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Non-random sampling is a source of bias in empirical research. It is common for the outcomes of interest (e.g. wage distribution) to be skewed in the source population. Sometimes, the outcomes are further subjected to sample selection, which is a type of missing data, resulting in partial observability. Thus, methods based on complete cases for skew data are inadequate for the analysis of such data and a general sample selection model is required. Heckman proposed a full maximum likelihood estimation method under the normality assumption for sample selection problems, and parametric and non-parametric extensions have been proposed. We generalize Heckman selection model to allow for underlying skew-normal distributions. Finite-sample performance of the maximum likelihood estimator of the model is studied via simulation. Applications illustrate the strength of the model in capturing spurious skewness in bounded scores, and in modelling data where logarithm transformation could not mitigate the effect of inherent skewness in the outcome variable.

Abstract Background Clinical studies are often limited by resources available, which results in constraints on sample size. We use simulated data to illustrate study implications when the sample size is too small. Methods and Results Using 2 theoretical populations each with N = 1000, we randomly sample 10 from each population and conduct a statistical comparison, to help make a conclusion about whether the 2 populations are different. This exercise is repeated for a total of 4 studies: 2 concluded that the 2 populations are statistically significantly different, while 2 showed no statistically significant difference. Conclusions Our simulated examples demonstrate that sample sizes play important roles in clinical research. The results and conclusions, in terms of estimates of means, medians, Pearson correlations, chi-square test, and P values, are unreliable with small samples. Clinical studies are often limited by resource availability that results in constraints on sample size. This study used simulated data to illustrate study implications when the sample size is too small.

Does the public know much more about politics than conventionally thought? A number of studies have recently argued, on various grounds, that the “don’t know” (DK) and incorrect responses to traditionally designed and scored survey knowledge items conceal a good deal of knowledge. This paper examines these claims, focusing on the prominent and influential argument that discouraging DKs would reveal a substantially more knowledgeable public. Using two experimental surveys with national random samples, we show that discouraging DKs does little to affect our picture of how much the public knows about politics. For closed-ended items, the increase in correct responses is large but mainly illusory. For open-ended items, it is genuine but minor. We close by examining the other recent evidence for a substantially more knowledgeable public, showing that it too holds little water.

During the COVID-19 pandemic, testing of all persons except those who are symptomatic, is not feasible due to shortage of facilities and staff. This article focuses on estimating the number of COVID-19-positive persons over a geographical domain. The Horvitz– Thompson and Hansen—Hurwitz type estimators under adaptive cluster sampling-based design have been suggested. Two case studies are discussed to demonstrate the performance of the estimators under certain assumptions. Advantages and limitations are also mentioned.

Unsafe acts contribute dominantly to construction accidents, and increasing safety behavior is essential to reduce accidents. Previous research conceptualized safety behavior as an interaction between proximal individual differences (safety knowledge and safety motivation) and distal contextual factors (leadership and safety climate). However, relatively little empirical research has examined this conceptualization in the construction sector. Given the cultural background of the sample, this study makes a slight modification to the conceptualization and views transformational leadership as an antecedent of safety climate. Accordingly, this study establishes a multiple mediator model showing the mechanisms through which transformational leadership translates into safety behavior. The multiple mediator model is estimated by the structural equation modeling (SEM) technique, using individual questionnaire responses from a random sample of construction personnel based in Hong Kong. As hypothesized, transformational leadership has a significant impact on safety climate which is mediated by safety-specific leader–member exchange (LMX), and safety climate in turn impacts safety behavior through safety knowledge. The results suggest that future safety climate interventions should be more effective if supervisors exhibit transformational leadership, encourage construction personnel to voice safety concerns without fear of retaliation, and repeatedly remind them about safety on the job.

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images. Generally, during the acquisition of images in real-time, motion blur, caused by camera shaking or human motion, appears. Deep learning-based intelligent control applied in vision can help us solve the problem. To this end, we propose a 3D reconstruction method for motion-blurred images using deep learning. First, we develop a BF-WGAN algorithm that combines the bilateral filtering (BF) denoising theory with a Wasserstein generative adversarial network (WGAN) to remove motion blur. The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image. Then, the blurred image and the corresponding sharp image are input into the WGAN. This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions. Next, we use the deblurred images generated by the BF-WGAN algorithm for 3D reconstruction. We propose a threshold optimization random sample consensus (TO-RANSAC) algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately. Compared with the traditional RANSAC algorithm, the TO-RANSAC algorithm can adjust the threshold adaptively, which improves the accuracy of the 3D reconstruction results. The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms. In addition, the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

