On-line Measurement Research Articles

Direct and indirect effects of childhood adversity on psychopathology: Investigating parallel mediation via self-concept clarity, self-esteem and intolerance of uncertainty.

The aim was to extend previous work on the identity disruption model (IDM) of adult psychological distress. According to the IDM, aversive childhood experiences (ACEs) disrupt the development of identity, resulting in an unclear sense of self and a reliance on external sources of self-definition, leading to psychological distress in adulthood. In line with this model, self-concept clarity (SCC) in parallel with self-esteem (SE) and intolerance of uncertainty (IU) has been shown to mediate the relationship between childhood adversity and depression and anxiety. The current study examined if SCC, SE and IU mediated the influence of childhood adversity on depression, anxiety and hypomania. A community sample of 159 adults completed online measures of childhood adversity, self-esteem, self-concept clarity, intolerance of uncertainty, depression, anxiety and hypomania. Structured equation modelling using bias corrected bootstrapping was used to test the mediation model. Direct effects of childhood adversity were found for depression and anxiety, but not hypomania. The influence of ACEs on depression and anxiety was mediated by self-concept clarity and self-esteem. Self-concept clarity also mediated the influence of ACEs on hypomania, which is an important novel finding. The indirect effect of childhood adversity via intolerance of uncertainty was limited to anxiety. Results suggest that the identity disruption model generalizes to hypomania. The clinical implications are that interventions to improve clarity of the self-concept might be useful in reducing psychopathology.

Online assessment in young children: Challenges and considerations.

In the years following the COVID-19 pandemic, online assessment has emerged as an increasingly popular method for testing children. This review aims to review key advantages and disadvantages of online assessment in young children, specifically in the area of literacy and mathematics assessment. We first consider the recent history of online assessment and give evidence from our own experiences with online assessments in both Cebu, Philippines and Hong Kong. We then discuss the benefits of conducting assessments online. One of the key strengths of online assessments is the efficient use of resources, such as ease of staff training, increased participation and decreased costs for both participants and researchers. Difficulties related to testing environment, parental interference and exclusion of certain populations are also highlighted as focal challenges that must be considered when designing and implementing online assessment. The reliability and validity of online assessments are then specifically discussed in relation to the question of how to best ensure that online measures function as intended, stressing the importance of establishing separate norms for the various administration modes. Notably, online assessments delivered via phone screen must have separate norms than those delivered on a computer screen, as device differences may account for differences in performance. Finally, we present broader considerations for designing online assessments with young children. This includes practical tips for pilot testing, user interface design, data confidentiality and automatic scoring, both to enhance the efficiency of future online assessments and increase participation. Specifically, online assessments for children must account for their developing attentional abilities, meaning assessments should be brief, engaging and contain clear task instructions. The implementation of these considerations will support the development of more reliable, valid and inclusive assessments which will allow researchers and practitioners to support equitable and feasible online testing moving forward.

The association between subjective sleep and stress in recreational athletes

Subjective sleep and stress are strongly associated, at multiple levels, and the current body of evidence highlights a bi-directional association. Previous research has highlighted that issues with sleep can impact on several stress responses. On the other side of this relationship, research has shown that stress-inducing factors can significantly impact sleep. The present study examined this association in a sample of recreational athletes, a population that has received little to no research focus to date Recreational athletes are defined as individuals who exercise >4 hours per week for health, fitness, or unofficial competitions. Recreational athletes (n = 34) completed online measures of subjective sleep, subjective stress, subjective anxiety/depression and training load (PSQI, PSS, HADS and DALDA). Pearson correlations were carried out to examine associations between variables. There was a significant positive correlation between subjective sleep quality and subjective stress. There was a significant positive correlation between subjective stress and training load. There was a significant positive correlation between subjective sleep quality and training load. The positive associations between sleep, stress and training load are consistent with previous research, but the present study adds to the literature by highlighting the associations in recreational athletes. Recreational athletes should proactively manage their sleep and stress, as due to the bi-directional relationship, improving sleep may benefit stress, and improving stress may benefit sleep quality. This is also likely to benefit overall mood and reduce the likelihood of overtraining in recreational athletes.

