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Accurate measurement of rice amylose content, defined as the percentage of amylose in the total starch content of the rice endosperm, is crucial for determining its taste and quality, but can prove challenging for rice-grain elevators and milling plants. We therefor investigated the potential of a newly developed near-infrared (NIR) spectrometer to provide reliable and accurate measurements of the rice amylose content in a total of 868 samples of 14 rice varieties collected in Hokkaido, Japan, between 2013 and 2021. Calibration models to determine rice amylose content are developed using the NIR spectrometer. The models are created by mapping the NIR spectra and amylose content of rice samples collected between 2013 and 2018 using a moving-window partial least squares regression algorithm. The accuracy of the calibration models is validated using rice samples with an unknown amylose content collected between 2019 and 2021. The coefficient of determination between the NIR-predicted and chemically measured amylose contents (> 0.96) and the standard error of prediction (0.4–0.5%) validate the accuracy, stability, and robustness of the calibration models for application in rice grain elevators and milling plants. The NIR technique can screen high-quality rice to improve its quality and increase its value. This technology is currently being applied to rice grain elevators and milling plants in Hokkaido and will soon be implemented throughout Japan. [ABSTRACT FROM AUTHOR]

Zero-shot learning aims to identify unseen (novel) objects, using only labeled samples from seen (base) classes. Existing methods usually learn visual-semantic interactions or generate absent visual features of unseen classes to compensate for the data imbalance problem. However, existing methods ignore the representation quality of visual-semantic pairs, resulting in unsatisfactory alignment and prediction bias. To tackle these issues, we propose a Hierarchical Contrastive Representation learning paradigm, termed HCR, which fully exploits model representation capability and discriminative information. Specifically, we first propose a contrastive embedding, which preserves not only high quality representations but also discriminative enough information from class-level and instance-level supervision. Then, we introduce a regressor by valuable prior knowledge for conducting more desirable visual-semantic alignment for unseen classes. A pluggable calibrator is also aggregated to further alleviate prediction bias in contrastive embedding. Extensive experiments show that the proposed HCR can significantly outperform the state-of-the-arts on popular benchmarks under ZSL and challenging GZSL settings. [ABSTRACT FROM AUTHOR]

Background. Calibration models were developed for rapid assessment of crucial agronomic indicators in the grain of winter and spring triticale from the VIR collection using near-infrared spectroscopy (NIRS). These calibration models will enable users to find the most optimal utilization trends for individual triticale accessions. Materials and methods. Indicators of agronomic value (coloration, moisture, and the content of ash, protein, starch, amylose and fiber) were studied on a set of 32 grain samples collected from winter and spring triticale forms (Triticosecale Wittm. & A. Camus) reproduced in 2021–2022 at the Dagestan Experiment Station, a branch of the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (VIR). Calibration models for the assessment of the said agronomic characters in triticale grain were developed on the platform of the MATRIX-I IR analyzer (Bruker Optics, Germany) and based on the values obtained by classical methods of biochemical analysis adopted at VIR. Results. Reliability of the calibration models was tested on a randomized batch of triticale grain samples: the results of the new calibrations were compared with those of the conventional biochemical analysis. This verification showed that the data obtained through the use of calibration curves were reliable for protein and ash content, while other models required further improvement. Conclusion. Calibration models for measuring protein and ash content developed on the basis of the MATRIX-I IR analyzer can be used to perform rapid assessment of the winter and spring triticale grain. This method makes it possible to accelerate the process of obtaining crop characteristics for a number of basic agronomic characters and preserve valuable breeding material.

Background. The purpose of this work was to develop calibration models for rapid assessment of the main biochemical parameters of grain quality of naked and covered black oat (Avena strigosa Schreb.) using near-infrared reflectance (NIR) spectroscopy. The use of whole oat grains makes it possible to preserve breeding material of special value, which is very important for breeders. Materials and methods. Black oat grain from 50 accessions from the VIR collection was grown in 2014–2020 in various agro-ecological conditions of the Russian Federation. Chemical parameter values for constructing calibration models were determined in the Laboratory of Biochemistry and Molecular Biology of VIR using traditional methods, such as Kjeldahl for protein/nitrogen, Soxhlet extraction (modified by S.V. Rushkovsky) for oil, polarimetry according to Evers for starch, and gravimetry for beta-glucans. Results. The work resulted in the construction of calibration models for the determination of protein, oil, starch and beta-glucans in whole grains of black oat by a Bruker's MATRIX-I IR analyzer (Germany). A test batch of 20 accessions was used for assessing reliability of the constructed models. The models for determining protein, starch and oil were found to be reliable, while the model for determining beta-glucans needs improvements. It was found that the difference between the values obtained by spectroscopy and by standard chemical methods averaged 0.38 % for protein, 0.57 % for starch and 0.30 % for oil, which does not exceed the maximum permissible error of parallel measurements. The relative difference between the studied indicators does not exceed 3-6 % on an average. Conclusion. The proposed physical method for the analysis of black oat grain allows an express preliminary assessment of breeding material with a high speed of analysis. The main advantages of the method include the possibility of preserving valuable starting material, economizing on reagents, increasing labor efficiency, and obtaining data simultaneously on several parameters of the scanned sample with a specified repeatability.

According to the World Health Organization survey, the global incidence of adolescent mental illness is 28%, while the disease detection rate is only 24.6%. Many existing works use complex eye-tracking devices to study adolescent autism, depression, and other mental illness. In this paper, we propose a gaze estimation method to replace eye-tracking devices. Appearance-based methods with deep learning can predict the point of gaze by using a monocular camera, which requires a large number of samples to learn. However, the samples collected in publicly available gaze estimation datasets are mainly adults and not adolescents. To address the above issue, our work makes two contributions. First, we collected images from 107 adolescents aged 10–14 years by laptops under uncontrolled conditions to create the Young-Gaze dataset. Second, we propose a Multi-scale Feature Fusion-based Calibration Network (MFFC-Net) to deeply fuse the eye-face features for gaze estimation. The proposed MFFC-Net achieves the better performance on Young-Gaze and other public datasets. [ABSTRACT FROM AUTHOR]

Alcohol consumption is a major social and forensic issue. It is often the cause of road accidents, industrial accidents, suicides and other crimes. On account of this, it is of fundamental importance in forensic toxicology to correctly quantify blood alcohol concentration (BAC). In this work, a straightforward method for the quantification of ethanol from blood samples by means of headspace gas chromatography with flame ionization detection is presented and validated. For method validation linearity, limit of detection (LOD), lower limit of quantification (LLOQ), accuracy, precision (% CV) and interference studies were carried out. All the validation conditions were satisfied according to the acceptance criteria. Proof of applicability was performed on 50 real blood samples, showing that the method was effective. [ABSTRACT FROM AUTHOR]