The application of LiDAR technology in extracting individual trees and stand parameters plays a crucial role in forest surveys. Accurate identification of individual tree trunks is a critical foundation for subsequent parameter extraction. For LiDAR-acquired forest point cloud data, existing two-dimensional (2D) plane-based algorithms for tree trunk detection often suffer from spatial information loss, resulting in reduced accuracy, particularly for tilted trees. While cylinder fitting algorithms provide a three-dimensional (3D) solution for trunk detection, their performance in complex forest environments remains limited due to sensitivity to parameters like distance thresholds. To address these challenges, this study proposes an improved individual tree trunk detection algorithm, Random Sample Consensus Cylinder Fitting (RANSAC-CyF), specifically optimized for detecting cylindrical tree trunks. Validated in three forest plots with varying complexities in Tianhe District, Guangzhou, the algorithm demonstrated significant advantages in the inlier rate, detection success rate, and robustness for tilted trees. The study showed the following results: (1) The average difference between the inlier rates of tree trunks and non-tree points for the three sample plots using RANSAC-CyF were 0.59, 0.63, and 0.52, respectively, which were significantly higher than those using the Least Squares Circle Fitting (LSCF) algorithm and the Random Sample Consensus Circle Fitting (RANSAC-CF) algorithm (p < 0.05). (2) RANSAC-CyF required only 2 and 8 clusters to achieve a 100% detection success rate in Plot 1 and Plot 2, while the other algorithms needed 26 and 40 clusters. (3) The effective distance threshold range of RANSAC-CyF was more than twice that of the comparison algorithms, maintaining stable inlier rates above 0.9 across all tilt angles. (4) The RANSAC-CyF algorithm still achieved good detection performance in the challenging Plot 3. Together, the other two algorithms failed to detect. The findings highlight the RANSAC-CyF algorithm’s superior accuracy, robustness, and adaptability in complex forest environments, significantly improving the efficiency and precision of individual tree trunk detection for forestry surveys and ecological research.

This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In the edge detection step, AGPN analyzes geometric properties of each query point’s neighborhood, and then combines RANdom SAmple Consensus (RANSAC) and angular gap metric to detect edges. In the feature line tracing step, feature lines are traced by a hybrid method based on region growing and model fitting in the detected edges. Our approach is experimentally validated on complex man-made objects and large-scale urban scenes with millions of points. Comparative studies with state-of-the-art methods demonstrate that our method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds. Moreover, AGPN is insensitive to the point density of the input data.

As the global demand for clean energy continues to rise, wind power has become one of the most important renewable energy sources. However, wind power data often contains a high proportion of dense anomalies, which not only significantly affect the accuracy of wind power forecasting models but may also mislead grid scheduling decisions, thereby jeopardizing grid security. To address this issue, this paper proposes an adaptive threshold robust regression model (RPR model) based on the combination of the Random Sample Consensus (RANSAC) algorithm and polynomial linear regression for wind power data cleaning. The model successfully captures the nonlinear relationship between wind speed and power by extending the polynomial features of wind speed and power, enabling the linear regression model to handle the nonlinearity. By combining the RANSAC algorithm and polynomial linear regression, a robust polynomial regression model is constructed to tackle anomalous data and enhance the accuracy of data cleaning. During the cleaning process, the model first fits the raw data by randomly selecting a minimal sample set, then dynamically adjusts the decision thresholds based on the median of residuals and median absolute deviation (MAD), ensuring effective identification and cleaning of anomalous data. The model’s robustness allows it to maintain efficient cleaning performance even with a high proportion of anomalous data, addressing the limitations of existing methods when handling densely distributed anomalies. The effectiveness and innovation of the proposed method were validated by applying it to real data from a wind farm operated by Longyuan Power. Compared to other commonly used cleaning methods, such as the Bidirectional Change Point Grouping Quartile Statistical Model, Principal Contour Image Processing Model, DBSCAN Clustering Model, and Support Vector Machine (SVM) Model, experimental results showed that the proposed method delivered the best performance in improving data quality. Specifically, the method significantly reduced the average absolute error (MAE) of the wind power forecasting model by 72.1%, which is higher than the reductions observed in other methods (ranging from 37.3 to 52.7%). Moreover, it effectively reduced the prediction error of the Convolutional Neural Network (CNN) + Gated Recurrent Unit (GRU) forecasting model, ensuring high prediction accuracy. The adaptive threshold robust regression model proposed in this study is innovative and has significant application potential. It provides an effective new approach for wind power data cleaning, applicable not only to conventional scenarios with low proportions of anomalous data but also to complex datasets with a high proportion of dense anomalies.

Plane detection in point clouds is a common step in interpreting environments within robotics. Mobile robotic platforms must interact efficiently and safely with their surroundings, which requires capabilities such as detecting walls to avoid collisions and recognizing workbenches for object manipulation. Since these environmental elements typically appear as plane-shaped surfaces, a fast and accurate plane detector is an essential tool for robotics practitioners. RANSAC (Random Sample Consensus) is a widely used technique for plane detection that iteratively evaluates the fitness of planes by sampling three points at a time from a point cloud. In this work, we present an approach that, rather than seeking planes directly, focuses on finding lines by sampling only two points at a time. This leverages the observation that it is more likely to detect lines within the plane than to find the plane itself. To estimate planes from these lines, we perform an additional step that fits a plane for each pair of lines. Experiments conducted on three datasets, two of which are public, demonstrate that our approach outperforms the traditional RANSAC method, achieving better results while requiring fewer iterations. A public repository containing the developed code is also provided.