Accuracy Of Monitoring Research Articles

In-Situ Quality Intelligent Classification of Additively Manufactured Parts Using a Multi-Sensor Fusion Based Melt Pool Monitoring System

Although laser powder bed fusion (LPBF) technology is considered one of the most promising additive manufacturing techniques, the fabricated parts still suffer from porosity defects, which can severely impact their mechanical performance. Monitoring the printing process using a variety of sensors to collect process signals can realize a comprehensive capture of the processing status; thus, the monitoring accuracy can be improved. However, existing multi-sensing signals are mainly optical and acoustic, and camera-based signals are mostly layer-wise images captured after printing, preventing real-time monitoring. This paper proposes a real-time melt-pool-based in-situ quality monitoring method for LPBF using multiple sensors. High-speed cameras, photodiodes, and microphones were used to collect signals during the experimental process. All three types of signals were transformed from one-dimensional time-domain signals into corresponding two-dimensional grayscale images, which enabled the capture of more localized features. Based on an improved LeNet-5 model and the weighted Dempster-Shafer evidence theory, single-sensor, dual-sensor and triple-sensor fusion monitoring models were investigated with the three types of signals, and their performances were compared. The results showed that the triple-sensor fusion monitoring model achieved the highest recognition accuracy, with accuracy rates of 97.98%, 92.63%, and 100% for high-, medium-, and low-quality samples, respectively. Hence, a multi-sensor fusion based melt pool monitoring system can improve the accuracy of quality monitoring in the LPBF process, which has the potential to reduce porosity defects. Finally, the experimental analysis demonstrates that the convolutional neural network proposed in this study has better classification accuracy compared to other machine learning models.

Continuous Glucose Monitoring and the Effect of Liraglutide in Cardiac Surgery Patients: A Substudy of the Randomized Controlled GLOBE Trial

ObjectivesFirstly, to evaluate the accuracy and reliability of continuous glucose monitoring (CGM) in patients undergoing cardiac surgery and, secondly, to assess the impact of preoperative liraglutide administration on perioperative glucose control as captured by CGM. DesignProspective, single-center, pre-specified analyses of the GLOBE trial, a randomized controlled trial comparing preoperative liraglutide treatment to placebo in patients undergoing cardiac surgery. SettingA single center academic hospital in the Netherlands. ParticipantsTwenty-five patients undergoing cardiac surgery, recruited from the hospital's cardiac surgery department. InterventionsParticipants received the Dexcom G5 CGM system from the day before surgery until discharge from the Intensive Care Unit after surgery. Additionally, participants were randomized to receive either preoperative liraglutide or placebo. Measurements and Main ResultsArterial blood gas (ABG) glucose measurements were collected as reference and matched to CGM readings to assess accuracy and reliability. In 240 paired CGM–ABG glucose measurements, the mean absolute relative difference (MARD) was 14.4±12.5%. Temporary sensor interruption occurred mainly intraoperatively (92% of patients). The median duration of intraoperative sensor interruption was 65 (48-95) minutes. Liraglutide increased glycemic time in range, 72% vs 47% in the control group (absolute difference 25%, 95% CI -41.4 to -8.9, p = .004). ConclusionsDespite intraoperative sensor interruption, CGM seems an accurate method to use as a semi-invasive, real-time assessment of blood glucose levels. CGM can provide a detailed observation of the pre- and postoperative glycemic trajectory, demonstrating increased time in range following perioperative liraglutide treatment compared to placebo.

Intelligent Dust Monitoring System Based on IoT

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system train and test part was applied to dust phenomena historical data. Its data has been collected through the Iraqi Meteorological Organization and Seismology (IMOS) raw dataset with 170237 of 17023 rows and 10 columns. The LSTM model achieved small time, computationally complexity of, and layers number while being effective and accurate for dust prediction. The simulation results reveal that the model's mean square error test reaches 0.12877 and Mean Absolute Error (MAE) test is 0.07411 at the same rates of learning and exact features values of vector in the dense layer, representing a neural network layer deeply is connected to the LSTM training proposed model. Finally, the model suggested enhances monitoring performance.

