Smaller Absolute Error Research Articles

How to Explain the Huge Differences in Rebound Estimates: A Meta-Regression Analysis of the Literature

Rebound effects are commonly defined as the relative gap between the potential and realized savings in resource use following efficiency improvements or sufficiency changes. While a considerable number of studies quantify rebound effects, empirical estimates vary widely. Reliable information on the magnitude of rebound effects is therefore still lacking, despite being essential to devise and adjust, for example, energy efficiency policies accordingly. Here, we present the first meta-regression analysis of microeconomic rebound effects at the household level, using forty-three studies with 1,118 estimates to determine average rebound effects and to explain heterogeneous empirical findings. We find that the total microeconomic rebound is, on average, about 41%–52%. The variance can be explained by differences in the type of data used, the scenario setup, and the specifics of the rebound estimation in the primary studies. Furthermore, we find only small absolute transfer errors, indicating a good predictability of rebound effects using our meta-regression model.

Assessing Normalized Difference Vegetation Index as a proxy of urban greenspace exposure

The Normalized Difference Vegetation Index (NDVI) is a popular proxy of urban greenspace (UGS). However, it’s unclear how NDVI approximates physical characteristics of UGS in the context of urban health studies, causing ambiguities in translating research findings to UGS management. Therefore, we collected data from Landsat and MODIS satellites and Lidar 3D scans in New York City as of circa 2013, and we evaluated linear and non-linear relationships between NDVI and UGS characteristics. We found that: (1) % UGS was the best predicted UGS characteristic by NDVI (R2: 0.35–0.90, varies by data source and unit of analysis), whereas average tree height was the worst (R2: 0.09–0.46). The predictive power on % canopy cover, tree density, and crown volume density was in a similar range (R2: 0.10–0.67). Prediction improved with finer-resolution NDVI sources and larger units of analysis at the cost of losing useful variations; (2) There was a saturation effect where a linear relationship underestimated UGS characteristics in areas of high NDVI. These areas typically had NAIP-NDVI greater than the range of 0.08–0.25, Landsat-NDVI greater than the range of 0.42–0.65, and MODIS-NDVI greater than the range of 0.49–0.75; (3) Smaller absolute errors from a linear NDVI-UGS relationship were often found in more developed locations. We therefore recommend NDVI as a reliable predictor of UGS coverage and its use in longitudinal studies. Future studies should also consider fine resolution land cover maps and Lidar, which are increasingly available to derive detailed UGS characteristics.

Short-Term Photovoltaic Power Prediction Based on a Digital Twin Model

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation.

Reparation with moving least squares sampling and extraction of body sizes of beef cattle from unilateral point clouds

Body measurements of beef cattle at various growth stages are crucial for assessing breeding quality. To decrease the stress responses of live cattle during measurement, noncontact body size measurement using a deep sensor is conducted. After directly obtaining the point cloud data (PCD) of a unilateral surface, novel point cloud repair and body measurement are proposed. The PCD of beef cattle are first preprocessed using Euclidean clustering, conditional filtering, and Random Sample Consensus. To merge the unilateral PCD into the entire contour, a Euclidean transformation matrix and moving least squares are applied. To address holes caused by stitching due to camera angle restrictions, various upsampling techniques based on least squares deformation are investigated to repair holes in different surfaces with varying growth stages. Finally, polynomial fitting is chosen to be in accordance with the feature point distribution of the body height (BH), chest girth (CG), back height (AH), waist height (WH), ischial tuberosity, and shoulder end of beef cattle. Using the formulas of Euclidean distance and cubic B–spline curve fitting, the values of BH, AH, WH, CG, and body length are obtained and then compared with manual measurement data. The predicted values of the five body sizes of 100 beef cattle had a mean absolute error and mean absolute percentage error of 7.77 cm and 5.42 %, respectively. The largest absolute error was 12.05 cm, and the smallest absolute error was 4.12 cm. A series of PCD processing and repair methods provides a new method for the noncontact automated measurement of beef cattle. This approach promotes more effective practices in livestock welfare and trait assessment compared with traditional management.

