application programmer Research Articles (Page 1)

Pilot evaluation of a large language model-mediated pre-consultation interview for vestibular disorders.

Intraguild predation (IGP) regulates predator populations through direct predation and risk effects, shaping the life-history traits of intraguild prey. This study examines the impacts of IGP on life-history traits of two biocontrol agents, Neoseiulus barkeri and Scolothrips takahashii, reared on their shared prey Tetranychus urticae, using age-stage, two-sex life-table analysis and computer simulations. Life-table analysis revealed that IGP significantly reduced pre-adult survival of both predators. Neoseiulus barkeri developed faster, but exhibited reduced fecundity under IGP, which resulted in declines in its net reproductive rate (R0, from 30.76 to 10.51 offspring per individual), intrinsic rate of increase (r, from 0.2555 to 0.1872 day-1), and finite rate of increase (λ, from 1.2911 to 1.2059 day-1). Conversely, S. takahashii maintained stable development and fecundity, showing no significant differences in R0, r, λ and mean generation time (T) between IGP and control groups. The net predation rate (C0) of N. barkeri decreased from 381.00 to 172.97 prey per individual, and that of S. takahashii from 416.58 to 25.31, under IGP. Computer simulations indicated that IGP led to smaller populations and reduced predation potential for N. barkeri, whereas S. takahashii showed an increase in both. IGP differentially alters the population parameters and predation capacity of these two species. Neoseiulus barkeri experiences a decline in population growth, whereas S. takahashii benefits from IGP. These findings highlight species-specific adaptive strategies in response to IGP, providing insights for designing compatible multipredator application programs in biological control. © 2026 Society of Chemical Industry.

Dilated cardiomyopathy (DCM), characterized by ventricular dilation and systolic dysfunction, has a 10-year survival rate of 25%. The number of young and middle-aged patients with DCM is increasing, with progressive physical limitations and psychosocial distress substantially impairing quality of life. However, illness experiences in this population remain underexplored, particularly in non-Western contexts. Social media platforms such as Zhihu and Weibo offer a novel avenue for exploring patient narratives and peer support. A qualitative descriptive study was conducted to explore the multidimensional experiences of young and middle-aged Chinese patients with DCM through the social narratives shared on social media. A qualitative reflexive thematic analysis was conducted on 872 questions and responses from Zhihu, a Chinese Q&A platform. Data were extracted using a Python (Python Software Foundation) application programming interface, manually filtered, and analyzed with NVivo 14.0 (Lumivero). Thematic codes were developed iteratively, with researcher reflexivity and team discussions ensuring analytical rigor. Five themes emerged: (1) bodily control loss (persistent sleep disturbances, severe respiratory compromise, and physical activity limitations), (2) enmeshed in emotional turmoil (anxiety over survival challenges, guilt over burdening family, and heterogeneous responses to death), (3) social and family role disruption (impacts on academic and career paths and family responsibility interruptions), (4) support needs in health care (urgent demand for information on diseases and treatments and desire for continuous care and guidance), and (5) life reconstruction-between disease and life (lifestyle modifications, exploring diversified treatment paths, peer support and experience sharing, and reconstructing the meaning of life). This study provides an interpretive account of the intertwined physical, psychological, and social experiences of young and middle-aged patients with DCM in China, emphasizing the influence of life-course stage and sociocultural context. Patients' perceptions of family responsibilities, societal role expectations, and death shape their understanding of illness and their processes of meaning reconstruction. These findings underscore the importance of incorporating life-stage and culturally sensitive perspectives in future research and highlight the potential value of tailored psychological support and digital health platforms in improving access to illness-related information and ensuring continuity of care.

