Accurate Size Estimation Research Articles

A systematic review of sample size estimation accuracy on power in malaria cluster randomised trials measuring epidemiological outcomes

IntroductionCluster randomised trials (CRTs) are the gold standard for measuring the community-wide impacts of malaria control tools. CRTs rely on well-defined sample size estimations to detect statistically significant effects of trialled interventions, however these are often predicted poorly by triallists. Here, we review the accuracy of predicted parameters used in sample size calculations for malaria CRTs with epidemiological outcomes.MethodsWe searched for published malaria CRTs using four online databases in March 2022. Eligible trials included those with malaria-specific epidemiological outcomes which randomised at least six geographical clusters to study arms. Predicted and observed sample size parameters were extracted by reviewers for each trial. Pair-wise Spearman’s correlation coefficients (rs) were calculated to assess the correlation between predicted and observed control-arm outcome measures and effect sizes (relative percentage reductions) between arms. Among trials which retrospectively calculated an estimate of heterogeneity in cluster outcomes, we recalculated study power according to observed trial estimates.ResultsOf the 1889 records identified and screened, 108 articles were eligible and comprised of 71 malaria CRTs. Among 91.5% (65/71) of trials that included sample size calculations, most estimated cluster heterogeneity using the coefficient of variation (k) (80%, 52/65) which were often predicted without using prior data (67.7%, 44/65). Predicted control-arm prevalence moderately correlated with observed control-arm prevalence (rs: 0.44, [95%CI: 0.12,0.68], p-value < 0.05], with 61.2% (19/31) of prevalence estimates overestimated. Among the minority of trials that retrospectively calculated cluster heterogeneity (20%, 13/65), empirical values contrasted with those used in sample size estimations and often compromised study power. Observed effect sizes were often smaller than had been predicted at the sample size stage (72.9%, 51/70) and were typically higher in the first, compared to the second, year of trials. Overall, effect sizes achieved by malaria interventions tested in trials decreased between 1995 and 2021.ConclusionsStudy findings reveal sample size parameters in malaria CRTs were often inaccurate and resulted in underpowered studies. Future trials must strive to obtain more representative epidemiological sample size inputs to ensure interventions against malaria are adequately evaluated.RegistrationThis review is registered with PROSPERO (CRD42022315741).

Spatiotemporal Bayesian Machine Learning for Estimation of an Empirical Lower Bound for Probability of Detection with Applications to Stationary Wildlife Photography

An important parameter in the monitoring and surveillance systems is the probability of detection. Advanced wildlife monitoring systems rely on camera traps for stationary wildlife photography and have been broadly used for estimation of population size and density. Camera encounters are collected for estimation and management of a growing population size using spatial capture models. The accuracy of the estimated population size relies on the detection probability of the individual animals, and in turn depends on observed frequency of the animal encounters with the camera traps. Therefore, optimal coverage by the camera grid is essential for reliable estimation of the population size and density. The goal of this research is implementing a spatiotemporal Bayesian machine learning model to estimate a lower bound for probability of detection of a monitoring system. To obtain an accurate estimate of population size in this study, an empirical lower bound for probability of detection is realized considering the sensitivity of the model to the augmented sample size. The monitoring system must attain a probability of detection greater than the established empirical lower bound to achieve a pertinent estimation accuracy. It was found that for stationary wildlife photography, a camera grid with a detection probability of at least 0.3 is required for accurate estimation of the population size. A notable outcome is that a moderate probability of detection or better is required to obtain a reliable estimate of the population size using spatiotemporal machine learning. As a result, the required probability of detection is recommended when designing an automated monitoring system. The number and location of cameras in the camera grid will determine the camera coverage. Consequently, camera coverage and the individual home-range verify the probability of detection.

Prostate volume on computed tomography correlates well with magnetic resonance imaging measurements and is reproducible across rater training levels.

