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AbstractThe Network Scale-up Methods (NSUM) are methods to estimate unknown populations based on indirect surveys in which the participants provide information about aggregated data of their acquaintances. This preserves the privacy and may lead to higher participation. During the last thirty years, new NSUM estimators have emerged. However, conditions related to the design of the experiments and the robustness of the estimators have not been studied in depth, especially in a realistic simulation environment. This study aims to compare nine NSUM estimators under relevant conditions in the literature through simulation experiments. We have analyzed how the NSUM is affected by the network topology, transmission and recall errors, the distribution of the unknown subpopulation, the number and sizes of subpopulations, and sample size. This article shows that some NSUM estimators barely used are better and more robust to some conditions, especially when the network is scale-free or under barrier effects. In addition, some methods are very sensitive to recall errors. In terms of the subpopulations configuration, we observe that the number of known subpopulations usually employed is quite large and that the most common NSUM is robust to the number and sizes of the subpopulations. [ABSTRACT FROM AUTHOR]

The network scale-up method (NSUM) is a cost-effective approach to estimating the size or prevalence of a group of people that is hard to reach through a standard survey. The basic NSUM involves two steps: estimating respondents' degrees and estimating the prevalence of the hard-to-reach population of interest using respondents' estimated degrees and the number of people they report knowing in the hard-to-reach group. Each of these two steps involves taking either an average of ratios or a ratio of averages. Using the ratio of averages for each step has so far been the most common approach. However, the authors present theoretical arguments that using the average of ratios at the second, prevalence-estimation step often has lower mean squared error when the random mixing assumption is violated, which seems likely in practice; this estimator was proposed early in NSUM development but has largely been unexplored and unused. Simulation results using an example network data set also support these findings. On the basis of this theoretical and empirical evidence, the authors suggest that future surveys that use a simple estimator may want to use this mixed estimator, and estimation methods based on this estimator may produce new improvements. [ABSTRACT FROM AUTHOR]

Purpose: We determined the size of the transgender population in Shiraz, Iran. Methods: In this cross-sectional study, the respondent-driven sampling technique was used by choosing eight seeds, three waves, and six coupons for each participant. The estimated population size was calculated by wisdom of the crowds, multiplier, and successive sampling–population size estimation (SS-PSE) methods. Pooling of results was done by an Anchored Multiplier calculator. Results: The mean age of participants (n=200) was 22.7±4 years, 197 (98.5%) were single, 86 (43%) were educated <12 years, 25 (12.5%) were not living with their families, and 52 (26%) were not financially supported by their parents. The transgender population was estimated by the wisdom of the crowds, multiplier, and SS-PSE methods to be 300 (95% confidence interval [CI]: 200–400), 677 (95% CI: 655–696), and 665 (95% CI: 624– 677), respectively. Their prevalence was found to be 0.017% (95% CI: 0.011–0.022%), 0.038% (95% CI: 0.036–0.039%), and 0.037% (95% CI: 0.034–0.038) using the same methods, respectively. Pooled results revealed that 22–37 per 100,000 general population were transgender individuals. Weighted estimation showed that trans men (56.6%) are more prevalent than trans women (43.4%), and only 17% of transgender people succeeded in gender reassignment. Conclusion: Transgender people should not be considered as marginalized groups of the community; they should be respected, heard, and valued. Establishing a standard and routine procedure for the collection of data on the status of transgender people and gender identity is necessary for policymaking and intervention programs. [ABSTRACT FROM AUTHOR]

Purpose: Accurate measurement of renal mass size is crucial in the management of renal cancer. With the burdensome cost of imaging yet its need for management, a better understanding of the variability among patients when determining mass size remains of urgent importance. Current guidelines on optimal imaging are limited, especially with respect to body mass index (BMI). The aim of this study is to discern which modalities accurately measure renal mass size and whether BMI influences such accuracy. Methods: A multi-institutional chart review was performed for adult patients undergoing partial or radical nephrectomy between 2018 and 2021, with 236 patients ultimately included. Patients were categorized by BMI (BMI 1: 18.5–24.9, BMI 2: 25–29.9, BMI 3: 30–34.9, and BMI 4: ≥ 35). The greatest mass lengths were compared between the pathology report and the following: computerized tomography (CT), renal ultrasound, and magnetic resonance imaging (MRI). Results: The difference between greatest length on CT with contrast and MRI were significantly different when compared to pathologic measurement. BMI groups 3 and 4 were found to have a significant difference in size estimates compared to BMI 2 for CT with contrast. No difference was found between size estimates by BMI group for any other imaging modality. Conclusion: CT with contrast becomes less accurate at estimating mass size for patients with BMI > 30. While contrast-enhanced CT remains a vital imaging modality for tissue enhancement in the context of unknown renal masses, caution must be used for mass size estimation in the obese population. [ABSTRACT FROM AUTHOR]

Abstract Introduction Correct estimation of the size of key and bridge populations is crucial for an efficient HIV/AIDS response in resource-limited settings, enabling efficient program planning and resource allocation. The hidden nature of these groups poses challenges to traditional methods, leading to the adoption of innovative approaches like the network scale-up method (NSUM). In this article we present the results of a NSUM study conducted in 2020 in Ukraine, focusing on four key populations and three bridge populations, highlighting challenges and contributions to development of the method. Methods From July to September 2020, we conducted a nationally representative survey in Ukraine via computer-assisted telephone interviews, and applied the known population method and summation method to estimate social networks sizes. Results were weighted based on individual sampling probability and adjusted for social respect and visibility factors to address potential limitations. Results Our study achieved a 20% response rate with 10,000 completed interviews. The social network size, using the known population method, was 213 people, and 125 using the summation method. Adjusting for the social respect and visibility, estimated key populations sizes were 295,857 [248,714–343,001] people who inject drugs, 152,267 [109,960–194,573] men who have sex with men, 78,385 [57,146–99,619] sex workers, and 9,963 [7,352–12,571] transgender people, detailed by age and gender. Bridge populations were estimated at 62,162 [50,445–73,879] sexual partners of people who inject drugs, 284,348 [233,113–335,583] clients of sex workers, and 13,697 [7,370–20,026] female partners of men who have sex with men. Conclusions NSUM proves reliable for estimating key populations size with appropriate corrections. It shows promise for further use in Ukraine, considering limited geographic coverage of the integrated bio-behavioral studies to use multiplier-based methods. However, the validity concerns persist for estimating bridge populations size, emphasizing the need for further method refinement and addressing implementation issues, particularly those related to data collection.

