Current Monitoring Systems Research Articles

Selection of Internet of Things-enabled sustainable real-time monitoring strategies for manufacturing processes using a disc spherical fuzzy Schweizer–Sklar aggregation model

The emergence of the Internet of Things (IoT) for monitoring in real-time is geared towards sustainable energy consumption practices by taking control over energy loss. The promising potential of current IoT real-time monitoring systems paves the way for future developments in monitoring devices with eco-friendly sensing capabilities. As a result, the creation of effective IoT real-time monitoring devices targeted at decreasing energy loss becomes crucial. This modeling procedure falls under the realm of multiple-attribute group decision-making (MAGDM), aiming to integrate the Schweizer–Sklar (SS) τ-norm and τ-conorm within the disc spherical fuzzy (D-SF) framework. The objective is to enhance the flexibility of D-SF in dealing with intricate and uncertain data. The core focus of this research is on deriving SS τ-norm and τ-conorm for D-SF data, consequently introducing innovative aggregation operators. The article offers the fundamental D-SF operations using SS aggregation operators in a systematic manner, with thorough theorem justifications. A new MAGDM tool is presented, created simply to manage ambiguous and imprecise data utilizing the suggested operators. Our model is specifically designed to tackle the critical issue of reducing energy loss in IoT real-time monitoring systems. The research not only focuses on model accuracy but also emphasizes its effectiveness in solving this pressing problem, demonstrating significant advancements in sustainable energy practices. Moreover, the proposed aggregation operators are subjected to a comparative analysis. This comprehensive comparison not only enhances the operators’ efficacy but also underscores their relevance in real-world decision-making scenarios.

Hybrid TDR-MI Based Wireless Sensor Network for Underground Water Pipeline Leakage Detection and Localization Using Pressure Residuals and Classifiers

AbstractThe pipeline leakage detection and leak localization trouble is a highly demanding and dangerous issue. Underground pipelines are critical for transporting enormous fluid volumes (e.g., water) across extended distances. Not only would solving this issue save the nation a great deal of money and resources, but it will also save the environment. However, because of the harsh climatic conditions below earth, current leak detection systems are not enough for monitoring subterranean pipelines. To address these issues, this study suggests a hybrid wireless sensor network for monitoring subterranean pipelines that is based on magnetic induction and time domain reflectometry (TDR). TDR is installed in this instance below a wireless sensor network that is based on MI. TDR significantly reduces the time needed for inspection while accurately locating the leak. Based on MI technology, we provide a wireless sensor network for inexpensive, real-time leak detection in subterranean pipelines. Through the integration of data from several sensor types located within and around subterranean pipes, MISE-PIPE detects leaks. Ad-hoc WSNs are employed in pressure measurement. (WDNs) is a popular subject that has drawn attention from scholars lately. Since leak localisation has a significant influence on the human population and the economy, time and accuracy are essential components. A broad leak localisation technique is proposed using statistical classifiers operating in the residual space. Classifiers are trained using leak data from every node in the network, accounting for demand uncertainty, noise from sensor preservatives, and leak size. After localising and identifying leaks, all monitoring data is sent to the CH using the K-means clustering technique, which performs two vital tasks: optimum clustering, extending the Network Lifetime, and maintaining Quality of Service. The K-Means technique is used to optimise the clustering process. The K-means clustering technique is used to transfer all monitoring data to the CH for the purpose of pipeline leak identification and localisation. Unlike the current underground pipeline monitoring system, our proposed Hybrid TDR-MI-based wireless sensor network allows precise real-time leak identification and localisation.

