Innovative Monitoring Research Articles

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

This article delineates the design and deployment of an innovative real-time water quality monitoring system tailored for rural regions, focusing on monitoring the water resource quality parameters. We propose a solar-powered, waterproof, portable, and Internet of Things (IoT)-enabled solution that leverages Long Range Wide Area Network (LoRaWAN) technology. Central to this system is a sophisticated LoRa node outfitted with an array of sensors for capturing key water parameters, such as pH, total dissolved solids, turbidity and temperature. A conjunction of an Arduino microcontroller-based board and a LoRa shield facilitates real-time data capture and transmission to a LoRaWAN gateway. The acquired data is transmitted to The Things Network server, which is seamlessly integrated with a ThingSpeak web-based IoT server and ThingView mobile applications. We incorporate a solar cell with a solar shield to ensure sustainable energy provision for powering the entire system through a rechargeable battery. This allows users to access vital water quality information online simultaneously and continuously in real-time. As a testament to its robustness, the system was empirically tested at Gambang Lake to demonstrate its effectiveness, functionality, buoyancy, and waterproof capabilities. We further validated the results by comparing them with laboratory sample analysis findings. Experimental evaluations confirmed the system's reliability, as evidenced by the strong agreement between the water conditions measured using our solution and those obtained from laboratory instruments. Moreover, our system efficiently and remotely updated data across multiple IoT platforms using the LoRa radio interface over the LoRaWAN gateway.

Editorial Presentation - Perspectives on innovation governance: challenges and dilemmas

Editorial Presentation - Perspectives on innovation governance: challenges and dilemmas

Development of a Direct Non-Puncture Device for Measuring Portal Venous Pressure during Liver Transplantation-A Swine Model.

Portal hypertension-related complications pose a significant risk for liver failure post-transplantation. Thus, accurate monitoring of intraoperative portal venous pressure (PVP) is crucial. However, current PVP monitoring techniques requiring direct percutaneous puncture carry the risk of graft damage. In this study, we present an innovative non-puncture PVP monitoring device (PVPMD) using a 3D-printed prototype. PVPMD design is inspired by the sphygmomanometer principle, and strategically encompasses the portal vein and enables precise PVP measurement through blood flow ultrasonography after temporary occlusion. By a series of mini-pig experiments, the prototype PVPMD demonstrated a strong correlation with invasive catheter measurements in the main trunk of the portal vein (rs = 0.923, p = 0.000). There was a significant repeatability and reproducibility between the prototype PVPMD- and invasive catheter-measured PVP. This indicates that the PVPMD holds immense potential for direct application in liver transplantation and surgery. Moreover, it has the potential to replace catheter-based central venous pressure (CVP) measurements, thereby mitigating catheter-related complications during many surgeries. In conclusion, our innovative device represents a significant advancement in PVP monitoring during liver transplantation, with comprehensive validation from principle exploration to successful animal experiments. We anticipate that this groundbreaking PVPMD will attract the attention of researchers and clinicians, propelling the noninvasive measurement of PVP or other venous/arterial pressures into a new era of clinical practice.

IOT-Based Human Vital Sign Monitoring Tool Using Telegram Notifications (BPM and Respiration Rate Parameters)

Vital signs play a crucial role in monitoring the progress of adult or pediatric patients during hospitalization, as they enable prompt detection of delayed recovery or adverse events. Vital signs are measured to obtain fundamental indicators of the patient's health status. The measurement of vital signs, including blood pressure, temperature, pulse, and respiratory rate, is the most common intervention in hospital medicine. Advanced monitoring systems combine clinical and technological aspects to deliver innovative healthcare outcomes. Remote patient monitoring systems are increasingly becoming the cornerstone of healthcare delivery, replacing traditional manual recording with computer and smartphone-based electronic recording as a versatile and innovative health monitoring system. This study aims to design a Vital Sign Monitoring Parameter BPM and RR tool with Notifications via the IoT-Based Telegram application. The tool enables the monitoring of vital signs, particularly BPM and RR, regardless of the patient's location and at any given time. This allows doctors, health workers, and patients to stay informed about their health condition. Real-time display of vital sign data is available through the TFT LCD screen, and the data from the screen can be accessed via Telegram. The Telegram application will send notifications in the event of abnormal patient conditions. MAX30100, a digital sensor for detecting breathing rate and heart rate, is utilized in this research. Furthermore, the data obtained shows errors that are within the allowable limits for each parameter. The difference between the heart rate readings and the respiratory rate values on the device and the patient monitor is 1.14% for heart rate and 0.84% for respiratory rate. This study indicates that it is time to monitor vital signs that can be seen remotely and have a system that is an inexpensive and easy-to-operate device for health workers without interfering with activities of daily living.

