Variable Error Research Articles

Time Variability Correction of CMIP6 Climate Change Projections

AbstractAccurate projections of climate change and associated extreme events under differing emission scenarios are linked to realistic representations of the temporal variability of the atmosphere at a variety of time scales, for example, annual, seasonal, synoptic, and daily. Here a new method is employed to explicitly quantify a model's ability to accurately represent covariance at and between differing time scales. From our global‐scale analysis, on average, raw Coupled Model Intercomparison Project Phase 6 (CMIP6) models misrepresent temporal variances at differing time scales for maximum temperature (tasmax) by a considerable margin, particularly at 183‐, 92‐ and 46‐day time scales. To ameliorate such variability errors, we propose a novel Time Variability Correction (TVC) method that corrects temporal covariances while preserving the essential time‐event sequence of the model simulations. We adopt a model‐as‐truth framework to evaluate the effectiveness of the TVC method under future forcing conditions when applied to daily tasmax simulations from 23 CMIP6 models for 1% of the global grid cells. TVC‐corrected temperatures generally show improved matching of temporal variance and lag correlations in the out‐of‐sample projection period compared to simple mean‐corrected projections. By imparting more realistic temporal‐correlations to model series, TVC is expected to improve the projections of extreme events associated with persistent heat, such as heatwaves. Applying TVC to future temperature projections using actual observations significantly increases the temperature variance in most middle to high latitude land regions in the Northern Hemisphere, while decreasing it in most low to middle latitude land regions, compared to simple mean‐corrected projections.

Research on Speed Cooperative Control Strategy for Rudderless Dual-PMSM Propulsion Ships

In the navigation of rudderless dual-motor propulsion ships, it is often necessary for the dual motor to run stably at different given speeds. However, the existing coupling strategies cannot achieve precise cooperative control of the dual motor under different given commands. Therefore, in this study, we proposed a variable error difference compensation-coupling control (VEDC-CC) strategy. Simultaneously, a coupling compensation coefficient determination method based on the self-error difference of the dual motor was introduced. The VEDC-CC utilizes the self-error difference of the dual motor as the input for the coupling compensation controller, which enables the mutual coupling of self-errors, and effectively improves the cooperative controlling performance under different given commands. Moreover, the employed variable compensation coefficient determination method enhances the disturbance coupling performance and the response speed of the system. In this study, we analyzed the different navigation conditions and dual-propeller load characteristics of a rudderless dual-motor propulsion ship. Taking a rudderless dual-motor propulsion ship designed based on the cruise ship “Feng Hua Xue Yue” as the object, the VEDC-CC strategy was validated through simulation tests, considering the propeller load characteristics and actual operating conditions. The experimental results demonstrated that the VEDC-CC strategy could meet the practical control needs of the target ship.

Optimization and validation of a suprasystolic brachial cuff-based method for noninvasively estimating central aortic blood pressure.

Clinical studies have extensively demonstrated that central aortic blood pressure (CABP) has greater clinical significance in comparison with peripheral blood pressure. Despite the existence of various techniques for noninvasively measuring CABP, the clinical applications of most techniques are hampered by the unsatisfactory accuracy or large variability in measurement errors. In this study, we proposed a new method for noninvasively estimating CABP with improved accuracy and reduced uncertain errors. The main idea was to optimize the estimation of the pulse wave transit time from the aorta to the occluded lumen of the brachial artery under a suprasystolic cuff by identifying and utilizing the characteristic information of the cuff oscillation wave, thereby improving the accuracy and stability of the CABP estimation algorithms under various physiological conditions. The method was firstly developed and verified based on large-scale virtual subject data (n = 800) generated by a computational model of the cardiovascular system coupled to a brachial cuff, and then validated with small-scale in vivo data (n = 34). The estimation errors for the aortic systolic pressure were -0.05 ± 0.63 mmHg in the test group of the virtual subjects and -1.09 ± 3.70 mmHg in the test group of the patients, both demonstrating a good performance. In particular, the estimation errors were found to be insensitive to variations in hemodynamic conditions and cardiovascular properties, manifesting the high robustness of the method. The method may have promising clinical applicability, although further validation studies with larger-scale clinical data remain necessary.