GIS-Based Spatial Analysis and Strategic Placement of Fine Dust Alert Systems for Vulnerable Populations in Gangseo District

Air pollution, particularly fine particulate matter (PM), poses significant health risks to vulnerable populations such as children, older adults, and individuals with chronic illnesses. Understanding the spatial distribution of these populations and their access to air quality information is crucial for effective interventions. In urban areas like Gangseo District, the distribution of essential facilities and accessibility varies greatly. While studies have highlighted the health impacts of PM, research on optimizing air quality monitoring for at-risk groups remains limited. This study aims to identify optimal locations for air quality monitoring by analyzing the spatial distribution of vulnerable populations and facility accessibility. Using Geographic Information Systems (GIS) and isochrone maps, we identified areas with high concentrations of vulnerable groups and poor access to healthcare facilities. Our findings revealed significant disparities in access to air quality information, with some high-risk areas underserved by current monitoring systems. This study integrated demographic data and spatial analysis to propose strategic monitoring placements. The methodology can be applied to other urban settings and offers a framework for improving air quality management. This study underscores the importance of targeted air quality monitoring to protect vulnerable populations and suggests practical steps for policymakers to enhance public health.

Research on Driver Emotion Monitoring and Regulation System in Intelligent Assisted Driving

Abstract. With the rapid development of smart connected vehicles, the influence, recognition and regulation of driver's emotions are crucial for intelligent cockpits with "emotional interaction". This review aims to provide a comprehensive analysis of driver emotion monitoring systems in intelligent assisted driving. By summarizing and analyzing related literature, the structure and application of emotion monitoring systems are introduced, their principles and evaluation criteria are discussed in depth, and the advantages and limitations of the three sub-systems, namely, detection, decision-making and regulation, are analyzed in detail. The results show that although emotion monitoring and modulation technologies perform well in enhancing driving safety and comfort, their practical applications still face challenges in terms of technology maturity, cost control, and privacy protection. In addition, the reliability and accuracy of current emotion monitoring systems in complex driving environments have yet to be improved. Future research should focus on interdisciplinary cooperation to integrate the latest achievements in psychology, computer science and engineering. At the same time, it is necessary to develop unified technical standards and evaluation guidelines to promote compatibility and interoperability between different systems and enhance research on privacy protection measures to ensure the security and confidentiality of user data. Through these efforts, the field of intelligent driving is expected to achieve comprehensive development and wide application, further enhancing driver safety and user experience.

Concept of Sporadic E Monitoring Using Space-Based Low Power Multiple Beacon-Systems

Current monitoring systems to detect sporadic E use ground-based setups, ionosondes, and the network of GNSS satellites in order to assess the phenomenon of sporadic E. This paper aims to monitor sporadic E using a miniature space-based platform in an atypical way. The setup consists of multiple radio-amateur beacon systems aboard satellites, each having a specific modulation and transmission scheme. This Radio Amateur Beacon System for the Investigation of the Ionosphere (RABSII) is coupled to a GNSS receiver, revealing the location of the platform. Multiple beacon data streams are sequentially sent from a satellite platform towards the Earth. By receiving and comparing the Signal-to-Noise ratios of these streams using a dedicated ground-based radio-amateur network of receiving stations, the presence of sporadic E can be determined, and a location-based model can be built. The advantage of this miniaturized, low-power, low-cost instrument is its ability to be put on any satellite platform in the future in order to map sporadic E.

Single-stage NBI sampler for PM1 mass and five-stage NBI-NMCI sampler for PM1 mass distribution measurements

In recent years, concerns about the health and environmental risks associated with PM1 particles have increased. However, existing PM1 sampling instruments remain to be improved mainly because their PM1 inlets have particle loading and bounce issues. To address these challenges, PM1 inlets based on the Non-Bouncing Impactor (NBI) technique were developed. These inlets employ vacuum oil-wetted glass fiber filter (GFF) substrates to eliminate particle bounce and incorporate a daily vacuum oil injection to prevent particle loading. The 16.7 L/min PM1 NBI, modified from the PM2.5 M-WINS design with a reduced diameter of the single nozzle, was designed to adapt readily with current standard sampling and monitoring systems. The cut-size (Dpa50) of 0.99 ± 0.02 μm was determined by considering the effects of Reynold number and the ratio of jet-to-plate distance and nozzle diameter. Field tests comparing the 16.7 L/min PM1 NBI sampler to the 9-stage NCTU Micro-orifice Cascade Impactor (NMCI9) revealed small sampling biases, with a mean difference of +0.26 ± 2.28 μg/m3 for PM1 measurements when silicone oil-coated aluminum foil (AF) and GFF-AF were used in the NMCI9. The NBI with the oil-wetted GFF substrate effectively removed particles larger than 1.0 μm, resulting in more accurate PM1 mass concentration measurements. Additionally, the development of a 5-stage NMCI with the 30 L/min PM1 NBI as the first stage enabled detailed measurement of PM1 mass distribution in two modes, which was challenging when measuring the entire mass distribution of ambient aerosols. The distribution of size-dependent water-soluble inorganic ions in PM1 showed dominance of SO42− and NH4+ in PM1 compared to PM2.5. In summary, the PM1 NBI enhances accuracy for long-term atmospheric sampling by addressing particle loading and bounce, making it a more reliable standard sampling instrument.

