Complex Measurement Research Articles

Noninvasive Quantification of Contractile Dynamics in Cardiac Cells, Spheroids, and Organs-on-a-Chip Using High-Frequency Ultrasound.

Cell-based models that mimic in vivo heart physiology are poised to make significant advances in cardiac disease modeling and drug discovery. In these systems, cardiomyocyte (CM) contractility is an important functional metric, but current measurement methods are inaccurate and low-throughput or require complex setups. To address this need, we developed a standalone noninvasive, label-free ultrasound technique operating at 40-200 MHz to measure the contractile kinetics of cardiac models, ranging from single adult CMs to 3D microtissue constructs in standard cell culture formats. The high temporal resolution of 1000 fps resolved the beat profile of single mouse CMs paced at up to 9 Hz, revealing limitations of lower speed optical based measurements to resolve beat kinetics or characterize aberrant beats. Coupling of ultrasound with traction force microscopy enabled the measurement of the CM longitudinal modulus and facile estimation of adult mouse CM contractile forces of 2.34 ± 1.40 μN, comparable to more complex measurement techniques. Similarly, the beat rate, rhythm, and drug responses of CM spheroid and microtissue models were measured, including in configurations without optical access. In conclusion, ultrasound can be used for the rapid characterization of CM contractile function in a wide range of commonly studied configurations ranging from single cells to 3D tissue constructs using standard well plates and custom microdevices, with applications in cardiac drug discovery and cardiotoxicity evaluation.

R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement.

Digital image correlation (DIC) is an optical metrology method for measuring object deformation and has been widely used in many fields. Recently, the deep learning based DIC methods have achieved good performance, especially for small and complex deformation measurements. However, the existing deep learning based DIC methods with limited measurement range cannot satisfy the needs of real-world scenarios. To tackle this problem, a recursive iterative residual refinement DIC network (R3-DICnet) is proposed in this paper, which mimics the idea of the traditional method of two-step method, where initial value estimation is performed on deep features and then iterative refinement is performed on shallow features based on the initial value, so that both small and large deformations can be accurately measured. R3-DICnet not only has high accuracy and efficiency, but also strong generalization ability. Synthetic image experiments show that the proposed R3-DICnet is suitable for both small and large deformation measurements, and it has absolute advantages in complex deformation measurement. The accuracy and generalization ability of the R3-DICnet for practical measurement experiments were also verified by uniaxial tensile and wedge splitting tests.

Retracted: A Three-Dimensional Complex Measurement Model-Based Avionic Radio-Frequency Power Source Health Assessment Method

Retracted: A Three-Dimensional Complex Measurement Model-Based Avionic Radio-Frequency Power Source Health Assessment Method

Structure-function relationship and diagnostic value of macular ganglion cell complex measurement using Fourier-domain OCT in glaucoma.

To evaluate the relationship between visual function and macular ganglion cell complex (GCC) thickness measured by Fourier-domain optical coherence tomography (OCT) and evaluate the diagnostic value of GCC measurement compared to retinal nerve fiber layer (RNFL) thickness and macular thickness in detecting early, moderate, and severe glaucomas. Subjects underwent standard automated perimetry (SAP), OCT imaging with optic nerve head mode and GCC mode. The relationship between OCT parameters (mean GCC thickness, mean RNFL thickness, and macular thickness) and perimetry global indices (mean deviation [MD] and pattern standard deviation [PSD]) was evaluated by regression analysis. Diagnostic values of mean RNFL thickness, GCC parameters, and macular thickness were compared with the area under the receiver operating characteristic curves (AUC). A total of 84 eyes, 42 of each normal and primary open-angle glaucoma patients were included in the study. Compared with linear models, second-order polynomial models better described relationships between GCC thickness and MD ( P < 0.001), and between GCC thickness and PSD ( P = 0.00). RNFL parameter, inferior RNFL thickness had the highest AUC for detecting early glaucoma. The AUC of mean GCC thickness for early glaucoma was higher than that of mean RNFL; however, the difference was not significant ( P = 0.09), which was higher than that of macular thickness. The relationship between visual field (VF) sensitivity and GCC thickness is best expressed by the curvilinear function. Macular GCC thickness and RNFL thickness showed similar diagnostic values but were better than macular thickness for detecting early glaucoma but inferior to macular thickness and RNFL thickness for detecting moderate glaucoma.

DETECTION OF SILVER BIRCH GROWTH DYNAMICS AND TIMING WITH DENSE SPATIO-TEMPORAL LIDAR TIME-SERIES

Abstract. Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.