Data are lacking for the accuracy of computed tomography (CT) in measuring prostate size, which can streamline care and prevent invasive procedures. We evaluate agreement and intra/inter-observer variability in prostate sizing between CT and magnetic resonance imaging (MRI) planimetry for a wide range of gland sizes. We retrospectively reviewed 700 patients who underwent MRI fusion biopsy at a single institution and identified 89 patients that had a CT within 2years of the MRI. Six reviewers from different training levels were categorized as student, resident, or attending and each measured prostate size on CT by the prolate ellipse method. Bland-Altman analysis determined the degree of agreement between CT and MRI. Inter- and intra-observer reliability was calculated for CT. Mean CT volume was higher than MRI volume in the < 60g group (51.5g vs. 44.5g, p = 0.004), but not in the ≥ 60g group (101g vs. 100g, p = 0.458). The bias for overestimation of prostate volume by CT was 4.1g across prostate volumes, but the proportional agreement between modalities improved with size. The Pearson correlation coefficient between CT/MRI and inter/intra-rater reliability for CT increased in the ≥ 60g vs. the < 60g group for all training levels. Our data show that there is greater clinical utility for prostate size estimation by CT than previously established, particularly for larger glands where accurate size estimation may influence therapeutic decisions. In larger glands, prostate size estimation by CT is also reproducible across various training levels.

Factors shaping home ranges of Eurasian lynx (Lynx lynx) in the Western Carpathians

Understanding how large carnivores utilize space is crucial for management planning in human-dominated landscape and enhances the accuracy of population size estimates. However, Eurasian lynx display a large inter-population variation in the size of home ranges across their European range which makes extrapolation to broader areas of a species distribution problematic. This study evaluates variations in home range size for 35 Eurasian lynx in the Western Carpathians during 2011–2022 based on GPS telemetry and explains how intrinsic and environmental factors shape lynx spatial behaviour when facing anthropogenic pressure. The average annual home range size of lynx ranged from 283 (± 42 SE) to 360 (± 60 SE) km2 for males and from 148 (± 50 SE) to 190 (± 70 SE) km2 for females, depending on home range estimator (95% MCP, KDE and AKDE). Females with kittens had smaller annual and summer home ranges compared to non-reproducing females and subadults had smaller home ranges compared to adults. Lynx home range size was explained by availability of roe deer, except for summer, when alternative prey was likely available. We also found clear evidence of human-induced changes in lynx home range size, in particular, forest cover significantly decreased the home range size of male lynx during summer while road density led to an expansion of both annual and summer lynx home ranges. Lynx exhibited consistent fidelity to their home ranges throughout consecutive seasons, showing no seasonal variations. Strong territoriality was observed among competing males maintaining relatively low home range overlaps and considerable distances between centres of activity. The most pronounced tendency for association was observed between males and females, maintaining relatively close proximity year-round. The insights into lynx spatial requirements provided by our study will greatly enhance the accuracy of population size estimates and effectiveness of mitigation measures across the Western Carpathians.

Quantifying body size estimation accuracy and body dissatisfaction in body dysmorphic disorder using a digital avatar

BackgroundA core feature of body dysmorphic disorder (BDD) is body image disturbance. Many with BDD misperceive and are dissatisfied with the sizes and shapes of body parts, but detailed quantification and analysis of this has not yet been performed. To address this gap, we applied Somatomap 3D, a digital avatar tool, to quantify body image disturbances by assessing body size estimation (BSE) accuracy and body dissatisfaction. MethodsSixty-one adults (31 with BDD, 30 healthy controls) created avatars to reflect their perceived current body and ideal body by altering 23 body part sizes and lengths using Somatomap 3D. Physical measurements of corresponding body parts were recorded for comparison. BSE accuracy (current minus actual) and body dissatisfaction (ideal minus current) were compared between groups and in relation to BDD symptom severity using generalized estimating equations. ResultsIndividuals with BDD significantly over- and under-estimated certain body parts compared to healthy controls. Individuals with BDD overall desired significantly thinner body parts compared to healthy controls. Moreover, those with worse BSE accuracy had greater body dissatisfaction and poorer insight. ConclusionIn sum, this digital avatar tool revealed disturbances in body image in individuals with BDD that may have perceptual and cognitive/affective components.

