Monitoring System Research Articles

Computational Analysis of Stiffness Reduction Effects on the Dynamic Behaviour of Floating Offshore Wind Turbine Blades

This paper describes the study of a floating offshore wind turbine (FOWT) blade in terms of its dynamic response due to structural damage and its repercussions on structural health monitoring (SHM) systems. Using a finite element model, natural frequencies and mode shapes were derived for both an undamaged and a damaged blade configuration. A 35% reduction in stiffness at node 1 was applied in order to simulate significant damage. Concretely, the results are that the intact blade has a fundamental frequency of 0.16 Hz, and this does not change when damaged, while higher modes exhibit frequency changes: mode 2 drops from 2.05 Hz to 2.00 Hz and mode 3 from 6.15 Hz to 6.01 Hz. The shifts show a critical loss in the capability of handling vibrational energy due to the damage; higher modes (4, 5, and 6) show larger frequency deviations going down to as low as 18.06 Hz in mode 6. The mode shape change is considerable for the edge-wise and flap-wise deflection of the 2D contour plots, indicating possible coupling effects between modes. These results indicate that lower modes are sensitive to stiffness reductions, and the continuous monitoring of the lower harmonic modes early is required to detect damages. These studies have helped to improve blade design, maintenance, and operational safety for FOWT systems.

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

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

The Rise of Biochemical Sensors: Technology for Real-Time Health Monitoring (Applications and Future Scope)

Biochemical sensors integrated into wearable health technology represent one of the most transformative innovations of the 21st century. This review delves into the evolution, principles, and real-time applications of biochemical sensors and their role in personalized health monitoring. Wearable biochemical sensors, capable of continuously measuring various biomarkers like glucose, lactate, cortisol, and electrolytes, are revolutionizing healthcare by enabling proactive management of chronic diseases, including diabetes, cardiovascular disorders, and mental health issues. Advances in biosensing technologies, coupled with the use of AI and machine learning algorithms, have enhanced the sensitivity and accuracy of these devices, ensuring that critical health data is available in real-time. From glucose monitoring devices like Abbott's FreeStyle Libre to the latest nanomaterial-based sensors, these innovations are reshaping healthcare delivery by shifting the focus from hospital-centered treatments to patient-centric, continuous monitoring systems. This review provides an in-depth analysis of the technological advancements, challenges, and future directions in biochemical sensors, focusing on key technologies such as electrochemical, optical, and enzymatic sensors. It also highlights the critical role of AI in interpreting the complex data generated by these sensors, paving the way for more efficient diagnostics and predictive healthcare models. Furthermore, the paper explores how these sensors have been applied in infectious disease detection, particularly during the COVID-19 pandemic, and discusses their potential to enhance global health surveillance systems. In conclusion, wearable biochemical sensors represent a significant leap forward in the pursuit of personalized medicine, offering real-time diagnostics and timely interventions for disease management. The future of healthcare is closely tied to the ongoing innovations in sensor technology, with the promise of even more advanced and multifunctional devices on the horizon.

Real-time in-situ coatings corrosion monitoring using machine learning-enhanced triboelectric nanogenerator

Current methods for monitoring coating corrosion are limited by their inability to provide real-time data and dependence on external power sources. This study presents a novel in-situ corrosion monitoring system using a solid-liquid triboelectric nanogenerator (TENG) that converts mechanical energy into electrical signals for self-powered sensing. TENG signals and electrochemical impedance spectra were measured on a dopamine-modified lignin-polydimethylsiloxane coating on steel in 1 M NaCl solution under no corrosion, indentation, pitting, and broken conditions, respectively. We extract time-frequency features from the TENG signals to predict the coating’s corrosion condition by applying a customised convolutional neural network (CNN). By extracting time-frequency features from the TENG signals and applying a custom CNN, a prediction accuracy of 99 % for corrosion classification was achieved. Furthermore, the CNN regression model predicted coating impedance values with a high coefficient of determination (R² = 0.98), demonstrating its effectiveness in tracking corrosion progression. The developed TENG also facilitates defect localisation via a matrix electrode beneath the coating. Our approach introduces a promising real-time technology for in-situ corrosion monitoring.

Icodec ONWARDS: A review of the first once-weekly diabetes treatment for nurse practitioners and physician assistants.

