Advanced Data Analysis Research Articles

Data-Supported Prediction of Surface Settlement Behavior on Opencast Mine Dumps Using Satellite-Based Radar Interferometry Observations

To ensure the safe repurposing of post-mining landscapes, understanding and managing geotechnical risks, particularly ground movements such as settlements on opencast mining dump surfaces, is critical. Satellite-based radar interferometry (InSAR) technology offers highly detailed data on vertical ground movements with a high spatial and temporal resolution. By combining a data-driven approach, using InSAR-generated high-resolution datasets, with model-driven methods such as inverse modeling and classic time–settlement models, the efficient monitoring and prediction of opencast mine dump settlements can be achieved. This dual approach—leveraging advanced data analysis tools and precise modeling—yields valuable insights into spatial settlement behavior. In particular, classic time–settlement models are applied to the InSAR data through least square regression and Taylor approximation. The integration of both approaches enables the more robust, data-validated forecasts of key geotechnical indicators, such as the time to settlement stabilization and the expected maximum settlement over large areas. An application at a mine in central Germany illustrates the method by generating spatial predictions of the settlement behavior over more than 200 ha. In general, the results provide a comprehensive dataset for investigating other factors influencing the settlement behavior of opencast mine dumps.

Visual Search Interactive Model for Artificial Intelligence Robotics Model forthe Agricultural Field Analysis

The Visual Search Interactive Model for Artificial Intelligence (AI) is designed to enhance the efficiency and effectiveness of visual data analysis across various applications. By leveraging advanced computer vision techniques and machine learning algorithms, this model enables AI systems to interpret and analyze visual information in real-time, facilitating tasks such as object recognition, image classification, and scene understanding. The interactive nature of the model allows users to engage with the AI, refining searches and improving outcomes through iterative feedback. This paper introduces the Auxiliary Clustering k-means Machine Learning (AC k-means ML) model, designed to enhance agricultural efficiency through advanced data analysis and robotic integration. The study evaluates the performance of the AC k-means ML model using a dataset comprising 1,950 samples, achieving an overall accuracy of 91.5% and a precision of 89.2%. Key performance metrics such as F1 scores averaged 88.6%, with the highest individual cluster accuracy reaching 96% for Cluster 10. In addition to data classification, the model facilitated the completion of 250 tasks with a remarkable success rate of 92%, while maintaining an average task completion time of 15.4 minutes and an energy consumption of just 0.5 kWh per task. The implementation of the AC k-means ML model resulted in a 15% increase in crop yield and substantial cost savings estimated at $2,000. With a user satisfaction score averaging 8.7 and an adaptability score of 9.0, the findings indicate that the integration of machine learning and robotics significantly optimizes agricultural processes, promoting sustainability and efficiency in farming practices.

End-To-End Automated Intact Protein Mass Spectrometry for High-Throughput Screening and Characterization of Bispecific and Multispecific Antibodies.

Bispecific antibodies (bsAbs) and multispecific antibodies (msAbs) represent a promising frontier in therapeutic antibody development, offering unique capabilities not achievable with traditional monoclonal antibodies. Despite their potential, significant challenges remain due to their increased molecular complexity. One prominent challenge is the correct assembly of light and heavy chains, as improper pairing leads to mispaired or incompletely assembled species that lack therapeutic efficacy and possess undesired properties, impairing the developability, manufacturability, and safety. There is a critical need for rapid, sensitive analytical tools to monitor and control these undesired species and ensure the quality assessment of bsAbs and msAbs. To address this need, we present a novel high-throughput, format-agnostic intact mass workflow that significantly enhances the efficiency of detecting and quantifying biotherapeutic products and related impurities. This workflow integrates automated sample preparation, novel high-resolution rapid mass detection powered by SampleStream-MS, and an advanced data analysis pipeline. It offers increased throughput and data quality while substantially reducing analysis turnover time and labor. This was demonstrated in a pilot program where ∼800 multispecific antibodies were processed in 10 working days. The article details the evaluation and validation of our method, demonstrating its repeatability and intermediate precision in terms of measurement accuracy and relative quantification of various product-related species. We underscore the transformative potential of this end-to-end high-throughput workflow in expediting bispecific and multispecific antibody discovery, optimizing production processes, and ensuring high-quality development and manufacturing for therapeutic antibodies.

