Frontiers in Big Data最新文献

{"title":"Evolutionary multi-agent reinforcement learning for crisis-aware demographic policy optimization.","authors":"Anton V Dozhdikov, Arseniy M Sitkovskiy","doi":"10.3389/fdata.2026.1842233","DOIUrl":"10.3389/fdata.2026.1842233","url":null,"abstract":"<p><p>Demographic systems face unprecedented challenges from simultaneous crises. Conventional statistical demography techniques and agent-based models often struggle to capture nonlinear inter-regional interactions during periods of severe socio-economic disruption. To address this, we propose <i>MADDPG-EVO-DGM</i>, a hybrid algorithm that integrates multi-agent deep reinforcement learning with evolutionary optimisation and meta-learning principles to model regional demographic processes under multiple crisis scenarios. Each region is treated as an autonomous agent learning to steer demographic policy levers, while periodic evolutionary \"boosters\" overcome local optima via population-based perturbations of actor network parameters. Additionally, a Darwin-Gödel Machine-inspired meta-learning mechanism adapts the booster triggers, enabling self-improvement in the learning process. We evaluate MADDPG-EVO-DGM on a simulation environment calibrated with real demographic data for eight federal regions of the Russian Federation over the period 2000-2024 and subject to ten concurrent crisis scenarios (e.g., pandemic, geopolitical conflict, economic collapse). Experiments demonstrate significantly faster convergence and improved performance over a baseline MADDPG: the hybrid approach achieves a higher final average reward (252.57 vs. 243.07) and 3.4 × lower convergence variance (σ = 0.24 vs. 0.80), indicating more reliable training. It also exhibits qualitative performance jumps of +68% during evolutionary phases and maintains 35%-45% greater resilience under crisis shocks compared to the baseline. To our knowledge, this is the first application of multi-agent reinforcement learning to large-scale demographic modeling under crises, opening new possibilities for evidence-based, crisis-resilient population policy design. Code, data, and logs are provided to ensure reproducibility.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1842233"},"PeriodicalIF":3.8,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13388216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-model evaluation of phishing detectors against LLM-generated emails.","authors":"Rommel Gutierrez, William Villegas-Ch, Jaime Govea","doi":"10.3389/fdata.2026.1883452","DOIUrl":"https://doi.org/10.3389/fdata.2026.1883452","url":null,"abstract":"<p><p>Phishing remains a prevalent cyberattack vector, and the widespread adoption of large language models (LLMs) has enabled adversaries to generate grammatically correct and contextually coherent phishing emails at scale, against which conventional detection systems are less effective. Although stylometric methods achieve over 95% accuracy within a single generator, their performance has not been systematically evaluated when the source model changes between training and deployment. This represents a significant gap, as adversaries can switch generators rapidly. A balanced corpus of 9,986 phishing emails was assembled, comprising 4,986 emails generated by three modern LLMs (GPT-4.1, DeepSeek 3.2, and Llama 3.3 70B) across five thematic categories, and 5,000 human phishing emails sampled in a stratified manner from five public sources. Seventeen stylometric features were extracted, and Logistic Regression and XGBoost classifiers were evaluated under intra-model, cross-model, threshold-recalibrated, cross-dataset, and aggregated-pool settings. Intra-model F1 scores reached 0.96 under stratified cross-validation and 0.999 on held-out splits used for the cross-model matrix. However, cross-model F1 dropped by 28.0 percentage points under the default decision threshold of 0.5. Notably, the area under the receiver operating characteristic curve remained above 0.96 in every off-diagonal cell, indicating that discriminative information is preserved even though the decision threshold is generator-specific. Recalibrating the threshold on a small target subset reduced the gap to 4.0 percentage points (an 86% reduction), and an aggregated-pool detector achieved F1 = 0.997 on each generator. This work reframes cross-model phishing detection from a problem of model incompatibility to one of practical calibration, and provides two deployable solutions, threshold recalibration on a small target slice and aggregated-pool training, along with a publicly released multi-LLM corpus.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1883452"},"PeriodicalIF":3.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of localized roadway surface obstacles on vehicular emissions under real-world urban driving conditions.","authors":"Victor Cardoso Oliveira, Thiago Iachiley Araújo de Souza, Nicole Souza Batista, Bruno Vieira Bertoncini, Verônica Teixeira Franco Castelo Branco","doi":"10.3389/fdata.2026.1807559","DOIUrl":"10.3389/fdata.2026.1807559","url":null,"abstract":"<p><strong>Introduction: </strong>Vehicular emissions are a major source of air pollution in tropical urban environments. While the impacts of technology, traffic flow, and driving behavior on pollutant formation are well established, the influence of pavement surface remains insufficiently understood. Pavement defects such as potholes, cracks, and depressions disturb vehicle operation and may increase real-world emissions. This study evaluates the influence of pavement obstacles on emissions of CO₂, CO, and NO_x across five urban road segments in Fortaleza, Brazil.