Advanced Tools Research Articles

Efficient control of three-phase cascaded multilevel Z-Source inverter using SHEPWM technique

This paper presents an enhanced control strategy for three-phase cascaded multilevel Z-source inverters, focusing on the implementation of Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) techniques to improve efficiency. Z-source inverters are gaining prominence in various applications due to their inherent advantages, such as increased voltage boost capability and inherent shoot-through protection. However, achieving optimal control in cascaded multilevel configurations poses challenges that this study seeks to address. The proposed control strategy optimizes the switching patterns of the inverter, thereby enhancing its overall performance. It allows for precise control over the output voltage and reduces harmonic distortion, ensuring the inverter operates efficiently across varying load conditions. The research methodology involves a comprehensive analysis of the proposed technique, including its mathematical modeling and simulation using advanced software tools. Performance metrics such as total harmonic distortion, efficiency, and transient response are evaluated to quantify the improvements achieved. The results obtained demonstrate the efficiency of the proposed control strategy. This method significantly reduces harmonic distortion in the output voltage, leading to improved power quality. Furthermore, the efficiency of the inverter is enhanced, making it suitable for applications demanding high-performance power conversion.

Unveiling Synergistic Bonds: Exploring Spectroscopic Interactions between Tween 20 and Bromocresol Green for Enhanced Applications

Background: Surfactants and dyes interact in ways that can significantly alter the optical properties of solutions, a phenomenon with broad implications across various scientific fields, including healthcare. Understanding these interactions, especially in aqueous environments, is crucial for developing advanced diagnostic tools and enhancing drug delivery systems. Objective: This study aimed to investigate the interactions between the non-ionic surfactant Tween 20 and the dye Bromocresol Green (BCG) in aqueous solutions. By elucidating these interactions, the research sought to explore the potential for improving the accuracy and efficiency of spectroscopic assays used in medical diagnostics and therapeutic applications. Methods: Aqueous solutions of BCG were prepared in varying concentrations of Tween 20 surfactant. Spectroscopic analyses were conducted at two wavelengths, 510 nm and 520 nm, to observe the effects of surfactant concentration on dye absorbance. The critical micelle concentration (CMC) was determined by analyzing changes in absorbance and reciprocal absorbance (1/A) patterns. Beer’s Law was applied to calculate the molar absorption coefficients, providing a quantitative measure of the interaction strength between the dye and surfactant molecules. Results: The study found that the absorbance of BCG increased linearly with the concentration of Tween 20 up to the CMC, after which the increase continued at a different rate. At 510 nm, absorbance values ranged from 0.277 to 1.627 au as Tween 20 concentration increased from 0.005 mM to 0.017 mM, with molar absorption coefficients (ε) escalating from 0.483 to 2.839 (10^3 cm^2 mmol^-1). Similar trends were observed at 520 nm, underscoring the impact of micelle formation on dye-surfactant interactions. Conclusion: The interaction between Tween 20 and BCG significantly affects the absorbance properties of BCG in aqueous solutions, with marked changes observed upon reaching the CMC. These findings highlight the potential of surfactant-dye interactions to enhance the performance of spectroscopic assays in healthcare settings, offering pathways to more sensitive diagnostic techniques and efficient drug delivery systems.

AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data‐driven control

AbstractThe mission of the AI Institute in Dynamic Systems is to develop the next generation of advanced machine learning (ML) and AI tools for controlling complex physical systems by discovering physically interpretable and physics‐constrained data‐driven models through optimal sensor selection and placement. The research effort is anchored by a common task framework (CTF) that evaluates the performance of ML algorithms, architectures, and optimization schemes for the diverse tasks required in engineering applications. The aim is to push beyond the boundaries of modern techniques by closing the loop between data collection, control, and modeling, creating a unique and cross‐disciplinary architecture for learning physically interpretable and physics constrained models of complex dynamic systems from time series data. The CTF further supports sustainable and open‐source challenge datasets, which are foundational for developing interpretable, ethical, and inclusive tools to solve problems fundamental to human safety, society, and the environment.

Bibliometric analysis of breast cancer-related lymphedema research trends over the last 2 decades.