Winter oilseed rape (WOSR, Brassica napus L.) is the third largest oil crop worldwide that also provides a source of high quality plant-based proteins. Nitrogen (N) and carbon (C) play a key role in plant growth. Determination of N and C contents of plant tissues throughout the growth cycle is crucial in assessing plant nutritional status and allowing precise input management. In the dataset presented in this article, 2427 WOSR samples arising from a large diversity of tissues collected on WOSR diversity were analyzed by near infrared spectroscopy from 4000 to 12,000 cm−1. At the same time, reference chemical data for the N and C contents of the same samples were determined by elemental analysis using the Dumas method. Partial least squares regression has been used to develop predictive models linking spectral and chemical data, so that new samples can be characterized without the need for reference methods. This dataset could be used to test new calculation algorithms in order to enhance prediction performance or for training purposes. These models can be used as a rapid method for determining N and/or C content, adding to decision-support tools for fertilizer application throughout the plant developmental cycle.

Background. A calibration model has been developed for rapid assessment of economic value indicators (protein, oil, and quinolizidine alkaloid contents) in the seeds of narrowleaf lupine accessions from VIR using near-infrared spectroscopy, with the help of which it is possible to decide on the further use of the accessions.Materials and methods. Biochemical quality indicators (protein, oil, and quinolizidine alkaloid content) were studied in the seeds of narrowleaf lupine (Lupinus angustifolius L.) grown in 2019 in the northwest of Russia. Calibration models for measuring protein, oil and alkaloids in lupine seeds (62 accessions) were developed using a MATRIX-I IR analyzer (Bruker Optics, Germany). To construct calibration models, we used the values obtained by chemical analysis methods accepted at VIR. The oil content in lupine seeds was assessed by the defatted dry residue technique in Soxhlet extractors, protein by the Kjeldahl method, and quinolizidine alkaloids by gas chromatography coupled with mass spectrometry. All indicators were recalculated on the dry-weight basis.Results and conclusion. Statistical significance of the developed models was verified according to the results of measuring the content of protein, oil and alkaloids in the seeds of the test batch. The protein and oil content data obtained using a calibration curve did not differ significantly from the results of chemical studies, in contrast to alkaloid indicators. Consequently, the developed calibration model for the MATRIX-I IR analyzer can be used for rapid assessment of protein and oil content in narrowleaf lupine flour samples, thus accelerating the process of obtaining data on the main economic value indicators. The analysis does not require reagents and is safe.

Background. The possibility of applying near-infrared reflectance (NIR) spectroscopy for rapid assessment of various biochemical parameters in barley varieties and forms is discussed. The data obtained by the Biochemistry and Molecular Biology Department of VIR served to construct calibration models for the content of protein, starch, oil, beta-glucans, and total phenolic compounds (PhC) in grain, facilitating further screening of various barley samples.Materials and methods. The chemical composition of grain was studied in naked and covered spring barley (Hordeum vulgare L.) accessions grown in 2022 in the northwest of Russia. Calibration models were developed to measure the content of protein, oil, starch, beta-glucans, and PhC (80 accessions) in barley grain with the Matrix-I IR analyzer (Bruker Optics, Germany). The models were constructed on the basis of the data obtained by conventional techniques of chemical analysis. The protein/ nitrogen content was assessed using the Kjeldahl method, oil according to the method of defatted dry residue modified by S. V. Ruszkovsky, starch by the polarimetric method according to Evers, beta-glucans by gravimetric analysis, and the total PhC content by the Folin–Ciocâlteu method modified by Singleton and Rossi.Results. Statistical significance of the constructed calibration tests was compared with the results of measuring protein, starch, oil, beta-glucan and PhC levels by chemical methods. It was shown that the data of calibration techniques for protein and starch were significant, while the remaining models required improvement.Conclusion. The proposed method helps to preserve valuable source material, increases labor efficiency, and does not require chemical reagents. Scanning each sample makes it possible to obtain data for several indicators at once, with a specified replication and standard deviation.

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The development of calibration models using Raman spectra data has long been challenged owing to the substantial time and cost required for robust data acquisition. To reduce the number of experiments involving actual incubation, a calibration model development method was investigated by measuring artificially mixed samples. In this method, calibration datasets were prepared using spectra from artificially mixed samples with adjusted concentrations based on design of experiments. The precision of these calibration models was validated using the actual cell culture sample. The results showed that when the culture conditions were unchanged, the root mean square error of prediction (RMSEP) of glucose, lactate, and antibody concentrations was 0.34, 0.33, and 0.25 g/L, respectively. Even when variables such as cell line or culture media were changed, the RMSEPs of glucose, lactate, and antibody concentrations remained within acceptable limits, demonstrating the robustness of the calibration models with artificially mixed samples. To further improve accuracy, a model training method for small datasets was also investigated. The spectral pretreatment conditions were optimized using error heat maps based on the first batch of each cell culture condition and applied these settings to the second and third batches. The RMSEPs improved for glucose, lactate, and antibody concentration, with values of 0.44, 0.19, and 0.18 g/L under constant culture conditions, 0.37, 0.12, and 0.12 g/L for different cell lines, and 0.26, 0.40, and 0.12 g/L when the culture media was changed. These results indicated the efficacy of calibration modeling with artificially mixed samples for actual incubations under various conditions. [ABSTRACT FROM AUTHOR]

Alcohol consumption is a major social and forensic issue. It is often the cause of road accidents, industrial accidents, suicides and other crimes. On account of this, it is of fundamental importance in forensic toxicology to correctly quantify blood alcohol concentration (BAC). In this work, a straightforward method for the quantification of ethanol from blood samples by means of headspace gas chromatography with flame ionization detection is presented and validated. For method validation linearity, limit of detection (LOD), lower limit of quantification (LLOQ), accuracy, precision (% CV) and interference studies were carried out. All the validation conditions were satisfied according to the acceptance criteria. Proof of applicability was performed on 50 real blood samples, showing that the method was effective.