Real-time monitoring of multiparameter in 1,3-propanediol production process with near-infrared spectroscopy

1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocess control based on real-time information of feedstock and main products is crucial for reducing the cost of production. However, rapid quantification of 1,3-PDO and glycerol remains challenging due to their highly similar molecular structures. In this study, the feasibility of near-infrared (NIR) spectroscopy to monitor 1,3-PDO, glycerol, acetate, and butyrate concentrations in the fermentation process using strain Clostridium pasteurianum was evaluated. NIR spectra were acquired through at-line measurement involving sampling and ex-situ analysis or on-line measurement with a fiber optic probe immersed in fermentation broth, integrated with Partial Least Squares (PLS) regression to establish calibration models on a laboratory-scale and pilot-scale. The best PLS regressions of 1,3-PDO, glycerol, acetate, and butyrate with two measurement approaches provided excellent performance, with the root-mean-squared errors of prediction (RMSEP) of 1.656g/L, 1.502g/L, 0.746g/L, and 0.557g/L in at-line measurement and 1.113g/L, 1.581g/L, 0.415g/L, and 0.526g/L in on-line measurement. The cross-scale application performance of at-line measurement was evaluated by an external fermentation trial and an acceptable result was achieved. At-line measurement technique represents a superior choice for the optimization of fermentation process since the robustness across varying fermentation scales and its applicability in multiple bioreactors. Thus, a calibration model developed for one bioreactor is likely to be used in other bioreactors, which enables the reduction of modeling costs. On-line measurement technique, owing to its automated operation and frequent data acquisition, enables real-time monitoring and precise control of the fermentation process, thereby reducing cost and improving production efficiency.

The influence of temperature on ultrasonic signals in online measurement of oil film thickness

The influence of temperature on ultrasonic signals in online measurement of oil film thickness

Soft sensor modeling of steel pickling concentration based on IGEP algorithm

ABSTRACT Accurate measurement of acid concentration is paramount for ensuring the quality of strip steel pickling. Online measurement, a method that reduces operational complexity and lags effectively, is gradually replacing offline measurement of acid concentration. In this study, an indirect soft sensor model based on the improved gene expression programming (IGEP) algorithm has been constructed, leveraging easily measurable indexes from a large-scale dataset. The IGEP-based model predicted that the mean absolute errors for H+ and Fe2+ concentrations were 1.72 and 1.98 g/L, respectively. Additionally, the goodness of fit values for the H+ and Fe2+ prediction models were 0.945 and 0.933, respectively. Compared with the model based on support vector regression (SVR), which is suitable for small samples, it was demonstrated that the IGEP-based model achieved better predictive performance. Taken together, our study has designed a more effective and practical model for determining the acid concentration of strip steel pickling, providing a new ideal choice for the steel industry, which is of profound significance in the concentration control of pickling solution and the production of strip steel.

Dimensionality Reduction and Clustering Strategies for Label Propagation in Partial Discharge Data Sets

Deep learning approaches have been successfully applied to perform automatic classification of phase-resolved partial discharge (PRPD) diagrams. Under the supervised learning paradigm, however, the performance of classifiers strongly depends on the availability of large and previously labeled data sets. Labeling is an intensive and time-consuming labor, typically involving the manual annotation of a large number of data examples by an expert. In this work, we propose a label propagation algorithm applied to PRPD data sets, aiming to reduce the time necessary to manually label PRPDs. Our basic pipeline is composed of three phases: pre-processing, dimensionality reduction procedures, and clustering. Different configurations of the basic pipeline are tested by using PRPDs obtained from online measurements in hydrogenerators. The performance of each configuration is assessed by using the Silhouette, Caliński–Harabasz, and Davies–Bouldin scores. The clustering of the best three configurations is compared with annotated PRPDs by using the Fowlkes-Mallows index. Results suggest our strategy can substantially decrease the time for manual labeling.

Online in situ detection of deposited height deviation during additive manufacturing

Deposited height deviation (DHD) of printed layers is a common surface defect that restricts vertical printing accuracy during the additive manufacturing process. The accumulation of DHD layer by layer inevitably leads to the failure of subsequent additive manufacturing tasks. Therefore, accurate online measurement of DHD is crucial. This study proposed a novel amplification computer-vision measurement (ACVM) method that effectively utilizes both melt pool images and temperature information, achieving a DHD detection sensitivity of approximately 9.96 μm. Theoretical connections between image features and DHD, as well as the theoretical associations between instantaneous temperature characteristic and DHD, have been systematically deduced. Based on these two theoretical relationships, DHD can be accurately and synchronously detected directly through the positions of image features and temperature. A single-camera dual-channel multi-signal detection (SDMD) system was developed and implemented within a laser-engineered net shaping (LENS) additive manufacturing system. Subsequently, an online measuring and verification experiment was designed to assess the height deviation of thin-walled structural parts. The experimental results demonstrated that the ACVM method provided an early response to DHD. The method exhibits significant technical application value in quality control in future.