As breast cancer cases rise globally, post-mastectomy lymphedema garners increasing scholarly attention. This study aims to conduct a comprehensive bibliometric analysis of Breast Cancer-Related Lymphedema (BCRL) research from 2003 to 2022, identifying trends and providing global research insights for future studies. The literature for this analysis was extracted from the Web of Science (WoS) Core Collection, encompassing 1199 publications, including 702 articles and 101 reviews, totaling 803. Using advanced bibliometric tools such as VOSviewer and CiteSpace, quantitative and visual analyses were performed to map collaboration networks, research clusters, and emerging trends. The search strategy included specific terms related to lymphedema, breast cancer, and BCRL, ensuring a comprehensive representation of the research landscape. The bibliometric analysis revealed a steady increase in BCRL publications over the studied period, reaching a peak in 2018. The United States emerged as the leading contributor to BCRL literature, with China also demonstrating a significant presence. Collaboration networks were visualized, showcasing the interconnectedness of institutions and researchers globally. Key research hotspots identified include preventive strategies, complex decongestive therapy, and reconstructive interventions. In conclusion, this pioneering bibliometric analysis provides a comprehensive overview of BCRL research trends and collaborations globally. The findings contribute valuable insights into the evolution of the field, highlighting areas of focus and emerging research themes. This study serves as a foundational resource for researchers, clinicians, and policymakers, fostering evidence-based practices and interventions for BCRL in the future.

Precision Nutrition Unveiled: Gene-Nutrient Interactions, Microbiota Dynamics, and Lifestyle Factors in Obesity Management.

Obesity is a complex metabolic disorder that is associated with several diseases. Recently, precision nutrition (PN) has emerged as a tailored approach to provide individualised dietary recommendations. This review discusses the major intrinsic and extrinsic components considered when applying PN during the management of obesity and common associated chronic conditions. The review identified three main PN components: gene-nutrient interactions, intestinal microbiota, and lifestyle factors. Genetic makeup significantly contributes to inter-individual variations in dietary behaviours, with advanced genome sequencing and population genetics aiding in detecting gene variants associated with obesity. Additionally, PN-based host-microbiota evaluation emerges as an advanced therapeutic tool, impacting disease control and prevention. The gut microbiome's composition regulates diverse responses to nutritional recommendations. Several studies highlight PN's effectiveness in improving diet quality and enhancing adherence to physical activity among obese patients. PN is a key strategy for addressing obesity-related risk factors, encompassing dietary patterns, body weight, fat, blood lipids, glucose levels, and insulin resistance. PN stands out as a feasible tool for effectively managing obesity, considering its ability to integrate genetic and lifestyle factors. The application of PN-based approaches not only improves current obesity conditions but also holds promise for preventing obesity and its associated complications in the long term.

Two-frame wavefront reconstruction method with nonlinear optimization for Ronchi lateral shearing interferometry

Ronchi lateral shearing interferometry is widely applied in wavefront measurement of advanced lithography tools. In Ronchi lateral shearing interferometry, higher-order diffraction beam interference is a common challenge, currently mitigated by introducing more phase shifts, resulting in extended measurement times and added errors. In this study, we propose a nonlinear optimization approach that combines a 2-frame phase-shifting algorithm to suppress the influence of high diffraction orders, reducing the required number of phase shifts by more than 75%. We formulate a numerical model that aligns with the interferometry, construct a highly robust error function, and enable high-precision wavefront reconstruction by optimizing Zernike coefficients. The accuracy and robustness of the proposed method are verified through simulations and experiments.

