Real-time Measurement Research Articles

Real-time measurement of metals in submicron aerosols with particle-into-liquid sampler combined with micro-discharge optical emission spectroscopy.

The paper presents a novel technique for quantifying trace metals in aerosol samples in real time. Airborne metals were continuously collected for one week near the Baltic Sea in Finland using a particle-into-liquid sampler (PILS). The collected liquid samples were analyzed for metals using micro-discharge optical emission spectroscopy (µDOES). The micro-discharge analyzer is designed to perform real-time, on-site measurements of metal concentrations in aqueous solutions. Currently, µDOES can provide online measurements of 30 metals, with typical detection limits from 0.01µg/m3 to 0.06µg/m3 with a long-term repeatability less than 5%. The novelty of this analyzer lies in its compact design, rapid detection capabilities, and ease of operation and maintenance. Several metals, including potassium (K), calcium (Ca), sodium (Na), aluminum (Al), magnesium (Mg), and copper (Cu), were measured in the aerosol samples collected using PILS. The results indicate that this approach has significant potential for future automated online monitoring of airborne metal concentrations, facilitating investigations into their sources and daily variations. The development of real-time technologies for rapid, online, and accurate atmospheric aerosol measurements is essential for advancing climate change research. Such advancements allowing for continuous real-time data enhance our understanding of aerosol dynamics, improve source identification, and inform public health and environmental policies, ultimately contributing to more effective climate change monitoring and mitigation.

Edge-Driven Linux Platform for Power Quality Solutions

In the modern landscape of data centers, ensuring optimal power quality and efficiency is paramount. This paper describes designing and implementing an edge-driven Linux platform for power quality solutions for real-time measurement, analysis, and conformity with IEC 61000-4-30, IEEE 519, and EN 50160 standards. It employs an AM6442xx processor and has a web interface with the backing of an efficient embedded web server, a NodeJS, and a Nginx server. It supports industrial communication protocols like Modbus TCP to simplify integration into existing systems. In addition, it involves risk assessment, threat modeling, and enhanced ability of the platform to withstand tests that threaten the confidentiality and integrity of data. Thus, the proposed solution increases the efficiency of data center operations and provides reliable power quality metering that meets current international standards.

Time-resolved observation of DHR123 nano-clay radio-fluorogenic gel dosimeters by photoluminescence-detected pulse radiolysis

The importance of real-time dose evaluation has increased for recent advanced radiotherapy. However, conventional methods for real-time dosimetry using gel dosimeters face challenges owing to the delayed dose response caused by the slow completion of radiation-induced chemical reactions. In this study, a novel technique called photoluminescence-detected pulse radiolysis (PLPR) was developed, and its potential to allow real-time dose measurements using nano-clay radio-fluorogenic gel (NC-RFG) dosimeters was investigated. PLPR is a time-resolved observation method, and enables time-resolved fluorescence measurement. NC-RFG dosimeters were prepared, typically consisting of 100 μM dihydrorhodamine 123 (DHR123) and 2.0 wt.% nano-clay, along with catalytic and dissolving additives. We successfully achieved time-resolved observation of the increase in fluorescence intensity upon irradiation of the dosimeter. Dose evaluation was possible at 1 s after irradiation. The dose-rate effect was not observed for the deoxygenated dosimeter, but was observed for the aerated dosimeter. Besides the dose-rate effect, linear dose responses were obtained for both conditions. Furthermore, we made a novel observation of a decay in the fluorescence intensity over time in the early stages which named fluorescence secondary loss (FSL) and elucidated the conditions under which this phenomenon occurs.

Development and Experimental Study of an Experimental Setup for an Online Vibrating Tube Liquid Densitometer

Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, the real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an online vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density; it also conducts experiments with an online vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant-temperature stirring system, a walk-in constant-temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device’s evaluation is U = 0.08 kg/m3, k = 2. We built a set of pressure-density static test systems, statically testing the online vibrating tube’s liquid-density meter vibration frequency at different pressures; the whole set of systems can be used to assess the specific density, temperature, and pressure range of online vibrating tube liquid density meters in the experimental research to derive the standard temperature. Through the experimental research, we can accurately derive the fitting coefficients under the standard temperature, specific temperature, and pressure of online vibrating tube liquid densitometers, and calculate the fitting error of online vibrating tube liquid densitometers under different temperatures and pressures within the experimental range through fitting equations and coefficients, so as to realize the practical application of online vibrating tube liquid densitometers in engineering by utilizing straight-tube-type and curved-type online vibrating tube densitometers. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system’s vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device’s conformity. The study verified the device’s scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research. Using this device in subsequent experiments can verify the effects of viscosity, inlet, installation, and other factors on the online vibrating tube liquid densitometer’s metrological performance. Further experimental research on the pressure–frequency–density test system and the establishment of a wide range of temperatures and pressures within the pressure standard density test system are needed to achieve a wide range of temperatures and pressures under the standard density test.

Improving time-efficiency and accuracy in echocardiography: real-time automated measurements of left ventricular global longitudinal strain using deep learning

Abstract Background Accurate quantification of left ventricular (LV) systolic function is fundamental in echocardiography. LV Global Longitudinal Strain (GLS) offers advantages over LV ejection fraction, being more sensitive, reproducible and offering better prognostic value. However, existing semi-automatic methods are time-consuming and heavily operator-dependent. Additionally, poor image quality and foreshortening decrease accuracy and reproducibility of GLS. As a result, these challenges lead to suboptimal efficiency and underutilization of GLS quantification in clinical practice. Purpose Our objective was to develop a deep learning (DL) application for real-time automated measurements of GLS, capable of providing tracking quality and foreshortening feedback during scanning. Subsequently, we aimed to validate the novel method in a prospective cohort study. Methods Deep learning networks were trained to identify apical views, identify timing of end-systole and end-diastole, conduct cardiac segmentation, estimate myocardial motion, assess LV length in each view for foreshortening detection, and present real-time visualization of tracking quality and GLS values for three cardiac cycles to the operator for review (Figure 1). The DL method was validated in a prospective cohort of 40 patients included regardless of image quality. Agreement with manual measurements obtained using semi-automatic software (EchoPAC version 204, GE HealthCare, Horten, Norway) was evaluated. Bland-Altman statistics and Pearson's correlation coefficient were used to evaluate agreement and correlation. Results Feasibility for real-time DL measurements of GLS was 98%, with one patient excluded due to poor image quality. There was good agreement (Bias 0.7%, LoA: -1.6 to 3.1%, figure 2) and excellent correlation (Pearson’s correlation coefficient of 0.93, p<0.001) between the real-time DL application and manual reference measurements for GLS. Using the DL application during scanning resulted in a 58% (95% CI 56%-61%) reduction in time required for scanning and post-processing to obtain GLS values, compared to using the semi-automatic method. Average time consumption was 2 minutes and 16 seconds for the DL method and 5 minutes and 27 seconds for the reference method. Conclusion Using deep learning, real-time fully automated measurement of GLS during scanning is feasible, time-saving, and provides good agreement with reference methods. Real-time measurements also offer the potential to reduce operator-related variability by integrating tools to provide image and tracking quality feedback to the user, ultimately benefiting patients.Application user interfaceAgreement between DL and reference

The real-time spectral transient dynamics measurement of mode-locked semiconductor disk lasers with DFT

The real-time spectral transient dynamics measurement of mode-locked semiconductor disk lasers with DFT

Scalable enzymatic lactate sensor for continuous monitoring in interstitial fluid