New acoustic monitoring system quantifying aspiration risk during monitored anaesthesia care

Respiratory monitoring is crucial during monitored anaesthesia care (MAC) to ensure patient safety. Patients undergoing procedures like gastrointestinal endoscopy and dental interventions under MAC have a heightened risk of aspiration. Despite the risks, no current system or device can evaluate aspiration risk. This study presents a novel acoustic monitoring system designed to detect fluid retention in the upper airway during MAC. We conducted a prospective observational study with 60 participants undergoing dental treatment under MAC. We utilized a prototype acoustic monitoring system to assess fluid retention in the upper airway by analysing inspiratory sounds. Water was introduced intraorally in participants to simulate fluid retention; artificial intelligence (AI) analysed respiratory sounds pre and post-injection. We also compared respiratory sounds pre-treatment and during coughing events. Coughing was observed in 14 patients during MAC, and 31 instances of apnoea were detected by capnography. However, 27 of these cases had breath sounds. Notably, with intraoral water injection, the Stridor Quantitative Value (STQV) significantly increased; furthermore, the STQV was substantially higher immediately post-coughing in patients who coughed during MAC. In summary, the innovative acoustic monitoring system using AI provides accurate evaluations of fluid retention in the upper airway, offering potential to mitigate aspiration risks during MAC.Clinical trial number: jRCTs 062220054.

AIR QUALITY TESTING- A DESIGN THINKING APPROACH

This paper introduces a groundbreaking and holistic approach to revolutionize air quality testing by integrating design thinking principles, sensor technology, and the Internet of Things (IoT). Focused on addressing the critical challenge of air pollution, this research advocates for the development of user-centric, cost-effective, and scalable solutions for air quality monitoring. The design thinking process is comprehensively examined, covering crucial stages such as problem identification, ideation, prototype development, and user feedback. A central theme of this approach is the active involvement of stakeholders and cross- disciplinary collaboration, which ensures the creation of innovative and practical solutions. Real-world case studies underscore the tangible implications of this approach, emphasizing successful outcomes, including the development of affordable air quality monitoring devices and systems. Sensors and IoT technologies play pivotal roles in enabling real-time data collection, remote monitoring, and seamless integration with urban management systems, thereby contributing to enhanced environmental sustainability and public health. Additionally, this paper discusses the potential for adaptive air quality management strategies, wherein the data generated by these innovative monitoring systems can inform immediate response measures and long-term policy decisions. The adaptability and scalability of the proposed approach make it a valuable tool for addressing not only the challenges of today but also those of the future, such as climate change and evolving pollution sources.

Fish communities and diversity in river ecosystems on the Qinghai-Tibet Plateau revealed by environmental DNA (eDNA) method