Optical sensor to improve the accuracy of non-invasive blood sugar monitoring

Optical sensors offer a painless method of monitoring blood glucose levels using various light technologies to analyze blood characteristics without penetrating the skin. The literature review part reflects the progress in optical sensor technology evaluates its potential in blood glucose monitoring by overcoming the limitations of conventional methods and recognizes the challenges and future prospects in this rapidly developing area of research. The results of empirical studies are then presented. The methodology is presented as a non-invasive method of blood glucose monitoring based on near-infrared spectroscopy. To precisely evaluate blood glucose concentrations, spectroscopy techniques involving absorption and reflection are employed at wavelengths 450, 900, 1350, and 1800 nm. After absorption and reflection of glucose molecules, light is generated. An experimental study of different samples revealed a linear relationship between the final output voltage and sugar concentration. The results demonstrate a correlation between blood glucose level and signal intensity after transmission.

A lightweight convolutional neural network for working condition intelligent diagnosis of pumping units

Accurate diagnosis of pumping unit working conditions and prompt fault resolution are vital for ensuring safe production in oilfields. In response to the limitations of traditional convolutional neural networks in diagnosing pumping unit working conditions, a lightweight convolutional neural network is proposed. This network addresses the challenges of shallow networks, which cannot effectively extract indicator diagram features, and deep networks, which suffer from excessive parameterization and high redundancy. Firstly, the combination of depthwise separable convolution and dilated convolution efficiently extracts the contour features of the indicator diagram while reducing the model’s parameter count. Additionally, the Attention Embedded Atrous Spatial Pyramid Pooling and the coordinate attention mechanism are incorporated to further capture the shape and position information of indicator diagram lines. The experimental results demonstrate that the proposed lightweight network has 1.26 M parameters and storage size of 4.82 M. Compared to ResNet-50, VGG-16, and MobileNet-V2, its parameter count and storage size are approximately 1/18, 1/11, and 1/2, respectively, making it easily deployable on hardware-limited diagnostic platforms. Moreover, with an average diagnostic accuracy of 98.17%, surpassing existing networks, it enables more effective diagnosis of pumping unit working conditions, thereby enhancing the reliability and accuracy of pumping unit operational monitoring.

Application Research of Unmanned Aerial Vehicle Remote Sensing Technology in Agricultural Pest and Disease Monitoring

This study aims to explore the application of unmanned aerial vehicle (UAV) remote sensing technology in agricultural pest and disease monitoring to improve monitoring efficiency and accuracy. Through literature review and empirical research, the current application status, advantages, and challenges of UAV remote sensing technology in agricultural pest and disease monitoring were systematically studied. The study first analyzed and summarized the principles and characteristics of UAV remote sensing technology, and then conducted case studies and field investigations on its specific application in agricultural pest and disease monitoring. The research results show that UAV remote sensing technology has the advantages of high resolution, high spatiotemporal coverage, and high efficiency in agricultural pest and disease monitoring. It can provide multi-source data acquisition and analysis during crop growth to achieve rapid identification and dynamic monitoring of agricultural pest and disease. The findings of this study have important implications for guiding the application and promotion of UAV remote sensing technology in agricultural pest and disease monitoring.

Fault Detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network.

As one of the hot issues of concerns during modern social development, the wastewater treatment process is acknowledged to be a process with complex biochemical reactions and susceptible to an external environment, featuring strong nonlinear and time correlation characteristics, which are difficult for traditional mechanism-based models to tackle. For many classical data-driven fault detection methods, a complete retraining process is necessary to monitor every new fault, and most of the current neural network-based strategies rarely achieve satisfactory monitoring accuracy or robustness either. Giving full consideration to the aforementioned problems, this article takes advantage of position encoding, residual connection, and multihead attention mechanism embedded in the Transformer structure to establish an effective and efficient wastewater treatment process fault detection model, where offline modeling and online monitoring are performed successively to achieve accurate detection of the faults. In the experimental part, the advantages of the proposed method are strongly verified through the simulation monitoring of 27 faults on the benchmark simulation model 1 (BSM1), where the false alarm rate (FAR) and miss alarm rate (MAR) of the established method are proved to be significantly lower than those of the compared state-of-the-art methods.