Abstract Elevated lactate levels have been shown to be an indicator of poor outcome in cardiovascular patients [1]. Moreover, continuous monitoring of lactate may be more clinically valuable than the absolute value of lactate for risk stratification [2]. A promising approach involves the implementation of a lactate monitoring system inspired by advanced continuous glucose monitoring [3] to provide real-time measurements of lactate in interstitial fluid (ISF) based on miniaturized lactate sensors with submillimeter dimensions. The proposed amperometric lactate sensor employs Pt electrodes (WE = 0.785 mm²) on which the enzyme lactate oxidase is immobilized to selectively detect lactate. The sensing mechanism, shown in Fig. 1a, is based on the conversion of lactate to hydrogen peroxide, which then undergoes oxidation at the electrode surface, generating a current proportional to the concentration of lactate. Amperometric measurements are performed with an applied potential of 0.5 V, a sample volume of 2 µL and a flow rate of 1 µL/min, respecting real-life conditions. The response of the sensor towards lactate is shown in Fig. 1c, demonstrating excellent reversibility, long-term stability over 8 hours and negligible cross sensitivity to glucose (G), ascorbic acid (AA) and uric acid (UA). In addition, the sensor linear range can be extended by 10x by incorporating an outer Nafion layer, acting as a diffusion-limiter. As shown in Fig. 1d, by adjusting the thickness of Nafion layer, the linear range increases up to 20 mM lactate, as per ICU specifications. The physiological challenges associated with ISF extraction prompted us to develop a minimally invasive, submillimeter-sized sensor for direct placement inside the body. To address the most critical step of miniaturization, we have successfully fabricated a three-electrode platform on a Si ribbon measuring 300x1500 µm² and a thickness of 100 µm (Fig. 2a). The sensor is also drastically scaled down, with smallest design having a total sensing area of 200x500 µm² (WE = 0.026 mm², Fig. 2b). Its response to lactate is displayed in Fig. 2c, confirming a reliable functionality. Fig. 2d presents the calibration curves obtained from sensors with different sizes, indicating that the reduction in size does not affect the sensitivity. The developed lactate sensor demonstrates excellent performance: high sensitivity, excellent reversibility, and high stability. In addition, the linear range of the sensor is tunable by integrating an outer Nafion membrane. Finally, we demonstrate the successful fabrication and characterization of submillimeter-sized sensors, opening up their potential for minimally invasive in-vivo measurements.Working principle and characterizationCharacterization miniaturized sensors

Deep learning for fully automated echocardiographic measurements of left ventricular wall thickness and chamber dimensions in the parasternal long-axis view

Abstract Background Accurate quantification of left ventricular (LV) wall thickness and chamber dimensions in the echocardiographic parasternal long-axis view (PLAX) is crucial for clinical decisions in patients with heart disease. However, there is a need for more reproducible and time-efficient methods. Fully automated deep learning (DL)-based methods could address this need. Purpose To develop a DL-based method for fully automated measurements of LV wall thickness and chamber dimensions in PLAX, validate its performance in a large population cohort, and evaluate its capacity to differentiate between hypertensive and normotensive groups. Methods DL networks were trained on PLAX recordings from 207 subjects. The DL method performed cardiac segmentation, timing of end-systole and end-diastole, and measured LV wall thickness and chamber dimensions in PLAX. The model was validated in a large populational echocardiography dataset including a subset with test-retest evaluation. Agreement with expert reference measurements, test-retest reproducibility, and comparison of normotensive and hypertensive (defined as those on hypertensive medication or with systolic blood pressure >160 mmHg) groups were evaluated. Results In the populational study of 2047 subjects, the novel DL method demonstrated a feasibility of 97.6%. Strong agreement was observed between the automated DL method and expert readers in measuring end-diastolic septal thickness, posterior wall thickness, and LV diameter (bias 0.1 mm, 0.2 mm, and 2.2 mm, respectively, Figure 1). In a test-retest dataset (n=40, with 2 recordings per patient and 4 readers), the novel DL method displayed improved reproducibility compared to inter-reader measurements, with minimal detectable change for interventricular septal diameter 2.2 mm versus 2.6 mm, posterior wall diameter 1.2 mm versus 2.5 mm, and LV diameter 5.0 mm versus 6.3 mm (Figure 2). The DL method measured significantly wider septal diameter in the hypertensive group compared to normotensive group (p<0.001). Mean analysis time for wall thickness and chamber dimensions using the DL method was 1.2 seconds. Conclusion The novel DL method accurately measured LV wall thickness and chamber dimensions in the PLAX view, and demonstrated improved test-retest reproducibility compared to experienced echocardiographers. Additionally, it was able to detect significantly wider septal diameters in the hypertensive population. By providing fast and reliable measurements in retrospective data, and with potential for real-time measurements during image acquisition, this DL method has the potential to improve the yield, efficiency, and workflow in echocardiography.Bland-Altman posterior wall diameterTest-retest posterior wall diameter

Real-time optical fibre near-infrared chromatic dispersion analyser using collocated optical fibre gratings

