Monitoring Requirements Research Articles

Optical coherence tomography for in vivo longitudinal monitoring of artificial dermal scaffold.

Artificial dermal scaffold (ADS) has undergone rapid development and been increasingly used for treating skin wound in clinics due to its good biocompatibility, controllable degradation, and low risk of disease infection. To obtain good treatment efficacy, ADS needs to be monitored longitudinally during the treatment process. For example, scaffold-tissue fit, cell in-growth, vascular regeneration, and scaffold degradation are the key properties to be inspected. However, to date, there are no effective, real-time, and noninvasive techniques to meet the requirement of the scaffold monitoring above. In this study, we propose to use optical coherence tomography (OCT) to monitor ADS in vivo through three-dimensional imaging. A swept source OCT system with a handheld probe was developed for in vivo skin imaging. Moreover, a cell in-growth, vascular regeneration, and scaffold degradation rate (IRDR) was defined with the volume reduction rate of the scaffold's collagen sponge layer. To measure the IRDR, a semiautomatic image segmentation algorithm was designed based on U-Net to segment the collagen sponge layer of the scaffold from OCT images. The results show that the scaffold-tissue fit can be clearly visualized under OCT imaging. The IRDR can be computed based on the volume of the segmented collagen sponge layer. It is observed that the IRDR appeared to a linear function of the time and in addition, the IRDR varied among different skin parts. Overall, it can be concluded that OCT has a good potential to monitor ADS in vivo. This can help guide the clinicians to control the treatment with ADS to improve the therapy.

Clozapine is the approved option in treatment-resistant schizophrenia and requires careful management.

Clozapine is the only agent approved for treatment-resistant schizophrenia, but is underprescribed. Its adverse drug event (ADE) profile and patient monitoring requirements can discourage its use, but the benefits of clozapine generally outweigh its risks, as most ADEs are manageable. Careful patient assessment, gradual titration, minimum effective dosages, therapeutic drug monitoring and checks of neutrophils, cardiac enzymes and ADE symptoms are recommended. Neutropenia is common but does not necessarily warrant permanent clozapine cessation.

Rapid Compaction Monitoring and Quality Control of Embankment Dam Construction Based on UAV Photogrammetry Technology: A Case Study

The compaction quality of embankment dams directly affects the safe operation of power stations. The traditional monitoring method has the shortcomings of limited sample and time consumption. Compaction quality can be reflected by the compression ratio (CR) of the filling material. A novel method based on unmanned aerial vehicle (UAV) photogrammetry technology, which can rapidly acquire the CR of the entire filling area, is proposed in the present paper. Specifically, the CR nephogram is obtained by processing the terra information of the compaction body collected by the UAV. Validation of the CR results is performed by comparing them with the results obtained via leveling measurements. Mean absolute error between CR results and leveling measurements results is less than 1%, and the corresponding settlement value error is millimeter-level, reflecting a fairly good agreement. Furthermore, the reduced-scale experiment shows that the UAV-based CR method is more stable than manual measurements, and the efficiency is increased by more than five times, which meets the requirements of compaction quality monitoring and quality control. The CR nephogram obtained can reflect the compaction quality information rapidly, comprehensively, and accurately, thereby guiding the quality control of embankment dam construction.

Research on the dynamic monitoring system of intelligent cluster-chemical welding equipment

With the steady development of computer technology and communication technology, the research and application of intelligent group-dominated welding equipment dynamic monitoring system can provide an effective basis for improving the management level and production capacity of manufacturing enterprises. Welding, as the longest applied thermal processing method in the manufacturing field, contains a wide range of technical equipment, the production area is too scattered, covers a large area, and the practical operation requirements are high. Therefore, scholars from various countries have strengthened the research on the dynamic monitoring system of welding equipment based on the theory of artificial intelligence technology. In this paper, on the basis of understanding the cluster welding equipment and production process, a real-time monitoring system of multi-technology integration development is proposed. The ZigBee technology is used to transmit the electric signal generated during welding wirelessly, the MYSQL is used to build the welding process database, and the remote access function of enterprise LAN is completed by using DataSocket. The final experimental results show that the overall system design is stable and safe, which meets the real-time monitoring requirements in the manufacturing field in the new era.