A bibliometric review of Internet of Things (IoT) on cybersecurity issues

Purpose In response to the growing interest in Internet of Things (IoT) technology and its profound implications for businesses and individuals, this bibliometric study focuses on a critical yet understudied aspect, i.e. cybersecurity. As IoT adoption grows, so do concerns regarding user privacy and data security. This study aims to provide a comprehensive understanding of the current research in this vital area, shedding light on research trends, gaps and emerging themes. Design/methodology/approach The study conducted a bibliometric analysis and systematic review of literature spanning over two decades (2013–2023). Bibliometric analysis is conducted using Biblioshiny which is R-software-based advanced analytical tool. Further, VOSviewer is used to conduct network analysis. The study highlights the evolving landscape of IoT cybersecurity, emphasizing interdisciplinary intersections and the ethical dimensions of IoT technologies. Findings The study uncovers crucial concerns related to IoT adoption, emphasizing the urgent need for comprehensive cybersecurity protocols. It identifies emerging themes such as artificial intelligence and blockchain integration, indicating a shift toward interdisciplinary solutions. Furthermore, the research highlights ethical gaps in current IoT discussions, emphasizing the importance of responsible innovation. Research limitations/implications Businesses can bolster their cybersecurity strategies, policymakers can craft informed regulations and researchers are encouraged to explore IoT’s ethical dimensions. Originality/value This study pioneers a nuanced analysis of IoT cybersecurity, filling a crucial gap in the existing business and management literature. By synthesizing a decade of scholarly work, it provides foundational insights for researchers, businesses and policymakers. The research not only informs academic discourse but also offers practical guidance for enhancing IoT security measures and fostering ethical innovation.

Research and analysis of airline supply chain risk management-- Take Boeing Company as an example

In today’s business environment, supply chain risk management is becoming more and more important for the strategic position of airlines. As a highly complex organizational structure, the airline supply chain faces multiple challenges and potential risks that may negatively impact its operational efficiency, customer satisfaction and profitability. Therefore, a deep understanding of the key elements and practical experience of an airline’s supply chain risk management is essential to enhance its competitiveness. This paper aims to comprehensively explore the overview and core elements of airline supply chain risk management. Firstly, it clearly defines and introduces the characteristics of supply chain risk, as well as the main risk types faced by airline supply chain. Then, it delves into the key elements of airline supply chain risk management, including supply chain visibility and information sharing, supplier selection and evaluation, and inventory management and logistics planning. Then, a series of successful experiences of airline supply chain risk management are introduced, and methods of using advanced technologies and tools to mitigate supply chain risk are discussed.

Sequence introgression from exogenous lineages underlies genomic and biological differences among Cryptosporidium parvum IOWA lines

The IOWA strain of Cryptosporidium parvum is widely used in studies of the biology and detection of the waterborne pathogens Cryptosporidium spp. While several lines of the strain have been sequenced, IOWA-II, the only reference of the original subtype (IIaA15G2R1), exhibits significant assembly errors. Here we generated a fully assembled genome of IOWA-CDC of this subtype using PacBio and Illumina technologies. In comparative analyses of seven IOWA lines maintained in different laboratories (including two sequenced in this study) and 56 field isolates, IOWA lines (IIaA17G2R1) with less virulence had mixed genomes closely related to IOWA-CDC but with multiple sequence introgressions from IOWA-II and unknown lineages. In addition, the IOWA-IIaA17G2R1 lines showed unique nucleotide substitutions and loss of a gene associated with host infectivity, which were not observed in other isolates analyzed. These genomic differences among IOWA lines could be the genetic determinants of phenotypic traits in C. parvum. These data provide a new reference for comparative genomic analyses of Cryptosporidium spp. and rich targets for the development of advanced source tracking tools.

Researching Artificial Intelligence Applications in Evangelical and Pentecostal/Charismatic Churches: Purity, Bible, and Mission as Driving Forces

We explore in this article how Evangelical and Pentecostal/Charismatic Churches (EPCCs) view Artificial Intelligence (AI), and how they use it, either intentionally or indirectly. Considering first the digital habitus in which EPCCs are immersed, we have documented and analyzed three sample cases showing how EPCCs use advanced AI tools to improve the sanctification process for believers; how the Bible can be translated, distributed, and its reading can be fostered around the world, using machine intelligence; and how a spiritual revival among EPCCs can spread rapidly through AI-mediated algorithms. We discuss the implications of these developments and conclude finally with some ideas about how EPCCs should engage AI applications in the future.