The Qinghai-Tibet Plateau (QTP) serves as the source of many large rivers in Asia, and also serves as a natural habitat for rare wildlife and a genetic reservoir for plateau species. The exceptional ecological environment of the QTP has fostered diverse fish species. However, due to climate change and human activities, fish species in this area are facing serious threats, while large spatial scale investigation is a big challenge. Environmental DNA (eDNA) metabarcoding, as an innovative monitoring technology, offers a simple, fast, and environmentally friendly approach to explore fish communities on the QTP. This study utilized eDNA technology to assess the diversity, composition, and potential environmental influences of riverine fish communities across the QTP. A total of 90 fish species were identified belonging to 79 genera, 34 families, and 18 orders. The dominant species were Schizothorax chongi, Gymnocypris przewalskii, Carassius carassius, and Botia striata. PCoA and ADONIS indicated significant differences among fish community in different river basins of the Qinghai-Tibet Plateau. Canonical correspondence analysis (CCA) revealed that the ratio of SRP to TP, average NDVI, average elevation, and proportion of bare land significantly influenced the distribution of fish community. This study provided a novel monitoring method and insight for fish communities and implied potential species invasion on the QTP.

Analyzing electric vehicle battery health performance using supervised machine learning

Lithium-ion batteries having high energy and power densities, fast depleting cost, and multifaceted technological improvement lead to the first choice for electric transportation systems. A noble supervised K nearest neighbors (KNN), support vector regressor (SVR), decision tree (DT), and random forest (RF) regressors machine learning algorithm are developed with different accuracy and usefulness for the state of health estimation from direct measurable indices of voltage, current, and temperature without inherited electrochemical characteristics like voltage hysteresis, aging, degradation level, operational, and environmental effect. Based on the battery dataset from Sandia National Laboratories, the proposed technique is validated. The comparison characteristics are provided with the help of mean absolute percent error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) techniques. The MAPE error at 15 °C for different regression algorithms SVR, DT, KNN, and RF are 2.791E-02, 1.607E-03, 9.957E-04, and 4.323E-03 respectively. The MAE error at 25 °C for different regression algorithms SVR, DT, KNN, and RF are 5.593E-02, 2.379E-03, 2.429E-03, and 5.073E-03 respectively. The RMSE error at 35 °C for different regression algorithms SVR, DT, KNN, and RF are 5.062E-02, 6.862E-03, 6.333E-03, and 1.275E-02 respectively. The MSE error percentages for the testing sample at 35 °C for different regression algorithms are 0.37 %, 0.01 %, 0.01 %, and 0.02 % respectively. The results indicate that KNN and DT exhibit higher precision when applied to lithium-ion batteries for electric vehicles. Collaborating with hardware manufacturers to exchange data has the potential to foster the creation of innovative battery health monitoring systems.

Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework

The digital twin concept has found widespread application across diverse industries. Herein, we present a comprehensive conceptual framework for the cognitive soil digital twin, which embodies the intricate physical reality of the soil ecosystem, aiding in its holistic monitoring and comprehension. The digital twin can seamlessly integrate a multitude of sensor data sources, including field Internet of Things sensors, remote sensing data, field measurements, digital cartography, surveys, and other Earth observation datasets. By virtue of its duality, this digital counterpart facilitates data organisation and rigorous analytical exploration, unravelling the intricacies of physical, chemical, and biological soil constituents while discerning their intricate interrelationships and their impact on ecosystem services. Its potential extends beyond mere data representation, acting as a versatile tool for scenario analysis and enabling the visualisation of diverse environmental impacts, including the effects of climate change and transformations in land use or management practices. Beyond academic circles, the digital twin’s utility extends to a broad spectrum of stakeholders across the entire quadruple helix, encompassing farmers and agronomists, soil researchers, the agro-industry, and policy-makers. By fostering collaboration among these stakeholders, the digital twin catalyses informed decision-making, underpinned by data-driven insights. Moreover, it acts as a testbed for the development of innovative sensors and monitoring frameworks, in addition to providing a platform that can educate users and the broader public using immersive and innovative visualisation tools, such as augmented reality. This innovative framework underscores the imperative of a holistic approach to soil ecosystem monitoring and management, propelling the soil science discipline into an era of unprecedented data integration and predictive modelling, by harnessing the effects of climate change towards the development of efficient decision-making.

