Method For Real-time Monitoring Research Articles

Optimizing gula apong production with an IoT-based temperature monitoring system

<p>Determining the quality of <em>gula apong</em> is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The <em>gula apong</em> industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of <em>gula apong</em>. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality <em>gula apong</em> is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of <em>gula apong</em> to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of <em>gula apong</em> but also reduce the cooking time and cost.</p>

Comparative studies between humans and golden Syrian hamsters via thromboelastography.

Thromboelastography (TEG) is a widely utilized clinical testing method for real-time monitoring of platelet function and the thrombosis process. Lipid metabolism disorders are crucial risk factors for thrombosis. The lipid metabolism characteristics of hamsters resemble those of humans more closely than mice and rats, and their relatively large blood volume makes them suitable for studying the mechanisms of thrombosis related to plasma lipid mechanisms. Whole blood samples from golden Syrian hamsters and healthy humans were obtained following standard clinical procedures. TEG was employed to evaluate coagulation factor function, fibrinogen (Fib) function, platelet function, and the fibrinolytic system. The whole blood from hamster or healthy human was isolated following the clinical procedure, and TEG was employed to evaluate the coagulation factor function, Fib function, platelet function, and fibrinolytic system. Coagulation analysis used ACLTOP750 automatic coagulation analysis pipeline. Blood routine testing used XN-2000 automatic blood analyzer. TEG parameters revealed that hamsters exhibited stronger coagulation factor function than humans (reaction time [R], p = 0.0117), with stronger Fib function (alpha angle, p < 0.0001; K-time [K], p < 0.0001). Platelet function did not differ significantly (maximum amplitude [MA], p = 0.077). Hamsters displayed higher coagulation status than humans (coagulation index [CI], p = 0.0023), and the rate of blood clot dissolution in hamsters differed from that in humans (percentage lysis 30 min after MA, p = 0.02). Coagulation analysis parameters indicated that prothrombin time (PT) and activated partial thromboplastin time (APTT) were faster in hamsters than in humans (PT, p = 0.0014; APTT, p = 0.03), whereas the Fib content was significantly lower in hamsters than in humans (p < 0.0001). No significant difference was observed in thrombin time (p = 0.1949). In summary, TEG could be used to evaluate thrombosis and bleeding parameters in whole blood samples from hamsters. The platelet function of hamsters closely resembled that of humans, whereas their coagulation function was significantly stronger.

Motion state factor driven for doubly-curved shallow shell deformation reconstruction

The conventional shape sensing method, based on C0 continuity, is recognized for its computational efficiency. However, it is prone to shear locking due to low-order polynomial interpolation in shell analysis, introducing two primary challenges. Firstly, it lacks consideration for C1 continuity. Secondly, existing high-order polynomial interpolation methods significantly increase the number of degrees of freedom (DOF), reducing computational efficiency. Therefore, a brand-new real-time structural health monitoring (SHM) method driven by motion state factor instead of nodal factors is proposed. This method accommodates all deformation modes of shallow shells with C1 continuity and improves the shape function's order without additional DOF. It begins with deriving motion states from compatibility equations, leading to a polynomial shape function expression. Subsequently, conversion equations for real constants and DOF are developed, establishing a least-squares model for theoretical strains and measured values. Following that, the higher-order element formula, optimized for minimal DOF, is then inferred from varied displacement data. Finally, simulations and experiments demonstrate this method's effectiveness, accuracy, and reliability in analyzing shallow shell elements.

Seismic source of rock drill and its application in in-situ vibration monitoring of rock mass

This study suggests the use of a seismic source of a rock drill as a new excitation method for real-time monitoring of the vibration frequency of rock masses in tunnel engineering. In this study, a laboratory rock drill seismic source test system was developed. The rock mass excitation and vibration response under real in situ stress were simulated, and a natural frequency test of different lithologic materials under various in situ stresses was carried out based on the developed system. Empirical equations for estimating natural frequency were established using regression analysis. The test results show that (1) the laboratory rock drill seismic source test system exhibits good performance and can simulate the horizontal excitation process of the tunnel rock drill. Rock drill excitation may be used as the seismic source to continuously measure the in-situ vibration information of the rock mass. (2) The natural frequency of rock is positively correlated with rock elastic modulus and geostress and negatively correlated with rock density. This is not directly related to the seismic source.

