The rise of the gig economy and the increasing demand for flexible, remote work have transformed the freelancing landscape. This paper presents the development of a mobile application designed to streamline the process of project bidding, user management, and service matching for freelancers. Built using Java and Android Studio, the application employs Agile development methodologies to ensure robust performance and a seamless user experience. Key features include user registration and verification, a secure project-bidding platform, and efficient database management with SQLite. The app also utilises the Glide library for optimised image handling, ensuring smooth interaction for freelancers and clients alike. Initial results indicate that the platform effectively connects clients with freelancers by providing an intuitive, reliable, and secure service-matching environment. This paper explores the technical design, challenges, and future directions of the application, emphasising its potential to improve freelancing workflows and address key issues such as decision fatigue and trust in digital platforms.
The strategic location of emergency supply depots is critical for enhancing pre-disaster preparedness and post-disaster relief efforts. Given the inherent uncertainties and risks associated with natural and man-made disasters, ensuring the swift and effective delivery of relief materials to affected areas is pivotal for minimizing disaster impacts and safeguarding lives and property. This review synthesizes the current body of research on the siting of emergency stockpiles, providing a comprehensive analysis of the factors influencing site selection. Key factors such as the geographic scope of disaster response, hydrographic conditions, transportation infrastructure, and accessibility to affected populations are examined. Various siting models are evaluated to optimize resource allocation, minimize logistics costs, and improve supply chain responsiveness during emergencies. This review also identifies key challenges within the existing literature, including limitations in model algorithms, disaster stage considerations, optimization criteria, and the degree of stakeholder involvement in decision-making. Notably, while previous research has often focused on isolated factors, this study emphasizes the need for an integrated approach that accounts for dynamic, diversified, intelligent, and human-centered considerations. Dynamic models are essential to adapt to the unpredictable nature of disasters, while diversified approaches are necessary to address the varying needs of different disaster types and affected populations. Intelligent decision-making tools, incorporating data analytics and real-time information, can enhance the efficiency and accuracy of site selection processes. Human-centric models, focusing on the actual needs of disaster-affected communities, are critical for ensuring the effectiveness of relief operations. The review concludes by outlining future research directions, emphasizing the importance of developing adaptable, sustainable, and context-specific siting models. Future investigations should focus on the practical application of emerging technologies, such as big data analytics, artificial intelligence, and remote sensing, to refine siting models and improve their responsiveness in a rapidly changing global landscape. These advancements are expected to contribute to more efficient and cost-effective emergency supply systems, better equipped to address the evolving challenges of global disaster risks.
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.
In the field of jurisprudence, judgment element extraction has become a crucial aspect of legal judgment prediction research. The introduction of pre-trained language models has provided significant momentum for the advancement of Natural Language Processing (NLP) technologies, with the Bidirectional Encoder Representations from Transformers (BERT) model being particularly notable for its ability to enhance semantic understanding in unsupervised learning. A fusion model combining BERT and an attention mechanism-based Recurrent Convolutional Neural Network (RCNN) was utilized in this study for multi-label classification tasks, aiming to further extract contextual features from legal texts. The dataset used in this research was derived from the "China Legal Research Cup" judgment element extraction competition, which includes three types of cases (divorce, labor, and lending disputes), with each case type divided into 20 label categories. Four comparative experiments were conducted to investigate the optimization of the model by placing the attention mechanism at different positions. At the same time, previous models were learned and studied and their advantages were analyzed. The results obtained from replicating and optimizing those previous models demonstrate promising legal instrument classification performance.
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.
This study addresses the issues of fragmentation, unstructured information, and low reusability in the process knowledge management of aircraft engine component manufacturing. A process knowledge modeling method based on ontology is proposed. By constructing an ontology knowledge base tailored for the aircraft engine manufacturing domain, an improved top-down approach is employed. This method introduces feature-based constraints on process parameters and uses tools to create a Web Ontology Language (OWL) model. The manufacturing of a long tension bolt is chosen as the case study, and application verification is carried out based on the Model-Based Definition (MBD) model. The results demonstrate that the proposed method significantly improves the sharing and reusability of process knowledge, providing theoretical support for the intelligent process design of aircraft engine components.
The rapid advancement of digital finance has emerged as a crucial driver of sustainable urban development, yet its impact on green total factor productivity (GTFP) remains underexplored. This study investigates the mechanisms through which digital finance influences GTFP and examines its spatial spillover effects within Chinese prefecture-level cities. Utilizing panel data from 278 cities spanning 2011 to 2021, the Digital Financial Inclusion Index and an urban GTFP measurement framework are employed to conduct a dynamic analysis. The findings reveal that digital finance facilitates GTFP growth primarily through three channels: fostering technological innovation, promoting industrial upgrading, and mitigating resource misallocation. Significant regional heterogeneity is observed, with the impact being more pronounced in central and western China compared to the eastern region. Moreover, cities with lower levels of financial development experience a stronger enhancement in GTFP through digital finance than their more financially developed counterparts. A temporal analysis further indicates that the green efficiency effect of digital finance has intensified over time. Employing a Spatial Error Model (SEM), robust evidence of significant spatial spillover effects is identified, demonstrating a clustering pattern in regional green efficiency improvements. These findings underscore the need for tailored policy interventions to optimize the role of digital finance in promoting sustainable urban development. Policy recommendations include enhancing financial accessibility in underdeveloped regions, strengthening technological diffusion, and fostering coordinated regional green development strategies.