Efficient marketing of winegrapes involves negotiating with potential buyers long before the harvest, when little is known about the expected vintage. Grapevine physiology is affected by weather conditions as well as by soil properties and such information can be applied to build yield prediction models. In this study, Partial Least Squares Regression (PLSR), Cubist (CUB) and Random Forest (RF) algorithms were used to predict yield from imputed weather station data and soil sample analysis reports. Models using only soil variables had the worst general results (R2 = 0.15, RMSE = 4.16 Mg ha−1, MAE = 3.20 Mg ha−1), while the use of only weather variables yielded the best performance (R2 = 0.52, RMSE = 2.99 Mg ha−1, MAE = 2.43 Mg ha−1). Models built with CUB and RF algorithms showed signs of overfitting, yet RF models achieved the best average results (R2 = 0.58, RMSE = 2.85 Mg ha−1, MAE = 2.24 Mg ha−1) using only weather variables as predictors. Weather data imputation affected RF and CUB models more intensely while PLSR remained fairly insensitive. Plant age, yield level group, vineyard plot, May temperatures, soil pH and exchangeable concentrations of Zn, Cu, K and Mn were identified as important predictors. This exploratory work offers insights for future research on grape yield predictive modeling and grouping strategies to obtain more assertive results, thus contributing to a more efficient grapevine production chain in southern Brazil and worldwide. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agronomica Sinica is the property of Crop Science Society of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate, and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier transform infrared (FTIR) spectroscopy combined with multivariate curve resolution–alternating least squares (MCR-ALS) methodology was evaluated to verify the purity or adulteration of VCO with reference to low-cost commercial oils such as sunflower (SO), maize (MO) and peanut (PO) oils. A two-step analytical procedure was developed, where an initial control chart approach was designed to assess the purity of oil samples using the MCR-ALS score values calculated on a data set of pure and adulterated oils. The pre-treatment of the spectral data by derivatization with the Savitzky–Golay algorithm allowed to obtain the classification limits able to distinguish the pure samples with 100% of correct classifications in the external validation. In the next step, three calibration models were developed using MCR-ALS with correlation constraints for analysis of adulterated coconut oil samples in order to assess the blend composition. Different data pre-treatment strategies were tested to best extract the information contained in the sample fingerprints. The best results were achieved by derivative and standard normal variate procedures obtaining RMSEP and RE% values in the ranges of 1.79–2.66 and 6.48–8.35%, respectively. The models were optimized using a genetic algorithm (GA) to select the most important variables and the final models in the external validations gave satisfactory results in quantifying adulterants, with absolute errors and RMSEP of less than 4.6% and 1.470, respectively. [ABSTRACT FROM AUTHOR]

Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr2O3 and TiO2 as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R2cv and r2, respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R2cv =0.87 and RPD=2.77 and R2cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R2cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R2cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r2>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts. [ABSTRACT FROM AUTHOR]

The flexible strain sensor's measuring range is usually over 5000 με, while the conventional variable section cantilever calibration model has a measuring range within 1000 με. In order to satisfy the calibration requirements of flexible strain sensors, a new measurement model was proposed to solve the inaccurate calculation problem of the theoretical strain value when the linear model of a variable section cantilever beam was applied to a large range. The nonlinear relationship between deflection and strain was established. The finite element analysis of a variable section cantilever beam with ANSYS shows that the linear model's relative deviation is as high as 6% at 5000 με, while the relative deviation of the nonlinear model is only 0.2%. The relative expansion uncertainty of the flexible resistance strain sensor is 0.365% (k = 2). Simulation and experimental results show that this method solves the imprecision of the theoretical model effectively and realizes the accurate calibration of a large range of strain sensors. The research results enrich the measurement models and calibration models for flexible strain sensors and contribute to the development of strain metering. [ABSTRACT FROM AUTHOR]

Strain sensors, especially fiber Bragg grating (FBG) sensors, are of great importance in structural health monitoring, mechanical property analysis, and so on. Their metrological accuracy is typically evaluated by equal strength beams. The traditional strain calibration model using the equal strength beams was built based on an approximation method by small deformation theory. However, its measurement accuracy would be decreased while the beams are under the large deformation condition or under high temperature environments. For this reason, an optimized strain calibration model is developed for equal strength beams based on the deflection method. By combining the structural parameters of a specific equal strength beam and finite element analysis method, a correction coefficient is introduced into the traditional model, and an accurate application-oriented optimization formula is obtained for specific projects. The determination method of optimal deflection measurement position is also presented to further improve the strain calibration accuracy by error analysis of the deflection measurement system. Strain calibration experiments of the equal strength beam were carried out, and the error introduced by the calibration device can be reduced from 10 με to less than 1 με. Experimental results show that the optimized strain calibration model and the optimum deflection measurement position can be employed successfully under large deformation conditions, and the deformation measurement accuracy is improved greatly. This study is helpful to effectively establish metrological traceability for strain sensors and furthermore improve the measurement accuracy of strain sensors in practical engineering scenarious. [ABSTRACT FROM AUTHOR]

Snow pear is very popular in southwest China thanks to its fruit texture and potential medicinal value. Lignin content (LC) plays a direct and negative role (higher concentration and larger size of stone cells lead to thicker pulp and deterioration of the taste) in determining the fruit texture of snow pears as well as consumer purchasing decisions of fresh pears. In this study, we assessed the robustness of a calibration model for predicting LC in different batches of snow pears using a portable near-infrared (NIR) spectrometer, with the range of 1033-2300 nm. The average NIR spectra at nine different measurement positions of snow pear samples purchased at four different periods (batch A, B, C and D) were collected. We developed a standard normal variate transformation (SNV)-genetic algorithm (GA) -the partial least square regression (PLSR) model (master model A) - to predict LC in batch A of snow pear samples based on 80 selected effective wavelengths, with a higher correlation coefficient of prediction set (Rp) of 0.854 and a lower root mean square error of prediction set (RMSEP) of 0.624, which we used as the prediction model to detect LC in three other batches of snow pear samples. The performance of detecting the LC of batch B, C, and D samples by the master model A directly was poor, with lower Rp and higher RMSEP. The independent semi-supervision free parameter model enhancement (SS-FPME) method and the sequential SS-FPME method were used and compared to update master model A to predict the LC of snow pears. For the batch B samples, the predictive ability of the updated model (Ind-model AB) was improved, with an Rp of 0.837 and an RMSEP of 0.614. For the batch C samples, the performance of the Seq-model ABC was improved greatly, with an Rp of 0.952 and an RMSEP of 0.383. For the batch D samples, the performance of the Seqmodel ABCD was also improved, with an Rp of 0.831 and an RMSEP of 0.309. Therefore, the updated model based on supervision and learning of new batch samples by the sequential SS-FPME method could improve the robustness and migration ability of the model used to detect the LC of snow pears and provide technical support for the development and practical application of portable detection device. [ABSTRACT FROM AUTHOR]