Real-Time Monitoring on the Chinese Giant Salamander Using RPA-LFD.

The Chinese giant salamander (Andrias davidianus), listed as an endangered species under "secondary protection" in China, faces significant threats due to ecological deterioration and the expansion of human activity. Extensive field investigations are crucial to ascertain the current status in the wild and to implement effective habitat protection measures to safeguard this species and support its population development. Traditional survey methods often fall short due to the elusive nature of the A. davidianus, presenting challenges that are time-consuming and generally ineffective. To overcome these obstacles, this study developed a real-time monitoring method that uses environmental DNA (eDNA) coupled with recombinase polymerase amplification and lateral flow strip (RPA-LFD). We designed five sets of species-specific primers and probes based on mitochondrial genome sequence alignments of A. davidianus and its close relatives. Our results indicated that four of these primer/probe sets accurately identified A. davidianus, distinguishing it from other tested caudata species using both extracted DNA samples and water samples from a tank housing an individual. This method enables the specific detection of A. davidianus genomic DNA at concentrations as low as 0.1 ng/mL within 50 min, without requiring extensive laboratory equipment. Applied in a field survey across four sites in Huangshan City, Anhui Province, where A. davidianus is known to be distributed, the method successfully detected the species at three of the four sites. The development of these primer/probe sets offers a practical tool for field surveying and monitoring, facilitating efforts in population recovery and resource conservation for A. davidianus.

IoT-based Design and Implementation of an Intelligent Water Quality Monitoring System

Abstract: Water is an essential requirement for human survival, and a scarcity of water renders us unable to sustain our lives. The issue of water pollution and shortage is a pressing worldwide concern that necessitates the adaptation of water resources and the establishment of guiding principles at both the international and individual levels. Water contamination is the primary contributor to global disease outbreaks. In the past, the process of monitoring water quality relied on the physical collection of data, lacking the implementation of a smart sensor system. This resulted in inefficiencies, particularly in the areas of real-time data retrieval, device surveillance, and less precise data gathering. The integration of a real-time water quality monitoring method is important in order to guarantee the provision of a secure drinking water supply. Therefore, the concept of the Internet of Things (IoT), which facilitates the interconnection of diverse devices for the purpose of data interchange and collection, has emerged

A Real-Time Monitoring Method for Selective Laser Melting of TA1 Materials Based on Radiation Detection of a Molten Pool.

Selective laser melting (SLM) technology is a promising additive manufacturing technology. However, due to the numerous influencing factors in this complex process, a reliable real-time method is needed to monitor the forming process of SLM. The molten pool is the smallest forming unit in the SLM process, the consistency of which can effectively reflect the quality of the printing process. By using a coaxial optical path structure and a compound amplifier circuit, high-speed acquisition of molten pool radiation can be realized. Next, single factor analysis and orthogonal experimentation were used to investigate the influence levels of key process parameters on the radiation of molten pool. In addition, numerical simulation was carried out with the same parameter setting schemes, the results of which are consistent with those in radiation detection experiments. It is shown that the laser power has the greatest effect on the radiation of the molten pool, while the scanning speed and the hatch spacing have little effect on the radiation. Finally, the positioning experiment involving the small hole structure was carried out, and the experimental results showed that the device could accurately locate the position coordinates of the given hole structure.

Data-driven method embedded physical knowledge for entire lifecycle degradation monitoring in aircraft engines

The degradation of aircraft engine performance necessitates real-time monitoring. We introduce a real-time performance-degradation monitoring method comprising a baseline state (BLS) model for pristine engine performance prediction and a real-time model for ongoing engine performance prediction. Both models are LSTM-based and integrated with the engine physical topology; however, they vary in their input parameters. To counteract the data distribution disparities across various engine states during model training, a data augmentation method is proposed. In a case study, data from 600 h of engine running were employed, with thrust as the target monitoring parameter. The initial 50 h dataset covers the entire range of working conditions for the subsequent 550 h, serving as the training dataset for both models. The results demonstrated the accuracy of these models, with the BLS model achieving a mean absolute relative error (MARE) below 0.5% and a maximum absolute relative error (Emax) below 8% in the initial 50 h. Simultaneously, the real-time model yielded a MARE below 0.7% and Emax below 10% for the subsequent 550 h. A comparison of the real-time thrust degradation between this study and traditional methods highlights the feasibility and advantages of this approach.