Effects of Statistical Practices for Longitudinal Group Comparison of the Penetration-Aspiration Scale on Power and Effect Size Estimation: A Monte Carlo Simulation Study.

Multiple bolus trials are administered during clinical and research swallowing assessments to comprehensively capture an individual's swallowing function. Despite valuable information obtained from these boluses, it remains common practice to use a single bolus (e.g., the worst score) to describe the degree of dysfunction. Researchers also often collapse continuous or ordinal swallowing measures into categories, potentially exacerbating information loss. These practices may adversely affect statistical power to detect and estimate smaller, yet potentially meaningful, treatment effects. This study sought to examine the impact of aggregating and categorizing penetration-aspiration scale (PAS) scores on statistical power and effect size estimates. We used a Monte Carlo approach to simulate three hypothetical within-subject treatment studies in Parkinson's disease and head and neck cancer across a range of data characteristics (e.g., sample size, number of bolus trials, variability). Different statistical models (aggregated or multilevel) as well as various PAS reduction approaches (i.e., types of categorizations) were performed to examine their impact on power and the accuracy of effect size estimates. Across all scenarios, multilevel models demonstrated higher statistical power to detect group-level longitudinal change and more accurate estimates compared to aggregated (worst score) models. Categorizing PAS scores also reduced power and biased effect size estimates compared to an ordinal approach, though this depended on the type of categorization and baseline PAS distribution. Multilevel models should be considered as a more robust approach for the statistical analysis of multiple boluses administered in standardized swallowing protocols due to its high sensitivity and accuracy to compare group-level changes in swallowing function. Importantly, this finding appears to be consistent across patient populations with distinct pathophysiology (i.e., PD and HNC) and patterns of airway invasion. The decision to categorize a continuous or ordinal outcome should be grounded in the clinical or research question with recognition that scale reduction may negatively affect the quality of statistical inferences in certain scenarios.

Optimizing Mean Fragment Size Prediction in Rock Blasting: A Synergistic Approach Combining Clustering, Hyperparameter Tuning, and Data Augmentation

Accurate estimation of the mean fragment size is crucial for optimizing open-pit mining operations. This study presents an approach that combines clustering, hyperparameter optimization, and data augmentation to enhance prediction accuracy using the Xtreme Gradient Boosting (XGBoost) regression model. A dataset of 110 blasts was divided into 97 blasts for training and testing, whereas a separate set of 13 new, unseen blasts was used to evaluate the robustness and generalization of the model. Hierarchical Agglomerative (HA) and K-means clustering algorithms were used, with HA clustering providing a higher cluster quality. To address class imbalance and improve model generalization, a synthetic minority oversampling technique for regression with Gaussian noise (SMOGN) was employed. Hyperparameter tuning was conducted using HyperOpt by comparing Random Search (RS) with the Advanced Tree-structured Parzen Estimator (ATPE). The combination of ATPE with HA clustering and SMOGN in an expanded search space produced the best results, achieving superior prediction accuracy and reliability. The proposed HAC1-SMOGN model, which integrates HA clustering, ATPE tuning, and SMOGN augmentation, achieved a mean squared error (MSE) of 0.0002 and an R2 of 0.98 on the test set. This study highlights the synergistic benefits of clustering, hyperparameter optimization, and data augmentation in enhancing machine learning models for regression tasks, particularly in scenarios with class imbalance or limited data.