Prediction method of stalk content in strip particles after redrying of tobacco leaves

AbstractFour tobacco leaf modules processed by Yunnan Tobacco Redrying Co., Ltd. during the 2022 roasting season were used to investigate the method of stalk content in strip particles after redrying of tobacco leaves, effectively reducing the loss of strip particles. A total of 151 sets of experimental data were used to construct the prediction model for the stalk content in strip particles after redrying using the BP artificial neural network method, the linear regression method, and the support vector machine method. The results show that the prediction model constructed by the BP artificial neural network method has high accuracy and stability, with a relatively small absolute error of prediction (e = 0.0195%) and the root-mean-square error of interactive verification (RMSECV = 0.0227%), as well as a relatively small mean absolute error of production data validation (e = 0.0675%), while the prediction deviation ratio (RPD = 2.2435) is relatively large. Overall, the prediction model established by BP artificial neural network could provide new insight into the non-destructive detection of stalk content in strip particles of redried tobacco leaves after threshing and redrying and potentially leading to a reduction in tobacco leaf crushing by more than 112,500 kg per year.

Prediction of Biogas Production Volumes from Household Organic Waste Based on Machine Learning

The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression) to create an effective model for forecasting the volume of biogas production from household organic waste is considered. Based on the comparison of these algorithms by MSE and MAE indicators, the quality of training and their accuracy during forecasting are evaluated. The proposed algorithm for creating a model for forecasting biogas production volumes from household organic waste involves the implementation of 10 main and 3 auxiliary steps. Their advantage is that they aid in the performance of component data analysis, which is carried out based on the method of reducing the dimensionality of the data set, increasing interpretability, and minimizing the risk of data loss. An analysis of 2433 data is was carried out, which characterizes the formation of biogas from food (FW) and yard waste (YW) according to four features. Data preparation is performed using the Jupyter Notebook environment in Python. We select five machine learning algorithms to substantiate an effective model for forecasting volumes of biogas production from household organic waste. On the basis of the conducted research, the main advantages and disadvantages of the used algorithms for building forecasting models of biogas production volumes from household organic waste are determined. It is found that two models, “Random Forest Regressor” and “Gradient Boosting Regressor”, show the best accuracy indicators. The other three models (Linear Regression, Ridge Regression, Lasso Regression) are inferior in accuracy and were not considered further. To determine the accuracy of the “Random Forest Regressor” and “Gradient Boosting Regressor” models, we choose the MSE and MAE indicators. The Random Forest Regressor model is found to be a more accurate model compared to the Gradient Boosting Regressor. This is confirmed by the fact that the MSE of the “Random Forest Regressor” model on the training data set is 7.14 times smaller than that of the “Gradient Boosting Regressor” model. At the same time, MAE is 2.67 times smaller in the “Random Forest Regressor” model than in the “Gradient Boosting Regressor” model. The MSE and MAE of both models are worse on the test data set, which indicates overtraining tendencies. The Gradient Boosting Regressor model has worse MSE and MAE than the Random Forest Regressor model on both the training and test data sets. It is established that the model based on the “Random Forest Regressor” algorithm is the most effective for forecasting the volume of biogas production from household organic waste. It provides MAE = 0.088 on test data and the smallest absolute errors in predictions. Further systematic improvement of the “Random Forest Regressor” model for forecasting biogas production volumes from household organic waste based on new data will ensure its accuracy and maintain competitive advantages.

A Mine Water Source Prediction Model Based on LIF Technology and BWO-ELM.

