Temporal Inaccuracies Research Articles

Comparing an energy-based ship emissions model with AIS and on-board emissions testing

On-board emission testing data for two ocean-going vessels is used to assess the performance of a new Australian ship emissions model, and to also assess the impact of local currents on emission predictions. Prediction performance is only marginally affected by AIS post-processing method and inclusion of local current information. Model performance was assessed for three different aspects, fuel-based emission factors (g/g CO 2 ), engine work-based emission factors (g/kWh) and distance-based emission factors (g/km). Analysis of fuel-based and engine-work based emission factors suggest good performance and small to reasonable mean prediction errors for CO 2 (±10%), PM 10 (±15%) and SO 2 (±20%). For NO x and CO, on-board emissions testing suggest that model emission factors are biased high and low with mean prediction errors +60–70% and −60%, respectively. The results for distance-based emission factors were not considered to be meaningful due to spatial and temporal inaccuracies in linking on-board testing with the AIS data that could not be resolved. Given the importance of AIS data as input to fuel and emissions modelling, it is recommended that the spatial and temporal accuracy of AIS data is investigated and confirmed in future studies. Moreover, the differences found in this study between model predictions and on-board measurements highlight a few limitations in application of generic fleet-based models. • Use on-board emissions testing to validate new ship emission model. • Assess the impact of local currents. • On-board testing suggests accurate predictions for CO 2 , SO 2 and PM 10 . • On-board testing suggests biased predictions for CO and NO x . • Potential spatial issue with using AIS data in emission modelling.

A piecewise recursive semi-analytical method for effective evaluation of heterogeneous aging viscoelastic materials

A novel semi-analytical method in the time domain is proposed to predict the effective properties of heterogeneous aging viscoelastic materials. By virtue of a series expansion in a discretized time interval, variables and time varying constitutive coefficients can temporally be described more precisely, and a piecewise variable separation is realized. Consequently, a set of recursive constitutive equations are derived from a second-order differential constitutive equation with time-varying coefficients, and a spatially and temporally coupled effective evaluation is decoupled into a series of recursive spatial effective evaluations which can be tackled with Mori–Tanaka method (M–T), self-consistent method (SC), or other well developed skills, providing time-varying effective strain/stress. A recursive adaptive algorithm is stressed, so as to ensure stable temporal solution accuracy for different step sizes, and avoid the temporal inaccuracy caused either by the relatively lower truncation order or improper step size. Four numerical examples are provided to verify the effectiveness of the proposed approach by comparing the prediction results with reference solutions.

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks.

There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient ()] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.

Automated procedure to assess pup retrieval in laboratory mice

All mammalian mothers form some sort of caring bond with their infants that is crucial to the development of their offspring. The Pup Retrieval Test (PRT) is the leading procedure to assess pup-directed maternal care in laboratory rodents, used in a wide range of basic and preclinical research applications. Most PRT protocols require manual scoring, which is prone to bias and spatial and temporal inaccuracies. This study proposes a novel procedure using machine learning algorithms to enable reliable assessment of PRT performance. Automated tracking of a dam and one pup was established in DeepLabCut and was combined with automated behavioral classification of “maternal approach”, “carrying” and “digging” in Simple Behavioral Analysis (SimBA). Our automated procedure estimated retrieval success with an accuracy of 86.7%, whereas accuracies of “approach”, “carry” and “digging” were estimated at respectively 99.3%, 98.6% and 85.0%. We provide an open-source, step-by-step protocol for automated PRT assessment, which aims to increase reproducibility and reliability, and can be easily shared and distributed.

Sex differences in spatial accuracy relate to the neural activation of antagonistic muscles in young adults.

Sex is an important physiological variable of behavior, but its effect on motor control remains poorly understood. Some evidence suggests that women exhibit greater variability during constant contractions and poorer accuracy during goal-directed tasks. However, it remains unclear whether motor output variability or altered muscle activation impairs accuracy in women. Here, we examine sex differences in endpoint accuracy during ankle goal-directed movements and the activity of the antagonistic muscles. Ten women (23.1±5.1years) and 10 men (23±3.7years) aimed to match a target (9° in 180ms) with ankle dorsiflexion. Participants performed 50 trials and we recorded the endpoint accuracy and the electromyographic (EMG) activity of the primary agonist (Tibialis Anterior; TA) and antagonist (Soleus; SOL) muscles. Women exhibited greater spatial inaccuracy (Position error: t=-2.65, P=0.016) but not temporal inaccuracy relative to men. The motor output variability was similar for the two sexes (P>0.2). The spatial inaccuracy in women was related to greater variability in the coordination of the antagonistic muscles (R 2 0.19, P=0.03). These findings suggest that women are spatially less accurate than men during fast goal-directed movements likely due to an altered activation of the antagonistic muscles.