An autonomous robotic arm for efficient rock collection in uncharted territories

The autonomous rock collector using robotic arm for exploration of unknown territories (ARCAxUT) is introduced as an innovative solution for the efficient retrieval of rock samples in unexplored space regions. Traditional, human-reliant methods are costly and hazardous, prompting the development of ARCAxUT. Equipped with a smart robotic arm, an RGB-D camera, and NUC computer, the system autonomously detects and estimates the mass of various rock samples. Validated in simulated and real-world environments, the algorithm ensures precise gripper control, achieving an impressive 95.4% accuracy in rock size estimation. This breakthrough offers transformative capabilities for space missions, revolutionizing celestial body sample collection and advancing broader societal implications in space exploration technologies.

Cross-sectional analysis for lymphedema epidemiology in South Korea by HIRA data: An observational study.

Lymphedema is known to affect as many as 140 to 250 million people worldwide. Approximately 99% of lymphedemas are secondary cases after lymphadenectomy, radiation, or parasite infection. There has been no accurate estimate of the lymphedema population size in South Korea. This study aimed to quantify the epidemiology of lymphedema in South Korea and analyze the clinical characteristics of patients with lymphedema using a nationwide database. This large-scale cross-sectional study included patients who received one of the following three diagnostic codes - Q82.0 (hereditary lymphedema), I97.2 (breast cancer-related lymphedema), and I89.0 (unclassified lymphedema) from the Korean Health Insurance Review and Assessment service between January 2019 and July 2022 at tertiary hospitals, general hospitals, or other hospitals. The demographics of the lymphedema population, medication use, and medical characteristics were identified. Over the last 4 years, the annual incidence of lymphedema has steadily increased and peaked in 2021 with the number 1.85 out of 1000. Of all patients, 81% were located in the upper extremity, and 10.6% had previous cancer. Most patients were diagnosed in general hospitals (53.2%), at orthopedic surgery department (41.9%). On average, lymphedema patients spent 70.17 US dollar for medication and hospitalized for 16.9 days annually after diagnosis. Commonly prescribed medications were Entelon® (Vitis vinifera seed extract) (66.9%), steroids (40.5%), non-steroidal anti-inflammatory drugs (26.5%) and diuretics (21.8%). Only 5.7% of patients received surgery and 19.2% received herbal medicine. This is the first study in South Korea to quantify demographic and medical characteristics of lymphedema patients. These results will contribute to a comprehensive understanding of lymphedema diagnosis and treatment in our nation.

A Segmentation-based Automated Calculation of Patient Size and Size-specific Dose Estimates in Pediatric Computed Tomography Scans

Background and Purpose: Size-specific dose estimates (SSDE) have been introduced into computed tomography (CT) dosimetry to tailor patients’ unique sizes to facilitate accurate CT radiation dose quantification and optimization. The purpose of this study was to develop and validate an automated algorithm for the determination of patient size (effective diameter) and SSDE. Materials and Methods: A MATLAB platform was used to develop software of algorithms based on image segmentation techniques to automate the calculation of patient size and SSDE. The algorithm was used to automatically estimate the individual size and SSDE of four CT dose index phantoms and 80 CT images of pediatric patients comprising head, thorax, and abdomen scans. For validation, the American Association of Physicists in Medicine (AAPM) manual methods were used to determine the patient’s size and SSDE for the same subjects. The accuracy of the proposed algorithm in size and SSDE calculation was evaluated for agreement with the AAPM’s estimations (manual) using Bland–Altman’s agreement and Pearson’s correlation coefficient. The normalized error, system bias, and limits of agreement (LOA) between methods were derived. Results: The results demonstrated good agreement and accuracy between the automated and AAPM’s patient size estimations with an error rate of 1.9% and 0.27% on the patient and phantoms study, respectively. A 1% percentage difference was found between the automated and manual (AAPM) SSDE estimates. A strong degree of correlation was seen with a narrow LOA between methods for clinical study (r &gt; 0.9771) and phantom study (r &gt; 0.9999). Conclusion: The proposed automated algorithm provides an accurate estimation of patient size and SSDE with negligible error after validation.