Assessing the Spatial Distribution of NO2 and Influencing Factors in Urban Areas—Passive Sampling in a Citizen Science Project in Berlin, Germany

Nitrogen dioxide (NO2) is a major air pollutant with diverse impacts on human health and the environment. In urban areas, road traffic is the main emission source for NO2. In Berlin, Germany, a network of measurement stations is operated by the state, fulfilling the monitoring requirements set by the European Union. To get a more detailed overview of the spatial distribution of NO2 concentrations in Berlin, a citizen science project allowed for collection of additional data and an increase in the number of sampling sites. Passive samplers (modified Palmes tubes) were distributed to participants to collect NO2 at a site of their choice. When returned, the samplers were analyzed based on the Griess–Ilosvay reaction and spectrophotometric detection. The results confirmed a seasonal trend of higher NO2 concentrations in winter and lower concentrations during the summer period. Furthermore, the spatially and monthly averaged NO2 concentrations observed in the study period from March 2019 to October 2020 were in good agreement with the average urban background concentration. At small spatial scales, a tendency of decreasing NO2 concentrations with increasing distance from roads was observed. Overall, this study shows the added benefit of extensive low-cost measurements of NO2 concentrations across urban environments in a citizen science project to complement stationary air pollution monitoring networks.

Retrieve of total suspended matter in typical lakes in China based on broad bandwidth satellite data: Random forest model with Forel-Ule Index

Total Suspended Matter is the core parameter of water color remote sensing and the important indicator for water quality evaluation of lakes. Rapid and high-precision monitoring of TSM is an important guarantee for water quality remote-sensing applications. China has launched many broad-bandwidth remote sensing satellites, all of which have similar bandwidth. The coordinated observation of multiple satellites can effectively meet the large-scale and high-frequency dynamic monitoring requirements of TSM concentration in lakes. This study proposed a machine-learning model to retrieve the TSM concentration from broad bandwidth satellites. The reliability and accuracy of various retrieve models (i.e., linear regression model, support vector regression model, random forest model, and back propagation neural networks model) were evaluated through the in-situ datasets of TSM concentration in lakes. The RF model was selected as the retrieved model of TSM concentration using broad bandwidth satellites. The results showed that 1) Compared with four machine learning models, the RF model can provide better performance (R2=0.88, Mean Absolute Percentage Error (MAPE) = 22.5%). Similarly, compared with the documented six TSM retrieve model, the RF retrieve model also has substantial advantages. 2) the Forel-Ule Index (FUI) can effectively enhance the precision and accuracy of the TSM retrieve model. 3) The RF model has good generalization ability and accuracy in the validation datasets (Lake Chagan: MAPE = 3.7%, Lake Changdang: MAPE = 4.3%). 4) The RF model was applied to the broad bandwidth satellites retrieve of TSM concentrations in Lake Bosten, Lake Chagan, and Lake Changdang, and the MAPEs were 5.3%, 8.1%, and 12.1%, respectively. This study showed that the RF model could effectively improve the retrieve performance and generalization ability of the broad bandwidth satellite’s TSM concentration, which meets the accuracy requirements of high-frequency dynamic monitoring of TSM concentration.

