Mean Error Research Articles

Validation of oscillometric ratio and maximum gradient methods for non-invasive blood pressure measurement with intra-arterial blood pressure measurements as reference.

Most non-invasive blood pressure (BP) measurements are carried out using instruments which implement either the Ratio or the Maximum Gradient oscillometric method, mostly during cuff deflation, but more rarely during cuff inflation. Yet, there is little published literature on the relative advantages and accuracy of these two methods. In this study of 40 lightly sedated individuals aged 64.1 ± 9.6 years, we evaluate and compare the performance of the oscillometric ratio (K) and gradient (Grad) methods for the non-invasive estimation of mean pressure, SBP and DBP with reference to invasive intra-arterial values. There was no significant difference between intra-arterial estimates of mean pressure made via Korotkoff sounds (MP-OWE) or the gradient method (MP-Grad). However, 17.7% of MP-OWE and 15% of MP-Grad were in error by more than 10 mmHg. SBP-K and SBP-Grad underestimated SBP by 14 and 18 mmHg, whilst accurately estimating DBP with mean errors of 0.4 ± 5.0 and 1.7 ± 6.1 mmHg, respectively. Relative to the reference standard SBP-K, SBP-Grad and DBP-Grad were estimated with a mean error of -4.5 ± 6.6 and 1.4 ± 5.6 mmHg, respectively, noting that using the full range of recommended ratios introduces errors of 12 and 7 mmHg in SBP and DBP, respectively. We also show that it is possible to find ratios which minimize the root mean square error (RMSE) and the mean error for any particular individual cohort. We developed linear models for estimating SBP and SBP-K from a range of demographic and non-invasive OWE variables with resulting mean errors of 0.15 ± 5.6 and 0.3 ± 5.7 mmHg, acceptable according to the Universal standard.

A developed convolutional neural network model for accurately and stably predicting effective thermal conductivity of gradient porous ceramic materials

Accurate and stable prediction of the effective thermal conductivity (ETC) of porous ceramic materials is of great significance for their application in areas such as optimizing the design of thermal barrier coatings and improving energy conversion efficiency. Porous ceramic materials exhibit complex porous structures with gradient porosity distributions that pose a great challenge for effective medium theory (EMT) and conventional convolutional neural networks (CNN) in attempting to accurately and stably predict the ETC of porous media. In this study, a CNN model that integrates self-attention and a multiscale feature-fusion mechanism is proposed to predict the ETC of porous media with greater accuracy and stability. The integration of the self-attention and multiscale feature-fusion mechanisms enhances the CNN's ability to learn long-range dependencies and preserve detailed information. The optimization of the CNN's accuracy and stability is visually illustrated using gradient-weighted class activation mapping (Grad-CAM) for ETC. Additionally, by employing the proposed gradient quartet structure generation set (QSGS), a gradient porous ceramic media dataset comprising 10 000 images was built to train the CNN model. Finally, the prediction results and relative error distribution of the ETC were compared across different models. Our model demonstrated improvements in statistical metrics, including a 33.7 % decrease in mean error, 25.2 % decrease in median error, and 59.6 % decrease in maximum error. The decreases in these metrics and Grad-CAM for the ETC strongly demonstrates that the model proposed in this work greatly improved the accuracy and stability of predicting the ETC of ceramic materials with gradient porosity distributions.