</p><p><strong>Methods: </strong>A portable emissions measurement system (PEMS) collected second-by-second exhaust data during real driving. Roadway surface obstacles were captured through windshield-mounted images acquired at 1 Hz. A broader image pool comprising 44,175 roadway images was assembled from multiple urban roads for model development. From this pool, a final annotated dataset comprising 1,812 images with 3,158 labeled obstacles was used for training, validation, and testing the YOLOv8n detector, which was then applied to the five monitored road sections used in the synchronized emission analysis. Emission data and obstacle locations were synchronized, enabling comparison of pollutant rates along obstacle-present and obstacle-free segments, with emphasis on features likely to influence short-term driving behavior. Detection performance was evaluated using precision, recall, and mAP metrics.</p><p><strong>Results: </strong>Road segments with higher obstacle occurrence presented elevated emission rates. In the full dataset, maximum values reached 478.2 g/km for CO₂, 491.96 mg/km for CO, and 100.266 mg/km for NOx. A filtered analysis excluding curves, intersection buffers, and visible traffic or pedestrian interference showed that obstacle-present observations still exhibited higher emissions than obstacle-free observations, with average increases of 26% for CO₂, 31% for NOx, and 42% for CO. Spatial mapping showed that emission hotspots tended to occur in areas with frequent roadway surface obstacles and operational disturbances.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1807559"},"PeriodicalIF":3.8,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13337393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive class-aware feature selection for high-dimensional and imbalanced multi-class network intrusion detection.","authors":"Joseph P Mchina, Neema Mduma, Ramadhani S Sinde","doi":"10.3389/fdata.2026.1871346","DOIUrl":"10.3389/fdata.2026.1871346","url":null,"abstract":"<p><p>High-dimensional feature spaces and severe class imbalance remain fundamental challenges for Machine Learning-based Network Intrusion Detection Systems (ML-NIDS), where minority attack categories are frequently overlooked during feature selection. Existing feature selection approaches commonly rely on global feature relevance measures and manually specified feature counts, which favor majority traffic classes and reduce sensitivity toward rare but critical attack categories. To address these limitations, this study proposes the Adaptive Class-Aware Feature Selection (ACAFS) framework for multi-class intrusion detection. Unlike conventional approaches, ACAFS introduces a data-driven adaptive feature count mechanism based on permutation null hypothesis testing, a Class-Aware Composite Mutual Information scoring strategy that explicitly preserves minority-class discriminative information, and a coordinated two-stage feature selection framework that combines statistical filtering with XGBoost-based model refinement. The framework was evaluated independently on the CSE-CIC-IDS2018 benchmark dataset and a Simulated University Network Environment (SUNE) dataset representing Tanzanian higher learning institution networks. Experimental results demonstrate that ACAFS substantially reduces feature dimensionality while improving balanced intrusion detection performance. On CSE-CIC-IDS2018, ACAFS reduced the feature space from 74 to 22 features, representing a 70.3% dimensionality reduction, while the Two-Stage CNN achieved 99.39% accuracy, 99.40% F1-score, and a false positive rate of 0.09%. The framework further achieved 98.59% recall for Web_Attacks despite severe class imbalance, demonstrating effective preservation of minority-class discriminative features. On the SUNE dataset, ACAFS independently selected 18 features and maintained stable detection performance without dataset-specific manual tuning, confirming its adaptability across heterogeneous network environments. These results confirm that adaptive and class-aware feature selection can simultaneously reduce feature redundancy, improve minority attack detection, and maintain robust intrusion detection performance across diverse network traffic environments.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1871346"},"PeriodicalIF":3.8,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13337421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Where diverse populations gather: transit accessibility and the spatial structure of social mixing.","authors":"Yuan Liao","doi":"10.3389/fdata.2026.1857064","DOIUrl":"10.3389/fdata.2026.1857064","url":null,"abstract":"<p><p>Urban venues serve as arenas for social mixing, yet less is known about how public transit infrastructure shapes the geography of mixing at specific locations. This study examines how transit catchment diversity-the socioeconomic heterogeneity of populations reachable by public transit-associates with visitor diversity at points of interest (POIs) in nine Swedish and three US cities. Using mobile phone GPS traces and aggregated foot traffic data from 2024, we compute visitor diversity indices based on visitors' home-neighborhood birth-background composition and employ spatial regression models and geographically weighted regression (GWR). Transit catchment diversity positively predicts visitor diversity across nearly all cities, but this association is robust only in the largest metropolitan areas; in smaller cities, the coefficient attenuates to insignificance once geographic catchment composition, centrality, and venue density are controlled. Spatial spillovers in visitor diversity follow general geographic proximity rather than shared transit-stop connectivity, suggesting that the association operates through catchment population composition rather than station-level linkages. Transit-diversity hotspots occur not in already-diverse venues, but in lower-diversity POIs with lower commercial density, greater distance from transit in US cities, and greater centrality in Sweden. These patterns are consistent with transit-accessible population composition being associated with visitor diversity, particularly where alternative pathways to diverse co-presence are limited.