Search Results (71)

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Nd–Fe–B permanent magnets are vital for numerous key technologies in strategic sectors such as renewable energy production, e-mobility, defense, and aerospace. Accordingly, the demand for rare earth elements (REEs) enormously increases in parallel to a significant uncertainty in their supply. Thus, research and innovative studies are focus on the investigation of sustainable solutions to the problem and a closed-loop value chain. The present study is based on two benign-by-design approaches aimed at decreasing the recycling loop span by preparing standardized batches of EoL Nd–Fe–B materials to be treated separately depending on their properties, as well as using mechanochemical method for waste processing. The previously reported benefits of both direct recycling and mechanochemistry include significant improvements in processing metrics, such as energy use, ecological impact, technology simplification, and cost reduction. Waste-sintered Nd–Fe–B magnets from motorbikes were collected, precisely sorted, selected, and pre-treated. The study presents a protocol of resource-efficient recycling through mechanochemical processing of non-oxidized sintered EoL magnets, involving the extraction of Nd₂Fe₁₄B magnetic grains and refining the material’s microstructure and particle size after 120 min of high-energy ball milling in a zirconia reactor. The recycled material preserves the main Nd₂Fe₁₄B magnetic phase, while an anisotropic particle shape and formation of a thin Nd/REE-rich layer on the grain surface were achieved. Full article

Motorbike taxis are widely used in Yaoundé and Douala, despite their association with heightened accident risks and relatively high fares. This research combines qualitative methods, including 38 semi-structured interviews and direct field observations, with a quantitative survey of 280 motorbike taxi passengers (customers). It employs a dynamic risk approach to analyse both the factors motivating individuals to choose motorbike taxis and the strategies adopted by drivers and passengers to mitigate and prevent accidents. The findings reveal that speed, cost-effectiveness, and the limited accessibility of certain neighbourhoods to other transport options are key factors driving regular motorbike taxi use. Moreover, strategies for managing accident risks include regulating passenger positions based on gender, perceived age, or physical stature; invoking deities for protection; and passengers’ verbal interactions with drivers to ensure safer behaviour. This research also explores how overloading, a collectively tolerated deviance, is managed to avoid or minimize the impact of accidents. By addressing both risk acceptance and prevention strategies, this study provides new insights into passengers’ social perceptions, which are often overlooked in motorbike taxi research. It expands the understanding of motorbike taxi use in urban Global South transport contexts, particularly in terms of users’ risk management behaviours. Full article

Traffic congestions due to construction and maintenance works of road infrastructure cause travel delays, unpredictability and less tolerant road users. Bicyclists are more flexible with road closures, shifting to alternative routes, public transport and other active transport depending on the infrastructure, quality and transport services. However, the mixed traffic environment near road closures increases the safety risks for bicyclists. Traditional traffic monitoring systems rely on costly and demanding intrusive sensors. The application of wireless sensors and machine learning algorithms can enhance the analysis and prediction ability of traffic distribution and characteristics in the proximity of road closures. This paper applies artificial neural networks (ANNs) coupled with a Generalised Delta Rule (GDR) algorithm to analyse the sensor traffic data before, during and after the closure of the Mill Road Bridge in Cambridge City in the United Kingdom. The ANN models show that the traffic volume of motorbikes (44%) and buses (34%) and the proximity of Mill Road Bridge (39%) are significant factors affecting bicycle traffic before the closure. During the bridge closure, the proximity of the bridge (99%) and traffic volume of large rigid vehicles (51%) are the most important factors of bicycle distribution in nearby streets leading cyclists to unsafe detours. After the reopening of the Mill Road Bridge, unclear signage caused continued traffic impact, with motorbikes (17%) and large vehicles (24%) playing the most significant role in the spatial distribution of bicycle traffic. This paper emphasises safety concerns from mixed traffic and highlights the importance of cost-effective sensor-based traffic monitoring and analysis of the sensor data using neural networks. Full article

