Advanced Data Research Articles

Assessing the Impact of Technology on the Efficiency and Effectiveness of Investment Banking Services

This paper evaluates the effect of innovation on the effectiveness and adequacy of venture managing an account administrations. It reflects on the way digital innovations such as artificial intelligence, blockchain, and data management systems have transformed such missions as securities underwriting, mergers and acquisitions, and client advisory services. By combining these technologies, investment banks optimize their processes, improve precision, and bring in enhanced customer experiences. Deliberating through critical review of literature and specific cases from the industry, this paper explores how artificial intelligence changes the decision-making processes, automates trading strategies, and enhances risk management, while blockchain changes back-office operations by making transactions faster and more secure. Advanced data management tools equally provide improved insight into the enhanced client services and decisions in investments for the banks. Although this brings cost-cutting and operational efficiency, these technologies pose challenges in cybersecurity and regulatory compliance due to the expansion of digital platforms, which provide increased opportunities for cyber-attacks and a need for strict adherence to ever-changing regulations. The paper provides a panoramic view of the current and future role of technology in evolving investment banking.

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. The role of carbohydrates, particularly apolipoprotein E (ApoE), is pivotal in AD pathogenesis due to its involvement in lipid and cholesterol metabolism, and its status as a genetic predisposition factor for the disease. Despite its significance, the mechanistic contributions of Lipid Metabolism-related Genes (LMGs) to AD remain inadequately elucidated. This research endeavor seeks to bridge this gap by pinpointing biomarkers indicative of early-stage AD, with an emphasis on those linked to immune cell infiltration. To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers. Differentially expressed genes (DEGs) were identified by comparing gene expression profiles between healthy individuals and Alzheimer's disease (AD) patients, using data from two Gene Expression Omnibus (GEO) datasets: GSE5281 and GSE138260. Functional enrichment analysis was conducted to elucidate the biological relevance of the DEGs. To ensure the reliability of the results, samples were randomly divided into training and validation sets. The analysis focused on lipid metabolism-related DEGs (LMDEGs) to explore potential biomarkers for AD. Machine learning algorithms, including Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, were applied to identify a key gene biomarker. Additionally, immune cell infiltration and its relationship with the gene biomarker were assessed using the CIBERSORT algorithm. The Integrated Traditional Chinese Medicine (ITCM) database was also referenced to identify Chinese medicines related to lipid metabolism and their possible connection to AD. This comprehensive strategy aims to integrate modern computational methods with traditional medicine to deepen our understanding of AD and its underlying mechanisms. The study identified 137 genes from a pool of 751 lipid metabolism-related genes (LMGs) significantly associated with autophagy and immune response mechanisms. Through the application of LASSO and SVM-RFE machine-learning techniques, four genes-choline acetyl transferase (CHAT), member RAS oncogene family (RAB4A), acyl-CoA binding domain-containing protein 6 (ACBD6), and alpha-galactosidase A (GLA)-emerged as potential biomarkers for Alzheimer's disease (AD). These genes demonstrated strong therapeutic potential due to their involvement in critical biological pathways. Notably, nine Chinese medicine compounds were identified to target these marker genes, offering a novel treatment approach for AD. Further, ceRNA network analysis revealed complex regulatory interactions involving these genes, underscoring their importance in AD pathology. CIBERSORT analysis highlighted a potential link between changes in the immune microenvironment and CHAT expression levels in AD patients, providing new insights into the immunological dimensions of the disease. The discovery of these gene markers offers substantial promise for the diagnosis and understanding of Alzheimer's disease (AD). However, further investigation is necessary to validate their clinical utility. This study illuminates the role of Lipid Metabolism-related Genes (LMGs) in AD pathogenesis, offering potential targets for therapeutic intervention. It enhances our grasp of AD's complex mechanisms and paves the way for future research aimed at refining diagnostic and treatment strategies.

A Case Study on AI Integration for Global Services at a Bangalore-based Tech Company

This case study examines Justin's leadership in integrating AI technologies at a prominent Bangalore-based tech company to enhance global services and customer experiences. By deploying AI-powered chatbots, personalized recommendations, and predictive analytics, the company significantly improved customer satisfaction, operational efficiency, and scalability. However, challenges related to data management, employee resistance, and process alignment arose during integration, prompting the implementation of strategic solutions such as advanced data management systems and workforce upskilling to foster a collaborative environment.

An IoT Healthcare System With Deep Learning Functionality for Patient Monitoring

ABSTRACTCurrently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality.