A crowdsensing-based framework for sound and vibration data analysis in smart urban environments

In this paper, we introduce a framework tailored to the precise analysis of sound and vibration data in the dynamic context of smart cities. By harnessing the power of crowdsensing data, our system offers a robust and highly adaptable solution for the real-time monitoring and comprehensive assessment of acoustic and vibrational parameters within urban environments. The rise of smart cities, fueled by sensor tech and data analysis, demands advanced tools to tackle urban environmental issues and improve residents’ quality of life. Our framework empowers city stakeholders, offering insights for informed decisions in urban planning, transportation, infrastructure maintenance, and public health. Key features of our system include data acquisition through mobile crowdsensing application, advanced signal processing algorithms for noise and vibration identification, and the integration of geospatial information to provide location-specific context. Furthermore, our framework supports scalable and adaptable data analytics, ensuring the efficient utilization of resources and the effective management of urban environments. The presented framework addresses the challenges of noise pollution, structural health monitoring, transportation optimization, and public safety in smart cities. By exploiting crowdsourced data, it promotes a collaborative approach to data collection, analysis, and decision-making, fostering an environment where cities can continuously evolve and adapt to the evolving needs of their residents. This paper offers a detailed exploration of the system’s architecture, and showcases its practical implementation, affirming its potential as a powerful starting point for advancing the science of smart cities.

Are extreme floods on the Danube River becoming more frequent? A case study of Bratislava station

Abstract In this study, strong and extreme flood events were analysed on the basis of long-term daily runoff records of winter and summer floods in the Danube River between 1876 and 2020, using the peaks-over-threshold method. Based on the results, the following conclusions can be made: (1) There is a downward trend in strong winter floods, but it is not statistically significant. Additionally, there is an upward trend in summer floods, but this is not statistically significant. (2) There are statistically significant upward trends in extreme events for both the winter and summer seasons. The results have implications for flood protection and disaster management on the Danube River. Regulation of assets in flood-prone areas is essential for minimising economic damage. Public awareness of increasing extreme summer floods is vital for prevention. This study suggests that effective flood risk analysis requires (i) a local- to regional-scale approach to account for spatial variability and (ii) advanced statistical tools for robust detection of climate extremes and estimation of their occurrence rates.

AFM-NSOM Nano-Polarimeter: Wide-ranging sensing device for synchronized scanning

This paper explores the use of an enhanced AFM-NSOM system tip as a nanoscale polarimeter. The tip consists of a tungsten-silicon conical photodetector modified with four differently-shaped subwavelength apertures, which enable the determination of incident light's four Stokes parameters and in turn its polarization state on the Poincaré sphere. The system is mounted on a standard Atomic Force Microscope (AFM) cantilever and can serve as a triple-mode scanning system, with complementary topography scanning, optical data analysis, and polarization state detection. The device was designed and simulated using the Comsol Multi-Physics software package, and initial steps toward fabrication were taken using the application of advanced nanotechnology tools to a commercial AFM probe. After a discussion of the advantages and drawbacks of drilled scanning tips vis-a-vis standard ones, the article presents the simulation and fabrication results of the nano-polarimeter tip.

New reference genomes to distinguish the sympatric malaria parasites, Plasmodium ovale curtisi and Plasmodium ovale wallikeri

Despite Plasmodium ovale curtisi (Poc) and wallikeri (Pow) being important human-infecting malaria parasites that are widespread across Africa and Asia, little is known about their genome diversity. Morphologically identical, Poc and Pow are indistinguishable and commonly misidentified. Recent rises in the incidence of Poc/Pow infections have renewed efforts to address fundamental knowledge gaps in their biology, and to develop diagnostic tools to understand their epidemiological dynamics and malaria burden. A major roadblock has been the incompleteness of available reference assemblies (PocGH01, PowCR01; ~ 33.5 Mbp). Here, we applied multiple sequencing platforms and advanced bioinformatics tools to generate new reference genomes, Poc221 (South Sudan; 36.0 Mbp) and Pow222 (Nigeria; 34.3 Mbp), with improved nuclear genome contiguity (> 4.2 Mbp), annotation and completeness (> 99% Plasmodium spp., single copy orthologs). Subsequent sequencing of 6 Poc and 15 Pow isolates from Africa revealed a total of 22,517 and 43,855 high-quality core genome SNPs, respectively. Genome-wide levels of nucleotide diversity were determined to be 2.98 × 10–4 (Poc) and 3.43 × 10–4 (Pow), comparable to estimates for other Plasmodium species. Overall, the new reference genomes provide a robust foundation for dissecting the biology of Poc/Pow, their population structure and evolution, and will contribute to uncovering the recombination barrier separating these species.

Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition.