Although the scheme of designing transceiver with zero-IF architecture reduces the design cost, complexity and power loss of the system, this architecture is more sensitive to the I/Q mismatch problem existing in the analog front-end and it is difficult to eliminate the image interference caused by the mismatch. Therefore, a fast and low-cost I/Q imbalance calibration algorithm is needed. As for the problem of I/Q imbalance in zero-IF receiver, a blind estimation algorithm for extracting the calibration parameters and signal compensation is proposed because of the complexity of calculation and hardware circuit, as well as the power consumption of resources under the traditional algorithm. Based on the statistical property of the signal, this algorithm has the specific characteristics of low computational complexity and being easy to implement in hardware circuit. Due to different ideas of parameters extraction, a special calibration model is designed. The result of simulation shows that the proposed algorithm has not only better performance in compensation parameters estimation, but also better calibration performance than the traditional algorithm. By comparing the calibration performance in the case of different gain imbalances or phase imbalances, we can see that the algorithm can maintain a good calibration performance under different imbalance conditions. [ABSTRACT FROM AUTHOR]

In order to test the accuracy of the automatic instrument for measuring the fuels moisture content, the obtained moisture content data are compared with the manual measured moisture content data, analyze the causes of errors combined with environmental factors and the working principle of the instrument, and find a correction method to improve the accuracy of the instrument. In this study, three kinds of dead fuels on the ground surface of Harbin were taken as the research object, and a new generation of automatic measuring instrument of moisture content was set up to obtain moisture content data and meteorological data, and compared with the field measured moisture content data. The instrument data were corrected by establishing linear and nonlinear regression models. Finally, the extrapolation of the model was tested and the calibration method was determined. The results showed that (1)The difference between the instrumental data and the measured data was mainly due to the systematic error, which was caused by the water or other impurities attached to the net pocket. This was mainly related to the complex environment in the forest and the working principle of the instrument, which needed to be corrected. (2)Meteorological factors also had a certain influence on the significant difference between instrument measured moisture content data and manual measured data. (3)Among the two calibration models, only the linear regression model had good correction effect and extrapolation, and the moisture content data measured by the instrument can be corrected, and the corrected data can meet the accuracy requirements. There was a certain error in the automatic measuring instrument of fuel moisture content, which was significantly different from the manual measured data. It can be corrected by the linear regression model, and the corrected instrument data met the accuracy requirements of the measurement, which improved the accuracy of the instrument in measuring moisture content data, and can provide important technical support for fast and accurate fire risk prediction in the future. [ABSTRACT FROM AUTHOR]

Detection of fruit traits by using near-infrared (NIR) spectroscopy may encounter out-of-distribution samples that exceed the generalization ability of a constructed calibration model. Therefore, confidence analysis for a given prediction is required, but this cannot be done using common calibration models of NIR spectroscopy. To address this issue, this paper studied the Gaussian process regression (GPR) for fruit traits detection using NIR spectroscopy. The mean and variance of the GPR were used as the predicted value and confidence, respectively. To show this, a real NIR data set related to dry matter content measurements in mango was used. Compared to partial least squares regression (PLSR), GPR showed approximately 14% lower root mean squared error (RMSE) for the in-distribution test set. Compared with no confidence analysis, using the variance of GPR to remove abnormal samples made GPR and PLSR showed approximately 58% and 10% lower RMSE on the mixed distribution test set, respectively (when the type 1 error rate was set to 0.1). Compared with traditional one-class classification methods, the variance of the GPR can be used to effectively eliminate poorly predicted samples. [ABSTRACT FROM AUTHOR]

Pollution flashover is one of the main factors threatening the safety of power system. In order to prevent pollution flashover accident,it is necessary to regularly analyze the contamination of insulator surface. However,the traditional power outage sampling method has a long detection period and consumes a lot of manpower and material resources. In this paper,a strategy and a model for the surface pollution analysis of insulators using laser-induced breakdown spectroscopy are proposed. 10 glass insulators with different equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) are taken as research object,and the glass insulator's surface can be divided into inner,middle and outer ring. The calibration of coefficient of correlation of the model under different test strategy is studied,using Na 589.592 nm,Al 396.192 nm as characteristic spectral lines. The result shows that for ESDD,the coefficient of correlation can reach 0.948 1 when inner and middle rings are selected as test objects. And the relative error of detection is within 5%. For NSDD,the coefficient of correlation can reach 0.938 3 when middle ring is selected as test object. And the relative error of detection is within 15%. The analysis method can realize the quick on-site analysis of insulator surface contamination and improve the operation ability of transmission line operation safety maintenance,which has important engineering application value.