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1857064"},"PeriodicalIF":3.8,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13322799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148377994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning model to predict COPD hospital admissions based on meteorological data: a medical meteorological forecast.","authors":"Lei Zhang, Mingjie Zhang, Jinghong Zhang, Yajie Zhang, Tian Xie, Yipeng Ding, Shuyuan Chu, Haihong Wu","doi":"10.3389/fdata.2026.1687969","DOIUrl":"10.3389/fdata.2026.1687969","url":null,"abstract":"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) has placed a substantial health burden on the world. Meteorological conditions are associated with hospital admissions for COPD. In this study, we aim to develop a model of medical meteorological forecasting for COPD hospital admissions.</p><p><strong>Methods: </strong>A predictive model was developed using a Long Short-Term Memory (LSTM) algorithm applied to time series data on COPD hospital admissions and meteorological conditions. Data were collected daily from 25 September 2016 to 26 December 2020. Performance of the model was assessed using the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and <i>R</i> ². The association between the risk of COPD hospital admissions and meteorological conditions was assessed using a conditional logistic regression analysis and a conditional Poisson regression analysis in a time-stratified case-crossover design.</p><p><strong>Results: </strong>A total of 17,555 hospital admissions for COPD from 1 January 2017 to 31 December 2019 were included in the final LSTM model. Regarding the performance of the LSTM model, the MSE was 0.028, the RMSE was 0.167, the MAE was 0.134, and <i>R</i> ² was 0.416. Regression analysis revealed that the maximum temperature was positively associated with COPD hospital admissions.</p><p><strong>Conclusion: </strong>The LSTM model offers potential for medical meteorological forecasting to predict COPD hospital admissions among the general population according to the local climate. Higher maximum temperature may be a risk factor for COPD hospital admissions.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1687969"},"PeriodicalIF":3.8,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13322846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148378036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context.","authors":"Ampuan Shazani Bin Ampuan Haji Sadikin, Heru Susanto","doi":"10.3389/fdata.2026.1826953","DOIUrl":"10.3389/fdata.2026.1826953","url":null,"abstract":"<p><strong>Introduction: </strong>The rapid adoption of real-time digital payment systems introduces cybersecurity risks that extend beyond technical vulnerabilities to include significant human and organizational factors.</p><p><strong>Methods: </strong>This study evaluates a dual-layer data protection mechanism combining AES-128 encryption with spread spectrum audio steganography within Brunei's digital payment context. Stakeholder interviews with Cyber Security Brunei (CSB), National Digital Payments Network (NDPx), and a local bank were conducted alongside experimental testing using Stripe Sandbox.</p><p><strong>Results: </strong>Human factor issues accounted for 47% of identified cybersecurity concerns. Experimental results demonstrated that spread spectrum steganography achieved an 87.5% robustness rate across eight attack scenarios while maintaining near real-time performance with an average processing time of 568.82 ms and acceptable audio quality (PSNR 26.30 dB). Sandbox validation confirmed feasibility within realistic payment workflows.</p><p><strong>Discussion: </strong>The findings support data-centric security as a compensating control in human-dominated threat environments and demonstrate the viability of combining encryption and steganography to reinforce instant payment security.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1826953"},"PeriodicalIF":3.8,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13318576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148370867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models.","authors":"Safaa Hriez, Mohammad Haikal","doi":"10.3389/fdata.2026.1838191","DOIUrl":"10.3389/fdata.2026.1838191","url":null,"abstract":"<p><strong>Introduction: </strong>The rapid growth of artificial intelligence (AI) has significantly increased computing demand, intensifying the operational strain on the computing environment. While virtualization and containerization are established technologies for resource optimization, their comparative energy efficiency and environmental impact, particularly under GPU-accelerated AI workloads, are not well-quantified.