Accurate Size Estimation Research Articles

Role of Posterior Acoustic Shadow Width in Ultrasound in Determining Stone Size in Urolithiasis.

Introduction Ultrasound (US) is frequently the initial diagnostic tool for urolithiasis, though computed tomography (CT) remains the imaging modality of choice. However, due to potential overestimations, the accuracy of US in gauging stone size has been a point of contention. This study aims to compare the accuracy of stone size measurements in US, specifically evaluating the utility of the posterior acoustic shadow (PAS) width, against the CT measurements. Methods We conducted a cross-sectional study where 120 adult patients (aged >18 years) with confirmed urolithiasis through CT participated. Stone sizes were assessed via both CT and US, with the PAS width also being measured in the latter. Statistical analysis compared stone size discrepancies between both CT and US measurement techniques. Results The study enrolled 73 males and 47 females with various stone locations. The average stone sizes were 15.93 ± 4.59 mm (CT), 18.60 ± 4.80 mm (US), and 16.69 ± 4.61 mm (PAS width). There was a mean difference of 2.67 mm (p< 0.0001) between CT and US sizes, whereas the difference between the PAS width and CT sizes was only 0.75 mm (p= 0.203). Stone size miscalculation by US was 16.77%, whereas it was only 4.77% for PAS width. Conclusion US tends to significantly overestimate stone size when compared to CT. Conversely, the measurement of the PAS width in US presents a more aligned estimation to CT outputs. Integrating PAS width into routine US reporting can enhance the accuracy of stone size estimation, optimizing urolithiasis management and patient counseling.

Minimal Crew Size and Sensitive Reproductive Parameters on Multigenerational Interstellar Travel

Multigenerational interstellar travel to exoplanets, as well as manned missions to the Moon and Mars, has been investigated in the world. Defining the minimum crew size of interstellar ships is one of the most important research areas for interstellar travel, because designing multigenerational interstellar ships requires defining a critical crew size, which factors into many variables, including food production, air/water control, and propulsion. Anthropologists and astrophysicists have recently tackled with the minimum crew size of interstellar ships in the field of “Space anthropology”. Previously published computations in the field of space anthropology provided a critical crew number of 1,900- 2,000 (Sano, 2021) with constant reproductive parameters, but did not fluctuate reproductive parameters such as infertility, initial genetic diversity and number of children per woman. These parameters would fluctuate during the real multigenerational journey. Then, a more accurate estimate of the critical crew size would be obtained from the fluctuating parameters. This paper provides a critical crew size of 1,400-6,800 for interstellar travel using the fluctuating parameters, and clarifies which anthropological parameters are sensitive and how critical to be controlled aboard interstellar ships. It indicates that measuring infertility and controlling the number of children per woman for many generations onboard interstellar ships are essential to prevent extinction, and firstly quantifies the importance of high diversity of initial crew and procreation window for interstellar travel.

Universal Behavior of the Image Resolution for Different Scanning Trajectories

This study examines the characteristics of various scanning trajectories or patterns under the influence of scanning parameters in order to develop a theory to define their corresponding image resolutions. The lack of an accurate estimation of pixel size for a specified set of scanning parameters and their connection is a key challenge with existing scanning methods. Thus, this research aimed to propose a novel approach to estimate the pixel size of different scanning techniques. The findings showed that there is a link between pixel size and a frequency ratio NP, which is the ratio of two waveform frequencies that regulates the density of the scanning pattern. A theory has been developed in this study to explain the relationship between scanning parameters and scanning density or pixel size, which was not previously considered. This unique theory permitted the a priori estimate of the image resolution using a particular set of scanning parameters, including the scan time, frequencies, frequency ratio, and their amplitudes. This paper presents a novel and systematic approach for estimating the pixel size of various scanning trajectories, offering the user additional flexibility in adjusting the scanning time or frequency to achieve the desired resolution. Our findings also reveal that in order to achieve a high-quality image with high signal-to-noise and low error, the scanning trajectory must be able to generate a fairly uniform or regular pattern with a small pixel size.

