Data Analysis Modeling Research Articles

Global Warming – Our Greatest Challenge

The current state of Earth’s climate is the result of a long-lasting, continuous development: it is a snapshot from a film of the Earth’s history that has lasted around 4.5 billion years and is still running. Climate fluctuations are documented by geological findings (marine sediment cores: several million years; ice cores: 800,000 years), by tree rings (10,000 years), historical records (1000 years) and by modern instruments (e.g. thermometers and barometers, 250 years). These data reveal pronounced changes as a characteristic feature of Earth’s climate, which extend over time scales ranging from months to millions of years. In recent decades, climate scientists have analysed many of these data sets and developed dynamic models that have been successfully tested against modern observations and are used to project the future of our climate. Data analysis and climate modelling show that a pronounced impact of human activities on current climate change is apparent.

Video Games Market Forecast Based on Linear Regression Model

Abstract. In recent years, the video game industry has been developing rapidly and the global market scale has been expanding. In this case, pre-study of the video game market has important theoretical and practical significance. Based on the Video Game Sales dataset, this study comprehensively analyses the development of the video game market over the last forty years through data analysis and mathematical modelling. This study uses a linear regression model to analyse the trend of the video game market over the past forty years. The paper finds that the scale of its tends to grow year by year, and the growth rate shows a linear trend. The video game market has shown a linear growth trend in the past forty years, and the market size has been expanding. The significance of this study is to provide a new idea and method for the prediction of the video game market, which helps to better understand the market dynamics and trends and provides a decision-making basis for related enterprises and policymakers.

Analyzing and forecasting dengue fever incidence in Thailand: A comprehensive study for public health preparedness

Thailand is currently grappling with a severe dengue fever outbreak, with a rising threat to public health as the rainy season and El Niño draw near. This year has witnessed a troubling surge in dengue cases, prompting the Ministry of Public Health (MoPH) to issue warnings that the numbers may hit a three-year peak. Dengue outbreaks in Thailand have historically followed a cyclical pattern, excluding COVID-19 years. This research employs data analysis and predictive modeling to forecast the forthcoming dengue case numbers in Thailand, facilitating better public health preparedness. It also incorporates data visualization for enhanced data exploration. Various forecasting models, including Exponential Smoothing, Polynomial Fitting and Random Forest, are deployed to predict dengue cases within the constraints of our data. This study offers valuable insights into the potential trajectory of dengue cases in Thailand, aiding proactive measures to combat the outbreak.

The Combination of Topological Data Analysis and Mathematical Modeling Improves Sleep Stage Prediction From Consumer-Grade Wearables.

Wearable devices have become commonplace tools for tracking behavioral and physiological parameters in real-world settings. Nonetheless, the practical utility of these data for clinical and research applications, such as sleep analysis, is hindered by their noisy, large-scale, and multidimensional characteristics. Here, we develop a neural network algorithm that predicts sleep stages by tracking topological features (TFs) of wearable data and model-driven clock proxies (CPs) reflecting the circadian propensity for sleep. To evaluate its accuracy, we apply it to motion and heart rate data from the Apple Watch worn by young subjects undergoing polysomnography (PSG) and compare the predicted sleep stages with the corresponding ground truth PSG records. The neural network that includes TFs and CPs along with raw wearable data as inputs shows improved performance in classifying Wake/REM/NREM sleep. For example, it shows significant improvements in identifying REM and NREM sleep (AUROC/AUPRC improvements >13% and REM/NREM accuracy improvement of 12%) compared with the neural network using only raw data inputs. We find that this improvement is mainly attributed to the heart rate TFs. To further validate our algorithm on a different population, we test it on elderly subjects from the Multi-ethnic Study of Atherosclerosis cohort. This confirms that TFs and CPs contribute to the improvements in Wake/REM/NREM classification. We next compare the performance of our algorithm with previous state-of-the-art wearable-based sleep scoring algorithms and find that our algorithm outperforms them within and across different populations. This study demonstrates the benefits of combining topological data analysis and mathematical modeling to extract hidden inputs of neural networks from puzzling wearable data.

AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of bioremediation by enabling real-time monitoring and optimization of environmental cleanup processes. This paper explores the integration of AI-driven monitoring systems with bioremediation techniques, focusing on how real-time data analysis and predictive modelling can enhance the effectiveness of pollutant degradation. These AI systems continuously collect data from contaminated sites, such as soil and water, and analyse variables like microbial activity, pollutant concentration, and environmental conditions. By processing this data, AI models can optimize the bioremediation environment, adjusting factors such as pH, temperature, and nutrient levels to maximize microbial efficiency. Predictive modelling plays a crucial role in forecasting remediation outcomes, allowing for proactive adjustments to improve the speed and success of the process. The study also highlights the potential of AI in reducing operational costs by automating data collection and analysis, minimizing the need for manual intervention. Furthermore, it discusses challenges related to data quality, system integration, and the scalability of AI applications in real-world bioremediation projects. By leveraging AI's capability to provide real-time insights and predictive analytics, this research demonstrates its potential to significantly enhance the precision and sustainability of bioremediation efforts, paving the way for smarter environmental management.

An overview of machine learning applications in elastic power systems

Abstract. With the development of economy and the access of renewable energy, the power system is facing new challenges. Machine learning provides a new way for the intelligent management of power system through big data analysis, pattern recognition and predictive modeling. This paper reviews the application of machine learning technology in elastic power system, including load forecasting, transient stability assessment, intelligent scheduling and fault diagnosis. Machine learning will better improve the stability, reliability and economic benefit of the system. Future research will focus on algorithm optimization, model adaptability improvement and integration with traditional power system knowledge to promote the development of power system to a higher level of intelligence and automation.

Безплазовий метод конструкторської підготовки виробництва літаків авіації загального призначення

The article’s subject is the design preparation for producing light general aviation aircraft. This article aims to develop design solutions for a light cheap aircraft with low flight and ground costs, to substantiate the possibility and necessity of using modern CAD systems and new plazless methods to ensure the interchangeability of light aircraft components to reduce design and production costs. Objective: to analyze the global demand for light aircraft, to substantiate the possibility and feasibility of introducing independent (plazless) methods for linking technological equipment and aircraft design components, to develop a list of design work and expected results, to develop digital models (electronic drawings) of the aircraft as a whole and its components, to prove the effectiveness of such approaches. The research methods used are the analysis of scientific and technical documentation, numerical modeling of the aerodynamic flow around the aircraft, the stress-strain state of the units, and the design of the aircraft and its components considering the results of numerical modeling. The following results were obtained: a set of digital models (electronic drawings) of the whole aircraft, a fuselage, a wing, a horizontal and vertical tail, a chassis, digital models (electronic drawings) of the aircraft parts. The scientific and practical novelty of the obtained results is as follows: a set of aircraft design engineering documents developed based on statistical data analysis and numerical modeling will allow manufacturing, testing the aircraft, and obtaining a certificate of airworthiness, which, in turn, will make it possible to subsequently begin serial production of the aircraft and create full-fledged primary flight training classes designed to improve flight training activities for cadets of higher military educational institutions and create opportunities for training civilian pilots.

Application of probabilistic and stochastic models and data mining for forecasting the contingent of old age pension recipients in the context of systemic uncertainty

The object of the research is mathematical models for forecasting the contingent of pension recipients in conditions of uncertainty caused by both the reform of the pension system and the impact of armed aggression. Based on the study of statistical information on the structure and dynamics of the contingent of pension recipients, an approach to uncovering systemic uncertainty in the task of forecasting the contingent of pensioners is proposed. This work is part of a study of the application of data mining methods of intellectual data analysis and mathematical modeling in information technology intended for use in the pension system. The main focus of this work is on forecasting the dynamics of the contingent of pension recipients by age, in particular, forecasting the number of newly appointed pensions. The difficulty of forecasting the contingent of pension recipients, in particular by age, is connected with the fact that it is necessary to ensure the representativeness and variability of data sets. In addition, it should be taken into account that a significant number of factors must be included in the model in accordance with the requirements of regulatory documents. Another problematic issue is that the time series of the investigated indicators, such as data on the insurance experience of insured persons (based on the results of a sample survey), may contain significant (more than 40 %) gaps that can be filled only on the basis of primary (paper) documents. Therefore, the input data sets are formed with assumptions about the probability of the accumulation of insurance experience in various groups of insured persons. The paper proposes an analytical toolkit based on the use of probabilistic and statistical models in the form of Bayesian networks, intended for use in specialized decision-making support systems of the Ukrainian pension system. In the course of the study, a number of numerical experiments were carried out, in which the correctness of the proposed method was investigated. The proposals presented in the paper will improve the stability of the pension system of Ukraine, including through a more accurate definition of the dynamics of the contingent of pension recipients and, accordingly, the costs of pension payments. The proposed models and methods can be used as part of decision-making support systems of state and public administration bodies to analyze the results of reforming the pension system.

