Swarm intelligence (SI) has emerged as a transformative approach in solving complex optimization problems by drawing inspiration from collective behaviors observed in nature, particularly among social animals and insects. Ant Colony Optimization (ACO), a prominent subclass of SI algorithms, models the foraging behavior of ant colonies to address a range of challenging combinatorial problems. Originally introduced in 1992 for the Traveling Salesman Problem (TSP), ACO employs artificial pheromone trails and heuristic information to probabilistically guide solution construction. The artificial ants within ACO algorithms engage in a stochastic search process, iteratively refining solutions through the deposition and evaporation of pheromone levels based on previous search experiences. This review synthesizes the extensive body of research that has since advanced ACO from its initial ant system (AS) model to sophisticated algorithmic variants. These advances have both significantly enhanced ACO's practical performance across various application domains and contributed to a deeper theoretical understanding of its mechanics. The focus of this study is placed on the behavioral foundations of ACO, as well as on the metaheuristic frameworks that enable its versatility and robustness in handling large-scale, computationally intensive tasks. Additionally, this study highlights current limitations and potential areas for future exploration within ACO, aiming to facilitate a comprehensive understanding of this dynamic field of swarm-based optimization.
Ghana has enacted various policies and programmes, often with support from international agencies, to strengthen public sector financial management. These efforts aim to mitigate mismanagement and misappropriation of public financial resources, yet many reform policies have yielded suboptimal outcomes. A critical examination of Ghana's financial reform initiatives reveals a notable oversight: none adequately recognize the role of audit committees (ACs) as a governance mechanism, which diverges from international standards and best practices in public sector financial management. This study aims to identify and analyze the determinants influencing the effectiveness of ACs within Ghana’s public institutions. The research was motivated by persistent financial infractions and irregularities documented in the Auditor-General’s annual reports. An Interactive Qualitative Analysis (IQA) approach was employed to facilitate a focus group session, through which data were gathered, analyzed, and interpreted. Key factors, or affinities, impacting AC effectiveness were identified, including AC member characteristics, inter-stakeholder coordination, funding allocation, meeting frequency and attendance, AC independence, internal audit function (IAF) autonomy, institutional management commitment, the nature of the audited institution, regulatory policies governing ACs, political influence, professional competence of internal auditors, and the quality of quality control processes and recommendations. These affinities were validated through participant interpretation and researcher refinement. The study contributes to the body of knowledge on public sector audit governance by addressing a critical gap concerning the role of ACs in Ghana. By establishing an effective governance mechanism, this research seeks to enhance the strategic oversight and accountability of public financial resources in Ghana’s public institutions.
To address the complexities and inaccuracies associated with traditional methods of concrete compactness monitoring, in this paper, a real-time monitoring approach based on long short-term memory (LSTM) networks has been developed. Traditional methods often involve cumbersome data processing and yield large errors, especially in complex environments, in contrast, the proposed method leverages the LSTM network's ability to process time-series data, enhancing accuracy in detecting compactness defects within concrete structures, and the ultrasonic wave velocity through concrete under standard conditions has been set as a baseline value. The platform can visualize the curve of ultrasonic propagation speed in the monitored concrete over time, allowing for a direct comparison with the baseline to assess the extent and location of potential defects. The degree of deviation from the baseline indicates the compactness and defect severity, facilitating more accurate monitoring. Additionally, a user-friendly monitoring platform interface has been designed using Mock Plus, enabling rapid prototyping and optimization for enhanced data visualization and user interaction, this design allows for effective real-time monitoring, data processing, and user engagement. By integrating advanced machine learning techniques with intuitive platform design, the proposed method offers a significant improvement in monitoring concrete compactness, potentially benefiting both research and practical applications in structural health monitoring.
Parks play a pivotal role in urban environments, contributing to ecological balance, aesthetic appeal, and social well-being. At the neighbourhood level, they provide essential recreational spaces and promote community cohesion. However, many residential areas in Mosul, Iraq, lack adequate parks, disrupting the urban landscape and diminishing the quality of life. To address this issue, the potential of transforming school gardens—segregated by gender at the primary and intermediate levels—into public parks during non-school hours is explored. This adaptive reuse strategy is framed within a place-making approach, leveraging time as a resource and fostering community participation in the planning process. The study examines the feasibility of this intervention by assessing the interests and preferences of different demographic groups within the neighbourhoods, identifying key design considerations to ensure usability and long-term engagement. The findings confirm strong community support for this strategy, with adolescent boys (aged 12-14) expressing the highest interest, followed by grandmothers, fathers, adolescent girls (aged 12-14), grandfathers, girls aged 15 and above, mothers, and children aged 6-11. Each demographic group demonstrated unique preferences regarding the use and function of the proposed park spaces. These insights underscore the importance of designing adaptable, inclusive environments that cater to diverse needs, ensuring the success of place-making initiatives in Mosul. The integration of school gardens as shared community parks not only addresses the scarcity of recreational spaces but also strengthens social bonds through collaborative planning and shared use. This approach offers a sustainable and scalable solution for enhancing urban life in Mosul’s residential areas, promoting the creation of vibrant public spaces through local participation.