Tomato pose estimation using the association of tomato body and sepal

In facility horticulture smart farms, harvesting robotic systems to automate harvesting tasks are challenging due to the complex environment, irregular growth and fruits pose. To harvest a fruits, the vision information required by a harvesting robot is accurate position and pose. Especially, fruit pose information is essential for planning paths to avoid damaging stems, leaves, branches, and obstacles, preventing damage to the fruit and harvesting robots, and planning efficient harvest sequences. This paper presents a method that uses information from tomato sepals to estimate the orientation of tomatoes, which are a commonly grown crop in horticulture. First, we train a YOLOv8 model to detect and segment bodies and sepals of tomatoes. For robust training of the model, the training data is constructed using effective data augmentation, which synthesizes segmented foreground objects and inserting them into the background. Then, for finding association between the body and sepal of a tomato, we apply IoU score based matching and the Hungarian algorithm. Consequently, we obtain point clouds for both parts using RGB-D data. Finally, we compute the center point of each object by using spherical fitting and the statistics of the point cloud, respectively. Then, we estimate the pose of the tomato as a vector between two center points of the body and sepal. To accurately and practically evaluate the proposed method, we generated ground truth data using calibration patterns in tomato greenhouse. As the experimental results show, The segmentation results show that AP50,sepal is 94.7, AP50,tomato is 96.3, and mAP is 61.5. The result of the pose estimation for the pose validation dataset is a mean error angle of 6.79 ± 3.18 and angle errors of less than 10 degrees account for 87.2%. The total algorithm proposed in this paper requires about 0.038s for each tomato greenhouse image. As a result, our proposed pose estimation can be practically utilized in robot systems for tomato harvesting.

Car Price Prediction Using Machine Learning

Abstract: The objective of the "Car Price Prediction Using Machine Learning" project is to anticipate car prices based on pertinent variables by utilizing predictive modelling and advanced data analytics. The rising need for precise and active pricing systems in the automobile sector is addressed by this initiative. The system will evaluate past vehicle data, including brand, model, manufacturing year, the mileage, type of fuel, and other details, by utilizing machine learning techniques. In order to produce specific predictions, the suggested model will be trained on an extensive dataset, noticing patterns and parallels within the data. In order to create a reliable prediction model, the research focuses on using regression techniques, such as ensemble approaches or linear regression. System of measurement like as absolute mean error and R-squared coefficient will be used to evaluate the predicted accuracy of the system in order to regulate its usefulness. If this resourcefulness is employed successfully, it will have a big impact on the automobile business for clients and venders alike by giving them a tool to evaluate fair market prices and helping them make decisions. This study enhances to the body of knowledge in price analysis using machine learning and lays the footing for future improvements in the prognostication of dynamic market trends in the automobile sector.

Experimental validation of the accuracy of robotic-assisted radioactive seed implantation for tumor treatment.

An experimental validation of a robotic system for radioactive iodine-125 seed implantation (RISI) in tumor treatment was conducted using customized phantom models and animal models simulating liver and lung lesions. The robotic system, consisting of planning, navigation, and implantation modules, was employed to implant dummy radioactive seeds into the models. Fiducial markers were used for target localization. In phantom experiments across 40 cases, the mean errors between planned and actual seed positions were 0.98±1.05mm, 1.14±0.62mm, and 0.90±1.05mm in the x, y, and z directions, respectively. The x, y, and z directions correspond to the left-right, anterior-posterior, and superior-inferior anatomical planes. Silicone phantoms exhibiting significantly smaller x-axis errors compared to liver and lung phantoms (p<0.05). Template assistance significantly reduced errors in all axes (p<0.05). No significant dosimetric deviations were observed in parameters such as D90, V100, and V150 between plans and post-implant doses (p>0.05). In animal experiments across 23 liver and lung cases, the mean implantation errors were 1.28±0.77mm, 1.66±0.69mm, and 1.86±0.93mm in the x, y, and z directions, slightly higher than in phantoms (p<0.05), with no significant differences between liver and lung models. The dosimetric results closely matched planned values, confirming the accuracy of the robotic system for RISI, offering new possibilities in clinical tumor treatment.