Research on the monitoring system for induced voltage and ground current of 27.5 kV cable sheath in railways

PurposeThe purpose of this study is to address the deficiency in safety monitoring technology for 27.5 kV high-voltage cables within the railway traction power supply by analyzing the grounding methods employed in high-speed railways and developing an effective monitoring solution.Design/methodology/approachThrough establishing a mathematical model of induced potential in the cable sheath and analyzing its influencing factors, the principle of grounding current monitoring is proposed. Furthermore, the accuracy of data collection and alarm function of the monitoring equipment were verified through laboratory simulation experiments. Finally, through practical application in the traction substation of the railway bureau on site, a large amount of data were collected to verify the stability and reliability of the monitoring system in actual environments.FindingsThe experimental results show that the designed monitoring system can effectively monitor the grounding current of high-voltage cables and respond promptly to changes in cable insulation status. The system performs excellently in terms of data collection accuracy, real-time performance and reliability of alarm functions. In addition, the on-site trial results further confirm the accuracy and reliability of the monitoring system in practical applications, providing strong technical support for the safe operation of high-speed railway traction power supply systems.Originality/valueThis study innovatively develops a 27.5kV high-voltage cable grounding current monitoring system, which provides a new technical means for evaluating the insulation status of cables by accurately measuring the grounding current. The design, experimental verification and application of this system in high-speed railway traction power supply systems have demonstrated significant academic value and practical significance, contributing innovative solutions to the field of railway power supply safety monitoring.

Technical aspect of the operation of accelerometers as composition of a system for monitoring engineering structures of a cable-stayed bridge over the Petrovsky canal in the construction of the Western Speed Diameter highway in Saint Petersburg

Aim. This study analyzes the current monitoring system for the engineering structures of a cable-stayed bridge across the Petrovsky Canal, part of the Western High-Speed Diameter highway in St. Petersburg, focusing on emergency situations where accelerometers on pylons record values that exceed limits. Methods and Materials. The study utilizes statistical data from existing monitoring system databases. The tasks, set within the framework of the study, are accomplished by applying theoretical scientific methods including analytical techniques, mathematical statistics, and induction. Results. The analysis presents an overview of the current system used for monitoring engineering structures and highlights issues related to emergency situations in which accelerometer readings exceed threshold values. This study proposes a method for assessing the reliability of the monitoring system by classifying the data obtained from the accelerometer into two groups. The research identifies the need for further investigations to understand the root causes of these issues. Conclusion. Тhe results can assist in the design and installation of monitoring systems of the engineering structures for cable-stayed bridges, as well as for modernization and optimization of existing monitoring systems to improve the quality of structural technical condition assessments.

SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services

Edge computing changed the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy breach. However, advances in deep learning enabled Internet of Things (IoTs) to onload tasks and run cognitive tasks locally. This research introduces a decentralized-control edge model where computation and decision-making are moved to the IoT level. The model aims at decreasing communication and computation dependance on the edge which affect efficiency and latency. The model also limits data transfer to the edge to avoid security and privacy risks. Decentralized control is a key to many business applications that prioritizes safety, real-time response, and privacy such as ridesharing monitoring and industrial operations. To examine the model, we developed SAFERIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current monitoring systems require costly infrastructure and continuous network connectivity. However, SAFRIDES uses optimized deep learning models that run locally on IoTs to detect and record violations in ridesharing. The system achieved the lowest latency among current solution, while minimizing data sharing and maintaining privacy. Moreover, decentralized edge computing empowers IoTs and upgrades their functionality from sensing to independent decision-making.

