While significant empirical research has examined the use and benefits of designated bus stops in urban settings across Western and Asian countries, the factors influencing commuters' preference for non-designated bus boarding locations in developing cities remain understudied. This study investigates the determinants that lead commuters to board buses at non-designated locations in Enugu, a rapidly urbanizing city in southeast Nigeria. Data were collected through a questionnaire survey involving 424 commuters at 17 non-designated bus stop locations across three local government areas within Enugu metropolis. Descriptive statistics, commuters' perception index (CPI) and principal component analysis (PCA) were employed to analyze the data. The analysis identified seven key factors influencing the choice of non-designated bus boarding locations: environmental and social conditions, cost-related considerations and diversity of routes, concerns over unsanitary conditions, bus availability and access to information, convenience and time-saving benefits, proximity to the desired destination, and perceived safety and comfort. These findings provide valuable insights for urban planners in designing effective and commuter-friendly bus stop infrastructures that encourage the use of designated boarding locations, thereby optimizing the multifunctional benefits of such facilities in Nigerian cities and similar urban contexts globally. It is recommended that targeted strategies be developed to address these factors, enhancing the overall efficiency and attractiveness of public transport systems in developing urban centers.
Manufacturers are increasingly leveraging both online and offline channels to diversify their sales strategies. However, competition between these channels presents challenges in maximising profits for all parties involved. This study investigates the use of cost-sharing contracts by manufacturers to promote marketing in both online and offline channels, with the goal of achieving Pareto improvements in supply chain profitability. The model also accounts for consumers’ reference quality perceptions in online channels, offering a comprehensive evaluation of how cost-sharing contracts influence the operational strategies and performance of both online and offline enterprises. An empirical analysis is conducted using the “US Stores Sales” dataset from Kaggle, comprising 4,249 samples with 20 recorded characteristics per sample. The findings indicate that: (1) Cost-sharing in marketing efforts facilitates a Pareto improvement in profits for manufacturers, online enterprises, and offline retailers, with manufacturers experiencing the most significant benefit. (2) When the manufacturer assumes a larger share of marketing costs for one channel (e.g., online or offline) and a smaller share for the other, the party receiving the higher cost-sharing proportion typically sees increased profitability, while the other party’s profitability may diminish. (3) Empirical analysis suggests that manufacturers should prioritise supporting online businesses’ marketing activities, as this strategy is more likely to result in higher overall profits for the manufacturer. (4) Interestingly, when equal cost-sharing proportions are offered to both online and offline enterprises for the sake of fairness, the manufacturer’s profitability is enhanced. Moreover, the profitability of online enterprises tends to increase when the equal cost-sharing proportion is smaller. These findings validate the proposed model and underscore the critical role of strategic cost-sharing contracts in optimising Online to Offline (O2O) supply chain performance. Further research could explore the implications of varying consumer preferences and digitalisation trends on the effectiveness of such strategies.
The classification of fruit ripeness and detection of defects are critical processes in the agricultural industry to minimize losses during commercialization. This study evaluated the performance of three Convolutional Neural Network (CNN) architectures—Extreme Inception Network (XceptionNet), Wide Residual Network (Wide ResNet), and Inception Version 4 (Inception V4)—in predicting the ripeness and quality of tomatoes. A dataset comprising 2,589 images of beef tomatoes was assembled from Golden Fingers Farms and Ranches Limited, Abuja, Nigeria. The samples were categorized into six classes representing five progressive ripening stages and a defect class, based on the United States Department of Agriculture (USDA) colour chart. To enhance the dataset's size and diversity, image augmentation through geometric transformations was employed, increasing the dataset to 3,000 images. Fivefold cross-validation was conducted to ensure a robust evaluation of the models' performance. The Wide ResNet model demonstrated superior performance, achieving an average accuracy of 97.87%, surpassing the 96.85% and 96.23% achieved by XceptionNet and Inception V4, respectively. These findings underscore the potential of Wide ResNet as an effective tool for accurately detecting ripeness levels and defects in tomatoes. The comparative analysis highlights the effectiveness of deep learning (DL) techniques in addressing challenges in agricultural automation and quality assessment. The proposed methodology offers a scalable solution for implementing automated ripeness and defect detection systems, with significant implications for reducing waste and improving supply chain efficiency.
