Deviation Threshold Research Articles

Personalized AI Dietitian and Fitness Trainer

Abstract: Exercising is the most important thing to live a healthy life. To be free from injuries and getting the most from our workout proper form of exercise is important. Doing too much repetition and over training can cause muscle soreness. Unendingly doing exercise incorrectly could eventually cause severe future injuries. The rest between the sets should be optimum depending on the repetition of the exercise, what muscle group is being trained and the load that is being put in the muscle. Maximum Muscle hypertrophy is achieved with the correct form of exercise. The goal of the project is to build an exercise repetition counter and form corrector in a way so the optimum growth can be achieved with the help of cutting edge technology. The whole project is written in python. Simple easy to use tkinter GUI is used, Computer vision for live video input, media pipe for building machine learning solutions and numpy for mathematical calculations. Our system is built on the most recent advancements in deep learning for build cause estimation. We tend to the area units that are able to observe and find errors in user’s activity (pose) by effectively supporting some threshold deviation between the limb angles. If a person is doing exercises with the help of this system, the user's form is checked (corrections in form also will be given if any) repetition’s status and count is displayed. Along with this the AI dietitian calculates the maintenance calories, BMR, etc. This will increase muscle hypertrophy and reduce the risk of injury. Keywords: Exercise, Form corrector, Machine learning, Numpy, Tkinter

Manifold-Preserving Sparse Graph and Deviation Information Based Fisher Discriminant Analysis for Industrial Fault Classification Considering Label-Noise and Unobserved Faults

Fault classification is one of the most important topics in the area of industrial process monitoring. Existing industrial fault classification models based on Fisher Discriminant Analysis (FDA) and its variants still fail to cope with the issues of label-noise and unobserved faults simultaneously. To fill this important gap, a novel Manifold-preserving sparse graph and Deviation information based FDA (MDFDA) industrial fault classification model is originally presented in this paper. Firstly, the manifold-preserving sparse graph technique is utilized to filter training samples with label-noise; by keeping high connectivity between the obtained sub-graph and the original graph, not only the underlying manifold structure of training samples is preserved after being filtered, but also some mislabeled training samples with evident statistical characteristic deviations are filtered; in this way, the information quality of training samples is greatly improved. Secondly, based on the deviation information obtained from the filtered training samples, a deviation threshold is defined for each observed fault; if the deviation information of a testing sample is larger than the deviation threshold of each observed fault, it will be recognized as the testing samples from the unobserved faults. Experiments based on the data from the benchmark Tennessee Eastman process and a real industrial air separation unit demonstrate the effectiveness and superiority of the proposed MDFDA classification model.

Abstract 13804: True Anemia in Heart Failure Patients: Peripheral Hematocrits Lack Sensitivity and Specificity as a Predictor of Measured Red Blood Cell Volumes in a Mixed-LVEF Cohort

Introduction: Anemia is a common and serious co-morbidity of heart failure (HF) that is undertreated, impacting outcomes for HF patients and the overall economics of HF. Studies have shown that anemia is associated with worse outcomes in HF, but to date interventional studies aimed at treating anemia in HF have failed to demonstrate conclusive outcome improvements. A common feature of past studies is that they generally defined anemia via a threshold of peripheral Hct (or via Hemoglobin, which has a direct linear relationship to Hct). Methods: In a recently published study of a mixed-LVEF cohort of 245 HF patients, red cell volume status relative to patient norms was quantified using direct blood volume analysis (BVA, Daxor, Oak Ridge, TN). We evaluated the suitability of a Hct for detecting true anemia (as measured by BVA) in this group. Although Hct was somewhat correlated (R 2 =0.43) with red cell volume (RCV) deviation from ideal (as measured directly by BVA), actual RCV status was heterogeneous. We performed an analysis of the specificity and sensitivity of Hct as an indicator of true anemia, defined as RCV < 90% of normal value. Results: The distribution of true anemia is shown in Figure 1. Visual inspection shows mixed anemia status across a broad range of Hct values. We examined three Hct thresholds for determining true anemia. Two thresholds were defined based on deviations from normal Hct (M=45, F=40); a third threshold was chosen at 24 for both genders, corresponding to a common transfusion trigger. Using a moderate 10% deviation threshold yielded a sensitivity of 96%, but a specificity of only 50%, due to a False Positive (dilutional anemia) rate of 19%. Using a more extreme 25% deviation threshold yielded a specificity of 90%, but a sensitivity of only 62%, due to a False Negative (FN) rate of 23%. The transfusion trigger had a FN rate of 61%. Conclusions: Hct lacks sensitivity and specificity for detecting true anemia. Interventions for anemia in HF would benefit from BVA.

