Good Estimates Research Articles

Mapping Long COVID across the EU: definitions, guidelines and surveillance systems in EU Member States

Abstract A mapping study had been conducted to provide insight into 1) Long COVID definitions, 2) guidelines and intelligence on diagnosis and treatment, and 3) surveillance systems, as used/implemented in EU Member States plus Iceland, Norway, the United States, Canada, New Zealand, and Australia. Findings show that most Long COVID definitions align with WHO or NICE criteria, although there are also notable differences between definitions. The study also found Long COVID guidelines for diagnosis and treatment in 21 out of 34 selected countries. Common elements include advocating a multidisciplinary approach, a central role for primary care, and the focus on rehabilitation. Some guidelines include recommendations for referral to specialised care and follow-up procedures. Guidelines also differ in their target audience and in terms of their focus specific symptoms or organ systems that are affected by Long COVID. When it comes to Long COVID surveillance systems only a limited number of (nine) registries were found. Five of the Long COVID registries are based on self-registration. As limited information is available for most registries, a detailed understanding of their structures and goals in comparison to others is hindered. In conclusion, the study underlines the interconnectedness of Long COVID definitions, the development of guidelines, and surveillance systems. Given the variability in definitions and the voluntary entry of patients into the existing registries, there are no good estimates yet of the total numbers of patients and the severity of their disease in EU Member States. Linking information from cohort studies and clinical trials may be necessary to provide the full picture of the burden of disease of Long COVID. Still, preliminary estimates indicate that a significant proportion of those infected with COVID-19 experience long lasting symptoms, leading to impaired quality of life, also placing a significant burden on national health systems in the future.

On Covariance Switch-Based Invariant Extended Kalman Filter for Multi-Source Fusion

The good estimation consistency of Invariant Extended Kalman Filter (InEKF) is mainly attributed to its excellent trajectory-independent property. However, when the InEKF is applied to multi-source inertial-based navigation that coexists left-invariant and right-invariant observations, the observation matrix would be unavoidably trajectory-dependent and might lead to the degradation of estimation performance. This paper for the first time performs theoretical analyses on the covariance switch between the left error and the right error in multi-source inertial-based navigation. Furthermore, detailed realizations of the covariance switch-based InEKF are formulated in this work, which are lacked in previous works. Numerical simulations and experiments demonstrate that the covariance switch significantly reduces the attitude errors during the transient phase in contrast with the original method using the incompatible error definition.

The Internal Structure of the WISC-V in Chile: Exploratory and Confirmatory Factor Analyses of the 15 Subtests

The WISC-V is a widely used scale in clinical and educational settings in Chile. Given that its use guides critical decision-making for children and adolescents, it is essential to have evidence of its psychometric properties, including validity based on internal structure. This study analyzed the factor structure of the WISC-V through an exploratory (EFA) and confirmatory (CFA) approach considering the age range of 853 children and adolescents between 6 and 16 years. We obtained evidence favoring the four-factor structure in the EFA, which is a clearer organization in the 15–16 age group. In the confirmatory stage, the best four- and five-factor models showed factor loadings greater than 0.4, except for one subtest in the processing speed domain in the 6–8 age group. The internal consistency ranged from acceptable to good estimates for the best two models. The results support the use of hierarchical factor structures of four and five factors, which offer specific advantages and disadvantages discussed in the article. The implications of these findings in both the professional area of psychology and future research are discussed.

