Method For Real-time Monitoring Research Articles

Measurements of Spontaneous and External Stimuli Molecular Release Processes from a Single Optically Trapped Poly(lactic-co-glycolic) Acid Microparticle and a Liposome Containing Gold Nanospheres.

We investigated the single particle kinetics of the molecular release processes from two types of microcapsules used as drug delivery systems (DDS): biodegradable poly(lactic-co-glycolic) acid (PLGA) and a light-triggered-degradable liposome encapsulating gold nanospheres (liposome-GNP). To optimize the design of DDS capsules, it is highly desirable to develop a method for real-time monitoring of the release process. Using a combination of optical tweezers and confocal fluorescence microspectroscopy we successfully analyzed a single optically trapped PLGA particle and liposome-GNPs in solution. From temporal decay profiles of the fluorescence intensity, we determined the time constant τ of the release processes. We demonstrated that the release rate of spontaneously degradable microcapsules (PLGA) decreased with increasing size, while conversely, the release rate of external stimuli-degradable microcapsules (liposome-GNPs) increased in proportion to their size. This result is explained by the differences in the disruption mechanisms of the capsules, with PLGA undergoing hydrolysis and the GNPs in the liposome-GNP undergoing a photoacoustic effect under nanosecond pulsed laser irradiation. The present approach offers a way forward to an alternative microanalysis system for single drug delivery nanocarriers.

Acoustic emission monitoring for damage diagnosis in composite laminates based on deep learning with attention mechanism

During service, advanced composite materials are susceptible to various forms of damage. Developing an efficient real-time Structural Health Monitoring (SHM) method is of profound significance to ensure the normal operational integrity of the advanced composite structures. This study introduces a novel end-to-end SHM approach employing acoustic emission (AE) techniques and a deep learning model with attention mechanisms (AM) to automate the damage diagnosis process and improve prediction accuracy. Low-velocity impact (LVI) experiments were carried out, recording AE signals for subsequent bi-spectrum analysis. Three dedicated AM-based Convolutional Neural Network (CNN) architectures were designed for damage diagnosis, utilizing 2D bi-spectrum contour images as input data. A comparison was made between the proposed CNN model, deep learning and traditional machine learning models in the context of damage diagnostics, revealing that CNN models combined AM exhibit significantly higher accuracy in pattern recognition tasks. The accuracies of the baseline CNN, self-attention-CNN, multi-head attention-CNN and convolutional block attention module-CNN are 98.22%, 98.50%, 98.85%, and 98.87%, respectively. The CNN embedded with AM demonstrated superior predictive performance and fewer instances of misclassification. Considering the specific demands of damage diagnostics in composite materials, the CBAM-CNN is better suited for composite material health monitoring tasks.

Real-time dynamic damage monitoring of ultra-thin-ply composite bonded/bolted joint interference-fit installation based on evolutionary affinity propagation

This research introduced ultra-thin prepregs for designing equal-thickness laminates and conducted experimental tests on interference-fit. The uncertainty of damage, attributed to the bearing and friction exerted by the shank against the hole wall during installation, posed a significant challenge. Moreover, the absence of real-time damage monitoring methods in existing literature. In this paper, we proposed the utilization of acoustic emission (AE) technology in conjunction with the evolutionary affinity propagation (EAP) algorithm to facilitate dynamic, real-time monitoring and classification of damage. The findings indicated that employing ultra-thin in composite interference-fit installations substantially reduced damage to the hole walls. Furthermore, the EAP algorithm surpassed traditional methods in its ability to accurately and efficiently monitor and analyze damage dynamically throughout the interference-fit installation.

Dynamic Evolution and Reversibility of a Single Au25 Nanocluster for the Oxygen Reduction Reaction.