In an increasingly competitive energy sector, the strategic utilization of human resources is paramount to achieving sustainable competitive advantage. The alignment between human resource management (HRM) and strategic objectives plays a critical role in enhancing organizational performance, particularly in power plant operations. This study aims to prioritize human resource performance indicators within the context of power plants by employing a hybrid multi-criteria decision-making (MCDM) approach. Through a comprehensive literature review, the key evaluation criteria—preparation, implementation, and evaluation—were identified. A mixed-method research design was adopted, integrating the Delphi method for expert consensus with a fuzzy Analytic Hierarchy Process (AHP)-Complex Proportional Assessment (COPRAS) approach for quantitative analysis. The study was conducted as an applied case study, with data collected from 15 senior experts and managers at the Nowshahr Combined Cycle Power Plant. Findings indicate that among the main criteria, the implementation dimension was assigned the highest priority, with a final weight of 0.418. Within this category, the training system emerged as the most influential sub-criterion, receiving a weight of 0.1154. Additionally, continuous performance measurement was identified as the most effective strategy for sustaining workforce efficiency. The proposed methodology provides a systematic framework for decision-makers in the energy sector to enhance human resource performance from a strategic perspective, thereby improving overall operational effectiveness.
Human resource management plays a pivotal role in organizational success, with employee satisfaction being a critical factor in maximizing potential and productivity. This study investigates the relationship between leadership styles, organizational justice, and employee satisfaction within the hotel sector in Zadar County. The findings indicate that distributive justice has the most significant positive impact on employee satisfaction, while an autocratic leadership style is found to have a detrimental effect, contributing to lower motivation and higher stress levels among employees. Conversely, transformational leadership positively influences satisfaction by fostering motivation and encouraging employee participation in decision-making processes. The hotels in Zadar County generally report a high level of employee satisfaction, predominantly driven by a transactional leadership style, which emphasizes goal achievement through clearly defined tasks and performance-based rewards. Furthermore, managers exhibit a notable degree of interpersonal justice, treating employees with respect and empathy. These practices are considered to enhance the overall effectiveness of hotel management. In conclusion, the hotel sector in Zadar County benefits from a relatively high level of management effectiveness, characterized by efficiency and respect for employees. However, to further enhance employee satisfaction, it is recommended that management adopt a more democratic leadership style and focus on improving distributive justice, as these factors have the strongest positive influence on satisfaction. The integration of these strategies is expected to foster a more supportive work environment, thereby improving employee morale and retention.
The complexity and variability of Internet traffic data present significant challenges in feature extraction and selection, often resulting in ineffective abnormal traffic monitoring. To address these challenges, an improved Bidirectional Long Short-Term Memory (BiLSTM) network-based approach for Internet abnormal traffic monitoring was proposed. In this method, a constrained minimum collection node coverage strategy was first applied to optimize the selection of collection nodes, ensuring comprehensive data coverage across network nodes while minimizing resource consumption. The collected traffic dataset was then transformed to enhance data validity. To enable more robust feature extraction, a combined Convolutional Neural Network (CNN) and BiLSTM model was employed, allowing for a comprehensive analysis of data characteristics. Additionally, an attention mechanism was incorporated to weigh the significance of attribute features, further enhancing classification accuracy. The final traffic monitoring results were produced through a softmax classifier, demonstrating that the proposed method yields a high monitoring accuracy with a low false positive rate of 0.2, an Area Under the Curve (AUC) of 0.95, and an average monitoring latency of 5.7 milliseconds (ms). These results indicate that the method provides an efficient and rapid response to Internet traffic anomalies, with a marked improvement in monitoring performance and resource efficiency.
The theory of Complex T-Spherical Fuzzy Sets (CTSpFSs) is introduced along with their Einstein operational methods under induced variables. This research aims to extend the theoretical framework of complex fuzzy sets (CFSs) by exploring fundamental Einstein operational laws and proposing two novel aggregation operators: the induced complex T-spherical fuzzy Einstein ordered weighted averaging (I-CTSpFEOWA) operator and the induced complex T-spherical fuzzy Einstein hybrid averaging (I-CTSpFEHA) operator. Aggregation operators serve as powerful tools in data analysis, decision-making, and understanding complex systems by enabling the extraction of meaningful insights from large, multidimensional datasets. These operators contribute to the simplification of information, ultimately enhancing decision support in complex decision-making processes. The proposed operators, designed to handle complex and multidimensional fuzzy information, enhance the ability to refine these decision-making processes. Their effectiveness is demonstrated through the development of a numerical example, which illustrates their potential application in real-world scenarios. The proposed techniques not only improve the clarity and relevance of the aggregated information but also provide an efficient methodology for managing complex fuzzy environments, thus refining decision-making across diverse domains. By demonstrating the utility of the I-CTSpFEOWA and I-CTSpFEHA operators, the research highlights their practical application in systems where traditional fuzzy aggregation methods may fall short. This work contributes significantly to the field of fuzzy set theory by presenting advanced aggregation methods that support improved decision-making in environments characterised by uncertainty and complexity.
The inherent hierarchical and decentralized nature of decision-making within banking systems presents significant challenges in evaluating operational efficiency. This study introduces a novel bi-level programming (BLP) framework, incorporating Stackelberg equilibrium dynamics, to assess the performance of bank branches. By combining with data envelopment analysis (DEA), the proposed BLP-DEA model captures the leader-follower relationship that characterizes banking operations, wherein the leader focuses on marketability and the follower prioritizes profitability. A case study involving 15 Iranian bank branches was employed to demonstrate the model’s capacity to evaluate performance comprehensively at both decision-making levels. The results underscore the model's effectiveness in identifying inefficiencies, analyzing cost structures, and providing actionable insights for performance optimization. This approach offers a robust tool for addressing the complexities associated with decentralized decision-making in hierarchical organizations. The findings have significant implications for both theoretical development and practical application, especially in the context of improving the operational efficiency of banking institutions.
Open Access