Human activity recognition (HAR) in wearable and ubiquitous computing typically involves translating sensor readings into feature representations, either derived through dedicated pre-processing procedures or integrated into end-to-end learning approaches. Independent of their origin, for the vast majority of contemporary HAR methods and applications, those feature representations are typically continuous in nature. That has not always been the case. In the early days of HAR, discretization approaches had been explored-primarily motivated by the desire to minimize computational requirements on HAR, but also with a view on applications beyond mere activity classification, such as, for example, activity discovery, fingerprinting, or large-scale search. Those traditional discretization approaches, however, suffer from substantial loss in precision and resolution in the resulting data representations with detrimental effects on downstream analysis tasks. Times have changed, and in this paper, we propose a return to discretized representations. We adopt and apply recent advancements in vector quantization (VQ) to wearables applications, which enables us to directly learn a mapping between short spans of sensor data and a codebook of vectors, where the index comprises the discrete representation, resulting in recognition performance that is at least on par with their contemporary, continuous counterparts-often surpassing them. Therefore, this work presents a proof of concept for demonstrating how effective discrete representations can be derived, enabling applications beyond mere activity classification but also opening up the field to advanced tools for the analysis of symbolic sequences, as they are known, for example, from domains such as natural language processing. Based on an extensive experimental evaluation of a suite of wearable-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR.

Behavioural ecology meets oncology: quantifying the recovery of animal behaviour to a transient exposure to a cancer risk factor.

Wildlife is increasingly exposed to sublethal transient cancer risk factors, including mutagenic substances, which activates their anti-cancer defences, promotes tumourigenesis, and may negatively impact populations. Little is known about how exposure to cancer risk factors impacts the behaviour of wildlife. Here, we investigated the effects of a sublethal, short-term exposure to a carcinogen at environmentally relevant concentrations on the activity patterns of wild Girardia tigrina planaria during a two-phase experiment, consisting of a 7-day exposure to cadmium period followed by a 7-day recovery period. To comprehensively explore the effects of the exposure on activity patterns, we employed the double hierarchical generalized linear model framework which explicitly models residual intraindividual variability in addition to the mean and variance of the population. We found that exposed planaria were less active compared to unexposed individuals and were able to recover to pre-exposure activity levels albeit with a reduced variance in activity at the start of the recovery phase. Planaria showing high activity levels were less predictable with larger daily activity variations and higher residual variance. Thus, the shift in behavioural variability induced by an exposure to a cancer risk factor can be quantified using advanced tools from the field of behavioural ecology. This is required to understand how tumourous processes affect the ecology of species.

Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study

Abstract Significant social, economic, and environmental costs are associated with traffic congestion. The effectiveness of intersections makes a substantial contribution to the effectiveness of entire road networks. The three-leg at-grade Al-Husainea intersection, located 20 km from the center of holy Karbala city in Iraq, is subjected to serious congestion, resulting in an increase in delay time, reduction in capacity, and bad level of service (LOS). Therefore, it is essential to use advanced software tools to ensure that the current intersection can be controlled, evaluated, and improved. So, the major goal of this study is to use SIDRA, an acronym for signalized and unsignalized intersection design and research aid, software to evaluate the LOS of the Al-Husainea intersection, in which the traffic is assessed using the current LOS. LOS, delay time, and degree of saturation were the criteria utilized to evaluate the traffic flow performance. SIDRA is also used to assess benefits as a result of suggested changes in the design of particular junctions. The first stage is to gather field data regarding traffic volumes utilizing a method of traffic volume gathering. From 7 am to 6 pm, the SIDRA program gathered data for a full 7 days. The results showed that the LOS for the Al-Husainea intersection in the Al-Husainea arm is F, with an average delay of 52 s per vehicle and a saturation level of 0.86 v/c. Finally, it was determined that the Al-Husainea intersection needs additional improvements based on the study and findings from the SIDRA program, and some remedies are suggested in this study to improve the intersection traffic flow.

Assessing economic implications for micro, small and medium enterprises in Thailand post Covid-19 lockdown.

