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With density functional theory (DFT), it is possible to calculate the formation energy of charged point defects and in turn to predict a range of experimentally relevant quantities, such as defect concentrations, charge transition levels, or recombination rates. While prior efforts have led to marked improvements in the accuracy of such calculations, comparatively modest effort has been directed at quantifying their uncertainties. However, in the broader DFT research space, the development of Bayesian Error Estimation Functionals (BEEF) has enabled uncertainty quantification (UQ) for other properties. In this paper, we investigate the utility of BEEF as a tool for UQ of defect formation energies. We build a pipeline for propagating BEEF energies through a formation-energy calculation and test it on intrinsic defects in several materials systems spanning a variety of chemistries, bandgaps, and crystal structures, comparing to prior published results where available. We also assess the impact of aligning to a deep-level transition rather than to the VBM (valence band maximum). We observe negligible dependence of the estimated uncertainty upon a supercell size, though the relationship may be obfuscated by the fact that finite-size corrections cannot be computed separately for each member of the BEEF ensemble. Additionally, we find an increase in estimated uncertainty with respect to the absolute charge of a defect and the relaxation around the defect site without deep-level alignment, but this trend is absent when the alignment is applied. While further investigation is warranted, our results suggest that BEEF could be a useful method for UQ in defect calculations. [ABSTRACT FROM AUTHOR]

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability. [ABSTRACT FROM AUTHOR]

Background: The Veterans Health Administration (VHA) implemented the Clinical Resource Hub (CRH) program to fill staffing gaps in primary care (PC) clinics via telemedicine and maintain veterans' healthcare access. Objective: To evaluate PC wait times before and after CRH implementation. Design: Comparative interrupted time series analysis among a retrospective observational cohort of PC clinics who did and did not use CRH during pre-implementation (October 2018–September 2019) and post-implementation (October 2019–February 2020) periods. Participants: Clinics completing ≥10 CRH visits per month for 2 consecutive months and propensity matched control clinics. Main Measures: Two measures of patient access (i.e., established, and new patient wait times) and one measure of clinic capacity (i.e., third next available appointment) were assessed. Clinics using CRH were 1:1 propensity score matched across clinical and demographic characteristics. Comparative interrupted time series models used linear mixed effects regression with random clinic-level intercepts and triple interaction (i.e., CRH use, pre- vs. post-implementation, and time) for trend and point estimations. Key Results: PC clinics using CRH (N = 79) were matched to clinics not using CRH (N = 79). In the 12-month pre-implementation, third next available time increased in CRH clinics (0.16 days/month; 95% CI = [0.07, 0.25]), and decreased in the 5 months post-implementation (−0.58 days/month; 95% CI = [−0.90, −0.27]). Post-implementation third next available time also decreased in control clinics (−0.48 days/month; 95% CI = [−0.81, −0.17]). Comparative differences remained non-significant. There were no statistical differences in established or new patient wait times by CRH user status, CRH implementation, or over time. Conclusions: In a national VHA telemedicine program developed to provide gap coverage for PC clinics, no wait time differences were observed between clinics using and not using CRH services. This hub-and-spoke telemedicine service is an effective model to provide gap coverage while maintaining access. Further investigation of quality and long-term access remains necessary. [ABSTRACT FROM AUTHOR]

Several approaches to estimating the productivity function for a Hawkes point process with variable productivity are discussed, improved upon, and compared in terms of their root-mean-squared error and computational efficiency for various data sizes, and for binned as well as unbinned data. We find that for unbinned data, a regularized version of the analytic maximum likelihood estimator proposed by Schoenberg is the most accurate but is computationally burdensome. The unregularized version of the estimator is faster to compute but has lower accuracy, though both estimators outperform empirical or binned least squares estimators in terms of root-mean-squared error, especially when the mean productivity is 0.2 or greater. For binned data, binned least squares estimates are highly efficient both in terms of computation time and root-mean-squared error. An extension to estimating transmission time density is discussed, and an application to estimating the productivity of Covid-19 in the United States as a function of time from January 2020 to July 2022 is provided. [ABSTRACT FROM AUTHOR]

AbstractIdeal point estimation methods in the social sciences lack a principled approach for identifying multidimensional ideal points. We present a novel method for estimating multidimensional ideal points based on ℓ1 distance. In the Bayesian framework, the use of ℓ1 distance transforms the invariance problem of infinite rotational turns into the signed perpendicular problem, yielding posterior estimates that contract around a small area. Our simulation shows that the proposed method successfully recovers planted multidimensional ideal points in a variety of settings including non-partisan, two-party, and multi-party systems. The proposed method is applied to the analysis of roll call data from the United States House of Representatives during the late Gilded Age (1891-1899) when legislative coalitions were distinguished not only by partisan divisions but also by sectional divisions that ran across party lines. [ABSTRACT FROM AUTHOR]

Multidimensional measuring instruments are often used in behavioral, social, educational, marketing, and biomedical research. For these scales, the paper discusses how to find the optimal score based on their components that is associated with the highest possible reliability. Within the framework of structural equation modeling, an approach to point and interval estimation of the maximal reliability coefficient of general structure measuring instruments is outlined. The procedure allows obtaining in addition the linear combination of all scale components that possesses maximal reliability. The described method is applicable in empirical research with popular statistical software, and is illustrated using an example. [ABSTRACT FROM AUTHOR]

In the analysis of a train–bridge coupled system, fuzzy uncertainty is a factor that must be considered in the prediction of coupled vibration response, but it has not been considered so far. In this work, the concept of information entropy is used to unify the fuzzy uncertainty and random variables into the train–bridge coupled system, and the fuzzy random train–bridge coupled system is established. The fuzzy dynamic response of trains and bridges with fuzzy parameters of the bridge structures and the mass of the carriage were studied, and the mean and variance of the response quantities were calculated using the new point estimation method (NPEM). The combined effect of the fuzziness is considered and the fuzzy value of the system dynamics is obtained. The feasibility of applying this method to train–bridge problems was verified. The calculation results indicated that the maximum amplitude of the fuzzy vertical displacement of the bridge exceeded the conventional vertical displacement by 25.57%, and the maximum amplitude of the fuzzy vertical acceleration of the train exceeded the conventional vertical acceleration by 23.42%. Obviously, in this case, the traditional deterministic calculation method cannot comprehensively and accurately analyze the dynamic response of the train–bridge system. The method in this paper can provide theoretical guidance for evaluating the safety of bridge structures and running safety research in the future. [ABSTRACT FROM AUTHOR]

