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This study aimed to improve the measurement accuracy of the discrete element method (DEM) simulation parameters of sorghum seeds for enhanced model accuracy. We measured the intrinsic and contact parameters of sorghum seeds, extracted the outline of the seeds by 3D scanning, and used the multi-spherical particle model filling method in the EDEM (Version 2020) software to obtain the simulation model of the seeds. By simulating the free-fall, slope slip, slope rolling, and rolling experiments of sorghum seeds–photosensitive resin material, we calibrated the collision restitution coefficient of the sorghum seeds–photosensitive resin material to 0.690, static friction coefficient to 0.345, and rolling friction coefficient to 0.040. Through the steepest ascent search and central composite design experiments, we calibrated the collision restitution coefficient of sorghum seeds–sorghum seeds to 0.400, the static friction coefficient to 0.450, and the rolling friction coefficient to 0.043. The angle of repose of the parameter combination and the angle of repose of the physical experiment (33.82°) were verified, and the relative errors of the coefficients were 0.7%, 0.25%, and 0.3%, respectively, indicating that the established model had a high simulation accuracy and reliability. These results show that the established sorghum seeds model and the optimally calibrated parameter combination are effective for DEM studies on sorghum seeds. [ABSTRACT FROM AUTHOR]

Cell deformability is a useful feature for diagnosing various diseases (e.g., the invasiveness of cancer cells). Existing methods commonly inflict pressure on cells and observe changes in cell areas, diameters, or thickness according to the degree of pressure. Then, the Young's moduli (i.e., a measure of deformability) of cells are estimated based on the assumption that the degrees of the changes are inversely proportional to Young's moduli. However, manual measurements of the physical changes in cells are labor-intensive, and the subjectivity of the operators can intervene during this step, thereby causing considerable uncertainty. Further, because the shapes of cells are nonuniform, we cannot ensure the assumption for linear correlations of physical changes in cells with their deformability. Therefore, this study aims at measuring non-linear elastic moduli of live cells (degrees of cell deformability) automatically by employing conventional neural networks (CNN) and multilayer perceptrons (MLP) while preserving (or enhancing) the accuracy of the manual methods. First, we obtain photomicrographs of cells on multiple pressure levels using single-beam acoustic tweezers, and then, we suggest an image preprocessing method for emphasizing changes in cell areas on the photomicrographs. The CNN model is trained to measure the ratios of the cell area change at each pressure level. Then, we apply the multilayer perceptron (MLP) to learn the correlations of the cell area change ratios according to the pressure levels with cell deformability. The accuracy of the CNN was evaluated using two types of breast cancer cells: MDA-MB-231 (invasive) and MCF-7 (noninvasive). The MLP was assessed using five different beads (Young's moduli from 0.214 to 9.235 kPa), which provides standardized reference data of the non-linear elastic moduli of live cells. Finally, we validated the practicality of the proposed system by examining whether the non-linear elastic moduli estimated by the proposed system can distinguish invasive breast cancer cells from noninvasive ones. [ABSTRACT FROM AUTHOR]

The uplink (UL) throughput prediction is indispensable for a sustainable and reliable cellular network due to the enormous amounts of mobile data used by interconnecting devices, cloud services, and social media. Therefore, network service providers implement highly complex mobile network systems with a large number of parameters and feature add-ons. In addition to the increased complexity, old-fashioned methods have become insufficient for network management, requiring an autonomous calibration to minimize utilization of the system parameter and the processing time. Many machine learning algorithms utilize the Long-Term Evolution (LTE) parameters for channel throughput prediction, mainly in favor of downlink (DL). However, these algorithms have not achieved the desired results because UL traffic prediction has become more critical due to the channel asymmetry in favor of DL throughput closing rapidly. The environment (urban, suburban, rural areas) affect should also be taken into account to improve the accuracy of the machine learning algorithm. Thus, in this research, we propose a machine learning-based UL data rate prediction solution by comparing several machine learning algorithms for three locations (Houston, Texas, Melbourne, Florida, and Batman, Turkey) and determine the best accuracy among all. We first performed an extensive LTE data collection in proposed locations and determined the LTE lower layer parameters correlated with UL throughput. The selected LTE parameters, which are highly correlated with UL throughput (RSRP, RSRQ, and SNR), are trained in five different learning algorithms for estimating UL data rates. The results show that decision tree and k-nearest neighbor algorithms outperform the other algorithms at throughput estimation. The prediction accuracy with the R2 determination coefficient of 92%, 85%, and 69% is obtained from Melbourne, Florida, Batman, Turkey, and Houston, Texas, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Machine learning (ML) accelerometer data processing methods have potential to improve the accuracy of device-based assessments of physical activity (PA) in young children. Yet the uptake of ML methods by health researchers has been minimal and the use of cut-points (CP) continues to be the norm, despite evidence of significant misclassification error. The lack of studies demonstrating a relative advantage for ML approaches over CP methods maybe a key contributing factor. Purpose: The current study compared the accuracy of PA intensity predictions provided by ML classification models and previously published CPs for preschool-aged children. Methods: In a free-living study, 31 preschool-aged children (mean age = 4.0 ± 0.9 y) wore wrist and hip ActiGraph GT3X+ accelerometers while completing a video recorded 20-minute free play session. Ground truth PA intensity was coded continuously using the Children's Activity Rating Scale (CARS). Accelerometer data was classified as sedentary (SED), light intensity (LPA), or moderate-to-vigorous intensity (MVPA) using ML random forest PA classifiers and published CPs for preschool-aged children. Performance differences were evaluated in a hold-out sample by comparing weighted kappa statistics, classification accuracy for each intensity band, and equivalence testing. Results: ML classification models (hip: κ = 0.76; wrist: κ = 0.72) exhibited significantly higher agreement with ground truth PA intensity than CP methods (hip: κ = 0.38–0.49; wrist: κ = 0.31–0.44). For the ML models, classification accuracy for SED and LPA ranged from 83% - 88%, while classification accuracy for MVPA ranged from 68% - 78%. For the CP's, classification accuracy ranged from 50% - 94% for SED, 19% - 75% for LPA, and 44% - 76.1% for MVPA. ML classification models showed equivalence (within ± 0.5 SD) with directly observed time in SED, LPA, and MVPA. None of the CP's exhibited evidence of equivalence. Conclusions: Under free living conditions, ML classification models for hip or wrist accelerometer data provide more accurate assessments of PA intensity in young children than CP methods. The results demonstrate the relative advantage of ML methods over threshold-based approaches and adds to a growing evidence base supporting the feasibility and accuracy of ML accelerometer data processing methods. [ABSTRACT FROM AUTHOR]

