Size Estimation Research Articles

Interaction-focused music therapy with cancer-affected children and their significant others: a randomized controlled feasibility study with subsequent intervention (INMUT)

BackgroundPaediatric oncology/haematology patients and their families are confronted with a life-threatening situation for which music therapy can be a cross-linguistic field of action. The creative act of making music together offers the possibility to strengthen competences and make conflicts tangible. Besides its complementing of evidence-based biomedical care, there is little research on the feasibility and efficacy of interactive music therapy including the diagnosed child and their significant others.MethodsWe conducted an assessor blind, prospective, multicentric feasibility randomized controlled trial (RCT) with subsequent intervention. Including overall 52 child-significant other dyads, INMUT investigates interaction-focused music therapy with cancer-affected children and their significant others (INMUT-KB; n = 21) compared to music therapy only with the child (MUT-K; n = 21) and a wait-list group (WLG; n = 10). The measurement points include the screening for a cancer diagnosis, psychometric baseline (pre-T1), initial assessment (T1/T2), music therapy sessions (T3–T9), final assessment (T10), final psychometric evaluation (post-T10), and 3-month follow-up (cat-T11). Feasibility and acceptability of the (1) research methodology, (2) intervention and (3) estimation of effect sizes will be assessed using qualitative and quantitative data. The proposed primary outcome includes the parent–child interaction (APCI), and the proposed secondary outcomes refer to subjective goal achievement (GAS), quality of life (KINDL), system-related functional level (EXIS), psychosocial stress (BAS), psychosomatic complaints (SCL-9k), and resources (WIRF). We plan to investigate the efficacy of INMUT-KB and MUT-K post-intervention (post-T10) within the RCT design and at 3-month follow-up (cat-T11).DiscussionThis study will provide insights into the feasibility of INMUT and the final sample needed for a confirmatory RCT. We will reflect on successfully implemented study procedures and, if necessary, provide recommendations for changes considering the design, procedures, measures, and statistical analyses. The discussion will conclude with an evaluation whether a confirmatory RCT is worth the investment of future resources, including the calculated number of child-significant other dyads needed based on the efficacy trends derived from this feasibility study.Trial registrationClinicalTrials.gov: NCT05534282; date of registration: June 23, 2022.

Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R : Power simulation for SEMs.

Determining the compositional structure and dimensionality of psychological constructs lies at the heart of many research questions in developmental science. Structural equation modeling (SEM) provides a versatile framework for formalizing and estimating the relationships among multiple latent constructs. While the flexibility of SEM can accommodate many complex assumptions on the underlying structure of psychological constructs, it makes a priori estimation of statistical power and required sample size challenging. This difficulty is magnified when comparing non-nested SEMs, which prevents the use of traditional likelihood-ratio tests. Sample size estimates for SEM model fit comparisons typically rely on generic rules of thumb. Such heuristics can be misleading because statistical power in SEM depends on a variety of model properties. Here, we demonstrate a Monte Carlo simulation approach for estimating a priori statistical power for model selection when comparing non-nested models in an SEM framework. We provide a step-by-step guide to this approach based on an example from our memory development research in children.

Effectiveness of a chatbot in improving the mental wellbeing of health workers in Malawi during the COVID-19 pandemic: A randomized, controlled trial.

We conducted a randomized, controlled trial (RCT) to investigate our hypothesis that the interactive chatbot, Vitalk, is more effective in improving mental wellbeing and resilience outcomes of health workers in Malawi than the passive use of Internet resources. For our 2-arm, 8-week, parallel RCT (ISRCTN Registry: trial ID ISRCTN16378480), we recruited participants from 8 professional cadres from public and private healthcare facilities. The treatment arm used Vitalk; the control arm received links to Internet resources. The research team was blinded to the assignment. Of 1,584 participants randomly assigned to the treatment and control arms, 215 participants in the treatment and 296 in the control group completed baseline and endline anxiety assessments. Six assessments provided outcome measures for: anxiety (GAD-7); depression (PHQ-9); burnout (OLBI); loneliness (ULCA); resilience (RS-14); and resilience-building activities. We analyzed effectiveness using mixed-effects linear models, effect size estimates, and reliable change in risk levels. Results support our hypothesis. Difference-in-differences estimators showed that Vitalk reduced: depression (-0.68 [95% CI -1.15 to -0.21]); anxiety (-0.44 [95% CI -0.88 to 0.01]); and burnout (-0.58 [95% CI -1.32 to 0.15]). Changes in resilience (1.47 [95% CI 0.05 to 2.88]) and resilience-building activities (1.22 [95% CI 0.56 to 1.87]) were significantly greater in the treatment group. Our RCT produced a medium effect size for the treatment and a small effect size for the control group. This is the first RCT of a mental health app for healthcare workers during the COVID-19 pandemic in Southern Africa combining multiple mental wellbeing outcomes and measuring resilience and resilience-building activities. A substantial number of participants could have benefited from mental health support (1 in 8 reported anxiety and depression; 3 in 4 suffered burnout; and 1 in 4 had low resilience). Such help is not readily available in Malawi. Vitalk has the potential to fill this gap.