</p><p><strong>Methods: </strong>This study evaluates the energy consumption and environmental impact of virtualization and containerization technologies when using Graphics Processing Units (GPUs) for AI model execution. Employing a computer vision benchmark, the performance, GPU resource utilization, and power consumption were measured. The experiment involved training a DenseNet-121 model on the MNIST dataset within a VirtualBox virtual machine and a Docker container environment.</p><p><strong>Results: </strong>The analysis indicates that containerization consistently surpasses virtualization in energy efficiency. Specifically, Docker container configuration demonstrated an approximately 21.6% reduction in total energy consumption, and a corresponding reduction in carbon dioxide (CO₂) emissions compared to a VirtualBox virtual machine. Furthermore, containerization exhibited lower average and peak GPU utilization and power consumption.</p><p><strong>Discussion: </strong>These findings demonstrate that containerization offers a more energy-efficient and environmentally sustainable approach than VirtualBox virtualization for the specific GPU-enabled AI workload evaluated in this study. Statistical significance testing indicates that the observed performance differentials are significant, supporting the validity of the results within the experimental scope of this work.</p><p><strong>Conclusion: </strong>Implementing containerization in this experimental setup may reduce energy consumption and environmental impact without compromising computational performance. Future studies should extend these analyses to larger neural network models, diverse AI workloads, and heterogeneous GPU platforms to enhance the generalizability of these findings beyond the current single-system experimental configuration.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1838191"},"PeriodicalIF":3.8,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13316829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148363756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using artificial intelligence to improve governance and public services in Africa.","authors":"David Mhlanga","doi":"10.3389/fdata.2026.1835663","DOIUrl":"10.3389/fdata.2026.1835663","url":null,"abstract":"<p><p>African governments continue to face persistent challenges in delivering efficient, transparent, and inclusive public services due to institutional constraints, rapid population growth, and fragmented administrative systems. At the same time, accelerating digital transformation and expanding data ecosystems create new opportunities for governance reform. This study examines the role of Artificial Intelligence (AI) in enhancing public sector performance across welfare targeting, healthcare delivery, tax administration, and urban governance. Drawing on a structured narrative literature review, the paper develops a conceptual framework that conceptualizes governance outcomes as a function of data availability, AI capability, institutional capacity, and human oversight. The findings suggest that AI can improve service delivery by enabling predictive decision-making, reducing administrative inefficiencies, and enhancing targeting accuracy. However, these benefits depend on the alignment between technological adoption and institutional readiness, as weak governance systems may amplify risks such as bias, exclusion, and accountability gaps. The study concludes that AI must be embedded in inclusive, context-sensitive governance strategies to support sustainable development outcomes in Africa.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1835663"},"PeriodicalIF":3.8,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13310759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148354749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case count metric for comparative analysis of entity resolution results.","authors":"John R Talburt, Muzakkiruddin Ahmed Mohammed, Mert Can Cakmak, Onais Khan Mohammed, Mahboob Khan Mohammed, Khizer Syed, Leon Claassens","doi":"10.3389/fdata.2026.1736939","DOIUrl":"10.3389/fdata.2026.1736939","url":null,"abstract":"<p><strong>Introduction: </strong>Entity resolution (ER) systems often produce different clustering outcomes when applied to the same dataset, especially when parameters, algorithms, or system configurations change. However, in many real-world settings, the true linking structure is unknown, making traditional accuracy-based evaluation difficult.</p><p><strong>Methods: </strong>This paper presents the Case Count Metric System (CCMS), a process and software system for comparing two cluster ER outcomes without requiring a truth set. CCMS classifies how each cluster from the first ER process is transformed by the second process into four mutually exclusive cases: unchanged, merged, partitioned, or overlapping.</p><p><strong>Results: </strong>CCMS produces aggregate case counts, singleton summaries, and per-cluster transformation details to support diagnostic analysis. Example applications using synthetic demographic data and an industrial materials dataset show that CCMS can identify how clustering outcomes change under parameter adjustments and alternative ER systems.</p><p><strong>Discussion: </strong>CCMS provides a practical and interpretable method for comparing ER clustering results when labeled ground truth is unavailable. By distinguishing between over-linking, under-linking, and more complex cluster reorganizations, CCMS offers more actionable insight than single-value similarity measures and supports both research analysis and operational ER evaluation.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1736939"},"PeriodicalIF":3.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}