Background The swift advancement of Internet of Things (IoT) technology has revolutionized smart home settings; the prevalent automation systems are limited by their need for specific device identification and rigid rule-based configurations. These constraints impede natural human-device interaction, especially in dynamic or communal environments where spatial context is more instinctive than predetermined naming conventions. Current solutions frequently neglect spatial reasoning and multimodal inputs, resulting in heightened cognitive demands and diminished accessibility. The proposed work develops a spatial context-aware control system aimed at facilitating intuitive, vision-driven, and language-based interaction with smart devices to overcome these problems. Methods The proposed model is a modular, multimodal framework that integrates computer vision, natural language processing, and spatial inference for context-aware smart device control. The system comprises six core components: (i) an Onboarding Inference Engine for extracting device information via natural language input, (ii) Zero-Shot Device Detection using Open-World Localization–Vision Transformer (OWL-ViT) for object identification without prior training, (iii) Metadata Refinement and Filtering for structured annotation and disambiguation, (iv) a Geospatial Device Visualizer for annotated visual feedback, (v) Spatial Topology Inference using Generative Pre-trained Transformer 4 omni (GPT-4o) for reasoning about physical layouts, and (vi) Intent-Based Command Synthesis with Gemini Flash to generate precise, executable control commands. The final Agentic Execution Module interfaces with the Tuya Smart Device Application Programming Interface (API), ensuring vendor-agnostic actuation. The system supports multilingual input and adapts to various environmental contexts, including smart homes and assisted living facilities. Results A user study involving 15 participants (aged 18–80, diverse educational backgrounds) was conducted to evaluate the effectiveness of the proposed method in comparison to the Google Home Assistant. Quantitative findings demonstrate a statistically significant reduction in cognitive workload, with NASA Task Load Index (TLX) scores decreasing by an average of 13.17 points ( p = 0.0013, Cohen’s d = 1.0381). Participants rated the proposed method higher in terms of ease of use (mean = 4.67) compared to Google Home (mean = 3.8) on a 5-point Likert scale. Qualitative feedback highlighted the intuitive nature of spatial context commands, reduced cognitive burden due to the elimination of device name memorization, and enhanced accessibility via support for regional languages. 93.3% of users preferred the proposed method over the baseline system. These results affirm the feasibility and user-centric benefits of integrating vision-language models for context-aware smart device control.

The Copernicus Data Space Ecosystem is the official data platform for the Copernicus Programme’s satellites. CDSE combines instant access to satellite imagery with Application Programming Interfaces and virtual machine processing. Instead of downloading satellite imagery for local computation, CDSE utilizes cloud-optimized files to provide data according to the filtering and processing request of the user, facilitating large-scale scientific analysis. Cloud computing on CDSE eliminates the need for users to rely on their own data infrastructure. The incorporated standards support both Open Science and commercialization of scientific tools and algorithms. CDSE serves all users from beginners to professionals, from the interactive visualization of imagery to custom ML algorithms. Acquiring the skills required to process Earth Observation data is facilitated by the open-source codebase and tutorials. Access to public cloud processing is expected to foster the uptake of Earth Observation across new domains. CDSE now provides the critical mass to serve as a tool for knowledge exchange and to influence commercial and public providers alike to support cloud processing.

Downy mildew of hops represents a serious disease affecting hops production in all growing regions. Disease management is primarily based on the application of fungicides at regular intervals based on a short-term forecasting methodology that is essential for evaluating the occurrence of theoretical infections. To enable a more reliable assessment of the pathogen’s presence in a given area, spore traps capturing airborne Pseudoperonospora humuli sporangia can be utilized. The use of quantitative real-time PCR (qRT-PCR) for the detection of sporangia collected by these traps allows for the elimination of laborious and time-consuming microscopic counting. Among four tested P. humuli-specific nuclear DNA sequences, an effective qRT-PCR detection method was developed based on the c127233.5e3 sequence. This detection approach was used for the quantification of sporangia from volumetric spore trap samples collected in situ under field conditions at three selected localities in Bohemia and Moravia during the 2021–2022 period. The obtained results were compared with the short-term forecasting method of the downy mildew (HDM) weather index (I) based on meteorological data. The overall course of the HDM weather index (I) closely correlated with the occurrence of sporangia: after reaching the maximum HDM weather index (I) value, the highest sporangium detection was observed with a time delay of 1–2 weeks at all the monitored sites. The results corresponded well with data obtained from volumetric spore traps in Germany, and the qRT-PCR method proved to be fully comparable to light microscopy. The combination of volumetric spore traps and qRT-PCR can significantly improve the precision of short-term forecasting systems for P. humuli infection, thereby enabling more efficient fungicide application programs in hops protection and contributing to a better understanding of the pathogen’s dispersal dynamics.

