The policy of "separation of three rights" in China, which distinguishes rural land ownership (collective), contract rights (farmers), and management rights (transferable), has been implemented to optimize resource allocation, advance agricultural modernization, and protect farmers’ interests. To address the persistent issue of arable land abandonment, it is critical that the interactions among local governments, farmers, and agribusinesses be systematically understood. In this study, a tripartite evolutionary game model was developed to investigate the dynamic decision-making behaviors and stabilization strategies of the three primary stakeholders within the framework of three rights separation. The influence of variations in key parameters was quantitatively assessed. The results demonstrate that economic subsidies, cooperation costs, and loss of prestige significantly influence farmland utilization and transfer. It is emphasized that local governments must actively fulfill regulatory and facilitative roles during the pre-transfer phase of arable land, particularly by providing comprehensive economic and infrastructural support. Furthermore, the necessity of enhancing the construction of farmland mobility service systems is underscored, with the aim of reducing transaction barriers and enabling a more effective and sustainable separation of contracting and management rights. These findings offer theoretical and practical insights for strengthening farmland management systems, ensuring long-term farmland productivity, and supporting rural revitalization strategies in China.
This study critically investigates the strategic transformation of South Korea’s entrepreneurial ecosystem within the broader trajectory of national economic modernization and innovation-centric development. The principal objective is to understand how coordinated governmental strategies, targeted institutional reforms, and private sector alignment have collectively redefined entrepreneurship as a structural pillar of economic advancement. Drawing upon a synthesis of longitudinal economic data, comparative policy frameworks, and a refined production function incorporating entrepreneurship as a distinct variable, the research adopts a multidisciplinary lens. It evaluates key dynamics such as venture investment flows, research and development spending, and startup proliferation between 2005 and 2024. Through the construction of a comprehensive entrepreneurship performance index and the estimation of an entrepreneurship-augmented growth model, the analysis captures both the macroeconomic contribution and the policy effectiveness behind Korea’s startup landscape. The findings underscore that entrepreneurship in Korea functions not as a peripheral activity but as an embedded mechanism for addressing core economic vulnerabilities, including demographic contraction, employment mismatches, and structural dependence on large conglomerates. The paper concludes that Korea’s model, characterized by institutional agility and strategic foresight, offers instructive insights for nations navigating post-industrial transitions. Its broader significance lies in demonstrating how entrepreneurship, when interwoven into national policy, education systems, and regional development, can serve as a lever for sustainable competitiveness. Rather than offering a universal blueprint, the Korean experience presents a flexible framework adaptable to diverse socio-economic contexts, especially in emerging and resource-transitioning economies.
The contemporary landscape of the insurance industry has been drastically changed alongside the introduction of state-of-the-art technologies like Artificial Intelligence (AI), machine learning, big data, blockchain, and InsurTech. The present study traces the evolution of digital transformation in this sector through a bibliometric analysis of data published between 2015 and 2024 and indexed in the Scopus database. The dataset, consisted of 972 articles, could help identify publication trends, thematic focus areas, and collaborative networks. The findings suggested a rapidly expanding literature base with increasing scientific production in recent years due to the accelerated adoption of technology within the sector. The US, China, and India emerged as the dominant countries in their contribution to publications; in addition to their substantial influence in the field due to active national research programs. International co-authorship occupied around one-quarter of the publications, which demonstrated collaboration among global researchers in this topic. This article filled the existing research gap by examining the correlation between digital transformation and insurance with a bibliometric analysis, while drafting policy documents revealed more topics for discussion and patterns for collaboration. Valuable guidance was provided to policymakers and industrial stakeholders to identify the key strengths in the field with the emergence of AI applications and blockchain technology; furthermore, emphasis was placed in the areas for further research and concerted efforts.
