Economic Applications Research Articles

Assessing the Impact of Copula Selection on Reliability Measures of Type P(X < Y) with Generalized Extreme Value Marginals

In reliability studies, we are interested in the behaviour of a system when it interacts with its surrounding environment. To assess the system’s behaviour in a reliability sense, we can take the system’s intrinsic quality as strength and the outcome of interactions as stress. Failure is observed whenever stress exceeds strength. Taking Y as a random variable representing the stress the system experiences and random variable X as its strength, the probability of not failing can be taken as a proxy for the reliability of the component and given as P(Y<X)=1−P(X<Y). This way, in the present paper, it is considered that X and Y follow generalized extreme value distributions, which represent a family of continuous probability distributions that have been extensively applied in engineering and economic contexts. Our contribution deals with a more general scenario where stress and strength are not independent and copulas are used to model the dependence between the involved random variables. In such modelling framework, we explored the proper selection of copula models characterizing the dependence structure. The Gumbel–Hougaard, Frank, and Clayton copulas were used for modelling bivariate data sets. In each case, information criteria were considered to compare the modelling capabilities of each copula. Two economic applications, as well as an engineering one, on real data sets are discussed. Overall, an easy-to-use methodological framework is described, allowing practitioners to apply it to their own research projects.

RETRACTED ARTICLE: Efficient and economical smart healthcare application based on quantum optical neural network

RETRACTED ARTICLE: Efficient and economical smart healthcare application based on quantum optical neural network

Variable Selection Based Testing for Parameter Changes in Regression with Autoregressive Dependence

We consider a regression model with autoregressive terms and propose significance tests for the detection of change points in this model. Our tests are applicable to both low- or moderate dimension and to high-dimension with sparse regressors. The dimension may be high from the practical point of view of economic and business applications, but in our theoretical framework it is fixed. To accommodate practically high dimension, variable selection is incorporated as an integral part of our approach. The regressors and the errors can exhibit general nonlinear dependence and the model incorporates autoregressive dependence. We develop asymptotic justification and evaluate the performance of the tests both on simulated and real economic data. We test for and estimate changes in responses to risk factors of a U.S. energy stocks portfolio and the Industrial Production index. We relate our findings to macroeconomic policy changes and global impact events.

A General Inferential Framework for Singly-Truncated Bivariate Normal Models with Applications in Economics

A General Inferential Framework for Singly-Truncated Bivariate Normal Models with Applications in Economics

Optimization of Business Decision Accuracy through the Application of Mathematical Economics

In the dynamic landscape of contemporary business environments, the need for precise decision-making has become paramount for organizational succes. It explores the strategic integration of mathematical economics as a powerful tool for enchancing decision accuracy in business. Mathematical economics, a discipline that combines economic theories with mathematical methodologies, offers a structured framework to model, and analyze a complex business strategy decision. This study delves into a theorical foundations of mathematical economics and investigates it practical applications in real-world business decision-making processes. Through the utilization of mathematical model, optimization techniques, and statistical analyses, businesses can gain insights into market dynamics, resource allocation, and risk assessment. The objective is to establish a systematic approach that aids decision-makers in formulating well-informed strategies, minimizing uncertaintes, and maximizing overall business performance.

Cattle wastewater treatment using green microalga Coelastrella sp. KNUA068 as a promising bioenergy feedstock with enhanced biodiesel quality.

Global water scarcity increased the demand for clean water, leading to attention on microalgae-based biological treatment for wastewater due to economic feasibility and sustainable biomass applications. This study isolated indigenous microalga Coelastrella sp. KNUA068 from a wastewater treatment plant, observed its admissible growth rate in diluted cattle wastewater (DCW), and used it for wastewater treatment analysis. The microalga showed high growth rates in indoor and outdoor cultivation with 100% DCW. In addition, the ammonia nitrogen and nitrate nitrogen removal rates of the microalga were 69.97 and 60.35%, respectively, in indoor cultivation, and 50.63 and 67.20%, respectively, in outdoor cultivation. Carotenoid content analysis revealed lutein as the highest productivity carotenoid, and zeaxanthin production was higher in outdoor cultivation. The biomass exhibited suitable biodiesel quality with a cetane number of 50.8 for high-quality biodiesel production. Coelastrella sp. KNUA068 demonstrates potential for bioenergy feedstock, carotenoid production, and wastewater treatment.