Firefighting Water Jet Trajectory Detection from Unmanned Aerial Vehicle Imagery Using Learnable Prompt Vectors.

This research presents an innovative methodology aimed at monitoring jet trajectory during the jetting process using imagery captured by unmanned aerial vehicles (UAVs). This approach seamlessly integrates UAV imagery with an offline learnable prompt vector module (OPVM) to enhance trajectory monitoring accuracy and stability. By leveraging a high-resolution camera mounted on a UAV, image enhancement is proposed to solve the problem of geometric and photometric distortion in jet trajectory images, and the Faster R-CNN network is deployed to detect objects within the images and precisely identify the jet trajectory within the video stream. Subsequently, the offline learnable prompt vector module is incorporated to further refine trajectory predictions, thereby improving monitoring accuracy and stability. In particular, the offline learnable prompt vector module not only learns the visual characteristics of jet trajectory but also incorporates their textual features, thus adopting a bimodal approach to trajectory analysis. Additionally, OPVM is trained offline, thereby minimizing additional memory and computational resource requirements. Experimental findings underscore the method's remarkable precision of 95.4% and efficiency in monitoring jet trajectory, thereby laying a solid foundation for advancements in trajectory detection and tracking. This methodology holds significant potential for application in firefighting systems and industrial processes, offering a robust framework to address dynamic trajectory monitoring challenges and augment computer vision capabilities in practical scenarios.

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Diabetes Mellitus (DM) as a non-communicable disease (NCD) continues to increase every year. Continuous glucose monitoring (CGM) is essential for effective DM management. However, existing disposable glucose monitoring methods still rely on invasive techniques, cause pain, and lack continuous monitoring capabilities. On the other hand, non-invasive techniques are not feasible for CGM due to the biometric data's complexity and the classification system's inadequate performance. This study aims to develop a non-invasive technology to improve the performance of a non-invasive blood glucose classification system using Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) and an oversampling technique. The oversampling technique could improve data quantity by balancing the amount of data for each class. This study recruited twenty-three participants in the age range of 20 to 22 years comprising seven females and fifteen males. During data recording sessions, blood glucose levels were simultaneously assessed using a gold-standard glucometer and a non-invasive CGM prototype. The proposed CNN model successfully improved the classification accuracy of non-invasive blood glucose monitoring significantly. With the implementation of oversampling for augmenting the data, the accuracy of the proposed model increased to more than 88%. This study concludes that non-invasive approaches combined with AI technology have the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly improving diabetes management and enhancing the overall quality of life for those affected by this condition. These findings could revolutionize the field of diabetes management, offering a more comfortable and accurate monitoring solution that could potentially transform the lives of millions of diabetes patients.

An Automated Approach for Classroom Attendance Using OpenCV

Abstract: The "AI Attendance: Automated Attendance Monitoring System" employs artificial intelligence and facial recognition to revolutionize attendance tracking, replacing inefficient methods like roll calls and ID cards. This system eliminates manual record-keeping, reducing administrative burdens while ensuring accurate real-time identification of individuals' attendance. Technologies such as Flask Framework, OpenCV, and Pandas Library are utilized to develop a user- friendly web interface for face registration and attendance marking.With Pickle for serialization, registered faces persist seamlessly, enhancing efficiency and accuracy in attendance monitoring across various sectors

Self-powered porous polymer sensors with high sensitivity for machine learning-assisted motion and rehabilitation monitoring

