A doppler-exclusive computational diagnostic framework to enhance conventional 2-D clinical ultrasound with 3-D mitral valve dynamics and cardiac hemodynamics.

This study aims to formulate a strategic plan for financing management for the Islamic Education Foundation (YPI) through the utilization of Generative AI for the 2025-2035 period. Using a mixed methods approach, this study integrates a Systematic Literature Review (SLR) to build a conceptual model of AI's role with a qualitative case study of the Ulul Albab Social Education and Da'wah Foundation in Malang City, East Java, Indonesia. The SLR findings identify the crucial role of Generative AI in donation prediction, accountability automation, and strategic scenario design. Meanwhile, analysis of the Ulul Albab Foundation reveals a reliance on traditional funding and limited long-term projections. Through prompt engineering, Generative AI successfully designed an "Adaptive Balance Scenario" as the most feasible roadmap, combining philanthropic automation with diversification of independent business units. This study concludes that Generative AI functions as a strategic designer, transforming financial management from a reactive to a proactive and data-centric approach. Its gradual implementation over a decade is key to achieving sustainable and resilient financial independence for foundations.

Groundwater is increasingly relied upon in cities, particularly during drought, yet its management often lacks coordination and systems-based decision-making. Effective governance requires inclusive participation across sectors and scales, engaging actors with diverse knowledge, experiences, and priorities. In cities, this is challenging due to the wide range of roles and responsibilities tied to groundwater. This study examines the value of urban water metabolism analysis (UWMA) for enhancing groundwater governance in Cape Town and Nelson Mandela Bay, South Africa—both recently affected by severe drought. Through a series of Learning Labs, we convened groundwater-related actors to co-develop a shared understanding of urban water systems. We brought together two methods of systems enquiry, UWMA and governance network analysis to explore physical stocks and flows of water across metropolitan boundaries with governance processes shaping groundwater management. The UWMA revealed that, prior to the 2015 drought, Nelson Mandela Bay’s water supplies were more diversified than those of Cape Town, despite Cape Town progressively pursuing managed aquifer recharge and wastewater reuse. The governance analysis surfaced the diversity of actors influencing groundwater flows across the public, private, and civil society sectors, yet highlighted the fragmented nature of the network, with geohydrology and engineering consultants often acting as intermediaries. This research found that UWMA was perceived to be most useful at larger scales (e.g., watershed/urban scales) and was considered a valuable tool for strategic discussion, though clearer language would increase accessibility. We conclude that UWMA helps identify knowledge gaps, integrate diverse perspectives, and foster stakeholder cooperation. Coupled with scenario planning, it can support participatory and inclusive decision-making.

Research methodology This case was developed using a grounded theory approach and ethnographic observation. The authors conducted 14 semistructured interviews with internal stakeholders − including the founder, family members and staff − and gathered on-site field notes during immersive visits to elcielo’s Bogotá and Medellín locations. Partial financial data were disclosed to support internal projections and scenario planning. In addition, experiential data were collected through customer sessions, and preliminary biometric analyses (EEG and GSR) were reviewed to illustrate the potential business applications of neuromarketing. No external benchmarking was included, which enhances internal validity by preserving the authenticity of the firm’s perspective, though it limits direct cross-case comparison. Case overview/synopsis This case examines elcielo, a pioneering fine-dining restaurant founded in Medellín, Colombia, by chef and entrepreneur Juan Manuel Barrientos (JuanMa). Celebrated for its inclusion among Latin America’s 50 Best Restaurants, elcielo redefines traditional dining through a series of immersive “moments” − multisensory culinary experiences that blend neuroscience, storytelling and Colombian heritage. Rather than allowing customers to choose their dishes, elcielo curates a journey designed to trigger emotions, memories and sensations through food. After successful expansions to Bogotá and Miami, JuanMa now stands at a strategic crossroads. The case centers on a critical decision: how to scale elcielo without compromising its creative integrity. Three strategic paths are under consideration: optimizing operational efficiency and lunchtime traffic in domestic markets, accepting investor funding to accelerate international growth − at the risk of losing creative autonomy or launching a standalone venture focused on neuromarketing and experiential design that extends the brand beyond restaurants. Each alternative presents distinct trade-offs in terms of scalability, control, brand coherence and long-term sustainability. Students are challenged to evaluate the feasibility and alignment of each strategic option with elcielo’s core identity and values. The case is designed for MBA and executive education audiences and is particularly relevant for courses in strategy, innovation, entrepreneurship and brand management. It encourages critical reflection on the growth dilemmas faced by founder-led businesses operating in creative industries, and prompts debate around the balance between artistic vision and commercial scalability. Complexity academic level This case is well-suited for courses in strategic management, innovation, branding and entrepreneurship. It is especially effective in capstone modules where students are expected to analyze complex trade-offs involving growth, autonomy and brand authenticity, applying conceptual frameworks to real-world scenarios.