Vertical accuracy assessment of freely available global DEMs (FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM) in flood-prone environments

ABSTRACT Flood models rely on accurate topographic data representing the bare earth ground surface. In many parts of the world, the only topographic data available are the free, satellite-derived global Digital Elevation Models (DEMs). However, these have well-known inaccuracies due to limitations of the sensors used to generate them (such as a failure to fully penetrate vegetation canopies and buildings). We assess five contemporary, 1 arc-second (≈30 m) DEMs -- FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM -- using a diverse reference dataset comprised of 65 airborne-LiDAR surveys, selected to represent biophysical variations in flood-prone areas globally. While vertical accuracy is nuanced, contingent on the specific metrics used and the biophysical character of the site being assessed, we found that the recently-released FABDEM consistently ranked first, improving on the second-place Copernicus DEM by reducing large positive errors associated with forests and buildings. Our results suggest that land cover is the main factor explaining vertical errors (especially forests), steep slopes are associated with wider error spreads (although DEMs resampled from higher-resolution products are less sensitive), and variable error dependency on terrain aspect is likely a function of horizontal geolocation errors (especially problematic for AW3D30 and Copernicus DEM).

Development and performance evaluation of intelligent algorithm for optimal control of a hybrid heat pump system during the cooling season

The purpose of this study is to develop a control method for a hybrid heat pump system based on an artificial neural network (ANN) to reduce energy use and create a more comfortable thermal environment. The proposed optimal control method uses an ANN-based predictive model to predict the heat storage tank and indoor temperature during the cooling period and controls the flow rate of the circulation pump on the heat source and load side of the system. The performance of the predictive model for the heat storage tank temperature (R2 = 0.9988; coefficient of variation of the root mean square error [CV(RMSE)] = 1.06 %; normalized mean bias error [NMBE] = 0.16 %; and mean absolute error [MAE] = 0.09℃) and the indoor temperature (R2 = 0.9893; CV(RMSE) = 1.66 %; NMBE = 0.16 %; and MAE = 0.15℃) was excellent. The temperature control of the heat storage tank using the optimal algorithm exhibited an improvement of 18.39 % for the CV(RMSE), 3.10 % for the NMBE, and 1.31 °C for MAE compared with rule-based. For the indoor temperature, the optimal algorithm improved the CV(RMSE) by 1.30 %, NMBE by 0.42 %, and MAE by 0.29℃ compared to rule-based. The energy use was reduced by 52.85 % for the entire system using the optimal control method compared with the existing control strategy under similar outdoor conditions. Using the proposed control method, it is thus possible to improve thermal comfort and reduce carbon emissions in the building sector by improving the control and energy performance of hybrid heat pump systems.

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed.

Metrology for Indoor Radon Measurements and Requirements for Different Types of Devices.

Indoor radon measurements have been conducted in many countries worldwide for several decades. However, to date, there is a lack of a globally harmonized measurement standard. Furthermore, measurement protocols in the US (short-term tests for 2-7 days) and European Union countries (long-term tests for at least 2 months) differ significantly, and their metrological support is underdeveloped, as clear mathematical algorithms (criteria) and QA/QC procedures considering fundamental ISO/IEC concepts such as "measurement uncertainty" and "conformity assessment" are still absent. In this context, for many years, the authors have been advancing and refining the theory of metrological support for standardizing indoor radon measurements based on a rational criterion for conformity assessment within the ISO/IEC concepts. The rational criterion takes into account the main uncertainties arising from temporal variations in indoor radon and instrumental errors, enabling the utilization of both short- and long-term measurements while ensuring specified reliability in decision making (typically no less than 95%). The paper presents improved mathematical algorithms for determining both temporal and instrumental uncertainties. Additionally, within the framework of the rational criterion, unified metrological requirements are formulated for various methods and devices employed in indoor radon measurements.

Pinch force sense test–retest reliability evaluation using contralateral force matching task

A high test–retest reliability in measurement of pinch force sense is required to assess a clinical parameter accurately over a longitudinal study. Ipsilateral reproduction (IR) task and contralateral matching (CM) task have commonly been used for the assessment of force sense. To date, there has been little research on the test–retest reliability of pinch force sense utilizing the contralateral force matching task. This research aimed to explore this phenomenon across a spectrum of reference force levels (10, 30, and 50 percent maximum voluntary isometric contraction (MVIC)) using a contralateral matching task. Every participant in the study was tested twice by the same skilled experts, with each session separated by one week. Although normalized variable error indicated a poor level of reliability (intraclass correlation coefficient (ICC) = − 0.25 to 0.05) for these force sense tests, normalized constant error (ICC = 0.76–0.85) and normalized absolute error (ICC = 0.61–0.81) results indicated a fair to good of reliability. The lower bound of 95% CI of ICC for NAE and NCE indicated fair test–retest reliability (0.41–0.69). These findings suggest that investigators can reasonably obtain a fair to good test–retest reliability when investigating pinch force sense using the contralateral matching task. The Bland–Altman plots, SEM, and MDD95% were lower at these lower reference force level (10% MVIC) compared to the level of higher reference forces (30% and 50% MVIC). Therefore, when the reference force level increases, the participant needs a larger NAE or NCE decrease to show that their pinch force sense has indeed improved.