Problems of the management of habitat monitoring in settlements (ports) of the Northern Sea Route

Introduction. The Northern Sea Route (NSR) is the main Arctic shipping route in the Russian Federation. As part of the development of Arctic infrastructure, the state and private investors have launched large-scale work on the development of Arctic ports. The purpose of the study was to evaluate the current monitoring system for the state of atmospheric air, soil, and drinking water in the territory of settlements (ports) in the Northern Sea Route. Materials and methods. Official data from environmental pollution monitoring by (Russian Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet) and social and hygienic monitoring by Federal Service for Supervision in Protection of the Rights of Consumer and Man Wellbeing (Rospotrebnadzor) in twelve settlements and ports in the Northern Sea Route were used. An electronic database has been created. The analysis was carried out using MS Office Excel, visualization of the location of monitoring points – ESRI ArcGIS 9.3 Results. The conducted research has shown heterogeneity in the management of monitoring of environmental factors of the population living in settlements (ports) of the NSR. The largest monitoring coverage is typical for settlements in the North-West – primarily the cities of Murmansk and Arkhangelsk, where the largest number of posts are located and all environments are monitored – atmospheric air, soil of populated areas and drinking water. There is practically no monitoring of the habitat in the village of Sabetta, Dudinka. In the village. Dixon habitat monitoring is not carried out. Atmospheric air, the quality of which is especially important for territories with developed port infrastructure, is controlled only in 5 out of 12 settlements (ports) of the NSR. Limitations. The research had no limitations. Conclusion. The need to optimize the monitoring system was noted. As measures, it is recommended to manage monitoring of atmospheric air, drinking water, and soil in the village. Dixon; atmospheric air and soil in the village. Sabetta and Dudinka; atmospheric air in Beringovsky village, Igarka, Providence village; soil in Tiksi village. It is necessary to expand the list of controlled indices in atmospheric air, drinking water and soil, as well as increase the frequency of sampling.

Prototype of Voltage, Current, Temperature, and Oil Level Monitoring System in Transformers

Prototype of Voltage, Current, Temperature, and Oil Level Monitoring System in Transformers

Machine Learning Framework for Real-Time Pipeline Anomaly Detection and Maintenance Needs Forecast Using Random Forest and Prophet Model

This paper introduces an Intelligent Model for Real-Time Pipeline Monitoring and Maintenance Prediction to enhance infrastructure integrity and operational efficiency in Nigeria's oil and gas sector. Given the country's economic dependence on oil and gas revenue, efficient pipeline transportation is crucial. However, pipelines face challenges such as corrosion, mechanical failures, vandalism, and theft, leading to economic losses and environmental risks. Current monitoring systems are mainly reactive, lacking timely anomaly detection and predictive maintenance capabilities. To tackle these challenges, the study utilized sophisticated machine learning methods by combining the Random Forest classifier for real-time anomaly detection with the Prophet model for predictive maintenance forecasting. Datasets from Kaggle were used. The Random Forest classifier demonstrated robust performance with an accuracy of 93.48%, precision of 93.75%, recall of 96.77%, and an F1-score of 95.24%. The Prophet model provided accurate hourly forecasts of operational parameters, aiding proactive maintenance scheduling. Despite some errors encountered (RMSE: 21.48 and MAE: 18.17), the Mean Absolute Percentage Error (MAPE) of 14.87% indicates relatively minor discrepancies compared to actual values. In conclusion, the Intelligent Model shows significant advancements in pipeline monitoring and maintenance prediction by leveraging machine learning for early anomaly detection and timely maintenance interventions. This proactive approach aims to reduce downtime, prevent environmental damage, and optimize operational efficiency in Nigeria's oil and gas infrastructure. Future research could focus on enhancing system scalability across diverse terrains, employing advanced deep learning techniques such as Transformer Networks and Autoencoders for improved prediction accuracy, and exploring cybersecurity measures like blockchain integration to ensure data integrity and protect critical infrastructure from cyber threats.

Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras

Despite advancements in technology, safety equipment, and training within the construction industry over recent decades, the prevalence of fatal and nonfatal injuries and accidents remains a significant concern among construction workers. Hard hats and safety vests are crucial safety gear known to mitigate severe head trauma and other injuries. However, adherence to safety protocols, including the use of such gear, is often inadequate, posing potential risks to workers. Moreover, current manual safety monitoring systems are laborious and time-consuming. To address these challenges and enhance workplace safety, there is a pressing need to automate safety monitoring processes economically, with reduced processing times. This research proposes a deep learning-based pipeline for real-time identification of non-compliance with wearing hard hats and safety vests, enabling safety officers to preempt hazards and mitigate risks at construction sites. We evaluate various neural networks for edge deployment and find that the Single Shot Multibox Detector (SSD) MobileNet V2 model excels in computational efficiency, making it particularly suitable for this application-oriented task. The experiments and comparative analyses demonstrate the pipeline’s effectiveness in accurately identifying instances of non-compliance across different scenarios, underscoring its potential for improving safety outcomes.

Design of a Tunnel Anchor Monitoring System Based on Long Short-Term Memory–Autoregressive Integrated Moving Average Prediction

Considering the shortcomings of the current monitoring system for tunnel anchor support systems, a tunnel anchor monitoring system based on LSTM-ARIMA prediction is proposed in this paper to prevent the deformation and collapse accidents that may occur in the underground mine tunnels during the backfilling process, which combines the Internet of Things and a neural network deep learning algorithm to achieve the real-time monitoring and prediction of the tunnel anchor pressure. To improve the prediction accuracy, a time series analysis algorithm is used in the prediction model of this system. In particular, an LSTM-ARIMA model is constructed to predict the tunnel anchor pressure by combining the Long Short-Term Memory (LSTM) model and the Autoregressive Integrated Moving Average (ARIMA) model. And a dynamic weighted combination method is designed based on model prediction confidence to acquire the optimal weight coefficients. This combined model enables the monitoring system to predict the anchor pressure more accurately, thereby preventing possible tunnel deformation and collapse accidents in advance. Finally, the overall system is verified using the anchor pressure dataset obtained from the 21,404 section of the Hulusu Coal Mine transportation tunnel in real-world engineering, whose results show that the pressure value predicted using the combined model is basically the same as the actual value on site, and the system has high real-time performance and stability, proving the effectiveness and reliability of the system.

Monitoring and Evaluation Practices: Implication to Education Policy Implementation in Gambella Region, Ethiopia

The purpose of the study was to examine the practice of monitoring and evaluation in promoting effective policy implementation in the education sector of the Gambella Regional Sate. It also attempted to identify key challenges facing the current monitoring and evaluation system, and suggests ways to enhance the monitoring and evaluation system in education policy implementation in the region. Case study design was used to conduct the study. Primary data were collected through interview guides that targeted the bureau leader and experts in the Gambella region education bureau. Secondary data were collected through desk reviews. The results showed that the required monitoring, and evaluation framework, for effective monitoring, and evaluation are not in place. It was also found that the Gambella region education bureau has not fully established a system that can enhance a successful monitoring and evaluation system in the education sector of the region. The current staffs do not have adequate knowledge and skills, there is no proper monitoring, and evaluation coordination among stakeholders, and the bureau does not allocate adequate funds required for monitoring and evaluation activities. To undertake monitoring and evaluation, the Gambella bureau of education there needs a greater understanding of the value of monitoring and evaluation and there is a requirement for the bureau of education and the stakeholders to develop proper a legislative framework that considers the key performance indicator stetted in the education sector development program six for the effective implementation of monitoring and evaluation in the education sector for effective decision-making.