This report presents a framework that enables the reproduction and extension of our empirical evaluations using Large Language Models (LLMs) for automated web-form-test generation. The framework includes HTML pruning , context construction , prompt design , LLM communication , and web-form-test insertion . It involves the construction of three types of prompts (from HTML) to guide the test generation: Raw HTML for Task Prompt (RH-P); LLM-Processed HTML for Task Prompt (LH-P); and Parser-Processed HTML for Task Prompt (PH-P). The framework provides an LLM communication module that standardizes interactions with provider Application Programming Interfaces (APIs). Our study utilized public-API models and demonstrated that PH-P consistently achieved a higher successfully-submitted rate (SSR) than RH-P and LH-P. To support the replication of our work, we have released the source code, a dataset subset, and the relevant scripts.

As TikTok (ByteDance) grows as a major platform for health information, the quality and accuracy of Arabic-language cancer prevention content remain unknown. Limited access to culturally relevant and evidence-based information may exacerbate disparities in cancer knowledge and prevention behaviors. Although large language models offer scalable approaches for analyzing online health content, their utility for short-form video data, especially in underrepresented languages, has not been well established. We aimed to characterize and evaluate the quality of Arabic-language TikTok videos on cancer prevention and explore the use of large language models for scalable content analysis. We used the TikTok research application programming interface and a GPT-assisted keyword strategy to collect Arabic-language TikTok videos (2021-2024). From an initial collection of 1800 TikTok videos, 320 were eligible after preprocessing. Of these, the top 25% (N=30) most-viewed were analyzed and manually coded for content type, cancer type, uploader identity, tone and register, scientific citation, and disclaimers. Video quality was assessed using the Patient Education Materials Assessment Tool for Audiovisual Materials for understandability and actionability, and the Global Quality Scale (GQS). GPT-4 was used to generate artificial intelligence annotations, which were compared to human coding for select variables. The top 25% (N=30) most-viewed videos amassed a total of 21.6 million views. Diet and alternative therapies were most common (15/30, 50%), which included recommendations to reduce hydrogenated oils, increase fruit and vegetable intake, and the use of traditional remedies such as garlic and black seed. Only 6.6% (2/30) of videos cited scientific literature. General cancer (15/30, 53%), breast (5/30, 17%), and cervical (4/30, 13%) cancers were most frequently mentioned. Doctors led 30% (9/30) of videos and were more likely to produce higher quality content, including significantly higher global quality scores (GQS=4, median 4, IQR 4-4 vs 3, median 3, IQR 2-3, P=.06). Over half of the videos had low understandability (16/30, 53%) and actionability (18/30, 60%). Emotionally framed content had the highest engagement across likes and shares, although this did not reach statistical significance (P=.08 and P=.05, respectively). However, emotional tone was significantly associated with lower GQS scores (P=.01). GPT-4 showed high agreement with human coders for cancer type (Cohen κ=1.0), strong agreement for GQS (κ=0.94), but low agreement for tone classification (κ=0.15), due to misclassification of emotional delivery from text-only input. Arabic-language TikTok cancer prevention content is highly engaging but variable in quality, with emotionally framed videos attracting substantial attention despite lower informational value. Artificial intelligence-assisted tools show strong potential for scalable, multilingual health content analysis, but multimodal approaches are needed to accurately interpret tonal and audiovisual features.

