Real-time Monitoring Technique Research Articles

Electrophysiological Characteristics and Mechanism of His-Bundle Pacing Guided by Electrocardiograms in Uninterrupted Real-Time Monitoring Technique.

His-bundle pacing (HBP) is a pacing mode that provides near-physiological pacing and has more advantages over standard right ventricle (RV) septum pacing in positive clinical results. However, traditional HBP cannot accurately and stably place the lead tip into the His-Purkinje system. Hence, this study aimed to establish a novel strategy for HBP to strike a balancebetween an excellent pacing threshold and minor injuries to the conduction system. HBP, using continuous and real-time monitoring of unipolar His-bundle (HB) electrograms along with electrocardiograms, was performed in 29 consecutive atrial fibrillation patients. We analyzed the unipolar HB intracardiac electrogram (EGM) and electrophysiological characteristics during HBP using John Jiang's connecting cable and an electrophysiology recording system. All patients were followed up for 12 months. Of the 29 patients, 28 (96.55%) successfully received HBP. Twenty-one (75%) patients were confirmed to have the negative deflection of His potential with a negative amplitude of ≥ 0.1mV in HB EGM. The subgroup with a deep negative deflection was recorded with an HBP threshold (0.71 ± 0.41V), significantly lower than those thresholds with no deep negative deflection (1.83 ± 0.76V) (p < 0.05). The selective HBP rate was significantly different between the two groups: 20 (95.24%) in the deep negative deflection group and three (42.86%) in thenon-deep negative deflection group (p < 0.05); five (17.24%) patients presented right bundle branch block (RBBB) during the lead placement. This study supports the safety and feasibility of using an uninterrupted real-time monitoring technique for HBP. An uninterrupted real-time monitoring technique can guide theaccurate placement of the HB lead and may provide abalancebetween anexcellent pacing threshold and minor injury to the conduction system.

Plasmon-enhanced NIR-II photoluminescence of rare-earth oxide nanoprobes for high-sensitivity optical temperature sensing.

With ongoing advancements in photothermal therapy, achieving efficient tumor cell eradication while minimizing damage to healthy tissues necessitates a highly effective and non-invasive real-time temperature monitoring technique for human tissues. Herein, we report a near-infrared (NIR)-II optical temperature sensing nanoprobe featuring rare-earth-doped gadolinium oxide nanocrystals (RENCs) attached to the dumbbell mesoporous silica-coated gold nanorods (AuNRs). The composite nanoprobe presents an intense absorption in the NIR region, and NIR-II photoluminescence (PL) increases by 97.2 to 102-fold compared to pure RENCs upon 980 nm irradiation. The localized electric field generated through surface plasmon resonance effects of AuNRs demonstrated a dumbbell-shaped distribution that aligns with the structure of nanoprobes, maximizing the PL enhancement of RENCs. Moreover, the NIR-II emissions are changed with the rising temperature, with an exceptional relative sensitivity of 7.25% K-1 at 338 K based on PL lifetime, indicating the nanoprobe is highly potential for optical temperature sensing.

Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions.

Visualizing the Structure and Dynamics of Transition Metal-Based Electrocatalysts Using Synchrotron X-Ray Absorption Spectroscopy.

As the global energy structure evolves and clean energy technologies advance, electrocatalysis has become a focal point as a critical conversion pathway in the new energy sector. Transitional metal electrocatalysts (TMEs) with their distinctive electronic structures and redox properties show great potential in electrocatalytic reactions. However, complex reaction mechanisms and kinetic limitations hinder the improvement of energy conversion efficiency, highlighting the necessity for comprehensive studies on structure and performance of electrocatalysts. X-ray Absorption Fine Structure (XAFS) spectra stand out as a robust tool for examining the electrocatalyst's structures and performance due to its atomic selectivity and sensitivity to local environments. This review delves into the application of XAFS technology in characterizing TMEs, providing in-depth analyses of X-ray Absorption Near-Edge Structure (XANES) spectra, and Extended XAFS (EXAFS) spectra in both R-space and k-space. These analyses reveal intrinsic structural information, electronic interactions, catalyst stability, and aggregation morphology. Furthermore, the paper examines advancements in in-situ XAFS techniques for real-time monitoring of active site changes, capturing critical intermediate and transitional states, and elucidating the evolution of active species during electrocatalytic reactions. These insights deepen our understanding on structure-activity relationship of electrocatalysts and offer valuable guidance for designing and developing highly active and stable electrocatalysts.