The application of the K-class system in mine seismicity

As seismology has evolved, scientists have developed various earthquake magnitude scales tailored for specific scenarios, including Richter magnitude (ML) for local, moderate quakes, moment magnitude (Mw) for large-scale seismic events, energy magnitude (Me), which correlates directly with the total energy released, and duration magnitude (MD) for quickly assessing shallow or nearby seismic activities. Despite these advances, these scales were not specifically designed to address the unique challenges posed by mine earthquakes, particularly in coal mine environments where seismic events are typically of lower energy and lower perceptibility but have potentially significant impacts. This study applies the K-class magnitude system for the first time to address significant discrepancies in magnitude values for the same seismic events in mine earthquakes, particularly in coal mines when different formulas are used. However, because current seismic monitoring systems in domestic coal mines continue to employ a variety of magnitude scales, we have developed a corrected Richter scale (MLc) specifically tailored to the geological conditions of coal mines. This adaptation facilitates the conversion to and from the K class, enhancing the accuracy and relevance of seismic assessments in these environments. Therefore, we conducted continuous blasting experiments at the Dongtan Coal Mine and derived the corrected Richter magnitude MLc. Based on data analysis, we fitted the energy attenuation coefficient and compared the K values with four types of traditional magnitude results. Finally, we derived the conversion relationship between the K class and the corrected Richter magnitude MLc, establishing a mine earthquake measurement system. The successful application of the K class in coal mines suggests its potential to be extended to the entire field of induced microseismicity, including mining-induced microseismicity, shale gas extraction-induced microseismicity, and oil extraction-induced microseismicity, providing valuable insights and tools for a broader range of geophysical research and industry practices.

Online Student Monitoring and Evaluation System using Apriori Algorithm for Predicting Student Academic Performance

The study investigates the current student monitoring and evaluation system of Saint Michael College of Caraga with the goal of easy tracking of student academic performance and progression. The research employs a mixed-methods approach through the conduct of interview and utilizing online survey questionnaire. The perspectives of young female students regarding the current system and their suggestions for improvement were examined and analyzed using descriptive statistical method such as frequency distribution, weighted mean score, and standard deviation. The key findings indicate a generally positive perception of the existing system but highlight areas for improvement including addressing technical issues, accommodating diverse learning styles, ensuring system security and privacy, and providing personalized support to students. By engaging stakeholders and incorporating diverse viewpoints from the respondents, the research output contributes to the development of a robust monitoring and evaluation system capable of optimizing academic performance assessment and enriching the educational experience. The integration of Apriori Algorithm fosters informed decision-making for attaining student’s academic success

Outpatient blinatumomab with digital monitoring in patients with measurable residual disease positive (MRD+) B-ALL in a phase 4 study.

6544 Background: Blinatumomab, a CD3/CD19-directed BiTE® (bispecific T-cell engager) molecule, is an effective therapy for patients (pts) with MRD+ B-ALL. Hospitalization is recommended for days (D) 1–3 of cycle (C) 1 and D1–2 of C2 for these pts receiving blinatumomab to monitor for serious adverse events (SAEs). This phase 4 study evaluated the safety and feasibility of outpatient (outpt) digital monitoring to replace hospitalization in pts with MRD+ B-ALL (NCT04506086). Methods: Enrolled adults with MRD+ B-ALL in complete remission received continuous intravenous (cIV) blinatumomab at 28 μg/day. Current Health Wearable Monitoring System (CHWMS) was worn by pts at home for D1–3 (C1) and D1–2 (C2), in the presence of a caregiver, and provided heart rate, respiratory rate, and oxygen saturation; a separate patch monitored temperature. Manual blood pressure measurements were taken every 3-6 hrs. Vital signs (VS) were streamed to a provider smartphone/laptop; changes outside a predetermined threshold generated audible alarms. The primary endpoint was incidence of grade (G) ≥3 cytokine release syndrome (CRS), neurotoxicity, or any adverse event (AE) requiring hospitalization during the monitoring period. Results: As of 12/15/2023, 9 pts (median age 43 [20-73] years) received outpt blinatumomab for (completed C1, n=7; C2, n=6). During outpt monitoring, dose interruption was reported in 5 of 9 pts (1 pt had 2 dose interruptions: AE, n=2; dose administration error, n=2; other, n=2). CRS was reported in 2 pts (SAE, n=1; G≥3, n=0; led to interruption, n=1) and neurologic events in 4 pts (SAE, n=0; G≥3, n=0; led to interruption, n=0). Two pts experienced AEs requiring hospitalization (CRS, n=1; fever, n=1). Most VS changes were physiological or from routine pt activity. Median (range) number of alarms triggered/pt was 79 (12-135); among 3 pts, 1.2%, 5.1% and 8.9% of alarms led to therapeutic intervention (Table). Conclusions: To date, outpt administration of cIV blinatumomab in pts with MRD+ B-ALL was feasible and safe with appropriate outpt digital monitoring. Study enrollment is ongoing. Clinical trial information: NCT04506086 . [Table: see text]