Background: Accurate measurement of polyps size is crucial in predicting malignancy, planning relevant intervention strategies and surveillance schedules. Endoscopists' visual estimations can lack precision. This study builds on our prior research, with the aim to evaluate a recently developed quantitative method to measure the polyp size and location accurately during a simulated endoscopy session. Methods: The quantitative method merges information about endoscopic positions obtained from an electromagnetic tracking sensor, with corresponding points on the images of the segmented polyp border. This yields real-scale 3D coordinates of the border of the polyp. By utilising the sensor, positions of any anatomical landmarks are attainable, enabling the estimation of a polyp's location relative to them. To verify the method's reliability and accuracy, simulated endoscopies were conducted in pig stomachs, where polyps were artificially created and assessed in a test–retest manner. The polyp measurements were subsequently compared against clipper measurements. Results: The average size of the fifteen polyps evaluated was approximately 12 ± 4.3 mm, ranging from 5 to 20 mm. The test–retest reliability, measured by the Intraclass Correlation Coefficient (ICC) for polyp size estimation, demonstrated an absolute agreement of 0.991 (95% CI 0.973–0.997, p < 0.05). Bland & Altman analysis revealed a mean estimation difference of − 0.17 mm (− 2.03%) for polyp size and, a mean difference of − 0.4 mm (− 0.21%) for polyp location. Both differences were statistically non-significant (p > 0.05). When comparing the proposed method with calliper measurements, the Bland & Altman plots showed 95% of size estimation differences between − 1.4 and 1.8 mm (− 13 to 17.4%) which was not significant (p > 0.05). Conclusions: The proposed method of measurements of polyp size and location was found to be highly accurate, offering great potential for clinical implementation to improve polyp assessment. This level of performance represents a notable improvement over visual estimation technique used in clinical practice. [ABSTRACT FROM AUTHOR]

Introduction: Correct estimation of the size of key and bridge populations is crucial for an efficient HIV/AIDS response in resource-limited settings, enabling efficient program planning and resource allocation. The hidden nature of these groups poses challenges to traditional methods, leading to the adoption of innovative approaches like the network scale-up method (NSUM). In this article we present the results of a NSUM study conducted in 2020 in Ukraine, focusing on four key populations and three bridge populations, highlighting challenges and contributions to development of the method. Methods: From July to September 2020, we conducted a nationally representative survey in Ukraine via computer-assisted telephone interviews, and applied the known population method and summation method to estimate social networks sizes. Results were weighted based on individual sampling probability and adjusted for social respect and visibility factors to address potential limitations. Results: Our study achieved a 20% response rate with 10,000 completed interviews. The social network size, using the known population method, was 213 people, and 125 using the summation method. Adjusting for the social respect and visibility, estimated key populations sizes were 295,857 [248,714–343,001] people who inject drugs, 152,267 [109,960–194,573] men who have sex with men, 78,385 [57,146–99,619] sex workers, and 9,963 [7,352–12,571] transgender people, detailed by age and gender. Bridge populations were estimated at 62,162 [50,445–73,879] sexual partners of people who inject drugs, 284,348 [233,113–335,583] clients of sex workers, and 13,697 [7,370–20,026] female partners of men who have sex with men. Conclusions: NSUM proves reliable for estimating key populations size with appropriate corrections. It shows promise for further use in Ukraine, considering limited geographic coverage of the integrated bio-behavioral studies to use multiplier-based methods. However, the validity concerns persist for estimating bridge populations size, emphasizing the need for further method refinement and addressing implementation issues, particularly those related to data collection. [ABSTRACT FROM AUTHOR]

The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare. [ABSTRACT FROM AUTHOR]

Purpose: Polyp size is an important factor that may influence diagnosis and clinical management decision, but estimation by visual inspection during endoscopy is often difficult and subject to error. The purpose of this study is to develop a quantitative approach that enables an accurate and objective measurement of polyp size and to study the feasibility of the method. Methods: We attempted to estimate polyp size and location relative to the gastro-oesophageal junction by integrating data from an electromagnetic tracking sensor and endoscopic images. This method is based on estimation of the three-dimensional coordinates of the borders of the polyp by combining the endoscope camera position and the corresponding points along the polyp border in endoscopic images using a computer vision-based algorithm. We evaluated the proposed method using a simulated upper gastrointestinal endoscopy model. Results: The difference between the mean of ten measurements of one artificial polyp and its actual size (10 mm in diameter) was 0.86 mm. Similarly, the difference between the mean of ten measurements of the polyp distance from the gastroesophageal junction and its actual distance (~ 22 cm) was 1.28 mm. Our results show that the changes in camera positions in which the images were taken and the quality of the polyp segmentation have the most impact on the accuracy of polyp size estimation. Conclusion: This study demonstrated an innovative approach to endoscopic measurements using motion tracking technologies and computer vision and demonstrated its accuracy in determining the size and location of the polyp. The observed magnitude of error is clinically acceptable, and the measurements are available immediately after the images captured. To enhance accuracy, it is recommended to avoid identical images and instead utilise control wheels on the endoscope for capturing different views. Future work should further evaluate this innovative method during clinical endoscopic procedures. [ABSTRACT FROM AUTHOR]

The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments. [ABSTRACT FROM AUTHOR]