High-precision non-contact online measurement and predictive analysis of geometric parameters in large industrial components

Obtaining precise geometric parameters is fundamental in industry but often proves challenging, particularly for large-scale and irregularly shaped industrial components. This study introduces an innovative measurement method enabling precise determination of chord length, arc length, and curvature radius in curved sections of such components, even within dynamic industrial environments. Our solution relies on the implementation of a LiDAR sensor and the utilization of data processing techniques, including outlier detection and removal, as well as customized digital filters. Additionally, we incorporate analytic geometry to address specific measurement challenges. Finally, Machine Learning techniques were applied to forecast time series data of the measurements. The proposed system was validated in a real-world wind turbine tower manufacturing environment, comparing them with manual measurements performed by experienced operators. This validation confirms its effectiveness of our approach, achieving the industry-required minimum tolerances of 5 millimeters, where precise geometric data acquisition is crucial to ensure product quality.

Asymmetric flow field-flow fractionation for comprehensive characterization of hetero-aggregates made of nano-silver and extracellular polymeric substances

The present study explores the capability of asymmetrical flow field-flow fractionation (AF4) coupled online with diode array (DAD), fluorescence detectors (FLD), multi-angle light scattering (MALS) and dynamic light scattering (DLS) to characterize silver nanoparticles (nAg) hetero-aggregates formed with diatoms derived extracellular polymeric substances (EPS). The content of EPS varied from 10.5 to 105 mgC L-1 and nAg were dispersed at 4 mg L-1 in a freshwater medium. Good recoveries (∼ 76.9 ± 8.4 %) of nAg-EPS were obtained from AF4-DAD signals, but an anomalous elution was observed as EPS concentration increased: AF4 retention times decreased despite average gyration radii measured by MALS for nAg-EPS increased (from 16 nm to 24 nm), which suggests a change in the aggregation state, as evaluated by UV–Vis scans obtained from DAD. A regular Brownian relaxation of nAg-EPS was proven for each EPS concentration using these 2 detectors. The comparison of on-line and batch DLS measurements validated in addition, that no (dis)aggregation occurs upon injections. After a thorough comparison with classical AF4 using standards, the frit-inlet-AF4 was used. Slightly higher recovery (79.6 ± 4.6 %) was obtained but similar deviation of nAg-EPS elution occurred, excluding the implication of membrane differential fouling of nAg-EPS /conditioning effects of EPS. The investigation of physico-chemical parameters controlling the Brownian relaxation of nAg-EPS suggests the influence of nAg-EPS structure and EPS loading. This study demonstrates the suitable use of AF4 coupled to multiple detectors to probe-out eco-corona formation and characterize polydisperse systems containing NPs, EPS and their hetero-aggregates in freshwaters, even under a non-ideal size-fractionation scenario.

Controllable Fluorescence Quantum Yield of Carbon Quantum Dots Synthesised by Plasma Electrochemical Method

ABSTRACTCarbon quantum dots were synthesised in an environmentally friendly and efficient way using the plasma electrochemical method with folic acid as a precursor. By taking online measurements of the 90‐min synthesis process, we observed that the fluorescence quantum yield (QY) varied with the reaction time. The optimisation of reaction time and other experimental parameters can effectively improve the QY of carbon quantum dots. The results demonstrate that an increase in nitrogen doping leads to an increase in QY. In this paper, the QY of carbon quantum dots was increased from 0.64% to 13.72% by optimising the experimental parameters, which provides an effective way to increase the QY of carbon quantum dots synthesised by the plasma electrochemical method or other methods.

Competition & UV254 projection in odorants vs natural organic matter adsorption onto activated carbon surfaces: Is the chemistry right?