VOTES AND VALUATIONS: THE IMPACT OF LOK SABHA ELECTIONS ON STOCK MARKET

This paper investigates the impact of the 2024 Indian Lok Sabha elections on the stock market, a relationship of significant interest to economists, investors, and policymakers. Given the pivotal role of the Lok Sabha in shaping India's economic policies and legislative landscape, the outcomes of these elections are expected to have substantial implications for market dynamics. Historical data analysis reveals that past elections have consistently led to increased market volatility and activity, reflecting investor sentiment towards political stability and economic policy continuity. This study employs a combination of quantitative and qualitative approaches, including historical data analysis, event studies, and predictive modeling, to forecast potential market reactions. Key factors such as political stability, proposed economic reforms, global economic environment, and investor sentiment are examined to understand their influence on market behavior. The 2024 elections are especially significant as they occur during a period of post-pandemic economic recovery, technological advancements, and evolving geopolitical relations. By analyzing these factors, the paper aims to provide insights into how different election outcomes might affect various sectors, investment strategies, and overall market performance. The findings are intended to aid investors in navigating the election period, making informed decisions, and understanding the broader implications of political events on economic stability and growth. This comprehensive analysis contributes to the existing literature on political economy and market behavior, offering valuable perspectives for stakeholders involved in the Indian financial markets.

Artificial Intelligence in Dry Eye Disease: A Narrative Review.

Dry eye disease (DED) is a multifactorial condition affecting millions worldwide, characterized by discomfort, visual disturbance, and potential damage to the ocular surface. The complexity of its diagnosis and management, driven by the diversity of symptoms and underlying causes, presents significant challenges to clinicians. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering potential solutions to these challenges through its data analysis, pattern recognition, and predictive modeling capabilities. This narrative review explores the role of AI in diagnosing, treating, and managing dry eye disease. AI-driven tools such as machine learning algorithms, imaging technologies, and diagnostic platforms are examined for their ability to enhance diagnostic accuracy, personalize treatment approaches, and optimize patient outcomes. Furthermore, the review addresses the limitations of AI technologies in ophthalmology, including the need for robust clinical validation, data privacy concerns, and the ethical considerations of integrating AI into clinical practice. The findings suggest that while AI holds promise for improving the care of patients with DED, ongoing research and development are crucial to realizing its full potential.

Home literacy environment mediated socioeconomic status’ impact on handwriting skill of children with intellectual disabilities

Objectives Mastering Chinese handwriting is a fundamental skill, yet it poses a challenging task for children with intellectual disability (ID). Socioeconomic status (SES) and home literacy environment (HLE) are two important family factors known to influence children’s academic achievement. This study aimed to explore the HLE’s role in SES’ influence on Chinese handwriting skill of children with ID. Methods Chinese handwriting skill was tested directly on 192 children with ID (7-12 years old), while SES and HLE questionnaires were completed by their parents. Data entry, analysis and structural equation modeling were performed using the Statistical Package for Social Sciences software. Results SES was shown not directly correlated with any of the three handwriting skills (β = 0.02, p > 0.05). Instead, the former influenced the latter indirectly via HLE; informal HLE played a complete intermediary role in this relationship (SES-informal HLE: β = 0.42, p < 0.001; informal HLE-handwriting skill: β = 0.41, p < 0.001), while formal HLE had no effect (SES-formal HLE: β = 0.34, p < 0.001; formal HLE-handwriting skill: β=-0.10, p > 0.05). Conclusions SES influences Chinese handwriting of children with ID via mediation by informal HLE. These findings emphasize the importance of maintaining high SES and creating rich HLE in the development of Chinese handwriting skill of children with ID.