Optimizing Individual HIV Testing and Counseling for Emerging Adult Sexual Minority Men (Aged 18 to 24) in Relationships: A Pilot Randomized Controlled Trial of Adjunct Communication Components.

The majority of new HIV infections in the US occur among sexual minority men (SMM) with older adolescent and emerging adult SMM at the highest risk. Those in relationships face unique HIV prevention challenges. Existing sexual HIV transmission risk interventions for male couples often encounter implementation challenges and engaging younger SMM early in relationships may be particularly difficult. This pilot randomized controlled trial evaluated the acceptibility and feasibility of We Test HIV testing - a behavioral health intervention tailored for younger SMM in realtionships - and generated preliminary estimates of effect size. The intervention comprises two adjunct moduls - video-based communication skills training as well as communication goal setting and planning - delivered in conjunction with routine HIV testing and counseling in individual or dyadic formats. A sample of 69 SMM aged 17 to 24 were recruited online. Following baseline assessment, youth were randomized to receive either the experimental, We Test, intervention or routine HIV testing (the control condition). Follow-up assessments were completed 3 and 6 months post-baseline. Results suggested the study was feasible and the individually delivered format was acceptible. We Test HIV testing was associated with significant improvements in communication skills. In addition, youth who remained in a relationship experienced an increase in communal coping to reduce HIV infection risk and relationship power. While groups did not differ with respect to condomless anal sex with casual partners, these psycho-social constructs (communication, communal coping with HIV prevention, and relationship power) may serve as mediators of intervention effects on sexual risk reduction in a larger study.

Investigation of Sample Size Estimation for Measuring Quantitative Characteristics in DUS Testing of Shiitake Mushrooms

Investigation of Sample Size Estimation for Measuring Quantitative Characteristics in DUS Testing of Shiitake Mushrooms

Banana Bunch Weight Estimation and Stalk Central Point Localization in Banana Orchards Based on RGB-D Images

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots.

Enhanced versus standard hydration in acute ischaemic stroke: REVIVE - a randomised clinical trial.

Early neurological deterioration (END) within 72 hours of stroke onset is associated with poor prognosis. Optimising hydration might reduce the risk of END. To determine in acute ischaemic stroke patients if enhanced hydration versus standard hydration reduced the incidence of major (primary) and minor (secondary) END, as whether it increased the incidence of early neurological improvement (secondary), at 72 hours after admissionSample Size Estimate: 244 participants per arm. A prospective, double-blinded, multicentre, parallel-group, randomised controlled trial conducted at 4 hospitals from April 2014 to July 2020, with data analysed in August 2020. The sample size estimated was 488 participants (244 per arm). Ischaemic stroke patients with measurable neurological deficits of onset within 12 hours of emergency department presentation and blood urea nitrogen/creatinine (BUN/Cr) ratio ≥15 at point of admission were enrolled and randomised to 0.9% sodium chloride infusions of varying rates - enhanced hydration (20 mL/kg body weight, one-third given via bolus and remainder over 8 hours) versus standard hydration (60 mL/hour for 8 hours), followed by maintenance infusion of 40-80 mL/hour for the subsequent 64 hours. The primary outcome measure was the incidence of major early neurological deterioration at 72 hours after admission, defined as an increase in National Institutes of Health Stroke Scale of ≥4 points from baseline. 487 participants were randomised (median age 67 years; 287 females). At 72 hours: 7 (2.9%) in the enhanced-hydration arm and 5(2.0%) in the standard-hydration developed major early neurological deterioration (p=0.54). The incidence of minor early neurological deterioration and early neurological improvement did not differ between treatment arms. Enhanced hydration ratio did not reduce END or improve short term outcomes in acute ischaemic stroke. ClinicalTrials.gov (NCT02099383, https://clinicaltrials.gov/study/NCT02099383).