This study reviews the methodologies used in the literature to predict failure in small and medium-sized enterprises (SMEs). We identified 145 SMEs' default prediction studies from 1972 to early 2023. We summarized the methods used in each study. The focus points are estimation methods, sample re-balancing methods, variable selection techniques, validation methods, and variables included in the literature. More than 1,200 factors used in failure prediction models have been identified, along with 54 unique feature selection techniques and 80 unique estimation methods. Over one-third of the studies do not use any feature selection method, and more than one-quarter use only in-sample validation. Our main recommendation for researchers is to use feature selection and validate results using hold-out samples or cross-validation. As an avenue for further research, we suggest in-depth empirical comparisons of estimation methods, feature selection techniques, and sample re-balancing methods based on some large and commonly used datasets. [ABSTRACT FROM AUTHOR]

With the advancement of robotics technology, industrial automation has become a trend in many scenarios. In fields such as warehousing logistics and service robotics, a reliable method for estimating the grasp pose of objects has become a necessary task. At present, many robotic grasping systems have achieved good results. However, due to the irregularity of the object surface and variation of illumination, the point cloud obtained by a single view has large holes and errors at the edge of the object. These errors easily lead to wrong estimations of grasp poses. To address these issues, this paper proposes a practical robot grasping method. The method is based on the idea of 6D pose estimation and point cloud fusion and complements the input point cloud with the model through 6D pose estimation. Then, the fused object point cloud is used to estimate the grasp pose through the Angle-View Net and fast search strategy. In order to ensure the accuracy of point cloud fusion, we use iterative closest point to correct the 6D pose. Finally, we conduct experiments in simulated and real environments, and the performance evaluation shows the feasibility of the method. [ABSTRACT FROM AUTHOR]

LiDAR technology introduced the possibility of indirectly estimating various tree parameters. In 2020, Apple incorporated LiDAR sensors into their iPhone 12 Pro. This brings an opportunity to make data collection and plot scanning incredibly convenient. The main aim is to search for a tool and software to estimate the most accurate Diameter at Breast Height (DBH) for individual trees. Therefore, this study focuses on using an iPhone 12 Pro for data collection of individual trees. The performance of three software tools (ForestScanner, rTLS, RANSAC) for the estimation of DBH with field-estimated DBH was compared. The investigation of the significance of the estimation of DBH for each tree species was performed using the three algorithms. In this context, the scanning of 123 trees comprising six species: pine, oak, beech, hornbeam, spruce, and fir was done. DBH was estimated of the scanned tree point cloud using a built-in algorithm of ForestScanner application in the iPhone 12 Pro, RANSAC (CloudCompare plugin), and rTLS (an R-based package). In this study, ForestScanner showed the best results. Therefore, ForestScanner can be used as a reliable tool for measuring DBH. Inferences confirmed by ANOVA and Tukey post-hoc tests show that species significantly influence DBH. Accurate DBH estimation is crucial, and using lightweight devices like the iPhone can revolutionize the forest inventory sector in terms of the estimation of DBH. [ABSTRACT FROM AUTHOR]

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5 % . [ABSTRACT FROM AUTHOR]

In statistics, there exists several means such as the arithmetic, geometric, harmonic, or power means. Using axiomatic deduction, they have been unified into the generalized f-mean. Similarly, there are several standard deviations and they have been unified into the generalized f-standard deviation, even though without derivation. However, standard score, as known in its arithmetic and geometric versions, has never been f-generalized. The goal of this article is to expose a full derivation of generalized f-statistics, adopting a parameter estimation point of view. Generalized f-mean and generalized f-variance are derived thanks to a maximum likelihood estimation. The non trivial relation between generalized f-standard deviation and generalized f-variance is also studied. Furthermore, generalized f-standard score is defined and extended to multivariate case. Finally, these generalized f-statistics are applied on simulated data for pre-processing and outlier detection. [ABSTRACT FROM AUTHOR]

This paper presents a robust point cloud registration method based on a multi-scale covariance matrix descriptor and an accurate transformation estimation. Compared with state-of-the-art feature descriptors, such as FPH, 3DSC, spin image, etc., our proposed multi-scale covariance matrix descriptor is superior for dealing with registration problems in a higher noise environment since the mean operation in generating the covariance matrix can filter out most of the noise-damaged samples or outliers and also make itself robust to noise. Compared with transformation estimation, such as feature matching, clustering, ICP, RANSAC, etc., our transformation estimation is able to find a better optimal transformation between a pair of point clouds since our transformation estimation is a multi-level point cloud transformation estimator including feature matching, coarse transformation estimation based on clustering, and a fine transformation estimation based on ICP. Experiment findings reveal that our proposed feature descriptor and transformation estimation outperforms state-of-the-art feature descriptors and transformation estimation, and registration effectiveness based on our registration framework of point cloud is extremely successful in the Stanford 3D Scanning Repository, the SpaceTime dataset, and the Kinect dataset, where the Stanford 3D Scanning Repository is known for its comprehensive collection of high-quality 3D scans, and the SpaceTime dataset and the Kinect dataset are captured by a SpaceTime Stereo scanner and a low-cost Microsoft Kinect scanner, respectively. [ABSTRACT FROM AUTHOR]