In-field fruit size monitoring is useful for estimating fruit maturation and size distribution, making harvesting and marketing decisions, estimating profit, and controlling taste. Many algorithms have been proposed for size estimation, but estimation accuracy for occluded fruits (e.g., by branches and leaves) remains low. In this research, a method of estimating pixel area for occluded circular masks was developed. This method involves distant pixel estimation and radius of curvature estimation. This method was evaluated via application to randomly generated occluded circular masks. The results showed that this method can decrease root mean squared error (RMSE) by an average of 87.1% for generated occluded masks. This method was then applied to realistic segmentation masks generated from in-field snapshot data as a practical application. Snapshots of peach fruits were taken with an infrared camera once every 30 min at night during the growing season. The sizes of the fruits were then estimated by the following method. First, masks were detected using Mask-RCNN, which is a popular method for instance segmentation. Next, assuming a circular fruit, the developed size estimation method was applied. Estimated results for actual images were evaluated using a logistic model tuned by an annotated mask area, which showed a maximum of 9.55% mean absolute error (MAE)-based size improvement. This size estimation method can also be applied to the field monitoring of other circular plants. [ABSTRACT FROM AUTHOR]

Aggregated Relational Data (ARD), formed from "How many X's do you know?" questions, is a powerful tool for learning important network characteristics with incomplete network data. Compared to traditional survey methods, ARD is attractive as it does not require a sample from the target population and does not ask respondents to self-reveal their own status. This is helpful for studying hard-to-reach populations like female sex workers who may be hesitant to reveal their status. From December 2008 to February 2009, the Kiev International Institute of Sociology (KIIS) collected ARD from 10,866 respondents to estimate the size of HIV-related groups in Ukraine. To analyze this data, we propose a new ARD model which incorporates respondent and group covariates in a regression framework and includes a bias term that is correlated between groups. We also introduce a new scaling procedure using the correlation structure to further reduce biases. The resulting size estimates of those most-at-risk of HIV infection can improve the HIV response efficiency in Ukraine. Additionally, the proposed model allows us to better understand two network features without the full network data: (a) What characteristics affect who respondents know, and (b) How is knowing someone from one group related to knowing people from other groups. These features can allow researchers to better recruit marginalized individuals into the prevention and treatment programs. Our proposed model and several existing NSUM models are implemented in the networkscaleup R package. for this article are available online. [ABSTRACT FROM AUTHOR]

Hotspot mapping of People Who Inject Drugs (PWIDs) is important for harm reduction program planning. The aim of this study is to estimate the numbers of PWIDs and their geographic distribution in Iran. We used the hotspot mapping and size estimation method to estimate the number of drug users in four cities of Iran from December 2017 to January 2019. We calculated three estimates in each hotspot: a) Key Affected Population Key Informant (KAP-KI) estimate (drug users met in the hotspot), b) Non-KAP KI estimate (e.g., taxi drivers, shopkeepers nearby the hotspot); c) estimation made by field observers. Locations of hotspots were recorded by the field team and ArcGIS software was used for mapping. A total number of 335 hotspots were identified in four cities and 1524 key informants were interviewed. The forty four per cent of identified hotspots received harm reduction services. Ahvaz had the highest estimated number of PWIDs (843, range: 703–887), followed by Sari (346, range: 307–666), Yazd (228, range: 221–471) and district 2 of Tehran (18, range: 18–34). Findings highlight the need to use hotspot mapping to identify new PWIDs hotspots and best location for drop-in centers (DIC) and to provide targeted harm reduction programs. [ABSTRACT FROM AUTHOR]

Background. The size of Dunkleosteus and other late Devonian arthrodire placoderms has been a persistent problem in paleontology. The bony head and thoracic armor of these animals are typically the only elements preserved in the fossil record, with the rest of the body being lost during fossilization. Accurate length estimates of arthrodires are critical for reconstructing the paleobiology of these taxa and Devonian paleoecology more generally. Lengths of 5.3-8.8 m were proposed for Dunkleosteus based on allometric relationships between upper jaw perimeter and total length in extant large-bodied sharks. However, these methods were not statistically evaluated to determine if allometric relationships between body size and mouth size in sharks reliably predicted size in arthrodires. Several smaller arthrodire taxa are known from relatively complete remains, and can be used as independent case studies to test the accuracy of these methods. Results. Length estimates for Dunkleosteus are evaluated through an examination of mouth proportions in complete arthrodires and fishes more generally. Currently accepted lengths of 5.3-8.8m for D. terrelli are mathematically and biologically unlikely for three major reasons: (1) Arthrodires have larger mouths than sharks at similar body sizes. (2) upper jaw perimeter and mouth width produce extreme overestimates of body size (at least twice the actual value) in arthrodires known from complete remains. (3) Reconstructing Dunkleosteus using lengths predicted by upper jaw perimeter results in highly unusual body proportions, including extremely small, shrunken heads and hyper-anguilliform body plans, not seen in complete arthrodires or fishes more generally. Conclusions. Length estimates for arthrodires based on the mouth dimensions of extant sharks are not reliable. Arthrodires have proportionally larger mouths than sharks, more similar to catfishes (Siluriformes). The disproportionately large mouths of arthrodires suggest these animals may have consumed larger prey relative to their body size than extant macropredatory sharks, and thus the paleobiology and paleoecology of these two groups may not have been exactly analogous within their respective ecosystems. [ABSTRACT FROM AUTHOR]

Dunkleosteus terrelli, an arthrodire placoderm, is one of the most widely recognized fossil vertebrates due to its large size and status as one of the earliest vertebrate apex predators. However, the exact size of this taxon is unclear due to its head and thoracic armor being the only elements of its body regularly preserved in the fossil record. Lengths of 5–10 m are commonly cited, but these estimates are not based on rigorous statistical analysis. Here, I estimate the body size of D. terrelli using a new metric, orbit-opercular length, and a large dataset of arthrodires and extant fishes (3169 observations, 972 species). Orbit-opercular length strongly correlates with total length in fishes (r2 = 0.947, PEcf = 17.55%), and accurately predicts body size in arthrodires known from complete remains. Applying this method to Dunkleosteus terrelli results in much smaller sizes than previous studies: 3.4 m for typical adults (CMNH 5768) with the largest known individuals (CMNH 5936) reaching ~4.1 m. Arthrodires have a short, deep, and cylindrical body plan, distinctly different from either actinopterygians or elasmobranchs. Large arthrodires (Dunkleosteus, Titanichthys) were much smaller than previously thought and vertebrates likely did not reach sizes of 5 m or greater until the Carboniferous. [ABSTRACT FROM AUTHOR]

The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.