Background The analysis of large-scale public health data is crucial for evidence-based policymaking, but conventional workflows involving manual data handling and static reporting are inefficient and lack reproducibility. There is a need for automated tools that bridge the gap from live data sources to sophisticated, dynamically generated insights. Objective To design, implement, and validate a fully automated Python (Python Software Foundation,Wilmington,DE, USA) pipeline for real-time application programming interface (API)-based ingestion of existing datasets, analysis, and dynamic report generation in public health informatics. Methods We developed alightweight, Python-only, Word-report-oriented pipeline using packages including Pandas, scikit-learn,statsmodels, and python-docx. The pipeline ingests data from public APIs with automated retries, performs preprocessing, calculates composite health scores, applies K-means clustering (k = 3) for state stratification, and performs correlation analysis. A custom rule-based engine generates dynamic textual interpretations based on statistical results. The final output is a programmatically constructed Microsoft Word (Microsoft Corporation, Redmond, WA, USA) document containing narrative, tables, and embedded figures. The pipeline was tested using India's Health and Family Welfare Statistics 2015 dataset via the data.gov.in API. Results The pipeline executed successfully in approximately 95 seconds, ingesting 37 records. It generated a composite health score, identifying Meghalaya as the top performer (score: 100.0). K-means clustering stratified states into three distinct performance tiers. Correlation analysis revealed a significant negative association between sub-health centre (SHC) infrastructure and specialist availability (r = -0.446, p = 0.01), as well as between 24×7 service availability and auxiliary nurse midwife (ANM) staffing (r = -0.358, p = 0.05), highlighting a systemic disconnect between capital investment in facilities and human resource allocation. A complete Word report including these findings, figures, and tables was automatically generated. Conclusion This automated framework provides a robust, efficient, and reproducible solution for transforming raw public health data into actionable insights and can significantly accelerate data-driven discovery and reporting in public health and bioinformatics. This study validates a computational framework for automated public health data analysis and reporting.

This work presents a dialect-aware and noise-robust Kannada automatic speech recognition (ASR) system that bridges the gap between low-resource linguistic contexts and state-of-the-art deep learning models. We design a two-stage approach: (i) a scratch-built convolutional neural network (CNN)–Transformer hybrid trained on curated Kannada speech data with fast Fourier transform-based noise reduction and (ii) fine-tuning OpenAI’s Whisper-small model on a dialect-diverse corpus. The proposed pipeline integrates adaptive noise suppression, subword tokenization, and beam-search decoding to handle agglutinative morphology, speaker variation, and environmental noise. Extensive experiments were conducted on two datasets: the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) multilingual Kannada subset and a curated 150-sample, Custom-collected Kannada speech dataset covering both formal and conversational speech. On the FLEURS test set (≈ 2.5 hours), our fine-tuned Whisper model achieves a word error rate (WER) of 0.15, a character error rate (CER) of 0.255, and a BLEU score of 0.912, representing a 53% relative reduction in WER and 36% reduction in CER compared to the scratch CNN–Transformer baseline. For the customized dataset, the fine-tuned Whisper model achieves a WER of 0.2311 and a CER of 0.0453, outperforming Google Speech-to-Text Application Programming Interface (API) by 16.8% (relative WER reduction) and surpassing the scratch transformer by over 70% in WER. We further evaluate robustness under dialectal variation and noisy recordings, providing detailed error analysis and computational efficiency metrics. To our knowledge, this is the first comprehensive evaluation of Whisper fine-tuning for Kannada, demonstrating its viability for real-time, edge-deployable applications in education, accessibility, and public administration.

Abstract The logistics industry currently faces a prevalent "data silo" issue. This leads to information isolation and system fragmentation and severely restricts the ability of cross-platform business collaboration and intelligent decision-making. To address this problem, this paper proposes a non-intrusive logistics system architecture design for data silo integration. The architecture is based on key mechanisms such as edge access, protocol abstraction, and a unified data bus. It realizes lightweight integration of heterogeneous logistics information systems, and features low coupling, high compatibility, and strong adaptability. The designed system includes acquisition layer protocol adaptation components, transport layer edge routing mechanisms, and platform layer intelligent processing engines. It supports multi-protocol concurrent access and fault self-recovery capabilities. Deployment and testing in enterprise scenarios show that the system outperforms traditional solutions in data acquisition delay control, stability maintenance, and multi-protocol adaptation. The non-intrusive system not only leads significantly in throughput, reaching 12,000 entries per second. This is much higher than the 7,500 entries per second of traditional Application Programming Interface (API) integration and the 9,200 entries per second of the Enterprise Service Bus (ESB) architecture. It also performs excellently in average response time, which is only 45 milliseconds, significantly lower than the other two solutions. In particular, this system achieves efficient integration without modifying the original systems and effectively solves the problem of coexisting multi-systems and non-interoperable data in enterprises. This paper provides a promotable reference architecture for the informatization transformation of logistics.