Statistical process monitoring creates a hemodynamic trajectory map after pediatric cardiac surgery: A case study of the arterial switch operation.

Postoperative critical care management of congenital heart disease patients requires prompt intervention when the patient deviates significantly from clinician-determined vital sign and hemodynamic goals. Current monitoring systems only allow for static thresholds to be set on individual variables, despite the expectations that these signals change as the patient recovers and that variables interact. To address this incongruency, we have employed statistical process monitoring (SPM) techniques originally developed to monitor batch industrial processes to monitor high-frequency vital sign and hemodynamic data to establish multivariate trajectory maps for patients with d-transposition of the great arteries following the arterial switch operation. In addition to providing multivariate trajectory maps, the multivariate control charts produced by the SPM framework allow for assessment of adherence to the desired trajectory at each time point as the data is collected. Control charts based on slow feature analysis were compared with those based on principal component analysis. Alarms generated by the multivariate control charts are discussed in the context of the available clinical documentation.

Workload Risk Factors for Pitching-Related Injuries in High School Baseball Pitchers

Background: Pitch counts are only one measure of the true workload of baseball pitchers. Newer research indicates that workload measurement and prevention of injury must include additional factors. Thus, current monitoring systems gauging pitcher workload may be considered inadequate. Purpose/Hypothesis: The purpose of this study was to develop a novel method to determine workload in baseball pitchers and improve processes for prevention of throwing-related injuries. It was hypothesized that our pitching workload model would better predict throwing-related injuries occurring throughout the baseball season than a standard pitch count model. Study Design: Cohort study; Level of evidence, 2. Methods: This prospective observational study was conducted at an academic medical center and community baseball fields during the 2019 to 2023 seasons. Pitchers aged 13 to 18 years were monitored for pitching-related injuries and workload (which included pitching velocity; intensity, using preseason and in-season velocity as a marker of effort; and pitch counts). Results: A total of 71 pitchers had 313 recorded pitcher outings, 11 pitching-related injuries, and 24,228 pitches thrown. Gameday pitch counts for all pitchers ranged from 19 to 219 (mean, 77.5 ± 41.0). Velocity ranged from 46.8 to 85.7 mph (mean, 71.3 ± 5.8 mph). Intensity ranged from 0.7 to 1.3 (mean, 1.0 ± 0.08). The mean workload was 74.7 ± 40.1 for all pitchers. Risk factors significant for injury included throwing at a higher velocity in game (P = .001), increased intensity (eg, an increase in mean velocity thrown from preseason to in-season; P < .001), and being an older pitcher (P = .014). No differences were found for workload between injured and noninjured pitchers because the analysis was underpowered. Conclusion: Our workload model indicated that throwing at a higher velocity, throwing at a higher intensity, and older age were risk factors for injury. Thus, this novel workload model should be considered as a means to identify pitchers who may be at greater risk for injury.