Installation and Use of a Pavement Monitoring System Based on Fibre Bragg Grating Optical Sensors

The evolution of technological tools, namely affordable sensors for data collection, and the growing concerns about maintaining roads in adequate conditions have promoted the development of continuous pavement monitoring systems. This paper presents the installation and use of an innovative pavement monitoring system, which was developed to measure the effects of vehicle loads and temperature on the performance of a pavement structure. The sensors used are based on fibre Bragg grating optical technology, collecting data about the strains imposed in the pavement and the temperature at which those measurements are made. The site selection for the system’s installation and the essential installation details to ensure successful data collection are addressed. A calibration procedure was implemented by performing falling weight deflectometer tests and passing preweighed heavy vehicles over the sensors. In addition to validating the system installation, the results obtained in the calibration confirmed the importance of adequately choosing the distance between sensors. Differences of 50 mm in the position of the load may cause differences of about 20% to 25% in the resulting strains. These results confirmed the importance of increasing the sensor concentration in wheel paths. Furthermore, for loads between 25 kN and 65 kN, raising the temperature by 8 °C caused an increase of about 20% in the horizontal tensile strains measured in the pavement. In summary, it was possible to conclude that this innovative system is capable of capturing the effects of temperature and vehicle speed on the response of the pavement, which may be considered an advantage of this type of monitoring system when compared to those that are only used to determine the loads applied to the pavement or to characterise the type of vehicle.

An approach of on-board multistage health monitoring for heavy haul railway bridge using bogie acceleration

Recently on-board damage detection becomes a promising strategy in the field of bridge structural health monitoring. Due to the randomness of track irregularity and the weak vehicle vibration feedback from the bridge damages, it is difficult to effectively extract the damage characteristics from vehicle responses. This paper proposes an innovative multistage health monitoring approach with bogie accelerations for heavy haul railway bridge, which considers the influence of track irregularity and realizes the localization and quantification of bridge damage. Firstly, the bogie accelerations are preprocessed by the presented time-domain correlation analysis denoising method (TCADM) to suppress the unfavorable interference of track irregularity. Secondly, the Empirical Mode Decomposition (EMD) is exploited to decouple the effect of vibration coupling among bridge spans on bogie accelerations, and a confidence classification threshold related to Mahalanobis distance is defined by the Gaussian inverse cumulative distribution function to detect damage. Thirdly, the sliding window strategy is utilized to localize damaged region, realizing a trade-off between time cost and localization accuracy by the custom window length. Fourthly, the classification threshold and the k-means clustering analysis are combined to quantify the damage severity. The effectiveness and robustness of the proposed framework are appraised using blind tests, where one monitor operator simulates various damaged conditions by combining a 2D train-track-bridge coupling model with the Monte Carlo Sampling Method, and the other estimates the bridge health state using the provided bogie dataset without any prior knowledge. The results indicate that the proposed framework performs well in detecting, localizing and quantifying damage.

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

Healthy coasts have a high ecological service value. However, many coastal areas are faced with oil spill risks. The Synthetic Aperture Radar (SAR) remote sensing technique has become an effective tool for monitoring the oil spill risk in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative oil spill monitoring framework was established based on Polarimetric SAR (PolSAR) images and deep learning theory. Specifically, a DeepLabv3+-based semantic segmentation model was trained using 35 Sentinel-1 satellite images of oil films on the sea surface from maritime sectors in different regions all over the world, which not only considered the information from the PolSAR images but also meteorological conditions; then, the well-trained framework was deployed to identify the oil films in the Sentinel-1 images of Jiaozhou Bay from 2017 to 2019. The experimental results show that the detection accuracies of the proposed oil spill detection model were higher than 0.95. It was found that the oil films in Jiaozhou Bay were mainly concentrated in the vicinity of the waterways and coastal port terminals, that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, and that more than 80 percent of the oil spill events occurred at night, mainly coming from the illegal discharge of waste oil from ships. These data indicate that, in the future, the PolSAR technique will play a more important role in oil spill monitoring for Jiaozhou Bay due to its capability to capture images at night.