Superpixel generation of polarimetric synthetic aperture radar (PolSAR) images is widely used for intelligent interpretation due to its feasibility and efficiency. However, the initial superpixel size setting is commonly neglected, and empirical values are utilized. When prior information is missing, a smaller value will increase the computational burden, while a higher value may result in inferior boundary adherence. Additionally, existing similarity metrics are time-consuming and cannot achieve better segmentation results. To address these issues, a novel strategy is proposed in this article for the first time to construct the function relationship between the initial superpixel size (number of pixels contained in the initial superpixel) and the structural complexity of PolSAR images; additionally, the determinant ratio test (DRT) distance, which is exactly a second form of Wilks' lambda distribution, is adopted for local clustering to achieve a lower computational burden and competitive accuracy for superpixel generation. Moreover, a hexagonal distribution is exploited to initialize the PolSAR image based on the estimated initial superpixel size, which can further reduce the complexity of locating pixels for relabeling. Extensive experiments conducted on five real-world data sets demonstrate the reliability and generalization of adaptive size estimation, and the proposed superpixel generation method exhibits higher computational efficiency and better-preserved details in heterogeneous regions compared to six other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

While the electrical models of the membrane-based solid-state nanopores have been well established, silicon-based pyramidal nanopores cannot apply these models due to two distinctive features. One is its 35.3° half cone angle, which brings additional resistance to the moving ions inside the nanopore. The other is its rectangular entrance, which makes calculating the access conductance challenging. Here, we proposed and validated an effective transport model (ETM) for silicon-based pyramidal nanopores by introducing effective conductivity. The impact of half cone angle can be described equivalently using a reduced diffusion coefficient (effective diffusion coefficient). Because the decrease of diffusion coefficient results in a smaller conductivity, effective conductivity is used for the calculation of bulk conductance in ETM. In the classical model, intrinsic conductivity is used. We used the top-down fabrication method for generating the pyramidal silicon nanopores to test the proposed model. Compared with the large error (≥25% in most cases) when using the classical model, the error of ETM in predicting conductance is less than 15%. We also found that the ETM is applicable when the ratio of excess ion concentration and bulk ion concentration is smaller than 0.2. At last, it is proved that ETM can estimate the tip size of pyramidal silicon nanopore. We believe the ETM would provide an improved method for evaluating the pyramidal silicon nanopores. [ABSTRACT FROM AUTHOR]

In an iron ore pelletization plant, pellets are produced inside a rotating disc pelletizer. Online pellet size distribution is an important performance indicator of the pelletization process. Image processing-based system is an effective solution for online size analysis of iron ore pellets. This paper proposes a machine learning algorithm for estimating the size class of the pellets during their production by imaging from an area inside the disc pelletizer. Instead of computing the size of each individual pellets in the acquired image, this method proposes a qualitative approach to get the overall size estimate of the pellets in production. The key idea of this paper is to find out whether the disc is producing VERY SMALL, SMALL, MEDIUM, or BIG-sized pellets. A weighted average ensemble of different convolutional neural networks such as VGG16, Mobilenet, and Resnet50 is used to achieve this objective. Furthermore, batch normalization is applied to improve the estimation performance of the proposed model. A novel data augmentation method is applied to the in situ captured images to create the data set used to train and evaluate the proposed ensemble of CNN models. Results of experiments indicate that it is possible to detect the operating state of the pelletization disc by acquiring images from the inside area of the disc with sufficient accuracy. [ABSTRACT FROM AUTHOR]

Purpose: To compare the accuracy of Contrast-Enhanced Spectral Mammography (CESM), MG, US, and breast MRI in estimating the size of breast lesions requiring surgery. The postoperative histology size of the lesion was used as the gold standard. Material and methods: Two hundred thirty-three non-benign lesions in 189 patients were included in the analyses. All the selected patients underwent CESM and at least one other conventional diagnostic exam (US, MG, or MRI). Subsequently, all the patients underwent surgery preceded by cytological/histological examination. The largest diameter of the lesion at imaging was measured by a radiologist with more than 10 years' experience and then compared with the size of the lesion in the histological sample at the surgery (gold standard). Results: Among the 233 breast lesions, 196 were evaluated with US, 206 with MG and 160 with MRI. We found no statistically significant differences between size measurements using CESM and MRI compared with the measurements at the surgery (p value 0.63 and 0.51), whereas a significant difference was found for MG and US (p < 0.001). Conclusion: CESM is a reliable method for estimating the size of breast lesions: its performance seems superior to US and MG and comparable to MRI. [ABSTRACT FROM AUTHOR]

Software size prediction is a first step to compute information for software project development such as: project cost, effort, schedules, duration and so on. This paper presents the software prediction methods and the efforts of the researchers in this era. Methods such as Source line of Code, Model based estimation, function point and estimation from UML diagrams. For every method, the strength and weakness is presented. Also, it presents the evaluation criteria that is necessary for the researcher to evaluate the accuracy of the methods. This information will help the researcher in the future to choose the most effect method that fit to his/her software project. [ABSTRACT FROM AUTHOR]