Motorcycles are widely used vehicles in Indonesia, not only as private vehicles but also for public transportation. Currently, there are many applications that help customers to use motorbikes as public transportation and are known as “online ojek” applications. Although there have been many online ojek applications used by the public, there is no online ojek application specifically for women. This study uses the conjoint method to determine the attributes of female consumer preferences for online ojek applications so that further studies the result of this study as a reference for developing female online ojek applications. With a sample size of 130 respondents, the combination of attributes is formed using fractional factorial design. This study produces eight attributes and levels that are most desired by consumers: (1) colourless, 3D supporting icons with, (2) a primary application colour of pink, (3) button shapes with blunt rectangles, (4) bold text for the level selected in the thick and thin text design, (5) application menu containing homepage, promo, order, chat, profile, thick and thin text, (6) Indonesian application language, (7) payment menu containing accumulated prices and accumulated minutes and (8) 3D and colourful icons. Full article

Transportation mode selection is pivotal for navigating through cities plagued by heavy traffic congestion. This plays a crucial role in ensuring the efficient utilization of time and resources to achieve the desired objectives. Given the complex dynamics of urban mobility, strategically selecting a transportation mode can significantly mitigate delays and enhance overall productivity in densely populated areas. The objective of this study is to find the most efficient result among various transportation modes to make deliveries from different points on a university campus. To solve this problem, reinforcement learning was used and tested on the simulation environment SUMO. Traffic density was increased by using an equal number of different transportation modes, such as driving, cycling, motorbiking, and walking. Various traffic densities were generated, and different reward models were applied to select the best means of transportation. Various probability distributions were used as reward models to avoid the unfair distribution caused by how near or how far the road is when moving from random points to the destination region. As a result of the models created using the applied reward–penalty functions, it was determined that the best means of transportation in areas with a low traffic density is cycling, and in areas with high traffic density, the optimal mode of transportation is motorbiking. Full article

Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario. Full article

Timely and accurate detection of pavement cracks, the most common type of road damage, is essential for ensuring road safety. Automatic image segmentation of cracks can accurately locate their pixel positions. This paper proposes a Multi-Scale Feature Enhanced U-shaped Network (MFE-UNet) for pavement crack detection. This network model uses a Residual Detail-Enhanced Block (RDEB) instead of a conventional convolution in the encoder–decoder process. The block combines Efficient Multi-Scale Attention to enhance its feature extraction performance. The Multi-Scale Gating Feature Fusion (MGFF) is incorporated into the skip connections, enhancing the fusion of multi-scale features to capture finer crack details while maintaining rich semantic information. Furthermore, we created a pavement crack image dataset named China_MCrack, consisting of 1500 images collected from road surfaces using smartphone-mounted motorbikes. The proposed network was trained and tested on the China_MCrack, DeepCrack, and Crack-Forest datasets, with additional generalization experiments on the BochumCrackDataset. The results were compared with those of the U-Net model, ResUNet, and Attention U-Net. The experimental results show that the proposed MFE-UNet model achieves accuracies of 82.95%, 91.71%, and 69.02% on three datasets, namely, China_MCrack, DeepCrack, and Crack-Forest datasets, respectively, and the F1_score is improved by 1–4% compared with other networks. Experimental results demonstrate that the proposed method is effective in detecting cracks at the pixel level. Full article

Reducing CO₂ emissions is becoming a particularly important goal for motorcycle manufacturers. A fully electric transition still seems far away, given the difficulties in creating an electric motorcycle with an acceptable range and mass. This opens up opportunities for the application of hybrid powertrains in motorcycles. Managing mass, cost, and volume is a challenging issue for motorcycles; therefore, an MHEV architecture represents an interesting opportunity, as it is a low-complexity and low-cost solution. Firstly, in this work, an adequate sizing of the powertrain components is studied for the maximum reduction in fuel consumption. This is performed by analyzing many different system configurations with different hybridization ratios. A 1D simulation of the motorcycle traveling along the homologation cycle (WMTC) is performed, and the powerunit use strategy is optimized for each configuration using the Dynamic Programming technique. The results are analyzed in order to highlight the impact of kinetic energy recovery and engine load-point shifting on fuel consumption reduction. The results show the applicability of MHEV technology to road motorcycles, thus providing a useful tool to analyze the cost/benefit ratio of this technology. The developed methodology is also suitable for different vehicles once a specific test cycle is known. Full article