Transit time in the transportation and logistics sector is typically governed either by contractual agreements between the customer and the service provider or by relevant regulatory frameworks, including national laws and directives. In the context of postal services, where shipment volumes frequently reach millions of items per day, individual contractual definitions of transit time are impractical. Consequently, transit time expectations are commonly established through regulatory standards. These standards, as observed in numerous European Union (EU) countries and Serbia—the focus of the present case study—define expected delivery timelines at an aggregate level, without assigning specific transit time to individual postal items. Under this conventional model, senders are often unaware of the exact delivery schedule but are provided with general delivery expectations. An alternative approach was introduced and evaluated in this study, in which the transit time is explicitly selected by the sender for each shipment, offering predefined options such as D+1 (next-day delivery) and D+3 (three-day delivery). The impact of this individualized approach on operational efficiency and process organization within sorting facilities was examined through its implementation in a national postal company in Serbia. A comparative analysis between the traditional aggregate-based model and the proposed individualized model was conducted to assess variations in process management, throughput efficiency, and compliance with quality standards. The findings suggest that the new approach enhances the predictability of sorting operations, improves resource allocation, and facilitates more flexible workflow planning, thereby contributing to higher overall service quality and customer satisfaction. Furthermore, it was observed that aligning operational processes with explicitly defined transit time commitments can lead to more efficient industrial process management in logistics and postal centers.
SolidWorks used an optimization approach from the authors to strengthen the structural quality of edge weld designs. The current standard approaches for edge weld analysis evaluation remain insufficiently developed which causes limitations to the functionality of SolidWorks simulation software. A modern weldment analysis procedure stands as the selected research method to predict outcomes across various conditions through weld parameter definition. The SolidWorks simulation model provides an advanced method to construct 3D frame structures with edge-welding through precise weld specifications and effective boundary definition. Standard welding processes together with analytical methods affect outcome precision because weld measurements showed differences from projected values. The design process will split weld component inspections into two separate outcomes which will distinguish between passable dimensions and those that need additional evaluation. The scientific research confirms that all structures require weld modifications whenever external forces surpass either 2000 N or 3000 N during analysis. Results show that maximum stability requires either robust welds or reduced safety procedures or better welding electrodes according to the research data. Engineers leverage this simulated platform as it helps evaluate welded structure loading patterns to improve their live design work. Virtual data processing together with actual application parameters allows engineers to build precise weld designs producing better responses predictions for modern welded frameworks in operational environments.
Automobiles play a vital role in daily life, providing suitable and efficient transportation for work, school, and errands. They also support essential services like emergency response, goods delivery, and public transportation systems. This increased variety means that car manufacturers are competing intensely to attract customers and maximize their profits. However, making the right choice when buying a car can be challenging due to the wide range of factors to consider. This study introduces a new approach that uses Dombi operators combined with T-spherical fuzzy numbers (T-SFNs) to help improve the decision-making process. This method reduces the uncertainty and imprecision that often comes with decision-making, especially when selecting a car. The aim is to help customers make better, more informed choices and avoid financial difficulties. To achieve this, the study develops several innovative operators namely T-spherical fuzzy Dombi weighted averaging (T-SFDWA), T-spherical fuzzy Dombi ordered weighted averaging (T-SFDOWA), T-spherical fuzzy Dombi weighted geometric (T-SFDWG), T-spherical fuzzy Dombi ordered weighted geometric (T-SFDOWG). These methods offer flexibility, suppleness and can adapt to real-world problems where factors are constantly changing. By managing uncertainty and hesitation effectively, these approaches help decision-makers evaluate complex situations with multiple variables. A practical example, such as choosing a car, demonstrates how these approaches can evaluate important criteria like price, safety, and fuel efficiency. Ultimately, these methods ensure that consumers can make the best decision, even in uncertain and complex situations.
Rapid urban traffic growth often outpaces infrastructure development, especially at intersections, leading to safety risks and congestion. Roundabouts are increasingly implemented to improve traffic flow, safety, and environmental sustainability. However, the environmental and social dimensions of roundabout design remain underexplored. This study conducts a systematic review and bibliometric analysis of 1,000 publications from 2020 to 2024 to evaluate roundabout designs at unsignalized intersections. Key trends, themes, and research gaps are identified using bibliometric mapping and performance metrics such as Level of Service (LOS), vehicle delays, queue lengths, and emission levels. Findings highlight the effectiveness of roundabouts in enhancing urban traffic efficiency, reducing congestion, improving safety, and lowering environmental impacts. Despite these benefits, challenges persist in adapting roundabout designs to diverse urban settings and ensuring public acceptance. The study recommends adaptive and sustainable roundabout designs tailored to specific regional conditions. It also emphasises the integration of emerging technologies, such as smart traffic monitoring systems, to optimise performance. These insights offer guidance for urban planners and policymakers in rapidly urbanising areas.