During the past decade, substantial efforts have been dedicated to advancing cost-effective sensor platforms for air quality monitoring. Calibration is a crucial step in ensuring that the data quality of low-cost air monitoring systems meets established standards. Recent research has extensively evaluated low-cost air monitoring platforms against the data quality objectives set by the European Directive. This paper introduces a novel calibration model for low-cost air quality sensors, significantly improving the accuracy of the measurement of nitric oxide (NO), sulfur dioxide (SO 2), and particulate matter (PM 1 , PM 2.5 , and PM 10), while promoting accessibility and adaptability in environmental monitoring technologies. This study extends the evaluation of a developed platform capable of integrating a diverse array of sensors to measure up to 12 parameters. Our proposed models demonstrate a significant improvement, achieving a 60% better accuracy for SO 2. Additionally, these models deliver similar results for PM x and NO or exceed those of state of the art research. The calibration methodology meets the requirements of the Data Quality Objectives (DQO) for all monitored parameters and also achieves indicative levels for PM parameters. • The proposed calibration models enhance accuracy for NO, SO 2 , and PM parameters. • Both MLR and RF models achieve indicative levels for PM. • Only RF calibration model is effective for NO and SO 2. • RF model reached the DQO estimation level faster than MLR for all parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • N quantification by NIRS in stem and root of B. juncea is achieved for first time. • Diverse germplasm collections allowed rapid and reliable NIRS calibration. • The developed NIRS equations displayed the best prediction performance. Brassica juncea depends heavily on nitrogen (N) fertilizers for growth and accumulation of seed protein. However, it is an inefficient mobilizer of applied N which leads to accumulation of excess N in the soil, posing environmental risks. Hence, it is imperative to systematically examine spatial–temporal pattern of crop N to efficiently manage N application. The Kjeldahl method is commonly used to estimate N status of crops but it is a destructive method that entails the use of perilous and expensive chemicals. Near-infrared reflectance spectroscopy (NIRS) offers a safe, accurate, and non-destructive alternative for large-scale screening of seed metabolites. Currently, no NIRS model exists to quickly estimate N content in shoots and roots from large germplasm sets in any rapeseed-mustard crop. Developing such a model is essential to breed for enhanced nitrogen use efficiency (NUE). We used 738 shoot and 346 root samples from a B. juncea diversity set to construct the NIRS models. A diverse range of genetic variation in N content was recorded in the stem (0.21–6.61%) and root (0.15–3.04%) tissues of the crop raised on two different N levels (N0 and N100). Modified partial least squares (MPLS) method was employed to establish a regression equation linking reference N values with spectral changes. The developed models exhibited strong associations with reference values, with RSQ values of 0.884 for stem and 0.645 for roots. Furthermore, external validation confirms the reliability of the developed models. The developed models have strong predictive capabilities for rapid and reliable N estimation in various tissues of B. juncea plants. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) plays an important role in the global water and energy cycle. Compared with GNSS and radiosonde which are distributed in the form of scatters, near-infrared -derived PWV has a higher spatial resolution, meeting more comprehensive investigation of regional climate change. However, PWV derived from the near-infrared water vapor channels of the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard Aqua and Terra satellites, exhibits limitations of missing data and poor accuracy especially when data are collected under cloudy conditions. Many researches have been made to evaluate and calibrate MODIS-PWV with a cloud-free probability >95%, there is still very little research on improving accuracy of PWV under all-weather conditions. Therefore, an optimal calibration method was proposed, in which a filling algorithm was applied to enhance availability of the PWV data, and a linear and periodic calibration scheme based on the analysis of residuals was utilized to improve its accuracy. The experiment was conducted in Hong Kong with 11 uniformly distributed GNSS stations and the station cross-validation was employed using the GNSS-PWV as the references. The results show that the filling algorithms can first effectively fill the data vacancy and generate complete MODIS-PWV with data coverage reaching up to 100%, further make MODIS-PWV more conducive to construct following calibration model. The R-Square(R2), root mean square error (RMSE), mean absolute error (MAE) and relative error of the MODIS-PWV obtained by the proposed method are 0.67, 9 mm,7 mm and 22.5% compared with GNSS-PWV. In comparison to original MODIS-PWV, the RMSE and MAE are reduced by 62% and 59%. For the four seasons, the average value of RMSE is improved from 25 to 9 mm, 32 to 10 mm, 22 to 9 mm, and 12 to 8 mm, respectively. Moreover, the RMSEs and MAEs are about 6–8 mm and 5–6 mm for station cross-validation in every month. These results confirm that the proposed method can provide a complete and continuous PWV data and improve the accuracy of original MODIS-PWV, which is benefit for the hydrological and ecological research. • An optimal method was proposed to calibrate MODSI-PWV using GNSS observations. • The filling strategy of the proposed method can effectively fill the data missing caused by cloud cover of MODIS-PWV. • The accuracy of MODIS-PWV is improved by the optimal method that considers the periodic factors in PWV sequences. [ABSTRACT FROM AUTHOR]

Efficient marketing of winegrapes involves negotiating with potential buyers long before the harvest, when little is known about the expected vintage. Grapevine physiology is affected by weather conditions as well as by soil properties and such information can be applied to build yield prediction models. In this study, Partial Least Squares Regression (PLSR), Cubist (CUB) and Random Forest (RF) algorithms were used to predict yield from imputed weather station data and soil sample analysis reports. Models using only soil variables had the worst general results (R2 = 0.15, RMSE = 4.16 Mg ha−1, MAE = 3.20 Mg ha−1), while the use of only weather variables yielded the best performance (R2 = 0.52, RMSE = 2.99 Mg ha−1, MAE = 2.43 Mg ha−1). Models built with CUB and RF algorithms showed signs of overfitting, yet RF models achieved the best average results (R2 = 0.58, RMSE = 2.85 Mg ha−1, MAE = 2.24 Mg ha−1) using only weather variables as predictors. Weather data imputation affected RF and CUB models more intensely while PLSR remained fairly insensitive. Plant age, yield level group, vineyard plot, May temperatures, soil pH and exchangeable concentrations of Zn, Cu, K and Mn were identified as important predictors. This exploratory work offers insights for future research on grape yield predictive modeling and grouping strategies to obtain more assertive results, thus contributing to a more efficient grapevine production chain in southern Brazil and worldwide.

New approach in development of a calibration model for continuous monitoring of particle size distribution during crystallization of Fosamprenavir Calcium in a laboratory scale set-up is presented. Calibration models were developed based on data generated from Fosamprenavir Calcium crystallization experiments. The applicability of the method for continuous monitoring was explored comparing particle size distributions calculated from the developed model and particle size distributions determined by the laser light scattering method. Based on the calibration model of the particular process, partial least squares regression algorithm for continuous monitoring of particle size distribution was derived and applied and the results were validated. Presented method finds its application for real-time monitoring of particle size distribution, based on chord length distribution measurements.

This paper proposes a novel virtual engine calibration method for alternative fuels using thermodynamic simulation for in-cylinder pressure prediction. Based on known engine data, including the crank angle of the peak cylinder pressure, the optimization problem is defined for a desired indicated mean effective pressure. The decision variables are the combustion and heat transfer model parameters The method was tested for three different engines of different sizes, operating with ethanol, hydrogen and natural gas, and different equivalence ratios. The Wiebe model and a quasi-dimensional fractal combustion model were compared. The results showed that the method was able to successfully predict the in-cylinder pressure curve, with a coefficient of determination higher than 0.99. Furthermore, the method predicted the peak pressure and the crank angle corresponding to 50% of mass fraction burned with a maximum deviation of 2.5% and 1.5 °CA, respectively.

The accuracy and precision of laser-induced breakdown spectroscopy (LIBS) quantitative analysis are significantly limited by the spectral noise. Normalization and ensemble averaging of multiple spectra were often used to preprocess spectra. However, these methods cannot completely remove the spectral noise. Data uncertainty due to the irremovable spectral noise will affect LIBS quantitative analysis. Therefore, this paper proposes a method using data uncertainty to improve the performance of LIBS quantitative analysis. The proposed method uses several spectra to characterize each sample to preserve some data uncertainty in the calibration data matrix. Thus, the data uncertainty is used to optimize the calibration model for improving the toleration to the spectral signal variation. As a result, the optimized calibration model had better accuracy and robustness than the calibration model trained by conventional method. The best root mean square error of prediction (RMSEP) of the ash content of coal was 1.152% for the optimized calibration model, while that for the conventional calibration model was 1.718%. The optimized calibration model also showed a lower relative standard deviation (RSD) value of repeated predictions. Moreover, the calibration model for predicting the ash content in biomass was also optimized by the proposed method. The optimized calibration model outperformed the conventional calibration model again, which demonstrated the extensive applicability of the proposed method. Graphical Abstract [ABSTRACT FROM AUTHOR]

