The shear connection behaviour of steel-concrete composite beams is primarily governed by the strength of the connectors and concrete. Modern seismic evaluations and vibrational analyses of composite beams, particularly concerning their load-slip characteristics and shear strength, predominantly rely on push-out test data. In this study, the Finite Element Method (FEM) has been employed to simulate and analyse the shear, bending, and deflection responses of composite beams subjected to various load conditions, in accordance with Eurocode 4 standards. Failure modes, ultimate loads, and sectional capacities were examined in detail. The results indicate that increased strength of both steel and concrete significantly enhances the beam’s capacity in bending. Specifically, flexural and compressive resistance showed marginal improvements of 3.2%, 3.1%, and 3.0%, respectively, as concrete strength increased from 25 N/mm² to 30, 35, and 40 N/mm², while steel strength increased by 27% and 21%, with yield strengths of 275 N/mm², 355 N/mm², and 460 N/mm², respectively. Under seismic loading, however, the ultimate flexural load capacity exhibited a reduction with a fixed beam span, irrespective of steel strength. The shear capacity remained constant across varying beam lengths but demonstrated significant improvements with increased steel yield strength, with enhancements of 29% and 67% as steel yield strength increased from 275 N/mm² to 355 N/mm² and 460 N/mm², respectively. A detailed vibration analysis was also conducted to investigate the dynamic behaviour of these composite beams under seismic conditions. These findings underscore the critical influence of material strengths and loading conditions on the performance of steel-concrete composite beams, particularly in seismic scenarios, providing valuable insights for the design and assessment of such structures in seismic-prone regions.
This study investigates the harmonising potential of complex systems theory in non-financial reporting of sustainable finance practices within Zimbabwean commercial banks. The increasing prominence of sustainable finance in Zimbabwe can be attributed to the adoption of international frameworks such as the United Nations’ 2030 Agenda and the Paris Agreement, which have led to its integration into banks' non-financial reporting. Sustainable finance, however, is recognised as a wicked problem—an issue characterised by its complexity, involving numerous interacting agents, emergent properties, and the need for a holistic approach. Such problems cannot be adequately addressed through conventional financial theories, which are often insufficient to capture their complexity. Despite the existence of various sustainability reporting standards, a unified framework to harmonise non-financial reporting and enable comparability across banks is still lacking. Using content analysis, this research examines annual reports from 17 Zimbabwean commercial banks, analysing 136 reports spanning from 2016 to 2023. The findings suggest that most banks have adopted a weak sustainability approach, guided by complex systems theory, which enables some degree of harmonisation in reporting standards but ultimately compromises long-term sustainability. This weak approach has been found to encourage greenwashing practices, with policies and strategies that, while aligned with sustainability rhetoric, may perpetuate environmental and social harm. The study makes several key contributions: it provides empirical evidence on the current state of sustainable finance reporting in Zimbabwean banks, offers a theoretical framework for harmonising non-financial reporting using complex systems theory, and proposes the adoption of a stronger sustainability-oriented framework to ensure genuine, long-term sustainability outcomes.
The reuse of Reclaimed Asphalt Pavement (RAP) in road construction has become increasingly prevalent due to its potential environmental and economic benefits. The aging characteristics of RAP, particularly the degradation of its bitumen content, are critical for evaluating its suitability for future applications. The aging process is influenced by various meteorological factors, including solar radiation, temperature fluctuations, and precipitation. This study investigates the impact of these factors on the properties of bitumen in RAP, focusing on a pavement constructed between 2002 and 2005. After eight years of service, the pavement was milled and the material was stored in a stockpile for an additional eight years. The bitumen properties, specifically penetration and softening point, were measured at regular intervals over the 16-year period. Cumulative meteorological data, including temperature, solar radiation, and precipitation, were recorded and analysed in relation to the observed aging effects on the bitumen. The results demonstrated a linear correlation between the cumulative meteorological conditions and the degree of bitumen aging. Increased exposure to solar radiation and temperature fluctuations accelerated the aging process, while prolonged periods of precipitation appeared to have a moderating effect. These findings suggest that both the duration and intensity of exposure to specific environmental conditions must be considered when assessing the viability of using RAP in future pavement construction.
The challenge of providing students with practical, hands-on experience in realistic industrial environments is increasingly prevalent in modern technical education. The concept of a Learning Factory addresses this issue by facilitating skill acquisition through immersive, practice-oriented training that integrates advanced digital technologies. An innovative educational platform has been developed, incorporating Internet of Things (IoT) devices, Cyber-Physical Systems (CPS), and Digital Twin (DT) technology to enhance manufacturing education. This platform combines modular hardware and software, enabling immersive visualisation and real-time monitoring through DT-supported systems. These features offer a comprehensive, interactive learning experience that closely simulates real-world manufacturing processes. The system's smart reconfigurability further enhances its educational potential by enabling customisable training scenarios tailored to specific learning outcomes. The proposed approach aligns with the principles of Industry 4.0 and serves as a catalyst for the improvement of both educational and professional training environments. By leveraging digitalisation, this platform not only supports adaptive learning but also enhances the efficiency of educational models. Through the simulation of dynamic manufacturing systems, students are exposed to a variety of industrial scenarios, fostering deeper understanding and skill development. The integration of IoT, CPS, and DT technologies is expected to provide a scalable framework for future educational environments, ultimately improving the adaptability and effectiveness of manufacturing training.
The prospectus, as the primary vehicle for issuers to disclose information to the public, plays a crucial role in protecting investors’ rights. Review inquiries serve as an important tool to ensure the quality of the prospectus, as the inquiry and feedback mechanism helps to identify potential risks and enhance the quality of information disclosure. This paper, based on the theory of responsive regulation and the attention-based view, takes companies applying for Initial Public Offering (IPO) on the Science and Technology Innovation Board (STAR) Market and ChiNext Board between 2019 and 2023 as the research samples. Using text analysis methods such as the Latent Dirichlet Allocation (LDA) topic model and dictionary-based methods, this study measures the intensity of review inquiries and the extent of information disclosure. It examines the impact of inquiry topics on the disclosure of corresponding information in the prospectus and explores the moderating effects of company ownership structure, sponsor reputation, and auditor reputation on these relationships. Empirical results indicate that: (1) an increase in the formality of review inquiries enhances the optimization of information disclosure in the prospectus; (2) the focus of review inquiries on specific topics has a significant positive impact on the update of relevant information disclosure in the prospectus; and (3) at the ownership structure level, state-owned enterprises dampen the positive influence of review inquiries on the textual features of the prospectus.
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.
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