Time-dependent corrosion behavior of EH36 steel caused by Pseudomonas aeruginosa based on big data monitoring technology

Marine microbial corrosion poses a significant threat to the safe service of marine engineering equipment. Previous studies have often failed to thoroughly analyze the continuous and prolonged microbial corrosion process, resulting in an incomplete understanding of microbial corrosion mechanisms involved at various stages and the development of ineffective control strategies. This study employed a corrosion big data online real-time monitoring technique to investigate the time-dependent corrosion behavior of EH36 steel caused by Pseudomonas aeruginosa in aerobic environments over a 30-d incubation period. It was found that P. aeruginosa accelerated the corrosion of EH36 steel in the early stages by enhancing the cathodic oxygen reduction process. As oxygen levels declined, P. aeruginosa transitioned from aerobic to anaerobic respiration, promoting corrosion through biocatalytic nitrate reduction. In the later stages, the reduction in sessile cell counts, extreme low oxygen concentration, and dense surface film increased the charge transfer and film resistances, ultimately leading to corrosion inhibition. The weight loss and electrochemical data confirmed the effectiveness of the big data monitoring technique in investigating microbial corrosion, which provides new approaches for diagnosing and preventing microbial corrosion.

Sensitive and real-time monitoring of microbial growth using a dielectric sensor with a 65-GHz LC-oscillator array and polytetrafluoroethylene membrane

In this study, we report a sensitive real-time microbial growth monitoring technique using a complementary metal-oxide semiconductor (CMOS) dielectric sensor with a polytetrafluoroethylene (PTFE) membrane. The sensor comprised an LC oscillator array operating at 65-GHz, whose resonant frequency was altered according to the dielectric properties of the region approximately 15 μm from the surface. We previously reported the rapid detection of viable Escherichia coli suspended in a liquid medium using the dielectric sensor; however, sensing growing cells was challenging owing to their tendency to float outside the effective sensing area in the suspended medium. To address this, we propose a new method to enhance the sensitivity of the device using a PTFE membrane that retains cells inside the effective area during measurement. Experiments using Escherichia coli suggested that the use of the membrane more than doubled sensitivity, reducing inspection times for practical applications. Furthermore, experiments with Lactococcus lactis, Staphylococcus epidermidis, and Saccharomyces cerevisiae demonstrated that this method can be used to monitor the growth of various microbes. In addition, variations in the output values of each oscillator facilitated the determination of microbial characteristics, such as cell size and growth distribution. This microbial growth monitoring technique is expected to find applications across a wide range of fields, such as food inspection, environmental hygiene monitoring, antibiotic susceptibility testing, new drug discovery, and the exploration of beneficial microbes.

Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor

Changes in the freshness of pork due to microbial action during complex transportation and storage indicate an urgent need for in-situ, real-time monitoring techniques for chemical spoilage of meat. This study reported the use of a portable detection device based on a miniaturized visible/near-infrared spectrometer, combined with data noise reduction and machine learning methods, to predict the total viable count (TVC) in pork samples. A rapid detection device for pork TVC was designed based on the miniaturized spectrometer; a spectral preprocessing method based on the resolution of the spectrometer was proposed; the effects of different preprocessing methods on the full-wavelength modeling effect were compared; and four different feature wavelength extraction algorithms were utilized for the feature wavelengths of pork TVC. The modeling effects of different simplified models were compared. The results showed that resolution interval correction combined with standard normal variation was the most effective in full-wavelength modeling, with correlation coefficients of prediction set (RP), root mean square errors in prediction set (RMSEP), and relative percent deviation (RPD) of 0.918, 0.464 (lg CFU/g), and 2.508, respectively; interval random frog − partial least squares regression (iRF-PLSR) had the best predictive ability among all simplified models, the number of wavelengths used in the simplified model was reduced by 85.45% compared with the full-wavelength model. In contrast, the model performance was improved with RP, RMSEP, and RPD of 0.948, 0.392 (lg CFU/g) and 2.970, respectively. The combination of a rational spectral acquisition setup and a data processing methodology, the miniaturized spectrometer showed competitive results with the complex detection system in predicting meat TVC.

Determining aqueous deuterium detection limits via infrared spectroscopy to understand its capabilities for real-time monitoring of tritiated water

Tritiated water will be present in many demonstrator class fusion devices in a wide array of locations and concentrations. Accurate and fast measurement will be a key requirement to ensure suitable process monitoring, safety assurance and tritium tracking is in place. This work quantifies the deuterium detection limits achievable using a benchtop transmission Fourier transform infrared (FTIR) instrument and an industrialised attenuated total reflectance (ATR)-FTIR instrument to understand the capabilities of these techniques for real-time monitoring of aqueous tritium. Deuterium limits of detection of 4.62 × 10-5 mol mL-1 and 1.07 × 10-3 mol mL-1 were demonstrated with measurement times of 10s for the transmission FTIR and ATR-FTIR, respectively. These instruments are considered viable for the measurement of high concentration tritiated water and have many potential benefits associated with their deployment.