Background: Silver nanoparticles (AgNPs) are toxic to microorganisms and can potentially kill multidrug-resistant bacteria. Nanoparticles can be synthesized in many ways, such as physical or chemical methods. Recently, it has been found that plant molecules can perform the same reduction reactions necessary for the production of nanoparticles but in a much more efficient way. Results: Here, green chemistry was employed to synthesize AgNPs using leaf extracts of Cymbopogon citratus. The effects of different parameters such as temperature, pH, and the volume of plant extract were also tested using their absorbance pattern at different wavelengths. The Surface Plasmon Resonance (SPR) changed with the changes in parameters. Changes in temperature from 20 °C to 60 °C have changed the highest absorbance from 0.972 to 3.893 with an SPR of 470 nm. At higher pH (11.1), the particles become highly unstable and have irregular shapes and sizes. The peak shifts to the right at a lower pH level (3.97), indicating a smaller but unstable compound. We have also investigated the effect of the volume of plant extracts on the reaction time. The sample with the highest amount of plant extract showed the most absorbance with a value of 0.963 at λmax, calculated to be 470 nm. The total formation of the AgNPs was observed visually with a color change from yellow to brownish-black. UV-visible spectroscopy was used to monitor the quantitative formation of AgNPs, showing a signature peak in absorbance between 400 and 500 nm. We have estimated the size of the nanoparticles as 47 nm by comparing the experimental data with the theoretical value using Mieplot. The biosynthesized AgNPs showed enhanced antibacterial activity against several multidrug-resistant bacteria, determined based on the minimal inhibitory concentration and zone of inhibition. Conclusion: The findings of this study indicate that an aqueous extract of C. citratus can synthesize AgNPs when silver nitrate is used as a precursor, and AgNPs act as antimicrobial property enhancers, which can be used to treat antibiotic-resistant bacteria. Hence, mass production and green synthesis of AgNPs from C. citratus will be able to increase the overall health of the general population. Moreover, it will enormously reduce the costs for drug development and provide employment options in the remotely located source areas. Finally, our findings will influence further studies in this field to better understand the properties and applications of AgNPs and ultimately contribute to improving planetary health by increasing immunity with high biocompatibility and less drug toxicity. [ABSTRACT FROM AUTHOR]

The size estimating of fluidized Titania agglomerates in a conical fluidized bed was studied by force balance model and fractal scaling analysis. The primary size of Titania Nano Particles (NPs) was 21 nm, while for complex agglomerates was in the size range of several hundred micrometers. The formation mechanism of simple-agglomerate and complex-agglomerate structures was studied experimentally. The size distribution and morphology of agglomerates were determined by advanced laser dynamic imaging and scanning electron microscopy. The AFM-nanoindentation test was used to determine the elastic modulus of agglomerates with porous structures. The size distribution of Titania NP agglomerates was estimated by the fractal analysis through the relationship between the number of particles and gyration diameter. The fractal exponent obtained from the power-law scaling of agglomerates and the complex agglomerate sizes were determined experimentally and theoretically. A simple theoretical model was applied to estimate the complex agglomerates' size based on the equilibrium of the separation and cohesion forces. The proposed model showed satisfactory results compared with the experimental data. The results of the present study can help to determine the critical gas velocity in achieving the desired agglomerate size of Titania NPs.

Atlantic bluefin tuna (Thunnus thynnus; BFT) is a large (up to 3.3 m in length) pelagic predator which has been exploited throughout the eastern Atlantic and Mediterranean since prehistoric times, as attested by its archeological remains. One key insight derivable from these remains is body size, which can indicate past fishing abilities, the impact of fishing, and past migration behavior. Despite this, there exists no reliable method to estimate the size of BFT found in archeological sites. Here, 13 modern Thunnus spp. skeletons were studied to provide power regression equations that estimate body length from vertebra dimensions. In modern specimens, the majority of BFT vertebrae can be differentiated by their morphological features, and thus, individual regression equations can be applied for each rank (position in vertebral column). In an archeological context, poor preservation may limit one's ability to identify rank; hence, "types" of vertebrae were defined, which enable length estimates when rank cannot be determined. At least one vertebra dimension, height, width, or length correlated highly with body length when vertebrae were ranked (R2 > 0.97) or identified to types (R2 > 0.98). Whether using rank or type, length estimates appear accurate to approximately ±10%. Finally, the method was applied to a sample of Roman‐era BFT vertebrae to demonstrate its potential. It is acknowledged that further studies with larger sample sizes would provide more precision in BFT length estimates. [ABSTRACT FROM AUTHOR]

Fossil snakes are most often identified from isolated vertebrae, complicating estimations of total body lengths of extinct taxa. Here we estimate the range of total body length of the late Eocene North American palaeophiid marine snake Pterosphenus schucherti based on 29 recently collected fossil vertebrae from Wilkinson County, Georgia, USA. Previous research suggests that the palaeophiids are most closely related to modern boids. Total body length estimates here are based on family-specific regressions of centrum length versus known total body length in extant members of Boidae, Pythonidae, and Colubridae. The high correlation coefficients for the family specific regressions supports previous studies that used centrum length to estimate total body length in extinct snakes. Here, size estimates for Pterosphenus schucherti ranged from 2.5 to 4.8 m using boids, 3.4 to 13.0 m using pythonids, and 2.8 to 5.3 m using colubrids. This study demonstrates how the lumping of multiple extant clades greatly increases the range of variability for length estimations. For the TL estimates of fossil snakes, it appears that regressions generated from the lowest taxonomic level of the most closely related extant species should be used. [ABSTRACT FROM AUTHOR]

It is challenging to estimate satellite targets’ absolute attitude and size with limited observational data. This article proposes an innovative way to jointly estimate satellite targets’ absolute attitude and size in the 3-D stable coordinates based on inverse synthetic aperture radar (ISAR) image interpretation with only one image. By taking advantage of the rectangular solar panels commonly equipped on satellites, this article extracts solar panel’s principal components, line features, and phase features of single ISAR imagery with principal component analysis (PCA), radon transform (RT), and minimum-entropy (ME)-based autofocus method, respectively. The projection relationship between these features and the absolute attitude and size of the satellite are established separately. Through multi-features fusion, a joint parameter estimation optimization function is established. This optimization is solved iteratively by the quasi-Newton method. The attitude and size parameters can be estimated simultaneously and rapidly, realizing the satellite state estimation under limited observation data. The excellent performance of the proposed algorithm is verified through different experiments. [ABSTRACT FROM AUTHOR]