- Setting the lateral dimensions of the anthropometric measurements of middle school students for the three intermediate stages (first, second and third) in the city of Tikrit. - Designing the profile grid for anthropometric measurements for middle school students for grades (first, second and third) in the middle school in the city of Tikrit. - Identifying the distinctive anthropometric measurements of middle school students for middle school grades (first, second and third) in the city of Tikrit. The researchers used the descriptive approach using the survey method, and the research sample consisted of (342) middle school students in the city of Tikrit - Iraq and for grades (first, second and third) that were chosen by the stratified random method, then the process of obtaining data through (20) physical measurements she: Body weight (1). The lengths included (6) measurements: (Total body length, torso length with head, lower limb length, arm length with palm, palm length and foot length) - Oceans: It included (6) measurements: (Chest circumference, abdomen circumference, hip circumference, thigh circumference, humerus circumference and calf circumference) Symptoms: included (3) measurements: (The width of the shoulders, the width of the chest and the width of the hips).The thickness of the leather folds included (4) measurements: (The thickness of the skinfolds behind the humerus, the thickness of the skinfolds at the iliac process, the thickness of the skinfolds at the calf of the leg and the thickness of the skinfolds under the shoulder board). [ABSTRACT FROM AUTHOR]

Background Wearable devices have been proposed as a novel method for monitoring patients after surgery to track recovery, identify complications early, and improve surgical safety. Previous studies have used a heterogeneous range of devices, methods, and analyses. This review aimed to examine current methods and wearable devices used for monitoring after abdominal surgery and identify knowledge gaps requiring further investigation. Methods A scoping review was conducted given the heterogeneous nature of the evidence. MEDLINE , EMBASE , and Scopus databases were systematically searched. Studies of wearable devices for monitoring of adult patients within 30 days after abdominal surgery were eligible for inclusion. Results A total of 78 articles from 65 study cohorts, with 5153 patients were included. Thirty-one different wearable devices were used to measure vital signs, physiological measurements, or physical activity. The duration of postoperative wearable device use ranged from 15 h to 3 months after surgery. Studies mostly focused on physical activity metrics (71.8 per cent). Continuous vital sign measurement and physical activity tracking both showed promise for detecting postoperative complications earlier than usual care, but conclusions were limited by poor device precision, adherence, occurrence of false alarms, data transmission problems, and retrospective data analysis. Devices were generally well accepted by patients, with high levels of acceptance, comfort, and safety. Conclusion Wearable technology has not yet realized its potential to improve postoperative monitoring. Further work is needed to overcome technical limitations, improve precision, and reduce false alarms. Prospective assessment of efficacy, using an intention-to-treat approach should be the focus of further studies. [ABSTRACT FROM AUTHOR]

The research aimed at: 1- Recognizing the values of some physical measurements bio kinematics variables of the arms movement and some physical measurements values in the back swimming . 2- Finding the relationship between the values of some bio kinematics variables of the arms movement and values of some physical measurements . 3- Finding of the relationship between the values of some biokinematics variables of the arms under study . In the research procedures: The researchers used the descriptive approach to suit the nature of the research problem. The sample of the research was chosen in the deliberate manner by the students of the college of physical education and sport sciences / University of Mosul for the academic year (2018-2019). The researchers used (6) students scientific observation , analysis , measurement , to collect data to obtain the values of some biochemical parameters of the swimmers and the physical measurements of the swimmers . The technical scientific observation was achieved through the use of video imaging. The study included a number of physical measurements and some biochemical variables of the arms, and the data was processed statistically using the following means through the use of the researchers statistical bag (SPSS). The arithmetic mean, the standard deviation, and the simple correlation coefficient(person) In light of the results of the research, the researchers reached the following conclusions: 1- There is a significant correlation between some physical measurements with some biokiniematics variables values in arms movement . 2- There is significant correlation between the values of some biokiniematics variables of the arms movements in the back swimming. The researchers recommended a set of recommendations to be used in the field of education and training to achieve the best achievement.

Despite its potential usefulness, the Lifshitz–van der Waals/acid–base approach is a controversial method for surface tension measurement in the physical engineering community, due to its apparent inconsistencies. In the present work, a vector method using matrix equations is studied for desirable test liquid screening, rearranging a commonly used governing equation into symmetric forms. Additionally, a new geometric perspective using vector volumes is proposed as a simple measure for selecting a set of test liquids, and compared to a state-of-art method that relies on the condition number of a matrix containing the liquid surface tension components. It is found that these two approaches agree to a high degree. On the other hand, a commonly stated approach of using a large number of test liquids is critically discussed in terms of practical applicability. The method presented for calculating solid surface tension values is further extended for calculating surface tension components of an unknown liquid, using known solid surface tension components. [Display omitted] [ABSTRACT FROM AUTHOR]