Intrusive effects of dual-tip conductivity probes on bubble measurements in a wide velocity range

High-velocity air-water flows in hydraulic structures, such as spillways and low-level outlets, are characterised by high turbulence levels and strong self-aeration. In air-water flows, the void fraction varies from values close to zero at the invert up to near unity in the upper spray region. Because of the strong aeration, non-intrusive optical measurement techniques can only be applied to a limited extent, and intrusive phase-detection probes are widely used to estimate air-water flow properties, including void fraction, interfacial velocity, and chord sizes. However, these probes are often used without independent validation. Herein, this study systematically investigated the uncertainties of phase-detection probe measurements of individual bubbles traveling at a wide range of velocities (up to 7.5 m/s) in a vertically upward bubbly pipe flow. Velocities and chord times of air bubbles were simultaneously measured with an intrusive dual-tip conductivity probe and stereo-view high-speed videos, and a comparative analysis identified various effects of bubble-probe interactions on the measurement accuracy of the bubble velocities. The bubble deceleration due to these bubble-probe interactions were quantified. For standard interactions (i.e., bubbles undergoing no significant deformation and slowing down), the velocity estimates compared well with a theoretical model (errors less than 2 %), while larger errors were found for non-standard interactions such as those characterised by crawling, drifting, and waking mechanisms. In addition, a novel correction scheme for chord times was proposed, improving the accuracy of chord length, bubble size and void fraction estimations in air-water flows.

Generative adversarial networks assisted machine learning based automated quantification of grain size from scanning electron microscope back scatter images

In this research, a novel approach for the automated quantification of grain size in single-phase materials using scanning electron microscopy (SEM) based back scattering electron (BSE) images is presented. Grain size analysis is crucial in understanding material properties, but manual analysis is time-consuming and subject to human error. The proposed method leverages Generative Adversarial Networks (GANs) to automatically segment grains to enable grain size quantification thereby substantially enhancing the efficiency and accuracy of the process. The grain boundary segmentation is achieved by treating the problem of grain segmentation as an ‘image translation task’ in which GANs are known to be efficient. Due to the learned ‘perceptual ability’ of the trained network, the model was able to delineate the grain boundaries even from low contrast regions and regions obscured due to sample preparation artefacts, enabling better segmentation performance over the conventional algorithms. Application of the present approach to test data set has shown mean grain size estimation accuracy of 92% and grain size distribution similarity of 80% against EBSD (Electron Back Scatter Diffraction) ground truth data. The ability of the approach in enabling reliable quantification of the microstructures even from noisy images has been demonstrated.

Advances in wildlife abundance estimation using pedigree reconstruction.

The conservation and management of wildlife populations, particularly for threatened and endangered species are greatly aided with abundance, growth rate, and density measures. Traditional methods of estimating abundance and related metrics represent trade-offs in effort and precision of estimates. Pedigree reconstruction is an emerging, attractive alternate approach because its use of one-time, noninvasive sampling of individuals to infer the existence of unsampled individuals. However, advances in pedigree reconstruction could improve its utility, including forming a measure of precision for the method, establishing required spatial sampling effort for accurate estimates, ascertaining the spatial extent of abundance estimates derived from pedigree reconstruction, and assessing how population density affects the estimator's performance. Using established relationships for a stochastic, spatially explicit simulated moose (Alces americanus) population, pedigree reconstruction provided accurate estimates of the adult moose population size and trend. Novel bootstrapped confidence intervals performed as expected with intensive sampling but underperformed with moderate sampling efforts that could produce abundance estimates with low bias. Adult population estimates more closely reflected the total number of adults in the extant population, rather than number of adults inhabiting the area where sampling occurred. Increasing sampling effort, measured as the proportion of individuals sampled and as the proportion of a hypothetical study area, yielded similar asymptotic patterns over time. Simulations indicated a positive relationship between animal density and sampling effort required for unbiased estimates. These results indicate that pedigree reconstruction can produce accurate abundance estimates and may be particularly valuable for surveying smaller areas and low-density populations.

Waiting for the right time and tide: The fine‐scale migratory behavior of river herring in two coastal New England streams

AbstractObjectiveAlewife Alosa pseudoharengus and Blueback Herring A. aestivalis, collectively referred to as river herring, are anadromous clupeids inhabiting the North American Atlantic coast. Although the seasonal patterns of their spawning migration are well studied and predictable, the fine‐scale movements are still poorly resolved.MethodsThis study used high‐resolution acoustic imaging to study the fine‐scale behavior of river herring during their spring spawning migration in two coastal rivers in Massachusetts, United States.ResultObservations suggest that seasonal patterns of migration appear to be influenced by temperature, while fine‐scale patterns in migration were associated with the time of day, tidal state, and their interaction. River herring were consistently observed during both day and night, with elevated peaks of activity in the early morning and late afternoon. Daytime movement consisted of the intermittent passage of large, dense schools, while nighttime movement consisted of the frequent passage of individuals or small groups. The highest numbers of herring observations were associated with flood and high tides. Additionally, the interactions between time of day and tidal state resulted in synergistic positive effects during crepuscular incoming tides, which were favorable to movement, and synergistic negative effects during midday low tides, which inhibited fish movement.ConclusionThe changing relationship between the time of day and tidal state within the season manifested in changing periodicity in fish movement to correlate with favorable movement conditions. Since movements may be related to both time of day and tide, previous run size estimates from visual counts made only during the day may be too low. Sampling methodologies that collect information during all 24 h would likely produce the most accurate run size estimates. This study highlights the need to view river herring migration in a holistic context predicated by multiple environmental and biological factors that vary at several temporal scales (i.e., seasonally, daily, and hourly).