Determining the Anticipation Times of Athletes Participating and Non-Participating in Volleyball, Handball, and Basketball School Sports at Secondary Schools

The aim of this study is to find out the anticipation times of fifth-to eighth-grade students who participate and those who do not participate in school sports in volleyball, handball, and basketball in secondary schools, and also to determine whether the anticipation time varies according to gender, different speeds and sports experience of students participating in school sports. A total of 267 athletes (150 girls and 117 boys) participated in volleyball, handball, and basketball from the 5th to 8th grades of secondary schools, and a total of 214 athletes (90 girls and 124 boys) who did not participate in school sports in these branches were studied. The “Basin Anticipation Timer” device measured the anticipation time. As a result of the analysis, it was found that female students participating in school sports have better anticipation time than that of male students. The anticipation times of students participating in school sports were determined at speeds of 5 m/s and 8 eight m/s with average constant, absolute, and variable errors of (0.018, 0.044, 0.059) and (0.017, 0.041, 0.055), respectively. Even though the 8 m/s error results seem better than the 5 m/s results, no statistically significant difference was found between them.

Predicting melting temperature of inorganic crystals via crystal graph neural network enhanced by transfer learning

The melting temperature, a crucial material property, is particularly challenging to measure accurately for inorganic crystals. The data-driven approach emerges as a potential solution, although its effectiveness is hindered by the limitation of available data. To counter this challenge, we implement transfer learning, leveraging a vast computational database of atomization energy. We first pre-train a geometric-information-enhanced crystal graph neural network (GeoCGNN) using atomization energies of approximately 36,000 materials that are computed by the density functional theory. Subsequently, the pre-trained model is fine-tuned using melting temperatures measured for 799 crystals, encompassing 83 elements, ranging from unary to quaternary systems. This transfer learning strategy decreases the root mean square error from 407 to 218 K, attesting to a marked improvement in prediction accuracy. Furthermore, transfer learning significantly mitigates error variability across unary, binary, and ternary (or higher-order) systems, thereby enhancing the reliability of predictions across a broader range of crystals. We also show that transfer learning allows effective task adaptation by leveraging representation learned from pre-training. Therefore, it can achieve better prediction performance even with a limited number of data for predicting melting points.

The influence of elbow and forearm posture on grip force perception in healthy individuals

AbstractThis study aimed to examine the influence of elbow and forearm postures, as well as sex, on the perception of grip force in a sample of individuals without any known health conditions. A total of 21 healthy participants (10 women and 11 men) from college were included and completed a force reproducibility assignment with four elbow and forearm positions (full pronation, supination, and extension, and at 90° of flexion) at three force levels (10%, 30%, and 50% of maximal voluntary isometric contraction [MVIC]). Our results show that participants were more sensitive in detecting variations in their grip force when their elbow was in full supination (14.1 ± 8.5% MVIC, p < .05) and full extension (13.8 ± 10.1% MVIC, p < .01) than when it was at 90° of flexion (19.9 ± 20.1% MVIC). The normalized absolute error exhibited comparable patterns among both male and female participants. Specifically, when the working range of the muscles increased (as indicated by higher maximum voluntary isometric contraction values in males), the accuracy decreased (as reflected by the more significant absolute error in men). Moreover, men exhibited a greater degree of both constant and variable error than women. Recent research indicates that the prevalence of musculoskeletal disorders is higher in women than in males. The results we obtained may contribute to developing strategies to reduce injury risk.

General low alertness in people with obstructive sleep apnea.