Smart building digital twins represent a significant paradigm shift to optimize building operations, thereby reducing their substantial energy consumption and emissions through digitalization. The objective is to virtually replicate existing buildings' static and dynamic aspects, leveraging data, information, and models spanning the entire life cycle. The virtual replica can then be employed for intelligent functions, including real-time monitoring, autonomous control, and proactive decision-making to optimize building operations. To enable proactive decisions, models within the digital twin must continually evolve with changes in the physical building, aligning their outputs with real-time measurements through calibration. This continuous updating requires real-time physical measurements of model inputs. However, challenges arise in the uncertain conditions of buildings marked by sensor absence, malfunctions, and inherent limitations in measuring certain variables. This study introduces a novel calibration framework for physics-based models, addressing the challenges of continuous model calibration in smart-building digital twins while considering the uncertain environment of physical buildings. Within this framework, a novel generative model-based architecture is proposed. This architecture enables a fast and scalable solution while quantifying uncertainty for reliable calibration. Furthermore, a continuous model calibration procedure is presented based on a pre-trained generative calibrator model. A comprehensive evaluation was conducted via a case study employing a building energy model and multiple experiments. The experimental results demonstrated that the proposed framework effectively addresses the challenges of continuous model calibration in smart-building digital twins. The calibrator model accurately quantified uncertainties in its predictions and solved a single calibration problem in an average time of 0.043 second. For facility-level electricity consumption, Coefficient of Variation Root Mean Squared Error (CVRMSE) values of 6.33%, 10.18%, and 10.97% were achieved under conditions of observations without noise or missing data, with noise, and with noise and missing data, respectively. Similarly, for facility-level gas consumption, the corresponding values were 18.75%, 20.53%, and 20.7%. The CVRMSE scores in both cases met the standard hourly thresholds for building energy model calibration. • Proposed probabilistic calibration framework based on generative inverse models. • Proposed a novel generative calibrator model architecture for physics-based models. • The calibrator model is designed to handle noisy and missing sensor observations. • Presented a continuous model calibration procedure for smart-building digital twins. • The proposed approach enabled fast model calibration without iterative search. [ABSTRACT FROM AUTHOR]

The welfare of farmed fish has gained increasing attention during recent decades, and as technological advances have facilitated measurements of brain activity in fish, the slaughter process has been highlighted as an area for assessment and potential improvement. Here, we used electroencephalograms (EEG) to assess brain activity in channel catfish (Ictalurus punctatus), and used commercial slaughter practices to guide optimization of stunning conditions in a laboratory setting. Following in-air electro-stunning at processing plants, individual fish responses to the shock varied based on EEG and corresponding ventilation measurements prior to physical euthanasia. Results from laboratory experiments showed stunning efficacy is dependent on shock duration and the location where electrodes contact fish. Electrodes contacting the head for 1 s using 50 Hz 132 AC V RMS , with a current >380 mA RMS , caused immediate loss of consciousness lasting 10–40 s. When the exposure period was prolonged to 6 s, recovery time was significantly longer, ranging from 45 to 240 s (mean 125 s). If the electrodes contacted the body instead of the head, shock delivered for 6 s resulted in a shorter recovery time of 0–100 s (mean 48 s). These findings highlight that shock duration and electrode position are important when stunning channel catfish and presumably other fishes, and indicate the time from stunning to killing should be kept as short as possible. • Electrical stunning can cause unconsciousness in channel catfish. • The effect of electric stunning is often temporary. • Shock duration affect time to recovery. • Electrode position affect time to recovery. • Time between stunning and killing should be kept short. [ABSTRACT FROM AUTHOR]

We present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our method uses adversarial autoencoders, which reduce the dimensionality of the full order model to a set of latent variables that are enforced to match a prior, for example a normal distribution. Adversarial autoencoders are seen as generative models, and they can be trained to generate high-resolution samples from low-resolution inputs, meaning they can address the so-called super-resolution problem. Then, a second neural network is trained to learn the physical structure of the latent variables and predict their temporal evolution. This neural network is known as a structure-preserving neural network. It learns the metriplectic-structure of the system and applies a physical bias to ensure that the first and second principles of thermodynamics are fulfilled. The integrated trajectories are decoded to their original dimensionality, as well as to the higher dimensionality space produced by the adversarial autoencoder and they are compared to the ground truth solution. The method is tested with two examples of flow over a cylinder, where the fluid properties are varied between both examples. • Given some scarce experimental measurements, our method is able to provide estimations of the measured fields at a higher resolution. • Then, a second neural network is trained to learn the physical structure of the latent variables and predict their temporal evolution. • Given a set of initial conditions at low resolution, our method predicts the temporal evolution of the physical system at high resolution. • To ensure the physical consistency of the results, the fulfilment of the principles of thermodynamics is enforced. [ABSTRACT FROM AUTHOR]

A Bayesian peak identification method based on physical model had been proposed in this study to perform qualitative analysis for the low count rate gamma spectrum under short-time measurement. Based on the prior knowledge about gamma detection, the distribution of energy deposited in the detector near the region of interest of a target peak through photoelectric effect, Compton scattering and multiple scattering had been analyzed and approximated using three elementary components, then, the distribution of each component after convolution had been studied, and at last, a probabilistic mixture model had been proposed to directly describe the distribution of measured energy within region of interest. When the data measurement has been completed, a Gibbs sampler was used to estimate the posterior distribution of model parameters to discriminate the existence of target peaks. The numerical and physical experiments were performed to verify the effectiveness of the parameter estimation algorithm and the performance of the proposed identification method. The numerical experiments showed the identification precision is proportional to counts and peak-to-total ratio, when the peak-to-total ratio is greater than 0.5, the proposed method could identify the peak with a probability greater than 50% given 50 observations. The physical experiment used a NaI(Tl) detector to measure 137Cs, 60Co and 152Eu sources in a laboratory environment at different equivalent distances, the peak identification precision had been tested for the proposed method, and the experiments proved that the probabilistic model is a good approximation to the physical process, and the proposed identification method outperformed the conventional method under short-time measurement conditions. [ABSTRACT FROM AUTHOR]

Data-driven artificial intelligence models play an important role in mechanical fault diagnosis. Generally, it is difficult to collect relative complete fault samples, which limits the application of artificial intelligence models for complex mechanical systems. To address this issue, numerical model-based or called physical model-based fault sample generation methods attracted many attentions but still an open problem: the difference in fault samples between numerical simulation and measurement of a physical system needs to be well decreased. Therefore, a noise generative network (NGN) for mechanical fault classification is developed. First, the NGN is trained using the simulation and measurement normal samples. The simulation fault samples are further fed into the trained NGN to obtain more solid simulation fault samples with minor differences from measurements of mechanical systems with faults. Second, deep convolutional neural network is trained by solid simulation fault samples, and the test samples of unknown fault types will be finally recognized. Finally, validation experiments using rotation vector reducers, bearings, and rotors classified test samples with average accuracies of 99.5%, 95.7%, and 99.9%, respectively. They indicate that the proposed NGN model effectively reduces the difference between the simulation and measurement samples to lead more precision results for fault classification in mechanical systems. [Display omitted] [ABSTRACT FROM AUTHOR]

Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data. However, these methods omit measurement errors and physical assumptions in modeling, resulting in several problems in practical applications. In particular, full waveform inversion methods are very sensitive to erroneous observations (outliers) that violate the Gauss–Markov theorem. Herein, we propose a method for addressing spurious observations or outliers. Specifically, we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution. To achieve this, we apply a waveform-like noise model based on a specific covariance matrix definition. Finally, we build an inversion problem based on the updated data, which is consistent with the wavefield reconstruction inversion method. Overall, we report an alternative optimization inversion problem for data containing outliers. The proposed method is robust because it uses uncertainties. This method enables accurate inversion, even when based on noisy models or a wrong wavelet. [ABSTRACT FROM AUTHOR]

In this paper, we introduce PDE-LEARN, a novel deep learning algorithm that can identify governing partial differential equations (PDEs) directly from noisy, limited measurements of a physical system of interest. PDE-LEARN uses a Rational Neural Network, U , to approximate the system response function and a sparse, trainable vector, ξ , to characterize the hidden PDE that the system response function satisfies. Our approach couples the training of U and ξ using a loss function that (1) makes U approximate the system response function, (2) encapsulates the fact that U satisfies a hidden PDE that ξ characterizes, and (3) promotes sparsity in ξ using ideas from iteratively reweighted least-squares. Further, PDE-LEARN can simultaneously learn from several data sets, allowing it to incorporate results from multiple experiments. This approach yields a robust algorithm to discover PDEs directly from realistic scientific data. We demonstrate the efficacy of PDE-LEARN by identifying several PDEs from noisy and limited measurements. [ABSTRACT FROM AUTHOR]

• The application of digital speckle technique in large deformation measurement of large-scale physical models is realized. • The fracture field, strain field, displacement field and strata movement trend of the working face with steeply inclined are obtained by monitoring. • The characteristics of roof fracture and the 2D migration law of overlying strata under the length effect of steep working face are obtained. • The main disaster-causing forms and preventive measures with steeply inclined long working faces are put forward. • The critical working face length of catastrophe and reasonable layout size of steep working face are determined. In order to determine the optimal inclined length of steeply inclined working face, the fracture field, strain field, displacement field and strata movement trend of steeply inclined working face were monitored through physical simulation test and digital speckle technique. At the same time, combined with theoretical analysis, the movement law of overlying strata and the catastrophe mechanism of working face caused by the length effect of working face are deeply studied. The research results show that with the increase of working face length, the length and growth rate of middle extremely unstable region are always largest, followed by lower and upper. The spreading failure characteristics of "roof-overlying strata-roof" appear along the inclined direction. When the working face length is less than 125 m, the larger vertical displacement and horizontal displacement are concentrated in the middle extremely unstable region. When the working face length is greater than 140 m, the larger vertical displacement is transferred to the lower stable region and the larger horizontal displacement is transferred to the upper unstable region. Based on this, it is obtained that the working face boundary length of roof and overlying strata catastrophe is 125 m and 140 m respectively. Furthermore, it is put forward that the optimal working face length is 95 m ∼ 125 m on the premise of effectively controlling the hanging space instability in the upper goaf and the flying gangue disaster in the working face. The application of digital speckle technique in large deformation measurement of large-scale physical models is realized. [ABSTRACT FROM AUTHOR]

The ability to offer long-range, high scalability, sustainability, and low-power wireless communication, are the key factors driving the rapid adoption of the LoRaWAN technology in large-scale Internet of Things applications. This situation has created high demand to incorporate location estimation capabilities into large-scale IoT applications to meaningfully interpret physical measurements collected from IoT devices. As a result, research aimed at investigating node localization in LoRaWAN networks is on the rise. The poor localization performance of classical range-based localization approaches in LoRaWAN networks is due to the long-range nature of LoRaWAN and the rich scattering nature of outdoor environments, which affects signal transmission. Because of the ability of fingerprint-based localization methods to effectively learn useful positional information even from noisy RSSI data, this work proposes a fingerprinting-based branched convolutional neural network (CNN) localization method enhanced with squeeze and excitation (SE) blocks to localize a node in LoRaWAN using RSSI data. Results from the experiments conducted to evaluate the performance of the proposed method using a publicly available LoRaWAN dataset prove its effectiveness and robustness in localizing a node with satisfactory results even with a 30% reduction in both the principal component analysis (PCA) variances on the training data and the size of the original sample. A localization accuracy of 284.57 m mean error on the test area was achieved using the Powed data representation, which represents an 8.39% increase in localization accuracy compared to the currently best-performing fingerprint method in the literature, evaluated using the same LoRaWAN dataset. [ABSTRACT FROM AUTHOR]

Cyber–physical systems face the threat of sensor attacks, which can result in incorrect measurements of the physical state for the controller and lead to erroneous decisions and control instructions. To address this issue, our paper introduces a sensor attack detection method involving the active injection of excitation into the system and a comparison of actual sensor data with the expected response. First, we address challenges posed by sensing noise and uncertain delays by compensating for them and presenting a probability interval derived from the convolution of noise and delay. Second, we create an anticipated response spectrum by adjusting the probability interval within a defined time window under specific active excitation. Third, the disparity between the expected response spectrum and the actual sensor data within the time window is utilized to calculate the probability of a sensor being attacked. Finally, we evaluate the effectiveness of our method through extensive simulations in a vehicle-platoon case study. [ABSTRACT FROM AUTHOR]