The aim of this cross-sectional study was investigation of geographical distribution and size estimation of people who inject drugs (PWIDs) in Dogonbadan, Iran, in 2018 using geographical mapping method. Data was obtained through interviewing primary and secondary key informants as well as field team observations. Population size was estimated by median in Stata software (version 14). GIS software (version 10.6.1) was used to prepare the density and distribution maps of the hotspots. We identified 52 hotspots of drug users; injection of drugs occurred in 31 of the hotspots. The prevalence of injecting drugs was 232.4 per 100,000 adults, while it was 21.9 and 441.8 among females and males, respectively. A large number of identified hotspots are not covered by harm reduction services. Increasing outreach teams, equipping of harm reduction centers, and moving them to the nearest location to hotspots are suggested. [ABSTRACT FROM AUTHOR]

The aim of this study was to solve the problem that the existing identification parameters of rotor unmanned aerial vehicles (UAVs) are few and limited by the detection mode, and an identification method for estimating the rotor blade width based on the peak time-shift effect is proposed for the first time. Taking the width of the rotor blade as the parameter to identify the rotor of UAVs, the time-shift effect and its relationship with rotor blade width are verified by theoretical analysis and simulation. The proposed time-shift method has the characteristics of high-precision extraction of rotor width, and its effectiveness is verified by simulation and experiments. The aspect ratio of the rotor is accurately extracted based on the proposed time-shift method under the condition of an unknown pitch angle. Simulation results show that the estimation accuracy of the width and aspect ratio is up to 98 and 98.4%, respectively. The experimental results show that the relative errors of the width and aspect ratio are less than 7 and 4%, respectively. This study provides the theoretical basis and technical support for the high-accuracy identification of rotorcraft UAVs.

Background: Network Scale Up (NSU) is applied in many settings to estimate the size of hidden populations.The visibility of alcohol consumption - as a hidden behavior - in Iran has not been yet set. Our aim is to estimatethe visibility factor (VF) of alcohol consumption in Iran which is an Islamic country in the Middle East.Methods: Ninety persons who had a history of alcohol consumption were recruited. Relationships in networkwere aligned in three main subgroups: immediate family, extended family, and non-family. According to thegame of contact methodology, participants answered questions about total and aware number of personsthey know in each relationship category. VF was calculated by dividing total number of people aware aboutthe respondent’s alcohol consumption by total number of respondent’s social network. The 95% confidenceintervals (CIs) were computed through bootstrapping.Findings: The mean and standard deviation (SD) of participants’ age was 32.9 ± 10.2, the sex ratio was 3.Overall VF (95% CI) was 40% (33% to 47%). VF was estimated at 44% and 23% among men and women’snetwork, respectively. The immediate family was the highest informed group, followed by non-family andextended family members.Conclusion: The visibility of alcohol consumption in Iran was not high. This is due to religious and legalprohibitions around it

Background: Targeted interventions (TIs) are one of the most effective strategies to control HIV/AIDS transmission, especially among the high-risk groups (HRGs). Implementation of HIV/AIDS control strategies relies heavily on estimation of the size of HRG population. Size estimation for key populations such as female sex workers (FSWs), men who have sex with men (MSM), and injecting drug users (IDUs) is a crucial component of national HIV strategic planning. Objective: The objective of this study was to estimate the size of FSWs, MSM, and IDUs in various states of India. Methods: The program multiplier method was used to estimate the size of FSWs, MSM, and IDUs across the country using two distinct but overlapping data sources – Integrated Bio-Behavioral Surveillance and TI program from the same geographical area at the same time period. Results: In India, as on 2018–2019, there were nearly 18.2 lakhs estimated FSWs accounting to 0.53% among female population aged 15–49 years, with a highest in West Bengal (4.5 lakhs); 5.7 lakhs estimated MSM accounting to 0.16% among male population aged 15–49 years, with a highest in Gujarat (0.7 lakh); and 3.9 lakhs estimated IDUs accounting to 0.11% among male population aged 15–49 years, with a highest in Uttar Pradesh (0.5 lakh). Conclusions: The current size estimates on HRGs will support the development of projections and estimations of the HIV epidemic at national and state levels. These estimates also help in framing national guidelines such as HIV strategic planning, program design, allocation of resources, prioritizing the interventions, and monitoring and evaluation.

Dunkleosteus terrelli, an arthrodire placoderm, is one of the most widely recognized fossil vertebrates due to its large size and status as one of the earliest vertebrate apex predators. However, the exact size of this taxon is unclear due to its head and thoracic armor being the only elements of its body regularly preserved in the fossil record. Lengths of 5–10 m are commonly cited, but these estimates are not based on rigorous statistical analysis. Here, I estimate the body size of D. terrelli using a new metric, orbit-opercular length, and a large dataset of arthrodires and extant fishes (3169 observations, 972 species). Orbit-opercular length strongly correlates with total length in fishes (r2 = 0.947, PEcf = 17.55%), and accurately predicts body size in arthrodires known from complete remains. Applying this method to Dunkleosteus terrelli results in much smaller sizes than previous studies: 3.4 m for typical adults (CMNH 5768) with the largest known individuals (CMNH 5936) reaching ~4.1 m. Arthrodires have a short, deep, and cylindrical body plan, distinctly different from either actinopterygians or elasmobranchs. Large arthrodires (Dunkleosteus, Titanichthys) were much smaller than previously thought and vertebrates likely did not reach sizes of 5 m or greater until the Carboniferous.