Muscle contraction and relaxation inherently contains valuable information crucial for monitoring physical states, rehabilitation, and injury prevention. However, the majority of flexible wearable devices struggle to offer customizable sensors with high sensitivity, durability, and stability, resulting in suboptimal biofeedback performance. Herein, the machine learning-assisted smart motion and rehabilitation monitoring system (SMRMS) is demonstrated for full-body motion recognition and rehabilitation assessment using a designed porous triboelectric nanogenerator (TENG) array. A theoretical model of porous materials is built to demonstrate the effect mechanism of porosity on TENG output. Through pore design on the tribolayer, theoretical simulation and experimental are performed to determine the key features of porous TENG sensors with high sensitivity (1.76 kPa−1), fast response time (50 ms) and high durability (over 100,000 cycles). A ring-shaped TENG (RS-TENG) is fabricated based on the porous TENG sensor, enabling real-time recording of leg/arm force without additional attachment. By integrating the RS-TENG array with a multichannel signal acquisition system, an SMRMS is designed to capture motion information during human subjects’ exercise and rehabilitation training. These data are then fused for machine learning analytics, resulting in significantly improved accuracy in motion pattern recognition (98.75 %) and rehabilitation monitoring (100 %). The simple, precise and durable porous sensors could help mitigate the risk of excessive exercise-induced muscle injuries, expanding self-powered wearable functionalities and adaptabilities.

Integrated high-precision monitoring method for surface subsidence in mining areas using D-InSAR, SBAS, and UAV technologies

The use of unmanned operations to monitor mining induced land subsidence is increasing. This study conducts a detailed comparative analysis of accuracy of measured ground deformation provided by Differential Interferometric Synthetic Aperture Radar (D-InSAR), Small Baseline Subset (SBAS), and Unmanned Aerial Vehicle (UAV) tilt photogrammetry with respect to levelling measurements. Based on such analysis we propose an integrated approach that combines multiple remote sensing methods to achieve a better global accuracy in the land subsidence monitoring in mining areas. Conducted at the Banji Coal Mine, this study collected subsidence data from April 10, 2021, to June 28, 2022, through D-InSAR, SBAS, and UAV techniques. After segmenting the subsidence basin into distinct zones, we qualitatively assessed each area with UAV-derived 3D models and quantitatively evaluated the precision of all applied techniques, benchmarking against leveling data. Our findings indicate that integrating D-InSAR, SBAS, and UAV technologies significantly enhances monitoring accuracy over any single method, demonstrating their combined effectiveness in different subsidence areas. Consequently, the synergistic integration of D-InSAR, SBAS, and UAV technologies, capitalizing on their complementary strengths, enables the achievement of intuitive, comprehensive, and high-precision monitoring of subsidence basins in mining areas.

Gaining Insight into Updated MR Imaging for Quantitative Assessment of Cartilage Injury in Knee Osteoarthritis.

Knee Osteoarthritis (KOA) entails progressive cartilage degradation, reviewed via MRI for morphology, biochemical composition, and microtissue alterations, discussing clinical advantages, limitations, and research applicability. Compositional MRI, like T2/T2* mapping, T1rho mapping, gagCEST, dGEMRIC, sodium imaging, diffusion-weighted imaging, and diffusion-tensor imaging, provide insights into cartilage injury in KOA. These methods quantitatively measure collagen, glycosaminoglycans, and water content, revealing important information about biochemical compositional and microstructural alterations. Innovative techniques like hybrid multi-dimensional MRI and diffusion-relaxation correlation spectrum imaging show potential in depicting initial cartilage changes at a sub-voxel level. Integration of automated image analysis tools addressed limitations in manual cartilage segmentation, ensuring robust and reproducible assessments of KOA cartilage. Compositional MRI techniques reveal microstructural changes in cartilage. Multi-dimensional MR imaging assesses biochemical alterations in KOA-afflicted cartilage, aiding early degeneration identification. Integrating artificial intelligence enhances cartilage analysis, optimal diagnostic accuracy for early KOA detection and monitoring.