Valuing drinking water quality after a PFAS contamination event: Results from a meta-analysis benefit transfer

Drawing upon an extensive body of valuation literature focused on water quality, this paper performs a meta-analysis benefit transfer exercise aimed at quantifying willingness to pay (WTP) for an enhancement in drinking water quality for households that have been directly exposed to Perfluoroalkyl Substances (PFAS) over recent decades in Italy. The analysis compiles metadata of 72 WTP estimates extracted from 40 previous valuation studies conducted in advanced economies. The benefit transfer is realized estimating a meta regression model (MRM) which includes both study design and socio-economic explanatory variables, according to the Weak Structural Utility Theoretic approach. To determine the most suitable MRM specification, a comparative evaluation of various model configurations is developed exploiting the Least Absolute Shrinkage and Selection Operator (LASSO) selection criterion, and assessing their predictive performances in terms of transfer error and explanatory power. The mean transfer error (MTE) and the adjusted R-squared of the preferred MRM are in line with past published meta-analyses (0.665 and 0.607, respectively). The parameters estimated in the model align with both economic theory and intuition. The benefit transfer process results in an estimated annual WTP of € 250.80 per household for improved drinking water quality in the PFAS-affected area and an aggregated value of social benefits from PFAS decontamination of around € 12 million.

Towards an accurate rolling resistance: Estimating intra-cycle load distribution between front and rear wheels during wheelchair propulsion from inertial sensors

ABSTRACT Accurate assessment of rolling resistance is important for wheelchair propulsion analyses. However, the commonly used drag and deceleration tests are reported to underestimate rolling resistance up to 6% due to the (neglected) influence of trunk motion. The first aim of this study was to investigate the accuracy of using trunk and wheelchair kinematics to predict the intra-cyclical load distribution, more particularly front wheel loading, during hand-rim wheelchair propulsion. Secondly, the study compared the accuracy of rolling resistance determined from the predicted load distribution with the accuracy of drag test-based rolling resistance. Twenty-five able-bodied participants performed hand-rim wheelchair propulsion on a large motor-driven treadmill. During the treadmill sessions, front wheel load was assessed with load pins to determine the load distribution between the front and rear wheels. Accordingly, a machine learning model was trained to predict front wheel load from kinematic data. Based on two inertial sensors (attached to the trunk and wheelchair) and the machine learning model, front wheel load was predicted with a mean absolute error (MAE) of 3.8% (or 1.8 kg). Rolling resistance determined from the predicted load distribution (MAE: 0.9%, mean error (ME): 0.1%) was more accurate than drag test-based rolling resistance (MAE: 2.5%, ME: −1.3%).

Treatment of portal hypertension complicated by variceal bleeding

Background. Mortality in variceal bleeding (VB) on the background of portal hypertension (PH) is highest among patients with gastrointestinal bleeding. The treatment of PH complicated by VB is a complex problem that is far from being solved, especially in case of Child-Pugh class B and C decompensation. The aim of the study was to determine the causes of PH complicated by VB, features of conservative and interventional therapy, indications and nature of surgical procedures, treatment efficacy, as well as causes of complications and mortality. Materials and methods. We analyzed treatment outcomes of 923 inpatients with PH complicated by VB who were treated in the clinic of the Department of Surgery of the Faculty of Dentistry of Bogomolets National Medical University on the basis of the Kyiv City Clinical Emergency Hospital from 2012 to 2023. It was possible to talk about the cause of PH based on anamnestic data in 61 % of cases. In 711 (77 %) patients, the source of bleeding was detected endoscopically within 30–60 minutes from the moment of admission to the clinic. Conservative therapy included the administration of hemostatic and agents lowering pressure in the portal system, as well as vasoconstrictors that reduce portal blood flow. For the failure of conservative therapy, unstable hemostasis, and repeated previous bleedings, 59 (6.4 %) patients were operated. The methodology of the research analysis consisted in the fact that the arithmetic mean and standard error (M ± m) were determined as the characteristics of the groups for features with a distribution corresponding to the Gaussian law. This reference method of descriptive statistics was applied to evaluate surgical treatment in comparison with conservative therapy and interventional measures. Results. Endoscopic hemostasis by the ligature method was achieved in 97 (10.5 %) patients. Repeated endoscopy due to VB recurrence was done in 276 (29.9 %) participants. In cases of unsatisfactory endoscopic hemostasis, as well as recurrent VB, a Sengstaken-Blakemore probe was installed. In case of right-sided PH, the formation of varices in the esophagus is caused by the discharge of blood mainly through the coronary vein, which required its transection during еsophagogastric devascularization. In compliance with this rule, the patients underwent esophagogastric devascularization, circular transection of the esophagus, which provided portoazygous disconnection. The total mortality rate was 43.1 %, among operated people — 8.5 %, and among non-operated ones — 42.1 %. Conclusions. PH complicated by VB is evidence of decompensated processes in the liver parenchyma or obstruction in the portal system. Conservative therapy and interventional measures make it possible to stop bleeding and prepare the patient for elective surgery, if necessary. An operation that stops blood flow through dilated veins of the esophagus or stomach is an effective method of treating bleeding in portal hypertension.