Superpixel generation of polarimetric synthetic aperture radar (PolSAR) images is widely used for intelligent interpretation due to its feasibility and efficiency. However, the initial superpixel size setting is commonly neglected, and empirical values are utilized. When prior information is missing, a smaller value will increase the computational burden, while a higher value may result in inferior boundary adherence. Additionally, existing similarity metrics are time-consuming and cannot achieve better segmentation results. To address these issues, a novel strategy is proposed in this article for the first time to construct the function relationship between the initial superpixel size (number of pixels contained in the initial superpixel) and the structural complexity of PolSAR images; additionally, the determinant ratio test (DRT) distance, which is exactly a second form of Wilks’ lambda distribution, is adopted for local clustering to achieve a lower computational burden and competitive accuracy for superpixel generation. Moreover, a hexagonal distribution is exploited to initialize the PolSAR image based on the estimated initial superpixel size, which can further reduce the complexity of locating pixels for relabeling. Extensive experiments conducted on five real-world data sets demonstrate the reliability and generalization of adaptive size estimation, and the proposed superpixel generation method exhibits higher computational efficiency and better-preserved details in heterogeneous regions compared to six other state-of-the-art approaches.

Estimating the size of hard-to-reach populations is an important problem for many fields. The network scale-up method (NSUM) is a relatively new approach to estimate the size of these hard-to-reach populations by asking respondents the question, "How many X's do you know," where X is the population of interest (e.g., "How many female sex workers do you know?"). The answers to these questions form aggregated relational data (ARD). The NSUM has been used to estimate the size of a variety of subpopulations, including female sex workers, drug users, and even children who have been hospitalized for choking. Within the network scale-up methodology, there are a multitude of estimators for the size of the hidden population, including direct estimators, maximum likelihood estimators, and Bayesian estimators. In this article, we first provide an in-depth analysis of ARD properties and the techniques to collect the data. Then, we comprehensively review different estimation methods in terms of the assumptions behind each model, the relationships between the estimators, and the practical considerations of implementing the methods. We apply many of the models discussed in the review to one canonical dataset and compare their performance and unique features, presented in the . Finally, we provide a summary of the dominant methods and an extensive list of the applications, and discuss the open problems and potential research directions in this area. [ABSTRACT FROM AUTHOR]

While studies have increasingly used virtual hands and objects in virtual environments to investigate various processes of psychological phenomena, conflicting findings have been reported even at the most basic level of perception and action. To reconcile this situation, the present study aimed 1) to assess biases in size perception of a virtual hand using a strict psychophysical method and 2) to provide firm and conclusive evidence of the kinematic characteristics of reach-to-grasp movements with various virtual effectors (whole hand or fingertips only, with or without tactile feedback of a target object). Experiments were conducted using a consumer immersive virtual reality device. In a size judgment task, participants judged whether a presented virtual hand or an everyday object was larger than the remembered size. The results showed the same amplitude of underestimation (approximately 5%) for the virtual hand and the object, and no influence of object location, visuo-proprioceptive congruency, or short-term experience of controlling the virtual hand. Furthermore, there was a moderate positive correlation between actual hand size and perception bias. Analyses of reach-to-grasp movements revealed longer movement times and larger maximum grip aperture (MGA) for a virtual, as opposed to a physical, environment, but the MGA did not change when grasping was performed without tactile feedback. The MGA appeared earlier in the time course of grasping movements in all virtual reality conditions, regardless of the type of virtual effector. These findings confirm and corroborate previous evidence and may contribute to the field of virtual hand interfaces for interactions with virtual worlds.

The growth and the harvestability of a broccoli crop is monitored by the size of the broccoli head. This size estimation is currently done by humans, and this is inconsistent and expensive. The goal of our work was to develop a software algorithm that can estimate the size of field-grown broccoli heads based on RGB-Depth (RGB-D) images. For the algorithm to be successful, the problem of occlusion must be solved, which is the partial visibility of the broccoli head due to overlapping leaves. This partial visibility causes sizing errors. In this research, we studied the use of deep-learning algorithms to deal with occlusions. We specifically applied the Occlusion Region-based Convolutional Neural Network (ORCNN) that segmented both the visible and the amodal region of the broccoli head (which is the visible and the occluded region combined). We hypothesised that ORCNN, with its amodal segmentation, can improve the size estimation of occluded broccoli heads. The ORCNN sizing method was compared with a Mask R–CNN sizing method that only used the visible broccoli region to estimate the size. The sizing performance of both methods was evaluated on a test set of 487 broccoli images with systematic levels of leaf occlusion. With a mean sizing error of 6.4 mm, ORCNN outperformed Mask R–CNN, which had a mean sizing error of 10.7 mm. Furthermore, ORCNN had a significantly lower absolute sizing error on 161 heavily occluded broccoli heads with an occlusion rate between 50% and 90%. Our software and data set are available on https://git.wur.nl/blok012/sizecnn. • The image-based size estimation of broccoli heads was studied. • A data set of 2560 broccoli images was acquired with systematic levels of occlusion. • Two deep-learning methods were tested on their ability to size occluded broccolis. • One deep-learning method used a dual segmentation to better deal with the occlusion. • The software and the data set have been made available. [ABSTRACT FROM AUTHOR]

Abstract Background Pneumothorax can present as a respiratory emergency and has varied etiology. However, literature available on assessment and management of primary spontaneous pneumothorax (PSP) based on radiological quantitation is scarce. This study was undertaken to compare two different methods of quantitating pneumothorax size with that recommended in American Thoracic Society (ATS) guideline on chest radiogram with respect to possible change in management practices. Patients and methods A prospective cohort of adults presenting with spontaneous pneumothorax (SP) over 3 years to Emergency and Pulmonology Department, St John’s Medical College Hospital, Bangalore, was included. Demographic characteristics and clinical presentation were compiled. Management of SP was based on ATS guidelines. PSP size on chest radiogram was requantitated in the included patients using Light’s index and Collin’s method and was compared with that proposed by the ATS guideline. Results Seventy-six participants with SP were studied; their mean age was 43.7 years, with a preponderance of male patients (84.2%). Twenty-four (31.6%) patients had PSP and 52 (68.4%) patients had secondary spontaneous pneumothorax. In PSP, there was poor agreement between various methods of estimating size of pneumothorax on chest radiogram (Kappa statistic=0.23; ICC of 0.263). Three (12.5%) of the 24 incidences of PSP, which were treated conservatively as per ATS guidelines, would have required invasive intervention if Light’s index or the Collin’s formula were taken into consideration. Conclusion There was poor agreement of radiological pneumothorax size estimation by Collin’s, Light’s, and that proposed in the ATS guidelines. Thus, a unified, standardized method of radiological assessment of PSP is required.