Ultrasmall metallic nanoclusters (NCs) protected by surface ligands represent the most promising catalytic materials; yet understanding the structure and catalytic activity of these NCs remains a challenge due to dynamic evolution of their active sites under reaction conditions. Herein, we employed a single-nanoparticle collision electrochemistry method for real-time monitoring of the dynamic electrocatalytic activity of a single fully ligand-protected Au25(PPh3)10(SC2H4Ph)5Cl22+ nanocluster (Au252+ NC) at a cavity carbon nanoelectrode toward the oxygen reduction reaction (ORR). Our experimental results and computational simulations indicated that the reversible depassivation and passivation of ligands on the surface of the Au252+ NC, combined with the dynamic conformation evolution of the Au259+ core, led to a characteristic current signal that involves "ON-OFF" switches and "ON" fluctuations during the ORR process of a single Au252+ NC. Our findings reinvent the new perception and comprehension of the structure-activity correlation of NCs at the atomic level.

Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning

Periodic blood transfusion is a need in cancer patients in which the disease process as well as the chemotherapy can disrupt the natural production of blood cells. However, there are concerns about blood transfusion side effects, the cost, and the availability of donated blood. Therefore, predicting the timely requirement for blood transfusion considering patient variability is a need, and here for the first-time deal with this issue in blood cancer using in vivo data. First, a data set of 98 samples of blood cancer patients including 61 features of demographic, clinical, and laboratory data are collected. After performing multivariate analysis and the approval of an expert, effective parameters are derived. Then using a deep recurrent neural network, a system is presented to predict a need for packed red blood cell transfusion. Here, we use a Long Short-Term Memory (LSTM) neural network for modeling and the cross-validation technique with 5 layers for validation of the model along with comparing the result with networking and non-networking machine learning algorithms including bidirectional LSTM, AdaBoost, bagging decision tree based, bagging KNeighbors, and Multi-Layer Perceptron (MLP). Results show the LSTM outperforms the other methods. Then, using the swarm of fuzzy bioinspired nanomachines and the most effective parameters of Hgb, PaO2, and pH, we propose a feasibility study on nano fuzzy alarming system (NFABT) for blood transfusion requirements. Alarming decisions using the Internet of Things (IoT) gateway are delivered to the physician for performing medical actions. Also, NFABT is considered a real-time non-invasive AI-based hemoglobin monitoring and alarming method. Results show the merits of the proposed method.

Synthesis, Characterization, and Potential Usefulness in Liver Function Assessment of Novel Bile Acid Derivatives with Near-Infrared Fluorescence (NIRBAD).

Conventional serum markers often fail to accurately detect cholestasis accompanying many liver diseases. Although elevation in serum bile acid (BA) levels sensitively reflects impaired hepatobiliary function, other factors altering BA pool size and enterohepatic circulation can affect these levels. To develop fluorescent probes for extracorporeal noninvasive hepatobiliary function assessment by real-time monitoring methods, 1,3-dipolar cycloaddition reactions were used to conjugate near-infrared (NIR) fluorochromes with azide-functionalized BA derivatives (BAD). The resulting compounds (NIRBADs) were chromatographically (FC and PTLC) purified (>95%) and characterized by fluorimetry, 1H NMR, and HRMS using ESI ionization coupled to quadrupole TOF mass analysis. Transport studies using CHO cells stably expressing the BA carrier NTCP were performed by flow cytometry. Extracorporeal fluorescence was detected in anesthetized rats by high-resolution imaging analysis. Three NIRBADs were synthesized by conjugating alkynocyanine 718 with cholic acid (CA) at the COOH group via an ester (NIRBAD-1) or amide (NIRBAD-3) spacer, or at the 3α-position by a triazole link (NIRBAD-2). NIRBADs were efficiently taken up by cells expressing NTCP, which was inhibited by taurocholic acid (TCA). Following i.v. administration of NIRBAD-3 to rats, liver uptake and consequent release of NIR fluorescence could be extracorporeally monitored. This transient organ-specific handling contrasted with the absence of release to the intestine of alkynocyanine 718 and the lack of hepatotropism observed with other probes, such as indocyanine green. NIRBAD-3 administration did not alter serum biomarkers of hepatic and renal toxicity. NIRBADs can serve as probes to evaluate hepatobiliary function by noninvasive extracorporeal methods.