Integrated Analysis of Utilization Efficiency of Intelligent Sports Venues Based on Data Mining Algorithms

With the rapid development of information technology and the increasing prosperity of the sports industry, the construction and management of intelligent sports venues have become an important way to improve the efficiency of sports resource utilization and meet diverse fitness needs. This paper focuses on the integrated analysis of the utilization efficiency of intelligent sports venues based on data mining algorithms. Aiming to reveal the inherent laws and potential value in the operation of sports venues through advanced data processing and analysis techniques, and provide a scientific basis for optimizing resource allocation and improving service quality. Intelligent sports venues utilize modern information technologies such as the Internet of Things, big data, and cloud computing to achieve comprehensive and intelligent monitoring and management of venue facilities, personnel activities, environmental conditions, and more. This new management model not only improves the operational efficiency of the venue but also enhances the user experience and meets personalized and diverse fitness needs. This paper focuses on the current situation of the opening of university sports venues to the outside world, aiming to reveal the pain points and bottlenecks in their operation process through detailed investigation and analysis. This study introduces advanced concepts of middleware architecture and constructs a multi-level data integration framework that includes local models, global models, user models, and mapping rules. This framework can effectively integrate distributed heterogeneous data sources, achieving seamless integration and efficient interaction of data. At the same time, with the help of distributed programming technology in cloud computing, it is possible to conduct in-depth mining and analysis of integrated massive data, discover potential value, and provide a scientific basis for decision support.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease

Parkinson's disease (PD) presents a significant global health challenge, characterized by the progressive degeneration of dopamine-producing neurons in the brain, resulting in both motor and non-motor symptoms that severely impact quality of life. This study addresses the complexities of PD, highlighting the critical need for early diagnosis to slow disease progression. This research addresses the challenges of early diagnosis, such as the use of unreliable diagnostic techniques and limited healthcare resources. It uses the MMU Parkinson Disease Dataset and applies camera-based data collection to analyze gait patterns that can identify a risk of Parkinson's Disease. The study utilizes computer vision and the AlphaPose framework to analyze video data and detect body key points. By employing machine learning algorithms, including Support Vector Machines (SVM) and CatBoost, showing highly effective in identifying temporal dependencies in gait patterns. The algorithms achieved a high accuracy of 83.33% on the MMU dataset. This method enhances the accuracy of PD detection and enables immediate detection and control of the disease. The combination of advanced data analysis methods and medical knowledge offers new possibilities to develop targeted treatments that improve patient outcomes, demonstrating the potential of machine learning in effectively managing and treating Parkinson's disease. To enhance the generalizability of models, future research should collect extensive and diverse datasets covering various backgrounds and different stages of Parkinson's disease and utilize advanced techniques for extracting features to improve the accuracy of gait analysis.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.98±0.02 for lung cancer subtype differentiation and 0.83±0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

Markers of low field NMR relaxation features of tissues

This work presents an approach to exploiting Nuclear Magnetic Resonance (NMR) relaxometry data (1H spin-lattice relaxation rates covering the frequency range from below 1 kHz to 10 MHz) for the purpose of differentiating between pathological and reference tissues. Characteristic quantities (markers) that can be obtained in a straightforward manner, not resorting to an advanced analysis of 1H spin-lattice relaxation data, have been identified and compared for pathological and reference colon tissues. Moreover, the relaxation data have been parametrised in terms of Lorentzian spectral densities and the possibility of using the obtained dipolar relaxation constants and correlation times as biomarkers to assess the state of tissues has been discussed. It has also been demonstrated that the relaxation data for the reference and the pathological tissues can be attributed to two groups (for each case). The studies are a step towards exploiting the potential of NMR relaxometry for characterisation of pathological changes in tissues.

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.

Design and implementation of an IoT-based monitoring system for early detection of lumpy skin disease in cattle

Livestock farming serves a crucial role in global food security and sustainability, yet infections like Lumpy Skin Disease (LSD) present huge difficulties. LSD reduces the marketability and productivity of cattle, but it is not fatal. Current preventive measures that rely on routine veterinary testing can be time-consuming and miss early signs of infection, requiring a more effective management strategy first works This article presents an IoT-enabled intelligent system to prevent LSD in animals. The system uses a smart wearable sensor device to collect real-time health information including body temperature, heart rate and 3-axis motion data. Advanced analysis of data is considered to identify early diagnosis of potential LSD infection results based on deviations from normal parameters, this data prevents further infection within livestock by supporting immediate isolation. On a cloud-based online platform, and integrated into the mobile application provides a comprehensive experience and timely alerts to provide potential health issues. Furthermore, the IoT features of the system enable farmers and veterinarians to quickly guide and optimize disease control strategies, improving biosecurity protocols and overall livestock health management. This creative approach represents a significant improvement over traditional methods, providing a cost-effective, accurate and real-time solution for animal health monitoring and management.

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.