High-Performance Defect Detection Methods for Real-Time Monitoring of Ceramic Additive Manufacturing Process Based on Small-Scale Datasets

Vat photopolymerization is renowned for its high flexibility, efficiency, and precision in ceramic additive manufacturing. However, due to the impact of random defects during the recoating process, ensuring the yield of finished products is challenging. At present, the industry mainly relies on manual visual inspection to detect defects; this is an inefficient method. To address this limitation, this paper presents a method for ceramic vat photopolymerization defect detection based on a deep learning framework. The framework innovatively adopts a dual-branch object detection approach, where one branch utilizes a fully convolution network to extract the features from fused images and the other branch employs a differential Siamese network to extract the differential information between two consecutive layer images. Through the design of the dual branches, the decoupling of image feature layers and image spatial attention weights is achieved, thereby alleviating the impact of a few abnormal points on training results and playing a crucial role in stabilizing the training process, which is suitable for training on small-scale datasets. Comparative experiments are implemented and the results show that using a Resnet50 backbone for feature extraction and a HED network for the differential Siamese network module yields the best detection performance, with an obtained F1 score of 0.89. Additionally, as a single-stage defect object detector, the model achieves a detection frame rate of 54.01 frames per second, which meets the real-time detection requirements. By monitoring the recoating process in real-time, the manufacturing fluency of industrial equipment can be effectively enhanced, contributing to the improvement of the yield of ceramic additive manufacturing products.

Deep learning-based method for real-time spinach seedling health monitoring

Deep learning-based method for real-time spinach seedling health monitoring

Visually evaluating drug efficacy in living cells using COF-based fluorescent nanoprobe via CHA amplified detection of miRNA and simultaneous apoptosis imaging

BackgroundsCancer is a highly fatal disease which is close relative of miRNA aberrant expression and apoptosis disorders. Elucidation of the therapeutic efficacy through investigating the changes in miRNA and apoptosis holds immense importance in advancing the development of miRNA-based precision therapy. However, it remains a challenge as how to visually evaluate the efficacy during protocol optimization of miRNA-based anticancer drugs at the cellular level. Therefore, exploring effective and noninvasive methods for real-time monitoring of therapeutic efficacy in living cells is of great significance. ResultsHerein, we reported a novel fluorescent nanoprobe COF–H1/H2-Peptide for visually evaluating drug efficacy in living cells through amplified imaging of low-abundant miRNA-221 with catalytic hairpin assembly (CHA) circle amplification, as well as simultaneous caspase-3 imaging. With strong stability and good biocompatibility, this newly fabricated amplified nanoprobe showed high sensitivity and specificity for the detection of miRNA-221 and caspase-3, and the limit of detection (LOD) of miRNA-221 was as low as 2.79 pM. The fluorescent imaging results showed that this amplified nanoprobe could not only detect caspase-3 in living cells, but also effectively detect low levels of miRNA-221 with increasing anticancer drug concentration and treatment time. The smart nanoprobe had effective performance for optimizing miRNA-based drug treatment schedules by dual-color fluorescence imaging. SignificanceThis nanoprobe combined CHA amplified detection of intracellular miRNA-221 and synchronous apoptosis imaging, with excellent sensitivity for the detection of cellular low-level miRNA, enabling the realization of real-time assessment of the efficacy of miRNA-based therapy in living cells. This work presents a promising approach for revealing the regulatory mechanisms between miRNAs and apoptosis in cancer occurrence, development, and treatment.

A Real-Time Monitoring Method for Droplet Transfer Frequency in Wire-Filled GTAW Based on Arc Sensing.

