Continuous Monitoring Data Research Articles

The Indian Network for Detecting Radon Anomaly signal for Seismic Application.

The Indian Network for Detecting Radon Anomaly signal for Seismic Application.

Characterizing Neurocardiovascular Responses to an Active Stand Test in Older Women: A Pilot Study Using Functional Data Analysis.

This observational pilot study investigated neurocardiovascular responses to an active stand test using continuous physiological monitoring and functional data analysis (FDA) in older women. A sample of 25 community-dwelling female adults aged 59-78 years (mean age: 70.3 years) participated. Participants were dichotomized into comparison groups based on five factors: age (<70 vs. ≥70 years); the presence of initial orthostatic hypotension (IOH, yes/no); body mass index (BMI < 25 vs. ≥25 kg/m2); antihypertensive medication use (yes/no); and physical frailty status assessed by the Survey of Health, Ageing and Retirement in Europe-Frailty Instrument (SHARE-FI score < -0.5 vs. ≥-0.5). Each participant completed an active stand test during which six physiological signals were continuously recorded: systolic (sBP) and diastolic (dBP) blood pressure and heart rate (HR) via digital artery photoplethysmography and left frontal oxygenated hemoglobin (O2Hb), deoxygenated hemoglobin (HHb), and tissue saturation index (TSI) via near-infrared spectroscopy (NIRS). The signal analysis focused on a standardized 200 s window spanning 50 s before to 150 s after the stand, with all signals resampled and synchronized at 5 Hz. FDA was used to statistically compare the full time series between groups for each signal. Group-level differences revealed that younger participants (<70 years) exhibited significantly higher HR in multiple periods following the stand (~10 s, ~30 s, ~90 s, and ~140 s post-stand) compared to their older counterparts. Participants with IOH demonstrated significantly lower sBP at ~10 s, ~80 s, and ~130 s post-stand and lower dBP at ~10 s post-stand. Among participants classified as overweight/obese (BMI ≥ 25 kg/m2), significantly lower levels of HHb were observed at ~10 s, ~30-50 s, and ~60 s post-stand, while O2Hb levels were reduced at ~50 s, ~60 s, ~70-110 s, ~130 s, and ~140 s post-stand. No statistically significant group-level differences were observed based on antihypertensive medication use or frailty status. These findings demonstrate the utility of FDA in detecting subtle, time-dependent physiological variations during orthostatic challenge and underscore the value of continuous neurocardiovascular monitoring in assessing orthostatic tolerance in aging populations.

Prospective insights into pediatric neurosurgery: transforming care through adverse event analysis.

Recent advancements in pediatric neurosurgery have significantly enhanced patient care and monitoring. Despite these improvements, the complexity of these procedures continues to pose a high risk of adverse events (AEs). The current literature lacks comprehensive AE data, underscoring a critical gap in research. This study addresses this void by using a prospectively collected database from a premier neurosurgical tertiary center, aiming to develop critical care guidelines, optimize resource allocation, and foster interdisciplinary collaborations to mitigate AEs. This prospective study enrolled pediatric patients undergoing neurosurgery between January 2020 and December 2023. AEs were defined as any undesirable outcomes occurring within 30 days postoperatively, with each event peer-reviewed at discharge. Among the 1008 patients studied, ranging from newborns to 17-year-olds (mean age 10.5 years), 82.5% underwent elective procedures and 14.4% emergency procedures. The overall incidence of surgery-related AEs was 9.2%, with 5.2% requiring revision surgery. Cranial pathologies, accounting for 36.3% of interventions, were the most common, with wound infections and CSF leaks the most prevalent. The mortality rate was notably low at 0.4%, primarily attributable to severe underlying conditions such as medulloblastoma progression and severe traumatic brain injuries. Non-surgery-related AEs occurred at a rate of 2.4%. Logistic regression analysis identified age as a significant protective factor against postoperative complications, with each additional year reducing the odds of complications by approximately 5.4% (odds ratio 0.946, p = 0.002). Gender, however, was not a significant predictor of adverse outcomes. The study highlights a significantly low incidence of AEs in pediatric neurosurgery, demonstrating the effectiveness of systematic AE documentation and continuous data monitoring. Logistic regression analysis identified age as a significant protective factor against complications, while gender showed no significant association, underscoring the multifactorial nature of AE development. These findings provide actionable insights into patient risk stratification, particularly emphasizing the role of age, and contribute to enhancing patient education, guiding quality-based healthcare reforms, and supporting the implementation of prospective AE tracking systems to improve patient safety and care standards in pediatric neurosurgery.

