Search Results (309)

In developed countries such as Spain, where the population is increasingly aging, retirement planning and longevity risk represent major societal challenges. In Spain, in particular, a significant proportion of household wealth is concentrated in real estate, primarily in the form of owner-occupied housing. For this reason, one emerging financial product in the retirement savings space is the reverse mortgage (RM). This study examines the determinants of acceptance of this financial product using survey data collected from Spanish individuals. The intention to take out an RM is explained through performance expectancy (PE), effort expectancy (EE), social influence (SI), bequest motive (BM), financial literacy (FL), and risk (RK). The analysis applies machine learning techniques: decision tree regression is used to visualize variable interactions that lead to acceptance; random forest to improve predictive capability; and Shapley Additive Explanations (SHAP) to estimate the relative importance of predictors. Finally, Importance–Performance Map Analysis (IPMA) is employed to identify the variables that merit greater attention in the acceptance of RMs. SHAP values indicate that PE and SI are the most influential predictors of intention to use RMs, followed by BM and EE with moderate importance, whereas the positive influence of RK and FL is more reduced. The IPMA highlights PE and SI as the most strategic drivers, and RK and BM act as relevant barriers to the widespread adoption of RMs. Full article

In Turkey, flexible pavement containing bituminous material is widely preferred on highways. Rigid pavement, which is based on concrete, is generally used in small-scale, specific projects. This situation, which has arisen due to historical and technical reasons, has also brought with it certain prejudices against rigid pavement applications. A review of the literature reveals that many factors influence the choice of highway pavement type, but decision-makers tend to make their selection based on the most important factors, disregarding other parameters. The lack of a systematic factor analysis is a shortcoming in this regard. In this research, a combined multi-criteria decision-making study was conducted, including the neglected factors, to address this technical deficiency in the pavement type selection process. Through detailed analysis, parameters likely to influence pavement type selection were identified and analyzed using the hybrid AHP-TOPSIS approach, guided by the opinions of experts in the field. The analysis shows that comfort (user ride quality), financial, and environmental factors are the most effective main criteria, while maintenance and repair costs, eco-friendliness, and initial construction costs were identified as the most critical sub-criteria influencing the choice of pavement type. Based on the analysis results, a detailed decision support system was presented to decision-makers according to the characteristics of the alternatives obtained. The results highlight the need for decision-making frameworks that prioritize both long-term cost efficiency and user safety, contributing to more sustainable and resilient pavement applications. Full article

Contemporary discussions of AI are often framed according to generally held assumptions which have largely escaped serious critical analysis. For instance, those who promote AI tout its predictive prowess: powerful algorithms fed massive amounts of data are able to discover knowable patterns that can accurately forecast the behaviors in anything from individual movie preferences to financial markets. Armed with this type of knowledge we can then use AI, the hope goes, to be more objective in our ethical practices. And most seriously, we must extend this to the way we develop AI, not only do we want AI to function ethically, but we caution ourselves that if Artificial General Intelligence (AGI), superintelligence, or anything like the “singularity” is ever developed, it should be positively aligned with human values. Reflecting on these positions from the perspective of classical Daoism gives us reason to pause. While Daoist texts also assume there are patterns in the world which one can successfully go along with, they are not enthusiastic about the rational or knowable nature of these patterns—rather, they encourage us to appreciate them as fundamentally complex and mysterious. In this article, some Daoist attitudes are also concretely applied to ethical considerations, which cannot easily be controlled or known, much less put into code. Inspired by Daoist texts, we might cultivate an attitude less filled with hubris than humility, where we are allowed more space from which we can reflect on how we think about AI. Many of the most pressing issues associated with AI could, in fact, be significantly alleviated simply by shifting the way we think about, use, and develop these technologies. Full article

Evaluating green data centers is a multi-attribute decision problem. To enhance the rigor and precision of green data center assessment, this study verifies the weighting of the national green data center evaluation index system using the Analytic Hierarchy Process (AHP) with the participation of 19 domain experts from various data center sectors. The aim is to gain an in-depth understanding of the perspectives and priorities of different types of institutions regarding evaluation indicators and to investigate the underlying reasons for these perspectives and priorities. Through an analysis of expert sample distribution, this paper reveals the preferences of financial, internet, research, and design, as well as technical consulting service institutions, regarding indicators such as energy-efficient utilization, computational resource utilization, green low-carbon development, scientific layout, and intensive construction. Specifically, financial institutions tend to place a relatively lower emphasis on energy efficiency due to their focus on transaction speed and security. In contrast, internet companies prioritize efficient utilization of computational resources. Research and design institutions consider scientific layout and intensive construction more crucial, while technical consulting service institutions emphasize green and low-carbon development. Meanwhile, we identified substantial discrepancies among experts in determining the weights of specific indicators, suggesting a lack of consensus within the industry about the correlation between these indicators and green data centers. To propel the sustainable development of green data centers, future assessments should refine evaluation dimensions, consider disparities such as data center types and embrace regional differences, actively adopt novel technologies and innovative practices, and establish mechanisms for long-term monitoring and evaluation. Full article