Droplet transfer frequency is a decisive factor in welding quality and efficiency in gas tungsten arc welding (GTAW). However, there still needs to be a monitoring method for droplet transfer frequency with high precision and good real-time performance. Therefore, a real-time monitoring method for droplet transfer frequency in wire-filled GTAW using arc sensing is proposed in this paper. An arc signal acquisition system is developed, and the wavelet filtering method filters out noise from the arc signal. An arc signal segmentation method-based on the OTSU algorithm and a feature extraction method for droplet transition based on density-based spatial clustering of applications with noise (DBSCAN)-is proposed to extract the feature signal of the droplet transition. A new conception of droplet transition uniformity is proposed, and it can be used to monitor the weld bead width uniformity. Numerous experiments for monitoring droplet transfer frequency in real time are conducted with typical welding parameters. This method enables the real-time observation of droplet transfer frequency, and the result shows that the average monitoring error is less than 0.05 Hz.

The effect of freeze-thaw action on the dynamic change of supra-permafrost water sources: A stable isotope perspective

Due to the continuous degradation (gradual thawing) of permafrost, supra-permafrost water has become an important component of runoff that occurs in cold regions. However, current research has only focused on the amount of water provided by permafrost, and little has been reported regarding the source and formation mechanisms of supra-permafrost water. Due to the difficulty of observation and sampling in cold regions and insufficient data accumulation, model simulations face various difficulties in regard to solving problems related to hydrological processes. Considering the advantages of stable isotope tracer methods in hydrology, the source of supra-permafrost water in Qilian Mountain was analyzed based on 1,840 samples, and the source of supra-permafrost water was determined by end-member mixing analysis (EMMA). Negative line-conditioned excess (lc-excess), lower slope, and particularly the negative intercept of the evaporation line (EL) indicates strong evaporation effects on supra-permafrost water. Remarkably, the evolutionary process, influencing factors, and relationship with other water bodies all indicate that supra-permafrost water is replenished by precipitation, ground ice meltwater, and snow meltwater. The results indicated that from May to October, the contributions of precipitation to the supra-permafrost water were 79%, 83%, 90%, 84%, 87%, and 83%, respectively. Snow meltwater contributed 11%, 13%, 10%, 16%, 11%, and 9%, respectively. Permafrost degradation impacts the water cycle and can increase the minimum monthly runoff and increase groundwater storage. To mitigate the effects of this change, monitoring and early warning systems are essential for detecting signs of permafrost degradation in a timely manner so that appropriate measures can be taken. This may involve the use of remote-sensing technologies, sensor networks, and other methods for real-time monitoring. Establishing mechanisms for sharing information with the relevant departments is crucial. The research results provide scientific and technological support and aid in decision-making to mitigate the negative effects of continuous permafrost degradation in a changing environment.

Intracardiac Echocardiography-guided Left Atrial Appendage Occlusion.

Left atrial appendage occlusion (LAAO) is a suitable alternative to oral anticoagulant therapy to prevent stroke in patients with AF. Most procedures are performed under transoesophageal echocardiography (TOE) guidance, which facilitates transseptal puncture, reduces the risk of procedurerelated complications and provides an additional method for device selection and real-time monitoring during device deployment. However, TOE has significant shortcomings, including the need for general anaesthesia/deep sedation as well as a significant risk of procedure-related adverse events. More recently, intracardiac echocardiography has been proposed as an alternative to TOE guidance during LAAO procedures. In this review, we summarise the available evidence as well as providing a step-by-step approach for intracardiac echocardiography-guided LAAO.

Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices

INTRODUCTION: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring. OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases. METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases. RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals. CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.

Computer Vision-Based Biomass Estimation for Invasive Plants.

We report on the detailed steps of a method to estimate the biomass of invasive plants based on UAV remote sensing and computer vision. To collect samples from the study area, we prepared a sample square assembly to randomize the sampling points. An unmanned aerial camera system was constructed using a drone and camera to acquire continuous RGB images of the study area through automated navigation. After completing the shooting, the aboveground biomass in the sample frame was collected, and all correspondences were labeled and packaged. The sample data was processed, and the aerial images were segmented into small images of 280 x 280 pixels to create an image dataset. A deep convolutional neural network was used to map the distribution of Mikania micrantha in the study area, and its vegetation index was obtained. The organisms collected were dried, and the dry weight was recorded as the ground truth biomass. The invasive plant biomass regression model was constructed using the K-nearest neighbor regression (KNNR)by extracting the vegetation index from the sample images as an independent variable and integrating it with the ground truth biomass as a dependent variable. The results showed that it was possible to predict the biomass of invasive plants accurately. An accurate spatial distribution map of invasive plant biomass was generated by image traversal, allowing precise identification of high-risk areas affected by invasive plants. In summary, this study demonstrates the potential of combining unmanned aerial vehicle remote sensing withmachine learning techniques to estimate invasive plant biomass. It contributes significantly to the research of new technologies and methods for real-time monitoring of invasive plants and provides technical support for intelligent monitoring and hazard assessment at the regional scale.