Skin feature point tracking using deep feature encodings

AbstractFacial feature tracking is a key component of imaging ballistocardiography (BCG) where accurate quantification of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantification of motor degradation in Parkinson’s disease. While traditional computer vision algorithms like Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method (LK) have been benchmarks due to their efficiency and accuracy, they often struggle with challenges like affine transformations and changes in illumination. In response, we propose a pipeline for feature tracking, that applies a convolutional stacked autoencoder to identify the most similar crop in an image to a reference crop containing the feature of interest. The autoencoder learns to represent image crops into deep feature encodings specific to the object category it is trained upon. We train the autoencoder on facial images and validate its ability to track skin features in general using manually labelled face and hand videos of small and large motion recorded in our lab. Our evaluation protocol is comprehensive, including quantification of errors in human annotation. The tracking errors of distinctive skin features (moles) are so small that we cannot exclude the fact that they stem from the manual labelling based on a $$\chi ^2$$ χ 2 -test. With a mean error of 0.6–3.3 pixels, our method outperformed the other methods in all but one scenario. More importantly, our method was the only one that did not diverge. We also compare our method with the latest state-of-the-art transformer for feature matching by Google—Omnimotion. Our results indicate that our method is superior at tracking different skin features under large motion conditions and that it creates better feature descriptors for tracking, matching, and image registration compared to both traditional algorithms and the latest Omnimotion.

Thinking beyond the closure assumption: Designing surveys for estimating biological truth with occupancy models

Abstract Occupancy models estimate distributions of imperfectly detected species, but violations of the closure assumption can bias results. However, researchers working with mobile animals may find it impossible to eliminate such violations. Here, we tested the hypothesis that occupancy models fit to realistic sampling data can generate unbiased occupancy estimates for an itinerant Wood Thrush (Hylocichla mustelina) population. In 2013 and 2014, we tracked movements of 41 breeding Wood Thrush males. We modelled territory shift probabilities using logistic exposure models and within‐territory movements using continuous‐time stochastic process models. We then constructed an individual‐based model, simulated (1000 iterations) spatiotemporal locations for individuals and simulated sampling these populations using 162 different point count protocols with variable spatial (sampling radius and point placement method), and temporal (survey length, between‐survey intervals and number of surveys) characteristics. We compared occupancy estimates with true values of instantaneous, daily and seasonal occupancy from the simulations. We parameterized continuous time stochastic process models based on movements within 34 unique territories and estimated a daily territory shift probability of 0.0099 (95% CI: 0.0060, 0.0152). Simulated data indicated that estimates of occupancy ranged from 0.18 (0.06, 1.00) to 0.80 (0.71, 0.89) depending on protocol characteristics. Occupancy estimates increased with increasing survey radius, survey length and between‐survey interval. Protocols using shorter surveys and between‐survey intervals were good estimators for instantaneous occupancy (low bias and mean‐squared error) but poor estimators for daily and seasonal occupancy; longer surveys and intervals generated unbiased estimators of daily occupancy but underestimated seasonal occupancy. Logistic regression models that ignored imperfect detection outperformed occupancy models for estimating instantaneous occupancy but not daily or seasonal occupancy. For mobile animals, occupancy of sampling sites changes in space and time. Consequently, the spatial and temporal aspects of a sampling protocol have strong, but predictable, effects on occupancy model parameter estimates. Our results demonstrate that how these factors interact is critical for designing surveys that produce occupancy estimates representative of the biological process of interest to a researcher.

Real‐Time Nuclear Magnetic Resonance Detection Using Maximum Likelihood Estimation with Single‐Shallow‐Nitrogen‐Vacancy Centers in Quantum Heterodyne Measurements

Single‐nitrogen‐vacancy (NV) centers in diamond are highly promising quantum nuclear magnetic resonance (NMR) sensors. However, their exposure to many sources of noise, such as surface impurities, shot noise from avalanche photodiode overlaps, and spin‐state projection errors inherent in quantum systems, limits their usefulness. Often, long measurement durations are required to accumulate sufficient data for NMR signal detection via fast Fourier transform spectrometry. For practical reasons, methods to shorten the necessary accumulation time for NMR signal detection are greatly desired. In this article, an on‐line formulation of maximum likelihood estimation (MLE) signal processing for quantum heterodyne NMR measurements is presented as a step toward this goal. This MLE method reduces the required data accumulation time by orders of magnitude and provides good estimates of target frequency locales in real time. These results are significant to practitioners of NMR detection with single‐NV centers in diamond who require a quick litmus test for potential signals when probing a wide area.