Evaluating renewable energy policies is crucial for fostering sustainable development, particularly within the European Union (EU), where energy management must account for economic, environmental, and social criteria. A stable framework is proposed that integrates multiple perspectives by synthesizing the rankings derived from four widely recognized Multi-Criteria Decision Analysis (MCDA) methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Stable Preference Ordering Towards Ideal Solution (SPOTIS), and Multi-Objective Optimization by Ratio Analysis (MOORA). This approach addresses the inherent variability in individual MCDA techniques by applying Copeland’s compromise method, ensuring a consensus ranking that reflects the balanced performance of renewable energy systems across 16 EU countries. To further enhance the reliability of the framework, the Stochastic Identification of Weights (SITW) approach is employed, optimizing the criteria weights and strengthening the consistency of the evaluation process. The results reveal a strong alignment between the rankings generated by individual MCDA methods and the compromise rankings, particularly among the highest-performing alternatives. This alignment highlights the stability of the framework, enabling the identification of critical drivers of renewable energy policy performance—most notably energy efficiency and environmental sustainability. The compromise approach proves effective in balancing multiple, sometimes conflicting perspectives, offering policymakers a structured tool for informed decision-making in the complex domain of energy management. The findings contribute to the development of advanced frameworks for decision-making by demonstrating that compromise rankings can offer robust solutions while maintaining methodological consistency. Furthermore, this framework provides valuable insights into the complex dynamics of renewable energy performance evaluation. Future research should explore the applicability of this methodology beyond the EU context, incorporating additional dimensions such as social, technological, and institutional factors, and addressing the dynamic evolution of energy policies. This framework offers a solid foundation for refining policy evaluation strategies, supporting sustainable energy management efforts in diverse geographic regions.
Field data collection is a crucial component of geological surveys in hydraulic engineering. Traditional methods, such as manual handwriting and data entry, are cumbersome and inefficient, failing to meet the demands of digital and intelligent recording processes. This study develops an intelligent speech recognition and recording method tailored for hydraulic engineering geology, leveraging specialized terminology and speech recognition technology. Initially, field geological work documents are collected and processed to create audio data through manual recording and speech synthesis, forming a speech recognition training dataset. This dataset is used to train and construct a speech-to-text recognition model specific to hydraulic engineering geology, including fine-tuning a Conformer acoustic model and building an N-gram language model to achieve accurate mapping between speech and specialized vocabulary. The model's effectiveness and superiority are validated in practical engineering applications through comparative experiments focusing on decoding speed and character error rate (CER). The results demonstrate that the proposed method achieves a word error rate of only 2.6% on the hydraulic engineering geology dataset, with a single character decoding time of 15.5ms. This performance surpasses that of typical speech recognition methods and mainstream commercial software for mobile devices, significantly improving the accuracy and efficiency of field geological data collection. The method provides a novel technological approach for data collection and recording in hydraulic engineering geology.
The integration of Electric Vehicles (EVs) into modern power grids presents both challenges and opportunities. This study investigates the influence of slack bus compensation on the stability of voltage levels within these grids, particularly as EV penetration increases. A comprehensive simulation framework is developed to model various grid configurations, accounting for different scenarios of EV load integration. Historical charging data is meticulously analysed to predict future load patterns, indicating that heightened levels of EV integration lead to a notable decrease in voltage stability. Specifically, voltage levels were observed to decline from 230 V to 210 V under conditions of 100% EV penetration, necessitating an increase in slack bus compensation from 0 MW to 140 MW to sustain system balance. Advanced machine learning techniques are employed to forecast real-time load demands, significantly reducing both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), thereby optimising slack bus performance. The results underscore the critical role of real-time load forecasting and automated control strategies in addressing the challenges posed by EV integration into power grids. Furthermore, the study demonstrates that intelligent systems, coupled with machine learning, can enhance power flow management and bolster grid stability, ultimately improving operational efficiency in the distribution of energy. Future research will focus on refining machine learning models through the utilisation of more granular data sets and exploring decentralized control methodologies, such as federated learning, thereby providing valuable insights for grid operators as the adoption of EVs continues to expand.