AI-driven scenarios for urban mobility: Quantifying the role of ODE models and scenario planning in reducing traffic congestion

This study presents a UAV-based fuzzy inference framework for real-time air quality monitoring that integrates symmetric and asymmetric fuzzy rules. Symmetric rules capture baseline pollutant dynamics, ensuring computational stability, while asymmetric rules account for local anomalies, turbulence, and environmental disturbances, effectively regularizing the inherently ill-posed backward problem of reconstructing pollutant concentrations from noisy UAV measurements. Simulation and field experiments demonstrate that this hybrid fuzzy approach provides both mathematical robustness and practical reliability, outperforming purely symmetric models in dynamic, asymmetric environments. The proposed framework offers a generalizable methodology for environmental monitoring, emphasizing the critical role of symmetry and its breaking in modeling real-world ecological processes. Future developments will focus on atmospheric dispersion integration, fuzzy rule optimization, and large-scale UAV deployment. The results indicate that the hybrid fuzzy inference system can enhance the accuracy and reliability of UAV-based air quality monitoring under real-world disturbances, providing a robust framework applicable for urban planning, environmental policy, and large-scale deployment scenarios.

Emerging lessons on how participatory scenario planning can support sustainable land management and climate resilience

Exploring the future of Mobility as a Service (MaaS): A co-design approach to scenario planning in European cities

ABSTRACT Fusion energy is increasingly recognized as a potential game‐changer in addressing the grand challenge of achieving deep decarbonization while ensuring long‐term energy security. Recognizing the uncertainty surrounding fusion energy's technological maturity, commercialization timelines, and cost trajectories, this study adopts an anticipatory foresight approach tailored to high‐uncertainty contexts. The research employs a mixed‐methods framework incorporating horizon scanning, expert elicitation, trend analysis, and exploratory scenario planning. These methods were selected to account for deep technological uncertainty (e.g., plasma containment breakthroughs, cost convergence, fuel supply chain development), as well as systemic uncertainties related to sociopolitical acceptance and infrastructure readiness. For the case of Saudi Arabia, three distinct scenarios—Optimistic, Moderate, and Conservative—are developed to reflect a spectrum of plausible futures. Under the Optimistic Scenario, fusion could supply 10%–15% of Saudi Arabia's electricity mix by 2045 (50–75 TWh annually). The Moderate Scenario forecasts a 5%–10% contribution by 2050 (25–50 TWh), while the Conservative case sees fusion reaching under 5% by 2060 (< 25 TWh). These projections are framed within the broader uncertainty landscape, with sensitivity analyses on cost assumptions, technological learning curves, and policy interventions. A comparative assessment of anticipatory methodologies under these uncertainty levels underscores the limitations of deterministic forecasting and the value of scenario‐based planning in guiding long‐term energy policy. While fusion's economic feasibility remains uncertain, potential cost parity with advanced nuclear fission and gas‐fired plants by mid‐century is plausible. The paper concludes with strategic policy recommendations to reduce uncertainty and accelerate fusion adoption: increasing national R&D funding, fostering international and public‐private collaborations, investing in adaptive grid infrastructure, and developing flexible regulatory frameworks.

Welfare-Based Healthcare Planning: Methodology and Application to Thoracic Surgical Treatment of Lung Cancer in Germany.