How reliable is the Real Adsorbed Solution Theory (RAST) for estimating ternary mixture equilibrium in microporous host materials?

Microporous crystalline adsorbents such as zeolites, and metal-organic frameworks (MOFs) have potential use in a wide variety of separations applications. In applications such as CO2 capture, the Ideal Adsorbed Solution Theory (IAST) often fails to provide a quantitative description of mixture adsorption equilibrium especially in cation-exchanged zeolites. The failure of the IAST is ascribable to non-compliance with one or more tenets mandated by the IAST such as (a) homogeneous distribution of adsorbates within the pore landscape, (b) no preferential location of guest species, and (c) absence of molecular clustering due to say hydrogen bonding. The focus of this article is on the reliability of the Real Adsorbed Solution Theory (RAST) models for quantitative estimation of adsorption equilibrium. Configurational-Bias Monte Carlo (CBMC) simulations are undertaken to determine the adsorption equilibrium for ternary CO2/CH4/N2, CO2/CH4/C3H8, CO2/CH4/H2, and water/methanol/ethanol mixtures in NaX, LTA-4A, CHA, DDR, and MFI zeolites. Additionally, CBMC simulations of the constituent binary pairs are used to determine the Wilson or NRTL parameters, taking due account of the dependence of the activity coefficients on the spreading pressure. Use of the binary pair Wilson or NRTL parameters allows the estimation of ternary mixture adsorption equilibrium, that is tested against the CBMC data on component loadings. In all investigated guest/host combinations, the RAST provides a good estimation of ternary mixture adsorption equilibrium.

Exploring Soil–Water Characteristic Curves in Transitional Oil Sands Tailings

Soil–water characteristics curves (SWCC) have proved useful in estimating parameters used in modeling unsaturated geotechnical properties of soils including permeability and strength. Either saturation, gravimetric, and instantaneous and initial volumetric water content designation can be used to develop SWCCs. Studies have shown that any of the designations will give good estimates for soils that do not undergo volume change with suction change whereas, for soils that undergo substantial volume change, only saturation and instantaneous volumetric water content designation obtained by incorporating shrinkage curves can give correct estimates. Transition oil sands tailings have fines content that cannot be categorized as sandy or fine materials, and research on volume change with suction change in these materials is limited. In this study, HyProps, Tempe cells, and a chilled-mirror water potential meter were used to measure suction and corresponding water contents for samples that were prepared by mixing coarse sand and Fluid Tailing by ratios that mimic transition zone tailings. Shrinkage tests were also performed to observe the extent of volume change with suction increase. Air Entry Values (AEV) estimated from SWCCs based on gravimetric water content were found to be lower than those estimated from saturation-based SWCCs due to substantial volume changes in these materials with suction increase. The use of saturation water content designation is recommended in estimating AEV for transitional oil sands tailings. This is useful information in predicting the long term unsaturated geotechnical behavior of these materials for environmental management and safety purposes.

Magnetic connectivity from the Sun to the Earth with MHD models. I. Impact of the magnetic modelling on connectivity validation