Externalities and the (pre)nucleolus in cooperative games

In most economic applications of cooperative games, externalities prevail: the worth of a coalition depends on how the other players are organized. We show that there is a unique natural way of extending the prenucleolus to games with coalitional externalities. This is in contrast to the Shapley value and the core for which many different extensions have been proposed.

Advancements in Energy Efficiency Technologies for Thermal Systems: A Comprehensive Review

This comprehensive literature review investigates the diverse landscape of energy efficiency technologies in thermal systems, offering an extensive overview of current research and development in this crucial domain. Thermal systems, vital in industries like heating, ventilation, air conditioning, and industrial processes, play a pivotal role. With escalating global energy demands and growing environmental concerns, optimizing energy use in thermal systems is imperative. The paper explores a wide range of energy efficiency technologies, from traditional methods to cutting-edge innovations, examining the implementation of advanced materials, smart sensors, and control strategies to enhance thermal system performance. Additionally, it scrutinizes the integration of renewable energy sources and waste heat recovery mechanisms, addressing the dual challenge of energy conservation and sustainability. The literature review critically evaluates the economic feasibility and practical applicability of these technologies, highlighting barriers and opportunities for widespread adoption. Synthesizing information from various sources provides a holistic understanding of existing knowledge gaps and future directions in energyefficient thermal systems. This research paper serves as a valuable resource for researchers, practitioners, and policymakers seeking insights into state-of-the-art technologies and strategies to optimize energy utilization in thermal processes. Keywords: Energy efficiency, thermal systems, design optimization, fuels and combustion, boilers and turbines, cogeneration, circulating cooling water systems and heat exchangers

Fuzzy cognitive map and mean square method in empirical modeling: Application in economics

This article offers a Fuzzy Cognitive Map (FCM) - based method of empirical modeling which can be considered as an alternative to multivariate regression analysis for the input–output relationship extraction from experimental data. Vertices of the FCM graph are interpreted as input–output variables of the empirical model, and the weights of arcs are unknown parameters that are estimated in two stages. The first stage consists of the arcs weights estimating based on the proximity of the columns’ values in the input–output observation data table. At the second stage, the observation table is used for the offline weights adjustment using the least squares method and the optimization procedure by genetic algorithm. The method is illustrated by the example of the World Bank data testing the relations between Gross Domestic Product per Capita vs demographic, educational, economic, technological and other factors. The novelty of the method in comparison to the Soft Regression technique lies in the utilization of the FCM arcs weights adjustment according to the least squares criterion.

Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms.

Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. Camelina sativa (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.

The K-pop Economic Model's Application in China

This paper examines the potential introduction and development of the K-pop economic model in China, specifically focusing on Wajijiwa Entertainment. K-pop has gained global recognition for its systematic talent development, fan engagement, merchandising, and branding. This paper employs a qualitative research method to investigate the potential introduction and development of the K-pop economic model in China, specifically focusing on Wajijiwa Entertainment. Wajijiwa Entertainment, a prominent player in the Chinese idol industry, has adopted elements of the K-pop model. Despite initial success, several challenges, including the impact of the COVID-19 pandemic and product quality issues, need addressing. The research concludes that though the Chinese idol industry has made strides in implementing the K-pop economic model, several issues still need addressing. To further succeed, it must guide fan behavior towards rational consumption, provide more platforms for idol promotion, enhance training systems, and improve operational strategies and product quality. Effective communication with fans is also essential to bolster engagement. While this study primarily focuses on Wajijiwa Entertainment, it highlights the potential for growth and economic impact in the evolving Chinese idol industry.