Low-coverage sequencing and Wahlund effect severely bias estimates of inbreeding, heterozygosity and effective population size in North American wolves.

vonHoldt etal. ((2024), Molecular Ecology, 33, e17231) (vH24) used low-coverage (average ~ 7X read depth) restriction site-associated DNA sequence data to estimate individual inbreeding and heterozygosity, and recent effective population size (Ne), in Great Lakes (GL) and Northern Rocky Mountain (RM) wolves. They concluded that RM heterozygosity rapidly declined between 1991 and 2020, and that Ne declined substantially in GL and RM over the last 50 generations. Here, we evaluate the effects of low sequence coverage and sampling strategy on vH24's findings and provide general recommendations for using sequence data to evaluate inbreeding, heterozygosity and Ne. Low-coverage sequencing resulted in downwardly biased estimates of individual inbreeding and heterozygosity, and an erroneous large temporal decline in RM heterozygosity due to declining read depth through time. Additionally, vH24's sampling strategy-which combined individuals from several genetically differentiated populations and across at least eight wolf generations-is expected to have resulted in severe downward bias in estimates of recent Ne for RM. We recommend using high-coverage sequence data ( 15-20X) to estimate inbreeding and heterozygosity. Carefully filtering individuals, loci and genotypes, and using genotype imputation or likelihoods can help to minimise bias when low-coverage sequence data must be used. For estimation of contemporary Ne, the marginal benefits of using more than 103-104 loci are small, so aggressive filtering of loci with low average read depth potentially can retain most individuals without sacrificing much precision. Individuals are relatively more valuable than loci because analyses of contemporary Ne should focus on roughly single-generation samples from local breeding populations.

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 > 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<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<0.001). In the same line when studying lesions in those segments with > 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<0.001), L6 25.2±5.7 vs 16±5.48 (p<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<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

Perception of optical illusions in ungulates: insights from goats, sheep, guanacos and llamas

Optical illusions have long been used in behavioural studies to investigate the perceptual mechanisms underlying vision in animals. So far, three studies have focused on ungulates, providing evidence that they may be susceptible to some optical illusions, in a way similar to humans. Here, we used two food-choice tasks to study susceptibility to the Müller-Lyer and Delboeuf illusions in 17 captive individuals belonging to four ungulate species (Lama guanicoe, Lama glama, Ovis aries, Capra hircus). At the group level, there was a significant preference for the longer/larger food over the shorter/smaller one in control trials. Additionally, the whole group significantly preferred the food stick between two inward arrowheads over an identical one between two outward arrowheads in experimental trials of the Müller-Lyer task, and also preferred the food on the smaller circle over an identical one on the larger circle in the experimental trials of the Delboeuf task. Group-level analyses further showed no significant differences across species, although at the individual level we found significant variation in performance. Our findings suggest that, in line with our predictions, ungulates are overall susceptible to the Müller-Lyer and the Delboeuf illusions, and indicate that the perceptual mechanisms underlying size estimation in artiodactyls might be similar to those of other species, including humans.

IoT Video Delivery Optimization Through Machine Learning-Based Frame Resolution Adjustment

Providing acceptable video quality in the Internet of Things (IoT) implementation poses a significant challenge, mainly when the application is performed on low-cost and low-power devices. This research focuses on developing a frame resolution adjustment system that maintains the frame rate value of video delivery in wireless IoT environments with resource-constrained devices. Consistent frame rates prevent motion lag and data loss, improving user experience. The system works by predicting the upcoming throughput values using machine learning methods to adjust the sensing parameter, which is the resolution of the video frame to be captured by camera nodes. Hence, the proposed system is equipped with a file size estimator to estimate the size of the next video frame and then adjust the resolution in accordance with the throughput prediction. In this research, we conducted extensive experiments to evaluate the accuracy of the file size estimator and the throughput prediction. The experiment generated a dataset to evaluate throughput prediction and file size estimator model. The evaluation results for the file size estimator showed a Mean Absolute Percentage Error (MAPE) of 6.73% in the experiment using 317 frames with video resolutions between 72p and 720p. Experiments were also conducted to compare several machine learning methods for predicting throughput values. Compared to Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA), Simple Exponential Smoothing (SES) outperforms the others with the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Building upon these findings, we implemented the frame resolution adjustment system using SES as the method for predicting the upcoming throughput values. Finally, we demonstrated that the proposed system can maintain the frame rate according to the threshold set by the system while the resolution is being maximized, thereby addressing the challenges of maintaining video quality in resource-constrained IoT environments.