The magnetic connectivity between the Sun and the Earth is crucial to our understanding of the solar wind and space weather events. However, establishing this connectivity is challenging because of the lack of direct observations, which explains the need for reliable simulations. The method most often used to make such measurements over the last few years is the two-step ballistic method, but it has many free parameters that can affect the final result. Thus, we want to provide a connectivity method based on self-consistent magnetohydrodynamics (MHD) models. To this end, we combined the COCONUT coronal model with the EUHFORIA heliospheric model to compute the magnetic field lines from the Earth to the Sun. We then developed a way to quantify both the spatial and temporal uncertainty associated with this computation. To validate our method, we selected four cases already studied in the literature and associated with high-speed-stream events coming from unambiguous coronal holes visible on the disk. We always find a partial overlap with the assumed CH of origin. The extent of this overlap is 19<!PCT!> for event 1, 100<!PCT!> for event 2, 45<!PCT!> for event 3, and 100<!PCT!> for event 4. We looked at the polarity at Earth over the full Carrington rotation to better understand these results. We find that, on average, MHD simulations provide a very good polarity estimation, showing 69<!PCT!> agreement with real data for event 1, 36<!PCT!> for event 2, 68<!PCT!> for event 3, and 69<!PCT!> for event 4. For events 1 and 3, we can then explain the mixed results by the spatial and temporal uncertainty. An interesting result is that, for MHD models, minimum-activity cases appear to be more challenging because of the multiple recurrent crossings of the HCS, while maximum-activity cases appear easier because of the latitudinal extent of the HCS. A similar result was also found with Parker Solar Probe data in another study. We demonstrate that it is possible to use MHD models to compute magnetic connectivity and that this approach provides results of equal quality to those from the two-step ballistic method, with additional possibilities for improvements as the models integrate more critical physics.

A scheme of combining DFO and channel estimation scheme for mobile OFDM systems

AbstractTo reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high‐speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time‐varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high‐precision DFO estimator based on the convolutional neural network with anti‐noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time‐varying channel. Moreover, to improve the practicality of the algorithm, the non‐ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high‐speed mobile scenarios.

A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance.

Fatigue Life Assessment of Corroded AlSi10MgMn Specimens

This study investigates the influence of pre-corrosion damage on the fatigue behavior of AlSi10MgMn high-pressure die-cast specimens, using the statistical distribution of corrosion depths. The analysis is conducted on two different surface conditions: an unmachined rough surface (Ra=5.05μm) and a machined, polished surface (Ra=0.25μm). For the unmachined specimens, the corrosive damage manifests as homogeneously spread localized corrosion, whereas the polished specimens exhibit less uniform but deeper corrosion. The average corrosion depth of the polished specimens is found to be slightly higher (313 μm compared to 267 μm) with a broader depth distribution. Specimens are tested under a constant bending load amplitude in laboratory conditions at a stress ratio of R=0 until fracture. A fracture mechanics-based methodology is developed to assess the remaining fatigue life of corroded specimens, utilizing short and long crack fracture mechanical parameters derived from SENB specimens. This model incorporates a thickness reduction of the critical specimen cross-section based on the corrosion depth distribution and combines it with a small initial crack of the intrinsic defect size (aeff=14μm). Regardless of the surface condition, using the most frequent corrosion depth for thickness reduction provides a good estimate of the long-life fatigue strength, while using the 90th percentile depth allows for a conservative assessment.

Single time-point analysis of product and substrate inhibition.

When enzyme inhibition by either the product or excess substrate occurs, it is possible to determine the characteristic kinetic parameters based on [P]/t measurements, even when a large proportion of the substrate is converted. The advantages of various approaches are discussed. Most of them allow a good estimation of the V and Km values. Conversely, the determination of Kp (product inhibition) and Ki (inhibition by excess substrate) can be more challenging. In the first case, determination of the type of inhibition requires more complex experiments that are beyond the scope of the present contribution. In the second, the inhibition constant Ki can only be roughly estimated. In an experimental approach, we compared the results obtained either with initial rate measurements or with 50 to 60% conversion of the substrate. Similar values of V and Km were obtained. Measurements involving the conversion of a large proportion of substrate are particularly advantageous when the assay method is difficult or time-consuming, or when obtaining the substrate presents experimental difficulties or involves substantial costs.