For the chemistry of cultural heritage (Part V – The cultural heritage with paper basis exhibits and preventive conservation planning)

The article is devoted to aspects of restoration-conservation processes of cultural heritage exhibits created on the basis of paper using chemical-technological approaches. The paper focuses on the systematic topology of the complex measure of preventive conservation planning in museums and repositories. The article is intended for specialists working in the field of cultural heritage protection and students of all three levels of relevant educational programs.

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms.

Close-range photogrammetry methods are widely used for non-contact and accurate measurements of surface shapes. These methods are based on calculating the three-dimensional coordinates of an object from two-dimensional images using special digital processing algorithms. Due to the relatively complex measurement principle, the accurate estimation of the photogrammetric measurement error is a non-trivial task. Typically, theoretical estimations or computer modelling are used to solve this problem. However, these approaches cannot provide an accurate estimate because it is impossible to consider all factors that influence the measurement results. To solve this problem, we propose the use of physical modelling. The measurement results from the photogrammetric system under test were compared with the results of a more accurate reference measurement method. This comparison allowed the error to be estimated under controlled conditions. The test object was a flexible surface whose shape could vary smoothly over a wide range. The estimation of the measurement accuracy for a large number of different surface shapes allows us to obtain new results that are difficult to obtain using standard approaches. To implement the proposed approach, a laboratory system for the error estimation of close-range photogrammetric measurements was developed. The paper contains a detailed description of the developed system and the proposed technique for a comparison of the measurement results. The error in the reference method, which was chosen to be phasogrammetry, was evaluated experimentally. Experimental testing of the stereo photogrammetric system was performed according to the proposed technique. The obtained results show that the proposed technique can reveal dependencies that may not be detected by standard approaches.

BF2-Functionalized Benzothiazole Amyloid Markers: Effect of Donor Substituents on One- and Two-Photon Properties.

Investigation of amyloids with the aid of fluorescence microscopy provides crucial insights into the development of numerous diseases associated with the formation of aggregates. Here, we present a series of BF2-functionalized benzothiazoles with electron-donating methoxy group(s), which are tested as amyloid fluorescent markers. We evaluate how the position of donor functional group(s) influences optical properties (fluorescence lifetime (τ) and fluorescence quantum yield (FQY)) in a solution and upon binding to amyloids. We elucidate the importance of surrounding environmental factors (hydrogen-bonding network, polarity, and viscosity) on the observed changes in FQY and evaluate how the localization of a donor influences radiative and nonradiative decay pathways. We conclude that a donor attached to the benzothiazole ring contributes to the increment of radiative decay pathways upon binding to amyloids (kr), while the donor attached to the flexible part of a molecule (with rotational freedom) contributes to a decrease in nonradiative decay pathways (knr). We find that the donor-acceptor-donor architecture allows us to obtain 58 times higher FQY of the dye upon binding to bovine insulin amyloids. Finally, we measure two-photon absorption (2PA) cross sections (σ2) of the dyes and their change upon binding by the two-photon excited fluorescence (2PEF) technique. Measurements reveal that dyes that exhibit the increase/decrease of σ2 values when transferred from highly polar solvents to CHCl3 present a similar behavior upon amyloid binding. Our 2PA experimental values are supported by quantum mechanics/molecular mechanics (QM/MM) simulations. Despite this trend, the values of σ2 are not the same, which points out the importance of two-photon absorption measurements of amyloid-dye complexes in order to understand the performance of 2P probes upon binding.

Internal structural analysis of the nasomaxillary complex in patients with skeletal class III asymmetry: A study on asymmetry patterns.

To investigate the internal structure of the nasomaxillary complex, including the maxillary sinus, nasal cavity and nasal septum according to the facial asymmetry pattern and to evaluate its correlation with external maxillomandibular asymmetry in Class III patients based on cone-beam computerized tomography (CBCT) images. Facial asymmetry was analysed in a total of 100 Class III patients aged 16 years or older using CBCT scans. Patients were categorized into subgroups based on asymmetry pattern. Measurements of the nasomaxillary complex were obtained from the CBCT scans, including the volume and width of the maxillary sinuses and nasal cavities on deviated and non-deviated sides, as well as the displacement of the nasal septum. Statistical analysis was performed to compare the internal nasomaxillary variables within and between groups, and regression analysis was conducted to evaluate the correlation between facial asymmetry and the internal nasomaxillary variables. Group comparisons showed that there were no significant differences in the volume of the maxillary sinus and nasal cavity. However, the direction and extent of nasal septum deviation, as well as the width of the nasal cavity, varied depending on the maxillary asymmetry pattern. Regression analysis indicated a correlation between nasal septum deviation and the difference in maxillary height, while the difference in nasal cavity width was correlated with the difference in maxillary width. A comprehensive evaluation of the internal nasal anatomy is vital for understanding the intricate relationship between nasal structure and maxillary growth.

