Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Fuzzy Logic Based Software Reliability Quantification Framework: Early Stage Perspective (FLSRQF)

AbstractToday, the influence of information technology has been spreading exponentially, from high level research going on in top labs of the world to the home appliances. Such a huge demand is compelling developers to develop more software to meet the user expectations. As a result reliability has come up as a critical quality factor that cannot be compromised. Therefore, researchers are continuously making efforts to meet this challenge. With this spirit, authors of the paper have proposed a highly structured framework that guides the process of quantifying software reliability, before the coding of the software start. Before presenting the framework, to realize its need and significance, the paper has presented the state-of-the-art on software reliability quantification. The strength of fuzzy set theory has been utilized to prevail over the limitation of subjectivity of requirements stage measures. Salient features of the framework are also highlighted at the end of the paper

Computational tools for low energy building design : capabilities and requirements

Integrated building performance simulation (IBPS) is an established technology, with the ability to model the heat, mass, light, electricity and control signal flows within complex building/plant systems. The technology is used in practice to support the design of low energy solutions and, in Europe at least, such use is set to expand with the advent of the Energy Performance of Buildings Directive, which mandates a modelling approach to legislation compliance. This paper summarises IBPS capabilities and identifies developments that aim to further improving integrity vis-à-vis the reality

Measuring Sustainable Development: The Use of Computable General Equilibrium Models

This paper advocates computable general equilibrium models as a methodological tool that is particularly suitable for measuring the impacts of policy interference on the three dimensions of sustainable development, i.e. environmental quality, economic performance (gross efficiency) and equity. These dimensions are inherently intertwined and subject to trade-offs. Computable general equilibrium models can incorporate various important sustainable development indicators in a single consistent framework and allow for a systematic quantitative trade-off analysis. --computable general equilibrium modeling (CGE),sustainability impact assessment (SIA),sustainable development (SD)

Risk Assessment of Urban Gas Pipeline Based on Different Unknown Measure Functions

Several risk factors threaten the safety of urban gas pipeline. How to effectively identify various risk factors affecting urban gas pipeline and put forward scientific risk assessment method is the focus in the field of urban safety research. To explore the uncertain factors in the process of gas pipeline risk assessment, and propose a practical assessment method, a three-layer index system for the risk assessment of urban gas pipeline was established using unascertained measure theory, which included 5 first-class evaluation factors and 34 second-class evaluation indexes. Four unascertained measure models (linear, parabolic, exponential and sinusoidal) were constructed, and the unascertained measure values of each evaluation index under four unknown measure function models were calculated. The weight of evaluation factors was determined by Analytic Hierarchy Process (AHP), and the confidence criterion was used for discriminant evaluation. Results demonstrate that the risk assessment models constructed with different measurement functions can effectively reduce the uncertainty of urban gas pipeline risk assessment, but for the same object, the risk level of the linear measurement model in 4# pipeline is lower than other measurement functions, and the risk level of sinusoidal measurement model in 8# pipeline is higher than other measurement functions. Therefore, considering the evaluation results under different measure functions and focusing on monitoring objects with different results is necessary when using unascertained measure theory for risk assessment. The conclusions obtained from this study clarify the application conditions of unascertained measure theory in urban gas pipeline risk assessment, which helps to reduce the uncertainty in the assessment process and improve the accuracy of the assessment results

Employment diversification of farm households and structural change in the rural economy of the New Member States

Changes in the rural labour market, especially the increase of rural non-farm employment are recognised as one dimension of structural change. Labour allocation decisions are driven by economic incentives such as wage differentials, but also non-economic motives may play a decisive role. This paper summarises theoretical insights and presents an integrated conceptual framework reflecting the drivers of employment shifts. Methodologically, the conceptual framework is implemented in fuzzy logic to analyse the household potential to diversify its income activities. The empirical analysis draws on a survey of 1,077 farm households in rural Bulgaria, Hungary, Poland, Romania, and Slovenia. [...] This report is structured as follows: Chapter 2 presents a review of theory, an integrated theoretical framework as well as an overview of current trends of employment diversification. Chapter 3 then introduces fuzzy logic methodology and presents the model that is implemented to assess the non-farm income diversification potential in the survey countries. This is followed by a brief description of the database in Chapter 4. Simulation results are presented in Chapter 5 and Chapter 6. The last chapter summarises the main outcomes and gives policy recommendations. --