IOT BASED BATTERY MONITORING SYSTEM FOR ELECTRIC VEHICLE

The rapid adoption of electric vehicles (EVs) represents a critical step towards reducing greenhouse gas emissions and achieving sustainable transportation solutions. One of the key components in an EV is its battery, which plays a pivotal role in determining the vehicle's performance, range, and overall efficiency. Ensuring the health and longevity of EV batteries is essential for promoting EV adoption and optimizing their operational capabilities. This project presents an IoT-based Battery Monitoring System (BMS) designed to address the challenges associated with monitoring and managing the batteries in electric vehicles. The system leverages the power of Internet of Things (IoT) technology to continuously gather real-time data from the EV's battery pack. The IoT-based BMS consists of various components, including battery sensors, microcontrollers, wireless communication modules, and cloud-based data analytics. Battery sensors collect data on parameters such as voltage, current, temperature, and state of charge (SoC) from individual cells within the battery pack. In conclusion, this project presents an innovative IoT-based Battery Monitoring System that has the potential to revolutionize the way we manage and maintain electric vehicle batteries. Key Words: Battery pack, Electric vehicle Voltage, State of charge, Vehicle performance.

STM32 BASED WATER QUALITY MONITORING SYSTEM

Water is a precious and essential resource for all life on Earth. Ensuring the quality of water is vital for human health, environmental sustainability, and various industrial applications. This project introduces a novel STM32-based Water Quality Monitoring System designed to address the need for continuous and real-time monitoring of water parameters in various settings like pH and electrical conductivity sensors. The STM32-based Water Quality Monitoring System offers several advantages, including early detection of water pollution, enabling prompt action to mitigate environmental damage and health risks. It provides historical data for trend analysis, aiding in the identification of long-term water quality patterns and trends. Additionally, it reduces the need for manual sampling and laboratory testing, thus lowering operational costs for water treatment facilities. Furthermore, the system is highly adaptable and can be integrated into a variety of water management scenarios, including municipal water supplies, industrial processes, aquaculture, and environmental monitoring. It promotes sustainable water resource management by ensuring that water quality remains within safe and regulated standards In summary, this project presents an innovative STM32-Based Water Quality Monitoring System that leverages advanced microcontroller technology to address the critical issue of water quality monitoring. Key Words: water quality, stm32, pH sensor, conductivity sensor, aquaculture

Construction Progress Monitoring through the Integration of 4D BIM and SLAM-Based Mapping Devices

In the architecture, engineering and construction industry, site management during construction is a key phase. Scheduling activities and monitoring their progress allow any deviations from the schedule to be identified so that timely action can be taken. Until now, the monitoring phase has mainly been characterised by inspections in which the construction site manager manually collects data and produces a summary report. This proves to be a time-consuming process and is prone to errors. The authors propose an innovative construction progress monitoring method that combines BIM-based construction scheduling (4D BIM) with periodic geometric surveying using an indoor mobile mapping system (iMMS). Ten surveys were carried out on a real case study, producing point clouds to be compared with the 4D BIM, thereby comparing the as-built with the as-planned. The comparison was carried out using Sitemotion exploiting a custom class, the work breakdown structure (WBS), added to the BIM to associate each element with its scheduled construction date. The results show how the proposed method can effectively support the evaluation of construction progress, allowing the monitoring to be performed digitally and linked to the BIM. The paper details the proposed methodology, highlighting the problems encountered and suggesting adjustments for future implementation.

Temporal trends in microplastic accumulation in placentas from pregnancies in Hawaiʻi

Microplastics are created for commercial use, are shed from textiles, or result from the breakdown of larger plastic items. Recent reports have shown that microplastics accumulate in human tissues and may have adverse health consequences. Currently, there are no standardized environmental monitoring systems to track microplastic accumulation within human tissues. Using Raman spectroscopy, we investigated the temporal exposures to plastic pollution in Hawaiʻi and noted a significant increase in the accumulation of microplastics in discarded placentas over the past 15 years, with changes in the size and chemical composition of the polymers. These findings provide a rare insight into the vulnerability and sensitivity of Pacific Island residents to plastic pollution and illustrate how discarded human tissues can be used as an innovative environmental plastic pollution monitoring system.