Measured and predicted sound transmission through a laboratory concrete floor

The direct sound transmission through a simple, massive, homogeneous building element with and without lining is investigated experimentally and compared with analytical and empirical models from the literature. A 150 mm thick concrete floor and a 100 mm thick floating floor were measured in the floor transmission facility of the National Research Council Canada, i.e. without flanking sound transmission. In addition to the sound reduction index and the normalized impact sound pressure level, loss factors, radiation efficiencies, and mean squared velocities were recorded in the laboratory. The measured data is compared to established models of sound transmission. A focus is placed on the calculation model of ISO 12354-1 (Appendix B), which uses calculated radiation efficiencies and loss factors to predict the sound reduction index of heavy, homogeneous building elements. Some difficulties with the calculation model and the examples in the standard are discussed. In principle, the results in this study show that the calculation model of ISO 12354-1 gives good estimates when the correct input parameters are used. However, deviations between the calculated and the measured loss factors and radiation efficiencies have large effects on the accuracy of the estimates, particularly in the region around the coincidence frequency.

Porous material characterization with Bayesian and machine learning method

Two main classes of characterization methods are usually employed for acoustic parameters of porous media, namely the analytical inversion method or the numerical fitting procedure. While analytical inverse methods suffer from a high sensivity to the operator, the numerical fitting methods can lead to non physical sets of parameters. The fitting procedure can be bounded by the analytical inversion. The Bayesian method, which is presented in this work, is the appropriate tool to couple these methods. It enables to carry out a numerical fitting procedure using the analytical inversion as prior estimation while increasing the confidence in the resulting uncertainties. In a second part, machine learning method and data mining are used as an alternative characterization method which could be a good prior estimation for the Bayesian method. Results and performances will be shown for various porous media.

Measurement of anisotropic airflow resistance characteristics of mineral wool samples

In the field of acoustics, the airflow resistance of a porous materials like stone wool is one of the governing parameters. As a starting point, measuring the airflow resistance in the normal direction can be used for good estimations of sound-absorbing abilities when using empirical models. However, more complex models, like equivalent fluid models and Biot models, need more input parameters. The next natural step is to move into 2D and 3D interactions. An obstacle to overcome is obtaining accurate input parameters to qualify the models. The production process of stone wool introduces an inherent geometric anisotropy in the material. The aim of the study is hence to develop test and sample preparation methods that enable the measurement of the airflow resistance of stone wool sample in 3 directions by means of testing a cubic specimen. The airflow resistance was measured using a modified unidirectional static airflow apparatus. The paper presents test results obtained with the apparatus for a range of stone wool samples of varying densities. The results show noticeable differences in airflow resistance of more than 1.5 times between testing directions. This paper also, includes comparisons to results obtained using an alternating airflow method as per ISO 9053-2:2020.

A deep learning approach to predicting UAV sound affect

The most prevalent method by which the impact of an acoustic environment upon people is assessed is the `environmental noise' approach, in which the sounds are quantified based entirely on their recorded weighted sound pressure levels. This does not account, however, for the character of sounds outside of their loudness. Objective metrics such as psychoacoustic annoyance have been developed to attempt to rectify this, however this is still heavily weighted towards loudness as the most important factor and may not provide a good estimate for subjective perceived annoyance for all sound sources, including unconventional aircraft such as unmanned aerial vehicles (UAVs). Here we present a novel method using a convolutional neural network (CNN) to predict perceived annoyance directly from mel-spectrograms derived from recorded audio featuring both traditional and unconventional aircraft sources. The network is trained and tested using data collated from five separate listening tests, and is shown to outperform conventional regression models based on psychoacoustic annoyance as a predictor of perceived annoyance. The time-frequency resolution of the calculated mel-spectrograms is shown to be a key factor in performance.