Challenges in Applying Circular Economy Concepts to Food Supply Chains

In recent years, Circular Economy (CE) has captured vast global attention with regard to its potential in mitigating contemporary economic, social, and environmental challenges. This study aims to present the barriers that impede the application of CE concepts in the food supply chain (FSC) which received limited literature recognition. A systematic literature review is utilized to scrutinize challenges, resulting in 17 factors that burden CE adoption. The challenges were categorized under six subsets and were prioritized based on two perspectives: literature importance and empirical importance. A combination of literature frequency analysis and Field-Weighted Citation Impact was employed to derive the rankings related to literature importance. The pragmatic importance of challenging factors is derived using the Fuzzy Best-Worst method. Both rankings reveal that cost efficiency consideration is the most critical barrier that hinders the transition to CE in FSC. Thus, this paper highlights similarities and differences in the perspectives of academia and practicality by comparing the two prioritizations. The findings can be used to remove obstacles, create policies and strategies, and assist governments in implementing circular practices throughout FSC

Dynamic Data Scaling Techniques for Streaming Machine Learning

This research delves into innovative dynamic data scaling techniques designed for streaming machine learning environments. In the realm of real-time data streams, conventional static scaling methods may encounter challenges in adapting to evolving data distributions. To overcome this hurdle, our study explores dynamic scaling approaches capable of adjusting and optimizing scaling parameters dynamically as the characteristics of incoming data shift over time. The objective is to augment the performance and adaptability of machine learning models in streaming scenarios by ensuring that the scaling process remains responsive to changing patterns in the data. Through empirical evaluations and comparative analyses, the study aims to showcase the efficacy of the proposed dynamic data scaling techniques in enhancing predictive accuracy and sustaining model relevance in dynamic and fast-paced streaming environments. This research contributes to the advancement of scalable and adaptive machine learning methodologies, particularly in applications where timely and accurate insights from streaming data are crucial

Risk assessment of deep excavation construction based on combined weighting and nonlinear FAHP

Deep excavation construction safety has become a challenging and crucial aspect of modern infrastructure engineering, and its risk assessment is frequently carried out using the Fuzzy Analytic Hierarchy Process (FAHP). However, when using FAHP to evaluate the risks of deep excavation construction, the results of the weightings obtained through subjective weighting are heavily influenced by the subjective factors of the evaluators. In addition, using linear operators to calculate the risk level can easily cause a weakening effect on the influence of prominent risk factors, resulting in poor rationality of the evaluation results. To address these problems, this paper constructs a deep excavation construction risk evaluation model based on combined weighting and nonlinear FAHP. The WBS-RBS method is used to guide the construction of the risk evaluation index system for deep excavation construction. The combined weighting values of subjective and objective weightings are calculated through the game theory combined weighting method. The fuzzy relation matrix is constructed using the membership degree vector obtained from the expert evaluation method. Nonlinear operators are introduced for comprehensive calculation. According to the maximum membership degree principle, the final risk level of the excavation construction is obtained. The newly constructed model is applied to the risk analysis of the deep excavation construction of the Rongmin Science and Innovation Park project in Xi’an. The evaluation result for the excavation construction risk is N= [0.3125, 0.3229, 0.1939, 0.0854, 0.0854], and according to the maximum membership degree principle, the risk level of the excavation is classified as Level 2, which is a relatively low risk. Based on the deep excavation construction of the Rongmin Science and Innovation Park project, this paper discusses the differences between the new model and the traditional FAHP evaluation method, further verifies the reliability of the new model, optimizes the construction plan based on the evaluation results, avoids risks, and determines its guiding significance