A Non-Intrusive Monitoring System on Train Pantographs for the Maintenance of Overhead Contact Lines.

The condition monitoring of an overhead contact line (OCL) is investigated by developing an innovative monitoring system for a pantograph on an electrical multiple unit of a regional line. Kinematic and dynamic modelling of the pantograph is conducted to support the designed monitoring system. The modelling is proved through rigorous test-rig experiments, while the proposed methodology is then validated through extensive field tests. The field tests serve a dual purpose: First, to validate the monitoring system using benchmark measurements of the tCat® trolley, and second, to assess the reproducibility of measurements in a realistic case. This paper presents the OCL monitoring system developed in the framework of the H2020 project SIA. The accuracy of our results is not far from that of other commercial systems, with just 12 mm of absolute error in the height measurement. Therefore, they provide reliable information about trends in various key performance indicators (KPIs) that facilitates the early detection of failures and the diagnosis of anomalies. The results highlight the importance of model calibration and validation in enabling novel health monitoring capabilities for the pantograph. By continuously monitoring the parameters and tracking their degradation trends, our approach allows for optimized scheduling of maintenance tasks for the OCL.

Pressure Monitoring in Medium-Voltage Vacuum Interrupters

Innovation in the economy is closely tied to energy development, encompassing the exploration of new energy sources, increased energy production efficiency, and the integration of diverse energy sources for safe and effective supply to industries and households. Outdated energy infrastructure disrupts electricity continuity and hampers economic innovation. Power interruptions lead to higher SAIDI and SAIFI reliability indices. Quality and reliability requirements have sparked interest in enclosed energy devices. Vacuum technology has been pivotal in electrical switchgear insulation and arc-quenching for over four decades. However, the lack of real-time pressure monitoring systems for vacuum equipment, especially enclosed disconnectors, limits their use as isolation connectors. Potential insulation failure poses risks to power line maintenance teams and can lead to unplanned shutdowns, further compromising energy supply quality. This article explores an innovative pressure monitoring system for vacuum interrupters, utilizing fiber optic Bragg grids as a measuring sensor, enabling pressure measurement within the vacuum chamber ranging from 2 × 101 Pa to 5 × 105 Pa.

Mixed-methods study protocol to identify expectations of people with type 1 diabetes and their caregivers about voice-based digital health solutions to support the management of diabetes distress: the PsyVoice study

IntroductionType 1 diabetes (T1D) requires continuous management to obtain a good metabolic control and prevent acute complications. This often affects psychological well-being. People with T1D frequently report diabetes distress (DD)....

Commercialization of Handicraft Products from Water Hyacinth in Aren Handicrafts

The superior product of Aren Handicraft is a product made from water hyacinth which has not been commercialized in the market and is not well known by the public, therefore this product needs to carry out product commercialization with innovation and promotion. The objectives of this activity are: online promotional assistance through social media and e-commerce and product innovation assistance, through the creation of unique and attractive new products. The approach methods offered to solve partner problems consist of surveys, interviews, online promotion assistance, product innovation assistance, monitoring and evaluation. Based on the results of the PKM implementation, several informants said that the product was quite interesting because there were not many businesses that could make handicrafts made from water hyacinth. The business capital is IDR 5,000,000. The price of the product depends on the type of product, baskets of water hyacinth are priced at 55,000-65,000 while palcemates cost 24,000-30,000. The marketing area for water hyacinth products is the local area of Jember, namely the area around the sub-districts of Bangsalsari, Umbulsari, Balung, Puger and its surroundings. The scope of online marketing covers all regions of Indonesia. Promotion through online media such as Instagram, TikTok, website and marketplace Shopee, Bukalapak. The form of product innovation from Aren Handicraft varies from new product innovations such as tissue holders and several products to photo props and hampers.