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate, and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier transform infrared (FTIR) spectroscopy combined with multivariate curve resolution–alternating least squares (MCR-ALS) methodology was evaluated to verify the purity or adulteration of VCO with reference to low-cost commercial oils such as sunflower (SO), maize (MO) and peanut (PO) oils. A two-step analytical procedure was developed, where an initial control chart approach was designed to assess the purity of oil samples using the MCR-ALS score values calculated on a data set of pure and adulterated oils. The pre-treatment of the spectral data by derivatization with the Savitzky–Golay algorithm allowed to obtain the classification limits able to distinguish the pure samples with 100% of correct classifications in the external validation. In the next step, three calibration models were developed using MCR-ALS with correlation constraints for analysis of adulterated coconut oil samples in order to assess the blend composition. Different data pre-treatment strategies were tested to best extract the information contained in the sample fingerprints. The best results were achieved by derivative and standard normal variate procedures obtaining RMSEP and RE% values in the ranges of 1.79–2.66 and 6.48–8.35%, respectively. The models were optimized using a genetic algorithm (GA) to select the most important variables and the final models in the external validations gave satisfactory results in quantifying adulterants, with absolute errors and RMSEP of less than 4.6% and 1.470, respectively.

Generalized Zero-shot Learning (GZSL) seeks to identify objects from both seen and unseen classes, relying solely on labeled samples from the seen classes. One of GZSL's main challenges is the problem of bias towards seen classes, i.e. , the model tends to classify all test samples as seen classes due to the data imbalance and over-fitting. To reduce the prediction bias, we proposed to train a novel M ultiple C riteria C alibration (MCC) model with C onfusion eli M ination contrastive E mbedding (CME). Specifically, we designed a novel divide-and-conquer calibration network by leveraging the learned multiple Class-Level and Instance-Level criteria to separate unseen samples from seen samples. To enhance feature separation, we further formulated supervised confusion-elimination contrastive constraints in the latent space to ensure the alignment properties of visual-semantic features and simultaneously encourage the learned features to distribute uniformly, providing a reasonable distribution for MCC and classification. After that, to adaptively mitigate bias, we can follow the learned criteria to calibrate the predictions of both the seen and unseen classes in the latent space, or leave the original predictions unchanged. Extensive experiments under GZSL settings show that the proposed method outperforms the state-of-the-art methods on four popular benchmarks. [ABSTRACT FROM AUTHOR]

The prediction of analyte concentration by spectral responses using a calibration model is a commonly used method in chemical analysis. However, insufficient modeling samples will limit the performance of the calibration model. Artificial generation of spectral samples with analyte concentration values is an effective way to address the shortage of modeling samples. However, traditional methods for generating spectral samples with concentration values still have problems in terms of diversity and accuracy. We proposed a method for generating spectral samples with analyte concentration values based on an adversarial autoencoder (AAE). The proposed method combined spectral responses and analyte concentration as the inputs and fitted the extracted latent variables into a prior distribution. By decoding the random sampling points of the prior distribution, the spectral samples with analyte concentration values were generated. Four spectral datasets were used to validate the effectiveness of the proposed method. Two traditional spectral generation methods were used to evaluate the performance of the proposed methods. It was found that the proposed method performed significantly better than traditional ones. The spectral responses generated by the proposed method had good diversity and similarity to the real ones. In addition, the generated spectral samples could also accurately simulate the actual relationship between spectral responses and analyte properties. The proposed method is an effective solution to the problem of insufficient modeling samples in the quantitative analysis of spectral technology. • A method was proposed for generating spectral samples based on an adversarial autoencoder. • The generated spectral samples contain concentration values of the analyte. • The relationship between spectral responses and analyte properties was well simulated. • The quality of spectral samples was significantly superior to traditional methods. [ABSTRACT FROM AUTHOR]

The flexible strain sensor’s measuring range is usually over 5000 με, while the conventional variable section cantilever calibration model has a measuring range within 1000 με. In order to satisfy the calibration requirements of flexible strain sensors, a new measurement model was proposed to solve the inaccurate calculation problem of the theoretical strain value when the linear model of a variable section cantilever beam was applied to a large range. The nonlinear relationship between deflection and strain was established. The finite element analysis of a variable section cantilever beam with ANSYS shows that the linear model’s relative deviation is as high as 6% at 5000 με, while the relative deviation of the nonlinear model is only 0.2%. The relative expansion uncertainty of the flexible resistance strain sensor is 0.365% (k = 2). Simulation and experimental results show that this method solves the imprecision of the theoretical model effectively and realizes the accurate calibration of a large range of strain sensors. The research results enrich the measurement models and calibration models for flexible strain sensors and contribute to the development of strain metering.

• Measurement errors of ε were determined for eight EM sensors in high EC solutions. • Visualized measurement errors for each sensor under varying EC conditions. • Verified feasibility of insulation-coated probes for measuring ε in high EC conditions. Soil water content stands as a pivotal parameter across numerous research disciplines and engineering applications. Electromagnetic (EM) sensors have gained widespread usage in monitoring soil water content. While commonly employed sensors are suitable for typical agronomic- or landscape-soil environments, they may exhibit significant deviations under conditions of high-salinity or -electrical conductivity (EC). We investigated sensing limits under the influence of increasing EC and the resulting accuracy of dielectric permittivity (ε) determinations using commercial soil water content sensors. Tests were conducted using eight commercially available EM sensors (CS655, HydraProbe II, TDR-305N, TDR-310N, TDR-315N, TEROS 12, TEROS ONE, and WET150). For these uncoated rod sensors, most were accurate within ±3 % relative permittivity errors in saline solutions within the range of 0‒4 dS m−1 while TDR-305N, TEROS ONE and WET150 maintained an accuracy of 5 % for EC values up to 9.24 dS m−1. We explored extending the working range of some sensors to high-saline environments by adding an insulating coating on sensor rods. Since the coated probe not only perceives the characteristics of the immersed medium but also interacts with the coating material itself, a calibration model was evaluated for determining the actual ε of the evaluated medium. The strong correlation between a four-point calibration and the model demonstrates the effectiveness of calibrating coated EM sensors for ε determination using only a limited number of calibration materials. The findings presented here could serve as a valuable resource when selecting EM sensors for use in high-saline environments. Results also shed light on the potential to extend the sensing range through the integration of insulating coatings. [ABSTRACT FROM AUTHOR]