Development of raw-image ray-bundling for focused and unfocused plenoptic 3D-PTV

This paper introduces a new plenoptic 3D particle tracking velocimetry (3D-PTV) technique called raw-image ray-bundling (RIRB), which combines the 3D clustering algorithm of light-field ray-bundling (LFRB) with the raw image segmentation concept of epipolar triangular connectivity (ETC). The combined approach provides flexibility towards accommodating both unfocused and focused plenoptic cameras. Additionally, the RIRB algorithm bypasses the computational step of perspective-view generation in LFRB, thereby allowing lower specification microlens arrays (MLA) to be used without excessive loss of measurement resolution. As part of RIRB’s development, synthetic particle-field data and experimental dot-panel targets were used to demonstrate the viability of RIRB. The latter was performed through a VEO640 high-speed camera retrofitted for plenoptic imaging with a 2.5 mm focal-length, F/10 MLA. For the synthetic dataset, RIRB demonstrated better performance than LFRB in processing low-resolution images, with mean errors below 0.85% of the field-of-view (FOV). Additionally, a synthetic Hagen-Poiseuille flow dataset was used to demonstrate the feasibility of RIRB for 3D flow velocimetry using both high and low-resolution images. For the experimental dataset, x/y errors below 0.15% of the FOV and z error below 1.0% were achieved. The combination of synthetic and experimental validations suggests RIRB is a promising approach for 3D triangulation from both focused and unfocused raw plenoptic images, overcoming the low-resolution issue inherent with perspective-view decoding in plenoptic systems.

Evaluation of Satellite-Based Rainfall Estimates against Rain Gauge Observations across Agro-Climatic Zones of Nigeria, West Africa

Satellite rainfall estimates (SREs) play a crucial role in weather monitoring, forecasting and modeling, particularly in regions where ground-based observations may be limited. This study presents a comprehensive evaluation of three commonly used SREs—African Rainfall Climatology version 2 (ARC2), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) and Tropical Application of Meteorology using SATellite data and ground-based observation (TAMSAT)— with respect to their performance in detecting rainfall patterns in Nigeria at daily scales from 2002 to 2022. Observed data obtained from the Nigeria Meteorological Agency (NiMet) are used as reference data. Evaluation metrics such as correlation coefficient, root mean square error, mean error, bias, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are employed to assess the performance of the SREs. The results show that all the SREs exhibit low bias during the major rainfall season from May to October, and the products significantly overestimate observed rainfall during the dry period from November to March in the Sahel and Savannah Zones. Similarly, over the Guinea Zone, all the products indicate overestimation in the dry season. The underperformance of SREs in dry seasons could be attributed to the rainfall retrieval algorithms, intensity of rainfall occurrence and spatial-temporal resolution. These factors could potentially lead to the accuracy of the rainfall retrieval being reduced due to intense stratiform clouds. However, all the SREs indicated better detection capabilities and less false alarms during the wet season than in dry periods. CHIRPS and TAMSAT exhibited high POD and CSI values with the least FAR across agro-climatic zones during dry periods. Generally, CHIRPS turned out to be the best SRE and, as such, would provide a useful dataset for research and operational use in Nigeria.