Research on the Application of Computer Technology in Biostatistics

This paper explores the application of computer technology in biostatistics, focusing on the technological advancements and their impact on data management, data processing and analysis, and data visualization. With the development of computer technology, data storage and management methods have significantly improved, particularly with the introduction of database systems and big data technologies, which have greatly enhanced the efficiency and accuracy of handling biostatistical data. Additionally, the application of machine learning and artificial intelligence has made biostatistical data analysis more intelligent and automated, providing new tools for identifying and predicting complex data patterns. Furthermore, advanced data visualization techniques have enhanced the interpretability of statistical results through clear graphical presentations, promoting the application of biostatistics. Through the analysis of specific case studies, this paper demonstrates the practical effects and challenges of computer technology in improving biostatistical research capabilities. Finally, the paper anticipates the future impact of emerging computing technologies, such as quantum computing, on biostatistics and suggests future research directions and recommendations.

Sustainable watersheds management in Peru: Challenges and perspectives

Sustainable watershed management stands at the forefront of global efforts to tackle pressing environmental and socio-economic challenges. This study offers a pioneering assessment of the state of research on Peru's watersheds, integrating geographical, environmental, and socio-economic dimensions. By harnessing cutting-edge bibliometric tools, including Bibliometrix and VOSviewer, and adhering to PRISMA guidelines for a systematic review, this work maps the scientific landscape of watershed management in Peru. Utilizing comprehensive databases such as Scopus and Web of Science, the study identifies and analyses key thematic clusters, revealing an escalating focus on the urgent issues of climate change and urbanization, particularly within the critical Rimac River Basin. This basin, vital to the water supply of Lima, the world's second-largest desert city, faces severe threats from pollution and climate instability. Our findings not only chart the evolution of research in this domain but also spotlight emerging opportunities to modernize water management practices through the integration of climate change models, advanced data monitoring, and artificial intelligence. The study makes a compelling case for a collaborative approach, urging stronger alliances among local communities, research institutions, and international stakeholders to foster more resilient and sustainable watershed management strategies in Peru. This paper provides actionable insights, making it an indispensable resource for policymakers and practitioners aiming to secure Peru's water future.

Smart forest monitoring: A novel Internet of Things framework with shortest path routing for sustainable environmental management

AbstractForests play a pivotal role in protecting the environment, preserving vital natural resources, and ultimately sustaining human life. However, the escalating occurrences of forest fires, whether of human origin or due to climate change, poses a significant threat to this ecosystem. In recent decades, the emergence of the IoT has been characterised by the utilisation of smart sensors for real‐time data collection. IoT facilitates proactive decision‐making for forest monitoring, control, and protection through advanced data analysis techniques, including AI algorithms. This research study presents a comprehensive approach to deploying a dynamic and adaptable network topology in forest environments, aimed at optimising data transmission and enhancing system reliability. Three distinct topologies are proposed in this research study: direct transmission from nodes to gateways, cluster formation with multi‐step data transmission, and clustering with data relayed by cluster heads. A key innovation is the use of high‐powered telecommunication modules in cluster heads, enabling long‐range data transmission while considering energy efficiency through solar power. To enhance system reliability, this study incorporates a reserve routing mechanism to mitigate the impact of node or cluster head failures. Additionally, the placement of gateway nodes is optimised using meta‐heuristic algorithms, including particle swarm optimisation (PSO), harmony search algorithm (HSA), and ant colony optimisation for continuous domains (ACOR), with ACOR emerging as the most effective. The primary objective of this article is to reduce power consumption, alleviate network traffic, and decrease nodes' interdependence, while also considering reliability coefficients and error tolerance as additional considerations. As shown in the results, the proposed methods effectively reduce network traffic, optimise routing, and ensure robust performance across various environmental conditions, highlighting the importance of these tailored topologies in enhancing energy efficiency, data accuracy, and network reliability in forest monitoring applications.

Quantum Machine Learning for Advanced Data Processing in Business Analytics: A Path Toward Next-Generation Solutions

Quantum Machine Learning (QML) is a rising paradigm of computing that holds high possibilities to revolutionise data analysis in the business analytics industry. This paper aims at presenting how QML can solve the increasingly challenging demands of big contributions to large-scale data analysis in business contexts, achieving better performance than orthodox machine learning methods in terms of time and efficacy. LIQUID: This research utilises actual business data and scenarios and solving problems by using quantum algorithms – VQE and QSVM. At the core of our approach, we perform extensive validation of QML models on platforms such as IBM’s Quantum Experience and D-Wave systems pertaining to critical use cases for businesses would include financial modelling, supply chain management, and customer behaviour prediction. The results presented here suggest that quantum algorithm speeds processing by 30-50% when compared with conventions approaches of computation. Moreover, QML has shown better results for prediction in analytics for various firms and provide them better aptitudes for decision-making. Hence the uniqueness of this research by grounding real world business problems in quantum algorithms and demonstrating empirically how superior they are compared to the classical alternatives in dealing with large datasets. Considering the future of business analytics, QML looks set to be a real game changer for industries that heavily rely on big data analysis. In this it has filled a gap which would cause a scholarly without hard gap between theoretical quantum computing and real business applications.