Diabetes management is challenged by the complexity of treatment regimens and the need for frequent injections, affecting patient adherence and quality of life. Insulin icodec, a once-weekly basal insulin analog, represents a significant innovation, potentially simplifying diabetes care and improving outcomes. This review aims to evaluate the safety, efficacy, and clinical implications of insulin icodec for individuals with type 1 and type 2 diabetes, highlighting its potential to affect current treatment paradigms. A review was conducted comparing once-weekly insulin icodec with daily basal insulin analogs using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to ensure transparent reporting of systematic reviews. A search was performed in the following databases: PubMed, Google Scholar, Embase, and ClinicalTrials.gov, focusing on efficacy and safety outcomes. Insulin icodec has demonstrated effective glycemic management and a safety profile comparable to daily basal insulins. Its extended half-life and steady-state glucose-lowering effect have the potential to reduce the burden of daily injections and improve patient adherence. The introduction of once-weekly insulin icodec represents an advancement in diabetes care. For front-line clinicians, this innovation aligns with the need for more straightforward medication regimens. Coupled with continuous glucose monitoring systems, it enables a more personalized and efficient approach to diabetes management, with the potential to improve patient satisfaction and clinical outcomes. This underscores the impact of integrating such advancements into practice, highlighting the role of nurse practitioners and physician assistants in adopting these innovations to optimize patient care.

Advances of Triboelectric and Piezoelectric Nanogenerators toward Continuous Monitoring and Multimodal Applications in the New Era

Abstract Benefiting from the widespread potential applications in the era of Internet of Thing (IoT) and metaverse, triboelectric and piezoelectric nanogenerators have attracted considerably increasing attention. Their outstanding characteristics, such as self-powered ability, high output performance, integration compatibility, cost effectiveness, simple configurations and versatile operation modes, could effectively expand the lifetime of vastly distributed wearable, implantable and environmental devices, eventually achieving self-sustainable, maintenance-free and reliable systems. However, current triboelectric/piezoelectric based active (i.e., self-powered) sensors still encounter serious bottlenecks in continuous monitoring and multimodal applications due to their intrinsic limitations of monomodal kinetic response and discontinuous transient output. This work systematically summarizes and evaluates the recent research endeavours to addressing the above challenges, with detailed discussions on the challenge origins, designing strategies, device performance and corresponding diverse applications. Finally, conclusions and outlook regarding the research gap in self-powered continuous multimodal monitoring systems are provided, proposing the necessity of future research development in this field.

Evaluation of Electronic Hand Hygiene Monitoring Systems in UAE Hospitals

Abstract Introduction/Objective Introduction: Hand hygiene (HH) compliance is pivotal in preventing infections in healthcare settings, necessitating robust monitoring systems. This abstract outlines the assessment of three electronic HH monitoring systems across four United Arab Emirates (UAE) hospitals. Methods/Case Report Methods: A prospective observational study was conducted in four UAE governmental hospitals, evaluating three HH monitoring systems. Sensors were strategically positioned on alcohol-based dispensers, healthcare workers’ ID badges, and patients’ beds in these systems. Additionally, a comparative analysis was performed between direct observation data and automated system data. Results (if a Case Study enter NA) Results: System 1 was evaluated at Hospitals A and B, revealing HH compliance rates of 65% and 46%, respectively, contrasting with direct observation rates of 88% and 80%. Hospital C assessed System 2, reporting a compliance rate of 70%, compared to direct observation’s 94%. System 3, evaluated at Hospital D, showed a compliance rate of 39%, in contrast to the 88% observed directly. Conclusion Conclusion: While electronic monitoring systems offer continuous surveillance and data analysis benefits, they also present limitations. These include challenges in accurately distinguishing HH indications and opportunities, identifying all required HH moments, and capturing technique nuances. Understanding these strengths and limitations is vital for healthcare institutions aiming to implement effective HH monitoring strategies to enhance patient safety and reduce healthcare-associated infections (HAI

MONITORING ACTIVITY IN THE RISK-ORIENTED INTERNAL AUDIT: A MODEL PROPOSAL

The purpose of this article is to make a proposal for process development for monitoring activities in a way that includes experiences gained from audits conducted in order to achieve the goal of risk-oriented internal audit function in institutions and to contribute a guiding study on the subject to the literature. In this study, first of all, the risk-oriented internal audit approach and monitoring process are explained with the document analysis method, and then a model for the monitoring process is presented and conclusions and recommendations are made. By using the document analysis method and the features of the risk-oriented internal audit approach, an original application process was compiled to test the risk and control processes for the internal audit activities required for an effectively functioning monitoring system. It is suggested that risk- oriented internal audit will be an important tool in increasing the added value for institutions with the monitoring system designed for risk- oriented internal audit activity, which examines the processes and offers suggestions for areas of development in order to ensure that all financial and non-financial resources are used effectively, economically and efficiently and to add value to the work of the organization.