The rapid increase in the number of online job portals brings new challenges in the recruitment process, including the posting of job advertisements aimed to exploit job seekers in fraudulent practices. In this paper, we presents a detailed machine learning-based approach to identifying fraudulent job postings integrating Natural Language Processing (NLP) and ensemble learning. For a dataset of 5,000 job postings, we developed a binary classification model using TF-IDF vectorization coupled with an XGBoost classifier and attained an accuracy of 94.2%. The system incorporates SHAP (SHapley Additive exPlanations) to address model interpretability for the various stakeholders in a prediction scenario. We also created an interactive web app using Streamlit which allows users to analyze a single job, as well as, import files for batch predictions. For the first time, we propose a comprehensive approach for fraud detection which integrates feature extraction from the body of a job posting, suspicious keyword lists, and contact number analysis. Above all, we demonstrate that our model exceeds the performance of a standard logistic regression baseline by 8.3% in F1-score, especially for the detection of advanced fraudulent job postings.

Purpose. This study aims to develop innovative pricing models for digital educational products in higher education institutions, focusing on diversification and digitalization, especially for short-term and online programs. The research addresses a significant gap in pricing methods for online courses and digital learning materials, which have become increasingly important as universities expand beyond traditional degree programs. Methodology. The study uses a multi-method approach combining comparative analysis of European pricing practices, typological classification of educational products, and mathematical modeling for cost calculation and price setting. Mathematical algorithms are created for both synchronous and asynchronous online courses, integrating cost-based foundations with market-driven adjustment coefficients that reflect institutional reputation, seasonal demand, level of innovation, digital marketing effectiveness, content relevance, labor market needs for skills, and instructor expertise. Results. The study presents a comprehensive two-part typology that distinguishes traditional educational products (degree programs, professional development courses, corporate training) from online programs (synchronous online courses, asynchronous video courses, electronic materials). Each type shows different cost structures, demand elasticity, and scalability potential. The proposed pricing algorithms factor in fixed costs, variable costs per student, marketing expenses, and nine adjustment coefficients ranging from 0.80 to 1.40, with built-in limits to prevent overpricing. The research shows that digital products have high demand elasticity, substantial economies of scale due to minimal marginal costs, and need to incorporate behavioral and value-based factors along with traditional cost calculations. Financial modeling includes break-even analysis and scenario planning for various enrollment levels. Practical implications. The models developed enable universities to set prices that are economically justified and competitive, adopt flexible pricing strategies (such as subscriptions, freemium, pay-per-course), predict financial outcomes under different scenarios, optimize marketing spending, and diversify revenue sources, especially important during wartime when government funding is limited and institutional relocations have disrupted operations. Value/originality. This research fills a critical methodological gap by developing concrete, formula-based pricing algorithms specifically for digital learning environments. The integration of behavioral factors, quality measures, and labor market relevance with cost-based calculations presents a new approach to competitive pricing in higher education, situated within Ukraine's wartime context and the urgent need for financial independence while maintaining educational accessibility.

Subject. This article discusses the issues of development of the hospitality industry. Objectives. The article aims to develop tools that can help assess the economic efficiency of the hotel business in the context of digital transformation and high competition. Methods. For the study, I used the methods of systems and comparative analyses, scenario planning, and economic-mathematical modeling. Results. The article presents a comprehensive methodology for the quantitative assessment of customer loyalty programme effectiveness taking into account financial and non-financial factors. Conclusions. Considering non-financial effects when calculating return on investment improves the justification of managerial decisions. The results obtained expand the theoretical foundation of marketing in the service sector.

Abstract. With the rapid development of low-altitude economy in China, path planning for low-altitude aircraft faces challenges such as strong environmental dynamics, complex constraints, and difficult real-time decision-making. To achieve efficient and universal path planning capabilities, this paper proposes a three-layer Spatiotemporal knowledge graph(3L-STKG) architecture comprising a conceptual layer, an instance layer, and a spatiotemporal layer. Guided by ontological spatiotemporal knowledge, this architecture enables efficient route planning in complex scenarios and addresses the challenges of path planning in low-altitude environments. The proposed method pre-matches the maneuverability constraints from the conceptual layer with the grid traversal attributes of the spatiotemporal layer through a cross-layer semantic association mechanism, dynamically constructing an accessible correlation network. On this basis, the path planning problem is transformed into a minimum weighted connected subgraph search problem in the ontology-constrained spatiotemporal network, and an improved A* algorithm is used to solve for the global optimal path. The experimental results show that the planning time in specific scenarios is reduced by 88.54% compared with traditional methods, while supporting rapid adaptation to multiple aircraft types. The research results provide a theoretical framework for intelligent decision-making of low-altitude aircraft in complex environments and have broad application prospects in fields such as emergency rescue and urban logistics.