Effect of one-lung ventilation on the correlation between left and right cerebral saturation

BackgroundTo investigate if the correlation between left and right cerebral tissue oxygen saturation (SctO2) was affected by one-lung ventilation (OLV) in patients undergoing lung cancer surgery.MethodsPatients who underwent surgery for lung cancer were enrolled. Left and right SctO2 were collected during anesthesia. The primary outcome was the correlation between left and right SctO2 at 30 min after OLV which was analysed by Pearson correlation and linear regression model. Secondary outcomes included the trend of left–right SctO2 change over the first 30 min after OLV, correlation of left–right SctO2 during OLV for each patient; maximal difference between left–right SctO2 and its relationship with postoperative delirium.ResultsLeft–right SctO2 was moderately correlated at baseline (r = 0.690, P < 0.001) and poorly correlated at 30 min after OLV (r = 0.383, P < 0.001) in the Pearson correlation analysis. Linear regression analysis showed a poor correlation between left and right SctO2 at 30 min after OLV (r = 0.323, P < 0.001) after adjusting for confounders. The linear mixed model showed a change in left–right SctO2 over the first 30 min after OLV that was statistically significant (coefficient, -0.042; 95% CI, -0.070–-0.014; P = 0.004). For the left–right SctO2 correlation during OLV in each patient, 62.9% (78/124) patients showed a strong correlation, 19.4% (24/124) a medium correlation, and the rest a poor correlation. The maximal difference between the left and right SctO2 was 13.5 (9.0, 20.0). Multivariate analysis showed that it was not associated with delirium (odds ratio [OR], 1.023; 95% CI, 0.963–1.087; P = 0.463).ConclusionsThe correlation between left and right SctO2 was affected by one-lung ventilation in patients undergoing lung cancer surgery. This result indicates the requirement of bilateral SctO2 monitoring to reflect brain oxygenation.Trial registrationThis study was a secondary analysis of a cohort study approved by the Clinical Research Review Board of Peking University First Hospital (#2017–1378) and was registered in the Chinese Clinical Trial Registry on 10/09/2017 (http://www.chictr.org.cn, ChiCTR-ROC-17012627).

Research on the dynamic monitoring system of intelligent cluster-chemical welding equipment

With the steady development of computer technology and communication technology, the research and application of intelligent group-dominated welding equipment dynamic monitoring system can provide an effective basis for improving the management level and production capacity of manufacturing enterprises. Welding, as the longest applied thermal processing method in the manufacturing field, contains a wide range of technical equipment, the production area is too scattered, covers a large area, and the practical operation requirements are high. Therefore, scholars from various countries have strengthened the research on the dynamic monitoring system of welding equipment based on the theory of artificial intelligence technology. In this paper, on the basis of understanding the cluster welding equipment and production process, a real-time monitoring system of multi-technology integration development is proposed. The ZigBee technology is used to transmit the electric signal generated during welding wirelessly, the MYSQL is used to build the welding process database, and the remote access function of enterprise LAN is completed by using DataSocket. The final experimental results show that the overall system design is stable and safe, which meets the real-time monitoring requirements in the manufacturing field in the new era.

Vulnerable underground entrance understanding for visual surveillance systems

Protecting critical infrastructure through visual surveillance is of vital importance, especially in underground entrance environments where large chunks of sloping glass ceilings are particularly susceptible to various types of terrorist activity. However, owing to the diversity of underground entrance environments, understanding them remains a challenge. Traditional 3D layout and object pose estimation that are evaluated on 3D point clouds or RGB-D data are energy-consuming and difficult to account for semantic information in environments. In this study, we present a methodology to understand underground entrance environments, and to recover their 3D reconstruction from a monocular camera. Clusters of sloping angle projections are extracted. Through their corresponding vanishing points (VPs), surfaces of sloping structures are estimated. Relative geometric constraints of different planes are built to bridge the gap between 2D sloping surfaces and 3D reconstruction without precise depth or point clouds. An underground entrance scene is approximated by Manhattan and sloping non-Manhattan structures in 3D reconstruction. The approach requires no prior training, and it requires neither the camera being calibrated nor the camera internal parameters being constant. Compared to the ground truth, the percentage of incorrectly understood pixels were measured and the results demonstrated that the method can successfully understand underground entrance scenes, meeting the requirements in safety monitoring for critical infrastructures from a resource-constrained surveillance camera.

Clozapine Use in Early-Onset Schizophrenia.