Brainwaves in the Cloud: Cognitive Workload Monitoring Using Deep Gated Neural Network and Industrial Internet of Things

Monitoring and classifying cognitive workload in real time is vital for optimizing human–machine interactions and enhancing performance while ensuring safety, particularly in industrial scenarios. Considering this significance, the authors aim to formulate a cognitive workload monitoring system (CWMS) by leveraging the deep gated neural network (DGNN), a hybrid model integrating bi-directional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) networks. In our experimental setup, each of the four virtual users is equipped with a Raspberry Pi Zero W module to ensure efficient data transmission, thereby enhancing the reliability and efficacy of the monitoring process. This seamless monitoring framework utilizes the constrained application protocol (CoAP) and the Things Board platform to evaluate cognitive workload in real time. The most popular EEG benchmark dataset, the STEW is utilized for workload classification in this study. We employ the short-time Fourier transformation (STFT) to extract frequency bands corresponding to users in both high and low cognitive workload modes. The proposed DGNN models achieve a perfect accuracy of 99.45%, outperforming every previous state-of-the-art model. We meticulously monitored critical parameters, including latency, classification processing time, and cognitive workload levels. This research demonstrates the importance of continuous monitoring for increasing productivity and safety in industries by introducing a novel method of real-time cognitive workload monitoring. The implementation codes for each experiment are documented and made available for reproducibility.

Domain adaptation neural network based prediction of real-time drill bit tooth wear

Drill bit wear reduces drilling efficiency and escalates operational risks. The drill bit tooth wear grading is a measure of lost tooth height. Accurate monitoring of drill bit tooth wear grade is crucial to adjust drilling parameters and plan timely bit replacement, significantly enhancing drilling efficiency and safety. Current real-time monitoring methods for drill bit wear exhibit limitations, particularly in accounting for variations in the geological formation and drilling parameters, resulting in poor model stability. This paper proposes an intelligent modeling approach that utilizes a Domain Adaptation Neural Network (DANN) to establish an ideal drilling rate of penetration (ROP) prediction model. By incorporating measured ROP, wear coefficients are calculated, and a relationship between wear coefficient and tooth wear grade is established, facilitating real-time monitoring of drill bit tooth wear grade. Test results on ten drill bits demonstrate that the DANN ideal ROP prediction model, leveraging data from new drill bits, enhances the model's adaptability to varying working conditions for new drill bits, thereby improving the accuracy of ideal ROP predictions. We establish relationship between calculated wear coefficients and tooth wear grades. The results indicate that the average error in inner wear grade is 0.6 (7.5%) and the average error in outer wear grade is 1.3 (16%) on an 8-point scale which demonstrates the model's effectiveness in monitoring drill bit tooth wear.

Multitimescale Template Matching: Discovering Eruption Precursors across Diverse Volcanic Settings

Abstract Volcanic eruptions pose significant risks, demanding precise monitoring for timely hazard mitigation. However, interpreting noisy seismic data for eruptive precursors remains challenging. This study introduces a novel methodology that extends an earlier time-series feature engineering approach to include template matching against prior eruptions. We aim to identify subtle signals within seismic data to enhance our understanding of volcanic activity and future hazards. To do this, we analyze the continuous seismic record at a volcano and identify the time-series elements that regularly precede eruptions and the timescales over which these are observable. We conduct tests across various time lengths, ranging from 1 to 60 days. For Copahue (Chile/Argentina), Pavlof (Alaska), Bezymianny (Russia), and Whakaari (New Zealand) volcanoes, we confirm statistically significant eruption precursors. In particular, a feature named change quantiles (0.2–0.8), which is related to the conditional dynamics of surface acceleration at the volcano, emerges as a key indicator of future eruptions over 14-day timescales. This research offers new methods for real-time seismovolcanic monitoring, minimizing the effects of unknown, spurious noise, and discerning recurrent patterns through template matching. By providing deeper insights into pre-eruptive behavior, it may lead to more effective hazard reduction strategies, enhancing public safety around active volcanoes.