Concordance Rate of Pre-Operative Radiological Stage with Postoperative Pathological Stage in Colon Cancer

Aim: This study aimed to assess the concordance rate between pre-operative radiological staging using CT imaging and postoperative pathological staging in patients with colon cancer. Methodology: A prospective observational study was conducted at the Oncology Department of Jinnah Postgraduate Medical Center, Pakistan, with ethical committee approval between February 2023 to July 2023. Inclusion criteria involved patients diagnosed with colon adenocarcinoma aged >18 receiving treatment at the center, while exclusion criteria comprised unconfirmed colon cancer, lack of consent, benign colonic polyps, metastatic cancer, chemotherapy recipients, and incomplete data. Sample size estimation yielded 227 patients. Recruitment used consecutive sampling, and data were collected using a predefined proforma. CT scans were performed, and T-stage was assessed by radiologists and pathologists. Histological analysis followed established guidelines, with final pathology serving as the gold standard. Results: The diagnostic accuracy of CT imaging was evaluated, with statistically significant concordance found between CT scans and histopathological diagnoses (p-value = 0.046). CT scans demonstrated a sensitivity of 57.14% for Stage I tumors and a specificity of 88.18% for Stage II-III tumors. The positive predictive value (PPV) was 13.3%, and the negative predictive value (NPV) was 98.48%, resulting in an overall accuracy of 87.22%. These findings suggest that CT imaging is valuable for identifying Stage II-III tumors, exhibiting good specificity and NPV, although sensitivity and PPV for Stage I tumors were comparatively lower. Chi-square testing confirmed the statistical significance of these results (p-value ≤ 0.05) Conclusion: This study highlights the utility of CT imaging in pre-operative staging of colon cancer, particularly for Stage II-III tumors, where it exhibits notable accuracy. However, improvements may be needed to enhance sensitivity and PPV for Stage I tumors. Keywords: Colon cancer, CT imaging, pathological staging, concordance rate, diagnostic accuracy.

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.

Intervention to increase colorectal cancer screening among emergency department patients: results from a randomised pilot study

BackgroundColorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Emergency departments (ED) represent a promising setting to address preventive health measures like CRC screening.ObjectivesThe current study adapted...

Green synthesis of CuO nanoparticles using Ligustrum lucidum extract, and the antioxidant and antifungal evaluation

Abstract Biosynthesis of metal oxide nanoparticles using plant extract is an inexpensive, simple, rapid, and environmentally friendly approach to obtaining nanoparticles for biological applications. Herein, copper oxide nanoparticles (CuO-NPs) were successfully synthesized using an aqueous extract from Ligustrum lucidum leaves. The structural, optical, and morphological characteristics of the nanoparticles were assessed using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, UV-visible spectrophotometer, transmission and scanning electron microscopy (TEM and SEM), and energy-dispersive X-ray (EDX). Nanocrystalline CuO with an average crystalline size of 22.0 nm and a band gap energy of 1.4 eV were confirmed from the XRD and UV vis spectrophotometer, respectively. Morphological studies showed spherical nanoparticles, whose particle size estimation (30±5 nm) agrees with the crystalline size deduced from the XRD pattern. A free radical scavenging activity of the CuO nnanoparticles, evaluated using the 1, 1-diphenhyl-2-picrylhydrazyl (DPPH) assay, showed that it exhibited high antioxidant activity (IC50: 63.35 µg/mL) that is concentration dependent. Antifungal evaluation using four different fungal strains (Aspergillus flavus, Aspergillus niger, Fusarium oxysporum, and Trichoderma harzianum) indicated a direct relationship between the potency of the particles and their concentration, with 1 ppm solution exhibiting the highest potency. The green synthesized CuO-NPs using Ligustrum lucidum may be potentially used as an antioxidant and antifungal agent for therapeutic applications.