The theological rendering of economics in the Bahá’í Faith—particularly from ‘Abdu’l-Bahá—advocated progressive taxation, a strong welfare state, the abolition of trusts, and the redistribution of wealth. These orientations directly diverge from “neoliberal” economic theory, especially as articulated by Frederick Hayek: concerns that social justice exacerbates poverty and claims that progressive taxation is “discrimination.” Despite these seemingly antithetical orientations, there has been a slow and tentative, if not uneasy, meeting of Bahá’í and neoliberal ideals in global organizations and scholarship. Through a comparative analysis of the writings of both ‘Abdu’l-Bahá and Friedrich Hayek, I first illuminate the fundamental disagreements on economy and society between Bahá’í theology and neoliberalism. Second, I cover recent scholarship on the moralization of markets and the sacralization of financial actors in order to contextualize the historical and contemporary unions of theology and economy. Third, I outline how ‘Abdu’l-Bahá’s theological vision and Hayek’s neoliberal theories accrete around four mutual worldviews, which can tempt hermeneutic deemphases of the fundamental divergences in Bahá’í and neoliberal logics: (1) the duality of human nature, (2) the limits of materialist reason, (3) the apotheosis of the market and self-love, and (4) sacrificial submission to transcendent authority. Full article

In this work, we propose a novel quantum-informed epistemic framework that extends the classical notion of probability by integrating plausibility, credibility, and possibility as distinct yet complementary measures of uncertainty. This enriched quadruple (P, Pl, Cr, Ps) enables a deeper characterization of quantum systems and decision-making processes under partial, noisy, or ambiguous information. Our formalism generalizes the Born rule within a multi-valued logic structure, linking Positive Operator-Valued Measures (POVMs) with data-driven plausibility estimators, agent-based credibility priors, and fuzzy-theoretic possibility functions. We develop a hybrid classical–quantum inference engine that computes a vectorial aggregation of the quadruples, enhancing robustness and semantic expressivity in contexts where classical probability fails to capture non-Kolmogorovian phenomena such as entanglement, contextuality, or decoherence. The approach is validated through three real-world application domains—quantum cybersecurity, quantum AI, and financial computing—where the proposed model outperforms standard probabilistic reasoning in terms of accuracy, resilience to noise, interpretability, and decision stability. Comparative analysis against QBism, Dempster–Shafer, and fuzzy quantum logic further demonstrates the uniqueness of architecture in both operational semantics and practical outcomes. This contribution lays the groundwork for a new theory of epistemic quantum computing capable of modelling and acting under uncertainty beyond traditional paradigms. Full article

Population growth, urbanization and climate change are all factors that have made it increasingly difficult for governments to manage, allocate and permit the development and use of the finite land resource that is available in any society. Land administration plays a crucial role in supporting governments in these efforts. The World Bank and other donors have provided financial backing to help governments implement land administration reforms. While many of these reform projects focus on finance and technology, some succeed, while others fail to create lasting, meaningful change. The reasons for this usually do not relate to problems with finance or technology but are often due to political economy factors. Approaches to consider political economy are reviewed. The usefulness and viability of using political economy analysis in the design of land administration reform is confirmed in a policy Delphi survey. An innovative framework to systematically consider the use of political economy analysis in the design of land administration reform is developed and validated. This research addresses a significant gap, as there has been limited exploration of the political economy of land administration. The findings will be valuable for governments, development partners, and specialists involved in designing and implementing large-scale land administration reform initiatives. Full article

Background/Objectives: Pre-anesthetic consultations (PACs), conducted by anesthesiologists, are an established procedure to assess individual perioperative risk in surgical patients. Cancellation of surgery leads to unnecessary PACs, which have to be avoided in times of staff shortage. The aim of the study is to evaluate the costs and time loss of unnecessary PACs at Hannover Medical School, a university hospital in Germany. Methods: All PACs conducted at Hannover Medical School in September 2023 were included in the analysis. The duration and associated costs were calculated based on electronic documentation. Results were compared to data from the university hospital Aachen in Germany. Results: In 4.2% of all PACs, no surgical or interventional procedure was subsequently performed and therefore no anesthesiologic services were required. Reasons for surgery or intervention cancellation included choosing a conservative approach over surgery or proceeding with surgery under local anesthesia without the presence of an anesthesiologist. The additional costs for unnecessary PACs were EUR 1612 which corresponded to 43.2 h of working time. Projected over a year, this would result in EUR 19,344 in costs and 518.4 h of time spent. In comparison to university hospital Aachen, we observed lower cancellation rates after PACs and a greater reliability of the planned interventions and surgeries. Conclusions: The results of this study suggest that the financial and time burden associated with these consultations was not substantial. To ensure optimal use of temporal and financial anesthesiologic resources, it is essential to avoid PACs for patients who will not undergo surgery or intervention. Full article