Electric mobility is pervasive and strongly affects everyone in everyday life. Motorbikes, bikes, cars, humanoid robots, etc., feature specific battery architectures composed of several lithium nickel oxide cells. Some of them are connected in series and others in parallel within custom architectures. They need to be controlled against over current, temperature, inner pressure and voltage, and their charge/discharge needs to be continuously monitored and balanced among the cells. Such a battery management system exhibits embarrassingly parallel computing, as hundreds of cells offer the opportunity for scalable and decentralized monitoring and control. In recent years, tiny machine learning has emerged as a data-driven black-box approach to address application problems at the edge by using very limited energy, computational and storage resources to achieve under mW power consumption. Examples of tiny devices at the edge include microcontrollers capable of 10–100 s MHz with 100 s KiB to few MB embedded memory. This study addressed battery management systems with a particular focus on state-of-charge prediction. Several machine learning workloads were studied by using IEEE open-source datasets to profile their accuracy. Moreover, their deployability on a range of microcontrollers was studied, and their memory footprints were reported in a very detailed manner. Finally, computational requirements were proposed with respect to the parallel nature of the battery system architecture, suggesting a per cell and per module tiny, decentralized artificial intelligence system architecture. Full article

Recent studies revealed that the high production of reactive oxidative species due to exposure to fine or ultrafine particles are involved in many chronic respiratory disorders. However, the poor standard of clinical data in sub-Saharan countries makes the assessment of our knowledge on the health impacts of air pollution in urban cities very difficult. Objective: The aim of this study was to evaluate the distribution of respiratory disorders associated with exposure to fine and ultrafine air particles through the changes of some oxidative stress biomarkers among motorbike drivers from two cities of Cameroon. Methods: A cross-sectional survey using a standardized questionnaire was conducted in 2019 on 191 motorcycle drivers (MDs) working in Douala and Dschang. Then, the activities of superoxide dismutase (SOD) and the level of malondialdehyde (MDA) were measured using colorimetric methods. The data of participants, after being clustered in Microsoft Excel, were analyzed and statistically compared using SPSS 20 software. Results: The motorbike drivers recruited from both cities were from 21 to 40 years old, with a mean age of 29.93 (±0.82). The distribution of respiratory disorders, such as a runny nose, cold, dry cough, chest discomfort, and breathlessness, was significantly increased among MDs in Douala. According to the results of biological assays, SOD and MDA were significantly greater among the MDs recruited in Douala compared to those of Dschang. The change in these oxidative stress markers was significantly positively correlated with the mobilization of monocytes and negatively correlated with neutrophils, showing the onset and progression of subjacent inflammatory reactions, and it seemed to be significantly influenced by the location MDs lived in. Conclusions: Through this study, we have confirmed the evidence supporting that the onset and progression of oxidative stress is caused by the long-term exposure to fine or ultrafine air particles among working people living in urban cities. Further studies should be conducted to provide evidence for the cellular damage and dysfunction related to the chronic exposure to fine particulate matter (PM) in the air among working people in the metropolitan sub-Saharan Africa context. Full article