A Knowledge-Guided Approach for Landslide Susceptibility Mapping Using Convolutional Neural Network and Graph Contrastive Learning

Landslide susceptibility mapping (LSM) constitutes a valuable analytical instrument for estimating the likelihood of landslide occurrence, thereby furnishing a scientific foundation for the prevention of natural hazards, land-use planning, and economic development in landslide-prone areas. Existing LSM methods are predominantly data-driven, allowing for significantly enhanced monitoring accuracy. However, these methods often overlook the consideration of landslide mechanisms and uncertainties associated with non-landslide samples, resulting in lower model reliability. To effectively address this issue, a knowledge-guided landslide susceptibility assessment framework is proposed in this study to enhance the interpretability and monitoring accuracy of LSM. First, a landslide knowledge graph is constructed to model the relationships between landslide entities and summarize landslide susceptibility rules. Next, combining the obtained landslide rules with geographic similarity principles, high-confidence non-landslide samples are selected to optimize the quality of the samples. Subsequently, a Landslide Knowledge Fusion Cell (LKF-Cell) is utilized to couple landslide data with landslide knowledge, resulting in the acquisition of informative and semantically rich landslide event features. Finally, a precise and credible landslide susceptibility assessment model is built based on a convolutional neural network (CNN), and landslide susceptibility spatial distribution levels are mapped. The research findings indicate that the CNN-based model outperforms traditional machine learning algorithms in predicting landslide probability; in particular, the Area Under the Curve (AUC) of the model was improved by 3–6% after sample optimization, and the AUC value of the LKF-Cell method was 6–11% higher than the baseline method.

Monitoring soil salinity based on Sentinel-1/2 remote sensing parameters and two-dimensional space theory

Multi-spectral satellite remote sensing is widely used in soil salinity monitoring by virtue of its wide coverage, fast update and low cost, but it is susceptible to cloudy weather, limiting the accuracy and application conditions of the inversion, while SAR (synthetic aperture radar) is hardly affected by cloud with strong penetration ability and enables almost round-the-clock, climate-wide monitoring of soil salinity. Two-dimensional feature space model, being easily interpretive and widely used in soil salinity monitoring, requires only some easily extracted parameters and can reduce other influences on soil salinity inversion. Therefore, in order to take full advantages of the SAR and multi-spectral satellite, achieve monitoring of soil salinity in more climatic conditions and longer time, and further improve soil salinity monitoring accuracy, this paper combine SAR-Spectral data with two-dimensional space theory to construct two-dimensional models for monitoring soil salinity. In this study, two-dimensional space models are constructed using Sentinel-1 and Sentinel-2 feature parameters, and models are evaluated with R2 with measured soil salinity data of the Shahaoqu irrigation area in Hetao irrigation district, Inner Mongolia. The results show that it is feasible to use the parameters extracted from Sentinel-1 after H/a polarization for constructing two-dimensional feature space models for the soil salinity inversion(R2parameters of Sentinel-1 = 0.467,0.587,0.517), and the two-dimensional space models combining Sentinel-1/2 parameters are more accurate and stable in soil salinity monitoring than those using Sentinel-1 or Sentinel-2 alone((Rparameters of Sentinel-2 = 0.320,0.517,0.341),(R2parameters of Sentinel-1/2 = 0.507,0.545,0.562,0.512,0.557,0.568)).

The Afternoon/Morning Ratio of Tower-Based Solar-Induced Chlorophyll Fluorescence Can Be Used to Monitor Drought in a Chinese Cork Oak Plantation