Automated prediction of phosphorus concentration in soils using reflectance spectroscopy and machine learning algorithms

A method is presented for predicting total phosphorus concentration in soils from Santander de Quilichao, Colombia, using a UV-VIS V-750 Spectrophotometer and machine learning techniques. A total of 152 soil samples, prepared with varying proportions of P2O5 fertilizer and soil, were analyzed, obtaining reflectance spectra in the 200 to 900 nm range with 3501 wavelengths. Additionally, 152 laboratory results of total phosphorus concentration were used to train the prediction model. The spectra were filtered using a Savitzky-Golay filter. Key wavelengths were identified using Variable Importance in Projection - Partial Least Squares (VIP-PLS) and Random Forest (RF), reducing the spectral bands to 1085. Principal Component Analysis (PCA) further reduced data dimensionality. A feedforward artificial neural network was then trained to predict phosphorus concentration. This method is faster than traditional lab tests by leveraging advanced data analysis and machine learning, offering results in less time. While sample preparation remains consistent with standard spectroscopic analysis, the value added by the proposed method lies in its data processing and interpretation. Currently applied to a single soil type, future improvements will include more soil types and other macronutrients, enhancing nutrient management in agriculture. Accurate macronutrient measurements aid in better fertilizer uses planning.• Filtering spectra and determining relevant wavelengths using VIP-PLS and RF.• Dimensionality reduction with PCA.• Training feedforward artificial neural networks.

GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show “GoToCloud”, a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD.

Smart Supervision of Public Expenditure: A Review on Data Capture, Storage, Processing, and Interoperability with a Case Study from Colombia

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Assessment of ChatGPT-4 in Family Medicine Board Examinations Using Advanced AI Learning and Analytical Methods: Observational Study.

This research explores the capabilities of ChatGPT-4 in passing the American Board of Family Medicine (ABFM) Certification Examination. Addressing a gap in existing literature, where earlier artificial intelligence (AI) models showed limitations in medical board examinations, this study evaluates the enhanced features and potential of ChatGPT-4, especially in document analysis and information synthesis. The primary goal is to assess whether ChatGPT-4, when provided with extensive preparation resources and when using sophisticated data analysis, can achieve a score equal to or above the passing threshold for the Family Medicine Board Examinations. In this study, ChatGPT-4 was embedded in a specialized subenvironment, "AI Family Medicine Board Exam Taker," designed to closely mimic the conditions of the ABFM Certification Examination. This subenvironment enabled the AI to access and analyze a range of relevant study materials, including a primary medical textbook and supplementary web-based resources. The AI was presented with a series of ABFM-type examination questions, reflecting the breadth and complexity typical of the examination. Emphasis was placed on assessing the AI's ability to interpret and respond to these questions accurately, leveraging its advanced data processing and analysis capabilities within this controlled subenvironment. In our study, ChatGPT-4's performance was quantitatively assessed on 300 practice ABFM examination questions. The AI achieved a correct response rate of 88.67% (95% CI 85.08%-92.25%) for the Custom Robot version and 87.33% (95% CI 83.57%-91.10%) for the Regular version. Statistical analysis, including the McNemar test (P=.45), indicated no significant difference in accuracy between the 2 versions. In addition, the chi-square test for error-type distribution (P=.32) revealed no significant variation in the pattern of errors across versions. These results highlight ChatGPT-4's capacity for high-level performance and consistency in responding to complex medical examination questions under controlled conditions. The study demonstrates that ChatGPT-4, particularly when equipped with specialized preparation and when operating in a tailored subenvironment, shows promising potential in handling the intricacies of medical board examinations. While its performance is comparable with the expected standards for passing the ABFM Certification Examination, further enhancements in AI technology and tailored training methods could push these capabilities to new heights. This exploration opens avenues for integrating AI tools such as ChatGPT-4 in medical education and assessment, emphasizing the importance of continuous advancement and specialized training in medical applications of AI.

Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data

The extensive accumulation of tailings can potentially cause heavy metal contamination in the surrounding farmland soil. Accurately predicting the spatial distribution of heavy metals in farmland soil is crucial for assessing the potential environmental hazards of tailings.This study focuses on the spatial distribution and the quantitative prediction of heavy metals (chromium (Cr), vanadium (V), and copper (Cu)) in soils surrounding mine tailings using advanced spectral data analysis and multiple prediction models. The original hyperspectral reflectance data were processed using first-order differential (FD), second-order differential (SD), reciprocal logarithmic (LR), and continuum removal (CR) transformations to highlight the positions of characteristic bands. Multiple linear regression (MLR), stepwise linear regression (SLR), partial least squares regression (PLSR), random forest (RF), and back propagation artificial neural network (BP-ANN) models were used to establish inversion models for Cr, V, and Cu based on bands with high correlation coefficients. The performance of the inversion models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). The results indicate that the raw hyperspectral data from the measured soil exhibit a weak response to heavy metal content in the study area. However, applying FD, SD, and CR transformations significantly enhances the sensitivity of soil spectral data to heavy metal concentrations, facilitating subsequent modeling. Among these, the SD transformation is particularly beneficial for modeling the Cr and Cu elements in the soil. For the V element, the FD transformation yields data that are more suitable for modeling. Regarding the inversion models based on the measured spectral data, the BP-ANN model exhibited the best predictive performance. Specifically, when combined with SD spectral data, the BP-ANN achieved the highest predictive accuracy for Cu content (R² = 0.85, RPD = 2.12). The RF model demonstrated the next best performance, with its optimal inversion model also utilizing SD spectral data for predicting Cu content (R² = 0.76, RPD = 1.90). On the other hand, the MLR model exhibited the poorest performance and is unsuitable for predicting heavy metal content in the region using the measured spectral data. This study highlights the potential of spectral data in environmental monitoring and provides a technical reference for the inversion assessment and regulation of heavy metals in farmlands surrounding tailing sites.