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>

Real-time operation risk monitoring method for power grid based on cloud edge collaboration technology

Real-time operation risk monitoring method for power grid based on cloud edge collaboration technology

Real-time monitoring of vegetable oils photo-oxidation kinetics using differential photocalorimetry

This study introduced differential photocalorimetry (DPC) as a method for real-time monitoring of the photo-oxidation kinetics of vegetable oils. DPC measures the heat flow generated during the oxidation of oils upon light exposure. Experiments conducted with stripped linseed oil (SLSO), an oil depleted from its natural antioxidants, showed no induction time (τ). Conversely, spiking SLSO with increasing concentrations of trans-ferulic acid resulted in an induction time (τ) proportional to the antioxidant concentration (R2 = 0.99). A comparative study among different vegetable oils revealed that rice bran oil exhibited the highest resistant to photo-oxidation, followed by corn, soybean, and sunflower oils. The results are discussed in terms of sample oxidizability and antioxidant efficiency (A.E.), and validated through high-performance liquid chromatography with diode array detection (HPLC-DAD). Furthermore, the measured heat flow enabled the determination of the rates of inhibited (Rinh) and uninhibited (Runi) periods, as well as the rate constant of propagation (kp) and inhibition (kinh) reactions.

Real-time monitoring method for power system voltage based on optical voltage sensors

Abstract A real-time monitoring method for power system voltage based on optical voltage sensors is proposed to obtain transient voltage signals and achieve real-time tracking of instantaneous changes in electrical parameters of the power system accurately and effectively. Analyzing the topology principle of power system transformers clarifies the installation location of optical voltage sensors. By combining the measurement principle of optical voltage sensors, the electrical, optical modulation, and optical-electrical conversion of voltage signals are achieved, and the voltage signal of the power system is output. Using the short-time Fourier transform method to perform temporal processing on the output signal and obtaining a voltage signal sliding window based on wavelet packet decomposition, we set the normal range threshold of the voltage signal in the sliding window, combined with information entropy theory to extract key features of the voltage signal and achieve real-time monitoring of the power system voltage. The experimental results show that the proposed method can effectively obtain transient voltage signals of the power system, with an average detection accuracy of 92.61%, and the output voltage can be adjusted promptly to reflect the instantaneous changes in the power system voltage.

Revealing evolution law and failure mechanism of interface damage in concrete-encased CFST columns by acoustic emission technology

Concrete-Encased Concrete-Filled Steel Tubular (CFST) has extensive applications in high-rise buildings and bridge engineering as a high-performance composite structure. The severe bond failure between Concrete-Filled Steel Tubular and the outer concrete poses a significant threat to the integrity and safety of the structure. Considering the concealed and random nature of interface damage, this study proposes a real-time monitoring and evaluation method for Concrete-Encased CFST interface damage based on acoustic emission (AE) technology. Four groups of experiments on Concrete-Encased CFST column interface shear was conducted to investigate the influence of the number of shear connectors on the bond failure mechanism between CFST and the outer concrete. The signal characteristics of three kinds of AE sensors with different operating frequency ranges were analyzed to guide the selection of sensors according to the monitoring targets. The structural damage development trend has been recognized by analyzing the change rule of AE feature parameters. RA-AF correlation analysis has been used to identify the evolution law of cracks and the failure mechanism of the structure. The b-value analysis has been used to evaluate the damage severity of the structure. From the above three aspects, the damage evolution law and failure mechanism of the interface can be revealed. The results indicate that the R3α sensor is suitable for early warning of damage, the R15α sensor can be used for monitoring and warning of structural failure, and the R6α sensor can be used to describe and evaluate the whole process of damage. Based on the features of AE hit rate, cumulative hits, and cumulative energy, the damage process of Concrete-Encased CFST column interface can be accurately divided into the elastic stage, local slip stage, shear connector fracture stage, and slip stage. The RA-AF correlation analysis showed that the shear cracks were detected to develop rapidly before the peak load, and a significant number of tensile cracks caused by cracking of the outer concrete appear around the peak load. The changes of b-value can effectively reflect the magnitude of damage and record the occurrence of major damage.