Shark and ray genome size estimation: methodological optimization for inclusive and controllable biodiversity genomics

Estimate of nuclear DNA content serves as an independent tool for validating the completeness of whole genome sequences and investigating the among-species variation of genome sizes, but for some species, the requirement of fresh cells makes this tool highly inaccessible. Here we focused on elasmobranch species (sharks and rays), and using flow cytometry or quantitative PCR (qPCR), estimated the nuclear DNA contents of brownbanded bamboo shark, white spotted bamboo shark, zebra shark, small-spotted catshark, sandbar shark, slendertail lanternshark, basking shark, megamouth shark, red stingray, and ocellate spot skate. Our results revealed their genome sizes spanning from 3.18 pg (for ocellate spot skate) to 13.34 pg (for slendertail lanternshark), reflecting the huge variation of genome sizes already documented for elasmobranchs. Our qPCR-based method ‘sQuantGenome’ enabled accurate genome size estimation without using live cells, which has been a severe limitation with elasmobranchs. These findings and our methodology are expected to contribute to better understanding of the diversity of genome sizes in elasmobranchs even including species with limited availability of fresh tissue materials. It will also help validate the completeness of already obtained or anticipated whole genome sequences.

Robust variable step P&O algorithm based MPPT for PMSG wind generation system using estimated wind speed compensation technique

To expedite the cost recovery of the wind energy conversion system (WECS), maximizing power availability at all times is of utmost importance. Therefore, the primary objective of Maximum Power Point Tracking (MPPT) algorithms is to optimize power output by ensuring the achievement of maximum power under varying wind speeds. The perturbation and observation algorithm are widely recognized as the most well-known technique used in wind energy conversion systems (WECS) due to its versatility. Although this algorithm is valued for its simplicity and cost-effectiveness, it does have a significant drawback. Peculiarly, it is insensitive to fluctuations in instantaneous wind speed conditions and lacks accurate step size estimation. To address these limitations, a novel approach called the Robust Variable Step P&O (RVS-P&O) technique has been proposed. The RVS-P&O technique aims to overcome the aforementioned drawbacks. The control strategy employed by the proposed RVS-P&O-MPPT algorithm is based on three fundamental concepts: a new normalization of the selection of the step size, a novel correction procedure to estimate the power variation and wind speed estimation (WSE) algorithm based on the artificial neural network (ANN) controller. Simulations have been carried out by means of MATLAB/Simulink environment. The findings indicate the superiority of the proposed RVS-P&O algorithm compared to several P&O implementations: fixed Large Step (LS), fixed Small Step (SS), Variable Step (VS) and Modified (M)-P&O. The developed algorithm allows reaching 99.79% of wind energy conversion system (WECS) efficiency. This allows obtaining an enhancement of 2.19% in comparison to VS-P&O technique which is the most competing algorithm. Further, the response speed is clearly improved compared to the various P&O versions, as the settling time (sec) is of 0.0074 while that of VS and M−P&O is of 0.0157 and 0.0079, respectively. Indeed, to enhance the quality of the injected power into the grid, a Second Order Sliding Mode Controller (SOSMC) based Super Twisting Algorithm (STA) is proposed. The results show the suggested technique outperforms the PI and First Order (FO)-SMC for current THD minimization by attenuating the odd harmonics without chattering phenomenon.

Endoscopic measurement of the size of gastrointestinal polyps using an electromagnetic tracking system and computer vision-based algorithm

PurposePolyp 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.MethodsWe 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.ResultsThe 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.ConclusionThis 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.