The key of current research on behavior recognition algorithms based on deep learning lies in enhancing the accuracy and stability of key point extraction, in order to achieve more accurate action recognition of targets. However, many current algorithms tend to just add attention mechanisms that appear to perform better in the feature extraction stage of the target, without considering the impact of different attention mechanisms on different models and tasks. Therefore, this study proposes an algorithmic model for pose estimation based on various attention mechanisms, which further highlights the importance of selecting an appropriate attention mechanism by comparing the impact of different attention mechanisms on the model. In addition, considering the stability of key point extraction, the initialization of the model is fine tuned to select a more suitable initialization method that improves the performance by increasing the category of weights on network layer judgments. Compared with the performance of the benchmark network model, the model enhances all evaluation metrics on both multiscale and no multiscale CrowdPose datasets, where the average accuracy improvement in both cases is more than 1%. [ABSTRACT FROM AUTHOR]

Hierarchical Bayes, or E‐Bayes, is frequently used to estimate the failure probability when solving a zero‐failure reliability evaluation model; however, the accuracy of the reliability estimation using these methods is not very good in practice. Due to this, a novel double‐modified hierarchical Bayes (DMH‐Bayes) is proposed for Weibull characteristic data in this study to enhance failure probability estimation and improve reliability point estimation accuracy. Meanwhile, in order to guarantee the preservation of the assessment findings' consistency and confidence level, the parametric Bootstrap method (P‐Bootstrap) and the L‐moment estimation method based on point estimation are introduced to obtain reliability confidence interval estimates. Based on Monte–Carlo simulation testing and analysis of a gyroscope bearing, the new model is confirmed to have better applicability and robustness while improving the accuracy of reliability assessment. [ABSTRACT FROM AUTHOR]

This article addresses the design of angular velocity observer for rigid bodies using vector measurements directly. The main contributions of our observer compared with the classical estimation algorithms lie in that it does not need to reconstruct the attitude information from the measurements nor be implemented with velocity sensors. Meanwhile, it provides three‐degrees‐of‐freedom (3DOF) full state estimates. To avoid the unstable critical points for the attitude estimation errors on SO(3), the synergistic potential functions based hybrid method is adopted. Afterwards, an output feedback attitude tracking controller with a proportional‐derivative (PD) plus feedforward form is built based on the estimates from the observer, which can also be implemented using vector measurements directly. Rigorous analysis of the stability of the observer and the combined observer‐controller are presented in detail, with a separation property shown between the observer and the controller. Simulations are conducted to illustrate the effectiveness of the proposed observer and the combined attitude tracking law. [ABSTRACT FROM AUTHOR]

The determination of flash points is a critical aspect of chemical safety, essential for assessing explosion hazards and fire risks associated with flammable solutions. With the advent of new chemical blends and the increasing complexity of chemical waste management, the need for accurate and reliable flash point prediction methods has become more pronounced. This study introduces a novel predictive approach using Bayesian kernel machine regression (BKMR) with Gaussian process priors, designed to meet the growing demand for precise flash point estimation in the context of chemical safety. The BKMR model, underpinned by Bayesian statistics, offers a comprehensive framework that not only quantifies prediction uncertainty but also enhances interpretability amidst experimental data variability. Our comparative analysis reveals that BKMR surpasses traditional predictive models, including support vector machines, kernel ridge regression, and Gaussian process regression, in terms of accuracy and reliability across multiple metrics. By elucidating the intricate interactions between molecular features and flash point properties, the BKMR model provides profound insights into the chemical dynamics that influence flash point determinations. This study signifies a methodological leap in flash point prediction, offering a valuable tool for chemical safety analysis and contributing to the development of safer chemical handling and storage practices. [ABSTRACT FROM AUTHOR]

The proposed RMS-FlowNet++ is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation that can operate on high-density point clouds. For hierarchical scene flow estimation, existing methods rely on expensive Farthest-Point-Sampling (FPS) to sample the scenes, must find large correspondence sets across the consecutive frames and/or must search for correspondences at a full input resolution. While this can improve the accuracy, it reduces the overall efficiency of these methods and limits their ability to handle large numbers of points due to memory requirements. In contrast to these methods, our architecture is based on an efficient design for hierarchical prediction of multi-scale scene flow. To this end, we develop a special flow embedding block that has two advantages over the current methods: First, a smaller correspondence set is used, and second, the use of Random-Sampling (RS) is possible. In addition, our architecture does not need to search for correspondences at a full input resolution. Exhibiting high accuracy, our RMS-FlowNet++ provides a faster prediction than state-of-the-art methods, avoids high memory requirements and enables efficient scene flow on dense point clouds of more than 250K points at once. Our comprehensive experiments verify the accuracy of RMS-FlowNet++ on the established FlyingThings3D data set with different point cloud densities and validate our design choices. Furthermore, we demonstrate that our model has a competitive ability to generalize to the real-world scenes of the KITTI data set without fine-tuning. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to use fuzzy logic to provide a systematic approach for designing fuzzy control charts and compare the average run length type 2 of these charts in three different modes: Gaussian fuzzy, triangular fuzzy, and crisp cases. The process of solving and presenting the research results is by calculating the fuzzy control chart conditions and fuzzy descriptive capability indices through theoretical calculations and a project-based organizational case study and comparing the average run length type 2, which represents the expected interval between out-of-control events. Average run length type 2 illustrates that Gaussian and triangular quality control charts are able to detect an out-of-control signal more quickly. The main contributions of this paper are to use Gaussian fuzzy numbers in order to provide a systematic approach for designing fuzzy control charts, estimating descriptive capability indices, and analyzing the results of their estimation under fuzzy logic. [ABSTRACT FROM AUTHOR]