The traditional methods for identifying water sources in coal mines lack the ability to quickly detect water sources and are prone to causing secondary pollution of samples. In contrast, laser induced fluorescence (LIF) technology has been introduced for the identification of coal mine water sources due to its high sensitivity and real-time performance. However, extreme learning machine (ELM) have shortcomings in randomly selecting weights and biases. The Beluga Whale Optimization (BWO) algorithm has efficient optimization capability, global search capability, adaptability and parallelism, and can find the optimal weights and biases in a short time. The combination of LIF technology and BWO-ELM model can be applied to quickly identify the welling water source in coal mine. Select sandstone water and old goaf water from the Huainan mining area as experimental samples, and mix them in different proportions to prepare 7 mixed water samples for testing. Utilize LIF technology to obtain spectral curve images, preprocess them with polynomial smoothing algorithm (SG) and spectral multiple scattering correction (MSC), and perform dimensionality reduction using factor analysis (FA) and linear discriminant analysis (LDA) methods. Finally, construct ELM models, Long Short Term Memory (LSTM) models, BWO-ELM models, and Particle Swarm Optimization Extreme Learning Machine(PSO-ELM) models for the dimensionality reduced data. In order to improve the reliability and accuracy of the results, the experimental results were kept to 5 decimal places. From the experimental results, it can be seen that SG-LDA-BWO-ELM has the best fitting effect, with a fitting coefficient of 0.99990, a root mean square error of 0.00041, a mean square error approaching 0, and an average absolute error of 0.00021. It has the best convergence and the smallest absolute error among all models, making it the most suitable for identifying mine water inrush. It is of great significance for preventing and controlling mine water disasters and ensuring coal mine production safety.

A Novel Time-frequency Domain Reflectometry Method Based on S-transform for Power Cable Defect Localization

Background:: The large-scale aging of cross-linked polyethylene (XPLE) leads to an increase in the frequency of power cable accidents, which poses a threat to the safe operation of the power grid. Objective:: Traditional time-frequency domain reflectometry (TFDR) techniques for fault localization in power cables are susceptible to cross-terms interference, which makes it impossible to accurately locate defects. To overcome the limitation, this paper has proposed a novel cable fault localization method based on S-transform. Method:: The method employs S-transform to perform time-frequency analysis on the collected signals of TFDR, mapping the time-domain signals into time-frequency domain signals. Then, cross-correlation of the time-frequency domain signals is estimated, allowing for accurate positioning of cable defects. Results:: The results of the field experiments conducted on a 10 kV cross-linked polyethylene cable with artificially introduced defects have demonstrated that the proposed method could effectively locate the cable defects with small absolute error. Conclusion:: Compared to existing TFDR methods, the proposed method has been found to be free from interference of cross-terms, resulting in higher reliability and smaller blind spots for detecting cable defects.

A Comparative Study of Mathematical Models for the Tropical Cyclone Intensity–Size Relation

Despite considerable progress in tropical cyclone (TC) research, our current understanding and prediction capabilities regarding the TC intensity–size relation remain limited. This study systematically analyzes the key characteristics and performance of different types of mathematical models for TC intensity–size relations using the 6-hourly Tropical Cyclone Extended Best Track Dataset spanning 1988 to 2020. The models investigated include statistical, idealized (e.g., Rankine vortex), parametric, and theoretical models. In addition to directly comparing the solutions obtained from individual models to the observed TC records, we assess the models that can produce a unique finite-sized radial profile of surface winds for each TC record—a minimal requirement to ensure that the predicted radial profile of the surface winds would align with the observed profile. The results reveal that a sufficient condition to guarantee a unique radial profile of surface winds is that the associated model can be written as a radial invariant quantity, although it does not guarantee a finite-sized profile. Only the effective absolute angular momentum (eAAM) model, among all the models examined in this study, meets the minimum requirement. Furthermore, the solutions obtained from the eAAM model are well correlated with their observational counterparts (85 to 95%) with little systematic bias and small absolute mean errors that are very close to the observational resolution. The eAAM model’s ability to capture the complex intensity–size relation of observed TCs, in combination with these desirable features, suggests its high potential for gaining a better understanding of the underlying physics governing the observed TC intensity–size relation.

Recurrent neural network for the dynamics of Zika virus spreading

<abstract> <p>Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.</p> </abstract>

Comparative Study on Estimation Methods of Proportional Hazard Models for Interval-Censored Data

Purpose: In this study, we compare the estimation methods of interval-censored data using both simulated and real data. Many past studies have used fixed sample sizes in their simulation studies. We performed the best possible simulation study.
 Method: The methods include Finkelstein’s method with Piecewise and Spline and imputation methods (i.e., Efron’s method in the Cox model).
 Results: If the interval-censored data do not overlap, the same estimation results are obtained regardless of the assignment point for the estimation of the Cox model. The overlapping data also did not significantly affect the accuracy of the estimation. On the other hand, Finkelstein’s method showed differences in estimation depending on the two estimation methods of the baseline survival function. Although it was not possible to determine which method had better power, the Spline method had a smaller absolute error than the Finkelstein method. A comparison of Cox’s and Finkelstein’s methods showed that Finkelstein’s method was superior in terms of power.
 Conclusion: Interval-censored data is a form of data that can be found in a variety of fields. In this study, we compared estimation methods for interval-censored data, and the usefulness of Finkelstein’s method can be seen from simulation studies.