Direct Analysis of Chlorine Ionized Cement-Based Composites and Its Sensor Application By 2,5GHz Antennas

In this paper, we propose a system that discriminate cement-based composite including sea sand by using microwave patch antennas. The composites with sea sand have vital importance in construction engineering industry because ionised steel reinforced composites could cause life threatening damages. Firstly, to analyse cement-based composite including sea sand, 0%, 25%, 50%, 75% and 100% chlorine ionised sea sand containing concrete samples were produced and complex dielectric measurements were conducted. In addition, two 2.45GHz patch antennas were designed in microwave simulator and also it was integrated to the concrete models. Furthermore, simulated results shows that this approach could be used as a sensor with nearly 30MHz resonance shifts. Finally, proposed system was supported by experimental investigation. This type of configuration has many advantages as easy design, easy fabrication, cheap and flexible to non-destructive cement based composite measurements.

Machine Learning for Ultra High Throughput Screening of Organic Solar Cells: Solving the Needle in the Haystack Problem

AbstractOver the last two decades the organic solar cell community has synthesized tens of thousands of novel polymers and small molecules in the search for an optimum light harvesting material. These materials are often crudely evaluated simply by measuring the current–voltage (JV) curves in the light to obtain power conversion efficiencies (PCEs). Materials with low PCEs are quickly disregarded in the search for higher efficiencies. More complex measurements such as frequency/time domain characterization that could explain why the material performed as it is often not performed as they are too time consuming/complex. This limited feedback forced the field to advance using a more or less random walk of material development and has significantly slowed progress. Herein, a simple technique based on machine learning that can quickly and accurately extract recombination time constants and charge carrier mobilities as a function of light intensity simply from light/dark JV curves alone. This technique reduces the time to fully analyze a working cell from weeks to seconds and opens up the possibility of not only fully characterizing new devices as they are fabricated, but also data mining historical data sets for promising materials the community has overlooked.

Site evaluation of subsea tunnels with sightseeing function based on dynamic complex MARCOS method

Subsea tunnels with sightseeing function (STSF) are valuable for integrating transportation across different locations, particularly in cross-sea regions. Given their technical complexity, significant capital commitment, and substantial social impact, site evaluation of STSF has become a critical priority. In this study, a dynamic complex measurement alternative and ranking according to the compromise solution (MARCOS) framework is proposed for STSF site evaluation in a Pythagorean hesitant fuzzy environment. A novel score function for a Pythagorean hesitant fuzzy set is defined to handle an incomparable problem. Moreover, a growth coefficient matrix is constructed to reflect the internal development potential of decision objects, considering changes in expert and criteria weights during the dynamic decision-making process. An STSF evaluation system is designed to satisfy stability, sociality, and economic developments. An empirical study is conducted in Zhejiang Province, China, to verify the robustness and effectiveness of the proposed evaluation framework by comparing its results with other methods.

Design and development of OCR software for remote measurement and calibration

The advent of digital technology has led to significant changes in work processes in many organizations and industries worldwide including Thailand. This work aims to apply digital technology to improve measurement and metrology in terms of accuracy, productivity, and variability. We developed machine vision software for monitoring and collecting digital data from measurement systems. The application of Optical Character Recognition (OCR) technology in metrology is the first step toward digitalization, enabling enterprises to improve their measurement processes by automating data collection, analysis, and management. Moreover, it can be integrated with machine learning algorithms to enhance the automation of complex measurement tasks. To achieve this goal, an OCR software for data recording was designed in the LabVIEW environment. The image captured by the camera is processed by the LabVIEW OCR system which is trained continuously until it can recognize characters consistently and accurately. This software has been tested with the calibration systems which has a seven-segment digital format. The result shows that the software can acquire and transmit the instrument reading with reliable and exceptional accuracy. In addition, it can also reduce the steps and time required for measurement. In conclusion, the application of OCR technology can improve the efficiency, productivity, and accuracy of measurement. It also has a promising potential to enable remote measurement and calibration according to ISO/IEC 17025 possible.