Predicting somatic mutation origins in cell-free DNA by semi-supervised GAN models

MotivationDistinguishing between pathogenic cancer-associated mutations and other somatic variants present in cell-free DNA (cfDNA) is one of the challenges in the field of liquid biopsy. This distinction is critical, since the misclassification of mutations stemming from clonal hematopoiesis (CH) as tumor-derived and vice versa could result in inaccurate diagnoses and inappropriate therapeutic interventions for patients. ResultsWe addressed this by developing a specialized machine learning technique to differentiate tumor- or CH-related mutations in cfDNA. We established a comprehensive in-house reference catalog, comprising approximately 25,000 single nucleotide variants (SNVs), each linked to either tumor or CH origin. This reference serves as a foundation for training a deep learning model, which is structured on the semi-supervised generative adversarial network (SSGAN) architecture. By analyzing genomic coordinates and nucleotide composition of cfDNA variants, our model attains 95 % area under the curve (AUC) in classifying uncharacterized variants as CH or tumor-derived. In conclusion, our research emphasizes the potential of genomic feature prediction, using cfDNA data, to stand as a robust alternative to conventional multi-analyte sequencing methods. This approach not only enhances the accuracy of distinguishing CH from tumor mutations in liquid biopsy data, but also highlights the potential of advanced data analysis techniques and machine learning in genomics and personalized medicine. Availability: https://github.com/FPalizban/SSGAN.

Comparative analysis of machine learning algorithms for ECG-based heart attack prediction: A study using Bangladeshi patient data

This study aims to identify the most accurate machine learning algorithm for predicting heart attacks using demographic data, physiological measurements, and electrocardiogram (ECG) results. We utilized a dataset of 4,000 patient records, combining data from DMCH and Kaggle. Our methodology involved comprehensive data preprocessing, including ECG noise removal and feature selection using the Brouta algorithm. We implemented and compared six machine learning algorithms: Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, XGBoost, and K-Nearest Neighbors. The results demonstrate that our proposed method can accurately predict heart attacks with high sensitivity and specificity. Among the tested algorithms, Random Forest achieved the highest accuracy of 87%, with well-balanced precision (0.86), recall (0.85), and F1-score (0.87). K-Nearest Neighbors and XGBoost also showed strong performance, with accuracies of 81% and 80% respectively. This study contributes to the field by utilizing a large, diverse dataset and providing a comprehensive comparison of multiple algorithms. Our findings suggest the potential for integrating machine learning, particularly Random Forest models, into clinical practice for early heart attack risk assessment, representing a significant step towards improving cardiovascular care through advanced data analysis techniques.

Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis.

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.

Analysis of Factors Influencing Wait Times at Disneyland Resort

This paper aims to enhance the accuracy of wait time predictions and optimize park management at Disneyland, a popular leisure destination facing long wait times for attractions. These wait times adversely affect visitors' immediate experiences and overall evaluations, influencing their willingness to return. These wait times adversely affect visitors' immediate experiences and overall evaluations, influencing their willingness to return. The study employs advanced data analysis and machine learning algorithms to develop high-precision wait time prediction models. Utilizing big data techniques, the research integrates multi-dimensional data, including weather, visitor flow, and historical wait times, to optimize resource allocation and improve operational efficiency. optimize resource allocation and improve operational efficiency and visitor satisfaction. The study further explores the application of these models in other domains such as healthcare, healthcare, and healthcare services. The study further explores the application of these models in other domains such as healthcare and transportation, demonstrating the potential for cross-disciplinary technology transfer to enhance overall service quality and efficiency. The findings underscore the significant impact of big data behavior analysis in theme park management, contributing to the findings underscore the significant impact of big data behavior analysis in theme park management, contributing to increased visitor satisfaction, enhanced competitiveness, and long-term sustainability of the park.