We present what we believe to be a novel optical fibre device for real-time measurements of near-infrared chromatic dispersion of a liquid medium from 1100 nm to 1700 nm. This inline optical fibre chromatic dispersion analyser is based upon collocated fibre long-period gratings written adjacently in the core by femtosecond laser inscription yielding 8 attenuation bands associated with different cladding modes, yielding 8 independent measurements. This fibre device is tested on a series of chemical compounds associated with wines. The results for each compound yielded distinctive chromatic responses. A machine learning pipeline was developed that successfully clustered and classified spectral responses of the different chemicals to illustrate the distinctive responses. The limits of detection for these fibre devices ranged from 1×10−3 to 6×10−5 mol/L for the compounds associated with wines, demonstrating the potential usefulness of an optical fibre chromatic dispersion analyser is capable of monitoring in real-time effective chromatic dispersion that is not offered by any current technology.

Agent-based modeling system for real-time characterization of film thickness in digital twin coating

In the automated spraying workshop of ship segments, poor visibility caused by dust and fog hampers the effectiveness of the video monitoring system. The traditional method used during the automated spraying process fails to provide real-time measurement of film thickness and prediction of spraying quality, resulting in uneven film thickness and high rework rates. Robotic spraying technology also faces challenges, such as the time-consuming and labor-intensive process of building and computing a highly reliable Computational Fluid Dynamics (CFD) based spraying film-forming simulation model, which cannot be reflected in real-time during the spraying process. The digital twin characterization method, based on the agent model, offers a solution to these limitations and challenges, providing new ideas and approaches for improving and optimizing the spray film formation model. This paper presents the development of a real-time characterization system for sprayed film thickness based on digital twin technology. The spraying film thickness under different working conditions is obtained using Ansys-Fluent simulation software by varying the gun height and gun speed. The predictive model for spraying film thickness is then established using the obtained simulation data and the corresponding gun height and gun speed, employing the surrogate modeling method (BP neural network). The real-time characterization system for spraying film thickness is developed by combining C# and Unity3D. Finally, in order to ensure the accuracy of the predictive model, a spraying experimental platform is built to verify the accuracy of the predictive model.

Laboratory assessment of real-time water infiltration measurement in SPV200 bentonite using TDR for high-level radioactive waste disposal design

The water content movement within compacted bentonite holds significance in appraising the thermo-hydro-mechanical (THM) performance of the engineered barrier system (EBS) for the disposal of high-level radioactive waste (HLRW), but it demands considerable time to monitor water infiltration in a representative model. Therefore, employing bentonite with varying water content is favored in the laboratory. A comprehensive 3-D phase diagram was established as a benchmark for dielectric constant, temperature, and volumetric water content (VWC) using Time Domain Reflectometry (TDR). SPV200 bentonite at different densities (1400, 1500, and 1600 kg/m3) and temperatures (25, 40, and 60 °C) were prepared with TDR, achieving real-time VWC measurements within a 6 % relative error. Furthermore, numerical outcomes exhibited trends like the TDR tests. Comparation between the simulation and test data is within a relative error ±10 %, which showcased the potential for efficient and effective estimations at a reduced cost for HLRW disposal design.

A reproducible extended ex-vivo normothermic machine liver perfusion protocol utilising improved nutrition and targeted vascular flows

BackgroundNormothermic machine perfusion of donor livers has become standard practice in the field of transplantation, allowing the assessment of organs and safe extension of preservation times. Alongside its clinical uses, there has been expanding interest in extended normothermic machine perfusion (eNMP) of livers as a potential vehicle for medical research. Reproducible extended normothermic machine perfusion has remained elusive due to its increased complexity and monitoring requirements. We set out to develop a reproducible protocol for the extended normothermic machine perfusion of whole human livers.MethodsHuman livers declined for transplantation were perfused using a blood-based perfusate at 36 °C using the Liver Assist device (XVIVO, Sweden), with continuous veno-venous haemofiltration in-parallel. We developed the protocol in a stepwise fashion.ResultsPerfusion techniques utilised included: targeted physiological vascular flows, phosphate replacement (to prevent hypophosphataemia), N-acetylcysteine (to prevent methaemoglobin accumulation), and the utilisation of sodium lactate as both a nutritional source and real-time measure of hepatocyte function. All five human livers perfused with the developed protocol showed preserved function with a median perfusion time of 168 h (range 120–184 h), with preserved viability throughout.ConclusionsLivers can be reproducibly perfused in excess of 120 (range 121–184) hours with evidence of preserved hepatocyte and cholangiocyte function.