Predictor Scales of Cognitive Factors Affecting Physical Activity in Individuals Surviving Cancer: Psychometric Properties of Turkish Versions

Background: Increasing physical activity in individuals surviving cancer is crucial in reducing treatment-related side effects and recurrence. Valid and reliable measurement tools are needed that will enable the factors affecting exercise to be addressed holistically and theoretically. This study aimed to adapt the Exercise Goal-Setting Scale, Exercise Planning and Scheduling Scale, Multidimensional Outcome Expectations for Exercise Scale, and Physical Activity Neighborhood Environment Scale into Turkish language and to explore psychometric properties of the Turkish versions of these scales in individuals surviving cancer. Methods: This methodologically designed research collected data from 153 participants at a hospital in Istanbul, Türkiye. The scales used in the study were adapted to the Turkish language through forward and backward translation and expert opinion. Content validity was assessed with a 2-week test-retest reliability, Cronbach’s α reliability coefficient for stability, item-total correlation, and confirmatory factor analysis. Results: The scales showed good content validity (CVI = 0.95-0.99). Internal consistencies were acceptable to excellent (Cronbach’s α = .79-.94), and moderate to good reproducibility estimates were observed (r = .67-.84). The MOEES factor structure also showed excellent fit. Conclusions: This study demonstrates that the adapted scales evaluated are valid and reliable for examining physical activity based on social cognitive processes in individuals surviving cancer in Türkiye. The scales examined in the study can be used to determine the cognitive factors affecting activity and to plan effective programs when providing physical activity counseling to individuals surviving cancer in Türkiye.

The Role of Industrial Internships in Developing Work Self-Efficacy for Automotive Engineering Students

Ideally, vocational education graduates have the readiness and desire to work after graduation through various experiences at school. Industrial internships in vocational education are expected to support graduates' readiness to know the world of work according to their field of expertise. An anomaly, where so far it is suspected that there are still many vocational education graduates who are unemployed or work because they do not have the desire to work. This study aims to understand the role of the student experience in participating in an industrial internship program on automotive students' work self-efficacy at Yogyakarta State University (YSU). This is survey research with a student population of 102 people. A questionnaire was given to sample of industrial internship participants who were taken at simple random sampling from 91 people. The data were analysed using quantitative descriptive method and Partial Least Square Structural Equation Models using Smart PLS 3 software. The results showed that the developed instrument was proven to be valid for use and had a good estimate of reliability. Most students get internship experience in the good and very good categories. Some students have work self-efficacy in the high and very high categories after industrial internships. The experience of participating in industrial internships is proven to have a positive and significant contribution to student work self-efficacy. It is important for vocational education institutions, especially in the automotive sector, to support industrial internship programs that provide an optimal student experience.

Gravitational collapse at low to moderate Mach numbers: The relationship between star formation efficiency and the fraction of mass in the massive object

The formation of massive objects via gravitational collapse is relevant both for explaining the origin of the first supermassive black holes and in the context of massive star formation. Here, we analyze simulations of the formation of massive objects pursued by different groups and in various environments, concerning the formation of supermassive black holes, primordial stars, as well as present-day massive stars. We focus here particularly on the regime of small virial parameters, that is, low ratios of the initial kinetic to gravitational energy, low to moderate Mach numbers, and the phase before feedback is very efficient. We compare the outcomes of collapse under different conditions using dimensionless parameters, particularly the star formation efficiency є*, the fraction ƒ* of mass in the most massive object relative to the total stellar mass, and the fraction ƒtot of mass of the most massive object as a function of the total mass. We find that in all simulations analyzed here, ƒtot increases as a function of є*, although the steepness of the increase depends on the environment. The relation between ƒ* and є* is found to be more complex and also strongly depends on the number of protostars present at the beginning of the simulations. We show that a collision parameter, estimated as the ratio of the system size divided by the typical collision length, allows us to approximately characterize whether collisions are important. A high collision parameter implies a steeper increase in the relation between ƒtot and є*. We analyze the statistical correlation between the dimensionless quantities using the Spearman coefficient and further confirm via a machine learning analysis that good predictions of ƒ* can be obtained from є* together with a rough estimate of the collision parameter. This suggests that a good estimate of the mass of the most massive object can be obtained once the maximum efficiency for a given environment is known and an estimate for the collision parameter has been determined.