Radiation Hardness Assurance by Redundancy in Raspberry Pi Zero W Computation Metrics via Total Ionizing Dose 60Co Testing for Spacecraft Applications.

This work showed the outcome from conducting formal total ionizing dose (TID) testing with a traditional 60Co source on commercial Raspberry Pi computer boards. Two Raspberry Pi boards inside a Gammacell 220 irradiator with continuous biased operations, voltage monitoring, and live computational testing and data extraction were exposed to dose rates of 2.61 ± 0.09 mGy(Si) s-1. The results showed potential tolerance doses approaching 500 Gy(Si). Computational tests included memory, calculation capability, voltage rail output, and I/O. Memory degradation was not observed alongside voltage rail and Input/Output. Individual test result deviations for CPU performance slightly increased in likelihood throughout accumulation of dose. Particular observed weakness included the SD card with potential strengths being use of redundancy and novel spot-shielding opportunities.

Streamlining land surface model Initialization: Automated data retrieval for VELMA using HMS REST API and GDAL.

Streamlining land surface model Initialization: Automated data retrieval for VELMA using HMS REST API and GDAL.

Leveraging Digital Twins for Real-time Supply Chain Visibility and Decision-Making

Global supply chains today are characterized by increasing complexity, volatility, and uncertainty. Traditional supply chain systems often struggle to provide timely insights, leading to inefficiencies and vulnerability to disruptions. The COVID-19 pandemic exposed significant weaknesses in supply chain visibility, accelerating the adoption of digital technologies to enhance resilience and agility (Ivanov &amp; Dolgui, 2020). Among these technologies, Digital Twin solutions have emerged as a transformative approach, enabling virtual replicas of supply chain assets, processes, and networks that integrate real-time data for continuous monitoring, simulation, and predictive analytics. This paper explores the role of digital twins in supply chain management, examining their architecture, applications, benefits, and challenges. The study also presents examples of cases and discusses future trends that position digital twins as critical enablers of next-generation supply chain visibility and decision-making.

Integrating artificial intelligence with the Internet of Things for state-of-the-art medical care systems

The goal of this study is to investigate the AIoT the integration of artificial intelligence (AI) and the Internet of Things (IoT) in healthcare systems and to create a thorough research plan that will look at different machine learning approaches in IoT-based e-health systems. Using a survey-based methodology, this study independently investigates the current uses of machine learning and the Internet of Things in the healthcare industry. It emphasizes how effective these technologies are and how integrating AIoT could lead to benefits. Initial results show that although AI and IoT work well together in a variety of IT applications, their combination in healthcare with AIoT offers more potential. In order to enhance continuous monitoring of data and decision-making procedures in e-health systems, the study identifies important gaps and opportunities for developing machine learning algorithms. With an emphasis on overcoming obstacles and seizing opportunities to maximize e-health systems, the knowledge gathered from this study can direct future advancements in AIoT for healthcare. For IT developers and healthcare experts hoping to successfully deploy AIoT systems, this will have real-world repercussions.

Continuous Vital Sign Monitoring Data in the General Ward: Exploratory Analysis.

This paper explores and analyzes a high-frequency vital sign- and event dataset from surgical ward patients to prepare for the training and application of predictive models.

On Metric Choice in Dimension Reduction for Fréchet Regression

SummaryFréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data. Fréchet regression utilises the pairwise distances between the random objects, which makes the choice of metric crucial in the estimation. In this paper, existing dimension reduction methods for Fréchet regression are reviewed, and the effect of metric choice on the estimation of the dimension reduction subspace is explored for the regression between random responses and Euclidean predictors. An extensive numerical study illustrate how different metrics affect the central and central mean space estimators. Two real applications involving analysis of brain connectivity networks of subjects with and without Parkinson's disease and an analysis of the distributions of glycaemia based on continuous glucose monitoring data are provided, to demonstrate how metric choice can influence findings in real applications.