The accurate segmentation of visual data into semantically meaningful regions remains a critical task across diverse domains, including medical diagnostics, satellite imagery interpretation, and automated inspection systems, where precise object delineation is essential for subsequent analysis and decision-making. Conventional segmentation techniques often suffer from limitations such as sensitivity to noise, intensity inhomogeneity, and weak boundary definition, resulting in reduced performance under complex imaging conditions. Although fuzzy set-based approaches have been proposed to improve adaptability under uncertainty, they frequently fail to maintain a balance between segmentation precision and robustness. To address these challenges, a novel segmentation framework was developed based on Pythagorean Fuzzy Sets (PyFSs) and local averaging, offering enhanced performance in uncertain and heterogeneous visual environments. By incorporating both membership and non-membership degrees, PyFSs allow a more flexible representation of uncertainty compared to classical fuzzy models. A local average intensity function was introduced, wherein the contribution of each pixel was adaptively weighted according to its PyFS membership degree, improving resistance to local intensity variations. An energy functional was formulated by integrating PyFS-driven intensity constraints, local statistical deviation measures, and regularization terms, ensuring precise boundary localization through level set evolution. Convexity of the energy formulation was analytically demonstrated to guarantee the stability of the optimization process. Experimental evaluations revealed that the proposed method consistently outperforms existing fuzzy and non-fuzzy segmentation algorithms, achieving superior accuracy in applications such as medical image analysis and natural scene segmentation. These results underscore the potential of PyFS-based models as a powerful and generalizable solution for uncertainty-resilient image segmentation in real-world applications.
This research emphasizes analyzing existing transport logistics systems of the state, detecting problems within every mode of transport, and proposing solutions for them to advance towards the sustainable development of multimodal logistics. It also looks into how the nation's logistic infrastructure can be optimized, and challenges associated with shifting from one mode of transport to another within the Indian transport system are considered as such changes are deemed necessary to remedy the structural imbalance. Ex-ante and ex-post evaluations of the funding strategies were carried out as life cycle assessments using OpenLCA. The software and eco-invent database concluded that the new modal infrastructure would be less damaging when utilized than the available one. Building rail shipments' share of the total to 45% would significantly mitigate the adverse effects on the environment that the current structure of the modalities of freight transport. In addition, it was found that, hence why the changes were made, the displacement of transportation brought down global warming impacts by a commendable 9%, as well as the effects of emissions in ecotoxicity in the land, ocean and freshwater by 20% on average. These results highlight the need to boost rail traffic and build railway infrastructure as the most efficient strategy towards positive outcomes. The research admits some data-sourced weaknesses, but it contributes to appreciating the need to put in place an appropriate transport system that is environmentally sound for the country's anticipated development.
Heating, ventilation, and air-conditioning (HVAC) systems have been identified as major contributors to global energy consumption, underscoring the urgency of optimizing their performance for economic and environmental sustainability. This review presents a comprehensive examination of the thermofluid behavior, mathematical modeling techniques, and optimization strategies employed in HVAC systems. Particular emphasis is placed on the development and implementation of dynamic and steady-state models that enable predictive analysis and performance forecasting. The inherently nonlinear and time-varying nature of HVAC systems has necessitated the adoption of advanced computational approaches, including artificial intelligence (AI), machine learning (ML), genetic algorithm (GA), and simulated annealing (SA), to enhance system responsiveness and occupant comfort. AI- and ML- based control strategies have been shown to improve adaptability to real-time environmental and occupancy changes, thereby increasing operational efficiency. However, these approaches are often constrained by high data requirements and computational complexity. Multi-objective optimization frameworks have been proposed to balance energy efficiency with environmental impact, yet challenges remain regarding precision, scalability, and the seamless integration of emerging technologies. The application of digital twin technology has recently gained traction as a viable solution for real-time simulation and virtual testing, offering a non-intrusive means of performance evaluation and system tuning. It is suggested that the future of HVAC optimization lies in the convergence of classical thermodynamic and fluid dynamic modeling with intelligent control architectures, enabling the development of adaptive systems capable of autonomous decision-making. This integrated modeling paradigm is anticipated to support advancements in energy-aware design, occupant-centric climate control, and sustainable building operation. Through this synthesis of traditional and data-driven methodologies, new pathways were proposed for achieving robust, scalable, and intelligent HVAC systems that respond efficiently to evolving environmental and user-specific demands.