Scalable advertising analytics architectures are increasingly critical for organizations seeking unified insight across user behavior, inventory performance, and monetization outcomes. This study examines integrated analytics architectures that combine Google Analytics and Google Ad Manager to support scalable, data driven advertising intelligence in complex digital ecosystems. The abstract conceptualizes how event level user data, session attribution, and engagement metrics from analytics platforms can be systematically aligned with impression delivery, pricing, and revenue data from ad serving systems. Emphasis is placed on architectural patterns that enable interoperability, including standardized taxonomies, shared identifiers, automated data pipelines, and centralized reporting layers. The study discusses how combining behavioral analytics with ad delivery reporting enhances attribution accuracy by linking audience interactions to downstream advertising outcomes across devices, formats, and demand channels. It further explores how scalable architectures support revenue optimization by enabling granular yield analysis, cohort based performance evaluation, and adaptive pricing strategies informed by real time and historical data. Attention is given to data governance, latency management, and privacy preserving design, recognizing regulatory constraints and consent requirements as central architectural considerations. The abstract also highlights the role of cloud based processing, application programming interfaces, and visualization layers in ensuring extensibility and performance as data volumes and analytical complexity increase. Managerial implications are discussed, demonstrating how unified analytics environments improve cross functional decision making between marketing, ad operations, and commercial teams by providing consistent, trusted metrics. Limitations related to data sampling, schema alignment, and attribution bias are acknowledged, underscoring the need for continuous validation and iterative refinement. Overall, the abstract positions scalable advertising analytics architectures that integrate analytics and ad management platforms as foundational enablers of operational transparency, strategic monetization, and evidence based advertising management in competitive, data intensive digital markets. The study contributes conceptual clarity by outlining how architectural integration directly influences analytical depth, organizational agility, and sustainable revenue performance in modern digital publishing and advertising organizations. These insights inform future research directions, system design choices, and professional practice, supporting resilient analytics infrastructures capable of adapting to technological change, market volatility, and evolving advertiser, publisher, and audience expectations over time across global digital advertising ecosystems. Keywords: Scalable Analytics, Advertising Architecture, Google Analytics, Google Ad Manager, Attribution Modeling, Revenue Optimization, Data Integration, Digital Advertisings.

BackgroundConsumer-level drug recalls usually require action by individual patients. The Food and Drug Administration (FDA) has public-facing outlets to inform the public about drug safety information, including all recalls, but individual consumers may not be aware of them. And there is no system in place to notify individual prescribers which of their patients are affected by a specific recall.ObjectiveWe aimed to leverage the FDA’s Healthy Citizen prototype web-based software platform, which provides users with information about recalls, to automatically notify patients of relevant recalls.MethodsWe developed and evaluated an electronic notification system in the primary care and cardiology practices at a large, urban, academic medical center. The health care portal scanned the FDA Healthy Citizen application programming interface nightly to detect new recalls, identified patients who had those medications in their electronic health record (EHR) medication list, and sent them a message through the EHR patient portal with a link to a customized FDA information display. Using structured interviews, we assessed qualitative feedback on the system and portal messaging from a convenience sample of 9 patients.ResultsThe system was technically functional, but it was not possible to trace a medication prescription from the EHR to specific lot numbers dispensed to that patient by a community pharmacy. The qualitative feedback obtained from patients showed convergence of topics.ConclusionsLack of an accurate electronic audit trail from prescription to dispensed medication precludes clinical deployment of automated drug recall notification.