Schizophrenia in the pediatric population poses unique challenges and has been associated with poorer prognosis. Early intervention and symptom stability are paramount. There are several antipsychotic medications with U.S. Food and Drug Administration approval, but when treatment resistance occurs, clozapine is an option that has demonstrated superiority in effectiveness. Due to side effect burden and monitoring requirements, clozapine remains an alternative to other pharmacotherapy options that have failed rather than a mainstay of treatment. Clinicians, nurses, family members, and others involved in pediatric mental health care must be knowledgeable about this option and how to maintain safety with its use. [Journal of Psychosocial Nursing and Mental Health Services, 61(2), 6-8.].

Managing patients with tuberculosis in primary care

Tuberculosis is treated mainly in secondary care, but patients commonly present to primary care for investigation of initial symptoms and treatment monitoring. This article discusses the most frequently used anti‐tuberculosis drugs, their monitoring requirements and how to manage drug interactions and adverse reactions.

Construction and Test of Baler Feed Rate Detection Model Based on Power Monitoring

The existing methods of measuring the baler feed rate seldom consider the influence of machine vibration on the sensor signal during field operation, which leads to the low detection accuracy and poor stability of feeding quantity detection. We established a feed rate detection model of a baler based on power monitoring of the pickup platform. Through the dynamic analysis of the pickup platform, the functional relationship between the working power of the pickup platform and the feed rate was constructed. A power monitoring system of the pickup platform was developed, and the model construction experiment of the working power and the feed rate was performed. The influence mechanism of different running speeds on the torque noise signal of the power input shaft of the pickup platform was explored. The frequency of the noise signal was mainly concentrated at 0.5–6 Hz and 9–13 Hz employing a fast Fourier transform, and the noise signal was eliminated by the frequency-domain-filtering method. The function model of working power and feed rate of the pickup platform was established based on signal processing, and the determination coefficient R2 of the model was 0.9796. The field experiment results show that when the feed rate of the baler is between 1.6 and 4.88 kg/s, the determination coefficient R2 and RMSE between the actual and predicted feed rate are 0.989 and 0.2, respectively. The relative error range of feed-rate prediction is −9.37–8.77%, which indicates that the model has high detection accuracy and good stability and meets the requirements of feed-rate monitoring of a baler in field operation.

Highly Stretchable Graphene Scrolls Transistors for Self-Powered Tribotronic Non-Mechanosensation Application.

Owing to highly desired requirements in advanced disease diagnosis, therapy, and health monitoring, noncontact mechanosensation active matrix has drawn considerable attention. To satisfy the practical demands of high energy efficiency, in this report, combining the advantage of multiparameter monitoring, high sensitivity, and high resolution of active matrix field-effect transistor (FET) with triboelectric nanogenerators (TENG), we successfully developed the tribotronic mechanosensation active matrix based on tribotronic ion gel graphene scrolls field-effect transistors (GSFET). The tribopotential produced by TENG served as a gate voltage to modulate carrier transport along the semiconductor channel and realized self-powered ability with considerable decreased energy consumption. To achieve high spatial utilization and more pronounced responsivity of the dielectric of this transistor, ion gel was used to act as a triboelectric layer to conduct friction and contact electrification with external materials directly to produce triboelectric charges to power GFET. This tribopotential-driving device has excellent tactile sensing properties with high sensitivity (1.125 mm-1), rapid response time (~16 ms), and a durability operation of thousands of cycles. Furthermore, the device was transparent and flexible with the capability of spatially mapping touch stimuli and monitoring real-time temperature. Due to all these unique characteristics, this novel noncontact mechanosensation GSFET active matrix provided a new method for self-powered E-skin with promising potential for self-powered wearable devices and intelligent robots.

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge.

Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requirements of ECG monitoring. Edge inference is an emerging alternative that overcomes the concerns of cloud inference; however, it poses new challenges due to the demanding computational requirements of modern ML algorithms and the tight constraints of edge devices. In this work, we propose a tiny convolutional neural network (CNN) classifier for real-time monitoring of ECG at the edge with the aid of the matched filter (MF) theory. The MIT-BIH dataset with inter-patient division is used for model training and testing. The model generalization capability is validated on the INCART, QT, and PTB diagnostic databases, and the model performance in the presence of noise is experimentally analyzed. The proposed classifier can achieve average accuracy, sensitivity, and F1 scores of 98.18%, 91.90%, and 92.17%, respectively. The sensitivity of detecting supraventricular and ventricular ectopic beats (SVEB and VEB) is 85.3% and 96.34%, respectively. The model is 15 KB in size, with an average inference time of less than 1 ms. The proposed model achieves superior classification and real-time performance results compared to the state-of-the-art ECG classifiers while minimizing the model complexity. The proposed classifier can be readily deployed on a wide range of resource-constrained edge devices for arrhythmia monitoring, which can save millions of cardiovascular disease patients.

Development and application of a 6000-meter double decelerating lander

The deep-sea lander is an important equipment for in-situ detection and monitoring. It is of great significance to understand the benthic boundary layer’s physical, chemical, and ecological environment. A 6000-meter double decelerating lander was created to meet the deployment requirements of underwater detection and monitoring, allowing for long-term in-situ monitoring of several benthic boundary layer components. Protection of the installed ocean bottom seismometer (OBS) is required due to the lander’s and OBS’s different impact resistances. The double decelerating unit enables the OBS to avoid colliding with the seabed when the lander lands and then collides with the seabed at a slow speed rather than the speed at which the lander falls, which is intended to safeguard OBS from damage. To ensure a safe deployment, the lander’s static analysis and simulation were performed using ANSYS, and the motion characteristics of the application process were derived. Numerous data have been obtained after the lander’s successful application in the South China Sea. The lander provides an investigation approach for marine science and geochemistry, complementing a technical approach to marine environmental investigations.

Design and Application of an Airborne Radioactivity Survey System Based on Unmanned Aerial Vehicle

In this study, an airborne radioactivity survey (ARS) system based on an unmanned helicopter platform was designed to satisfy the requirements of geological exploration and environmental radiation monitoring under complex environmental conditions. The system is composed of an unmanned aerial vehicle platform, airborne equipment, and a ground control station, with both NaI(Tl) and CeBr3 crystals as detectors to meet the energy resolution and detection efficiency requirements of airborne radioactivity measurement. The designed ARS system is stable and reliable through the field flight test, and the measurement results are consistent with the existing data and can be widely used in fields such as large-scale uranium resource exploration, nuclear accident emergency monitoring, and environmental radiation investigation.

Cactus-Inspired Photonic Crystal Chip for Attomolar Fluorescence Multi-analysis.

Automation and efficiency requirements of environmental monitoring are the pursuit of spontaneous sampling and ultrasensitivity for current sensory systems or detection apparatuses. In this work, inspired by cactus hierarchical structures, we develop a cactus-inspired photonic crystal chip to integrate spontaneous droplet sampling and fluorescence enhancement for sensitive multi-analyte detection. A conical hydrophilic pattern on hydrophobic surfaces can give rise to unidirectional Laplace pressure, which drives droplet transport to the assigned photonic crystal site. The nanostructure of photonic crystals has bigger capillarity to drive the droplet wetting uniformly into the photonic crystal matrix while performing prominent fluorescence enhancement by their photonic bandgap. A low to attomolar (2.24 × 10-19 M) fluorescence limit of detection (LOD) sensitivity can be achieved by the synergy of spontaneous droplet sampling and fluorescence enhancement. Focused on eutrophic water problems and algae pollution monitoring, a femtomolar (1.83 × 10-15 M) LOD and identification of various microcystins in urban environmental water can be achieved. The suitable integration of the unidirectional droplet transport by Laplace pressure and fluorescence enhancement by photonic crystals can achieve the spontaneous sampling and signal enhancement for ultratrace detections and sample survey of environmental monitoring and disease diagnosis.