Micro, Small, and Medium-sized Enterprises (MSMEs) in Thailand were assessed in this study to determine the short-term and long-term economic effects of post-COVID- 19 -, with the goal of developing policy guidelines that focus on the methods and strategies that will further develop and help recover these sectors. MSMEs are the most vulnerable and require assistants to combat the pandemic. This study assesses the perspectives of stakeholders on the development of mechanisms and the strategies applied to support vulnerable groups in Thailand, which mostly consist of women and children. The main data collection was gathered through online questionnaires that were distributed to various stakeholder groups. The tools used for analysis were advanced quantitative analysis tools that aid in achieving this research study's objectives, and data was examined primarily through the usage of path modeling, structural equation modeling (SEM), and descriptive analysis was among the methods used. The findings reveal that in the short term, MSMEs' ability to respond to COVID-19 implications has a significant impact on both financial and non-financial performance. Non-financial performance, on the other hand, is more affected by adaptability than financial performance. Demand shock from lockdowns and other COVID-19 cautionary interventions has a negative and significant impact on MSMEs' adaptability, financial performance, and non-financial performance. The demand shocks increased the vulnerability of MSMEs significantly but it was found that proper management of demand shock has helped stabilized and improve MSMEs' financial and non-financial performances, as well as helped decrease their vulnerability. When it comes to government policy, the focus is usually on enhancing the flexibility and financial performance of MSMEs. The government's legislative actions have little impact on MSMEs' non-financial performance and vulnerability. This could be because the majority of the programs are more focused on providing financial assistance to businesses or their consumers. COVID-19's supply and demand shock only hindered MSMEs' ability to respond to the changes and challenges caused by the pandemic, according to vendors. The vulnerability of MSMEs caused by COVID-19 creates grave effects on their financial performance. The findings of this research paper will assist policymakers in identifying the most vulnerable aspects of MSMEs, as well as their expectations- and determine the forms of support that will be required to combat the current and future pandemic situations that may occur in Thailand. In addition, it will aid policymakers in the establishment of procedures and supporting strategies for MSMEs to reduce the unemployment rate and stimulate the Thai economy, among other factors of improvement.

Toward Controlled Synthesis of 2D Crystals by CVD: Learning from the Real-Time Crystal Morphology Evolutions.

The rich morphology of 2D materials grown through chemical vapor deposition (CVD), is a distinctive feature. However, understanding the complex growth of 2D crystals under practical CVD conditions remains a challenge due to various intertwined factors. Real-time monitoring is crucial to providing essential data and enabling the use of advanced tools like machine learning for unraveling these complexities. In this study, we present a custom-built miniaturized CVD system capable of observing and recording 2D MoS2 crystal growth in real time. Image processing converts the real-time footage into digital data, and machine learning algorithms (ML) unveil the significant factors influencing growth. The machine learning model successfully predicts CVD growth parameters for synthesizing ultralarge monolayer MoS2 crystals. It also demonstrates the potential to reverse engineer CVD growth parameters by analyzing the as-grown 2D crystal morphology. This interdisciplinary approach can be integrated to enhance our understanding of controlled 2D crystal synthesis through CVD.

Evaluating the Efficacy of Supervised Machine Learning Models in Inflation Forecasting in Sri Lanka

This study aims to forecast the inflation rate using supervised machine learning models (SMLM). While SMLMs are widely used in various fields, they have not been widely applied in forecasting inflation rates. Therefore, the main objective of this study is to identify the best model for forecasting inflation among four different SMLMs: LASSO regression (LR), Bayesian Ridge Regression (BRR), Support Vector Machine Regression (SVR), and Random Forest Regression (RFR) models. To achieve this objective, two different types of cross-validation techniques were employed: The k-fold cross-validation method (CVK) and walk forward validation (WFV) methods. These techniques were used to estimate the parameters and hyper-parameters for each machine learning model with root mean square error. The mean absolute percentage error (MAPE) was used to compare the performance of the different SMLMs. Empirical evidence from Sri Lanka between 1988 and 2021 was used to test the performance of the SMLMs in forecasting inflation rates. The results show that the SVR model with walk-forward validation is the best method for forecasting the future inflation rate of Sri Lanka based on the MAPE value. Overall, this study showcases the effectiveness of Supervised Machine Learning Models (SMLMs) in forecasting inflation rates, emphasizing the critical role of precise cross-validation techniques. These findings are invaluable for policymakers and investors, offering advanced tools for more informed economic decision-making and highlighting the potential of machine learning in enhancing macroeconomic stability and forecasting accuracy.