Powdered activated carbon (PAC) adsorption remains an indispensable method for addressing odor problems in drinking water. While natural organic matter (NOM) is ubiquitous and competes strongly in deteriorating odorant adsorption capacity, it can also serve as a promising indicator for predicting odorant adsorption through online measurement. However, the impact of PAC surface chemistry on NOM competition and feasibility of prediction across various adsorbents are not well understood. Here, we examined the role of PAC properties (pore structure and surface chemistry) in the competitive adsorption between odorants and NOM components, aligned with the applicability assessment of using NOM optical properties for odorant adsorption projection across various PAC samples. Chemical oxidation and thermal treatment achieved considerable changes in surface functional group composition, alongside minimal changes in pore structure, of two typical PAC products with microporous/mesoporous pore characteristics. The effect of NOM interference on the reduction of odorant adsorption exhibited a similar level regardless of the PACs with different pore structure (average pore size of 1.7 nm vs. 4.2 nm). Surface modification increased the equilibrium adsorption capacity (qe50) of odorants by 15.1% to 146.4% (thermal treatment) or decreased by -81.3% to -34.1% (chemical oxidation), respectively, but minimal changes in odorant-NOM selectivity. For various odorants, hydrophobicity (log D) influenced the adsorption capacity while the structural flexibility (reflected by the rotatable bonds) affected the vulnerability of odorant adsorption to NOM competition. It was found for the first time that four-parameter Richards model (RMSE = 2.6%) is superior to the linear model (RMSE = 12.5%) or logarithmic model (RMSE = 77.6%) to describe the S-shape UV254 projection curves associated with odorant adsorption on PAC. Moreover, the feasibility was confirmed to use UV254 projection curves of pristine PAC fitted with the Richard model to predict the odorant adsorption on surface-modified PAC in two different surface waters (RMSE 9.2% and 7.4%, respectively). This study provides insight into the role of PAC surface chemistry and pore characteristics in odorant adsorption in NOM-containing waters and enhances the feasibility of the NOM surrogate model for odorant monitor and control during PAC adsorption.

Research and application of on-line testing technology for drilling fluid properties in Chuanyu deep well

Abstract Viscosity measurement of drilling fluid while drilling is an important basis for real-time analysis of wellbore hydraulic characteristics and optimization of drilling parameters. When the current drilling fluid viscometer is used for on-line continuous detection, the drilling fluid is easy to settle and adhere to the measuring gap and stator end face, which affects the detection accuracy. This effect is particularly severe when measuring high density and high viscosity drilling fluids. Therefore, a larger measurement gap is more suitable for on-line inspection of the probe size. However, the larger measurement gap is more likely to produce eddy current and turbulence, which affects the detection accuracy. Therefore, based on Taylor Couette’s law of small gap fluid dynamics, combined with indoor evaluation experiments, and considering key influencing factors such as size requirements, laminar flow constraints, motor driving force and rotor limit speed, two different sizes of detection structures were designed. The measured gap range is 0.102 ∼ 0.146mm. Laboratory tests showed that the deposition and adhesion of drilling fluid in the measurement gap were significantly improved. A moment of inertia time-response model was established to determine the optimal reading time window: for standard fluid (GBW13607), the detection time was 33.7 seconds, and for drilling fluid (density ≤2.0g/cm³), the detection time was 67.4 seconds. The accuracy of on-line measurement of drilling fluid viscosity is ensured.

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 0.01 µg/m3 to 0.06 µg/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.

Evaluating long-term reductions in trace metal emissions from shipping in Shanghai

Shipping emissions are a major contributor to air pollution in coastal cities, and Shanghai Port, the busiest port in the world, handles over 40 million TEU annually. To mitigate shipping emissions, China introduced the Domestic Emission Control Area (DECA) policy in phases: DECA 1.0 in 2016 and DECA 2.0 in 2019. DECA 1.0 mandated low-sulfur fuels (0.5 %) at berth and near major ports, down from 1.5 %, and this requirement was extended to a 12-nautical-mile emission control zone in DECA 2.0. In this study, we conducted long-term online measurements of shipping emission tracer of vanadium (V) and nickel (Ni), at the Dian Shan Lake (DSL) supersite, located approximately 50 km from Shanghai waters. The observed V concentration exhibited a strong dependence on wind direction, with higher levels from spring to summer due to more frequent marine winds compared to other seasons. The long-term measurement showed a significant decrease in V concentrations, dropping from an annual mean of 7.08 ng m-³ during DECA 1.0 to 2.64 ng m-³ during DECA 2.0. The represents a year-on-year reduction of 63 % based on measurement. To remove the meteorological impact on the measured concentration, a Random Forest (RF) machine learning model and the Community Multiscale Air Quality (CMAQ 5.4) model were applied under a business-as-usual (BAU) scenario. Both models produced consistent results, showing a reduction of up to 82 % in V concentrations in April with more frequent marine winds, confirming regional air quality improvements post-DECA 2.0. Additionally, we found that Ni has other sources that require further control beyond shipping. The study highlights the importance of long-term measurements for understanding the impact of air quality policies on emission patterns.