In patients with obstructive sleep apnea (OSA), a previous study using a Go/No-Go task reported an average attention deficit. However, the temporal dynamics of such a deficit are unknown. Here, we investigated whether attention deficits in different subdomains increased as the test progressed. We also investigated the effect of target frequency and speed of stimulus presentation on performance. Twenty-seven untreated people with OSA and 27 age- and sex-matched controls underwent a 15-minute Go/No-Go task, divided into 6 blocks. Each block was subdivided into 3 different interstimulus intervals (1, 2, and 4 seconds). Three blocks had a low and three had a high target probability (20% and 80%, respectively). Reaction time (alertness), variability of reaction time (sustained attention), commission errors (response inhibition), and omission errors (focused attention) were measured. Alertness was lower in the group with OSA compared with controls, as evidenced by a significantly higher average reaction time. This effect was seen from the start of the task and continued until the end but did not increase in test progression. The temporal pattern of intrinsic alertness deficits in patients with OSA was found to be independent of target frequency or interstimulus interval. The primary attention problem in OSA is on the alertness subdomain irrespective of the number of required responses or speed of stimulus presentation. The present results support the notion that OSA is distinct from other neurological and psychiatric conditions, such as depression or chronic pain. The results also suggest significant concerns regarding daily life activities (eg, driving). de Souza Bezerra ML, van Duinkerken E, Simões E, Schmidt SL. General low alertness in people with obstructive sleep apnea. J Clin Sleep Med. 2024;20(5):689-698.

Digital coincidence counting with 4πβ(PPC)-γ for the standardization of 60Co

A 4πβ(PPC)-γ coincidence system has been made at KRISS based on a digital DAQ. 60Co sources were measured to verify the system. The maximum detection efficiency for beta particles was estimated to be 96.7 %. Massic activities for sample sources had 0.005 % of the sample variability error, which was well within the expanded standard uncertainty of 0.54 % (k = 2).

Current Transformer Condition Online Monitoring Platform Based on Big Data

Abstract As an important power equipment indispensable to the normal operation of the power grid, the good or bad working condition of the current transformer directly affects the operational efficiency of the whole power grid. In this paper, an online monitoring platform based on big data technology has been developed to monitor the current status of transformers. First of all, for the temperature of the transformer, through the decoupling of the heat generation mode of the transformer, the state monitoring method based on fiber optic temperature measurement technology and optical multiplexing technology is proposed, and the overall scheme of transformer state monitoring is designed. The data signal of the transformer’s status is collected and processed, and a fault state diagnosis model is constructed based on the decision tree algorithm. Finally, according to the data requirements of the current transformer, an online monitoring platform for transformer status has been constructed using big data technology. Through the simulation test, the variation of ratio error of this paper’s monitoring method is less than 0.60%, and the variation of phase error is less than 0.75 percent, which is in line with international requirements. The average loading time of the monitoring platform is between 123.591s~143.911s, the average loading speed is less than 13s, the average throughput is between 241ops/sec ~257ops/sec, and the average latency of the read and upload operations is 24.225ms. The current transformer condition monitoring platform in this paper has good stability, the overall performance is excellent, and it provides a good solution for the research of the current transformer condition monitoring platform provides practical support for the research of current transformer condition monitoring platform.

Evolução da tipologia dos erros e autocorreção na leitura textual em escolares

ABSTRACT Purpose: to verify the progress of error typology and percentage of self-correction in text reading and their association with reading fluency in second/third graders (group 1) and fifth/sixth graders (group 2) and the influence of education level on the typology of errors and percentage of self-correction. Methods: an observational, analytical, and longitudinal study. Altogether, 41 students were assessed during the COVID-19 pandemic regarding text reading fluency, percentage of self-corrections and errors, and error typology. The Wilcoxon and paired t-tests were used to compare means, and Pearson’s and Spearman’s tests for correlations. The p-value was set at p < 0.05. Results: group 1 had a decrease in the percentage of errors and an increase, followed by a decrease, in self-corrections. Group 2 had an increase in the percentage of self-corrections, with no variation in errors. The association analysis revealed that the higher the reading speed, the lower the percentage of self-corrections. In the second assessment, group 1 had more self-corrections than group 2 and, in the third one, group 2 had more errors than group 1. Conclusion: the increase in reading fluency and the decrease in errors occur progressively - unlike self-correction, which varies throughout the school year. However, there is an inverse relationship between speed and self-correction.

Temporal and Spatial Variations in the Radar Altimeter Signal Penetration Error in Greenland

Temporal and Spatial Variations in the Radar Altimeter Signal Penetration Error in Greenland

Location-Aware Encoding for Lesion Detection in 68Ga-DOTATATE Positron Emission Tomography Images.