To investigate the variation in the diffusion patterns of natural gas leaks in the Floating Production Storage and Offloading (FPSO) system, with the aim of formulating appropriate emergency response strategies and minimizing accident losses, a study was conducted on the gas leak issues of oil and gas processing equipment in the FPSO upper module. A consequence prediction and assessment model was established based on Computational Fluid Dynamics (CFD) methods. Sixteen working conditions and one control working condition were developed to simulate the diffusion characteristics of combustible gas leaks. The simulations provided insights into the gas leakage patterns under different conditions and identified the most hazardous scenario for gas leaks in the FPSO upper module. The results indicate that the density and shape of the equipment within the upper module significantly influence the diffusion outcome. After a leak, high concentrations of combustible gas were observed near the crude oil heat exchanger skid in Industrial Zone II. The effects of individual factors on gas diffusion were significant, and the interactions among multiple factors were complex. Wind speed had a more pronounced effect on longitudinal gas diffusion compared to wind direction and leak aperture, while wind direction significantly influenced lateral gas diffusion. The leak aperture, on the other hand, had a more substantial impact on vertical gas diffusion.
Rice is a staple food for a significant portion of the global population, particularly in countries where it constitutes the primary source of sustenance. Accurate classification of rice varieties is critical for enhancing both agricultural yield and economic outcomes. Traditional classification methods are often inefficient, leading to increased costs, higher misclassification rates, and time loss. To address these limitations, automated classification systems employing machine learning (ML) algorithms have gained attention. However, when raw data is inadequately organized or scattered, classification accuracy can decline. To improve data organization, normalization processes are often employed. Despite its widespread use, the specific contribution of normalization to classification performance requires further validation. In this study, a dataset comprising two rice varieties Osmancik and Cammeo produced in Turkey was utilized to evaluate the impact of normalization on classification outcomes. The k-Nearest Neighbor (KNN) algorithm was applied to both normalized and non-normalized datasets, and their respective performances were compared across various training and testing ratios. The normalized dataset achieved a classification accuracy of 0.950, compared to 0.921 for the non-normalized dataset. This approximately 3% improvement demonstrates the positive effect of data normalization on classification accuracy. These findings underscore the importance of incorporating normalization in ML models for rice classification to optimize performance and accuracy.
Water is regarded as the most critical natural resource in Idaho, with drinking water identified as its most essential aspect. To assess public perceptions and evaluations of drinking water quality, a survey instrument was developed and distributed to Idaho residents over the past 35 years. Key areas of focus included the safety of home drinking water, the use of in-home water filters, consumption of bottled water, frequency of water testing, and concerns about potential pollutants. Surveys were administered in 1988, 1993, 1998, 2002, 2005, 2010, 2015, 2018, and 2022, with findings indicating a gradual decline in perceived drinking water safety, from 90.2% in 1988 to 80.2% in 2022. The use of in-home water filtration systems increased significantly, rising from 16.2% in 1988 to 29.7% in 2022, potentially driven by extensive advertising campaigns rather than increased contamination concerns. Bottled water usage peaked at 33% in 2010 but has since declined to less than 11% in 2022, a trend attributed to heightened public awareness of tap water safety and environmental concerns related to plastic waste. No significant long-term patterns in water testing were observed, although rural residents, who rely on private wells, were more likely to test their water due to the absence of regular testing requirements. Hard water (with a high content of Ca and/or Mg) emerged as the primary contaminant identified by respondents, with no other significant pollutants widely reported. These findings offer valuable insights into shifting public perceptions of water quality and the factors influencing household water consumption practices in Idaho over the last three decades.
To accelerate the exchange of water rights between regions and address the uneven costs of water resource ecological protection among different districts in urban areas, it is essential to make an analysis of regional water resource ecological compensation responsibilities. Establishing a rational standard for ecological compensation based on water resources remains a key method for quantifying the ecological value of water resources. In this study, all districts within a national central city in southwestern China were divided into four functional zones as the research subjects. The water resource ecological footprint method was employed to calculate the water ecological footprint of each zone. The ecological carrying capacity was utilized as the benchmark to determine the water resource ecological deficit or surplus, and the corresponding ecological monetary value of water resources was estimated. The results indicated that the city, as a whole, exhibited a water resource ecological surplus, with a monetary value of 5.088 billion CNY. The western zone, a key urban development area, recorded the highest water resource ecological footprint and the largest ecological deficit. In contrast, the northeastern zone, abundant in water resources, presented the highest water resource ecological surplus, with a monetary value of 9.196 billion CNY. Compensation amounts for the central-eastern and western zones were calculated as 4.169 billion and 7.661 billion CNY, respectively. These findings align with the local water resources' sustainable utilization conditions. The relationship between regional economic development, water conservation, and sustainable development was further analyzed in this study, proposing a water resource ecological compensation model with certain districts and counties as beneficiaries.