Monitoring drought stress is crucial for estimating productivity and assessing the health status of forest ecosystems under global climate change. Solar-induced chlorophyll fluorescence (SIF) is mechanistically coupled to photosynthesis and has advantages over greenness-based vegetation indices in detecting drought. In recent years, SIF has commonly been used in monitoring drought stress in crop ecosystems. However, the response of tower-based SIF to drought stress in forest ecosystems remains unclear. In this study, we investigated the potential of tower-based SIF to monitor drought, which was quantified using the plant water stress index (PWSI) in a Chinese cork oak plantation. The results show the negative effect of drought on SIF, and afternoon depression of SIF emission under drought stress was observed. Canopy SIF (F) exhibited a nonlinear relationship with PWSI, while the quantum yield of SIF (ΦF) exhibited a significant linear relationship with PWSI at 687 nm and 760 nm (ΦF687: R2 = 0.90; ΦF760: R2 = 0.85). Incident radiation (PAR) and canopy structure affected the response of SIF to drought stress, with PAR as the main factor causing the nonlinear relationship between F and PWSI. Afternoon depression was described as the afternoon/morning ratio (AMR). AMRF and AMRΦF exhibited a negative linear response to PWSI. AMRF was less affected than F by PAR and canopy structures, and AMRΦF was more physiologically representative than ΦF. Moreover, AMRΦF was sensitive to VPD and REW, and it might be a good indicator of drought. Red SIF was more sensitive to drought than far-red SIF, as the R2 of PWSI with AMRΦF687 (R2 = 0.89) was higher than that with AMRΦF687 (R2 = 0.84). These results highlight the potential of tower-based SIF, especially red SIF, for drought monitoring in a plantation, and consideration of the physiological diurnal variation in SIF under drought stress is crucial for improving the accuracy of drought stress monitoring.

Real-time sports injury monitoring system based on the deep learning algorithm

In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human movements, followed by human joint detection. Polynomial fitting analysis is used to extract joint motion patterns, and the average of training data is calculated as a reference point. The raw data is then normalized to adjust position and direction, and dimensionality reduction is achieved through singular value decomposition to enhance processing efficiency and model training speed. A support vector machine classifier is used to classify and identify the processed data. The experimental section monitors sports injuries and investigates the accuracy of the system’s monitoring. Compared to mainstream models such as Random Forest and Naive Bayes, the SVM utilized demonstrates good performance in accuracy, sensitivity, and specificity, reaching 94.2%, 92.5%, and 96.0% respectively.

Vehicle Tracking System Based on Internet of Things Utilizing TTGO T-CALL ESP32 SIM800l

Nowadays, cases of stolen vehicles are increasing rapidly, particularly in urban areas. The stolen vehicle is difficult for the owner to trace since the GPS system is rarely used due to its expensive cost. Thus, the purpose of this research is to analyze the accuracy of the real-time monitoring provided by the Blynk application using information obtained from GPS modules and to design a simple and compact smart vehicle monitoring anti-theft system utilizing the TTGO T-CALL ESP32 SIM800l with the Blynk application. Next, a tracking system is configured and developed by utilizing the Arduino IDE software to flash the code into the microcontroller and the Internet of Things platform as the connectivity to the Blynk App. Furthermore, the usability and functionality of the Global Positioning System (GPS) and TTGO T-CALL ESP32 SIM800l are essential in determining the vehicle's location. The use of TTGO T-CALL ESP32 SIM800l is to make the device more compact than previous research. The data has been obtained using a comparison of two microcontrollers; TTGO T-Call ESP32 SIM800l and the Arduino Mega connected to the GSM SIM800l. The data will be analyzed under two different parameters; the accuracy of altitude and speed. It can be seen that the TTGO T-Call ESP32 SIM800l has a lower percentage of latitude and longitude errors. It also has great performance in terms of speed accuracy, which is 88.5%.

#2103 Accuracy of a continuous glucose monitor in people with diabetes on peritoneal dialysis