Misclassification of Eyes With Progressive Keratoconus Using the KISA% Index.

To determine the misclassification rate of the keratoconus percentage (KISA%) index efficacy in eyes with progressive keratoconus. This was a retrospective case-control study of consecutive patients with confirmed progressive keratoconus and a contemporaneous normal control group with 1.00 diopters or greater regular astigmatism. Scheimpflug imaging (Pentacam HR) was obtained for all patients. KISA% index and inferior-superior (IS) values were obtained from the Pentacam topometric/keratoconus staging map. Receiver operating characteristic curves were generated to determine the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. There were 160 eyes from 160 patients evaluated, including 80 eyes from 80 patients with progressive keratoconus and 80 eyes from 80 control patients. There were 20 eyes (25%) with progressive keratoconus misclassified by the KISA% index, with 16 eyes (20%) of the progressive keratoconus cohort classified as normal (ie, KISA% < 60). There were 4 eyes (5%) with progressive keratoconus that would classify as having "normal topography" using the published criteria for very asymmetric ectasia with normal topography of KISA% less than 60 and IS value less than 1.45. All controls had a KISA% index value of less than 15. The optimal cut-off value to distinguish cohorts was 15.31 (AUROC = 0.972, 93.75% sensitivity). KISA% index values of 60 and 100 achieved low sensitivity (80% and 73.75%, respectively). The KISA% index misclassified a significant proportion of eyes with progressive keratoconus as normal. Although highly specific for clinical keratoconus, the KISA% index lacks sensitivity, does not effectively discriminate between normal and abnormal topography, and thus should not be used in large data analysis or artificial intelligence-based modeling. [J Refract Surg. 2024;40(9):e614-e624.].

Exploring the response and prediction of phytoplankton to environmental factors in eutrophic marine areas using interpretable machine learning methods

Coastal marine areas are frequently affected by human activities and face ecological and environmental threats, such as algal blooms and climate change. The community structure of phytoplankton—primary producers in marine ecosystems—is highly sensitive to environmental factors, such as temperature, salinity, and nutrients. However, traditional methods for exploring the relationship between phytoplankton communities and environmental factors in eutrophic marine areas are limited by various factors. Therefore, this study employed interpretable machine learning models, integrating high-dimensional data analysis and complex system modeling, to quantitatively and thoroughly analyze the dynamic relationship between phytoplankton communities and environmental variables in high-frequency samples collected over 53 weeks from eutrophic marine areas. The cell abundance of phytoplankton exhibited a distinct “two-peak pattern” variation. Interpretable machine learning model analysis revealed the dynamic contributions of different environmental factors during changes in the phytoplankton community structure. The results showed that temperature was a key environmental factor that affected phytoplankton growth during peak periods. In addition, the contribution of salinity increased during the second peak in phytoplankton abundance, highlighting its central role in the ecological dynamics of this phase. During green tide outbreaks, particularly in Area 01, the contributions of factors such as temperature and salinity increased, whereas those of phosphates and silicates decreased, indicating that green tide outbreaks substantially altered the nutritional dynamics of the ecosystem. Furthermore, different phytoplankton species, such as Skeletonema costatum, Thalassiosira spp., and Nitzschia spp., exhibit varying responses to environmental factors. Hence, the predictions made using random forest and generalized additive models for phytoplankton cell abundance in two marine areas revealed complex nonlinear relationships between environmental factors, such as temperature, salinity, and phytoplankton abundance.