Ransomware attacks pose a serious threat to global cybersecurity, inflicting severe financial and operational damage on organizations, individuals, and critical infrastructure. Despite their pervasive impact, proactive measures to mitigate ransomware threats remain underdeveloped, with most efforts focused on reactive responses. Moreover, prior literature reveals a significant gap in systematic approaches for predicting such incidents. This research seeks to address this gap by employing time-series analysis to forecast ransomware attacks. Using 1880 ransomware incidents, we decompose the dataset into trend, seasonal, and residual components, fit a time-series model, and forecast future attacks. The results indicate that time-series analysis is useful for uncovering broad, structural patterns in ransomware data. To gain further insight into these results, we perform sub-analyses based on attacks targeting the top five sectors. The findings reveal reasonable predictive performance for ransomware attacks against government facilities and the healthcare and public health sector, with the latter showing an upward trend in attacks. By providing a predictive lens, our model equips organizations with actionable intelligence, enabling preemptive measures and enhanced situational awareness. Finally, this research underscores the importance of integrating time-series forecasting into cybersecurity strategies and seeks to pave the way for future advancements in predictive analytics for cyber threats. Full article

This study investigates economic decision-making behaviors among welfare recipients in Miami, Florida, by leveraging well-established experimental protocols: the Guessing Game, the Prudence Measurement Task, the Risk Aversion Task, and the Stag Hunt Game. For this purpose, our study defines financial decisions as the underlying individual preferences that serve as validated proxies for savings behavior, debt management, job-search intensity, and participation in cooperative finance. A central objective is to compare the behavior of welfare recipients to that of undergraduate students, a cohort typically used in experimental economics research. The analysis reveals significant differences between the two groups in strategic thinking and coordination, particularly across ethnic and gender lines. Non-Hispanic/Latino participants in Miami displayed significantly higher average guesses in the Guessing Game compared to their counterparts in Tucson, indicating potential discrepancies in the depth of strategic reasoning. Additionally, female participants in Tucson exhibited higher levels of coordination in the Stag Hunt Game compared to females in Miami, suggesting variance in cooperative behavior between these groups. Despite these findings, regression models demonstrate that location, gender, and ethnicity collectively account for only a small fraction of the observed variance, as evidenced by low R² values and substantial mean squared errors across all games. These results suggest that individual heterogeneity, rather than broad demographic variables, may be more influential in shaping economic decisions. This study underscores the complexity of generalizing findings from traditional student samples to more diverse populations, highlighting the need for further investigation into the socioeconomic factors that drive financial decision-making. Full article

To enhance the accuracy and reliability of large language models (LLMs) in regulatory question-answering tasks, this study addresses the complexity and domain-specificity of regulatory texts by designing a retrieval-augmented generation (RAG) testing framework. It proposes a dimensionality reduction-based semantic similarity measurement method and a retrieval optimization approach leveraging information reasoning. Through the construction of the technical route of the intelligent knowledge management system, the semantic understanding capabilities of multiple mainstream embedding models in the text matching of financial regulations are systematically evaluated. The workflow encompasses data processing, knowledge base construction, embedding model selection, vectorization, recall parameter analysis, and retrieval performance benchmarking. Furthermore, the study innovatively introduces a multidimensional scaling (MDS) based semantic similarity measurement method and a question-reasoning processing technique. Compared to traditional cosine similarity (CS) metrics, these methods significantly improved recall accuracy. Experimental results demonstrate that, under the RAG testing framework, the mxbai-embed-large embedding model combined with MDS similarity calculation, Top-k recall, and information reasoning effectively addresses core challenges such as the structuring of regulatory texts and the generalization of domain-specific terminology. This approach provides a reusable technical solution for optimizing semantic matching in vertical-domain RAG systems, particularly for MDSs such as law and finance. Full article