Online Optical Axis Parallelism Measurement Method for Continuous Zoom Camera Based on High-Precision Spot Center Positioning Algorithm

Ensuring precise alignment of the optical axis is critical for achieving high-quality imaging in continuous zoom cameras. However, existing methods for measuring optical axis parallelism often lack accuracy and fail to assess parallelism across the entire focal range. This study introduces an online measurement method designed to address these limitations by incorporating two enhancements. First, image processing methodologies enable sub-pixel-level extraction of the spot center, achieved through improved morphological processing and the incorporation of an edge tracing algorithm. Second, measurement software developed using Qt Creator can output real-time data on optical axis parallelism across the full focal range post-measurement. This software features a multi-threaded architecture that facilitates the concurrent execution of image acquisition, data processing, and serial communication. Experimental results derived from simulations and real data indicate that the maximum average error in extracting the center of the spot is 0.13 pixels. The proposed system provides critical data for optical axis calibration during camera adjustment and inspection.

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.

Research on the Detection Principle of Coal Ash by X-Ray Transmission Based on FLUKA

This study addresses the timely and accurate measurement of coal ash content by proposing a detection model based on nuclear science technology, which is validated using FLUKA 4-4.0 simulation software. The background provided highlights the fact that coal ash content is a critical sales indicator, and its precise measurement is essential for adjusting production parameters in coal preparation plants. In terms of methodology, this study employs the widely used FLUKA4-4.0 software in the field of nuclear physics to simulate X-ray transmission through coal, investigating the impact of changes in coal type on the accuracy of ash measurements. The results indicate that, when the proportions of high-atomic-number elements in coal remain constant, the ash measurement results are accurate and reliable. However, significant fluctuations occur when these proportions change. The conclusion emphasizes the fact that variations in coal type are the primary cause of inaccuracies in ash measurement, particularly when the ratios of high-atomic-number elements are altered. This research provides a new perspective on the online measurement of coal ash content and offers theoretical support for improving measurement accuracy.

REAL TIME LUMINESCENT DOSIMETRY SYSTEM FOR PRODUCT PROCESSING AT AN ELECTRON ACCELERATOR

Quality control in industrial product processing at an electron accelerator requires continuous monitoring of critical parameters of the irradiation regime, first of all, of absorbed dose. The method of contact-free on-line measuring the surface distribution of absorbed dose in a processed object is described. The novel technique is based on the use of optical radiation induced in the object with the electron beam (cathodoluminescence, CL). It is shown, that the intensity of CL is proportional to the absorbed dose rate. An automated system for online measurement of the CL signal and determination of the scanning beam parameters (profile, width, offset) has been developed. The results of calibration of the absorbed dose by the CL signal in the industrial dose range are obtained. In contrast to the well-known methods of luminescent dosimetry based on thermo- or optically stimulated luminescence, the new approach does not require a special material for detector, as well as its extra stimulation to read out the dosimetry information.

State and parameter estimation of a dynamic froth flotation model using industrial data

This paper investigates an observable dynamic model of froth flotation circuits aimed at online state and parameter estimation and model-based control. The aim is to estimate the model states and parameters online from industrial data. However, in light of limitations in the plant data, additional model analysis is conducted. It is shown that without online compositional measurements, only the states and parameters of a reduced model can be estimated online. The reduced model lumps all recovery mechanisms into a single empirical equation. The reduced model is used to develop a moving horizon estimator (MHE) which is implemented on the industrial data. The state and parameter estimates from the MHE are used to evaluate the model prediction accuracy over a receding control horizon as would be done in model predictive control (MPC). Given the uncertainty of the available data, unmeasured disturbances and missing online measurements, the estimation and prediction results are reasonably accurate, at least in a qualitative sense. If accurate and reliable online measurements are available for estimation, the reduced model shows potential to be used for long-term model-based supervisory control of a flotation circuit.