Legacy modernization constitutes a formidable technical and strategic challenge for enterprises maintaining large-scale software infrastructures. Systems accumulate complexity through decades of incremental development, resulting in tangled dependencies, obsolete frameworks, and inconsistent application programming interfaces. Manual migration approaches prove costly and hazardous due to dependence on institutional knowledge that frequently disappears over time. Conventional methods involving manual code rewriting introduce defects, prolong system unavailability, and impede innovation cycles. Recent advances in artificial intelligence have fundamentally altered modernization methodologies. Contemporary intelligent migration frameworks synthesize code comprehension models, dependency graph analytics, and predictive validation mechanisms to automate substantial portions of migration workflows. Machine learning architectures now parse, categorize, and translate complex codebases while maintaining high degrees of semantic integrity, though challenges remain as language models occasionally fail to preserve complete semantic equivalence. These enhanced systems diminish human error during transformation operations and strengthen system dependability, facilitating modernization efforts at scales previously deemed impractical. This article explores architectural underpinnings, operational mechanisms, security protocols, and implementation challenges within these frameworks, illustrating their capacity to convert legacy modernization from episodic reconstruction initiatives into perpetual evolutionary maintenance processes.

Wearable technologies are increasingly integrated into digital health systems to support continuous remote monitoring in oncology; however, the lack of standardized and reproducible criteria for device selection limits their scalable and regulation-compliant adoption in clinically oriented infrastructures. This study proposes a preclinical benchmarking framework for the systematic evaluation of commercially available wearable devices for oncology applications. Devices were assessed across six predefined dimensions: biometric data acquisition, application programming interface-based interoperability, regulatory compliance, battery autonomy, cost, and absence of mandatory subscription fees. From an initial pool of 23 devices, a stepwise screening process identified 6 eligible wearables, which were compared using a semi-quantitative weighted scoring system. The benchmarking analysis identified the Withings ScanWatch 2 as the highest-ranked device, achieving a score of 37/40 and representing the only solution combining medical-grade certification for selected functions, extended battery life (up to 30 days), declared General Data Protection Regulation-compliant data governance, and fully accessible application programming interfaces. The remaining devices scored between 17 and 23 due to limitations in certification, battery autonomy, or data accessibility. This work introduces a reproducible preclinical benchmarking methodology that supports transparent wearable device selection in oncology and provides a foundation for future scalable digital health integration under appropriate regulatory and interoperability governance.

The call for international collaboration on technology transfer has never been louder than now, owing to the problem of climate change that requires to be mitigated and adapted to. Quite the opposite, one of the paradoxes is that the very intellectual property rights protection mechanisms that have been put in place to encourage innovation may, at the same time, limit the availability of crucial technologies in the developing world. The article at hand is discussing the interplay between the new green patent incentives and the facilitative measures for technology transfer: the EPO's fast-tracking programs for patent applications and the regional fast-track systems, alongside patent pooling arrangements. The research is based on the data from the World Intellectual Property Organization, analysis of recent patent filings indicating a 20% rise in 2025, and the comparison of existing frameworks such as WIPO Green, Eco-Patent Commons, and the climate patent pools of the BRICS countries that are just emerging, which uncovers the critical gaps in the connection between patent-led innovation incentives and equitable technology diffusion. Through doctrinal legal analysis and systematic literature review, the research evaluates TRIPS flexibilities, compulsory licensing provisions, and alternative intellectual property mechanisms within the context of the climate priorities of developing nations. The results indicate that while green patent incentives effectively nurture innovation in the developed world, patent pools and liability-rule frameworks present innovative ways of reconciling private innovation incentives with global climate imperatives. The article wraps up with a proposal for policy mechanisms that are integrated and that use the incentive functions of patent law while at the same time, through strategic licensing arrangements and compulsory licensing limited to narrowly defined climate emergencies, technology transfer that is equitable is accelerated. Keywords: Patent pools, green technologies, climate change, intellectual property rights, technology transfer, developing nations, TRIPS flexibilities, patent pooling mechanisms, climate incentives

Modern microscopy generates multidimensional images. Graphical user interfaces allow visualization of multidimensional images, but image exploration is significantly harder in text-based environments. These include Jupyter notebooks, popular for the distribution of data analysis pipelines; and the terminal, often the only user interface available when accessing images in remote servers. We developed JuNkIE-CLImax, a pair of multidimensional image explorers designed to work in Jupyter notebooks and the terminal, respectively. JuNkIE-CLImax are written in Python, using Rust extensions to optimize performance; they are platform agnostic; and they provide application programming interfaces that enable integration into complex image visualization and analysis pipelines.

