in images. This hybrid model for X-ray noise removal and the contrast enhancement improves the quality of X-ray images and can, thus, be used for medical diagnosis. The performance of the proposed hybrid model was observed by using the standard data and was compared with the standard Poisson algorithms.

The architectural design of deep learning models significantly influences their predictive capabilities in environmental monitoring tasks. This paper investigates the individual and collective effects of residual learning and feature fusion mechanism to improve the performance of soil moisture estimation on the designed architecture of the deep learning model. In this study, the data fusion mechanism was used to integrate Normalized Difference Water Index (NDWI), Synthetic Aperture Radar (SAR), and satellite imagery datasets containing Red, Green, and Blue (RGB) color channels, which consist of images or data collected by a radar system that uses microwaves to produce images of the Earth's surface. Three model variants were developed, each selectively omitting one or more of these architectural elements, and their performance was evaluated using three standard metrics, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). The results of the final proposed model architecture showed that while each component contributes to accuracy improvements, the combination of residual learning and feature fusion yields the most significant gains. Improved results of RMSE = 0.0117, R²=0.814 and Mean Absolute Error=0.0148 were obtained. These performance indicators were superior to the results of most of the baseline models after comparative analysis. Thus, this study provides insights into model component selection for deep learning soil moisture prediction applications.

The recent development of High-Speed Trains (HST) operations requires real-time information transfer, high network capacity, and reliable communication for train signalling and control purposes. Rail operators face challenges as the high-speed railroad traffic grows. In a high-speed driving profile, the communication must be able to overcome fast handover, large Doppler shift, high penetration losses, limited visibility in tunnels, and the harsh electromagnetic environment. Global System for Mobile Communications for Railway (GSM-R) is still a standard-bearer for railway signalling communications. The link capacity and coverage prediction of the predefined Base Transceiver Station (BTS) locations are simulated using the Radio Planner RF planning tool and Matlab®. The terrain map and the signal levels along the railway corridor are observed. The analysis of the GSM-R for a fast-moving train is performed. Signal level coverage prediction, Doppler-shift effect and the received carrier frequencies due to Doppler shift along Dar–Pugu stations have been presented. The critical sites (locations) with weak signals of the first three BTSs have been identified. It is also observed that the Doppler effect may limit the proposed speed.