Intelligent optimal layout of drainage pipe network monitoring points based on information entropy theory

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

A numerical determination of complex solid gun propellant burn rates through closed bomb simulation

AbstractClosed bomb testing is a prominent means of characterizing the combustion behavior of solid gun propellants. This sub‐scale test allows the propellant to burn in a constant volume environment, where the resulting pressure‐time trace can be collected via a pressure transducer. Historically, numerical procedures have been developed to determine the burn rates of the gun propellants from these pressure‐time traces; however, no standardized procedure exists to determine the burn rates of grains with variable surface thermochemistry and ignition criteria. To address this capability gap, a non‐linearly constrained, multivariate optimization algorithm has been developed to decouple propellant grain surfaces and determine surface‐specific burn rates [1]. In this work, the optimization algorithm as well as the legacy Excel‐based Closed Bomb (XLCB) program [2] were used to determine the burn rates of homogeneous, deterred, and layered propellants from experimental data. Closed bomb simulations using these burn rates were then conducted with the two‐phase, multidimensional, interior ballistics solver, iBallistix [3]. The maximum mean error between the simulated and experimental pressure‐time curves was 6.8 % for the optimization algorithm and 23.8 % for XLCB, showing a marked improvement with our new approach. Furthermore, the approach discussed herein improves burn rate predictions of complex solid gun propellants when compared with legacy closed bomb data reduction analysis programs.

Non-Contact Blood Pressure Estimation from Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14mmHg and -0.20 ±5.50mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

Real-Time Shape Estimation of Hyper-Redundant Flexible Manipulator Using Coiled Fiber Sensors.

The shape of flexible endoscopic surgical robot should be obtained to increase control accuracy and prevent unwanted tissue damage. To estimate the shape of flexible manipulator, space efficiency, cost-effectiveness, system complexity, and ease of calibration should be considered to integrate sensors into the manipulator. In this article, we propose a real-time method to estimate the shape of a hyper-redundant manipulator having embedded coiled fiber sensors. The main advantage of this method is guaranteeing shape recognition even when the manipulator is subjected to an external load. The fiber sensors are highly flexible, compact, and inexpensive, as well as they can functionally measure both compressive and tensile strain of hyper-redundant manipulator. The sensor design was optimized to achieve durability and sensitivity. The numbers of sensor and the placements were determined by the analysis of the kinematics and moment distribution of the manipulator. The accuracy of shape estimation was validated experimentally under both free-loading and loading conditions. The proposed method achieved real-time estimating capability with a mean maximum error of each joint position smaller than 3.54% in free-loading condition and 5.47% in loading condition.

Accuracy of methods to estimate central aortic SBP via upper arm cuff: a systematic review and meta-analysis.

Central aortic BP may predict cardiovascular outcomes better than upper arm brachial BP. In recent years, technology has enabled central BP estimation by recording a peripheral BP waveform from a standard upper arm cuff. The accuracy of these devices is not well documented, and this study aimed to address this issue. This study was a systematic review, conducted according to PRISMA guidelines, of observational studies published between 2008 and 2023 that reported accuracy testing of cuff-based central BP devices, compared with reference invasive aortic BP. The primary analysis was stratified according to each commercially available device. Pooled estimates were calculated using random-effects models based on mean differences and standard errors. Six thousand four hundred and fifteen studies were screened, and 27 studies met inclusion criteria (plus one unpublished study). This generated data for seven devices that are commercially available, which were tested among 2125 adult participants. There was very high heterogeneity when all devices were pooled (I2 = 97.5%), and, when stratified by device, the accuracy of estimated central BP was highly device-dependent (range of accuracy across different devices -12.4 mmHg (-16.3 to -8.5) to 3.2 mmHg (0.2-6.1). Two of the seven commercially available devices had not undergone external validation testing. The accuracy of commercially available cuff-based central BP devices is highly device-specific and not all are accurate for the estimation of central SBP. These findings have major implications for the appropriate interpretation of studies that use cuff-based estimated central BP.