Efficacy of Reusable Rubber Moulage on Mannequin Versus Conventional Method for Burn Size Estimation

Background: Burn size estimation is crucial to burn fluid resuscitation. Therefore, training and practice to improve the accuracy of burn size estimation is important. Using reusable rubber moulage (RRM), the newly developed equipment to cooperating with simulation might help improve the accuracy of burn size estimation compared to conventional picture illustration. Objectives: To compare the accuracy of burn size estimation in simulation between picture illustration and RRM application, and to validate the face and content validity of RRM. Methods: Medical students, ER residents, EP staff, and paramedics were recruited and randomized into 2 groups. Each group completed 2 different burn pattern stations consisting of one RRM-decorated mannequin station and one picture illustration station. The estimation of total body surface area (TBSA) percentage was statistically analyzed to determine the difference between the 2 methods. Face and content validation were analyzed by a Likert scale. Results: A total of 70 participants were recruited. The means of total %TBSA of pattern 1 from RRM and picture illustration were 42.29% and 41.24% (P = .61), respectively. The means of pattern 2%TBSA estimation were 41.24% and 42.65% (P = .34), respectively. Participants rated RRM quality by Likert scale with means score more than 4 out of 5 in all questions. Written comments showed a preference toward RRM. Conclusions: There were no significant %TBSA differences between RRM decorated mannequin and picture illustration in terms of %TBSA. However, participants were satisfied with RRM quality and preferred RRM decorated mannequin.

Do people need guidance to estimate a food portion size? Evidence from an exploratory eye-tracker study

A promising strategy to encourage portion control involves clear guidance and portion information on product packaging alongside nutrition details. However, literature on the impact of this information on serving sizes is conflicting. To gather more objective evidence about how consumers interact with on-pack portion guidance, we conducted a laboratory-based eye-tracker study with 66 participants completing two portion-size estimation tasks. In the first, participants estimated serving sizes indirectly by gauging how many people a product pack could serve. The second task involved direct portion size selection using pictorial representations of the food portion on a virtual plate. Additionally, we explored how participants’ familiarity with products affected their portion estimations. Results: when portion guidance was presented, errors in portion estimation significantly decreased only in the indirect task, where 85% of participants showed an improved accuracy. This suggests that the way individuals are prompted to estimate portion sizes influences their approach and their reliance on the provided guidance. Higher error rates occurred when participants were more familiar with the products. For less familiar products, the lower portion estimation errors were associated with the longer fixations on the portion graphic in the indirect task. In addition, participants who quickly noticed the portion graphic provided more accurate portion size estimates. This emphasizes the practical importance of swiftly locating portion information on-pack to enhance estimation accuracy.

SED-RCNN-BE: A SE-Dual channel RCNN network optimized binocular estimation model for automatic size estimation of free swimming fish in aquaculture

Size of fish body is one of the most important morphological characteristics for cultured fish and plays a key role to enable effective management in aquaculture. Automatic, accurate and real-time monitoring of fish body size is therefore essential for improving aquaculture production. Computer vision facilitated the non-contact estimation of fish size and reduce labor consumption. While, current computer vision estimation techniques are still restricted in multi-scale size and multi-pose-variant of free-swimming fish, which limit the relevance application to aquaculture. Hence, we propose a novel method to facilitate automatic and accurate size estimation of free-swimming fish for growth monitoring of fish in aquaculture. This method comprised of an improved keypoint detection module based on Keypoint RCNN (SE-D-KP-RCNN) network and size estimation module based on CREStereo matching and spatial plane curve fitting. In the detection module, a squeeze and excitation Dual channel pooling layer (SE-D) is designed for the backbone network to improve the keypoint detection accuracy for free-swimming fish. In the estimation module, the global parallax map of binocular image is produced by CREStereo matching to obtain the 3D information of detection keypoint and build curve fitting of multi-pose variant fish body for height and length estimation. We implement and test our proposed method in different sizes and poses fish and the comprehensive experimental results show that the average accuracy of size estimation can reached 93.18% in the real aquaculture scene. This is important to help breeders to obtain underwater fish size information accurately and improve the efficiency for aquaculture management.