Assessment of the long-term hydrological performance of a green roof system in stormwater control

Green roof systems (GRs) are effective tools for urban stormwater management. However, there is limited documentation of the long-term hydrological performance of GRs to support decision-making. This study evaluated long-term field monitoring records (7 years) from a 12-year-old GR, situated in a Moist Subtropical Mid-Latitude Climate, to analyze seasonality in and evolution of hydrological performance. The monitoring system was built within a pan lysimeter buried under substrate layers matching the surrounding GR. The monitoring results highlight the efficacy of this GR in long-term stormwater runoff control. The GR can retain 87% of the annual precipitation and return 54% of the precipitation to the atmosphere through evapotranspiration (ET) and sustain long-term event-based mean runoff volume reductions, peak flow reductions, and flow delays of 82%, 93%, and 4.3 h, respectively. The initial moisture content prior to events was highly correlated with hydrological performance, with a seasonal mean Spearman correlation coefficient of 0.47, suggesting the potential of enhancing ET from the GR to improve performance. Substrate water holding capacity increased over time, but no obvious changes in water retention performance were observed. These monitoring results from the aging GR demonstrate the effectiveness of GR systems for long-term stormwater management.

Model Predictive Control and Service Life Monitoring for Molten Salt Solar Power Towers

A two-component system for control and monitoring of solar power towers with molten salt receivers is proposed. The control component consists of a model predictive control application (MPC) with a flexible objective function and on-line tunable weights, which runs on a Industrial PC and uses a reduced order dynamic model of the receiver’s thermal and flow dynamics. The second component consists of a service-life monitoring unit, which estimates the service-life consumption of the absorber tubes depending on the current mode of operation based on thermal stresses and creep fatigue in the high temperature regime. The calculation of stresses is done based on a detailed finite element study, in which a digital twin of the receiver was developed. By parallelising the model solver, the estimation of service-life consumption became capable of real-time operation. The system has been implemented at a test facility in Jülich, Germany, and awaits field experiments. In this paper, the modeling and architecture are presented along simulation results, which were validated on a hardware-in-the-loop test bench. The MPC showed good disturbance rejection while respecting process variable constraints during the simulation studies.

Development and Validation of a Portable EIT System for Real-Time Respiratory Monitoring.

This work contributes to the improvement of novel medical technologies for the prevention and treatment of diseases. Electrical impedance tomography (EIT) has gained attention as a valuable tool for non-invasive monitoring providing real-time insights. The purpose of this work is to develop and validate a novel portable EIT system with a small form factor for respiratory monitoring. The device uses a 16-electrode architecture with adjacent stimulation and measurement patterns, an integrated circuit current source and a single high-speed ADC operating with multiplexers to stimulate and measure across all electrodes. Tests were conducted on 25 healthy subjects who performed a pulmonary function test with a flowmeter while using the EIT device. The results showed a good performance of the device, which was able to recognize all respirations correctly, and from the EIT signals and images, correlations of 96.7% were obtained for instantaneous respiratory rate and 96.1% for tidal volume prediction. These results validate the preliminary technical feasibility of the EIT system and demonstrates its potential as a reliable tool for non-invasive respiratory assessment. The significance of this work lies in its potential to democratize advanced respiratory monitoring technologies, making them accessible to a wider population, including those in remote or underserved areas.

Gender-responsive monitoring and evaluation for health systems.

Gender-responsive monitoring and evaluation (M&E) for health and health systems interventions and programs is vital to improve health, health systems, and gender equality outcomes. It can be used to identify and address gender disparities in program participation, outcomes and benefits, as well as ensure that programs are designed and implemented in a way that is inclusive and accessible for all. While gender-responsive M&E is most effective when interventions and programs intentionally integrate a gender lens, it is relevant for all health systems programs and interventions. Within the literature, gender-responsive M&E is defined in different and diverse ways, making it difficult to operationalize. This is compounded by the complexity and multi-faceted nature of gender. Within this methodological musing, we present our evolving approach to gender-responsive M&E which we are operationalizing within the Monitoring for Gender and Equity project. We define gender-responsive M&E as intentionally integrating the needs, rights, preferences of, and power relations among, women and girls, men and boys, and gender minority individuals, as well as across social, political, economic, and health systems in M&E processes. This is done through the integration of different types of gender data and indicators, including: sex- or gender-specific, sex- or gender-disaggregated, sex- or gender-specific/disaggregated which incorporate needs, rights and preferences, and gender power relations and systems indicators. Examples of each of these are included within the paper. Active approaches can also enhance the gender-responsiveness of any M&E activities, including incorporating an intersectional lens and tailoring the types of data and indicators included and processes used to the specific context. Incorporating gender into the programmatic cycle, including M&E, can lead to more fit-for-purpose, effective and equitable programs and interventions. The framework presented in this paper provides an outline of how to do this, enabling the uptake of gender-responsive M&E.