Performance Evaluation Model for Matrix Calculation on GPU

Establishment of a performance evaluation model is a hotspot of current research. In this paper, the performance bottleneck is analyzed quantitatively, which provided programmers with a guidance to optimize the performance bottleneck. This paper takes a matrix as an example; the matrix is divided into a dense matrix or a sparse matrix. For dense matrix, the performance is first analyzed in a quantitative way, and an evaluation model is developed, which includes the instruction pipeline, shared memory, and global memory. For sparse matrix, this paper aims at the four formats of CSR, ELL, COO, and HYB, through the observation data obtained from the actual operation of large datasets, finds the relationship between the running time, dataset form, and storage model, and establishes their relational model functions. Through practical test and comparison, the error between the execution time of the test dataset that is predicted by the model function and the actual running time is found to be within a stable finite deviation threshold, proving that the model has certain practicability.

Trustee: A Trust Management System for Fog-Enabled Cyber Physical Systems

In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, bad-mouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasure the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e., CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system.

Time-Frequency Characteristics and the Influence Mechanism of the EMR from Coal with Different Joint Angles

It is vital to understand the electromagnetic radiation’s time-frequency characteristics in the process of coal and rock failure with different joint angles in order to reveal the generation mechanism of the electromagnetic radiation (EMR) and improve the accuracy of EMR early warning. We studied the time-frequency characteristics of EMR signals of coal samples with different joint angles. The study finds that, (1) with the increase of joint angle, the failure time and peak load of samples decrease first and increase later, and the postpeak failure time decreases gradually. The EMR counts’ peak value showed a slow rise, a sharp rise, and a slow rise in the three intervals of α = 0° to 45°, 45° to 60°, and 60° to 90°, respectively. The accumulated EMR counts showed a steady upward trend. The duration of the EMR waveform, the dominant frequency of the EMR, and the peak number of the frequency spectrum of coal samples are on the rise. (2) As the joint angle increases, the samples’ failure mode changes from the stage fracture dominated by tension cracks to the rapid fracture with the coexistence of shear and tension cracks and finally to the burst fracture which produces a large number of fragments. This is also the main reason for the difference of the EMR generation mechanism and the signal of samples with different joint angles. (3) According to the experimental results, we established the modified formulas for calculating the EMR threshold value and deviation of coal and rock with joints under different stress environments and revealed that the longer the EMR waveform duration, the higher the dominant frequency, and the more the number of spectrum peaks, the greater the burst risk of coal and rock.

Factors affecting the placement of agricultural best management practices in the agricultural conservation planning framework (ACPF) toolbox in the mid-Atlantic region.

There has been a recent push to conduct spatially explicit landscape planning at finer hydrologic unit scales to mitigate diffuse pollution. The Agricultural Conservation and Planning Framework (ACPF) helps identify potential locations for agricultural conservation practices by using high-resolution soils and elevation data. This spatially explicit approach attempts to identify runoff and nutrient pathways, but output may be influenced by user-specified parameters and the properties of the digital elevation model (DEM) being used. Here we assess differences in the density and location of conservation practices sited by the ACPF toolbox across three DEM resolutions in three agricultural catchments, each in distinct physiographies (Ridge and Valley, Piedmont, Coastal Plain) of the United States mid-Atlantic region. Output frequency did not vary much for contour buffer strips or water and sediment control basins (WASCOBs) across DEM resolution, particularly compared with landscape type. The DEM resolution was crucial for the density of grassed waterways but of little consequence for contour buffer strips. Placement density of WASCOBs and contour buffer strips varied by region. Grassed waterways are sited based on either discrete values or statistical distributions of stream power index (SPI). A higher density of grassed waterways was placed in lower relief landscapes when a single standard deviation threshold was applied. Using discrete SPI values for the grassed waterway tool generated more consistent output across watersheds than output based on statistical distributions. These and other reported findings can help guide user decisions in future applications of the ACPF toolbox, particularly across different areas of study.

Central macular OCTA parameters in glaucoma

Background/aimsTo investigate the relationship between the foveal avascular zone (FAZ) parameters assessed by optical coherence tomography angiography (OCTA) and central visual field parameters in glaucoma and healthy subjects.MethodsOne hundred and...

Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram.

Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST's performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds.