Shaping the Future of Social Security Management Beyond Retirement: A Review of Literature

This research delves into the advancements, in managing Social Security with the goal of navigating the complexities of today’s socio landscape. By conducting a literature review and examining studies this paper explores significant transformations taking place within social security systems. Notable innovations encompass the integration of technologies, contribution models, personalized retirement planning, inclusive coverage for nontraditional workers flexible policy adjustments, international coordination efforts, applications of behavioural economics and strategies for managing longevity risks. The analysis synthesizes insights from perspectives to inform policymakers researchers and stakeholders about how social security management's evolving in response to demographic changes, technological advancements and shifts, in the workforce.

Resourceful and harmless treatment of the steam-assisted gravity drainage produced fluid

The steam-assisted gravity drainage (SAGD) process is an effective technology for heavy crude oil extraction. SAGD produced fluid (including oil, water, and sludge) that has strong stability and difficult separation characteristics. It is very necessary to thoroughly separate SAGD produced fluid, as it can lead to environmental pollution and resource waste. In this study, a combined process of thermochemical treatment and electrocoagulation was proposed to deal with SAGD produced fluid. The influence of such parameters as type and structure of surfactants, temperature, stirring rate, and current density were examined on the separation efficiency of SAGD produced fluid. The results indicated that the oil recovery rate was 99%, with the oil and suspended solids content (4.4 and 0.58 mg/L, respectively) in the water meeting the reinjection standard (oil content <15 mg/L, suspended solids <3 mg/L) and the oil content in the sludge (1 wt%) attained the solid waste disposal standard (<2 wt%). The combined separation process of SAGD produced fluid allowed the obtaining of qualified oil, sludge, and water, with oily sludge and waste water simultaneously eliminated. Therefore, this process achieved oil and water resource utilization, as well as harmless sludge treatment, which would achieve great economic benefits and application prospects in the oil field.

The Ethical Dilemma with Open AI ChatGPT: Is it Right or Wrong to prohibit it?

Digitalisation and innovation in learning and research are rapidly becoming crucial drivers of society's sustainable and progressive growth. AI's technological advancements and landscapes have significant strengths, and their diversity and quality have grown in recent years. This has facilitated the impressive development of AI apps and software, such as ChatGPT, which has become popular around the world. ChatGPT is an OpenAI access to users in education to generate essays, song lyrics and stories. It is an AI language model that can understand and generate human-like responses to text inputs, making it a valuable tool for various economic and cultural applications. This study examines the ethical dilemma of banning ChatGPT. Using a range of argumentative examples, I address the concept of moral obligations to OpenAI access but also its limitations. Some possible ethical issues that may arise in the use of AI-powered chatbots include concerns about data privacy, algorithmic bias, and the potential for chatbots to replace human interaction and support. Can OpenAI's cutting-edge technology and tools truly help corporate operations and institutions, and improve decision-making? Can it also give students and researchers significant resources to help them develop their knowledge, critical thinking skills and understanding in a variety of fields? Allowing ChatGPT to operate freely could lead to unintended consequences, but it could also promote innovation in the field of AI. Ultimately, finding a balance between regulation and innovation is key to maximising the benefits of ChatGPT while minimising its potential harms. AI software has the potential to degrade and debase our ethics, which are fundamentally different from our critical thinking. Chomsky, Roberts &amp; Watumull concern is that AI software lacks the ability to understand and apply ethical principles in the same way that humans do, which could lead to unintended consequences and ethical dilemmas. Keywords: Critical Thinking; Ethical dilemma; Right or Wrong; ChatGPT

МЕТОДИ ТА МОДЕЛІ DATA SCIENCE В СУЧАСНІЙ ЕКОНОМІЦІ

In contemporary economics, data science models play a crucial role in analyzing complex relationships, predicting economic trends, and informing policy decisions. This article reviews the most commonly used data science models in economics, including econometric models like linear and logistic regression, Probit and Tobit models, time series analysis models such as ARIMA and Vector Autoregression (VAR), and panel data analysis methods like fixed and random effects models and Difference-in-Differences (DiD). Additionally, it explores machine learning algorithms, clustering and classification techniques, dimensionality reduction methods, Bayesian methods, and natural language processing (NLP) applications. The article highlights their purposes, applications, and relevant works, emphasizing the strengths and limitations of each model. It also discusses the impact of these models across various sectors, including finance, retail, energy, and healthcare. This comprehensive overview underscores the importance of aligning data science models with business objectives, ensuring data quality, investing in scalable technologies, fostering a data-driven culture, and addressing ethical considerations. The article concludes with future research directions, such as advanced neural network architectures, large language models, generative AI models, hybrid models, and the need for interpretable and ethical AI applications in economics. The importance of this topic lies in the transformative potential of data science models to enhance economic analysis and decision-making. By leveraging advanced data science techniques, economists can gain deeper insights into complex economic phenomena, improve forecasting accuracy, and develop more effective policies. As data-driven approaches continue to evolve, they provide powerful tools for addressing critical economic challenges, driving innovation, and fostering sustainable growth across various sectors.