Multiple covalent modification enables nylon fiber biosensor with robust scrub-resistant and signal-capture ability for multiscenario health monitoring and security warning

Nylon fibers have great potentials in smart textiles due to excellent wear resistance, resilience, and chemical stability, whereas poor combination between fibers and conductive materials causes discontinuous signal capture. In this work, nylon fibers/di-aldehyde cellulose nanocrystals/polypyrrole (NFACP) biosensors with robust scrub-resistant and signal-capture ability were fabricated by interfacial multiple covalent reactions. The best NFACP0.2 biosensor exhibited high conductivity (354 S/m), robust mechanical strength and stretching-releasing dynamic durability. Especially, its textile sensors still possessed high sensitivity and excellent sensing performance after repeated washing and friction. Moreover, NFACP0.2 biosensor was designed into various multifunctional health monitoring and security warning systems for “stress reducing exercise” enthusiasts, high-altitude activities, and deep-sea exploration, demonstating great potentials of conductive nylon fiber biosensor in flexible electronics.

Analyzing Antibiotic Resistance: A Comparative Study of E. coli Isolates from Wastewater and Clinical UTI Samples at a University Community in Grenada, West Indies

Abstract Introduction/Objective The rise of antibiotic resistance poses a significant global health threat, needing a robust monitoring system to track its emergence and spread. However, most developing countries lack the infrastructure to monitor such resistance effectively. Wastewater-based epidemiology (WBE) has been proven effective in surveilling various outbreaks and offers a promising method for monitoring antibiotic resistance at the community level. This study aims to assess and compare the antibiotic resistance of Escherichia coli isolated from raw wastewater with those isolated from clinical urinary tract infection (UTI) samples from the same community. Methods/Case Report Conducted from January 2022 to October 2023, this study analyzed antibiogram data from eighty-six Escherichia coli isolates sourced from wastewater and thirty-four isolates derived from clinical urinary tract infections (UTIs). The clinical samples were obtained and processed at the Clinical Microbiology Laboratory of a university campus. The isolates were evaluated against the eleven most prescribed antibiotics for UTIs in the University Clinic including: Amoxicillin/Clavulanic Acid, Ampicillin, Cefazolin, Cefoxitin, Ceftriaxone, Cefuroxime, Ciprofloxacin, Gentamicin, Nalidixic Acid, Nitrofurantoin, and Sulfamethoxazole-trimethoprim. Results (if a Case Study enter NA) Explaining the observed trends in both wastewater and clinical samples from the perspective of AWaRE-WHO classification (Access, Watch, Reserve), we see similar trends of resistance in both data sets. A) The antibiotics used in the Access category (Empiric first or second choice for treatment of most common infections) demonstrates similar resistance patterns for both wastewater and clinical isolates except for Nitrofurantoin. B) The antibiotics used in the Watch category (Antibiotics that have higher toxicity issues and higher potential to negatively impact AMR) resistance pattern shows negligible risk in the university community evaluated. Conclusion It is recommended that the university clinic prescribe Nitrofurantoin as the last antibiotic from the Access list to the university community to treat UTI patients. Furthermore, WBE is a useful tool to determine patterns of resistance under the studied settings.

PVDF/NiFe2O4 multiferroic composite membranes for dual mechanical and magnetic energy harvesting devices

A multiferroic composite membrane, combining PVDF piezoelectric polymer and nickel ferrite (NFO) nanofibers, was successfully fabricated and studied as an active material layer in multifunctional devices designed to harvest both mechanical and magnetic energy. Optimization of the manufacturing process ensured an even distribution of NFO fibers within the PVDF matrix, enhancing the crystallization of PVDF in the electroactive β phase. The resulting PVDF/NFO multiferroic films exhibited both piezoelectric and magnetic properties, along with a pronounced magnetoelectric (ME) effect. In a structure comprising Al/PVDF-NFO/PDMS/Al, the device operated as a piezoelectric generator (PEG) under a pressing force of 0.5 MPa, achieving a maximum output power density of 14.7 μW cm−12 with a peak-to-peak voltage of 12.2 V. When subjected to an AC magnetic field of 20 Oe at 50 Hz, the device functioned as a magneto-mechano-electrical (MME) generator, producing a sinusoidal waveform voltage of 486 mV. The cost-effective and easily integrable PVDF/NFO composite membrane presents promising opportunities for developing flexible, self-powered smart sensors for human health monitoring systems and implantable biomedical devices.