Manure slurry is a mixture of urine, feces, flushing water, and disinfectant in the livestock and poultry breeding industry. About two billion tons of manure slurry can be produced in China every year. Among them, the composition varies greatly, due to the complex sources. There are many influencing factors on the composition of manure slurry, such as the seasons, regions, breeding scale, fecal cleaning, and manure slurry treatment. Currently, the fixed composition value cannot accurately be calculated the amount of manure slurry, when returning to the field. Therefore, it is urgent to develop an accurate, and rapid detection system suitable for the compositions in the manure slurry on site. Fortunately, Near-Infrared (NIR) spectroscopy can offer a great potential to detect manure composition at present. Nevertheless, most reports were usually focused on the near-infrared spectrometer at the large-scale laboratory level. This kind of instrument can be confined to the application of near-infrared spectroscopy analysis in fields, due to the large bulk volume, low portability, and high price. The near-infrared spectrometer can be further developed towards the miniaturization for the technical and cost feasibility during on-site detection, particularly with the development of Micro-Electromechanical Systems (MEMS) and Micro-Opto-Electro-Mechanical Systems (MOEMS) in recent years. In addition to the need for a stable and reliable hardware system, the calibration model is another important application premise of near-infrared technology. But, there is a great challenge to establish the calibration model using cloud sharing technology. It is very necessary to access the shared resources via the various data computing services anytime, anywhere, and on-demand through the Internet. In this study, a micro NIR onsite and rapid system was proposed to detect the composition of manure slurry during field return using a calibration model under cloud sharing. A complete function was also achieved for the data acquisition, upload, prediction and storage at the same time, according to the design scheme of "micro NIR sensor + calibration model cloud sharing + Android client + mobile network". The micro NIR sensor was first used to collect the spectral data of the measured sample, then to transmit the data into the Android client through the Bluetooth protocol, and finally to the cloud server through the mobile network. A calibration model was deployed in the cloud server to calculate the received spectral data. A quantitative prediction was obtained to further send back to the Android client in real time. A rapid detection was realized for the ten parameters of manure slurry compositions, such as the Total Nitrogen (TN), Total Phosphorus (TP), Total Potassium (TK), ammonium nitrogen (NH4 +-N), nitrate nitrogen (NO3 --N), amide nitrogen (CONH2-N), Available Phosphorus (AP), Available Potassium (AK), Organic Matter (OM), and pH value. The prediction relative errors of AK, TN, AP, TP, pH, NH4 +-N, and TK in the manure slurry were less than 10%, and the rest of OM, CONH2-N, and NO3 --N were between 10%-18%. Consequently, the micro NIR technology combined with the calibration model in cloud sharing can be expected to realize the onsite, rapid and accurate detection of various compositions in the manure slurry, particularly for the full sharing of the quantitative model. Moreover, the broad market prospect can be gained for the practical application, due to the portable micro NIR sensor, the simple operation of the Android client, and the lower cost of the system. There is also no need for the professional requirements of the users in the cloud sharing of the calibration model. The finding can provide data support for the high precision return of manure slurry to the field. [ABSTRACT FROM AUTHOR]

The well inclination and azimuth are the main measurement parameters in the calculation of the wellbore trajectory. However, compared with the azimuth error of the conventional well inclination, the azimuth angle measurement error of the inclinometer under the small angle inclination is larger. In order to improve the azimuth angle measurement accuracy of the inclinometer under the small-angle well inclination, the azimuth angle measurement under the 5°～10° small-angle well inclination was compensated based on the BP neural network algorithm. In this paper, a two-dimensional vector composed of standard well inclination and measured azimuth is used as input, and a one-dimensional vector composed of standard azimuth is used as output, and a network model of double input and single output is established. The learning samples are divided into training set and test set by random selection, so that the network has better generalization ability. The simulation test results show that the BP neural network error correction model runs stably and the compensation accuracy reaches 10 -6, which can improve the measurement accuracy of the azimuth angle under the small-angle well inclination from ±5.3° to within ±1.7°. [ABSTRACT FROM AUTHOR]

Summary: This study assessed the ability of near‐infrared spectroscopy to predict intramuscular fat (IMF) and protein content of freeze‐dried ground beef and sheep meat determined using the modified Soxhlet extraction and Dumas combustion, and ascertain if robust calibration models could be developed for both assays using the combined spectral data from both species. Samples were sourced from sheep and cattle slaughtered for a variety of experiments, which covered a range in age, diet fed and carcase cut. All samples were scanned using a benchtop NIRS instrument to develop calibration models, a subset of which was used for later validation. Intramuscular fat and protein content of samples ranged from 5.1 to 32.8 and 63.4 to 89.8, respectively, with combined species of the R2 values of 98.4 and 96.5 (RMSE 0.74 and 0.76) for IMF and protein respectively. Additionally, animal age and carcase cut were found to have no significant impact on the precision of prediction models. Results from our study showed that individual species as well as combined calibration models can be successfully developed for predicting IMF and protein content of freeze‐dried ground beef and sheep meat. [ABSTRACT FROM AUTHOR]

The primary objective of this work was the utilization of the pulsed acoustic wave (PAW) method to explore the feasibility of measuring soil water content (SWC) based on the soil acoustic parameter acquisition (SAPA) system. Acoustic parameters (pulse acoustic velocity and acoustic attenuation coefficient) were collected from paddy soil (clay), red soil (loam), and lateritic-red soil (clay loam) under different SWCs. The calibration models of the pulse acoustic velocity, the attenuation coefficient, and the double acoustic parameters with the soil volumetric water content (SVWC) were established through fitting analysis. We studied the repeatability and reproducibility of the calibration models and conducted an extensive field experiment for 40 days to evaluate the calibration model's ability to predict SVWC. The results showed that the repeatability and reproducibility of the SVWC calibration model based on pulse acoustic velocity were the best, which showed that the acoustic velocity was more suitable for the inversion of SVWC than the acoustic attenuation coefficient. The 40-day field study's experimental results also verified the potential value of pulse acoustic velocity in SVWC detection.