Fast and Accurate Pupil Estimation Through Semantic Segmentation Fine-Tuning on a Shallow Convolutional Backbone

In the diverse realms of computer vision, psychology, biometrics, medicine, and robotics, the accurate estimation of pupil size and position holds paramount importance for applications like eye tracking, medical diagnostics, and facial recognition. Traditional pupil estimation techniques often grapple with speed and error issues, impeding their applicability in real-world scenarios. To address this challenge, our study introduces an innovative approach that significantly enhances both the speed and accuracy of pupil estimation. This method hinges on the fine-tuning of a pre-trained semantic segmentation model integrated with a shallow convolutional neural network (CNN) backbone. Our methodology employs a dual-phase process: initially leveraging a robust pre-trained semantic segmentation model, subsequently refined through targeted fine-tuning using a diverse collection of eye images. This process intricately learns pupil characteristics, substantially elevating detection precision. The incorporation of a shallow CNN backbone streamlines the model, ensuring rapid processing suitable for real-time applications. The novelty of our approach lies in its adept handling of varying lighting and camera conditions, establishing new benchmarks in both speed and accuracy, as evidenced by our experimental findings. This advancement marks a significant leap in pupil estimation technology, offering a practical, efficient solution with far-reaching implications in several key technological domains. Doi: 10.28991/HIJ-2024-05-02-016 Full Text: PDF

Local impedance drop as a predictor of sucessful pulmonary vein isolation

Abstract 1. Introduction Pulmonary vein isolation (PVI) is the cornerstone of AF treatment. An accurate estimation of the lesion size and a optimal evaluation of the catheter-myocardium coupling is crucial for achieving an effective and safe radiofrequency (RF) ablation. A few studies have suggested that local impedance (LI) drop is a better predictor than general impedance drop in terms of lesion size, electrical coupling and therefore acute PVI success. An open-irrigated ablation catheter designed to measure contact force (CF) and LI drop while applying RF has been recently developed. 2. Purpose The aim of this study is to evaluate if conduction GAPS are located in certain areas and if LI drop can predict the presence of conduction gaps in each of these segments. 3. Methods Observational prospective study including 55 consecutive patients who underwent PVI. The RF lesions were delivered with 30 W (posterior wall) or 35 W (anterior wall) for a maximum of 30 seg with a CF &amp;gt; 5 g. The applications were stopped if a LI of 35 Ohm was reached on the posterior wall or 40 Ohm on the anterior wall. PVs were divided into seven segments and ablation tags were assigned to a segment according to their anatomical position (figure 1) We categorized the tags included in each segment as successful or not according to the presence of conduction gaps detected with an Ultrahigh density mapping catheter once the WACA was completed. At the same time lesion by lesion were studied regardless of the segment where it was located. 4. Results We analyzed 4002 lesion tags of 55 patients who underwent PVI. 53 Gaps were found in 19/55 (34.5%) left WACA and 16/54 (29,6%) right WACA and were localized predominantly in three PVI segments (L1, L6 and R3) 34(54.85%) vs 19 (35,8%) (L2-L5, L7, R1-R2, R4-R7) (p&amp;lt;0.001) (figure 1). They were mainly circumscribed to anterior segments in left PVs and posterior-superior segment in right PVs (figure1). When examining lesions independently of their location we found a significant difference in LI drop among successful and unsuccessful lesions (24.22±8.31 vs 15.67±7.85) Ohm, p&amp;lt;0.001). In the same line when studying lesions in those segments with &amp;gt; 10% of GAPS (L1, L6 R1 and R3) LI drop was greater in successful lesion tags: L1 25.2 ± 5.35 vs 16.8 ± 5.17 (p&amp;lt;0.001), L6 25.2±5.7 vs 16±5.48 (p&amp;lt;0.001), R1 27.9±4.78 vs 17.1±5.73 (p=0.03) and R3 18.5±5.4 vs 10.7±2.49 (p&amp;lt;0.001). 5. Conclusions Gaps were located predominantly in anterior segments in left PVs and posterior-superior segment in right PVs. A low LI drop was an excellent predictor of conduction gaps, and it performs well regardless of the segment where it was analyzedFig. 1

Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation

Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model.