This study examines the development and trends in financial inclusion research between 2004 and 2023, with a focus on the trajectory of publication growth, key contributors (including influential authors, journals, and institutions), and dominant themes within the field. A systematic review and bibliometric analysis were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A total of 1,784 articles were identified from the Scopus database for inclusion. Analytical tools such as VOSviewer and Microsoft Excel were employed to explore publication patterns, citation networks, and thematic concentrations. The findings reveal a marked increase in financial inclusion research, with 2022 recording the highest output, contributing 473 publications. Among scholars, Ozili emerged as a leading author with significant influence in the domain. The Journal of Sustainability (Switzerland) was identified as the most prolific journal, publishing 173 relevant articles, while the University of International Business and Economics in Beijing, China, was found to be the most productive institution. Keyword analysis highlighted recurring themes and revealed underexplored areas, offering promising directions for future research. This comprehensive analysis not only provides insights into the past and current state of financial inclusion scholarship but also identifies gaps that warrant further academic investigation. By offering performance metrics and mapping the evolution of the field, the study serves as a valuable resource for scholars and practitioners seeking to understand emerging research trends and guide future inquiries.
Sustainable development has garnered significant attention due to its multifaceted benefits across social, economic, and environmental dimensions. This study investigates the influence of international performance indicators, specifically organisational agility, data science applications, and strategic partnerships, on the advancement of sustainable development initiatives. Additionally, the role of business intelligence (BI) techniques in augmenting this relationship is examined. A mixed-methods approach was employed, integrating both quantitative and qualitative analyses to comprehensively address the research objectives. A systematic review of the relevant literature was conducted, supplemented by data sourced from the World Bank, which was subsequently analysed using Power BI software. This global study encompassed diverse samples from various regions, ensuring a broad representation of perspectives. The findings reveal that the integration of organisational agility, data science applications, and partnerships, when enhanced by BI techniques, significantly accelerates the achievement of sustainable development goals (SDGs). It is concluded that leveraging these international performance indicators, alongside advanced data-driven methodologies, is critical for fostering a more sustainable future.
This study investigates the complex interrelationships between environmental quality, economic growth, and human capital across 34 provinces in Indonesia from 2017 to 2023, employing a vector autoregression (VAR) approach. The analysis seeks to elucidate how these three critical dimensions influence one another and to provide insights for formulating sustainable development policies that balance economic progress with environmental preservation and human capital enhancement. The findings reveal a bidirectional causality between environmental quality and economic growth, indicating that improvements in one are likely to promote advances in the other. A similar bidirectional causality is observed between environmental quality and human capital, suggesting that better environmental conditions may enhance human capital development, which in turn can contribute to environmental sustainability. However, the relationship between economic growth and human capital is found to be unidirectional, with evidence showing that human capital positively influences economic growth, but not vice versa. This unidirectional causality highlights the importance of investing in human capital to sustain economic growth without compromising environmental integrity. The study underscores the necessity of integrated policy approaches that simultaneously address environmental quality, economic growth, and human capital development. Focusing narrowly on economic growth without considering its environmental and social dimensions may lead to adverse outcomes, undermining long-term sustainability objectives. Therefore, it is recommended that policymakers in Indonesia adopt a holistic perspective, integrating environmental, economic, and social policies to achieve sustainable development goals. The findings of this study provide a nuanced understanding of the interplay among these factors and offer valuable guidance for designing policies that ensure balanced and sustainable development in Indonesia.
The Philippines possesses significant solar energy potential, yet the adoption of rooftop solar power (RTSP) among households remains limited despite its benefits in reducing electricity costs and contributing to the clean energy transition. This study investigates the determinants influencing households’ willingness to adopt RTSP in Metro Manila and surrounding provinces, utilizing the contingent valuation method. Survey results indicate that economic factors, particularly the potential for electricity bill reduction, along with environmental considerations, are positively associated with adoption intentions. While a substantial portion of households (82%) expressed some level of intention to adopt RTSP, the figure drops to 20% when focusing exclusively on households with definitive adoption plans. This suggests that perceived returns on RTSP investments are insufficient to spur broader adoption without further intervention. Policy measures, including increased financial incentives such as enhanced net metering rates, the accreditation of RTSP providers to mitigate perceived risks, and the provision of low-cost financing options, are deemed necessary to enhance adoption rates. Additionally, other economic advantages, such as property value appreciation and enhanced roof durability, could be emphasized in future marketing and public awareness campaigns to strengthen the case for RTSP adoption. Greater government support is critical to unlocking the potential of RTSP in the Philippines and aligning household energy practices with national sustainability goals.
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