Апроксимація дистрибутивної функції відбивної здатності поверхні поліном третього степеня

In the article a bidirectional reflectance distribution function based on a polynomial of the third degree is developed. The main disadvantages of Schlick, Phong, Blinn reflectance models are analyzed. The approximation of Blinn-Phong model by a cubic polynomial is proposed to improve the productivity of three-dimensional image formation. Formulas for the coefficients of the approximation cubic polynomial are calculated. The disadvantages of using the proposed cubic polynomial for reproducing the glare’s attenuation zone are considered. A function for high-precision reproduction of this zone is proposed, formulas of its coefficients are calculated. A combined function, combining a cubic polynomial for reproducing the glare’s epicenter zone and a function for reproducing the attenuation zone, is proposed. The plot of the combined function is built. It is shown that the combined function reproduces the glare’s epicenter and attenuation zones with small relative and absolute errors. The developed reflectance model provides a highly-productive formation of three-dimensional scenes in computer graphics systems. The proposed distribution function of surface reflectivity can be used in computer graphics systems.

Hip and lumbosacral joint centre locations in asian population: Biases produced by existing regression equations and development of new equations

The hip and lumbosacral joint centre (HJC and LSJC) predictions are required to analyse the lumbo-pelvic-hip dynamics during various human motions. Some HJC and LSJC regression equations based on pelvic dimension have been developed; however, the pre-existing methods need to be re-evaluated, and methodological reconsideration may improve the regression methods. Here we show that pre-existing methods produce biased predictions of the LSJC and HJC in 23 male and 24 female Japanese adults, and that the biases in the LSJC differ between sexes, using magnetic resonance imaging (MRI) around the pelvis. Compared with directly measured locations on MRI, the pre-existing regression equations predict LSJC to be more posterior in males and more inferior and posterior in females, and HJC to be more medial in both sexes. The better pre-existing regression equation for LSJC height differs between sexes, with pelvic-width-base better in males and pelvic-depth-base better in females, respectively. We suggest the unsuitability of pre-existing methods to our dataset consisting of Japanese adults and the importance of considering sex differences in regression methods. We propose regression equations to predict HJC and LSJC, considering soft-tissue thickness, sex differences, and a height-directional measure, using least absolute shrinkage and selection operator regression. We validate them using leave-one-out cross-validation (LOOCV). LOOCV shows that our model produces negligible biases and smaller absolute errors than the pre-existing regressions; in particular, the anteroposterior absolute error for LSJC is less than half that of the pre-existing regression. Our regression equation can be a powerful solution for accurate motion analysis.

Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland

Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. Machine learning has potential in large-scale GPP prediction, but its application accuracy and impact factors still need further research. This paper takes the Mongolian Plateau as the research area. Six machine learning methods (multilayer perception, random forest, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) were trained using remote sensing data (MODIS GPP) and 14 impact factor data and carried out the prediction of grassland GPP. Then, using flux observation data (positions of flux stations) and remote sensing data (positions of non-flux stations) as reference data, detailed accuracy evaluation and comprehensive trade-offs are carried out on the results, and key factors affecting prediction performance are further explored. The results show that: (1) The prediction results of the six methods are highly consistent with the change tendency of the reference data, demonstrating the applicability of machine learning in GPP prediction. (2) LightGBM has the best overall performance, with small absolute error (mean absolute error less than 1.3), low degree of deviation (root mean square error less than 3.2), strong model reliability (relative percentage difference more than 5.9), and a high degree of fit with reference data (regression determination coefficient more than 0.97), and the prediction results are closest to the reference data (mean bias is only −0.034). (3) Enhanced vegetation index, normalized difference vegetation index, precipitation, land use/land cover, maximum air temperature, potential evapotranspiration, and evapotranspiration are significantly higher than other factors as determining factors, and the total contribution ratio to the prediction accuracy exceeds 95%. They are the main factors influencing GPP prediction. This study can provide a reference for the application of machine learning in GPP prediction and also support the research of large-scale GPP prediction.