A critical analysis of the role of artificial intelligence and machine learning in enhancing nuclear waste management

Abstract Controlling and managing nuclear waste is a significant challenge due to the harmful effects of radioactive materials on human health. To address this, long-term storage solutions are essential. Artificial Intelligence (AI) and Machine Learning (ML) are being utilized to make nuclear waste management safer, more effective, and efficient. This paper evaluates various applications of AI and ML in the field of nuclear waste, covering aspects such as predictive maintenance, waste sorting, and classification. AI and ML enhance real-time monitoring of storage conditions and optimize waste handling procedures through advanced data processing capabilities. Implementing cutting-edge solutions is crucial to protect public health and the environment from radioactive waste. The purpose of this evaluation is to examine how AI and ML improve nuclear waste management processes. These technologies can reduce human exposure to harmful materials and increase the safety and efficiency of managing nuclear waste through advanced predictive capabilities. The introduction of AI and ML in nuclear waste management is driving significant changes and innovations, addressing current issues, and establishing new guidelines for future policies.

Deep Earth System Data Laboratory (DeepESDL)

Abstract. The Deep Earth System Data Lab (DeepESDL) provides an AI-ready, collaborative environment for researchers aiming to study the Earth's complex dynamics using various datasets and empirical approaches. Recently opened to Early Adopters, it builds on projects like CAB-LAB and ESDL, utilizing well-established Python and Julia technology stacks. DeepESDL offers programmatic access to extensive analysis-ready data cubes and computational resources, enabling researchers to focus on analysis without extensive preparations. Scientists can use persistently available data cubes or generate user-tailored cubes from own data or publicly available datasets. The goal is to streamline data processing through empirical or AI methods within high-dimensional Earth Observation workflows. DeepESDL addresses the complete research cycle, from discovery of earth data to powerful analyses, collaborative scientific research, advanced data visualisation and publication of results, promoting FAIR and Open Science. Apart from serving as a research environment, DeepESDL showcases scientific use cases and supports educational purposes through capacity building, academic programs, and Open Science initiatives. This paper presents an overview of DeepESDL.

3D micromorphological reconstruction and roughness characterization of wood surface based on sequence images

A novel non-contact optical inspection technique is proposed to precisely reconstruct the three-dimensional surface structure of wood and evaluate its roughness. The feasibility of using image fusion techniques to characterize the 3D roughness of wood surfaces is explored by combining high-resolution serial image acquisition techniques with advanced data processing methods. The work focuses on examining various cut surfaces of three distinct wood species: Larch, Catalpa, and Toona sinensis. The proposed 3D roughness parameters are verified using 3D reconstruction and precise measurements. The experimental results demonstrate that the proposed method exhibits strong adaptability in measuring wood surface roughness and achieves a high degree of consistency with the results obtained from traditional digital microscope systems. This study not only validates the possible use of multilayer sequence image fusion technology in measuring wood roughness, but also establishes a solid basis for the creation of 3D assessment criteria for wood surface roughness and associated inspection systems.

Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.

There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.

Bridging Theory and Practice : An Integrated Approach to Blockchain and Data Analytics Education

This article presents a comprehensive framework for building a strong foundation in blockchain technology and data analytics, two rapidly evolving fields that are increasingly intertwined in today's digital landscape. We propose a structured approach that combines theoretical knowledge acquisition with practical skill development, designed to equip aspiring professionals with the necessary tools to succeed in this dynamic sector. The framework encompasses key areas such as blockchain fundamentals, smart contracts, cryptography, data analysis techniques, and advanced machine learning concepts including neural networks, deep learning, and artificial intelligence. We emphasize the importance of leveraging diverse educational resources while highlighting the critical role of hands-on projects in reinforcing learning outcomes. By detailing strategies for developing blockchain applications and conducting advanced data analytics, this article bridges the gap between academic understanding and practical application. We explore real-world use cases demonstrating the synergy between blockchain and AI in areas such as financial crime prevention, supply chain management, and healthcare. Our findings suggest that this integrated approach not only enhances technical proficiency but also cultivates the problem-solving and analytical skills essential for advancing towards professional roles in blockchain and data analytics. This research contributes to the growing body of literature on technology education and provides a roadmap for individuals seeking to establish a robust foundation in these interconnected fields.

Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities

BackgroundThe clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model. MethodA retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores. ResultsThe frequency and mortality rates of COVID-19 among males (19.3 %) were higher than those among females (17 %) (p = 0.01, OR = 0.85, 95 % CI = 0.76–0.96). Cancer (p < 0.05, OR = 1.9, 95 % CI = 1.48–2.4) and Alzheimer's (p < 0.05, OR = 2.36, 95 % CI = 1.89–2.9) were the two most common comorbidities associated with long-term hospitalization (LTH). Kidney disease (KD) was identified as the most lethal comorbidity (45 % of KD patients) (OR = 5.6, 95 % CI = 5.05–6.04, p < 0.001). Age > 55 was the most predictive parameter for mortality (p < 0.001, OR = 6.5, 95 % CI = 1.03–1.04), and the CT scan score showed no predictive value for death (p > 0.05). WBC, Cr, CRP, ALP, and VBG-HCO3 were the most significant critical data associated with death prediction across all comorbidities (p < 0.05). ConclusionCOVID-19 is particularly lethal for elderly adults; thus, age plays a crucial role in disease prognosis. Regarding death prediction, various comorbidities rank differently, with KD having a significant impact on mortality outcomes.

Time-Encoded Information Encryption with pH Clock Guided Broad-Spectrum Emission by Dynamic Assemblies.

With growing threats from counterfeiting-based security breaches, multi-level and specific stimuli-responsive anti-counterfeiting devices and message encryption methods have attracted immense research interest. Fluorescence-based encryption from aggregation-induced emission (AIE)-based materials solves the problems to a considerable extent. However, the development of smarter patterns with hierarchical security levels alongside dynamic display is still challenging. To screen out this complication, we bring forward a pH-switchable fluorescent assembly of an AIEgen and an aliphatic acid. We later temporally direct the molecular assembly with the aid of a chemical trigger-regulated pH clock, generating a transitory multicolor emission, including transient white light generation. The pH-dependent emissions were further implemented in constructing smart multi-input fluorescent chemical AND gates. Subsequently, we integrate the time-gated emissive system to develop an advanced multi-dimensionally secure data encryption strategy. This novel approach enhances anti-counterfeiting measures by introducing an additional layer of security based on temporal characteristics.

Clinical adverse events to letairis: a real-world drug safety study based on FDA Adverse Event Reporting System (FAERS)

ABSTRACT Background Letairis (ambrisentan), an endothelin receptor antagonist (ERA), is a critical medication for pulmonary arterial hypertension (PAH). Despite its efficacy, its safety profile is under scrutiny, warranting a detailed analysis. Research design and methods This study leveraged the FDA Adverse Event Reporting System (FAERS) from Q1 2007 to Q4 2023, focusing on Letairis as the primary suspect in adverse events. Employing advanced data mining techniques, such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS), the study aimed to uncover safety signals. Results A total of 43,774 cases were identified, with Letairis implicated in 16,038,963 adverse event reports. There was a notable predominance of female patients (75.30%), with a median age around 64 years. Severe outcomes, including hospitalization (51.63%) and fatalities (20.44%), were prevalent. Signal strength analysis highlighted concerns in infections and infestations, as well as cardiac disorders. Conclusion The analysis underscores the need for vigilant pharmacovigilance and highlights Letairis’s potential to induce serious AEs, particularly in female and elderly populations. These findings are instrumental in guiding clinical practice and future drug safety assessments.

Changes of microbial active ingredients and antioxidant capacity during fermentation process of dark tea

Dark tea, a traditional fermented tea in China, is known for its unique flavor and enhanced health potential due to its fermentation process. However, previous studies on the evolution of its active ingredients and antioxidant properties have been limited by inadequate sample collection, single analytical methods, and insufficient data processing. To address these challenges, this study employed a comprehensive strategy to analyze the dynamic changes of active compounds and antioxidant efficacy during dark tea fermentation using refined sampling, diverse assay techniques, and advanced data analysis. A multi-point temporal sampling method was used to capture key stages of fermentation, ensuring comprehensive data. High-Performance Liquid Chromatography (HPLC) and various antioxidant assays (1,1-diphenyl-2-picryl-hydrazyl (DPPH), 2, 2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), Ferric ion reducing antioxidant power (FRAP)) enabled precise quantification of tea polyphenols, catechins, theaflavins, and thearubigins. Multivariate statistical analysis revealed that tea polyphenols and catechins decreased, theaflavins increased then slightly declined, and thearubigins steadily rose as fermentation progressed. These changes were linked to fluctuations in antioxidant capacity, peaking at around 30 mg/g of phenolic compounds. The study also explored optimizing fermentation to enhance the retention of beneficial components, maximizing antioxidant properties and improving product quality. This research advances the understanding of dark tea fermentation and supports the sustainable development of the dark tea industry.