Practicing open-wound suturing requires feedback, but evaluation of each suture practice takes instructors' time and is often subjective. Recent work showed that deep-learning models can automate the evaluation by analyzing an image or a video of suturing into either pass or fail. However, they lacked fine-grained feedback offered by previous systems that required specialized devices or manual annotations. This work introduced a system that automatically extract geometric measurements from a suture practice end-product image for further evaluation. We proposed the suture instance segmentation task and a hand-crafted algorithm to extract interpretable metrics from an image. We collected two new simple suture datasets consisting of 240 images with instance segmentation and physical measurement annotations. The experiment results shows that current deep-learning model can accurately segment suture instances and the extracted measurements from our system are highly correlated with physical measurements from humans. • Extracting metrics for objectively evaluating open-wound suture practice. • A Mask R-CNN model segments suture instances accurately for further processing. • A hard-crafted algorithm extracts metrics from the suture instance for assessments. • A new simple suture dataset containing 240 images with annotations is available. • Experiments show that the extracted metrics are highly correlated with humans. [ABSTRACT FROM AUTHOR]

• XCT's capability for dimensional metrology of AM parts is verified by CMS. • XCT beam hardening and XCT voxel size scale error are both investigated. • A new 3D algorithm to estimate uncertainty of the CMS mechanical filtering effect. • Simulation of CMS and XCT are conducted by virtual part with the AM surface texture. This paper aims to verify the capability of X-ray Computed Tomography (XCT) in the dimensional measurement of additive manufacturing (AM) parts by comparing it against a tactile coordinate-measuring system (CMS). Three factors are investigated: XCT beam hardening, XCT voxel size scale error, and CMS mechanical filtering effect. Their performances are compared via both physical and simulation experiments. The physical experiments show that the employed XCT beam hardening elimination aids in obtaining accurate inner dimensions but deteriorates external dimension measurements. XCT voxel size scale error can be compensated by either the two-sphere calibration or the CMS normalisation method. The physical measurement results also indicate that the dimensional deviation between XCT and CMS measurements is mainly due to the mechanical filter effect of CMS, which is further sustained by simulation measurements. The proposed 3D CMS probe scanning simulation method yields a reliable uncertainty estimation of the CMS mechanical filtering effect. [ABSTRACT FROM AUTHOR]

Despite technological advances over the last several decades, ship-based hydrography remains the only method for obtaining high-quality, high spatial and vertical resolution measurements of physical, chemical, and biological parameters over the full water column essential for physical, chemical, and biological oceanography and climate science. The Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) coordinates a network of globally sustained hydrographic sections. These data provide a unique data set that spans four decades, comprised of more than 40 cross-ocean transects. The section data are, however, difficult to use owing to inhomogeneous format. The purpose of this new temperature, salinity, and dissolved oxygen data product is to combine, reformat and grid these data measured by Conductivity-Temperature-Depth-Oxygen (CTDO) profilers in order to facilitate their use by a wider audience. The product is machine readable and readily accessible by many existing visualisation and analysis software packages. The data processing can be repeated with modifications to suit various applications such as analysis of deep ocean, validation of numerical simulation, and calibration of autonomous platforms. Measurement(s) temperature, salinity, and dissolved oxygen of sea water Technology Type(s) CTD (conductivity, temperature, depth profile) calibrated with bottle sampling [ABSTRACT FROM AUTHOR]

Sickle cell disease (SCD) is a genetic condition that causes abnormalities in hemoglobin mechanics. Those affected are at high risk of vaso-occlusive crisis (VOC), which can induce life-threatening symptoms. The development of measurements related to vaso-occlusion facilitates the diagnosis of the patient's disease state. To complement existing readouts, we design a microfluidic-informatics analytical system with varied confined geometries for the quantification of sickle cell disease occlusion. We detect an increase in physical occlusion events in the most severe hemoglobin SS group. We use bioinformatics and modeling to quantify the in vitro disease severity score (DSS) of individual patients. We also show the potential effect of hydration, clinically recommended for crisis management, on reducing the disease severity of high-risk patients. Overall, we demonstrate the device as an easy-to-use assay for quick occlusion information extraction with a simple setup and minimal additional instruments. We show the device can provide physical readouts distinct from clinical data. We also show the device sensitivity in separate samples from patients with different disease severity. Finally, we demonstrate the system as a potential platform for testing the effectiveness of therapeutic strategies (e.g. hydration) on reducing sickle cell disease severity. [ABSTRACT FROM AUTHOR]

Purpose: A method for calculating nuclear medicine ionization chamber (NMIC) calibration settings with a Monte Carlo model is presented and validated against physical measurements. This work provides Monte Carlo–calculated calibration settings for select isotopes with no current manufacturer recommendations and a method by which NMIC manufacturers or standards laboratories can utilize highly detailed specifications to calculate comprehensive lists of calibration settings for general isotopes. Methods: A Monte Carlo model of a Capintec PET series NMIC was developed and used to calculate the chamber response to relevant radioactive decay products over an energy range relevant to nuclear medicine. The photon detection efficiency (PDE) of a high purity germanium (HPGe) detector was modeled and physically validated to facilitate measurements of NMIC calibration settings with HPGe detector spectroscopy. Modeled NMIC response to various isotopes was compared against spectroscopic measurements and National Institute of Standards and Technology (NIST)‐validated calibration settings to validate the Monte Carlo–calculated NMIC calibration settings. Results: HPGe detector PDE was validated against the physical measurements to within 3.3%$3.3\%$ at 95%$95\%$ confidence and used to measure calibration settings, which produced activity readings 0.7%$0.7\%$, 1.6%$1.6\%$, 0.8%$0.8\%$, and 1.0%$1.0\%$ different than those validated by NIST for 11$^{11}$C, 18$^{18}$F, 68$^{68}$Ga, and 64$^{64}$Cu respectively. The Monte Carlo model of the NMIC reproduced measured calibration settings to within 7%$7\%$ at 95%$95\%$ confidence for isotopes with a sufficiently small yield of low energy photons. Conclusions: A method of calculating NMIC calibration settings with Monte Carlo modeling has been developed and validated against HPGe detector spectroscopy. NMIC manufacturers or standards laboratories can use more detailed specifications of the chamber geometries to extend the applicability of this method to a wider range of isotopes. [ABSTRACT FROM AUTHOR]