This study aims to develop and validate a mathematical model for estimating the number of emergency physicians required in post-earthquake scenarios, using actual data from the 2023 Kahramanmaraş earthquakes in Türkiye. The methodology follows a structured five-step framework to assess earthquake impact and emergency healthcare demand. First, population impact is analyzed using USGS PAGER data to estimate exposure levels. Second, household and building stock characteristics are profiled from TurkStat, focusing on construction year and building types, which influence structural vulnerability. Third, collapse probabilities are determined through empirical fragility functions that relate earthquake intensity to building failure rates. Fourth, casualties are estimated by combining structural damage with fatality and injury ratios specific to building types. Finally, physician demand is calculated using the M/M/s queuing theory model, incorporating key variables, such as emergency healthcare capacity, patient arrival rates, and examination duration, to forecast medical staffing needs in the aftermath of a disaster. The model estimated 11,645 potential emergency department visits in Hatay province within the first 144h post-earthquake. Based on this, 27 emergency physicians per shift-or 81 total physicians across three shifts-are required to operate at full capacity. These figures closely align with actual post-disaster hospital admission data, validating the model. This study presents a scalable, actual-data-validated model for physician workforce planning in disaster scenarios. The queuing-based approach supports strategic resource allocation and enhances organizational resilience. Unlike existing models, this study directly integrates field-specific damage and population data to forecast real-time health system needs.

Because the issue of distributed generation of electricity through photovoltaic plants is a recent topic in the accounting field, accountants still have doubts regarding the hypotheses of tax incidence, rates and calculation basis, as well as the economic classification that should be given to these activities, especially in order to provide the taxpayer with the greatest possible tax savings in a tax planning scenario. Therefore, a comparative study was carried out to define the best income tax regime to be applied to a 75-kilowatt photovoltaic power plant and, through tax avoidance, to establish the best tax regime: actual profit, presumed profit, simplified national tax regime, or individual income tax, and to mathematically prove that the latter system is the least rational, demonstrating which regime can provide the greatest savings for the income tax payer.

Adaptive management under volatile economic conditions is becoming a decisive factor for the development sector, where long investment cycles, high risks, and dependence on external factors converge. The growing economic turbulence, currency fluctuations, inflationary pressures, and regulatory transformations create an environment in which conventional management models lose their effectiveness. Adaptability emerges not only as a survival mechanism but also as a strategic development tool that ensures resilience, flexibility, and rapid managerial responsiveness. The essence of the adaptive approach lies in the continuous adjustment of actions, strategies, and processes in accordance with the changing external environment. In the field of real estate development, this model integrates digital technologies, business analytics, forecasting, and scenario planning. The foundation of modern adaptive architecture is the integration of information systems – BIM, ERP, CRM, and Business Intelligence – which provide end-to-end control over financial, technical, and operational processes. As a result, management acquires a proactive nature focused on preventing deviations rather than eliminating their consequences. Economic instability generates the need for flexible decision-making systems based on analytical instruments such as GAP, FMEA, and PESTEL analyses. These methods help identify strategic gaps, anticipate critical risks, and forecast external influences, forming a multilevel adaptation framework. In combination with digital KPI dashboards, they create a structure of adaptive controlling that records key performance indicators in real time, ensures informational feedback, and enhances managerial transparency. The effectiveness of adaptive management depends not only on technological advancement but also on an organization’s readiness for internal transformation. Flexible management systems require decentralization of decision-making, delegation of authority, transformation of corporate culture, and the implementation of Agile and Lean Construction principles. This approach minimizes institutional barriers, accelerates communication, and shortens the time lag between risk detection and managerial response. The development of adaptive models in the real estate business enables the integration of big data analytics, artificial intelligence, and machine learning to build predictive models that account for changing demand, resource costs, and macroeconomic parameters. Systematic implementation of these approaches shapes a new management paradigm – dynamic, analytically grounded, and digitally integrated. Adaptive management in real estate development transforms the very logic of strategic thinking: from reactive responses to anticipatory forecasting, from fixed plans to variable scenarios, from intuitive decisions to data-driven analytical models. As a result, companies gain the ability not only to maintain stability under crisis conditions but also to turn market fluctuations into a catalyst for innovative growth, creating new competitive advantages in the global environment.