Research on the economic security application of energy economy in a low-carbon sustainable development society

Research on the economic security application of energy economy in a low-carbon sustainable development society is an important research field. Its purpose is to explore how to achieve the safe development of the national economy in the context of low-carbon sustainable development, including economic structural adjustment, green technology innovation, resource conservation and recycling, environmental protection, etc. This article explores how to ensure green and sustainable development of energy security and the security risk assessment of green energy economy.

Research on the Construction of Economic Statistical Model and Application of Data Cleaning Technology in Big Data Environment

Abstract With the rapid development of big data technology, the number of economic data grows faster, and it is more challenging for people to grasp and calculate economic data. In this paper, an economic statistical model is proposed to optimize data-cleaning technology. The economic data application framework is constructed using Multi-Agents, and the mining of economic data is achieved using Microsoft time series and clustering algorithm. The GMDH algorithm, which is the core of self-organized data mining, is proposed and improved to complete economic data analysis and prediction by using the fitting error or prediction variance criterion as the identification criterion. The wild value identification method based on the regression model is utilized to eliminate potential wild values and carry out data cleaning. Statistical analysis of the economic data of the printing industry in Shanghai, China, reveals that the total assets, total industrial output value, and total profit of the printing industry in Shanghai in 2023 declined by 2.63%, 4.77%, and 5.68%, respectively. External investment and enterprise R &amp; D investment up to 109,440,100 U.S. dollars, 140,301,000 yuan. The overall number of employees declined, and the profit margin on output value decreased by 5.68%. It is predicted that the number of enterprises, total assets, total industrial output value, and total profit of Shanghai’s printing industry will rebound in 2024, and the external direct investment and R&amp;D investment will be appropriately reduced.

Linear and State-Dependent Impulse Responses in Agent-Based Models: A New Methodology and an Economic Application

Linear and State-Dependent Impulse Responses in Agent-Based Models: A New Methodology and an Economic Application

Properties, Preparation, and Application of Carbon Nanotubes

Carbon nanotubes (CNTs), due to their unique one-dimensional nanostructures and extraordinary physicochemical properties, have been a research hotspot in the field of materials science and nanotechnology. Their extremely high strength, good electrical conductivity, and excellent thermal conductivity make CNTs promising for a wide range of applications in many fields. In the medical field, CNTs are used in drug delivery, tumor treatment, and bioimaging due to their excellent biocompatibility and tunable surface properties. Especially in tumor treatment, CNTs can be used as drug carriers to deliver anti-tumor drugs directly to the lesion site and reduce damage to normal tissues. In the energy sector, the application of CNTs is mainly focused on energy storage devices and photovoltaic cells. Their high conductivity and large specific surface area make them ideal electrode materials, especially in supercapacitors and lithium-ion batteries. In addition, CNTs can also improve the photoelectric conversion efficiency of photovoltaic cells and promote the development of renewable energy technologies. In the future, the research of CNTs will continue to deepen, not only to find more efficient and economical applications in existing application fields, but also to explore new application fields, such as applications in environmental monitoring and biosensors. With the continuous progress of preparation technology and the reduction of cost, CNTs are expected to be commercialized in more fields and make greater contributions to the development of human society.

Applying L’Hospital’s Rule to Evaluate the Limit of a Function and Its Practical Applications in Physics and Economics

Applying L’Hospital’s Rule to Evaluate the Limit of a Function and Its Practical Applications in Physics and Economics