Biomechanical models and simulations of musculoskeletal function rely on accurate muscle parameters, such as muscle masses and lines of action, to estimate force production potential and moment arms. These parameters are often obtained through destructive techniques (i.e., dissection) in living taxa, frequently hindering the measurement of other relevant parameters from a single individual, thus making it necessary to combine multiple specimens and/or sources. Estimating these parameters in extinct taxa is even more challenging as soft tissues are rarely preserved in fossil taxa and the skeletal remains contain relatively little information about the size or exact path of a muscle. Here we describe a new protocol that facilitates the estimation of missing muscle parameters (i.e., muscle volume and path) for extant and extinct taxa. We created three-dimensional volumetric reconstructions for the hindlimb muscles of the extant Nile crocodile and extinct stem-archosaur Euparkeria, and the shoulder muscles of an extant gorilla to demonstrate the broad applicability of this methodology across living and extinct animal clades. Additionally, our method can be combined with surface geometry data digitally captured during dissection, thus facilitating downstream analyses. We evaluated the estimated muscle masses against physical measurements to test their accuracy in estimating missing parameters. Our estimated muscle masses generally compare favourably with segmented iodine-stained muscles and almost all fall within or close to the range of observed muscle masses, thus indicating that our estimates are reliable and the resulting lines of action calculated sufficiently accurately. This method has potential for diverse applications in evolutionary morphology and biomechanics. [ABSTRACT FROM AUTHOR]

Recently, the Varian multichannel vaginal cylinder (MCVC) set for high-dose-rate 192Ir brachytherapy was commercially released. This MCVC was distinct from our existing MCVC in its peripheral channel layout and tip design. This investigation sought to assess the dosimetric impact of these changes. The dimensions of the virtual model for each applicator were compared against both physical and radiographic measurements. Volumetric dose distributions were generated in silico using a model-based dose calculation algorithm (MBDCA). To characterize the effects of the new peripheral channel layout on dose to adjacent areas ("dose-spill"), point doses were compared using two sets of applicator-based reference points: at surface or 5 mm radially from surface. To evaluate the dose-shaping capabilities, a dose distribution was generated for the new applicator and assessed against a representative dose distribution for a patient previously treated with existing equipment. Based on both physical and radiographic measurements, virtual models were representative of each applicator within ±1 mm. Commissioning of the MBDCA was benchmarked based on AAPM Working Group on Dose Calculation Algorithms in Brachytherapy. The layout of the new applicator reduced dose-spill to other reference points significantly, as much as a factor of 16.3, compared with the existing equipment. The rounded tip shape and curve of the peripheral channels in the new applicator produced more conformity to its HR-CTV than existing equipment. Compared with our existing equipment, the design changes in the new Varian MCVC set offered improved control of dose spill and better conformality to HR-CTV. [ABSTRACT FROM AUTHOR]

The analysis of lung diseases at the early stage is a major need in medical field to overcome the patients' severity. Thus, in the proposed work, Support Vector Machine (SVM) based classification method is adopted for precise classification of lung cancer, solid or aerosol Deposition, liquid and semi-liquid Deposition. Initially, the gathered data are pre-processed for image transformation. Image binning is performed to divide the images into several sub-regions, and threshold setting is done for effective segmentation. To evaluate the cell size and infected areas, Region of Interest (ROI) extraction has performed. The physical characteristics encompassing reflection coefficient, mass density and impedance are estimated to achieve effective performance. The prediction and classification of four lung diseases are effectively classified using SVM classifier. The simulation tool used for evaluating the performance is MATLAB. The performance of different physical characteristics shows that the proposed method performs better in classification. [ABSTRACT FROM AUTHOR]

Today, the analysis of the behaviour of structures represents an important area of engineering, where we are mainly interested in the response of the structure to certain influences. The article and research are based on the theoretical foundations of structural monitoring using contactless methods and discuss the practical analysis and simulation of synchronization of structural response to various artificially induced load cases. The responses of the structure were recorded through various parameters, such as displacement, speed and acceleration, with the help of geodetic (Robotic Total Station - RTS and Global Navigation Satellite System - GNSS) and physical equipment (triaxial geophone). When using geodetic equipment, in addition to the possibility of final synchronization of results, we were also interested in the actual influence of the frequency of data acquisition on the accuracy of measured values. Special attention was paid to spectral analysis, to which we added practical value through data processing using Fourier transform algorithms. [ABSTRACT FROM AUTHOR]

In this study we compare somatic parameters of preschool-age children. The research was carried out in preschool facilities in the Sabinov district, Slovakia, in four age categories from 3 to 6 years. Anthropometric measurement involves the evaluation of 16 anthropometric parameters. We performed the following anthropometric measurements: Sitting height (M23), Arm span (M17), Upper limb length (M54), Lower limb length (M53), Biacromial breadth (M35), Transverse chest diameter (M36:), Bispinal breadth (M41), Maximum head length (M1b), Maximum head breadth (M3), Breadth of the face (M6), Morphological height of the face (M18), Hand length (M49), Hand width (M52), Forearm length (M48), Foot length (M58) and Foot width (M59). The results confirmed changes in parameters depending on age and gender. In the results, higher average values were recorded in boys than in girls in almost all measured parameters, and even younger boys demonstrated higher values than older girls. [ABSTRACT FROM AUTHOR]

Indoor wet-bulb globe temperature (WBGT) was extensively used to evaluate the indoor hot environment in a naturally ventilated building, but it does not consider the effect of air movement caused by the hot surface. A potential evaluation index, which is a difference between the mean radiant temperature and the air temperature (Δtr–a), was introduced to be integrated with the indoor WBGT to evaluate the indoor hot environment with hot surface. Subjective evaluation of the occupant's thermal perceptions and objective measurement of indoor physical parameters were carried out in a typical naturally ventilated room with hot surface ranged from 50°C to 250°C. The results show that the indoor air temperature, mean radiant temperature and WBGT were increased by 8.6°C–12.9°C, 11.2°C–29.7°C and 4.7°C–7.5°C, respectively, with participant's light activity, and by 9.9°C–13.5°C, 14.9°C–29.4°C and 5.7°C–8.0°C, respectively, with participant's heavy activity. Indoor air temperature was increased linearly with the indoor WBGT, whereas the mean radiant temperature was increased non-linearly with the indoor WBGT. Besides, the non-linear relationship between the occupant's thermal perceptions and Δtr−a coincided very well with those between occupant's thermal perceptions and indoor WBGT. This suggests that the Δtr–a may be a potential index to be integrated with the indoor WBGT for evaluating the indoor hot environment in naturally ventilated building with hot surface. [ABSTRACT FROM AUTHOR]