This study explores the application of Value Management (VM) and Value Engineering (VE) within Malta’s public service sector, focusing on environmental projects. Traditional cost-centric procurement approaches are proving insufficient in an era of heightened economic volatility, technological disruption, and increasing demands for public accountability. This research advocates for a structured, value-based decision-making framework that balances cost with quality, efficiency, and long-term sustainability considerations. A mixed-method, quasi-experimental design was employed, using Ambjent Malta as a case study. The methodology involved pre-intervention data collection, stakeholder information sessions, and post-intervention evaluation. Three environmental projects were analysed through stakeholder engagement workshops, incorporating VM tools such as function analysis and value criteria evaluation. The results demonstrate that VM can effectively address cost overruns, promote stakeholder collaboration, and improve project alignment with client and environmental objectives. Significant increases in stakeholder awareness and understanding of VM principles were observed, alongside a reported shift from cost minimisation to value optimisation in project planning. This research contributes to the limited knowledge of VM in the Maltese context. It underscores the potential of value-based methodologies to enhance public sector project outcomes and long-term efficiency.
This study's primary goals are to: Identify any damage that may have happened to the structural elements of steel I-girder-concrete composite spans; determine the static responses according to the influence of the vehicle and service loads (loads combination case) using numerical static analysis FEM using CSI-Bridge Ver. 25; measure the natural frequency of the bridge structure according to the influence of self-weight of the structure using modal analysis; determine the dynamic responses due to vehicle live load using numerical dynamic time history analysis by using Finite Element Method (FEM); assess the constructional effectiveness of bridge structures and identify methods for reinforcing and repairing damaged structural elements. Damage inspection results of steel I-girder span showed that the damage is not severe in the structural parts of span. Steel I-girders span shows no signs of rust or corrosion, but the main problem is in the expansion joints and they need to be repaired or replaced. Under the effect of vehicles live load and load combinations, maximum tensile stress appeared at the bottom of steel I-girder span, which was 13.56MPa and 86MPa respectively, lowering than the allowable value of tensile stresses from AASHTO LRFD BRIDGE, which is equal to 207MPa. The maximum deflection in the downward direction due to vehicles load and load combination was 10.9 mm and 91 mm, respectively. Meeting the allowable deflection values of 70 mm (live load) and 112 mm (loads combination). The Finite Element dynamic analysis described that the average value of vibration frequency is 6.42Hz. Compared with natural frequency, it is higher than 2.95Hz, indicating that the span of bridge will face vibration issues because this span has a long length. Therefore, this study recommended that to add more steel girders with more diaphragms (cross beams) to reduce the vibration of bridge span.
Poverty remains a pervasive and multifaceted challenge in developing countries, posing critical impediments to sustainable economic and social development. In alignment with the core objectives of the United Nations Sustainable Development Goals (SDGs), the present study aims to identify, evaluate, and prioritize the most effective poverty alleviation strategies within the context of developing economies. Through an extensive review of existing literature and expert consultation, seven primary strategies were identified, encompassing economic growth stimulation, economic and institutional reforms, prioritization of the basic needs of impoverished populations in national development policies, promotion of microfinance institutions and programs, development and improvement of marketing systems, provision of incentives to the private sector, and implementation of affirmative actions such as targeted cash transfers. To systematically assess the relative importance of these strategies, the Stepwise Weight Assessment Ratio Analysis (SWARA) technique was employed within a Fermatean fuzzy (FF) environment. The application of this hybrid method facilitated the extraction of nuanced expert judgments, thereby enhancing the robustness and credibility of the prioritization process. The findings indicate that fostering economic growth, implementing structural economic and institutional reforms, and promoting microfinance institutions and programs represent the most impactful and actionable strategies for poverty reduction. These results offer valuable insights for policymakers, development agencies, and stakeholders engaged in formulating targeted interventions to accelerate poverty eradication. The integration of the FF-SWARA approach further demonstrates its applicability in complex multi-criteria decision-making (MCDM) scenarios characterized by uncertainty and imprecise information, particularly in the domain of sustainable development planning.
Open Access
, 