Cartographic Generalization of Islands Using Remote Sensing Images for Multiscale Representation

The multi-scale representation of remote sensing images provides various levels of image information crucial for decision-making in GIS applications and plays a significant role in information processing, data analysis, and geographic modeling. Traditional methods for multi-scale representation of remote sensing images often struggle to simplify local details of individual targets while preserving the overall characteristics of target groups. These methods also encounter issues such as transitional texture distortion and rough final boundaries. This paper proposes a novel multi-scale representation method for remote sensing images based on computer vision techniques, which effectively maintains the overall characteristics of target groups. Initially, the K-means algorithm is employed to distinguish between islands and oceans. Subsequently, a superpixel segmentation algorithm is used to aggregate island groups and simplify the generated boundaries. Finally, texture synthesis and transfer are applied based on the original image to produce the aggregated island images. Evaluation metrics demonstrate that this method can generate multi-scale aggregated images of islands, effectively eliminate redundant information, and produce smooth boundaries.

Behavioral Intervention for Adults With Autism on Distribution of Attention in Triadic Conversations: A/B-Tested Pre-Post Study.

Cross-neurotype differences in social communication patterns contribute to high unemployment rates among adults with autism. Adults with autism can be unsuccessful in job searches or terminated from employment due to mismatches between their social attention behaviors and society's expectations on workplace communication. We propose a behavioral intervention concerning distribution of attention in triadic (three-way) conversations. Specifically, the objective is to determine whether providing personalized feedback to each individual with autism based on an analysis of their attention distribution behavior during an initial conversation session would cause them to modify their orientation behavior in a subsequent conversation session. Our system uses an unobtrusive head orientation estimation model to track the focus of attention of each individual. Head orientation sequences from a conversation session are analyzed based on five statistical domains (eg, maximum exclusion duration and average contact duration) representing different types of attention distribution behavior. An intervention is provided to a participant if they exceeded the nonautistic average for that behavior by at least 2 SDs. The intervention uses data analysis and video modeling along with a constructive discussion about the targeted behaviors. Twenty-four individuals with autism with no intellectual disabilities participated in the study. The participants were divided into test and control groups of 12 participants each. Based on their attention distribution behavior in the initial conversation session, 11 of the 12 participants in the test group received an intervention in at least one domain. Of the 11 participants who received the intervention, 10 showed improvement in at least one domain on which they received feedback. Independent t tests for larger test groups (df>15) confirmed that the group improvements are statistically significant compared with the corresponding controls (P<.05). Crawford-Howell t tests confirmed that 78% of the interventions resulted in significant improvements when compared individually against corresponding controls (P<.05). Additional t tests comparing the first conversation sessions of the test and control groups and comparing the first and second conversation sessions of the control group resulted in nonsignificant differences, pointing to the intervention being the main effect behind the behavioral changes displayed by the test group, as opposed to confounding effects or group differences. Our proposed behavioral intervention offers a useful framework for practicing social attention behavior in multiparty conversations that are common in social and professional settings.

Price Cannibalization Effect on Long-Term Electricity Prices and Profitability of Renewables in the Baltic States

This paper aims to evaluate price cannibalization effects in forecasts of long-term electricity prices and substantiate their relevance on the profitability of renewables in the Baltic States from 2024 to 2033. Statistical data analysis, literature review, scenario method, and PLEXOS modeling were applied. Five scenarios were analyzed for developing renewable energy sources (RES) and load in Lithuania. In contrast, scenarios for Estonia and Latvia were based on assumptions derived from the countries’ national RES strategies. The results showed that the increase in RES capacities will halve electricity market prices from around 130 EUR/MWh in 2024 to 58 EUR/MWh in Latvia, 60 EUR/MWh in Estonia, and 60–77 EUR/MWh in Lithuania in 2033. In time-waving, the absolute and relative price cannibalization effects of renewables were found. In 2033, the loss of revenue from solar photovoltaic (PV) generators was estimated to be 5.5–17.0 EUR/MWh in Lithuania, 7.1 EUR/MWh in Latvia, and 5.6 EUR in Estonia. The case of onshore wind demonstrated revenue losses of 10.5–22.0 EUR/MWh in Lithuania, 12.0 EUR/MWh in Latvia, and 10.0 EUR/MWh in Estonia. After 2029, revenues received by RES electricity generators could not guarantee project profitability; therefore, market flexibility options will be required. The key innovative strategy to mitigate the price cannibalization effect is the demand-side response when leveraging demand flexibility. Typically, this is achieved by sending price signals to the consumers who, if they have any, shift their demand to lower price periods. This is easily applied within HVAC systems, smart electric vehicle charging, and smart home appliance usage. Such behavior would allow the price cannibalization effect to be decreased.