Currently, there are problems regarding decreasing production rates and low recovery factors in the field test during depletion development of staged fractured horizontal wells in tight reservoirs. However, the production decline mechanism during the depletion development process is not clear at present; so, it is necessary to further explore the pressure distribution law and production decline mechanism in order to guide subsequent tight reservoir exploitation and to improve the recovery factor. In this study, a multi-point pressure measurement physical simulation experiment system, a pressure distribution mathematic model, and a mathematic productivity model were developed. After fitting the model with the experimental data, the production decline mechanism during depletion development of tight oil reservoirs was analyzed from four aspects. The results show that in lower permeability tight reservoir facies, the pressure propagation distance is shorter. The pressure gradient of tight oil reservoirs during depletion development is four times higher than that of low permeability reservoirs. As the permeability decreases, the seepage resistance coefficient increases exponentially. The seepage resistance in tight oil reservoirs is nearly six times higher than that in low permeability reservoirs. Compared with traditional enhanced oil recovery (EOR) techniques, inter-fracture flooding has obvious mechanism advantages in the EOR of tight reservoirs. [ABSTRACT FROM AUTHOR]

An effective method capable of measuring the topological charges of optical vortex beams by using a generalized ring Pearcey (GRP) phase is proposed theoretically and demonstrated experimentally in this paper. The number and orientation of fringes in the diffraction patterns respectively reveal the modulus and sign of topological charges. One can also tell the sign by observing the rotation of fringes during propagation. Moreover, the measurement of high‐order topological charges is shown to be significantly affected by the elliptical parameter in the GRP phase. Finally, the physical principle of these measurements is unveiled by applying the stationary phase approach. This work offers an alternative way of evaluating the photonic topological charges in vortex beams. [ABSTRACT FROM AUTHOR]

Because point clouds have reality measurements of physical objects, they are often used to reconstruct the as-built three-dimensional (3D) model of building construction sites through a modeling process called Scan-to-building information models (BIM). However, the reality measurements in point cloud data, such as actual color and deformed shapes of the original objects, could disappear during the solid modeling process. In addition, the conventional Scan-to-BIM pipeline still requires significant time and manual effort for object classification and shape representation. To address these problems, this study proposes a novel information modeling framework for point clouds, called point cloud information modeling (PCIM). PCIM can automatically recognize construction objects and their properties with deep learning approaches. Furthermore, it can store information in the original point cloud data with a hierarchical structure, rather than converting it to a solid or rigid model. To validate the overall PCIM concept, this research conducted a case study with an actual building construction project. The test results demonstrate that PCIM can be an effective tool for the as-is information modeling of structures and facilities during construction. [ABSTRACT FROM AUTHOR]

The technique of Body Condition Score (BCS) is a means of subjectively assessing the degree of fatness or condition of the live animal. It is a simple, non-invasive, time saving and effective management tool to rank goats according to their body reserves by touch and sight. To assess the relationships among body condition score (BCS), body weight and measurements of goats under field conditions, a study was conducted during the year 2019 on local goat flocks of farmers in Gundlasamudram and Marripudi villages of Prakasam district of Andhra Pradesh, India. A total of 100 does (2 to 4 years old) were assigned the BCS and the mean BCS of the test flock was 2.88±0.05. A total of 37 does were in the age group of 2 years with a mean BCS of 2.82±0.77, 52 does were in the age group of 3 years with a mean BCS of 2.96±0.75 and 11 does were in the age group of 4 years with a mean BCS of 2.75±0.16. Pin-shoulder length, height at withers, chest girth, abdominal girth, thigh circumference and body weight were significantly (p<0.01) positively correlated with each other. BCS was significantly (p<0.01) and positively correlated with all physical measurements studied. The mean BCS was significantly (p<0.01) higher for the does of age group of 3 years compared to does of age group of 2 and 4 years. The live weight of does increased by 2.66-2.80 kg for each unit increase in BCS. The present study suggested that BCS can be an effective indicator of degree of fitness of does. [ABSTRACT FROM AUTHOR]

I applied the methodology known as maximum likelihood conjoint measurement (MLCM) to diagnose how numerical magnitude affects the perception of physical size of numerals in the size congruity effect (SCE). Traditional theories of the SCE argue for automatic activation of numerical magnitude, and therefore the obligatory interaction in perception between number and size. Participants in the current study were presented with pairs of numerals varying on physical size and numerical magnitude. They judged which member of the pair was physically larger, while ignoring numerical magnitude. Three nested observer models (i.e. independent, additive, and saturated) of the contribution of physical size and numerical magnitude to perceived size were fit to the data using maximum likelihood. The independent observer model exhibited the best fit for the majority of observers. These results cast doubts on the view that numerical magnitude is activated automatically in judgments of physical size of numerals. [ABSTRACT FROM AUTHOR]

Thermal comfort depends on four environmental parameters such as air temperature, mean radiant temperature, air velocity and relative humidity and two personal parameters, including clothing insulation and metabolic rate. Environmental parameters can be measured via objective sensors. However, personal parameters can be merely estimated in most of the studies. Metabolic rate is one of the problematic personal parameters that affect the accuracy of thermal comfort models. International thermal comfort standards still use a conventional metabolic rate table which is tabulated according to different activity tasks. On the other hand, ISO 8996 underestimates metabolic rates, especially when the time of activity level is short and rest time is long. To this aim, this paper aims to determine metabolic rates from physical measurements of heart rate, mean skin temperature and carbon dioxide variation by means of nineteen sample activities. 21 male and 17 female subjects with different body mass indices, sex and age are used in the study. The occupants are subjected to different activity tasks while heart rate, skin temperature and carbon dioxide variation are measured via objective sensors. The results show that the metabolic rate can be estimated with a multivariable non-linear regression equation with high accuracy of 0.97. [ABSTRACT FROM AUTHOR]