Gas Endeavour: An Innovative Equipment for Estimating Methane Kinetics During In Vitro Rumen Fermentation.

The growing need to reduce methane emissions from ruminants while enhancing feed utilization has driven the development of innovative in vitro measurement techniques. This review examines the Gas Endeavour (GES), an automated volumetric apparatus that quantifies both total gas and methane production in real time during rumen fermentation. Utilizing the principles of liquid displacement and buoyancy, the GES integrates a thermostatically controlled water bath, specialized gas flow cells, and an alkaline CO2 absorption unit to deliver precise kinetic data on fermentation. Compared to conventional methods-which often rely on manual measurements and post-incubation gas chromatography-the GES provides continuous monitoring and immediate data acquisition, reducing labour and potential errors. This review discusses the system's design, operational challenges such as controlling headspace pressure and ensuring consistent inoculum preparation, and its applications in both animal nutrition and biomethane potential assessments. The findings suggest that, with further standardization and protocol refinement, the GES could significantly advance research aimed at optimizing feed digestibility and mitigating methane emissions in ruminant production systems.

5G EMF Exposure at 3.6 GHz in Greece Using Data From Frequency-Selective Monitoring Sensors.

The introduction of 5G networks has raised public concerns about potential changes in environmental electromagnetic field (EMF) exposure. This study analyzes continuous monitoring data collected over 2 years (August 2022-October 2024) from 13 frequency-selective monitoring sensors located in Greece's five largest cities. Focusing on the 3.6 GHz band, we evaluated trends and weekly variations in EMF levels. Results indicated a gradual increase in EMF exposure at 3.6 GHz, driven by the growing penetration of 5G infrastructure and devices. Notably, this band exhibited higher maximum-to-median power density ratios compared to other frequency bands, attributable to active antenna systems' characteristics and traffic variations. Applying the ICNIRP 2020 guidelines, we found that 30-min averaged values significantly reduced these variations. All measured EMF levels, including maximum values, remained well below Greek and international safety limits. These findings, especially the increasing trend identified for the EMF levels, underscore the importance of continuous monitoring networks for assessing EMF exposure to existing and emerging telecommunications networks and ensuring compliance with safety standards.

Distance Measurement and Display the Values in Lcd Using IOT with Rugged Board A5d2x

In modern industrial and smart infrastructure environments, accurate and automated distance measurement plays a critical role in ensuring safety, efficiency, and real-time monitoring. This paper presents a reliable, low-cost IoT-based distance measurement system utilizing the HC-SR04 ultrasonic sensor interfaced with the Rugged Board A5D2X. The system measures the distance of nearby objects by calculating the time delay of ultrasonic waves and displays the results on an LCD panel via I²C communication. The measured data is then transmitted to the Things Board cloud platform via Ethernet, enabling continuous remote monitoring and real-time data logging. Unlike traditional manual tools or microcontrollers lacking industrial-grade reliability, this implementation provides enhanced accuracy, long-term deployment capability, and cloud-based visualization. The proposed setup is ideal for applications in smart parking, obstacle detection, robotics, and industrial safety zones. This work highlights the seamless integration of embedded hardware with cloud services for scalable and intelligent monitoring solutions.

Bibliometric Insights into Terracing Effects on Water Resources Under Climate Change: Advances in Remote Sensing and GIS Applications

With the increasing impacts of global climate change and the continuous expansion of the population, the scarcity of food and water resources, along with the protection of agricultural land, have become significant constraints to sustainable agricultural development. Terraces plays a vital role in controlling water loss and promoting sustainable agriculture, and they have been widely adopted across the globe. Using CiteSpace, this study conducted a bibliometric review of the literature on the application of remote sensing and GISs in terrace studies under global climate change. The dataset included publications from the Web of Science spanning the years 1992 to 2024. Based on a systematical analysis of 508 publications, we investigated major institutions, cross-author collaborations, keyword co-occurrences, and the evolution of the research focus areas regarding the applications of remote sensing and GISs in terrace studies. The results show that the prominent research themes in this domain include remote sensing, erosion, and climate change. China (132, 26%) and the United States (108, 21%) are the top contributors in terms of publication numbers, while European countries and institutions are more active in collaborative efforts. The research emphasis has transitioned from analyzing the environmental characteristics of terraces to a broader consideration of ecological factors and multi-scenario applications. Moreover, analyses of the keyword co-occurrence and temporal trends indicate a rising interest in the application of machine learning, deep learning, and luminescence dating in terrace studies. Moving forward, it is essential to advance the deployment of automated monitoring systems, obtain long-term continuous monitoring data, encourage the adoption of conservation agriculture technology, and strengthen early warning networks for extreme climate events in terrace research. Overall, this study underscores the importance of interdisciplinary approaches and collaborative efforts to address the myriad challenges faced by terraced agriculture in an era of rapid environmental change.