Sleep State Monitoring System Based on Flexible Pressure Sensor Arrays for Evaluating Sleep Quality

Sleep State Monitoring System Based on Flexible Pressure Sensor Arrays for Evaluating Sleep Quality

Advancements in Flexible and Stretchable Electronics for Resistive Hydrogen Sensing: A Comprehensive Review.

Flexible and stretchable electronics have emerged as a groundbreaking technology with wide-ranging applications, including wearable devices, medical implants, and environmental monitoring systems. Among their numerous applications, hydrogen sensing represents a critical area of research, particularly due to hydrogen's role as a clean energy carrier and its explosive nature at high concentrations. This review paper provides a comprehensive overview of the recent advancements in flexible and stretchable electronics tailored for resistive hydrogen sensing applications. It begins by introducing the fundamental principles underlying the operation of flexible and stretchable resistive sensors, highlighting the innovative materials and fabrication techniques that enable their exceptional mechanical resilience and adaptability. Following this, the paper delves into the specific strategies employed in the integration of these resistive sensors into hydrogen detection systems, discussing the merits and limitations of various sensor designs, from nanoscale transducers to fully integrated wearable devices. Special attention is paid to the sensitivity, selectivity, and operational stability of these resistive sensors, as well as their performance under real-world conditions. Furthermore, the review explores the challenges and opportunities in this rapidly evolving field, including the scalability of manufacturing processes, the integration of resistive sensor networks, and the development of standards for safety and performance. Finally, the review concludes with a forward-looking perspective on the potential impacts of flexible and stretchable resistive electronics in hydrogen energy systems and safety applications, underscoring the need for interdisciplinary collaboration to realize the full potential of this innovative technology.

Electrode Droplet Microarray (eDMA): An Impedance Platform for Label-Free Parallel Monitoring of Cellular Drug Response in Nanoliter Droplets.

Label-free real-time monitoring of cellular behavior using impedance spectroscopy is important for drug development and toxicological assessments. Parallelization and miniaturization of such experiments are essential for increasing throughput and enabling experiments with low abundant stem or primary cells. Traditional methods are not miniaturized and require large volumes of reagents and number of cells, limiting their suitability for cost effective high-throughput screening of cells of limited availability. Here, the fabrication, optimization, and application of a bioelectrical signaling monitoring system - electrode droplet microarray (eDMA) are demonstrated. The eDMA platform is based on preparation of a hydrophilic-superhydrophobic patterns covering an array of individually addressable microelectrodes, which confines cells to individual microelectrodes, allowing for parallel, real-time, and label-free detection of cellular responses to drug treatments in nanoliter droplets. The real-time monitoring of cytotoxic effect of an anticancer drug is demonstrated over 48 h with real-time calculation of the half-inhibitory concentration (IC50) values through impedance spectroscopy. This demonstrates eDMA's ability to dynamically assess responses to various drugs in parallel at any given time point, which is crucial for functional personalized oncology. Specifically, the platform can be employed for monitoring anticancer drug toxicity using limited patient samples, where the miniaturization provided by eDMA is essential.

HOT Watch: IoT-Based Wearable Health Monitoring System

HOT Watch: IoT-Based Wearable Health Monitoring System

Hybrid WARIMA-WANN algorithm for data prediction in bridge health monitoring system

Application of data-driven algorithm to model and predict the future trend of the structural health monitoring (SHM) data serves as important references for the future safety evaluation and decision-making of the bridges. An innovative hybrid WARIMA-WANN algorithm combining the Wavelet model, the autoregressive moving average (ARIMA) model and the artificial neural network (ANN) model is proposed to predict massive monitoring data in the bridge SHM system. The WPT serves as the decomposition function for the linear and nonlinear components of the hybrid model. The separated linear/nonlinear components are individually predicted with the ARIMA/ANN models and then combined to obtain the prediction values of the future data. The hybrid method is applied to predict the deflection values of a single-tower cable-stayed bridge. Multiple parameters in individual models are discussed and selected to increase the credibility of the prediction results for the example bridge. The results show that the proposed hybrid algorithm successfully captures the linear and nonlinear coupling relationships inside the SHM data from the frequency perspective of view and shows the best prediction performance when compared with the other four existing models. Different prediction steps obviously influence prediction performance due to the periodic nature of the bridge SHM data.