Abstract Background and Aims Globally 40% of people receiving peritoneal dialysis (PD) have a diagnosis of diabetes. Good glycaemic control can by complicated by the obligate glucose load associated with PD. Monitoring and treatment of people with diabetes (PwD) on PD has been limited by reduced reliability of traditional markers of glycaemia such as HbA1c in end-stage kidney disease. Continuous glucose monitors (CGMs) have been increasingly used in the care of PwD over the last 20 years. Monitors worn on the upper arm or abdomen measure glucose in interstitial fluid at regular intervals over the 24 hour period. CGMs are currently unlicensed in PD populations as they have not been tested in large scale studies. Concerns remain around accuracy in the presence of potentially interfering substances generated by the breakdown of osmotic agents used in PD fluid (e.g. Icodextrin). We aimed to assess the analytical and clinical accuracy of the FreeStyle Libre(FSL) CGM system in PwD on maintenance PD. Method We conducted a single centre observational study of 10 PwD who had been on PD for at least 3 months. For the 10 day study period participants wore a blinded research model FSL monitor which measured interstitial glucose levels every 15 minutes. Results from the FSL were compared with; Analytical accuracy was assessed using three methods; Clinical accuracy was assessed using the Parkes consensus error grid to assign each pair of readings to one of five risk zones and quantify the clinical implications of any difference between the two measurements. The International Organisation for Standardisation guidelines stipulates 99% of pairs should be in zones A and B, denoting little or no effect on clinical actions. Results Of 11 recruited participants, 10 completed the 10 day study period resulting in 76 paired CGM-YSI measurements and 375 paired CGM-CBG measurements. 7 participants were on APD and 3 on CAPD. All participants were prescribed a PD regime that included one daily dwell with Icodextrin. The aggregate MARD was 9.4% (95% CI 7.9, 10.4) for CGM-YSI matched pairs and 17.6% (95% CI 17, 19.3) for CGM-CBG matched pairs. Spearman's r was 0.94 (p < 0.0001) for CGM-YSI and 0.89 (p < 0.0001) for CGM-CBG. From Bland-Altman analysis, the systematic error of the CGM compared with YSI and CBG was −0.81 mmol/l (SD 0.81) and −1.6 mmol/l (SD 1.9) respectively. Parkes consensus error grid analysis showed that compared with YSI and CBG respectively, 100% and 99.9% of sensor values were in the clinically acceptable zones A and B (Fig. 1). Conclusion We have demonstrated satisfactory performance of the FSL monitoring system by both analytical and clinical metrics in this cohort of PD patients using treatment regimens including Icodextrin based fluids. CGMs provide much more information for the PwD and their clinical care team compared with random CBGs (which in themselves have been shown to exhibit significant between user variability) and time averaged markers such as HbA1c. They allow detection of asymptomatic hypoglycaemia and direct monitoring of the systemic effects of any change to the PD prescription resulting in a larger or smaller glucose load. These results should be reassuring for clinicians wishing to utilise the greater depth of information provided by CGMs to improve clinical care for their patients with diabetes on PD.

Design of Non-Intrusive Online Monitoring System for Traction Elevators

With the increase in elevator usage, more and more elevator real-time monitoring equipment is being applied to the operation of elevators. Traditional elevator monitoring equipment adopts a multi-sensor decentralized installation and layout, and the monitoring accuracy is low, which directly affects the effective alarm of the monitoring system; however, existing online monitoring systems cannot quickly alarm for faults. Aiming to solve the above problems, an elevator online monitoring system based on narrow-band Internet of Things (NB-IoT) is designed. The system is highly integrated with an STM32 main control chip, a six-axis acceleration gyroscope sensor, and an air pressure sensor to realize the edge calculation of the monitoring system. At the same time, this paper eliminates the temperature drift of the pressure sensor by using a temperature compensation algorithm and inputs the extracted characteristic parameters into the BP neural network for training to eliminate the zero drift so as to obtain the real-time height data of the elevator. The six-axis acceleration gyroscope sensor is used to calculate the posture so as to avoid the problem that a three-axis acceleration sensor or a three-axis gyroscope sensor alone cannot obtain accurate posture data. In order to further improve the monitoring accuracy, the peak-to-peak value of the signal is calculated by using a 95% confidence interval algorithm to reduce the suppression of the high-frequency components of the signal by noise and ensure that the signal has a large signal-to-noise ratio so that the obtained elevator car posture and vibration operation data are more accurate. Finally, the effectiveness of the proposed method is verified by experiments.