Evaluation of a new predictive equation for automated calculation of size-specific dose estimate (SSDE) in CT imaging

IntroductionThe adoption of size-specific dose estimate (SSDE) in clinical practice is still limited owing to the tedious and complex manual measurement of individual patient size for the clinical calculation of SSDE. Thus, the automation of SSDE is imperative. This study aims to evaluate a predictive equation for the automated calculation of SSDE. MethodsA user-friendly software was developed to accurately predict the individual size-specific dose estimation of paediatric patients undergoing computed tomography (CT) scans of the head, thorax, and abdomen. The software includes a calculation equation developed based on a novel SSDE prediction equation that used a population's pre-determined percentage difference between volume-weighted computed tomography dose index (CTDIvol) and SSDE with age. American Association of Physicists in Medicine (AAPM RPT 204) method (manual) and segmentation-based SSDE calculators (indoseCT and XXautocalc) were used to assess the proposed software predictions comparatively. ResultsThe results of this study show that the automated equation-based calculation of SSDE and the manual and segmentation-based calculation of SSDE are in good agreement for patients. The differences between the automated equation-based calculation of SSDE and the manual and segmentation-based calculation are less than 3%. ConclusionThis study validated an accurate SSDE calculator that allows users to enter key input values and calculate SSDE. Implication for practiceThe automated equation-based SSDE software (PESSD) seems a promising tool for estimating individualised CT doses during CT scans.

Nanoscale dynamic mechanical analysis of a viscoelastic matrix inclusion within an elastic substrate

Advanced composite materials incorporate viscoelastic matrix inclusions in designated locations within elastic substrates, which grant them specialized energy dissipation capabilities of dynamic loadings. Identifying the local nanomechanical properties of the matrix-substrate complex within the composite is critical to its design and adaptation toward a specific target function. Here, we conduct FE simulations of nanoscale dynamic mechanical analysis (DMA) testing on hemispherical matrix inclusions within elastic substrates and analyze the variations in its indentation storage loss moduli measures for different matrix-substrate configurations. Then, we describe the mechanical system by simple spring models, identify the dominating parameters at different contact states of the nanoscale DMA testing, and obtain highly accurate analytical formulae that link the indentation measures of the matrix-substrate complex to the individual mechanical properties of the matrix and substrate parts. Our analysis can be directly integrated into benchmark nanomechanical testing methodologies of composite materials and promotes the local dynamic-mechanical characterization of complex materials systems, including nanomaterials, micro-architected structures, and bio-inspired designs.

Prediction of Quantum Yields of Monolayer WS2by Machine Learning

AbstractMonolayer transition metal dichalcogenides (1L‐TMDs) exhibits distinct light emissions in the visible range, making them suitable for 2D optoelectronic applications. Photoluminescence quantum yield (PLQY) is a key factor for practical applications of 1L‐TMDs. However, the methods for PLQY measurements of 1L‐TMDs suffer from limitations due to the small sample size and typically low PLQY, which require a complex measurement setup. In this study, machine learning (ML) models are developed to predict the PLQY of monolayer tungsten disulfide (1L‐WS2) using data extracted from 1208 PL spectra and corresponding measurement conditions as the ML training and testing data set. The ML model shows a high accuracy with R2value of 0.744 and a mean absolute percentage error of 44% in the prediction of widely ranged PLQYs of 1L‐WS2from 0.07% to 38%. This data‐driven prediction not only enables the convenient PLQY estimation of 1L‐TMDs, but also helps in identifying key parameters influencing PLQYs.

Inertia matrix identification of combined spacecraft using a deep neural network with optimized network structure

After the space robot captures the out-of-control spacecraft, they form a combined spacecraft. For the parameter identification problem of combined spacecraft with complex angular rate measurement noise, deep learning could provide a new solution, due to its ability to extract robust features from noisy data. However, the deep neural network (DNN) has substantial computing resource requirements, which restricts its application in spacecraft systems. To solve the above problems, an inertia matrix identification approach combining a structure optimization strategy of DNN is proposed to identify the inertia matrix in the presence of complex measurement noise using the DNN with optimized structure. The attitude dynamics of combined spacecraft are firstly derived, and the identification problem considering complex measurement noise is described. Based on the dynamics model, the construction process of DNN for accurately identifying the inertia matrix is proposed. A structure optimization strategy of DNN is then designed to optimize the structure of DNN for increasing computing efficiency. This strategy provides a whole set of solutions including the structure selection of the initial DNN, the redundant neurons pruning process, and the end-of-optimization condition. By combining the proposed construction process of DNN, the structure of the DNN is finally optimized by iterating the structured pruning and retraining step. The performance of the proposed DNN with optimized network structure is verified on a testing set with complex measurement noise. The results show that the proposed approach can accurately identify the inertia matrix of combined spacecraft, and meanwhile greatly improve the computing efficiency of DNN.