Failure probability function (FPF) is an important index that reflects the influence of designable distribution parameters on the safety degree of a structure, and it can be used for decoupling reliability optimization models. Thus, its efficient solution is expected. A decoupling algorithm based on statistical moment functions (SMFs) of performance function is proposed to solve the FPF efficiently in this paper. The proposed algorithm first constructs an extended density weight function (EDWF), which can cover the interested region of the distribution parameters and is independent of the distribution parameters so that the statistical moment integrals corresponding to different realizations of the distribution parameters can share the same EDWF. Then, using the same EDWF, a strategy is dexterously designed to estimate the SMFs by sharing a set of integral characteristic nodes. Finally, the FPF is approximated by the SMFs, which varies with the distribution parameters in the interested design region. In addition, the proposed algorithm introduces the Box-Cox transformation of the performance function to guide the high accuracy of FPF approximated by the SMFs. The main contribution of the proposed algorithm is constructing the EDWF to decouple the dependence of solving SMFs on the realizations of the distribution parameters over the interested region and designing the strategy of estimating the SMFs by sharing the same integral characteristic nodes. Since the proposed algorithm employs a point estimation method to evaluate the FPF, it has higher efficiency than the competitive methods. Numerical and engineering examples demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study proposes a novel hybrid prediction model for short-term Chl-a concentration prediction in the Chaohu Lake basin. The model adopts a two-layer decomposition, grouping prediction, and summation design, with an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) used for data decomposition and sample entropy (SampEn) for determining the complexity of the decomposed components. The component with the highest SampEn is then further decomposed using variational mode decomposition (VMD). In the grouping prediction part, a hybrid prediction model is utilized, based on bidirectional gate recurrent unit (BiGRU), temporal convolutional network (TCN), and attention mechanism (AM), to predict all components after the second decomposition separately. The final Chl-a concentration prediction is obtained by linearly summing the predicted values of each component. Through comparison with sensitivity analysis, point estimation, and interval estimation, this study finds that the proposed ICEEMDAN-VMD-BiGRU-TCN-AM model performs well in predicting the content of Chl-a concentration in lakeshore. The model has the highest fitting determination coefficient of 98.79%, the smallest root-mean-square error (RMSE) of only 0.0063 (ug/L), and the largest probability interval coverage percentage (PICP) of 97.57%. These results indicate that the IVBTA model has significant advantages in accuracy, precision, and stability compared to other models. This means that our method can more accurately predict the content of Chl-a in reservoirs, providing a new approach for water quality prediction and effective prevention and control of eutrophication in reservoirs. [ABSTRACT FROM AUTHOR]

This study proposes an innovative deep learning algorithm for pose estimation based on point clouds, aimed at addressing the challenges of pose estimation for objects affected by the environment. Previous research on using deep learning for pose estimation has primarily been conducted using RGB-D data. This paper introduces an algorithm that utilizes point cloud data for deep learning-based pose computation. The algorithm builds upon previous work by integrating PointNet + + technology and the classical Point Pair Features algorithm, achieving accurate pose estimation for objects across different scene scales. Additionally, an adaptive parameter-density clustering method suitable for point clouds is introduced, effectively segmenting clusters in varying point cloud density environments. This resolves the complex issue of parameter determination for density clustering in different point cloud environments and enhances the robustness of clustering. Furthermore, the LineMod dataset is transformed into a point cloud dataset, and experiments are conducted on the transformed dataset to achieve promising results with our algorithm. Finally, experiments under both strong and weak lighting conditions demonstrate the algorithm's robustness. [ABSTRACT FROM AUTHOR]

Automatic or smart irrigation systems benefit irrigation water management. However, measurement sensor networks in automatic irrigation systems are complex, and maintenance is essential. Regional soil moisture estimation avoids the multiple measurements necessary when deploying an irrigation system. In this sense, a fuzzy estimation approach based on decision-making (FEADM) has been used to obtain soil moisture point estimates. However, FEADM requires intelligent weather adjustment based on spatial features (IWeCASF) to perform regional soil moisture estimation. The IWeCASF-FEADM integrated approach for regional soil moisture estimation is developed in this work. IWeCASF provides the inputs for FEADM. FEADM is performed R times; R is the number of checkpoints at which a point estimate is obtained. In this way, regional estimation is achieved when the set of R soil moisture point estimates is completed. Additionally, IWeCASF-FEADM considers the irrigation water records, which are not included in either method individually. This method can detect when the soil moisture is deficient in a region, allowing actions to prevent water stress. This regional estimation reduces an irrigation system's operational and maintenance complexity. This integrated approach has been tested over several years by comparing the results of regional soil moisture estimation with measurements obtained at many points in the study region. [ABSTRACT FROM AUTHOR]

The precise control of soft and continuum robots requires knowledge of their shape, which has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built‐in sensors, resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on expensive tracking systems with reflective markers placed on all components, which are infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, a regression approach is presented for three‐dimensional key point estimation using a convolutional neural network. The proposed approach uses data‐driven supervised learning and is capable of online markerless estimation during inference. Two images of a robotic system are captured simultaneously at 25 Hz from different perspectives and fed to the network, which returns for each pair the parameterized key point or piecewise constant curvature shape representations. The proposed approach outperforms markerless state‐of‐the‐art methods by a maximum of 4.5% in estimation accuracy while being more robust and requiring no prior knowledge of the shape. Online evaluations on two types of soft robotic arms and a soft robotic fish demonstrate the method's accuracy and versatility on highly deformable systems. [ABSTRACT FROM AUTHOR]