Estimating distribution and abundance of wide-ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south-central Italy.

Estimating demographic parameters for wide-ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south-central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7-month sampling campaign in 2020-2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non-invasive genetic samples (NGS). The model comprised (i) a single-season, multiple data-source, multi-event occupancy model and (ii) a spatially explicit capture-recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation-based assessment, to estimate the best conditions for optimizing sub-sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south-central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence-absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide-ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.

FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.

Accuracy of Event Rate and Effect Size Estimation in Major Cardiovascular Trials

For the design of a randomized clinical trial (RCT), estimation of the expected event rate and effect size of an intervention is needed to calculate the sample size. Overestimation may lead to an underpowered trial. To evaluate the accuracy of published estimates of event rate and effect size in contemporary cardiovascular RCTs. A systematic search was conducted in MEDLINE for multicenter cardiovascular RCTs associated with MeSH (Medical Subject Headings) terms for cardiovascular diseases published in the New England Journal of Medicine, JAMA, or the Lancet between January 1, 2010, and December 31, 2019. Identified trials underwent abstract review; eligible trials then underwent full review, and those with insufficiently reported data were excluded. Data were extracted from the original publication or the study protocol, and a random-effects model was used for data pooling. This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. The primary outcome was the accuracy of event rate and effect size estimation. Accuracy was determined by comparing the observed event rate in the control group and the effect size with their hypothesized values. Linear regression was used to determine the association between estimation accuracy and trial characteristics. Of the 873 RCTs identified, 374 underwent full review and 30 were subsequently excluded, resulting in 344 trials for analysis. The median observed event rate was 9.0% (IQR, 4.3% to 21.4%), which was significantly lower than the estimated event rate of 11.0% (IQR, 6.0% to 25.0%) with a median deviation of -12.3% (95% CI, -16.4% to -5.6%; P < .001). More than half of the trials (196 [61.1%]) overestimated the expected event rate. Accuracy of event rate estimation was associated with a higher likelihood of refuting the null hypothesis (0.13 [95% CI, 0.01 to 0.25]; P = .03). The median observed effect size in superiority trials was 0.91 (IQR, 0.74 to 0.99), which was significantly lower than the estimated effect size of 0.72 (IQR, 0.60 to 0.80), indicating a median overestimation of 23.1% (95% CI, 17.9% to 28.3%). A total of 216 trials (82.1%) overestimated the effect size. In this systematic review of contemporary cardiovascular RCTs, event rates of the primary end point and effect sizes of an intervention were frequently overestimated. This overestimation may have contributed to the inability to adequately test the trial hypothesis.

Average grain size evaluation using scattering-induced attenuation of coda waves

Grain size is one of the key microstructural factors affecting the mechanical properties of polycrystalline metal materials. In this study, a novel method for grain size evaluation using ultrasonic coda waves is proposed. Different from conventional bulk wave methods that require a point-by-point scanning of the structure, the proposed method allows for a rapid evaluation of the average grain size of the whole part from a single inspection location using one-pass testing data. A piecewise energy attenuation function dealing with different attenuation mechanisms is proposed to obtain the effective attenuation coefficient of coda waves. A power-law model is constructed to correlate the effective attenuation coefficient with the average grain size. Ultrasonic testing on nickel-based superalloy plate specimens with different average grain sizes is performed for model calibration and method verification. The applicability and robustness of the proposed method are further validated using a realistic turbine disk specimen with an irregular shape and non-uniform grain sizes. Results show that the proposed method yields a reliable and accurate estimation of the average grain size with a maximum relative error less than 20 %.