Objective: To estimate (under reporting) UR of SDS (Suicide deaths) and SAS (suicide attempts) in Kermanshah Province which is among provinces with high suicide rate in Iran.Methods: For estimating the size of UR suicide death registers, all cases of SAS and suicide deaths were retrieved from forensic medicine and health centers. Then, using network scale up method, a sample of 500 cases, aged 18 to 65 years, were randomly selected from the general population on the basis of age - sex proportion. To find the 95% confidence interval, bootstrap technique was used.Results: The average coverage of SDS was 58.4%, the lowest and highest coverage rate of SDS were attributed to self-immolation (34.2%) and hanging (80.7%), respectively. The coverage rate of SAS for self-immolation and deliberate self-poisoning were 82.4% and 77.2%, respectively. Size estimation of SAS by NSU method revealed that deliberate self-poisoning with medication (61.7%), poisoning with toxins and chemicals (20.6%), and self-immolation (7.7%) were the most frequent methods of SAS.Conclusion: Given the low coverage of suicide registers, all causes of death, especially deaths classified as accident or deaths with undetermined category, are required to be accurately registered. Investigations of causes of death, correction of wrong codes, as well as interviews with survivors to give them assurance can reduce the rate of suicide denial and result in increased accuracy of death register coverage.

In comparison to field crops such as cereals, cotton, hay and grain, specialty crops often require more resources, are usually more sensitive to sudden changes in growth conditions and are known to produce higher value products. Providing quality and quantity assessment of specialty crops during harvesting is crucial for securing higher returns and improving management practices. Technical advancements in computer and machine vision have improved the detection, quality assessment and yield estimation processes for various fruit crops, but similar methods capable of exporting a detailed yield map for vegetable crops have yet to be fully developed. A machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the harvesting process. Coupled with a software developed in Python, the system is composed of a video logger and a global navigation satellite system. Computer vision analysis is performed within the tractor while an RGB camera collects real-time video data of the crops under natural sunlight conditions. Vegetables are first segmented using Watershed segmentation, detected on the conveyor, and then classified by size. The system detected shallots in a subsample of the dataset with a precision of 76%. The software was also evaluated on its ability to classify the shallots into three size categories. The best performance was achieved in the large class (73%), followed by the small class (59%) and medium class (44%). Based on these results, the occasional occlusion of vegetables and inconsistent lighting conditions were the main factors that hindered performance. Although further enhancements are envisioned for the prototype system, its modular and novel design permits the mapping of a selection of other horticultural crops. Moreover, it has the potential to benefit many producers of small vegetable crops by providing them with useful harvest information in real-time.

Purpose: Human immunodeficiency virus (HIV) risks are heterogeneous in nature even in generalized epidemics. However, data are often missing for those at highest risk of HIV, including female sex workers. Statistical models may be used to address data gaps where direct, empiric estimates do not exist.Methods: We proposed a new size estimation method that combines multiple data sources (the Malawi Biological and Behavioral Surveillance Survey, the Priorities for Local AIDS Control Efforts study, and the Malawi Demographic Household Survey). We used factor analysis to extract information from auxiliary variables and constructed a linear mixed effects model for predicting population size for all districts of Malawi.Results: On average, the predicted proportion of female sex workers among women of reproductive age across all districts was about 0.58%. The estimated proportions seemed reasonable in comparing with a recent study Priorities for Local AIDS Control Efforts II (PLACE II). Compared with using a single data source, we observed increased precision and better geographic coverage.Conclusions: We illustrate how size estimates from different data sources may be combined for prediction. Applying this approach to other subpopulations in Malawi and to countries where size estimate data are lacking can ultimately inform national modeling processes and estimate the distribution of risks and priorities for HIV prevention and treatment programs. [ABSTRACT FROM AUTHOR]

Background: Tumor size impacts the technical difficulty and histological curability of colorectal endoscopic submucosal dissection (ESD); however, the preoperative evaluation of tumor size is often different from histological assessment. Analyzing influential factors on failure to obtain an accurate tumor size evaluation could help prepare optimal conditions for safer and more reliable ESD., Aim: To investigate the tumor size discrepancy between endoscopic and pathological evaluations and the influencing factors., Methods: This was a retrospective study conducted at a single institution. A total of 377 lesions removed by colorectal ESD at our hospital between April 2018 and March 2022 were collected. We first assessed the difference in size with an absolute percentage of the scaling discrepancy. Subsequently, we compared the clinicopathological characteristics of the correct scaling group (> -33% and < 33%) with that of the incorrect scaling group (< -33% or > 33%), which was further subdivided into the underscaling group (-33% or less of the discrepancy) and overscaling group (33% or more of the discrepancy), respectively. As secondary outcome measures, parameters on size estimation were compared between the underscaling and correct scaling groups, as well as between the overscaling and correct scaling groups. Finally, multivariate analysis was performed in terms of the following relevant parameters on size estimation: Pathological size, location, and possible influential factors ( P < 0.1) in the univariate analysis., Results: The mean of absolute percentage in the scaling discordance was 21%, and 91 lesions were considered to be incorrectly estimated in size. The incorrect scaling was significantly remarkable in larger lesions (40 mm vs 28 mm; P < 0.001) and less experience ( P < 0.001), and these two factors were influential on the underscaling (75 lesions; P < 0.001). Conversely, compared with the correct scaling group, 16 lesions in the overscaling group were significantly small (20 mm vs 28 mm; P < 0.001), and the small lesion size was influential on the overscaling ( P = 0.002)., Conclusion: Lesions indicated for colorectal ESD tended to be underestimated in large tumors, but overestimated in small ones. This discrepancy appears worth understanding for optimal procedural preparation., Competing Interests: Conflict-of-interest statement: The authors have no conflicts of interest to declare., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)