Moving objects in the environment have a higher priority and more challenges in growing domains like unmanned vehicles and intelligent robotics. Estimating the motion state of objects based on point clouds in outdoor scenarios is currently a challenging area of research. This is due to factors such as limited temporal information, large volumes of data, extended network processing times, and the ego-motion. The number of points in a point cloud frame is typically 60,000–120,000 points, but most current motion state estimation methods for point clouds only downsample to a few thousand points for fast processing. The downsampling step will lead to the loss of scene information, which means these methods are far from being used in practical applications. Thus, this paper proposes a motion state estimation method that combines spatio-temporal constraints and deep learning. It starts by estimating and compensating the ego-motion of multi-frame point cloud data and mapping multi-frame data to a unified coordinate system; then the point cloud motion segmentation model on the multi-frame point cloud is proposed for motion object segmentation. Finally, spatio-temporal constraints are utilized to correlate the moving object at different moments and estimate the motion vectors. Experiments on KITTI, nuScenes, and real captured data show that the proposed method has good results, with an average vector deviation of only 0.036 m and 0.043 m in KITTI and nuScenes under a processing time of about 80 ms. The EPE3D error under the KITTI data is only 0.076 m, which proves the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Nonlinear state-space models (SSMs) are widely used to model actual industrial processes. System identification is an important method to reduce the uncertainty of the simulation model. In recent years, system identification has been greatly improved with the rise of machine learning. However, there are a few reviews on the latest identification methods based on machine learning. Therefore, this paper focuses on the latest development of identification methods for nonlinear SSM in recent years. In particular, this paper comprehensively compares the identification methods based on traditional methods and machine learning. In addition, according to the type of uncertainty, we divided the paper into the parameter's identification and the identification of unknown parts of the model. Compared with the classification of other reviews, our classification method is clearer. Briefly, this paper organizes the review according to the classification of uncertainty. Each type is extended from offline identification to online identification. Specifically, interval identification and point estimation methods are reviewed for offline parameter identification. For online parameter identification, point estimation methods are reviewed. In the case that the model is partially unknown or black-box, the modeling methods and identification methods are mainly reviewed. In addition to the traditional methods, this paper focuses on the latest progress in the application of machine learning in system recognition. Finally, at the end of the paper, this paper summarizes the existing methods and points out the key problems that still need to be solved. [ABSTRACT FROM AUTHOR]

This article introduces a paradigm shift in radar technology with field programmable gate array (FPGA)-driven Phase estimation using floating point arithmetic (FPA). Leveraging FPGA's parallel processing and the precision of FPA, this work promises enhanced accuracy and efficiency. The proposed system's key performance metrics include the following: number of slices: 20,941, number of look-up tables (LUTs): 22,371, number of digital signal processing (DSP) blocks: 2, delay: 112.9 ns, and power consumption: 7.2 mw. A comparative analysis showcases advantages in area utilization, LUT, and DSP blocks despite a trade-off with delay. The presented methodology and results demonstrate the feasibility of real-time phase estimation at GHz rates, positioning this approach as transformative for next-gen radar systems. [ABSTRACT FROM AUTHOR]

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This paper addresses the self-supervised learning of several critical factors in Visual Simultaneous Localization and Mapping (VSLAM) in low-level vision: interest point learning, descriptor learning, ego-motion estimation, and depth estimation. The key insight we have is that appearance and geometry constraints can be used to couple these fundamental vision issues. We propose a self-supervised framework for joint training of neural networks for multiple objectives to address complicated issues, simplify systems, and provide important information for deep monocular VSLAM systems. First, we input two adjacent images into pose and depth networks to obtain their corresponding depth maps and camera poses. Then, we employ a differentiable geometry module and utilize the depth maps and camera poses to generate pseudo-input images needed for the interest point network and construct the geometry loss. Further, we input the pseudo-input image and source image into the interest point network to obtain the corresponding interest points, descriptors, and scores. Subsequently, we construct the appearance loss. Finally, we combine the geometry and appearance losses to constrain the whole network in an unsupervised manner. The novelty of this paper is that it integrates the key information necessary in monocular VSLAM into a unified framework that takes into account interest point learning, descriptor learning, ego-motion estimation, and depth estimation at the same time. Without providing any ground truth, our model can combine sub-problems for self-supervised learning and achieve state-of-the-art performance in their respective domains. [ABSTRACT FROM AUTHOR]

AbstractAdvancements in technology have elevated the prominence of 3D point cloud data, making its analysis increasingly vital across various applications. This need drives the demand for advanced statistical analytic approaches to handle challenges such as size, sparsity, and irregularity in 3D point clouds while ensuring accurate and efficient information extraction. This article introduces a novel nonparametric distributed (NPD) learning framework that uses trivariate spline smoothing over a triangulation of domain. The proposed NPD algorithm features a straightforward, scalable, and communication-efficient implementation scheme that can achieve near-linear speedup. In addition, we provide rigorous theoretical support for the NPD estimation framework and demonstrate that the NPD spline estimators attain the same convergence rate as the global spline estimators obtained using the entire dataset and achieve the optimal nonparametric convergence rate established by Stone under some regularity conditions. To evaluate the efficacy of the proposed NPD method, we conduct simulation studies comparing it with several global nonparametric estimation methods used to smooth the 3D data. The results demonstrate the superior performance of the NPD method in accurately and efficiently processing and learning from 3D point clouds, highlighting its potential to advance large and complex data analysis. Supplementary material, which contains related technical details, proofs of the theoretical results, and additional results in simulation studies, is available online. [ABSTRACT FROM AUTHOR]

This paper presents an estimation of the launching points of unmanned aerial vehicles (UAVs) equipped with boosters. When UAVs are detected and tracked by sensors such as radars, the fast identification of vehicle launching points is needed for air traffic control and defense systems. When UAVs in a boosting phase are controlled by first-order pitch programming, this paper leverages inverse first-order pitch programming to analytically solve launching points. Furthermore, a two-point robust measurement selection (T-RMS) scheme is developed to reduce errors such as random noise and bias by utilizing multiple moving average filters. The proposed work is verified by various simulation results. [ABSTRACT FROM AUTHOR]