The aim of this study was to develop near-infrared spectroscopy (NIRS) prediction models for the estimation of chemical components and the fibre undegradable fractions (uNDF) of hay-based total mixed rations (TMR). A total of 205 TMR samples were used for the study. All the chemical components were measured using standard AOAC reference methods and expressed as percentages of dry matter (DM). Prediction models were developed using both cross- and independent validation and different mathematical treatments applied on spectral data. The best spectral treatment was chosen based on the method which simultaneously achieved the lowest root mean square error and the highest explained variance in cross-validation. The coefficient of determination in external validation (R2P) was the greatest for starch prediction model (R2P = 0.84), followed by acid detergent fibre (ADF; R2P = 0.79), and amylase-treated ash-corrected NDF with addition of sodium sulphite (aNDFom) and crude protein prediction models (CP; R2P = 0.73). The concordance correlation coefficient (CCC) in validation ranged from 0.66 (ash prediction model) to 0.92 (starch prediction model), indicating substantial to accurate models’ predictive ability. This study indicated that NIRS can be a screening method for the prediction of CP, Starch, aNDFom, ADF, acid detergent lignin (ADL), uNDF and Ash. The use of TMR utilised in various herds provided high variability for the NIRS calibration dataset, implying that the developed NIRS pre-diction models could be applicable to TMR collected from herds located in the Parmigiano Reggiano cheese production area.Highlights NIRS can be successfully employed to determine quickly and at cost-effective different compositional and digestibility traits in hay-based TMR. TMR analysis predicted by NIRS can support nutritionists in the formulation of diets containing a proper nutrient profile to sustain physiological, metabolic, and immunological processes. The use of NIR technology for TMR analysis can allow frequent monitoring of rations and increasingly timely corrections, maximising cows’ diet utilisation and conversion of the ingested feed.

This paper proposes a method for self-calibrating the star sensor on the Mars rover considering several years of exploration on the surface of Mars. The natural stars in the night sky are considered the control points, and a self-calibration model is deduced in detail according to an imaging model. An adaptive celestial positioning (ACP) algorithm is then introduced, and the calculation procedure is presented in detail to realise self-adjustment based on the self-calibration of the star sensor. Three field tests were conducted on Earth, the results of which show good self-calibration and celestial positioning performances. The positioning results indicate an obvious accuracy improvement using the ACP algorithm compared with that without calibration. Multiple positionings in one night can improve the celestial positioning accuracy to approximately 15 m. For future studies, this self-calibration model will be useful not only for star sensors but also for other optical sensors, such as sun sensors and binocular or stereo-vision cameras. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Emerging evidence suggests that excessive sitting has negative health effects. However, this evidence largely relies on research using self-reported sitting time, which is known to be biased. To correct this bias, we aimed at developing a calibration model estimating "true" sitting from self-reported sitting. METHODS: Occupational sitting time was estimated by self-reports (the International Physical Activity Questionnaire) and objective measurements (thigh-worn accelerometer) among 99 Swedish office workers at a governmental agency, at baseline and 3 and 12 months afterwards. Following compositional data analysis procedures, both sitting estimates were transformed into isometric log-ratios (ILR). This effectively addresses that times spent in various activities are inherently dependent and can be presented as values of only 0−100%. Linear regression was used to develop a simple calibration model estimating objectively measured "true" sitting ILR (dependent variable) from self-reported sitting ILR (independent variable). Additional self-reported variables were then added to construct a full calibration model. Performance of the models was assessed by root-mean-square (RMS) differences between estimated and objectively measured values. Models developed on baseline data were validated using the follow-up datasets. RESULTS: Uncalibrated self-reported sitting ILR showed an RMS error of 0.767. Simple and full calibration models (incorporating body mass index, office type, and gender) reduced this error to 0.422 (55%) and 0.398 (52%), respectively. In the validations, model performance decreased to 57%/62% (simple models) and 57%/62% (full models) for the two follow-up data sets, respectively. CONCLUSIONS: Calibration adjusting for errors in self-reported sitting led to substantially more correct estimates of "true" sitting than uncalibrated self-reports. Validation indicated that model performance would change somewhat in new datasets and that full models perform no better than simple models, but calibration remained effective.

Strain sensors, especially fiber Bragg grating (FBG) sensors, are of great importance in structural health monitoring, mechanical property analysis, and so on. Their metrological accuracy is typically evaluated by equal strength beams. The traditional strain calibration model using the equal strength beams was built based on an approximation method by small deformation theory. However, its measurement accuracy would be decreased while the beams are under the large deformation condition or under high temperature environments. For this reason, an optimized strain calibration model is developed for equal strength beams based on the deflection method. By combining the structural parameters of a specific equal strength beam and finite element analysis method, a correction coefficient is introduced into the traditional model, and an accurate application-oriented optimization formula is obtained for specific projects. The determination method of optimal deflection measurement position is also presented to further improve the strain calibration accuracy by error analysis of the deflection measurement system. Strain calibration experiments of the equal strength beam were carried out, and the error introduced by the calibration device can be reduced from 10 με to less than 1 με. Experimental results show that the optimized strain calibration model and the optimum deflection measurement position can be employed successfully under large deformation conditions, and the deformation measurement accuracy is improved greatly. This study is helpful to effectively establish metrological traceability for strain sensors and furthermore improve the measurement accuracy of strain sensors in practical engineering scenarious.

The measurement of the moisture content of building materials is of key importance both in the process of building structures and in their subsequent operation. In engineering practice, indirect techniques of moisture measurement, mainly, resistance and capacitive, are the most popular. The main objective of this research work was to compare the classic TDR measurement technique to the non-invasive, surface TDR sensors. Moisture measurements were carried out on samples made of cellular concrete with density class of 400 and 600. These samples were moist to various degrees, from 0 to 69% (400 c.c.) and from 0 to 55 (600 c.c.). For each sample, five measurements were carried out. Both the RMSE and the expanded uncertainty values were more favorable for the TDR FP/mts probe and were consistent with the literature data. Compared to them, the measurement result for the 400 c.c. samples with the S1 probe was 154.6%, and that with the S2 probe was 87.03% of the values obtained with the invasive probe. When measuring the 600 c.c. samples, we found values of 122.16% for S1 and of 120.1% for S2 of those obtained with the invasive probe. The use of surface TDR sensors provided an easy and quick measurement without damaging the surface and structure of the tested material, as there was no need to introduce the probe actuators inside the tested material.

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Spectra data of 300 samples from 6 Cucurbitaceae commodities, including zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber, were recorded using a handheld visible/near-infrared (Vis/NIR) instrument. Vis/NIR data were obtained in the form of absorbance spectra data at a wavelength of 381–1065 nm. The spectral data has the potential to be reused to predict quality attributes in the form of soluble solids and water content on several Cucurbitaceae commodities. The accuracy of the Vis/NIR calibration model can be increased by applying spectra preprocessing, for example, second derivative savitzky-golay (dg2). The calibration model was developed using the principal component regression (PCR) method on RAW and dg2 spectra. The enhanced Vis/NIR dataset can be used to evaluate the inner quality attributes of intact fruits in a rapid, non-destructive manner.