Strategic values play a pivotal role in the long-term success of logistics enterprises, influencing interactions with customers, employees, and stakeholders, and driving sustainable outcomes. In the context of the global logistics sector, the identification and alignment of strategic values are essential for maintaining competitive advantage and fostering resilience. This study systematically investigates the strategic values of the world’s 50 leading logistics companies, focusing on those most strongly associated with sustainable success. Using a qualitative approach, content analysis was employed to evaluate and interpret the strategic documents of these enterprises, revealing key values that contribute significantly to sustainability. Among the values identified, reliability, customer-centricity, and operational efficiency were found to be most influential in ensuring both operational and strategic sustainability. These values were consistently embedded within corporate practices, shaping decision-making processes, stakeholder engagement, and long-term growth strategies. The findings indicate that the integration of sustainability as a core strategic value is critical for enduring success in an increasingly competitive and environmentally conscious market. The results provide valuable insights for both academics and practitioners, offering a framework for logistics companies to refine their strategic management practices and align their operations with sustainable development goals. By highlighting the strategic values that underpin sustainable growth, this study contributes to the understanding of how logistics enterprises can navigate the complex challenges of the modern business environment.
The Weibull distribution (WD) is widely recognized as an effective statistical tool for characterizing wind speed (WS) variability. This study investigates the applicability of the WD to analyze WS data from a selection of African stations, with data spanning from 2000 to 2023, obtained from the Power Data archive in comma- separated values (CSV) format. The analysis aimed to assess the distribution's ability to represent the variations in WS across different regions in Africa. The results reveal significant spatial variability in the Weibull parameters across the selected stations. wind direction patterns were analyzed, with the highest frequency recorded from the east-north-east (ENE) direction, reaching a value of approximately 400 at certain locations. The lowest wind direction frequencies were observed in Abuja, where the predominant directions were north-northwest (NNW) and north (N). The probability distribution of WS demonstrated a considerable range, with Abuja exhibiting the highest values (exceeding 0.5), while Tunis recorded the lowest values (approximately 0.2). The mean WS for each location varied over the year, with Nairobi experiencing the highest recorded mean WS in October (5.72 m/s), accompanied by a standard deviation of 1.22 m/s. In contrast, the lowest mean WS was observed in Luanda during September (1.72 m/s), with a standard deviation of 0.46 m/s. The maximum and minimum wind power density (PDw) recorded across the selected station are ($>100 \mathrm{~W} / \mathrm{m}^2$) and ($>18 \mathrm{~W} / \mathrm{m}^2$). These findings highlight the considerable potential for wind energy across Africa, emphasizing the importance of incorporating wind energy into the region's renewable energy strategy. The results underscore the need for region-specific energy policies and further research to optimize the utilization of wind resources for sustainable development in Africa.
This study investigates the differences in the factors contributing to school dropout between rural and urban educational institutions in Romania, focusing on individual, family, school, and community dimensions. A sample of 557 participants, including educational directors, teachers, and administrators, was surveyed to assess the prevalence of various dropout causes. The Mann-Whitney U test was employed to identify statistically significant differences between rural and urban schools in specific dropout factors. The findings indicate that urban schools report higher incidences of individual-level issues such as substance abuse, juvenile delinquency, teenage pregnancy, and health-related problems. At the family level, urban institutions were more likely to encounter students with incarcerated parents or those placed in alternative care. School-related factors also varied, with urban schools being characterised by larger class sizes and insufficient access to counselling and guidance services, while rural schools were more affected by early school start times. In the community dimension, urban schools faced greater challenges with negative peer influences and a lack of educational facilities near students’ homes. These results suggest that the causes of dropout in urban settings are more complex, necessitating tailored interventions and resources. It is recommended that context-specific strategies be developed to address the distinct dropout factors in both rural and urban environments, thereby supporting more inclusive and effective educational policies in Romania.
Internal audits serve as critical assurance services that support the enhancement of operational efficiency and financial performance within organizations. This study examines the role of internal auditing in improving these aspects in privatised financial institutions, specifically focusing on BLESSING Finance. Given the profit-driven orientation of management in such institutions, there is a pressing need to identify strategies that maximize profitability. Enhancing operational efficiency is pivotal, as it reduces operational costs while increasing productivity. Internal auditing contributes significantly by identifying deficiencies within internal controls and providing audit opinions that inform management in drafting appropriate policies and procedures. This research utilized a mixed-methods approach, combining qualitative data from interviews and quantitative data from questionnaires, to assess the impact of internal auditing on operational efficiency and financial performance. The findings demonstrate that internal audits have a positive and significant effect on both operational efficiency and financial performance, highlighting the value of internal audits as a strategic tool for financial institutions. It is recommended that BLESSING Finance’s management prioritize the recruitment of qualified auditors with the necessary skills and expertise to perform audits effectively and efficiently, thereby further enhancing the institution’s operational efficiency and financial outcomes. The study underscores the importance of robust internal audit functions as a key driver of strategic and financial success in financial institutions.
Open Access