Ship detection and formation recognition in remote sensing have increasingly garnered attention. However, research remains challenging due to arbitrary orientation, dense arrangement, and the complex background of ships. To enhance the analysis of ship situations in channels, we model the ships as the key points and propose a context-aware DGCN-based ship formation recognition method. First, we develop a center point-based ship detection subnetwork, which employs depth-separable convolution to reduce parameter redundancy and combines coordinate attention with an oriented response network to generate direction-invariant feature maps. The center point of each ship is predicted by regression of the offset, target scale, and angle to realize the ship detection. Then, we adopt the spatial similarity of the ship center points to cluster the ship group, utilizing the Delaunay triangulation method to establish the topological graph structure of the ship group. Finally, we design a context-aware Dense Graph Convolutional Network (DGCN) with graph structure to achieve formation recognition. Experimental results on HRSD2016 and SGF datasets demonstrate that the proposed method can detect arbitrarily oriented ships and identify formations, attaining state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Since the full system deployment of BDS-3 in July 2020, it has provided stable and reliable positioning, navigation and timing (PNT) services for users around the world. It is an urgent task to explore the potential performance of precise point positioning (PPP) based on the full constellation of BDS-2/BDS-3. This research presented the global satellite visibility of BDS-2/BDS-3, analyzed the stability and accuracy of BDS-2 and BDS-3 rubidium clocks and hydrogen clocks, jointly estimated the BDS-2 and BDS-3 fractional cycle biases (FCB) and evaluated the PPP performance with ambiguity resolution (AR). Due to the fact that International GNSS Service (IGS) precise products have a great impact on narrow-lane (NL) FCB estimation, the FCB obtained by using the precise products from CODE (Center for Orbit Determination in Europe) and GBM (Deutsches GeoForschungsZentrum) in two different analysis centers and the FCB obtained by using GBM precise products with different calculation strategies are analyzed. In addition, the experimental data from Multi-GNSS Experiment (MGEX), EUREF (EPN) and Germany (GPN) observation networks are used to evaluate the influence of orbital errors on BDS FCB estimation. For wide-lane (WL) FCB, MGEX WL FCB has the highest accuracy, followed by EPN and GPN. On the contrary, for NL FCB, MGEX NL FCB has the worst accuracy, followed by EPN, and GPN is the best. Eventually, numerous experiment results showed that compared with the static float solution, the PPP fixed solutions in East (E), North (N) and Up (U) directions are improved by 32.9%, 27.7%, 13.8% for BDS-3 and 10.0%, 7.5%, 3.8% for BDS-2/BDS-3. Compared with the kinematic float solution, the PPP fixed solutions in E, N and U directions are improved by 27.9%, 23.4%, 16.6% for BDS-3 and 15.9%, 8.9%, 5.3% for BDS-2/BDS-3. [ABSTRACT FROM AUTHOR]

A concurrent heuristic search iterative process (CHSIP) is used for estimating groundwater pollution sources and aquifer parameters in this work. Frequent calls to carry out a numerical simulation of groundwater pollution have generated a huge calculated load during the CHSIP. Therefore, a valid means to mitigate this is building a substitute to emulate the numerical simulation at a low calculated load. However, there is a complicated nonlinear correlativity between the import and export of the numerical simulation on account of the large quantity of variables. This leads to a poor approach accuracy of the substitute compared to the simulation when using shallow learning methods. Therefore, we first built a stacked autoencoder substitute, using the deep learning method, to boost the approach accuracy of the substitute compared to the numerical simulation. In total, 400 training samples and 100 testing samples for the substitute were collected by employing the Latin hypercube sampling method and running the numerical simulator. The CHSIP was then employed for estimating the groundwater pollution sources and aquifer parameters, and the estimated outcome was obtained when the CHSIP was terminated. The data analysis, including interval estimation and point estimation, was implemented on the MATLAB platform. A relevant hypothetical case is set to verify our approaches, which shows that the CHSIP is helpful for estimating the groundwater pollution source and aquifer parameters and that the stacked autoencoder method can effectively boost the approach precision of the substitute for the simulator. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low positioning accuracy and poor mapping effect of the visual SLAM system caused by the poor quality of the dynamic object mask in an indoor dynamic environment, an indoor dynamic VSLAM algorithm based on the YOLOv8 model and depth information (YOD-SLAM) is proposed based on the ORB-SLAM3 system. Firstly, the YOLOv8 model obtains the original mask of a priori dynamic objects, and the depth information is used to modify the mask. Secondly, the mask's depth information and center point are used to a priori determine if the dynamic object has missed detection and if the mask needs to be redrawn. Then, the mask edge distance and depth information are used to judge the movement state of non-prior dynamic objects. Finally, all dynamic object information is removed, and the remaining static objects are used for posing estimation and dense point cloud mapping. The accuracy of camera positioning and the construction effect of dense point cloud maps are verified using the TUM RGB-D dataset and real environment data. The results show that YOD-SLAM has a higher positioning accuracy and dense point cloud mapping effect in dynamic scenes than other advanced SLAM systems such as DS-SLAM and DynaSLAM. [ABSTRACT FROM AUTHOR]

Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation. Therefore, we propose the multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards. Through the prior on the number of change points and their location, we impose dependence between change points across risks, as well as allowing for data-driven learning of their number. Then, conditionally on these change points, a multivariate Bernoulli prior is used to infer which risks are involved. Focus of posterior inference is cause-specific hazard rates and dependence across risks. Such dependence is often present due to subject-specific changes across time that affect all risks. Full posterior inference is performed through a tailored local-global Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation trick and MCMC updates from nonconjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches. [ABSTRACT FROM AUTHOR]

Many existing methodologies for analyzing spatiotemporal point patterns are developed based on the assumption of stationarity in both space and time for the second-order intensity or pair correlation. In practice, however, such an assumption often lacks validity or proves to be unrealistic. In this paper, we propose a novel and flexible nonparametric approach for estimating the second-order characteristics of spatiotemporal point processes, accommodating non-stationary temporal correlations. Our proposed method employs kernel smoothing and effectively accounts for spatial and temporal correlations differently. Under a spatially increasing-domain asymptotic framework, we establish consistency of the proposed estimators, which can be constructed using different first-order intensity estimators to enhance practicality. Simulation results reveal that our method, in comparison with existing approaches, significantly improves statistical efficiency. An application to a COVID-19 dataset further illustrates the flexibility and interpretability of our procedure. [ABSTRACT FROM AUTHOR]