High expression of CNOT6L contributes to the negative development of type 2 diabetes.

Type 2 diabetes (T2D) is a chronic metabolic disorder characterized by reduced responsiveness of body cells to insulin, leading to elevated blood sugar levels. CNOT6L is involved in glucose metabolism, insulin secretion regulation, pancreatic beta-cell proliferation, and apoptosis. These functions may be closely related to the pathogenesis of T2D. However, the exact molecular mechanisms linking CNOT6L to T2D remain unclear. Therefore, this study aims to elucidate the role of CNOT6L in T2D. The T2D datasets GSE163980 and GSE26168 profiles were downloaded from the Gene Expression Omnibusdatabase generated by GPL20115 and GPL6883.The R package limma was used to screen differentially expressed genes (DEGs). A weighted gene co-expression network analysis was performed. Construction and analysis of the protein-protein interaction (PPI) network, functional enrichment analysis, gene set enrichment analysis, and comparative toxicogenomics database (CTD) analysis were performed. Target Scan was used to screen miRNAs that regulate central DEGs. The results were verified by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR), western blotting (WB), and blood glucose measurements in mice. A total of 1951 DEGs were identified. GO and KEGG enrichment analysis revealed that differentially expressed genes were mainly enriched in the insulin signaling pathway, ECM-receptor interaction, and PPAR signaling pathway. Metascape analysis indicated enrichment primarily in the cAMP signaling pathway and enzyme-linked receptor protein signaling pathway. WGCNA analysis yielded 50 intersecting genes. PPI network construction and algorithm identification identified two core genes (CNOT6L and GRIN2B), among which CNOT6L gene was associated with multiple miRNAs. CTD analysis revealed associations of core genes with type 2 diabetes, diabetic complications, dyslipidemia, hyperglycemia, and inflammation. WB and RT-qPCR results showed that in different pathways, CNOT6L protein and mRNA levels were upregulated in type 2 diabetes. CNOT6L is highly expressed in type 2 diabetes mellitus, and can cause diabetes complications, inflammation and other physiological processes by regulating miRNA, PPAR and other related signaling pathways, with poor prognosis. CNOT6L can be used as a potential therapeutic target for type 2 diabetes.

Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT

Abstract The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error (MAPE) of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.

Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode

This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing online adaptive FD. The goal of this study is to present an artificial intelligence (AI) technique for detecting seven faults: inverter fault, feedback sensor fault, grid anomaly, nonhomogeneous partial shading, open circuit in PV array, MPPT controller fault, and boost converter controller fault. In this work, it was found that the application of Extreme Learning Machine (ELM) plays an important role in fault detection and localization. Nine (9) statistical features and eight (8) wavelet packet parameters are extracted from the data based on multiple default values. These features were used as an input vector to train and test the ELM and determine whether the system is operating under normal conditions or is faulty. The BDE feature selection algorithm is adopted to optimize the seven-fault classification procedure to reduce the number of features. The results showed that the Extreme Learning Machine (ELM), based on statistical parameters followed by BDE, can detect faults with high accuracy (98.3%) compared to a case without optimization.

Real-time detection of coke particles in blast furnace operations using machine learning: Case of a steel plant in India

The efficiency of blast furnace operations in a steel plant relies on the quality of raw materials, including coke. The particle size distribution (PSD) of coke plays a vital role in ensuring the efficiency of the blast furnace. Conventional methods of measuring average particle size are time-consuming and labour-intensive leading to delayed operations. To address the issue, various studies have used different techniques for real-time measurement of coke particle size, including laser diffraction, acoustic sounding, X-ray computed tomography, and computer vision. While these methods have shown successful applications, there have been limitations in their lower prediction accuracy and detection speed. Therefore, our study proposes a practical approach that leverages the integration of computer vision algorithms to enhance accuracy and detection speed. The proposed methodology is compared with the conventional method of measuring particle size in the lab at a blast furnace. Our results reveal that the YOLOv8 algorithm outperforms the conventional method, providing efficient detection of 5.419 frames per second and with a mean absolute error of [Formula: see text]1.77 mm within the lab results. YOLOv8 can perform object segmentation providing much more precise polygon masks of the coke particles instead of simply providing rectangular dimensions using traditional object detection, as explored in previous studies. This approach enables real-time monitoring of PSD and timely alerts to the onsite team, significantly improving the efficiency of blast furnace operations.