Effective map scales for soil transport processes and related process domains — Statistical and spatial characterization of their scale-specific inaccuracies

Digital Soil Mapping (DSM) aims at the creation of reliable, reproducible and dynamic spatial soil information according to specific users' requests and demands. Positional and temporal inaccuracies as well as the question of an optimal resolution of digital elevation models (DEMs) indicate scale-related issues which represent typical challenges for DSM.In this study, the effective map scale (EMS) approach is presented which enables the detection of operational scales where soil-related processes take place, the localization of corresponding process domains, as well as the statistical and spatial visualization of their scale-specific inaccuracies. The underlying algorithm can be considered as a test procedure for predictive efficiency where measurements, characterizing a soil-related process as well as a proxy variable and its scale-specific variation, are optimized. In doing so, positional and semantic inaccuracies of legacy data can be detected.The EMS approach is applied to the example of an agricultural parcel where soil erosion by tillage is assumed. Auger samples have been taken in order to quantify the amount of soil loss and accumulation during the last 80years in a German landscape with a complex topography and dominating loess parent material. The measurements have been related to the terrain attribute Mass Balance Index (MBI), which acts as an indicator for tillage erosion and has been varied according to both scale and soil surface complexity. The indicator MBI is derived from a high resolution digital elevation model and combines the basic terrain attributes slope, curvature and vertical distance to channel network due to their importance for tillage erosion processes. Different scale levels have been created by a region-growing segmentation algorithm. Each scale level contains discrete soil-terrain objects, represented by polygons.The scale-related analysis of MBI variations and measurements has revealed a range of EMSs where process domains are visible. Their accuracies are characterized from various perspectives: (1.) The analysis of single MBI variants of scale and complexity by linear regression expresses the spatial and statistical variance of EMSs. (2.) The application of the data mining algorithm random forest on all the MBI variants of complexity per scale level leads to a spatial and statistical suppression of uncertain process domains and an emphasis of process domains, which could be predicted with a higher reliability. In this study, the process domains of accumulation could be identified on a range of operational scale levels. Due to the positional inaccuracies of auger samples and temporal inaccuracies based on overlaying long-term soil transport processes, the process domains of soil loss could not be sufficiently located.

Visualizing the impact of space-time uncertainties on dengue fever patterns

In this article, we evaluate the impact of positional and temporal inaccuracies on the mapping and detection of potential outbreaks of dengue fever in Cali, an urban environment of Colombia. Positional uncertainties in input data are determined by comparison between coordinates following an automated geocoding process and those extracted from on-field GPS measurements. Temporal uncertainties are modeled around the incubation period for dengue fever. To test the robustness of disease intensities in space and time when accounting for the potential space-time error, each dengue case is perturbed using Monte Carlo simulations. A space-time kernel density estimation (STKDE) is conducted on both perturbed and observed sets of dengue cases. To reduce the computational effort, we use a parallel spatial computing solution. The results are visualized in a 3D framework, which facilitates the discovery of new, significant space-time patterns and shapes of dengue outbreaks while enhancing our understanding of complex and uncertain dynamics of vector-borne diseases.

The Effect of Data Inaccuracy on Tests of Space‐Time Interaction

AbstractSpace‐time event data are often subject to deficiencies in: (1) locational accuracy; (2), temporal accuracy; and (3) completeness. This work explores how these failings in the quality of input data may affect the results of global space‐time interaction tests. While previous work has partially investigated the impact of locational inaccuracy on the results of these tests, more work remains. The impacts of temporal inaccuracy and incomplete data reporting on the results of these tests remain completely unexplored. This study examines the influence of these problems individually and collectively, using a series of simulations. Findings demonstrate that even in cases of slight inaccuracy or underreporting, the consequences on results are potentially severe. Although the study is couched in terms of data inaccuracy, its relevance to situations where inaccuracy is replaced with uncertainty is self‐evident.

The impact of temporal inaccuracies on 4DCT image quality

Accurate delineation of target volumes is one of the critical components contributing to the success of image-guided radiotherapy treatments and several imaging modalities are employed to increase the accuracy in target identification. Four-dimensional (4D) techniques are incorporated into existing radiation imaging techniques like computed tomography (CT) to account for the mobility of the target volumes. However, these methods in some cases introduce further inaccuracies in the target delineation when further quality assurance measures are not implemented. A source of commonly observed inaccuracy is the misidentification of the respiration cycles and resulting respiration phase assignments used in the construction of the 4D patient model. The aim of this work is to emphasize the importance of optimal respiration phase assignment during the 4DCT image acquisition process and to perform a quantitative assessment of the effect of inaccurate phase assignments on the overall image quality. The accuracy of the phase assignment was assessed by comparison with an independent calculation of the respiration phases. Misplaced phase assignments manifest themselves as deformations and artifacts in reconstructed images. These effects are quantified as volumetric discrepancies in the localization of target objects represented by spherical phantoms. Measurements are performed using a fully programmable motion phantom designed and built at Mayo Clinic (Rochester, MN). Implementation of a case based independent check and correction procedure is also demonstrated with emphasis on the use of this procedure in the clinical environment. Review of clinical 4D scans performed in this institution showed discrepancies in the phase assignments in about 40% of the cases when compared to our independent calculations. It is concluded that for improved image reconstruction, an independent check of the sorting procedure should be performed for each clinical 4DCT case.