The objective of the study was to evaluate the effects of feed restriction and sex on the physical, chemical and histochemical parameters of meat quality in chickens, rabbits and nutrias. Feed restriction was applied at a rate of 70% ad libitum from 14 to 21 days of age in chickens, 70% ad libitum from 42 to 49 days of age in rabbits, and 70% ad libitum from 12 to 15 weeks of age in nutrias. Animals were fed ad libitum prior to and following restriction. The results showed stronger effects of feed restriction, sex, and their interaction on the meat quality of broiler chickens than in rabbits and nutrias. The effect of feed restriction, sex and their interaction were mainly observed in the nutritional value of meat, while physical measurements of meat were negligibly affected. The interaction between feed restriction and sex affected the cross-sectional area of muscle fibre only in rabbits and nutrias. The data indicated the relationships between compensatory growth, muscle fibre distribution, and nutritional value of meat. [ABSTRACT FROM AUTHOR]

Exposure to daylight has much to offer and should be optimised to maximise its potential. In order to harvest its benefits, any visual discomfort from daylight should be anticipated and minimised. Hence, there is the need to predict discomfort from daylight glare. While more than 20 models for predicting discomfort from daylight glare have been developed, none accurately predict it. The inclusion of additional factors in the models may improve the predictions. One such factor is the socio-environmental context of the observer. This study compares the evaluations of discomfort glare from daylight for office buildings in four socio-environmental contexts: Chile, Belgium, Japan and Switzerland. The evaluations of discomfort glare, each consisting of subjective assessments and physical measurements of a view condition, were collected at the office desks of 401 participants, although only 211 responses were used in the analyses due to exclusion rules. The results do not suggest evidence of an influence of socio-environmental context on discomfort from daylight glare. In other words, the participants in this study perceived discomfort glare similarly, regardless of their socio-environmental context. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of two large rock toppling/sliding events which occurred in January 2014 and February 2019 at the Cliets unstable slope (Savoie, French Alps). To understand the mechanism involved and its control by external forcings, a multi-technique analysis approach is used combining geological observations, meteorological data analysis, topographic measurements and simple physical modeling. The pre-failure stage of the events is more particularly analyzed. No direct relationships are found between triggering factors and surface motion though a kinematics analysis highlights the transition toppling-sliding. It showed that, at first order, this transition occurred 4 years before the first failure of 2014, while it happened 2 months before the second failure of 2019. From this date, the environment is considered like a block sliding on an inclined plane. By applying a frictional model (Helmstetter et al. in Journal of Geophysical Research: Solid Earth 109(B2), 2004), we illustrated that the two events belong to an unstable velocity-weakening sliding regime. The time to failure (Voight in Science 243(4888):200–203, 1989) is forecasted with the model, and the results are consistent with the observations. They confirm that the gravitational factor is predominant over the triggering factors for the two events. [ABSTRACT FROM AUTHOR]

This article examines to what extent nationalist and sexist sentiment and international politics shaped attempts to universalize measurement practices in physical anthropology. On the one hand, racial scientists were interested in creating an international community with a universalized methodology and developing a global taxonomy of human races. On the other hand, they chauvinistically guarded their localized practices from outside influences. By following the standardization efforts of British biometrician Miriam Tildesley, a female racial scientist adamant on unifying a research field largely dominated by men from different countries, this article argues that intersecting forces of nationalism, internationalism, and sexism shaped anthropological practices in the early twentieth century. [ABSTRACT FROM AUTHOR]

Raman spectral bandwidths and bandshapes can provide detailed information on the physics and chemistry of a sample. The Raman band reported by a spectrometer (observed Raman band [ORB]) is a result of contributions from the true, intrinsic Raman band (IRB) and the spectrometer slit function. To obtain the IRB spectral width and shape, the effects of the spectrometer slit function must be removed from the ORB by measuring the spectrometer slit function and modeling the ORB as a convolution of the IRB and slit function. Though this model has proven to be useful, it is not based on physical parameters of the measurement and is limited. Thus, new ways to model Raman bandshape and bandwidth are needed. I previously developed a new method of modeling Raman bands using spectrometer geometric optics to examine the dependence of Raman band intensity on slit width. The model calculates the intensity of the ORB from the contributions of a continuum of dispersed slit functions. Here, I extend the use of this model for examining ORB spectral widths and shapes. I show that the model produces the same result as the convolution model when using Lorentzian Raman and Gaussian slit functions, confirming the validity of the model. However, in contrast to the convolution model, the new model is based on spectrometer geometric optics allowing one to account for various optical effects when modeling Raman spectra. This is demonstrated by modeling the effects of slit width on the Raman spectrum. I use the model to provide a detailed, physical understanding of the effects of slit width and IRB width on Raman bandshapes. [ABSTRACT FROM AUTHOR]

Coding metasurfaces have been introduced as efficient tools allowing meticulous control over the electromagnetic (EM) scattering. One of their relevant application areas is radar cross section (RCS) reduction, which principally relies on the diffusion of impinging EM waves. Despite its significance, careful control of the scattering properties poses a serious challenge at the level of practical realization. This article is concerned with (global) design optimization of coding metasurfaces featuring broadband RCS reduction. We adopt a two-stage optimization procedure involving data-driven supervised-learning, sequential-search strategy, and direct EM-based design closure of the entire metasurface oriented toward maximizing the RCS reduction bandwidth. Our framework is then used to develop a two-bit coding metasurface. To handle the combinatorial explosion at the concurrent meta-atom optimization stage, a sequential-search strategy has been developed that enables global search capability at low computational cost. Finally, EM-based optimization is executed to maximize RCS reduction bandwidth at the level of entire metasurface. The properties of the coding metasurface are demonstrated using monostatic and bistatic RCS performance. The 10-dB RCS reduction can be obtained in the frequency range of 14.8–37.2 GHz, in a monostatic configuration. Also, 15-dB RCS reduction can be maintained in the frequency range of 16.7–37 GHz. Simulations are validated using physical measurements of the fabricated prototypes. Finally, the performance of the structure is benchmarked against recently reported designs. [ABSTRACT FROM AUTHOR]