Density dependent constitutive model for Bi-2212 powder compression deformation

Bi-2212 HTS materials are fabricated into multi-filamentary wires via powder-in-tube (PIT) method followed by proper heat treatment to obtain superconductivity, but how to predict the large compression deformation behaviors of the Bi-2212 powder is critical to design the processing of the Bi-2212 HTS wire. Drucker Prager/Cap (DPC) model was the most commonly used model for powders including Bi-2212 with soil-like mechanical behavior to consider its shear failure as well as hydrostatic compression. However, the parameters for DPC Cap evolve with densities change and the original model is inadequate to precisely describe the densification process of Bi-2212 powder with large strain. In this study, the modified DPC model with density dependent parameters was introduced for Bi-2212 powder compressions by measuring the failure strength and hydrostatic compressive behavior under different density states. The DPC yield surface was plotted with an evolution trend of non-linear outward expansion with density increased. FEM model of uniaxial compression based on the as-introduced model was built with subroutine VUSDFLD applied. The distribution of Mises stress and relative density were analyzed. The axial stress-density curve for FEM and experimental results were normalized and quantitively evaluated by Mean Square Error (MSE). The introduced model shows good convergence and could match the experimental results well with normalized MSE of 0.000207 and Root Mean Square Error (RMSE) of 0.0144, indicating the mean error percentage of 1.44%. The model introduced in this article provides supports toward large strain deformation simulation of Bi-2212 powder.

Fractional cover mapping of wildland-urban interface fuels using Landsat, Sentinel 1 and PALSAR imagery

Fuels within the immediate vicinity of a house (e.g., within 30–60 m), referred to as the ‘home-ignition zone’, are important determinants of structure damage during wildfires. Methods for mapping home-ignition zone fuels using earth observing satellites are lacking, limiting the capacity to quantify the spatial and temporal dynamics of urban fuel hazard and wildfire risk. Here, we (i) develop a methodology to map the fractional cover of five common fuel types (i.e., bare, buildings, herbaceous, woody and water cover) at the scale of the home-ignition zone (45 m radius area) using Landsat, Sentinel 1, and PALSAR imagery, and (ii) demonstrate that these maps improve prediction of house loss in wildfires in western Canada. A fractional cover database was created across thirteen cities via manual digitisation of aerial photos and used to train and validate random forest models consisting of combinations of Landsat, Sentinel 1 and PALSAR indices. The best models for the fractional cover of each fuel type explained most of the variation in the data (R2 = 0.66–0.96) and had high accuracy (Mean absolute error = 0.02–0.11) with low bias (Mean error = −0.005–0.008). The combination of optical (Landsat) and synthetic aperture radar (Sentinel 1 and/or PALSAR) improved model accuracy relative to models containing only multi-spectral indices. A house loss database was then derived for five major fires in western Canada and used to examine the effect of mapped fractional cover on house loss rates for 227 neighbourhood blocks. House loss rates were greater in blocks with a low amount of defensible space (i.e., areas of low fuel hazard; bare, herbaceous, or water cover) surrounding houses compared to those with extensive defensible space. This finding is consistent with house loss research globally and exposure assessment guidelines for communities in North America. Our fuel cover mapping methodology will facilitate improvements to models of house loss through the inclusion of important home-ignition zone fuel properties, allowing for spatially and temporally dynamic (i.e., annual) estimates of house loss likelihood and wildfire risk to communities.

Investigations on application of curve fitting in power system inertia estimation