Atmospheric deposition of chromophoric dissolved organic matter in the Athabasca Oil Sands Region, Canada, is strongly influenced by industrial sources during the winter months.

Atmospheric deposition of chromophoric dissolved organic matter in the Athabasca Oil Sands Region, Canada, is strongly influenced by industrial sources during the winter months.

Early warning and management of excessive discharge of water pollutants in municipal wastewater treatment plants based on fluctuation coefficients.

Early warning and management of excessive discharge of water pollutants in municipal wastewater treatment plants based on fluctuation coefficients.

Construction and application of a pollen emissions model based on phenology and random forests

Abstract. In recent years, the intensification of global climate change and environmental pollution has led to a marked increase in pollen-induced allergic diseases. This study leverages 16 years of continuous pollen monitoring data, alongside meteorological factors and plant functional type data, to construct a pollen emissions model using phenology and random forests (RFs). This model is then employed to simulate the emission characteristics of three primary types of autumn pollen (Artemisia, chenopods and total pollen concentration), elucidating the emission patterns throughout the seasonal cycle in Beijing. Phenology and RFs precisely simulate the start and end day of year of pollen, as well as the annual pollen production. There are significant spatiotemporal differences among the three types of pollen. On average, pollen dispersal begins around 10 August, peaks around 30 August and concludes by 25 September, with a dispersal period lasting approximately 45 d. Furthermore, the relationship between pollen emissions and meteorological factors is investigated, revealing that temperature, relative humidity (RH) and sunshine hours (SSHs) significantly influence annual pollen emissions. Specifically, temperature and RH exhibit a strong positive correlation with annual pollen emissions, while SSHs show a negative correlation. Different pollen types display varied responses to meteorological factors. Finally, the constructed pollen emissions model is integrated into the ICTP Regional Climate Model (RegCM) and validated using pollen observation data, confirming its reliability in predicting pollen concentrations. This study not only enhances the understanding of pollen release mechanisms but also provides scientific evidence for the selection and planting of urban greening plants.

Analyzing Consumer Behavior in E-Commerce: Insights from Data-Driven Approaches

Introduction; Understanding modern shopping behaviors and developing effective marketing strategies have made e-commerce consumer analysis a critical tool for businesses. Objective; This study delves into consumer behavior patterns by analyzing a comprehensive Kaggle dataset centered on online retail activities, which includes demographic details, purchasing trends, and user engagement metrics. Utilizing advanced data science methodologies—such as data preprocessing, exploratory data analysis, and predictive modeling—the research uncovers valuable insights into customer segmentation, spending behaviors, and strategies to enhance customer retention.Method; The analysis reveals significant trends, such as the impact of seasonal variations on online shopping, the role of demographic factors in shaping product preferences, and the effectiveness of personalized marketing in boosting conversion rates. Result; These findings underscore the importance of tailoring marketing efforts to align with consumer needs and preferences. Additionally, the study highlights the necessity for e-commerce businesses to adopt continuous data monitoring and implement robust analytical frameworks to remain competitive in an ever-evolving digital marketplace.Conclusion; This research provides actionable recommendations for e-commerce stakeholders, emphasizing the integration of data-driven insights into their strategies. By leveraging these insights, businesses can optimize their marketing approaches, improve customer satisfaction, and ultimately drive growth in the highly competitive online retail landscape.

Pitfalls and Strategies for Implementing and Sustaining an Otolaryngology Perioperative Registry in Mekelle, Ethiopia.