Unveiling the Impact of Transportation Infrastructure Construction on Rurality: A Case Study from Guangdong, China

Rurality is an important indicator to reflect the development of villages and reveal internal differences in rural areas. The unbalanced development of transportation infrastructure in the current period of rapid urbanization has become one of the principal elements triggering spatial differences in rurality and changes in rural territorial characteristics. However, there are few studies on the impact of transportation infrastructure accessibility on the multidimensional characteristics of rurality from the perspective of heterogeneity. This paper analyzed the spatio-temporal characteristics of transport accessibility (TA) and the county rurality index (CRI) in Guangdong in 2005, 2010, 2015 and 2020 using an accessibility model and the rurality index and explored the clustering characteristics and interactions of TA and the CRI through exploratory spatial data analysis (ESDA) and geographic weighted regression (GWR) modeling. The findings showed that (1) TA and the CRI in Guangdong were significantly unbalanced in terms of space. The CRI showed a weakening trend in general, forming a distribution pattern of “high in the north and low in the south, high in the west and low in the east”, while TA was on the rise, maintaining a stable pattern of “high in the middle and low in the periphery”. (2) Both TA and the CRI in Guangdong had a Moran’s I value greater than 0.6 during the study period, exhibiting strong spatial agglomeration, while the two showed a significant spatial negative correlation. (3) The influence of TA on the CRI in different dimensions showed dynamic changes in stages, with TA having a higher intensity of effect on society rurality and land rurality in 2005, while on society rurality and industry rurality in 2020. (4) This paper grouped 77 counties in Guangdong into four types of policy zonings—coordination types, lagged types of accessibility, lagged types of rurality and double lagged types—and put forward corresponding development recommendations. The study conducted in this paper contributes to an in-depth understanding of the impact of transportation infrastructure development on the multidimensional characteristics of rurality and provides a basis for policy formulation for coordinated urban–rural development and sustainable rural development.

Experimental research on Bayesian methods in large-scale datasets

The current study systematically examines the application of the Bayesian approach or Bayesian estimation methods in large-scale datasets, emphasizing their adaptability as well as predictive prowess across various domains. The study navigates the challenges inherent in the computational efficacy and scalability of these techniques to offer insights into their application in the fields of text analysis, image processing, recommendation systems, and social network analysis. The study uses experimental designs that highlight how well Bayesian methods perform in comparison to more conventional methods, emphasizing how much better they are able to handle uncertainty and incorporate prior knowledge. The future directions and possible enhancements of Bayesian techniques are also discussed, especially with regard to overcoming computational limitations by integrating machine learning and developing sophisticated algorithms. As a crucial tool for modern data analysis and predictive modelling, the study's conclusion upholds the critical role that Bayesian estimation plays in the world of big data.

Data modeling analysis of GFRP tubular filled concrete column based on small sample deep meta learning method.

The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis.

Incorporation of artificial intelligence into nursing research: A scoping review.

The integration of artificial intelligence (AI) across different sectors, notably healthcare, is on the rise. However, a thorough exploration of AI's incorporation into nursing research, as well as its advantages and obstacles, is still lacking. The aim of this scoping review was to map the roles, benefits, challenges, and potentials for the future development and use of AI in the context of nursing research. An exhaustive search was conducted across seven databases: MEDLINE, PsycINFO, SCOPUS, Web of Science, CINAHL, Google Scholar, and ProQuest. Articles were additionally identified through manual examination of reference lists of the articles that were included in the study. The search criteria were restricted to articles published in English between 2010 and 2023. The Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA-ScR guidelines guided the processes of source selection, data extraction, and data presentation. Twenty articles met the inclusion criteria, covering topics from ethical considerations to methodological issues and AI's capabilities in data analysis and predictive modeling. The review identified both the potentials and complexities of integrating AI into nursing research. Ethical and legal considerations warrant a coordinated approach from multiple stakeholders. The findings emphasized AI's potential to revolutionize nursing research, underscoring the need for ethical guidelines, equitable access, and AI literacy training to ensure its responsible and inclusive use.