ABSTRACT As the share of renewable energy sources in the power system keeps growing, a new issue with frequency stability – diminishing and changing inertia – has emerged. Estimating power system inertia has received a lot of attention lately in order to market design of synthetic inertia as an additional service and implement timely corrective steps for robust frequency control. Curve fitting technique has emerged as one of the promising techniques for estimation of power system inertia, however, many challenging aspects need to be addressed for its practical applications. This study aims to systematically assess the effectiveness of the curve fitting method under a range of operational parameters and conditions, such as the curve fitting order, the length of data to be used for curve fitting after an event begins, and the length of the moving average filter applied to the frequency data to remove oscillatory and noise from the frequency transient. The simulation studies have been performed on DIgSILENT and MATLAB platforms. The results show that very high and very low order curve fitting results are high errors and the 5th order model with mean error in estimated value of inertia less than 1% has outperformed others. The analysis on various window width of moving average filter found that increasing the window width results in more error. For IEEE-9 bus system, a filter up to 850 ms width has a mean percentage error below 1%. The analysis also revealed that the duration of the data used for curve fitting is not significant. For variation of the duration of the data used for the curve fitting from 50 ms to 500 ms, the estimated inertia values are found to be in a close range of 2.4881 s to 2.5262 s, for the single machine system with H = 2.5 s. The analysis presented in this study will be useful for effective application of curve fitting method in inertia estimation.

Evaluating Existing Ocean Glider Sampling Strategies for Submesoscale Dynamics

Abstract Mixing in the upper ocean is important for biological production and the transfer of heat and carbon between the atmosphere and deep ocean, properties commonly targeted by observational campaigns using ocean gliders. We assess the reliability of ocean gliders to obtain a robust statistical representation of submesocale variability in the ocean mixed layer of the Weddell Sea. A 1/48° regional simulation of the Southern Ocean is sampled with virtual ‘bow-tie’ glider deployments, which are then compared against the reference model output. Sampling biases of lateral buoyancy gradients associated with the arbitrary alignment between glider paths and fronts are formally quantified, and the magnitude of the biases are comparable to observational estimates, with a mean error of 52%. The sampling bias leaves errors in the retrieved distribution of buoyancy gradients largely insensitive to deployment length and the deployment of additional gliders. Notable sensitivity to these choices emerges when the biases are removed by sampling perpendicular to fronts at all times. Detecting seasonal change in the magnitude of buoyancy gradients is sensitive to the glider-orientation sampling bias but the change in variance is not. We evaluate the impact of reducing the number of dives and climbs in an observational campaign and find small reductions in the number of dive-climb pairs have a limited effect on the results. Lastly, examining the sensitivity of the sampling bias to path orientation indicates the bias is not dependent on the direction of travel, in our deep ocean study site.

Level of Agreement in Subjective Selection of Gingival Colour

Background and Objectives: Primary outcome: To assess the level of agreement between the objective and subjective methods for recording gingival colour in each area of the gingiva. Secondary outcome: To compare performance of the subjective visual method of gingival colour selection by a male observer and a female observer. Materials and Methods: A chromatic study was conducted on a total of 101 participants, in five gingival zones, from the free gingival margin to the mucogingival line, using a SpectroShade Micro spectrophotometer for the objective method and 21 ad hoc ceramic gingival shade tabs for the subjective method. A man and a woman of the same age, with the same amount of clinical experience in dentistry, selected the tab that most resembled the colour of participants’ gingiva. The “chromatic error” was then assessed by calculating the colour difference (using the Euclidean and CIEDE2000 formulae) between the CIELAB coordinates of the shade tab selected and the objective coordinates of the gingiva. The unweighted Kappa coefficient was used to calculate the level of agreement between observers. Results: For the male observer, the mean chromatic errors varied between ΔEab* 10.3 and 13.1 units, while for the female observer, the mean errors varied between ΔEab* 11.1 and 12.8: these differences were not statistically significant. Similarly, no statistically significant differences were found between the mean chromatic errors for the five gingival zones in either the male operator (p = 0.100) or the female operator (p = 0.093). The minimum level of agreement (unweighted Kappa) between the observers ranged from 0.1 to 0.4. Conclusions: Subjective selection of gingival colour was very inaccurate, by both the male observer and the female observer, for any area of the gingiva, with no differences identified between them. The level of agreement between the observers was low. These findings suggest that gingival colour should not be determined using solely subjective methods, given that the chromatic errors significantly exceeded the clinical acceptability threshold for gingiva (4.1 units for ΔEab* and 2.9 units for ΔE00). Both observers showed a tendency to select gingival shade tabs that were redder and bluer than the objective colours.