A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C

Semantic segmentation methods have been successfully applied in seabed sediment detection. However, fast models like YOLO only produce rough segmentation boundaries (rectangles), while precise models like U-Net require too much time. In order to achieve fast and precise semantic segmentation results, this paper introduces a novel model called YOLO-C. It utilizes the full-resolution classification features of the semantic segmentation algorithm to generate more accurate regions of interest, enabling rapid separation of potential targets and achieving region-based partitioning and precise object boundaries. YOLO-C surpasses existing methods in terms of accuracy and detection scope. Compared to U-Net, it achieves an impressive 15.17% improvement in mean pixel accuracy (mPA). With a processing speed of 98 frames per second, YOLO-C meets the requirements of real-time detection and provides accurate size estimation through segmentation. Furthermore, it achieves a mean average precision (mAP) of 58.94% and a mean intersection over union (mIoU) of 70.36%, outperforming industry-standard algorithms such as YOLOX. Because of the good performance in both rapid processing and high precision, YOLO-C can be effectively utilized in real-time seabed exploration tasks.

Estimating the Size of Clustered Hidden Populations

Abstract Successive sampling population size estimation (SS-PSE) is a method used by government agencies, aid organizations, and researchers around the world to estimate the size of hidden populations using data from respondent-driven sampling surveys. SS-PSE addresses a specific need in estimation, since many countries rely on having accurate size estimates to plan and allocate finite resources to address the needs of hidden populations. However, SS-PSE relies on several assumptions, one of which requires the underlying social network of the hidden population to be fully connected. We propose two modifications to SS-PSE for estimating the size of hidden populations whose underlying social network is composed of disjoint clusters. The first method is a theoretically straightforward extension of SS-PSE, but it relies on prior information that may be difficult to obtain in practice. The second method extends the Bayesian SS-PSE model by introducing a new set of parameters that allow for clustered estimation without requiring the additional prior information. After providing theoretical justification for both novel methods, we then assess their performance using simulations and apply the Clustered SS-PSE method to a population of internally displaced persons in Bamako, Mali.

Exploring the effect of various factors for ash content estimation via ensemble learning: Color-texture features, particle size, and magnification

Online estimation of ash content in coal based on machine vision has been paid more attention to by academia and industry. Existing research has mainly focused on feature extraction and model design for estimating ash content, but the exploration of the feature's contribution to the model is rarely reported. Therefore, this paper presented a method to analyze features' contribution to microscopic images. Expressly, ensemble learning could explicitly point out the assistance of different features to ash content estimation according to the results of weak regressors, which possessed good interpretability. Firstly, we explored the relationship among particle size, magnification, and precision to determine Adaboost as the optimal regressor. Secondly, we discussed the contribution of different features to the regressor quantitatively and qualitatively on the 0.5–0.25 (mm) particle size of datasets under magnification of 0.3, which we called the optimal condition. Ultimately, the experimental conclusions were validated by feature importance analysis and correlation analysis. The experimental results show that the magnification significantly influenced the precision of ash content estimation. Still, coal microscopic images' 0.5–0.25 (mm) particle size estimation accuracy was optimal under all magnifications. When magnification was 0.3 and particle size was 0.5–0.25 (mm), there was the best performance to ash content estimation (In the testing set, 98% of the samples had an error less than 0.5% between ash content estimated value and ash content value). Color features played a dominant role in ash content estimation in the optimal regressors. Only the building color feature regressor could achieve that 96.4% of samples had an estimated error of less than 0.5%. In contrast, only 68% of the samples using the texture feature regressors had an estimated error of less than 0.5%. Moreover, the building mixture of color and texture regressor possessed mutually reinforcing effects under specific conditions. The summarized features' contribution was essential for model building and design.

Developing and testing a workflow to identify microplastics using near infrared hyperspectral imaging