Evaluating performance is a necessary and specific process across all sectors and organizational levels, shaped by context, indicators, and purpose. Considering global sustainability transitions, understanding financial performance entails a deeper perspective on technical accuracy, conceptual clarity, and systemic integration. This study investigates how financial performance is assessed and interpreted in sustainability-focused research, drawing on a bibliometric analysis of 490 articles indexed in the Web of Science from 2007 to 2023. Using SciMAT, we traced thematic evolutions and revealed a fragmented research landscape marked by competing theoretical, methodological, and practical orientations. To address this conceptual dispersion, we applied the Analytic Hierarchy Process (AHP) to evaluate five key alternatives to financial-performance assessment (quantitative measurement, definition-oriented reasoning, theoretical frameworks, experiential comparison, and integration with sustainability and ethics) against three conceptual criteria (philosophical depth, holistic scope, and multidisciplinary relevance). The results highlight a strong preference for holistic and integrative models of financial performance, with quantitative measurement ranking highest in practical terms, followed by experiential and sustainability-driven approaches. These results underscore the need to align financial evaluation more closely with sustainability values, bridging short-term metrics with long-term societal impact. By combining diachronic thematic mapping with structured decision analysis, this study advances a more reflective and forward-looking framework for performance research. It contributes to sustainability research by identifying underexplored epistemological pathways and supporting the development of financial evaluation models that are inclusive, ethically grounded, and aligned with sustainable development goals. Full article

A major breakthrough in environmentally friendly building materials is the development of sustainable unfired clay bricks including alumina waste produced during liquid nitrogen generation. Though used extensively, conventional fired clay bricks require energy-intensive manufacturing techniques that produce significant amounts of CO₂ and aggravate environmental damage. By removing the need for high-temperature firing and allowing for the valorization of industrial byproducts including alumina waste and lateritic soil, unfired clay bricks offer a reasonable low-carbon alternative. High silica and alumina contents define the alumina waste, which shows pozzolanic reactivity, thus improving the physicomechanical performance of the bricks. With alumina waste substituting 0–8.57% of the cement content, seven different formulations showed improvements in compressive strength, reduced water absorption, and optimal thermal conductivity. Especially, the mechanical performance was much enhanced with alumina waste inclusion up to 30%, without sacrificing thermal insulation capacity or moisture resistance. Further supporting the environmental and financial sustainability of the suggested brick compositions is the economic viability of using industrial waste and regionally derived soils. A comparative analysis of the conventional fired bricks shows that the unfired substitutes have a much lower environmental impact and show better mechanical properties, including greater compressive strength and modulus of rupture. These results support the more general goals of circular economy systems and low-carbon urban development by highlighting the feasibility of including alumina waste and lateritic soil into sustainable building materials. Using such waste-derived inputs in building fits world initiatives to lower resource consumption, lower greenhouse gas emissions, and build strong infrastructure systems. Full article

As digital connectivity transforms financial decision-making, this study offers one of the first empirical investigations into the relationship between social media use and socially responsible investing (SRI). Using data from the 2021 National Financial Capability Study, multinomial regression analysis was used to explore whether people who rely on social media for investment decisions were more likely to invest in ways that reflect their values. The results show that investors who use social media for investment information are more likely to value being socially responsible as an important reason for investing. Younger, less experienced, and more risk-tolerant investors were especially likely to follow SRI strategies, and certain platforms like Twitter were more associated with SRI interest than others. These findings suggest that social media is not just a platform for sharing information; it may also shape how people think about investing and the role their money can play in making a societal difference. As online platforms continue to influence financial behavior, understanding their impact on values-based investing becomes increasingly important. This research contributes novel insights to the emerging intersection of social media, behavioral finance, and values-driven investing. Full article

A reasonable water price for interbasin water transfer projects (IWTPs) is vital for solving the problem of unequal water use among different water users caused by different water source supply prices, promoting external water transfer consumption, and ensuring the stable and equitable project operation. However, the formulation of the water price is influenced by many factors, and it is necessary to identify the key factors and their interactions in the water prices formulation for IWTPs. In this study, we identified 15 factors that affect it. This paper used the fuzzy decision-making trial and evaluation laboratory (DEMATEL) to analyze the causal relationships and importance levels among the influencing factors. A four-level hierarchical structural model was established using an interpretive structural model (ISM), which intuitively displayed the hierarchical structure and pathways of each factor. The role of each influencing factor was determined by using MICMAC. Finally, the grey relational analysis method was used to identify the top five key factors: the socioeconomic development level, diversification of water resources, water demand of water users, cost of the project’s water supply, and national policies and regulations. Strategies to improve the formulation of water prices have also been proposed. The results show that the top five factors influencing the water price for IWTPs are the socio-economic development level, diversification of water resources, water demand of water users, cost of the project’s water supply, and national policies and regulations. The water price should be formulated based on the water resource cost, supply–demand relationships of water resources, and policy objectives to ensure scientific and reasonable cost allocation and differentiated pricing. For water-transfer projects with strong public welfare, the government may lower water prices through financial subsidies to alleviate the burden on water users. Full article