This study aims to explore training and sleep characteristics among recreational runners, along with their interrelationships, to provide more scientific and personalised training guidance. Recreational runners wearing Garmin smartwatches were recruited, and continuous data on training and sleep were collected via the device Application Programming Interface from June 2024 to June 2025. Training data included pace, distance, duration, and heart rate, while sleep data included stage durations and nocturnal heart rate variability Linear mixed models were applied to examine differences in training and sleep characteristics across sex, performance classification, season, and day of the week, and to explore the effects of training load on subsequent sleep and the influence of sleep duration on training performance on the following day. The average distance per training session was 12.10 ± 3.36 km, with an average pace of 6.02 ± 1.00 min/km. Male runners exhibited significantly faster paces and longer distances than females, and elite runners outperformed other groups across all training parameters. Significant seasonal and weekly variations were observed. Specifically, pace was slowest and distance shortest in summer, while training volume, intensity, and heart rate were higher on rest days than on workdays. The average nightly sleep duration was 6.61 ± 0.69 h, indicating general insufficiency. Female runners had significantly longer deep sleep than males, and elite runners showed a more pronounced early sleep-wake rhythm. A bidirectional relationship was identified, with the finding that high training loads led to reductions in deep and REM sleep, increases in light sleep and wake duration, and lower HRV, collectively indicating impaired sleep quality and autonomic recovery despite longer total sleep duration. Conversely, insufficient sleep resulted in slower pace and decreased training efficiency on the following day, though runners tended to compensate by extending running duration. The training and sleep characteristics of recreational runners were significantly influenced by sex, performance classification, season, and weekday-weekend rhythm. A bidirectional association between training and sleep was observed, with high training loads and insufficient sleep posing potential risks to performance and recovery. It is recommended that recreational runners monitor both training and sleep, take individual differences and temporal rhythms into account when scheduling training, and avoid excessive training loads to achieve a balance between training and recovery.

A Review on Integration of Software Defined Networking with Blockchain Technology

Health information management education faces challenges in teaching claims data. Regulations prohibit the use of real, non-deidentified patient claims data within classroom settings. This limits hands-on experience with real-world data formats such as X12 837 institutional (837i) transaction files. Most existing approaches rely on textbook examples or static datasets that have been heavily redacted, which may lack the complexity required to prepare students for roles involving the handling of claims-related data. In this report, the authors describe the design and deployment of an open-source synthetic claims generator and parser designed to democratize access to realistic health care claims data, bridging the gap between theoretical knowledge and practical skills. A Python-based system was developed that generates realistic X12 formatted 837i claims using real Centers for Medicare & Medicaid Services (CMS) provider and payer databases. The tool features a free browser-based interface, plus local deployment via a command-line interface (CLI) or application programming interface (API) for advanced educational contexts. Finally, a modular two-lecture curriculum framework for integration into existing health information management courses is provided. The browser-based application enables instant access without installation, eliminating technical and privacy barriers. The tool supports generating 1-25 claims per session via the web interface, with the ability to perform batch generation of thousands of claims via CLI/API for analytics projects locally on a user's own machine. All generated files were validated against X12 5010 837i specifications using two independent electronic data interchange validation platforms, confirming zero structural errors and full conformance to industry standards. The framework aligns with American Health Information Management Association core competencies, offering a reproducible and scalable method to support workforce readiness for revenue cycle analyst, medical coding, and health care data analyst roles. By eliminating privacy concerns, financial barriers, and installation complexity, this method offers a scalable, reproducible model for teaching critical electronic data interchange and revenue cycle competencies related to the handling of claims data files. The open-source nature and multiple deployment options of this tool enable adoption across diverse institutional contexts, from community colleges to research universities, and from technical coding to nontechnical users.