State-of-the-Art Techniques for Real-Time Monitoring of Urban Flooding: A Review

In the context of the increasing frequency of urban flooding disasters caused by extreme weather, the accurate and timely identification and monitoring of urban flood risks have become increasingly important. This article begins with a bibliometric analysis of the literature on urban flood monitoring and identification, revealing that since 2017, this area has become a global research hotspot. Subsequently, it presents a systematic review of current mainstream urban flood monitoring technologies, drawing from both traditional and emerging data sources, which are categorized into sensor-based monitoring (including contact and non-contact sensors) and big data-based monitoring (including social media data and surveillance camera data). By analyzing the advantages and disadvantages of each technology and their different research focuses, this paper points out that current research largely emphasizes more “intelligent” monitoring technologies. However, these technologies still have certain limitations, and traditional sensor monitoring techniques retain significant advantages in practical applications. Therefore, future flood risk monitoring should focus on integrating multiple data sources, fully leveraging the strengths of different data sources to achieve real-time and accurate monitoring of urban flooding.

Analysis of speed using optocoupler sensors on electric boats in Sayung Demak

In the Indonesian archipelago, boats play a major role in people's daily mobility. In the realm of transportation, especially on sea vessels, it is crucial to keep an eye on the circumstances and state of affairs when traveling long distances. The objective of this study is to use an optocoupler sensor based on the Internet of Things (IoT) and a real-time monitoring technique to track the speed of an electric boat. This instrument can be tested using three different treatments: low-level gas, medium-level gas, and high-level gas with five repetitions. Motor speed was measured at 938 Rpm with a Knot difference of 0.7 utilizing XSpeed and an optocoupler sensor for low-speed measurements. Using an optocoupler sensor and XSpeed, the medium speed tests yielded a motor speed of 1205.6 Rpm and a Knot difference of 1.7. Using an optocoupler sensor and XSpeed, high-speed measurements yielded a motor speed of 1985.4 Rpm and a Knot difference of 1.2. Because the Blynk application may display data of both boat speed and motor rotational speed, readings obtained from the optocoupler sensor yield higher accuracy than those obtained from the comparison application XSpeed

Visualizing cancer resistance via nano-quenching and recovery detector of CD44

Drug resistance to chemotherapy in cancers remains significant clinical challenges. CD44 modulates cellular adhesion, migration and growth, which plays a pivotal role in driving cancer resistance and even recurrence. Despite ongoing efforts, accurate, safe, and real-time dynamic monitoring techniques for CD44 expression remain inadequate in guiding the management of drug-resistant cancer treatment. In this study, we developed a nano-quenching and recovery detector of CD44 (Cy3-AptCD44@BPNSs) for visualizing cancer drug resistance. The fluorescence recovery of the detector is directly related to the CD44 expression level on cancer cells, which can be used to indicate the degree of drug resistance. It’s confirmed that downregulating CD44 expression on cancer cells results in a corresponding decrease in the fluorescence intensity of the detector, which enables precise and dynamic monitoring of CD44. In addition, the Cy3-AptCD44@BPNSs also exhibited specificity in detecting CD44. This visualizing strategy may open up a wide range of possibilities for rapid recognition to cancer drug resistance, which is more efficient and flexible.

Characteristics analysis and monitoring of friction stir welded dissimilar AA5083/AA6061-T6 using acoustic emission technique

The joining of aluminum alloys using fusion welding often leads to issues such as hot tears, porosity, distortion, and solidifying cracks. To address these challenges, solid-state welding processes like Friction Stir Welding (FSW) are preferable. This study investigates the FSW of dissimilar aluminum alloys AA 5083 and AA 6061-T6, employing Acoustic Emission (AE) techniques for real-time monitoring. FSW was conducted under conditions with no defects, pinhole defects, and piping defects. The resulting joints were evaluated for their mechanical properties and metallurgical characteristics. Microstructural analysis was performed using an optical microscope, revealing that the defect-free condition achieved the highest tensile strength of 256 MPa and superior hardness of 93 HV at the stir zone. Tensile failure commonly manifested within the heat-affected zone (HAZ) of AA 6061-T6, primarily due to grain enlargement and the impact of Mg2Si precipitates. Examination of the fractured regions via scanning electron microscopy revealed ductile-mode fractures featuring elongated dimples and microvoids. AE parameters such as hits, amplitude RMS, and energy were analyzed, demonstrating that defect-free welds had consistent AE signal patterns, while significant variations were observed in pinhole and piping defect conditions due to larger defect areas. The findings suggest that AE monitoring is effective in detecting and analyzing welding defects, providing valuable insights into the FSW process for dissimilar aluminum alloys.