Lesion detection with positron emission tomography (PET) imaging is critical for tumor staging, treatment planning, and advancing novel therapies to improve patient outcomes, especially for neuroendocrine tumors (NETs). Current lesion detection methods often require manual cropping of regions/volumes of interest (ROIs/VOIs) a priori, or rely on multi-stage, cascaded models, or use multi-modality imaging to detect lesions in PET images. This leads to significant inefficiency, high variability and/or potential accumulative errors in lesion quantification. To tackle this issue, we propose a novel single-stage lesion detection method using only PET images. We design and incorporate a new, plug-and-play codebook learning module into a U-Net-like neural network and promote lesion location-specific feature learning at multiple scales. We explicitly regularize the codebook learning with direct supervision at the network's multi-level hidden layers and enforce the network to learn multi-scale discriminative features with respect to predicting lesion positions. The network automatically combines the predictions from the codebook learning module and other layers via a learnable fusion layer. We evaluate the proposed method on a real-world clinical 68Ga-DOTATATE PET image dataset, and our method produces significantly better lesion detection performance than recent state-of-the-art approaches. We present a novel deep learning method for single-stage lesion detection in PET imaging data, with no ROI/VOI cropping in advance, no multi-stage modeling and no multi-modality data. This study provides a new perspective for effective and efficient lesion identification in PET, potentially accelerating novel therapeutic regimen development for NETs and ultimately improving patient outcomes including survival.

An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis.

High-dimensional classification is an important statistical problem that has applications in many areas. One widely used classifier is the Linear Discriminant Analysis (LDA). In recent years, many regularized LDA classifiers have been proposed to solve the problem of high-dimensional classification. However, these methods rely on inverting a large matrix or solving large-scale optimization problems to render classification rules-methods that are computationally prohibitive when the dimension is ultra-high. With the emergence of big data, it is increasingly important to develop more efficient algorithms to solve the high-dimensional LDA problem. In this paper, we propose an efficient greedy search algorithm that depends solely on closed-form formulae to learn a high-dimensional LDA rule. We establish theoretical guarantee of its statistical properties in terms of variable selection and error rate consistency; in addition, we provide an explicit interpretation of the extra information brought by an additional feature in a LDA problem under some mild distributional assumptions. We demonstrate that this new algorithm drastically improves computational speed compared with other high-dimensional LDA methods, while maintaining comparable or even better classification performance.

Synergizing Neutrosophy and Randomized Blocks Design: Development and Analytical Insights

The design of the experiment is a strategy for effectively examining the relationship between input design parameters and process output and developing a greater understanding. A randomized block design is an experimental design that has two primary factors and is widely used in agriculture, environment, biological, animal, and food sciences, where experimental material is heterogeneous and precise. In a randomised block design, one or more observations may lose their true significance due to an accident, poor handling, pest infestations in agricultural trials, or other factors. It is prudent to treat this value as missing and estimate it. In today’s practical situations, uncertainty and inaccuracies are inevitable in most research areas. It is important to handle such data, which can lead to inaccurate and unreliable results. Neutrosophy is the branch of philosophy that provides an efficient method to study impreciseness among the data. Some of the common sources of Neutrosophy in randomised block design are incorrect blocking factor selection, measurement error, subjective factors, and natural variability. It is paramount to handle the Neutrosophy in a randomised block design; otherwise, it may lead to various problems, like a high risk of false positives. In this paper, the Neutrosophic Randomised Block Design (NRBD) is introduced to tackle data impreciseness. The study also, outlines a methodology for estimating missing observations in NRBD and presents its analysis. Additionally, the study compares the efficiency of NRBD to that of the Neutrosophic Completely Randomised Design (NCRD).

Hermite-Hadamard, Fejér and trapezoid type inequalities using Godunova-Levin Preinvex functions via Bhunia's order and with applications to quadrature formula and random variable.

Convex and preinvex functions are two different concepts. Specifically, preinvex functions are generalizations of convex functions. We created some intriguing examples to demonstrate how these classes differ from one another. We showed that Godunova-Levin invex sets are always convex but the converse is not always true. In this note, we present a new class of preinvex functions called $ (\mathtt{h_1}, \mathtt{h_2}) $-Godunova-Levin preinvex functions, which is extensions of $ \mathtt{h} $-Godunova-Levin preinvex functions defined by Adem Kilicman. By using these notions, we initially developed Hermite-Hadamard and Fejér type results. Next, we used trapezoid type results to connect our inequality to the well-known numerical quadrature trapezoidal type formula for finding error bounds by limiting to standard order relations. Additionally, we use the probability density function to relate trapezoid type results for random variable error bounds. In addition to these developed results, several non-trivial examples have been provided as proofs.