The analysis of microplastics (MP) is time-consuming which limits our capacity to monitor and mitigate plastic pollution. Here, near infrared (1000–2500 nm) hyperspectral imaging (NIR-HSI) offers an advantage over other spectroscopic techniques because it can rapidly image large areas relative to other systems. While NIR-HSI can successfully detect MP, accuracy and limitations of the method have not been fully explored. In addition, lack of open databases and analysis pipelines increases the barrier to use.In this work, we developed a spectral database containing preproduction pellets, consumer products and marine plastic debris, imaged using a Hyspex SWIR-320me imager. A SIMCA model identified four polymer types: polypropylene, polyethylene, polyethylene terephthalate and polystyrene (PP, PE, PET, PS) to identify MP in hyperspectral images. We determined the accuracy of size estimates for PS MP > 1000 μm using fluorescence microscopy and tested the impact of photooxidation on detection of plastics by NIR-HSI (PE, PP, PS, PET) and subsequent prediction by the SIMCA model.The model performed well across all polymers as shown by high specificity, sensitivity, and accuracy for internal cross validation (>88%), and sensitivity >80% for external validation. PS MP < 500 μm Feret diameter were not consistently detected by NIR-HSI when compared with fluorescence microscopy. However, estimates for Feret diameter were consistent for PS MP > 1000 μm. Analysis by NIR-HSI showed no spectral changes and SIMCA showed no decreased precision with increased photooxidation across polymer types. Recall varied across polymer type and photooxidation stage with no clear trends.This study shows that NIR-HSI is a rapid method which can accurately identify MP of the four most relevant polymer types, precluding the need to analyze particles one at a time. NIR-HSI can be a key technology for environmental monitoring of plastic debris where rapid analysis of multiple samples is required.

A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs

The effective population size is an important concept in population genetics. It corresponds to a measure of the speed at which genetic drift affects a given population. Moreover, this is most of the time the only kind of population size that empirical population genetics can give access to. Dioecious populations are expected to display excesses of heterozygosity as compared to monoecious panmictic populations, as measured by Wright's FIS. It can be shown that these excesses are negatively correlated with the population size. This is why FIS can be used to estimate the eigenvalue effective population size of dioecious populations. In this paper, we propose a new approximation that provides a very accurate estimate of the eigenvalue effective population size of a dioecious population as a function of the real population size. We then explore the accuracy of different FIS-based methods using the leading eigenvalue of transition matrices or coalescence. It appears that the eigenvalue-based method provides more accurate results in very small populations, probably due to approximations made by the coalescence approach that are less valid in such situations. We also discuss the applicability of this method in the field.

Population size and residency patterns of northern bottlenose whales (Hyperoodon ampullatus) using the Gully, Nova Scotia

A population of northern bottlenose whales (Hyperoodon ampullatus) uses the Gully, a submarine canyon off the coast of Nova Scotia, Canada. Eleven years of photo-identification records has permitted estimation of population size using mark-recapture techniques. The population estimate was small (133 individuals, 95% CI = 111-166 from left side identifications; 127 individuals, 95% CI = 106-160 from right side identifications). The population was not closed, with the combined mortality, mark change and emigration rate estimated at 13% per year for left side identifications (95% CI = 9-17) and 14% for right side identifications (95% CI = 10-18). There was no significant increase or decrease in the population size between 1988-1999 (change in population size: left side: –0.13% per year, 95% CI = -3.4 to 3.9; right side: –0.43% per year, 95% CI = -4.5 to 3.1). The sex ratio was roughly 1:1, with equal numbers of sub-adult and mature males. Over the summer field season, individuals emigrated from, and re-immigrated into the Gully, spending an average of 20 days within the Gully before leaving (left side identifications 19 days, SE = 17; right side identifications 23 days, SE = 10). Approximately 34% of the population was present in the Gully at any time. Individuals of all age and sex classes displayed similar residency patterns although there were annual differences as individuals spent less time in the Gully in 1996 than in 1990 and 1997. Sighting rates were similar in all years with extensive fieldwork, indicating little variability in the number of whales in the Gully each summer. Accurate estimates of population size and residency patterns will be useful in determining the regulations and required coverage for a marine protected area in the Gully.

Optimizing data quantity and quality for side-looking echosounder surveys in large rivers

The increased use of side-looking echosounder surveys in large rivers necessitates the creation and evaluation of standardized sampling protocols to effectively sample these habitats. Using data from five Ohio River pools, we evaluated the effects of habitat and transect direction on the quality and quantity of mobile, side-looking, split-beam echosounder data to determine how these factors may affect survey design in large rivers. Split beam echosounder data were collected using two multiplexing 200 kHz transducers offset in angle to maximize water column coverage. Using mixed-effects models, we evaluated the effects of habitat (main channel vs. tributary) and transect direction (upstream vs. downstream movement of the vessel) on fish density and the number of targets per fish track (series of acoustic targets from an individual fish). We also used a skewness test in conjunction with a Kolmogorov-Smirnov test to determine the distribution of fish orientations relative to the transducer and examine differences in the distribution of fish orientations among habitats, transect directions, and years. Mean fish densities were greater in tributary habitat relative to main channel habitat, whereas main channel habitat had a higher mean number of targets per fish track when compared to tributaries. Upstream-facing transects had greater mean fish density estimates and mean number of targets per fish track than downstream-facing transects. Although most fish were oriented perpendicular to the transducer, we identified habitat, transect direction, and annual differences in the distribution of fish orientations. These results suggest that researchers and managers that plan to use side-looking echosounders to assess fish populations in large rivers should ensure that standardized protocols include main channel and tributaries, and are expanded to encompass other relevant habitats (e.g., backwaters and side channels). Further, managers should use fish orientation data to select or develop TS-L equations that best matches the fish orientation distribution of each survey to ensure accurate density and size estimates.