Real-Time Monitoring of Particle Size in Emulsion Polymerization: Simultaneous Turbidity and Photon Density Wave Spectroscopy.

Particle size evolution in seeded semibatch emulsion polymerization is monitored by two real-time monitoring techniques: online turbidity spectroscopy (TUS) and inline photon density wave spectroscopy (PDWS). An automatic dilution system that withdraws a sample from the reactor and upon dilution transfers to the measurement cell is used for the online TUS analysis. A PDWS probe is immersed in the reactor and collects inline the scattered light directly from the reacting latex. The particle sizes retrieved from TUS and PDWS are compared to offline dynamic light scattering (DLS) values. The particle size obtained by TUS is close to the intensity-average particle size obtained offline by DLS, while the particle size obtained by PDWS lies closer to the number-average particle size from DLS.

Advancements in Real-Time Monitoring of Enteric Methane Emissions from Ruminants

The agricultural sector is responsible for a significant proportion of global anthropogenic methane (CH4) emissions, with enteric CH4 produced from ruminant livestock representing approximately 28% of the total. The development of effective mitigation strategies necessitates the accurate and actionable monitoring of CH4 emissions. However, a considerable research gap remains concerning real-time monitoring techniques capable of supporting on-farm enteric CH4 mitigation strategies. To bridge this research gap, this study explores the current status of real-time enteric CH4 emission monitoring techniques and technologies for ruminants. The study achieves this by reviewing key biomarkers and proxies for ruminant emissions, examining established animal-based measurement techniques, exploring emerging technologies, and critically assessing technological limitations and opportunities. By shedding light on this research area, this study aims to assist stakeholders in developing a viable pathway for on-farm emission monitoring, with the hope of facilitating a meaningful reduction in GHG emissions from the livestock sector.

New Opportunities in Real-Time Diagnostics of Induction Machines

This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance.

Optimizing traffic management in Danang: a comparative study of multi-object tracking techniques for real-time vehicle flow monitoring

Optimizing traffic management in Danang: a comparative study of multi-object tracking techniques for real-time vehicle flow monitoring

Research on predicting photosynthetic pigments in tomato seedling leaves based on near-infrared hyperspectral imaging and machine learning

Conventional chemical approaches could be limited in monitoring the concentration of pigments in plants in high volumes. To overcome these limitations, researchers often turn to non-invasive, high-throughput, and real-time monitoring techniques, such as spectroscopy and hyperspectral imaging, which allow for the assessment of pigment concentration in plants without the need for destructive sampling and offer the ability to monitor large volumes of plants efficiently. This research focused on the utilization of machine learning in conjunction with hyperspectral imaging to develop models for predicting the concentration of three pigments, namely chlorophyll-a, chlorophyll-b, and carotenoids, in tomato seedlings. The sample tomato seedlings were sourced from two distinct varieties: the wild type and the Long Hypocotyl-5-deficient (HY5) type. The spectral data were acquired using a near-infrared (NIR) camera with a spectral range spanning approximately 900–1700 nm. Machine learning algorithms such as partial least squares regression (PLSR) and extreme learning machine (ELM) were utilized to explore the latent relationship between hyperspectral information and chemical measurements. In addition, principal component analysis (PCA), independent component analysis (ICA), and competitive adaptive reweighted sampling (CARS) methods were used to extracted informative wavelengths from the reflectance spectrum. And a comprehensive analysis regarding to spectroscopy was conducted to investigate the validity and efficiency of the results of feature extraction. The ELM model demonstrated the highest effectiveness, achieving R2 values of 0.86, 0.83, and 0.83 for chlorophyll-a, chlorophyll-b, and carotenoids, respectively, on the test set. By integrating the predictive models with classifiers such as Logistic Regression, Support Vector Classifier (SVC), and K-nearest Neighbors (KNN), tomato seedlings were categorized into wild type and HY5 type. The findings showed that the proposed approach efficiently predicted the concentration of the pigments in tomato seedlings and explored the feasibility of using these results to identify tomato gene types.