Quantifying the observational requirements of a space-borne LiDAR snow mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments (OSSEs). The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface model with multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE) estimates and water budget variables such as runoff. Results from the OSSE runs show that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when the LiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm. Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case, this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates via assimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWE root mean square errors and more than a 15% increase in correlation coefficients are achieved when σerror ≤ 40 cm using a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Detailed cross comparison of building energy simulation tools results using a reference office building as a case study

Building Energy Simulation (BES) tools play a key role in the optimization of the building system during the different phases, from pre-design through commissioning to operation. BES tools are increasingly used in research as well as in companies. New BES tools and updated versions are continuously being released. Each tool follows an independent validation process but rarely all the tools are compared against each other using a common case study. In this work, the modelling approaches of widespread dynamic simulation tools (i.e. EnergyPlus, TRNSYS, Simulink libraries CarnotUIBK and ALMABuild, IDA ICE, Modelica/Dymola and DALEC), as well as PHPP (a well-known quasi-steady-state tool), are described and the results of all the tools modelling the same characteristic office cell, defined within the IEA SHC Task 56, are compared on a monthly and hourly basis for the climates of Stockholm, Stuttgart and Rome. Unfortunately, different tools require different levels of input detail, which are often not matching with available data, hence the parametrization process highly influences the quality of the simulation results. In the current study to evaluate the deviation between the tools, frequently used statistical indices and normalization methods are analysed and the problems related to their application, in a cross-comparison of different tools, are investigated. In this regard, the deviation thresholds indicated by ASHRAE Guideline 14-2014 are used as a basis to identify results that suggest an acceptable level of disagreement between the predictions of a particular model and the outcomes of all models. The process of reaching a good agreement between all tools required several iterations and great effort on behalf of the modellers. To aid the definition of building component descriptions and future references for inter-model comparison a short history of the executed steps is presented in this work. Together with the comparison of the results of the tools, their computational cost is evaluated and an overview of the modelling approaches supported by the different tools for this case study is provided aiming to support the users in choosing a fit-for-purpose simulation tool.

Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest

Feature selection is a pre-processing technique used to remove unnecessary characteristics, and speed up the algorithm's work process. A part of the technique is carried out by calculating the information gain value of each dataset characteristic. Also, the determined threshold rate from the information gain value is used in feature selection. However, the threshold value is used freely or through a rate of 0.05. Therefore this study proposed the threshold rate determination using the information gain value’s standard deviation generated by each feature in the dataset. The threshold value determination was tested on 10 original datasets transformed by FFT and IFFT and classified using Random Forest. On processing the transformed dataset with the proposed threshold this study resulted in lower accuracy and longer execution time compared to the same process with Correlation-Base Feature Selection (CBF) and a standard 0.05 threshold method. Similarly, the required accuracy value is lower when using transformed features. The study showed that by processing the original dataset with a standard deviation threshold resulted in better feature selection accuracy of Random Forest classification. Furthermore, by using the transformed feature with the proposed threshold excluding the imaginary numbers leads to a faster average time than the three methods compared.

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

Conditional attribution of climate change and atmospheric circulation contributing to the record-breaking precipitation and temperature event of summer 2020 in southern China

Quantification of the contribution of atmospheric circulation to damaging extreme weather and climate events and the attribution of extreme events in the context of climate change has been gaining worldwide interest. Attribution analysis helps us to better understand the risks associated with the effects of climate change on extreme events. However, the contribution of atmospheric circulation, as well as the influence of climate change, to the record-breaking precipitation event in the middle and lower reaches of the Yangtze River and the concurrent record-breaking hot event in South China during the Meiyu period (June–July) in 2020 are still unclear. In this study, we use flow analogues to estimate how much the atmospheric circulation can explain these two extreme events and the influence of climate change. The results show that the atmospheric circulation explains 70.73% and 43.61% of the extreme precipitation event and the concurrent hot event, respectively. Compared with past climate, the occurrence risk of an event reaching or exceeding the 2020 Meiyu amount under similar atmospheric circulation conditions increased by 5.1 times under the present climate, 80% of which can be attributed to climate change. In addition, hot events similar to the 2020 event cannot occur under past climate, while those reaching or exceeding a one standard deviation threshold increased from 0.58% under past climate conditions to 68.83% under the present climate, 99% of which can be attributed to climate change. These results are beneficial for the understanding and prediction of extreme events in the context of climate change in this region.