Detection of defects during laser-powder interaction by acoustic emission sensors and signal characteristics

Acoustic Emission (AE) sensing is an in-situ real-time nondestructive monitoring method proposed for Additive Manufacturing (AM) to detect defects such as cracks. Previous AE research in AM mainly focused on developing algorithms to automatically detect the defects from AE signals without understanding the physical mechanisms or the signal characteristics that could be used as identifiers. We study AE signals during a laser spot welding on a powder bed to clearly distinguish between different physical mechanisms using their signal characteristics. We identified specific signals associated with 1) tensile cracks from cooling, 2) a powder effect on the substrate, and 3) sudden thermal expansion of the substrate. We used the spectral ratio between high frequency (70–150 kHz) and low frequency (10–40 kHz) spectral amplitudes in the frequency domain to classify and differentiate the source types. We found that porosity due to insufficient energy density did not produce detectable AE signals. Using a ball drop calibration technique, we used AE signals to estimate the absolute sizes of the tensile cracks. Crack sizes ranged from 40 μm to 1 mm and were in general agreement with scanning electron microscope images of the fractures. We performed a line scanning test and successfully validated its potential for the application. Our findings provide a basic understanding of AE signal characteristics in AM, as well as the practical parameters used to separate the signal types.

Real-Time Monitoring Method of Power Grid Voltage Stability During Geomagnetic Storms

Real-Time Monitoring Method of Power Grid Voltage Stability During Geomagnetic Storms

Monitoring of distance between laser beam and arc in laser-arc hybrid welding based on deep learning

The laser beam to arc distance (DLA) in laser-arc hybrid welding will significantly affect the heat source’s thermal flow distribution and the weld joint qualities. Therefore, the development of real-time monitoring methods for DLA is highly urgent. This study proposes classification and regression methods for DLA monitoring in laser-arc hybrid welding based on top-surface molten pool images and the convolution neural network (CNN) model. Molten pool images obtained under six fixed DLA values are used to train and test the classification CNN model. The classification CNN model can discriminate six kinds of DLA conditions with an accuracy of 99.83 %. Further, workpieces with different thicknesses are welded with different change strategies of DLA to capture molten pool images. Four training and testing cases are designed to verify the accuracy and generalization ability of the regression CNN model. The mean absolute errors of the estimation results in the four training and testing cases are less than 0.3 mm. The regression CNN model can predict the DLA value within 2 ms, proving that the proposed model exhibits tremendous applicability for monitoring DLA in real-time.

Electrochemical Monitoring of Heterogeneous Peroxygenase Reactions Unravels LPMO Kinetics.

Biological conversion of plant biomass depends on peroxygenases and peroxidases acting on insoluble polysaccharides and lignin. Among these are cellulose- and hemicellulose-degrading lytic polysaccharide monooxygenases (LPMOs), which have revolutionized our concept of biomass degradation. Major obstacles limiting mechanistic and functional understanding of these unique peroxygenases are their complex and insoluble substrates and the hard-to-measure H2O2 consumption, resulting in the lack of suitable kinetic assays. We report a versatile and robust electrochemical method for real-time monitoring and kinetic characterization of LPMOs and other H2O2-dependent interfacial enzymes based on a rotating disc electrode for the sensitive and selective quantitation of H2O2 at biologically relevant concentrations. The H2O2 sensor works in suspensions of insoluble substrates as well as in homogeneous solutions. Our characterization of multiple LPMOs provides unprecedented insights into the substrate specificity, kinetics, and stability of these enzymes. High turnover and total turnover numbers demonstrate that LPMOs are fast and durable biocatalysts.