Simulation Research on the Dual-Electrode Current Excitation Method for Distance Measurements While Drilling

Based on a comprehensive analysis of the existing methods for measuring adjacent well distances, along with their advantages and disadvantages, this study employs theoretical analysis, simulation experiments, and other comprehensive research methods to investigate a distance measurement method based on current excitation. In response to the need for measuring and controlling the connection of relief wells, a method utilizing dual-electrode current excitation during drilling is proposed. This approach facilitates synchronous excitation measurements while drilling, significantly reducing both time and costs while ensuring safety and efficiency, making it particularly suitable for the connection operation of relief wells that involve safety risks. Firstly, this paper establishes a drilling with measurement model corresponding to the excitation mode, which derives the calculation formulas for the target casing current amplitude attenuation, as well as the induced magnetic field distribution within the formation. Additionally, it provides the calculation methods for determining the target well distance and azimuth direction. Lastly, the impact levels of various key factors are verified through numerical calculations and simulation analyses, which confirm the correctness and effectiveness of this distance measurement method. The findings from this research establish both a core theoretical foundation and a technological basis for the real-time measurement of adjacent well distances during relief well operations.

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

Hydrogen Measurement & Extraction at Nevada Solar One

Hydrogen was measured and extracted from the headspace gas of four expansion vessels that are components of the heat transfer fluid (HTF) subsystem in the Nevada Solar One (NSO) power plant, Boulder City, Nevada. Direct, real-time measurements of hydrogen partial pressures in the headspace gas were accomplished using the recently installed hydrogen mitigation process that was developed jointly by the National Renewable Energy Laboratory (NREL) and Acciona Energy, the owner/operator of NSO. This method combines the two functions of extracting hydrogen from the headspace gas and measuring hydrogen partial pressure in the headspace gas. During 2022 and 2023, we operated the process regularly and made real-time measurements of hydrogen partial pressures. In addition, DLR in Germany measured dissolved hydrogen concentration in the circulating HTF. These two measurements were compared via Henry’s Law and found to be consistent. Measured values also were compared to model results that were generated by the NSO plant model that was written to predict hydrogen levels in the power plant piping and components. The model predicted hourly hydrogen partial pressures in the expansion tanks for two cases - hydrogen extraction off, and hydrogen extraction on. Real-time hydrogen measurements and sampled HTF measurements were compared to hydrogen levels that were predicted for these two cases. We found good agreement between the hydrogen measurements and the modeled hydrogen for the extraction on case. These results indicate that the extraction process is working properly with its expected performance.

Sustainable and Advanced LED-Implanted Microfluidic Photoreactor with Coupled Optical Fiber Enabling Efficient Photocatalytic Synthesis Beside Online Reaction Progress Monitoring.

The conventional methods for the photoinduced synthesis of benzimidazoles may need further improvement regarding a safer and energy-efficient synthesis platform with in-process monitoring for maximum yield within a much reduced reaction time. This work describes the use of a novel optofluidic Lab-on-a-Chip technology for safer, energy-efficient, and expedited synthesis of benzimidazole derivatives in excellent yields along with real-time quantitative measurement during product formation. This innovative method involves synthesis within LED-embedded microfluidic reactors, allowing for rapid synthesis of benzimidazole derivatives via photoinduced condensation cyclization reactions of aryl aldehydes and o-phenylenediamines using a Rose Bengal/fluorescein photocatalyst. It results in good to excellent yields (85-94%) in a notably shorter period of time (10 min) as compared to that for the batch protocol reaction (2-3 h). The incorporation of a reaction-monitoring microfluidic device precisely connected to the microfluidic reactor (microreactor) unit successfully avoids interference from light sources, ensuring consistent UV-vis spectroscopic observations of the produced benzimidazole derivatives. This LED-embedded microfluidic device's capability of photoinduced synthesis, along with real-time spectroscopic analysis, represents a promising breakthrough in organic synthesis. The proposed approach minimizes the potential for accidents, prevents waste of chemicals, maximizes atom economy, and designs an energy-efficient synthesis route, along with real-time in-process monitoring.