The integration of digital technologies into the process of construction project management forms a new paradigm of planning, control, and decision-making in the industry. The development of digital platforms, artificial intelligence, IoT systems, ERP, and BIM technologies transforms classical static management models into dynamic, analytically oriented systems capable of responding to changes in real time. Digitalization ensures the creation of a continuous flow of information among all participants in the project cycle, providing conditions for automated rescheduling of works, resource optimization, and risk management. A key role in this process is played by intelligent platforms that implement the principle of reconfigurable planning. They enable rapid adaptation of schedules and control procedures depending on changes in the external and internal environment. This approach allows for improved forecasting accuracy of time and cost, reduced deviations, and greater transparency in project implementation. The integration of IoT technologies, artificial intelligence, and cloud solutions creates opportunities for analytical management in real time. Scenario modeling, digital twins, and GIS systems form the basis for developing predictive scenarios of project development. They make it possible not only to model alternative implementation options but also to assess risks, formulate response strategies, and create prerequisites for the resilience of construction enterprises to unforeseen changes. As a result, the integration of digital technologies into construction management ensures the transition from reactive to proactive management, where decisions are made based on real data and analytical models. Thus, the modern digital ecosystem of construction project management combines tools for forecasting, analysis, optimization, and control, forming a comprehensive architecture capable of self-adaptation to new conditions. This opens prospects for creating “smart” construction enterprises, where every element – from design to operation – is subordinated to a unified information and analytical logic.

Purpose: The Arthaśāstra of Kautilya, written over two millennia ago, remains one of the most comprehensive treatises on governance, economics, and strategic management. Far beyond its relevance to the Mauryan Empire, it presents timeless insights that resonate with contemporary corporate strategy. This paper explores the parallels between Kautilya’s principles of statecraft and the frameworks employed in modern management. The emphasis is on how concepts such as dandanīti (rule of law), mandala theory (alliances and rivalries), and arthashakti (economic strength) can be adapted to corporate governance, stakeholder management, and competitive positioning. Additionally, Kautilya’s strategic foresight in risk assessment, contingency planning, and long-term sustainability is compared with modern business practices such as scenario planning, SWOT analysis, and corporate social responsibility. Methodology: In this paper, the exploratory qualitative research method is used. The relevant information is collected using keyword-based search in Google search engine, Google Scholar search engine, and AI-driven GPTs. This information is analysed and interpreted as per the objectives of the paper. Analysis/Results: The fusion of ancient wisdom and modern management demonstrated how Kautilya’s timeless principles extended beyond their historical context to remain relevant in contemporary practice. His insights into statecraft, strategy, and ethics offered lessons in organizational governance, competitive positioning, and sustainable growth. By integrating these values with present-day management theories, businesses could balance profit with responsibility, efficiency with ethics, and innovation with stability. This convergence not only validated the universality of Kautilya’s thought but also served as a practical roadmap for companies striving to stay competitive while maintaining accountability to society and future generations. Originality/Values: By analysing Kautilya’s multidimensional view of power—economic, political, and ethical—this study argues that the Arthaśāstra offers not only a historical framework but also a forward-looking model for resilient leadership in dynamic business environments. The paper concludes that integrating Kautilya’s insights into corporate strategy can help modern leaders balance profit with prudence, ethics with expediency, and competition with collaboration, thereby providing a unique synthesis of ancient wisdom and contemporary managerial practice. Type of paper: Qualitative Exploratory Research Paper.

Predicting the properties of deep-sea sediments offers critical insights into past oceanic conditions, including sediment composition, stratigraphy, and geochemical signals. However, accurate prediction is hindered by the high spatial variability of these sediments. This study presents a data-driven machine learning framework to predict five key sediment properties. Five prediction scenarios were developed with tailored preprocessing and hyperparameter tuning, and Shapley additive explanations were employed to assess feature importance and the relationships between depth and sediment properties. Among the five tested algorithms, the extreme gradient boosting (XGBoost) model achieved the highest predictive performance. Depth and compressional wave velocity emerged as the most and second most influential features for estimating porosity, grain density, calcite content, and thermal conductivity. The depth-dependent predictions with quantified uncertainties generated by the XGBoost model demonstrate that the proposed framework provides a robust approach for predicting deep-sea sediment properties.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-29402-7.