Comparison of the Barrett toric calculator using measured and predicted posterior corneal astigmatism and the Kane and Abulafia-Koch calculators.

To compare the accuracy of the Barrett toric calculator with measured and predicted posterior corneal astigmatism (MPCA and PPCA, respectively), the Abulafia-Koch (AK) formula, and the toric Kane formula. Ein-Tal Eye Center, Tel-Aviv, Israel. Retrospective cohort. Consecutive cases of patients who underwent uneventful cataract extraction surgery with implantation of a toric intraocular lens between March 2015 and July 2019 were retrospectively reviewed. 1 eligible eye from each patient was included. The predicted postoperative refractive astigmatism was calculated using each method and compared with the postoperative refractive astigmatism to give the prediction error. 80 eyes of 80 patients were included in this study. The mean centroid and the mean and median absolute prediction errors using Kane (0.25 diopters [D] ± 0.54 @ 6 degrees, 0.50 D ± 0.31 and 0.45 D, respectively) were significantly different compared with MPCA (0.12 D ± 0.52 @ 16 degrees, P < .001, .44 D ± 0.28 and 0.36 D, P = .027, respectively), PPCA (0.09 D ± 0.49 @ 12 degrees, P < .001, .41 D ± 0.27 and 0.35 D, P < .001, respectively), and AK (0.11 D ± 0.49 @ 11 degrees, P < .001, .42 D ± 0.27 and 0.35 D, P = .004, respectively). No significant differences were found between the calculators in the predictability rates within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 D. The measured posterior corneal curvature in the Barrett calculator yielded comparable outcomes to its prediction by the Barrett and AK formulas. The Kane calculator showed a slight against-the-rule prediction error compared with the other methods, resulting in a small higher median absolute error with marginal clinical importance.

Genetic algorithm-based analysis of heat production prediction in electronic devices

Abstract In daily production life, heat generation and dissipation of electronic devices are important issues concerning the safety of the devices. To enhance the prediction and analysis of heat production of electronic devices, this paper analyzes and studies the heat production and dissipation of electronic systems of different electronic devices by collecting their historical operating power, hot spot temperature, ambient temperature, and other data, and iteratively optimizes these data using genetic algorithms to seek the best temperature fitting curve according to the research progress of existing artificial intelligence algorithms. The experimental results show that the population genes are sufficiently optimized as the number of iterations increases. The prediction model established by the genetic algorithm has a global optimization-seeking ability, high prediction accuracy, relatively small absolute and relative errors, and a fast convergence rate. This model has practical feasibility and can play a good role in the operation and maintenance of electronic devices.

Accelerating the discovery of acid gas-selective MOFs for natural gas purification: A combination of machine learning and molecular fingerprint

The rational construction of high-performing MOF adsorbents for H2S/CO2/CH4 separation is essential for natural gas (NG) purification. However, trial-and-error experiments usually face significant challenges associated with unclear design routes. Especially the chemical design, which is decisive in geometrical properties and adsorption sites, thus playing substantial roles in molecule separation, has not been comprehensively understood yet. In this work, a combination of machine learning (ML)-assisted computational screening protocol based on ∼ 1,000 simulation data and molecular fingerprint was performed to accelerate the discovery of promising MOFs for H2S/CO2/CH4 separation. The reliability of the adopted ML algorithm on our proposed comprehensive separation metric (TNSR) was further verified by another 50 MOFs with a small absolute mean error (AME) of 2.95. Encouragingly, 463 out of over 10,000 unpredicted MOFs were rapidly identified using the developed ML-assisted screening protocol. Among them, five additional top-performing MOFs (TNSR > 65) were successfully located, with the best one showing a TNSR of 82.39, 5–9 times higher than some well-known adsorbents (i.e. CuBTC, ZIF-69) at the same condition. During establishing the screening protocol, the chemical characteristics particular to the high-performing MOFs and the different effects of aromatic and alkyl ligands on separation were understood in detail. Different from the previous work, it was found that MOFs with transitional metals can also be unfavorable, mainly due to the specific metal coordination formed under various conditions. This work emphasizes the importance of chemical design and aims to provide an accurate screening strategy to discover promising MOFs for natural gas purification.