Application of 3D tree modeling using point cloud data by terrestrial laser scanner

Constructing 3D models for trees such as those found in Japanese gardens, in which many species exist, requires the generation of tree shapes that combine the characteristics of the tree's species and natural diversity. Therefore, this study proposes a method for constructing a 3D tree model with highly-accurate tree shape reproducibility from tree point cloud data acquired by TLS. As a method, we attempted to construct a 3D tree model using the TreeQSM, which is open source for TLS-QSM method. However, in TreeQSM, since processing is based on the assumption that the tree point cloud consists of data related to trunks and branches, measuring trees in which leaves have fallen is recommended. To solve this problem, we proposed an efficient classification process that mainly uses thresholds for deviation and reflectance, which are the adjunct data of the object that can be acquired by laser measurement. Furthermore, to verify accuracy of the model, position coordinates from the constructed 3D tree model were extracted. The extracted coordinates were compared with the those of the tree point cloud data to clarify the extent to which the 3D tree model was constructed from the tree point cloud data. As a result, the 3D tree model was constructed within the standard deviation of 0.016 m from the tree point cloud data. Therefore, the reproducibility of the tree shape by the TLS-QSM method was also effective in terms of accuracy.

A novel intelligent round robin CPU scheduling algorithm

In the present era of smart computing and digital transformation multiprogramming multitasking, multi-user operating environment is playing an important role where rapid arrival, termination and switching of multiple processes is crucial for performance. CPU Utilization is one of the key performance parameter and CPU scheduling crucial for CPU utilization. On the way of improving performance, substantial research and efforts are being done by community researchers and operating system processing is becoming fast day by day. Major performance evaluations criteria like waiting time, response time turnaround time number of context switching are sophisticatedly enhanced to achieve better processing. Round-Robin (RR) scheduling is a well-known important scheduling algorithm where quantum time slot pointedly affect performance. In case of very large quantum time it works like First come first serve or if it is too small then RR works like a process sharing algorithm which increases the system overheads in view of context switching. Thus setting up an effective quantum time is a major challenge for researchers. In this work a clustering inspired novel intelligent approach has been proposed to significantly enhance the round robin algorithm performance. An intelligent sub ready queue by using standard deviation and optimal threshold is created. Dynamic quantum is generated for each cluster and performance is evaluated on sample data in terms of waiting time, response time, turnaround time and context switching. The results of the proposed intelligent approach are compared with other existing algorithms and are found better.

Wave Anomaly Detection in Wave Measurements

AbstractQuality control measures for ocean waves observations are necessary to give confidence of their accuracy. It is common practice to detect anomalies or outliers in surface displacement observations by applying a standard deviation threshold. Besides being a purely statistical method, this quality control procedure is likely to flag extreme wave events erroneously, thereby impacting higher-order descriptions of the wave field. In this paper we extend the use of the statistical phase-space threshold, an established outlier detection method in the field of turbulence, to detect anomalies in a wave record. We show that a wave record in phase space (here defined as a diagram of displacement against acceleration) can be enclosed by a predictable ellipse where the major and minor axes are defined by the spectral properties of the wave field. By using the parameterized ellipse in phase space as a threshold to identify wave anomalies, this is a semiphysical filtering method. Wave buoy data obtained from a mooring deployed near King George Island, Antarctica [as part of the Antarctic Modeling Observation System (ATMOS)], and laser altimeter data obtained at the Northwest Shelf of Australia were used to demonstrate the functioning of the filtering methodology in identifying wave anomalies. Synthetic data obtained using a high-order spectral model are used to identify how extreme waves are positioned in phase space.

First Assessment of HY-1C COCTS Thermal Infrared Calibration Using MetOp-B IASI

The Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C (HY-1C) satellite was launched in September 2018. Accurate and stable calibration is one of the important factors when deriving geophysical parameters with high quality. The first assessment of HY-1C COCTS thermal infrared calibration is conducted in this research. We choose the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-B satellite as the reference instrument, mainly due to its hyper-spectral characteristic and accurate calibration superiority. The brightness temperatures (BTs) from the two HY-1C COCTS thermal infrared bands centered near 11 and 12 µm are collocated with the IASI in the spatial window of 0.12° × 0.12° and temporal window of half an hour. The homogeneity filtering of matchups is also carried out by setting the relative standard deviation (RSD) thresholds on each collocated grid and its neighboring grids. Based on the filtered matchups, the HY-1C COCTS BTs from the 11 and 12 µm channels are compared with IASI. The mean differences of COCTS minus IASI are 2.68 and 3.18 K for the 11 and 12 μm channels, respectively. The corresponding standard deviations (SDs) are also 0.29 and 0.28 K, respectively. In addition, the BT differences show latitude-dependence and BT-dependence. In order to correct the HY-1C COCTS thermal infrared BTs, the latitude-dependent coefficients are obtained to express the relationship between the BT differences and IASI BTs using the linear robust regression. After the BT correction, the biases and BT-dependence of the COCTS original BT minus IASI differences are removed. Further, the SDs decrease to 0.21 K for the 11 and 12 μm channels. Overall, the calibration of the HY-1C COCTS thermal infrared channels remains stable and the accuracy is around 0.2 K after inter-calibration.