In this paper, we study a sequential decision-making problem faced by e-commerce carriers related to when to send out a vehicle from the central depot to serve customer requests and in which order to provide the service, under the assumption that the time at which parcels arrive at the depot is stochastic and dynamic. The objective is to maximize the expected number of parcels that can be delivered during service hours. We propose two reinforcement learning (RL) approaches for solving this problem. These approaches rely on a look-ahead strategy in which future release dates are sampled in a Monte Carlo fashion, and a batch approach is used to approximate future routes. Both RL approaches are based on value function approximation: One combines it with a consensus function (VFA-CF) and the other one with a two-stage stochastic integer linear programming model (VFA-2S). VFA-CF and VFA-2S do not need extensive training as they are based on very few hyperparameters and make good use of integer linear programming (ILP) and branch-and-cut–based exact methods to improve the quality of decisions. We also establish sufficient conditions for partial characterization of optimal policy and integrate them into VFA-CF/VFA-2S. In an empirical study, we conduct a competitive analysis using upper bounds with perfect information. We also show that VFA-CF and VFA-2S greatly outperform alternative approaches that (1) do not rely on future information (2) are based on point estimation of future information, (3) use heuristics rather than exact methods, or (4) use exact evaluations of future rewards. Funding: This work was supported by the CY Initiative of Excellence [ANR-16- IDEX-0008]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0366. [ABSTRACT FROM AUTHOR]

Differentiable neural architecture search delivers point estimation to the optimal architecture, which yields arbitrarily high confidence to the learned architecture. This approach thus suffers in calibration and robustness, in contrast with the maximum a posteriori estimation scheme. In this paper, we propose a novel Variational Neural Architecture Search (VNAS) method that estimates and exploits the weight variability in the following three steps. VNAS first learns the weight distribution through variational inference which minimizes the expected lower bound on the marginal likelihood of architecture using unbiased Monte Carlo gradient estimation. A group of optimal architecture candidates is then drawn according to the learned weight distribution with the complexity constraint. The optimal architecture is further inferred under a novel training-free architecture-performance estimator, designed to score the network architectures at initialization without training, which significantly reduces the computational cost of the optimal architecture estimator. Extensive experiments show that VNAS significantly outperforms the state-of-the-art methods in classification performance and adversarial robustness. [ABSTRACT FROM AUTHOR]

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Humans inherently perceive 3D scenes using prior knowledge and visual perception, but 3D reconstruction in computer graphics is challenging due to complex object geometries, noisy backgrounds, and occlusions, leading to high time and space complexity. To addresses these challenges, this study introduces 3DRecNet, a compact 3D reconstruction architecture optimized for both efficiency and accuracy through five key modules. The first module, the Human-Inspired Memory Network (HIMNet), is designed for initial point cloud estimation, assisting in identifying and localizing objects in occluded and complex regions while preserving critical spatial information. Next, separate image and 3D encoders perform feature extraction from input images and initial point clouds. These features are combined using a dual attention-based feature fusion module, which emphasizes features from the image branch over those from the 3D encoding branch. This approach ensures independence from proposals at inference time and filters out irrelevant information, leading to more accurate and detailed reconstructions. Finally, a Decoder Branch transforms the fused features into a 3D representation. The integration of attention-based fusion with the memory network in 3DRecNet significantly enhances the overall reconstruction process. Experimental results on the benchmark datasets, such as ShapeNet, ObjectNet3D, and Pix3D, demonstrate that 3DRecNet outperforms existing methods. [ABSTRACT FROM AUTHOR]

We present a unique triangulation measurement of Strong Thermal Emission Velocity Enhancement (STEVE) observed on Sept 3rd, 2022, at Athabasca, Canada. Using two Digital single‐lens reflex (DSLR) color cameras with all‐sky fish‐eye lenses, we show the profile of STEVE altitude variation over time in 1 min resolution for the first time. We estimate the altitude variation of its visible purplish arc and green picket fence structures. We also compare the DLSR camera images with narrowband all‐sky images of an Optical Mesosphere Thermosphere Imager (OMTI) to see the correspondence of color camera images with 630 nm and OH‐band auroral/airglow emission images. The height of the purplish STEVE arc was stable at 150–170 km while present (∼0,546–0,633 UT), except for a short excursion to ∼200 km at 0,600 UT. The green picket fence structures appeared at 0,549 UT when the intensity of the STEVE arc started to intensify. They presented only for ∼7 min, and their altitude was steady at ∼110 km. The vertical movement of the STEVE arc to ∼200 km was found to be accompanied by the motion across the local magnetic field lines, suggesting a southward E × B drift underlying the westward ion drift. From the comparison with the OMTI images, we find that the purplish STEVE arc moved closer to the 630 nm arc in the all‐sky image when it rose to a higher altitude, indicating the occurrence of electron heating at a same or slightly higher altitude than the STEVE. Plain Language Summary: The Strong Thermal Emission Velocity Enhancement (STEVE) is a longitudinally extended purplish/mauve arc observed at subauroral latitudes, a region slightly equatorward of the typical aurora region. The morphological (shape and motion) characteristics of STEVE indicate that STEVE originates differently from typical aurora. Although it has been suggested that some chemical progresses are possibly triggering a STEVE, significant differences between observation and model still remain. The understanding of STEVE morphological characteristics, especially temporal features of STEVE development, will be helpful to improve our understanding about the STEVE generation. This paper presents a case study regarding the time variation of STEVE altitudes. The altitudes of STEVE are determined by triangulation using two color all‐sky cameras. We found that though the height of STEVE arc was stable for most of the time, it also rose and then descended for a short period. We also compared the images with camera detecting several wavelengths, and found that the STEVE moved closer to the red (wavelength: 630 nm, originating from oxygen) auroral arc in the all‐sky image when the STEVE elevated. This paper presents the first estimation of the temporal variation of STEVE altitudes. Key Points: First estimation of temporal variation of the altitudes of a Strong Thermal Emission Velocity Enhancement (STEVE)The estimated height of the STEVE arc was stable at ∼150–170 km for most of the time, except for a short elevation to ∼200 kmThe vertical motion of the STEVE arc entailed crossing of local magnetic field lines, and it moved closer to the 630 nm emission [ABSTRACT FROM AUTHOR]