Real-time Performance Monitoring of HPC Clusters: Techniques and Challenges

In the era of exascale computing, High-Performance Computing (HPC) clusters have become essential for addressing complex computational challenges across various domains. The increasing scale and complexity of HPC systems pose significant challenges in ensuring optimal performance and resource utilization. Real-time performance monitoring has emerged as a critical requirement to address these challenges effectively. The paper offers a comprehensive analysis of real-time performance monitoring techniques and challenges within HPC clusters focusing on three key monitoring approaches: Prometheus for agent-based monitoring, the ELK (Elasticsearch, Logstash, Kibana) stack for log-based monitoring, and machine learning/artificial intelligence techniques for proactive analysis. The implementation of Prometheus and Node Exporter for real-time metrics collection, the utilization of the ELK stack for log aggregation and analysis, and the integration of ML/AI for anomaly detection and predictive analytics are explored. Challenges such as scalability, heterogeneity, and dynamic workloads are addressed in the context of each monitoring approach. The findings demonstrate that each technique offers unique advantages and trade-offs, providing valuable insights for improving HPC performance monitoring practices. Key Words: High-Performance Computing, real-time performance monitoring, Prometheus, ELK stack, Machine Learning, Artificial Intelligence, HPC clusters.

Nucleation and growth rate determination on alkali-activated slag under various sodium hydroxide molarity

Alkali-activated slag has been noted as one of potential alternatives to the ordinary Portland cement due to its properties including high early strength performance and capability of ambient curing. However, there is still limited studies available on elucidating the reaction processes towards producing the excellent properties. This study aims to elucidate the mechanism of alkali activation of slag under different molarities of sodium hydroxide, which is one of the most influential factors on the properties of alkali-activated slag. Heat evolution of alkali-activated slag was used as a real-time monitoring technique. For mix designation, the molarity of sodium hydroxide was varied from 6M to 14 M, with solid-to-liquid ratios of 0.6 and alkali activator ratios of 2.0 remaining constant. The calorimetric data obtained was further used for determination of degree of reaction, nucleation and growth rate mechanism using Johnson–Mehl Avrami Kolmogrov model. According to the findings, it was found that regardless of various molarity of sodium hydroxide applied, the nucleation mechanism and growth is governed by instantaneous heterogeneous nucleation with rod-like growth as the n value is approaching 1 in which is observed from the morphology of the alkali-activated slag at lowest molarity applied (6 M). Furthermore, increasing in molarity of sodium hydroxide was found to decrease the total heat evolved and the lowest was obtained when using 14 M.

A comprehensive review of cased hole sand control optimization techniques: Theoretical and practical perspectives

Cased hole sand control is a critical aspect of oil and gas well management, ensuring efficient production by mitigating sand production issues. This review presents a comprehensive review of cased hole sand control optimization techniques, offering theoretical insights and practical perspectives to enhance well productivity and longevity. The review encompasses various aspects, including the challenges of sand production, the importance of sand control, and the evolution of sand control techniques. Sand production in oil and gas wells can lead to equipment damage, wellbore instability, and reduced production efficiency. Effective sand control is thus essential to maintain well integrity and maximize hydrocarbon recovery. This review explores the fundamental principles of sand control, highlighting the key parameters influencing sand production, such as reservoir properties, fluid properties, and wellbore geometry. The review discusses traditional sand control methods, such as gravel packing and standalone screens, along with emerging technologies like expandable sand screens and autonomous inflow control devices. It evaluates the advantages and limitations of each technique, considering factors such as installation complexity, cost-effectiveness, and long-term reliability. Furthermore, the review delves into advanced sand control optimization strategies, including the use of modeling and simulation tools to predict sand production and optimize sand control design. It discusses the integration of data analytics and machine learning techniques for real-time monitoring and decision-making, enhancing sand control effectiveness and reducing operational risks. Practical case studies and field applications are presented to illustrate the implementation of sand control optimization techniques in real-world scenarios. These case studies highlight the challenges faced, the solutions implemented, and the outcomes achieved, providing valuable insights for industry practitioners and researchers. In conclusion, this review provides a comprehensive overview of cased hole sand control optimization techniques, emphasizing the importance of integrating theoretical knowledge with practical considerations to enhance sand control efficiency and maximize well productivity. his review paper will systematically analyze existing methodologies and the latest advancements in cased hole sand control treatments. It explores theoretical frameworks, compares practical outcomes, and discusses the implications of optimized frac geometries for reservoir performance enhancement across varied geological settings.