Variable rate application accuracy of a centrifugal disc spreader using ISO 11783 communication data and granule motion modeling

In recent years, map-based variable rate (VR) fertilization has become more and more established in conventional crop farming, as it enables a better match of inputs to the growing conditions of cultivated crops and thus gives the opportunity for further environmental and economic optimization of plant production processes. Nonetheless, the VR application of mineral fertilizer performed by a centrifugal spreader is susceptible to deviations from the prescribed rates due to the expansion of the spread pattern on a large plane, as well as the high relation between common working widths and the management zone sizing. Application errors mainly occur because the spread pattern might cover an area of different zones at the transition from one zone to another, but the spreader still reads the prescribed rate from a specific location of one zone. The extremity of the rate deviations also depends on the response of the spreader to changes in the prescribed rates from one zone to another. By developing a model that could implement an accurate in-field position of the spreader to define the final position of each granule in the field with its corresponding mass, the application accuracy could be quantified and improved.Towards this aim, a sensor frame was mounted on the spreader to obtain its in-field positions in a georeferenced coordinate system while the application data, in terms of the applied mass, was recorded from the ISO 11783 communication data. A mathematical model of the spreader performance with granule dynamics on– and off-the spreader disc was developed. The developed model used the measured data as an input to define each granule position in the field and its corresponding mass. The developed model was verified by comparing the transversal distribution of the applied mass from the model with the measured one that showed a root-mean-squared error of 2.1% and a correlation coefficient of 0.96. Subsequently, the developed model was applied for the dynamic mode of the spreader to assess the application accuracy, in terms of deviations from the prescribed rates. The assessment of the deviations indicated that the applied rates of the model differed from the prescribed rates of the application with a mean value of 5.3 and 19.8 kg ha−1 for under- and over-applied areas of the field, respectively. These values corresponded to 2.3 and 8.2% of the maximum prescribed rate and were far below the defined deviation threshold equal to 10% of the applied rate.

Pulse Arrival Time Segmentation Into Cardiac and Vascular Intervals - Implications for Pulse Wave Velocity and Blood Pressure Estimation.

This study demonstrates a novel method for pulse arrival time (PAT) segmentation into cardiac isovolumic contraction (IVC) and vascular pulse transit time to approximate central pulse wave velocity (PWV). 10 subjects (38 ± 10 years, 121 ± 12 mmHg SBP) ranging from normotension to hypertension were repeatedly measured at rest and with induced changes in blood pressure (BP), and thus PWV. ECG was recorded simultaneously with ultrasound-based carotid distension waveforms, a photoplethysmography-based peripheral waveform, noninvasive continuous and intermittent cuff BP. Central PAT was segmented into cardiac and vascular time intervals using a fiducial point in the carotid distension waveform that reflects the IVC onset. Central and peripheral PWVs were computed from (segmented) intervals and estimated arterial path lengths. Correlations with Bramwell-Hill PWV, systolic and diastolic BP (SBP/DBP) were analyzed by linear regression. Central PWV explained more than twice the variability (R2) in Bramwell-Hill PWV compared to peripheral PWV (0.56 vs. 0.27). SBP estimated from central PWV undercuts the IEEE mean absolute deviation threshold of 5 mmHg, significantly lower than peripheral PWV or PAT (4.2 vs. 7.1 vs. 10.1 mmHg). Cardiac IVC onset signaled in carotid distension waveforms enables PAT segmentation to obtain unbiased vascular pulse transit time. Corresponding PWV estimates provide the basis for single-site assessment of central arterial stiffness, confirmed by significant correlations with Bramwell-Hill PWV and SBP. In a small-scale cohort, we present proof-of-concept for a novel method to estimate central PWV and BP, bearing potential to improve the practicality of cardiovascular risk assessment in clinical routines.