In-situ controllable preparation of ZIF-8-wrapped ZnO heterojunction nanorods array for ppb-level Cl2 detection operated at near room temperature

Real-time monitoring of chlorine gas (Cl2) with a strong irritating odor is an indispensable means of avoiding its contamination of the air and harm to the body. To achieve this purpose, a novel gas-sensitive material capable of detecting low concentrations of Cl2 at near room temperature is developed in this paper. ZIF-8 with a kinetic diameter of 3.4 Å in the designed ZnO@ZIF-8 composites is able to sieve gas molecules with different kinetic diameters, enabling effective improvement of the selectivity of the gas-sensitive materials. In addition, the porous structure of ZIF-8 can provide more reactive sites, which is conducive to improving gas adsorption capacity and providing of channels for gas diffusion. In this study, ZnO@ZIF-8 heterojunction composites are prepared by combining the advantages of ZnO and ZIF-8, which provides a new method for real-time monitoring of low concentrations of Cl2, and also has potential practical applications in fields such as environmental monitoring and industrial safety.

Pixel-Level Recognition of Trace Mycotoxins in Red Ginseng Based on Hyperspectral Imaging Combined with 1DCNN-Residual-BiLSTM-Attention Model.

Red ginseng is widely used in food and pharmaceuticals due to its significant nutritional value. However, during the processing and storage of red ginseng, it is susceptible to grow mold and produce mycotoxins, generating security issues. This study proposes a novel approach using hyperspectral imaging technology and a 1D-convolutional neural network-residual-bidirectional-long short-term memory attention mechanism (1DCNN-ResBiLSTM-Attention) for pixel-level mycotoxin recognition in red ginseng. The "Red Ginseng-Mycotoxin" (R-M) dataset is established, and optimal parameters for 1D-CNN, residual bidirectional long short-term memory (ResBiLSTM), and 1DCNN-ResBiLSTM-Attention models are determined. The models achieved testing accuracies of 98.75%, 99.03%, and 99.17%, respectively. To simulate real detection scenarios with potential interfering impurities during the sampling process, a "Red Ginseng-Mycotoxin-Interfering Impurities" (R-M-I) dataset was created. The testing accuracy of the 1DCNN-ResBiLSTM-Attention model reached 96.39%, and it successfully predicted pixel-wise classification for other unknown samples. This study introduces a novel method for real-time mycotoxin monitoring in traditional Chinese medicine, with important implications for the on-site quality control of herbal materials.

A real-time intelligent monitoring method for indoor evacuee distribution based on deep learning and spatial division

Providing real-time and detailed indoor evacuee distribution information is of great significance for the on-site formulating and dynamic adjustment of indoor emergency evacuation strategies. However, due to limitations in hardware costs and privacy protection, existing video surveillance and other evacuee counting methods cannot achieve real-time and accurate monitoring of the number and distribution of evacuees in all indoor areas. To solve the problem, using video data from an indoor surveillance system as a data source, an intelligent monitoring method for indoor pedestrian real-time distribution based on deep learning and spatial division is proposed. Firstly, the whole indoor area is divided many subareas according to the layout of indoor space and the size of spatial units. Then, deep learning detection and tracking algorithms are used to detect and track evacuees to achieve the number of evacuees at the boundary of each subarea and their movement directions; Finally, the numbers of evacuees entering and leaving each subarea are counted to obtain the spatial distribution information of evacuees. The experimental results show that within an appropriate monitoring distance, the proposed method achieves an average F1 score of 91.85% for evacuee counting at the boundaries of each subarea, with a processing speed of 22 FPS. More comprehensive and accurate real-time monitoring of evacuee distribution in the entire indoor space can be achieved, including no covered areas of cameras. It not only helps to formulate on-site emergency evacuation in case of a fire, but also enhances the daily operation and management capabilities of buildings.

Research on real-time monitoring methods for intelligent tower base health based on machine vision systems

This article introduces an innovative real-time health monitoring method for the tower foundation a system based on machine vision technology. This system is committed to overcoming the issues associated with traditional manual inspection methods and the high cost and technical challenges faced by sensor-based online monitoring systems during implementation. We delve into the principles of stereo imaging technology, employing a tilt monitoring algorithm to accurately measure the relative tilt angle, and perform an evaluation of the tower foundations health status based on this tilt angle range. We believe this technology has the potential to revolutionize the field of real-time tower foundation health monitoring, offering a more flexible, accurate, and cost-effective solution.

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.