Image processing methods based on physical models

In order to achieve better image processing results, the author proposes research on image processing methods based on physical models. The author first developed a simple and user-friendly GUIDE interactive interface based on Matlab software and image processing technology. Calculate and process the interference fringe patterns captured in static and dynamic experimental measurements in optical experiments separately to achieve experimental measurement objectives. Secondly, the image processing program was analyzed to highlight important information using methods such as Gaussian blur and binarization, and algorithms were used to construct surface 3D images or detect stripe motion features, establish 3D models, and accurately calculate corresponding experimental results. The final experimental results indicate that the phase transition value of piezoelectric devices increases linearly with the increase of voltage, which is consistent with the working characteristics of piezoelectric devices in the linear region. This program can analyze the image sets of horizontal, vertical, and oblique stripes, and calculate the piezoelectric moduli of piezoelectric devices to be 3.355 nm/V, 3.120 nm/V, and 3.557 nm/V, respectively, with relative errors of E1 = 4.9 %, E2 = 2.9 %, and E3 = 12 %. This method is suitable for various complex experimental measurement scenarios and provides an intuitive method for innovative design of optical interference experimental measurement teaching.

Managing multi-functional peri-urban landscapes: Impacts of horse-keeping on water quality.

Eutrophication assessments in water management to quantify nutrient loads and identify mitigating measures seldom include the contribution from horse facilities. This may be due to lack of appropriate methods, limited resources, or the belief that the impact from horses is insignificant. However, the recreational horse sector is growing, predominantly in multi-functional peri-urban landscapes. We applied an ecosystem management approach to quantify nutrient loads from horse facilities in the Stockholm Region, Sweden. We found that horses increased the total loads with 30-40% P and 20-45% N, with average area-specific loads of 1.2kg P and 7.6kg N ha-1year-1. Identified local risk factors included manure management practices, trampling severity, soil condition and closeness to water. Comparisons of assessment methods showed that literature standard values of area-specific loads and water runoff may be sufficient at the catchment level, but in small and more complex catchments, measurements and local knowledge are needed.

Abstract 14964: Unsupervised Machine Learning of Imaging-Based Sub-Phenotypes of Aortic Stenosis With Differential Risks of Conduction Disturbances After Transcatheter Aortic Valve Replacement

Background: It is currently unclear whether various measurements of the aortic valvular complex routinely performed on pre-operative computed tomography angiograms (CTA) can collectively predict the risk of conduction disturbances after transcatheter aortic valve replacement (TAVR) for aortic stenosis (AS). Here, we aimed to use unsupervised machine learning to analyze a multitude of CTA features and identify distinct patient sub-phenotypes with different risks for TAVR-related conduction disturbances. Methods: The pre-TAVR CTAs of 660 AS patients (330 males, 330 females) were analyzed using TeraRecon to extract 21 features that included aortic valve leaflet calcification loads, dimensions of the aortic root (annulus, sinus of Valsalva, sinotubular junction) and the ascending aorta, coronary ostial heights, and aortic angle. Agglomerative hierarchical clustering was performed separately on male and female datasets using R, with assessment of clusterability based on Hopkins statistics and agglomerative coefficient and the optimal number of clusters based on 30 previously validated indices (NbClust package). Multivariable logistic regression was conducted to assess the dependence of significant conduction disturbances (new left bundle branch block, advanced atrioventricular block) on cluster type. Results: Both male and female datasets were fairly clusterable (Hopkins statistics, 0.70 vs 0.68; agglomerative coefficient, 0.93 vs 0.93), with 3 and 2 as the optimal number of clusters, respectively. Of the 3 male clusters identified, the cluster with intermediate median values of aortic valve leaflet calcification loads and aortic root dimensions (M2) was associated with the greatest risk of conduction disturbances, compared to clusters with low (M1) and high (M3) median values of these variables (OR M2/M1 =9.24, p =0.03; OR M2/M3 =1.33, p =0.40; OR M3/M1 =6.97, p =0.07). The risk of the M1 cluster was not statistically different from those of the female clusters (F1, F2; OR M1/F1 =0.31, p =0.27; OR M1/F2 =0.25, p =0.21), which were not different from each other (OR F1/F2 =0.82, p =0.68). Conclusion: Machine learning of pre-TAVR CTAs can help identify subgroups of patients with similar or varying risks of TAVR-related conduction disturbances.