Two rod-shaped, non-spore-forming, Gram-negative bacteria, strain KR3-3^T isolated from fresh soil in Korea and strain VNH31^T obtained from soil samples from motorbike repair workshop floors in Vietnam, were identified. Phylogenetic analysis utilizing 16S rRNA gene sequences revealed their affiliation with the family Sphingobacteriaceae and their relation to the genus Pedobacter, exhibiting 16S rRNA gene sequence similarities lower than 98.00% with all known species within the genus Pedobacter. Growth of VNH31^T and KR3-3^T was impeded by NaCl concentrations exceeding >0.5% and 1.5%, respectively, while they both thrived optimally at temperatures ranging between 25 and 30 °C. Notably, neither strain reduced nitrate to nitrite nor produced indole. Negative results were observed for the acidification of D-glucose and hydrolysis of urea, gelatin, casein, and starch. VNH31^T exhibited growth on β-galactosidase, sodium acetate, L-serine, and L-proline, whereas KR 3-3^T demonstrated growth on D-glucose, D-mannose, D-maltose, N-acetyl-glucosamine, sucrose, sodium acetate, L-serine, 4-Hydroxybenzoic acid, and L-proline. Core genome-based phylogenetic analysis revealed that the two isolates formed distinct clusters within the genus Pedobacter. The DNA G+C contents of KR3-3^T and VNH31^T were determined to be 44.12 mol% and 32.96 mol%, respectively. The average nucleotide identity and in silico DNA-DNA hybridization relatedness values (67.19–74.19% and 17.6–23.6%, respectively) between the Pedobacter isolates and the closely related type strains fell below the threshold values utilized for species delineation. Following comprehensive genomic, chemotaxonomic, phenotypic, and phylogenetic analyses, the isolated strains are proposed as two novel species within the genus Pedobacter, named Pedobacter albus sp. nov. (type strain KR3-3^T = KACC 23486^T = NBRC 116682^T) and Pedobacter flavus sp. nov. (type strain VNH31^T = KACC 23297^T = CCTCC AB 2023109^T). Full article

Motorbikes are an integral part of transportation in emerging countries, but unfortunately, motorbike users are also one the most vulnerable road users (VRUs) and are engaged in a large number of yearly accidents. So, motorbike detection is very important for proper traffic surveillance, road safety, and security. Most of the work related to bike detection has been carried out to improve accuracy. If this task is not performed in real-time then it loses practical significance, but little to none has been reported for its real-time implementation. In this work, we have looked at multiple real-time deployable cost-efficient solutions for motorbike detection using various state-of-the-art embedded edge devices. This paper discusses an investigation of a proposed methodology on five different embedded devices that include Jetson Nano, Jetson TX2, Jetson Xavier, Intel Compute Stick, and Coral Dev Board. Running the highly compute-intensive object detection model on edge devices (in real-time) is made possible by optimization. As a result, we have achieved inference rates on different devices that are twice as high as GPUs, with only a marginal drop in accuracy. Secondly, the baseline accuracy of motorbike detection has been improved by developing a custom network based on YoloV5 by introducing sparsity and depth reduction. Dataset augmentation has been applied at both image and object levels to enhance robustness of detection. We have achieved 99% accuracy as compared to the previously reported 97% accuracy, with better FPS. Additionally, we have provided a performance comparison of motorbike detection on the different embedded edge devices, for practical implementation. Full article

Vehicular wireless networks are one of the most valuable tools for monitoring platforms in the automotive domain. At the same time, Internet of Things (IoT) solutions are playing a crucial role in the same framework, allowing users to connect to vehicles in order to gather data related to their working cycle. Such tasks can be accomplished by resorting to either cellular or non-cellular wireless technologies. While the former can ensure low latency but require high running costs, the latter can be employed in quasi-real-time applications but definitely reduce costs. To this end, this paper proposes the results of two measurement campaigns aimed at assessing the performance of the long-range wide-area network (LoRaWAN) protocol when it is exploited as an enabling technology to provide vehicles with connectivity. Performances are evaluated in terms of packet loss (PL) and received signal strength indicator (RSSI) in wireless links. The two testing scenarios consisted of a transmitter installed on a motorbike running on an elliptical track and a receiver placed in the centre of the track, and a transmitter installed on the roof of a car and a receiver placed next to a straight road. Several speeds were tested, and all the spreading factors (SFs) foreseen by the protocol were examined, showing that the Doppler effect has a marginal influence on the receiving performance of the technology, and that, on the whole, performance is not significantly affected by the speed. Such results prove the feasibility of LoRaWAN links for vehicular network purposes. Full article