Green synthesized silver nanoparticles in two stages: Box Behnken Design to machine learning

In order to solve the modeling issues due to data scarcity problems in the disciplines utilizing statistical approximations, a novel two-stage idea is proposed. As a use case, nanoparticle biosynthesis was selected, for which an environmentally friendly process is of vital importance. First, Box Behnken Design was used for experimental setup, quadratic model formulation and data generation. The second stage consists of Machine Learning, in which the data generated in the previous stage were fed into a Neural Network to determine the relationship between the parameters. Obtained results showed that the proposed combined strategy provided better nanoparticle size estimations than the statistical approach alone. In the absence of publicly available databases, data generation using experimental design and machine learning, as proposed here, could be a faster, lower-cost, and greener solution. Our proposed method can be applied to a wide range of biotechnology and bioengineering applications with significant advanced knowledge.

Improving Supercooled Water Forecast Through Real‐Time Aerosol Input: A Case Study

AbstractAccurately simulating and forecasting supercooled water has been crucial for addressing the threat of aircraft icing. This study utilized the Weather Research and Forecast model and Aerosol‐Aware Thompson‐Eidhammer microphysics (TE14) scheme to simulate supercooled water properties. Three high‐resolution experiments were conducted to explore the impact of increased model resolution and different aerosol initial conditions. The synoptic‐scale characteristics and cloud extent were well‐matched between observations and the model simulation, which established a background for supercooled water comparisons. Microphysical characteristics of supercooled droplets and the formation of supercooled large drops demonstrated the TE14 scheme's reasonable simulation of supercooled water microphysics under different aerosol loadings. In situ aircraft measurements were used to validate the simulated supercooled water properties. Results highlighted the sensitivity of supercooled water simulation to aerosol number concentration via droplet activation in the scheme. The choice for the aerosol initial condition could result in a significant underestimation or overestimation of supercooled water number concentration, consequently leading to biased estimations of droplet size. Incorporating real‐time forecast aerosol data improved the simulation by increasing cloud droplet number concentration and reducing droplet size. These findings underscored the importance of aerosols via droplet activation in accurately simulating cloud water properties and emphasized their role in forecasting aircraft icing hazards.

Coastal Sediment Grain Size Estimates on Gravel Beaches Using Satellite Synthetic Aperture Radar (SAR)

Coastal sediment grain size is an important factor in determining coastal morphodynamics. In this study, we explore a novel approach for retrieving the median sediment grain size (D50) of gravel-dominated beaches using Synthetic Aperture Radar (SAR) spaceborne imagery. We assessed this by using thirty-six Sentinel-1 (C-band SAR) satellite images acquired in May and June 2022 and 2023, and three NovaSAR (S-band SAR) satellite images acquired in May and June 2022, for three different training sites and one test site across England (the UK). The results from the Sentinel-1 C-band data show strong positive correlations (R2≥0.75) between the D50 and the backscatter coefficients for 15/18 of the resultant models. The models were subsequently used to derive predictions of D50 for the test site, with the models which exhibited the strongest correlations resulting in Mean Absolute Errors (MAEs) in the range 2.26–5.47 mm. No correlation (R2 = 0.04) was found between the backscatter coefficients from the S-band NovaSAR data and D50. These results highlight the potential to derive near-real time estimates of coastal sediment grain size for gravel beaches to better inform coastal erosion and monitoring programs.

Surface Crack Size Estimation Based on Quantification and Decoupling of Magnetic Flux Leakage (MFL) Signals of Circular Array Sensors

Surface Crack Size Estimation Based on Quantification and Decoupling of Magnetic Flux Leakage (MFL) Signals of Circular Array Sensors

Starving off death: Mortality salience impacts women’s body image and disordered eating

From a Terror Management perspective, the pursuit of thinness that characterizes eating disorders (EDs) is arguably a culturally endorsed way to mitigate death anxiety. In the present studies, we used the mortality salience (MS) paradigm to examine whether priming death increases ED symptoms. We recruited two samples of women from undergraduate (Study 1, N = 120), and clinically relevant (Study 2, N = 154) populations. After priming, participants completed measures of potential confounds (e.g., neuroticism, affect). Next, we assessed ED attitudes and behavior using a portion size estimation task, and measures of body dissatisfaction and eating intention. Study 1 findings were inconsistent with the claimed role of death anxiety in ED related behavior. However, in Study 2, MS priming led to increased dissatisfaction with current thinness and smaller portion sizes for high-fat compared to low-fat food. Overall, the results suggest that death anxiety may, at least partially, drive ED symptoms.