Developing a geological disaster monitoring system based on electrical prospecting

The functions and performances of current geological disaster monitoring systems have significantly improved in recent years. However, they still possess certain shortcomings, such as lack of deep monitoring functions and being affected by weather, atmosphere, and light. This study aims to combine electrical prospecting with geological disaster monitoring to develop an improved system. The proposed system has a high level of data gathering accuracy and involves deep monitoring. It is not easily affected by natural factors, such as the weather. We designed circuits for the high-precision data acquisition, high-voltage power transmission, and main control and developed an acquisition and monitoring software, which provides adequate technical support for geological disaster monitoring. The system was used to monitor the Lannitan landslide. Our results indicated that the proposed geological disaster monitoring system had stable functionality, high acquisition accuracy, simple operation of the human‒computer interface, and remote monitoring, meeting the requirements for geological disaster monitoring.

Research on Instance Segmentation Algorithm of Greenhouse Sweet Pepper Detection Based on Improved Mask RCNN

The fruit quality and yield of sweet peppers can be effectively improved by accurately and efficiently controlling the growth conditions and taking timely corresponding measures to manage the planting process dynamically. The use of deep-learning-based image recognition technology to segment sweet pepper instances accurately is an important means of achieving the above goals. However, the accuracy of the existing instance segmentation algorithms is seriously affected by complex scenes such as changes in ambient light and shade, similarity between the pepper color and background, overlap, and leaf occlusion. Therefore, this paper proposes an instance segmentation algorithm that integrates the Swin Transformer attention mechanism into the backbone network of a Mask region-based convolutional neural network (Mask RCNN) to enhance the feature extraction ability of the algorithm. In addition, UNet3+ is used to improve the mask head and segmentation quality of the mask. The experimental results show that the proposed algorithm can effectively segment different categories of sweet peppers under conditions of extreme light, sweet pepper overlap, and leaf occlusion. The detection AP, AR, segmentation AP, and F1 score were 98.1%, 99.4%, 94.8%, and 98.8%, respectively. The average FPS value was 5, which can be satisfied with the requirement of dynamic monitoring of the growth status of sweet peppers. These findings provide important theoretical support for the intelligent management of greenhouse crops.

Effect of Fabric Electrode Surface Coating Medium on ECG Signal Quality under Dynamic and Static Conditions

This study intends to explore the influence of a fabric electrode skin interface filling medium on ECG signals under dynamic and static conditions, so as to be able to better monitor both stable and dynamic ECG signals. In this paper, cotton fabric electrodes were prepared using electroless silver-plated conductive cotton fabric, and then the electrodes were integrated into a physiotherapy-grade elastic bandage to assemble the fabric ECG tape. The electrode impedance tester and the BIOPAC physiological recorder were used to measure the fabric electrode-skin impedance and electrocardiogram under dynamic and static conditions, respectively. Later, the influence of the fabric electrode coated medium on the acquisition effect of an electrocardiogram signal under dynamic and static conditions is discussed. The results show that in the absence of a dielectric reagent coating, the cotton fabric electrode is easily disturbed by external noise which leads to large electrode-skin contact impedance. However, the electrode-skin contact impedance is greatly reduced after coating the fabric electrode with the dielectric reagent. At the same time, after applying the conducive paste coating, the corresponding change curve of the fabric electrode became more stable, and the impedance value was less than 0.005 MΩ·cm2, which meets the standard requirement for textile dry electrode skin impedance. In a state of rest, a clear and stable ECG can be collected regardless of whether the medium reagent is coated or not. However, only after applying the conductive paste can a stable ECG signal be collected under the three motion modes, or can a P wave, QRS wave group, or T wave be clearly distinguished. In addition, there is a high correlation with the disposable gel electrode, which can satisfy the requirements of the long-term monitoring of ECG signals under dynamic conditions.