Zoobooth: A portable, open-source and affordable approach for repeated size measurements of live individual zooplankton

Repeated size measurements of individual animals are valuable data for many research questions, but it is often hard to obtain without causing stress or damage to the animal. We developed a video-based approach called Zoobooth to size individual zooplankton, which involves a low risk of handling accidents and stress. Here we describe the process of assembling the instrument we used to acquire video recordings of single zooplankton and the procedure to obtain size estimates from the recorded videos. Our setup produces accurate size estimates for Daphnia magna (correlation to manual measurements = 0.97), and was also tested with other zooplankton species. Zoobooth is especially advantageous when one needs size measurements of live, individual mesozooplankton. The device is small, portable, and comprised of very affordable and readily available components. It can easily be modified for other purposes, such as studies of coloration or behavior of micro-and macro-plankton. We share all the files to build and use Zoobooth.

Continuance intention in financial technology: a framework and meta-analysis

PurposeAlthough many studies have sought to address the topic of continuance intention among Fintech customers, the reported findings are fragmented. Therefore, the present study proposes a research model that integrates the main constructs involved in Fintech continuance intention.Design/methodology/approachThe current study uses a meta-analytic-based correlation analysis of effect sizes, meta-regression analysis and meta-analytic structural equation modeling, with 247 effect sizes in 69 studies involving 26,140 respondents.FindingsThe results reveal continuance intention is driven by satisfaction and trust, with ease of use and usefulness being antecedents of satisfaction and trust. The authors also found evidence to show satisfaction partially mediates the relationship between ease of use and continuance intention and that trust fully mediates the relationship between ease of use and continuance intention and partially mediates the relationship between usefulness and continuance intention. In addition, the authors found that in Western countries, with higher Human Development Index levels and greater of use of electronic payment, satisfaction has more impact on continuance intention.Practical implicationsFrom a theoretical standpoint, this meta-analytic study has implications for the literature on Fintech by offering an empirical generalization on the strength of the antecedents of Fintech continuance intentions and by testing possible moderators in a wide range of countries and studies. In other words, this study’s goal is to broaden the scope of the research. Regarding managerial implications, it is important to listen to user opinions regarding the positive and negative points of their experience with these technologies and take them into consideration when planning improvements. Additionally, the analysis shows the importance of using data from user interaction with technology, obtained, for example, through big data analytics, whereby companies can see how users behave, how much time they spend accessing certain functions and which technological features they use most, and thus seek to improve whatever is needed.Originality/valueThis meta-analytic study advances the understanding of Fintech continuance intentions. Using the proposed approach, it is possible to generate accurate estimates of the effect size of each analyzed antecedent as the meta-analytic method jointly evaluates the results produced by a wide variety of studies performed in different contexts, allowing more accurate conclusions to be drawn.

Predicting diet in brachyuran crabs using external morphology.

Morphological traits have often been used to predict diet and trophic position of species across many animal groups. Variation in gut size of closely related animals is known to be a good predictor of dietary habits. Species that are more herbivorous or that persist on low-quality diets often have larger stomachs than their carnivorous counterparts. This same pattern exists in crabs and in most species, individuals exhibit external markings on the dorsal side of their carapace that appear to align with the position and size of their gut. We hypothesized that these external markings could be used as an accurate estimate of the crab's cardiac stomach size, allowing an approximation of crab dietary strategies without the need to sacrifice and dissect individual animals. We used literature values for mean diet and standardized external gut size markings taken from crab photographs across 50 species to show that percent herbivory in the diet increases non-linearly across species of brachyuran crab with the external estimate of gut size. We also used data from dissections in four species to show that external gut markings were positively correlated with gut sizes, though the strength of this correlation differed across species. We conclude that when rough approximations of diet quality such as percent herbivory will suffice, measuring external carapace markings in crabs presents a quick, free, non-lethal alternative to dissections. Our results also provide important insights into tradeoffs that occur in crab morphology and have implications for crab evolution.