Rereferencing of clinical EEGs with nonunipolar mastoid reference to infinity reference by REST

ObjectiveConventional electroencephalography (EEG) offline subtraction rereferencing is invalid for many clinical practices when adopting a specific nonunipolar recording montage (e.g., the ipsilateral mastoid (IM) and contralateral mastoid (CM)). Further comparative analyses would thus be blocked due to the lack of a uniform offline reference. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform nonunipolar mastoid montages into a computational zero reference at infinity (IR) offline. MethodsFor EEG signals and power/connectivity configurations, simulation and clinical schizophrenia resting-state EEG datasets were used to investigate the performance of REST. ResultsREST produced small absolute errors (signal level: 1.21–1.26; power: 0.0057–0.021; connectivity: 0.066–0.088) and high correlations (>0.9) between the IM/CM-IR and true IR references. Using clinical data with the IM online reference, REST revealed valuable changes in spectral and connectivity (P < 0.05) in schizophrenia patients, consistent with previous studies. ConclusionsThese results demonstrated that REST transformation could be adopted to resolve the offline rereferencing of clinical EEGs with specific nonunipolar mastoid references. SignificanceREST could be an effective and robust resolution for nonunipolar clinical EEGs and could therefore retrieve these data for further analysis by deriving a favorable offline reference IR.

Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms

The presence of concrete cracks in structures indicates possible structural deterioration, but it is quite difficult to measure crack width accurately. While much research has been conducted on crack detection using image processing, there is a gap in the accurate quantification of crack width in millimeters. Current methods either measure in pixels or require the attachment of scales or markers onto a measured surface, which can pose safety hazards in hard-to-reach areas. This paper addresses these issues by proposing a novel image-based method for measuring concrete crack width in millimeters using a laser beam and image processing. The proposed method was validated in the laboratory by capturing images of concrete cracks with two cameras of different resolutions, each attached with lasers. The lasers projected a circular laser beam onto the surface of the concrete under inspection. The images were then pre-processed, segmented, and skeletonized for crack width measurement in pixels. The relationship between the laser diameter and camera distance from the surface was used to convert the measured crack width from pixels to millimeters. The method was performed with high accuracy, as indicated by the absolute error. The largest absolute error was 0.57 mm, while the smallest absolute error was 0.02 mm. The proposed method allows real-world interpretation of results in millimeters. As a result, measured crack widths can easily be compared to allowable limits in international standards, which are typically expressed in metric or SI units. The proposed method can also promote safer inspections in areas of low accessibility by attaching the laser to devices such as drones.

Finger-Stick Artifactual Hypoglycemia in the Emergency Department: A Case Report

BackgroundArtifactual hypoglycemia is a low glucose measurement in a normoglycemic patient. Patients in a shock state or with extremity hypoperfusion can metabolize a higher proportion of the glucose in the poorly perfused tissue, and blood obtained from those tissues may have far lower glucose concentration than the blood in the central circulation. Case ReportWe present the case of a 70-year-old woman with systemic sclerosis, progressive functional decline, and cool digital extremities. The initial point-of-care testing (POCT) for glucose was 55 mg/dL from her index finger, with subsequent repeated low POCT glucose reading, despite glycemic repletion and contradictory euglycemic serologic readings from her peripheral i.v. sites. Two separate POCTs were then obtained from her finger and her antecubital fossa, which had vastly different glucose readings; the latter was in congruence with her i.v. draws. The patient was diagnosed with artifactual hypoglycemia. Alternative sources of blood to avoid artifactual hypoglycemia on POCT samples are discussed. Why Should an Emergency Physician Be Aware of This?Artifactual hypoglycemia is a rare but commonly misdiagnosed phenomenon that can occur in emergency department patients when peripheral perfusion is limited. We encourage physicians to confirm peripheral capillary results with a venous POCT or explore alternative sources of blood to avoid artificial hypoglycemia. Small absolute errors can matter when the erroneous result is hypoglycemia.