Quantifying global warming potential variations from greenhouse gas emission sources in forest ecosystems

Forest ecosystems play a crucial role in regulating greenhouse gas (GHG) emissions and mitigating climate change. This research aimed to evaluate the GHG emissions of various sources within forested ecosystems and assess their respective contributions to global warming potential (GWP), vital for developing more targeted strategies to mitigate climate change, shaping climate policies, carbon accounting, sustainable forest management, and advancing scientific comprehension of ecosystem-climate dynamics. The study comprehensively analysed carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions EDGAR data of deforestation, fires, and natural processes such as organic soil decomposition within forested ecosystems. The assessment quantified the CO2 equivalent emissions for each category from 1990 to 2022 and forecasted till 2030. Our forecast shows that CO2 emissions from deforestation could reach between 3,990 and 4,529 metric ton (Mt) by 2030, with forest fires contributing an additional 750 Mt. Forestland CO2 absorption is expected to decline to -5134.80 Mt by 2030. There is uncertainty surrounding the forecasts for Organic soil CO2 (829.78 Mt) and Other land CO2 (-764.53 Mt). In addition, deforestation was a significant contributor to CO2 emissions, with a GWP ranging from 4000 to 4500, highlighting the complex interplay between natural processes and human activities in shaping atmospheric warming patterns. Additionally, forest fires emit a complex mix of GHGs. The potency of these gases in warming the planet varies considerably, with CH4 exhibiting a GWP range of 500 to 700 Mt CO2 equivalent, and CO2 ranging from 900 and 1350 Mt. These variations depend on fire intensity and its overall impact on the climate system. Forestland acts as powerful carbon sink, capturing atmospheric CO2 with negative GWP values between -7000 and -6000. Researchers suggest a multifaceted strategy such as stricter enforcement of sustainable forestry regulations, investing in projects that promote carbon sequestration, and reforestation. Additionally, advancements in drone technology, satellite imagery, remote sensing and advanced data analytics can aid in detecting and mitigating climate change impacts, ultimately paving the way for carbon neutrality.Graphical

Vigilancia Inteligente del Comercio Exterior: Detección de Anomalías en las Importaciones del Ecuador con Minería de Datos

This study employs advanced data mining techniques to comprehensively analyze Ecuador's import data spanning three decades, from 1990 to 2021. By leveraging the k-means clustering algorithm, we meticulously identified anomalies within tariff items. The study utilized a comprehensive dataset that encompassed tariff items, years, and critical variables such as import volume, CIF and FOB values, import cost, and trade attractiveness. The data mining model generated insightful reports that accurately pinpointed an omalous patterns in tariff items. These reports empower experts to delve into the underlying causes of these anomalies, enabling them to make well-informed decisions to optimize Ecuador's import strategies. Our research underscores the transformative potential of data mining in detecting import anomalies, providing valuable intelligence for the strategic management of Ecuador's foreign trade. The findings contribute significantly to the prevention of customs fraud and unfair trade practices, ultimately enhancing the competitiveness of the country's import sector.

Talent scouting and standardizing fitness data in football club: systematic review

Talent scouting and fitness data standardization in professional football clubs have been the focus of research in recent years. The role of technology, big data, and data analytics is crucial in reshaping sports performance and bolstering the competitiveness of professional football teams. This review aims to consolidate current research on these technological advancements, examining how they can be effectively harnessed to revolutionize talent sourcing and elevate the competitive standards within football clubs. The review will focus on participants, including professional football players, and intervention interventions such as the implementation of digital technologies, data mining, and machine learning techniques. The study designs will include experimental, observational, and mixed-method studies conducted in football club settings. The analysis highlights the transformative potential of integrating quantitative player statistics with advanced data science and data-driven approaches in revolutionizing sports performance, enabling clubs to make informed recruitment decisions and enhance team performance. In addition, the review focuses on the impact of data analytics on transforming sports performance. The research has demonstrated that these models, specifically classification and regression models, can forecast a football player's performance score with up to 94% accuracy for forward positions.