Surgical registries have been widely adopted in high-income countries to improve patient outcomes. However, similar data-driven initiatives are still scarce in low- and middle-income countries (LMICs). This study aimed to address the challenges of implementing a perioperative registry for otolaryngology-head and neck surgery (OHNS) in Mekelle, Ethiopia, and to assess strategies for ensuring its long-term sustainability. The registry was developed using REDCap, through a collaborative effort between otolaryngologists in the United States and Ethiopia, ensuring its relevance to the local context. On-site training sessions were conducted for 13 OHNS residents and four senior surgeons to facilitate their use of the registry. A Wi-Fi router was installed in the operating room to enable real-time data entry. Continuous support was provided through remote communication between the local team and the U.S. research team. Sustainability strategies focused on fostering local ownership, integrating the registry into existing workflows, and maintaining continuous data monitoring. Despite facing challenges like intermittent internet connectivity and issues with workflow integration, the local team successfully integrated the registry into routine clinical and surgical practices. Key strategies included providing dedicated Wi-Fi routers, modifying registry fields for improved efficiency, and emphasizing the registry's value to the institution. Ongoing collaboration between the local team and the U.S. team enabled continuous optimization and data collection. The successful implementation of this perioperative registry underscores the importance of engaging local stakeholders and integrating sustainable workflows. This initiative serves as a model for other LMICs seeking to establish surgical registries that enhance data-driven decision-making at both the patient and institutional levels.

A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records.

Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data. The TECO model was developed using multiple baseline and time-dependent clinical variables from 2579 hospitalized COVID-19 patients to predict ICU mortality and was validated externally in an acute respiratory distress syndrome cohort (n = 2799) and a sepsis cohort (n = 6622) from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Model performance was evaluated based on the area under the receiver operating characteristic (AUC) and compared with Epic Deterioration Index (EDI), random forest (RF), and extreme gradient boosting (XGBoost). In the COVID-19 development dataset, TECO achieved higher AUC (0.89-0.97) across various time intervals compared to EDI (0.86-0.95), RF (0.87-0.96), and XGBoost (0.88-0.96). In the 2 MIMIC testing datasets (EDI not available), TECO yielded higher AUC (0.65-0.77) than RF (0.59-0.75) and XGBoost (0.59-0.74). In addition, TECO was able to identify clinically interpretable features that were correlated with the outcome. The TECO model outperformed proprietary metrics and conventional machine learning models in predicting ICU mortality among patients with COVID-19, widespread inflammation, respiratory illness, and other organ failures. The TECO model demonstrates a strong capability for predicting ICU mortality using continuous monitoring data. While further validation is needed, TECO has the potential to serve as a powerful early warning tool across various diseases in inpatient settings.

IT Governance Maturity and Business Alignment: A COBIT 2019 Evaluation at RSUD ODSK

IT is crucial for organizational performance and competitiveness, especially in healthcare, where service quality affects patient outcomes. Effective IT governance aligns investments with goals, delivers value, and reduces risks. This study assesses IT governance priorities at RSUD ODSK using the COBIT 2019 framework, identifying key governance and management objectives. The research employs a structured methodology, beginning with problem identification and then data collection through structured interviews using the COBIT 2019 Design Toolkit and the analysis of 10 Design Factors. These factors help determine the most critical governance objectives in RSUD ODSK's operational environment and strategic priorities. Findings indicate that Client Service/Stability is the hospital's top strategic priority, supported by key enterprise goals, including compliance with external regulations, business-service continuity, and internal process optimization. The prioritization process highlights Managed Assurance (MEA04) and Managed Security Services (DSS05) as the most critical governance objectives, both receiving importance scores of 100%, emphasizing the need for continuous IT performance monitoring and strong data security measures. Other highly prioritized objectives include Managed Security (APO13) at 85% and Managed Compliance with External Requirements (MEA03) at 70%, reflecting the hospital's focus on regulatory adherence and risk management. The results provide practical guidance for RSUD ODSK in improving IT governance, aligning IT with organizational goals, and ensuring reliable IT services to enhance patient care and operational efficiency. This study contributes valuable insights for hospitals seeking to strengthen IT governance using COBIT 2019.