The presence of a substantial short‐circuit current generates a large electromagnetic repulsive force. The energy of such a short‐circuit current increases the likelihood of equipment damage. Furthermore, the occurrence of short‐circuits disrupts the stable supply of electric power. When a short‐circuit occurs in a distribution line, short‐circuit point identification is realized based on information such as overcurrent indicators and equipment damage caused by the short‐circuit. However, there is no established method for promptly pinpointing the short‐circuit location. Therefore, this study proposes a short‐circuit point estimation method for distribution lines using smart‐meters; whose performance is anticipated to improve in the future. The proposed method involved using smart‐meter voltage measurements to measure and determine the changes in the voltage drop slope, caused by the distribution‐substation short‐circuit currents and load currents, from the short‐circuit location to the end point. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

We present a novel method for estimating species abundance using presence–absence maps. Our approach takes the spatial context into consideration, distinguishing it from conventional methods. The proposed method is built upon a well-known kernel estimation for point pattern intensity, with the addition of a new parameter representing the mean abundance in each occupied cell. The parameter estimate is obtained through maximum likelihood estimation. The expected abundance corresponds to the integral of the intensity over the study area, which can be estimated by taking the Riemann sum of the intensity. The implementation of our method is straightforward, using existing packages in the R software. We compared various bandwidth selection methods within our approach and assessed the estimation performance against some approaches based on the random placement model or negative binomial model through the simulation study and an empirical forestry data in Barro Colorado Island (BCI), Panama. The results of the simulation and the application demonstrate that our method, with a carefully chosen bandwidth, outperforms the alternatives for highly aggregated data and improves the issue of underestimation. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

In reliability and life analysis, the accelerated life test is frequently used because it can reduce cost and obtain additional reliability and lifetime information. In addition, when a unit fails in a life test, there are often two or more risk factors related to the cause of the failure. In this article, we consider the inference from a step-stress-accelerated life test with competing risks using progressive type-II censored data. Based on the assumption that the parameters affected by stress follow a log-linear model with the stress level, the proportional hazard model with the Kumaraswamy distribution is established. The point estimation of the unknown parameters is derived using maximum likelihood and Bayesian methods. Accordingly, the logarithmic asymptotic confidence and the highest posterior density credible intervals are derived and constructed. Moreover, the algorithm for multi-parameter sampling and simulation technology based on this model is given. Simulation results show that the proposed methods have good performance. In light of the estimates of the parameters, the estimated reliability function under normal conditions can be expressed, and its images under different methods are drawn. Last, a real dataset and a set of simulated data are presented for illustrative purposes. [ABSTRACT FROM AUTHOR]

AbstractData heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this article, we consider potential structural changes and complicated tail distributions. There are various existing methods proposed to handle either structural changes or heteroscedasticity. However, it is difficult to handle them simultaneously. To overcome this limitation, we consider statistically and computationally efficient change point detection and localization in high-dimensional quantile regression models. Our proposed framework is general and flexible since the change points and the underlying regression coefficients are allowed to vary across different quantile levels. The model parameters, including the data dimension, the number of change points, and the signal jump size, can be scaled with the sample size. Under this framework, we construct a novel two-step estimation of the number and locations of the change points as well as the underlying regression coefficients. Without any moment constraints on the error term, we present theoretical results, including consistency of the change point number, oracle estimation of change point locations, and estimation for the underlying regression coefficients with the optimal convergence rate. Finally, we present simulation results and an application to the S&P 100 dataset to demonstrate the advantage of the proposed method. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation increasingly relevant, with significant potential for enhancing accuracy. This study introduces a novel model for software effort estimation (SEE) utilizing a deep learning (DL)-based sentence-BERT (SBERT) model for feature extraction combined with advanced gradient-boosted tree (GBT) algorithms. A comprehensive evaluation shows that the proposed model outperforms standard SEE and state-of-the-art models, demonstrating a mean absolute error (MAE) of 2.15 and a median absolute error (MdAE) of 1.85, representing a 12% improvement over the baseline model and an 18% improvement over the best-performing state-of-the-art model. The standardized accuracy (SA) is 93%, which is 7% higher than the next best model, across a dataset of 31,960 issues from 26 open-source Agile projects. This study contributes to software engineering by offering a more accurate and reliable decision support system for estimating project efforts. [ABSTRACT FROM AUTHOR]

Human pose estimation is an important task in computer vision, which can provide key point detection of human body and obtain bone information. At present, human pose estimation is mainly utilized for detection of large targets, and there is no solution for detection of small targets. This paper proposes a multi-channel spatial information feature based human pose (MCSF-Pose) estimation algorithm to address the issue of medium and small targets inaccurate detection of human key points in scenarios involving occlusion and multiple poses. The MCSF-Pose network is a bottom-up regression network. Firstly, an UP-Focus module is designed to expand the feature information while reducing parameter computation during the up-sampling process. Then, the channel segmentation strategy is adopted to cut the features, and the feature information of multiple dimensions is retained through different convolutional groups, which reduces the parameter lightweight network model and makes up for the loss of the feature information associated with the depth of the network. Finally, the three-layer PANet structure is designed to reduce the complexity of the model. With the aid of the structure, it also to improve the detection accuracy and anti-interference ability of human key points. The experimental results indicate that the proposed algorithm outperforms YOLO-Pose and other human pose estimation algorithms on COCO2017 and MPII human pose datasets. [ABSTRACT FROM AUTHOR]

Estimation of the 100 p percent lethal dose (LD 100 p ) is of great interest to pharmacologists for assessing the toxicity of certain compounds. However, most existing literature focuses on the interval estimation of LD 100 p and little attention has been paid to its point estimation. Currently, the most commonly used method for estimating the LD 100 p is the maximum likelihood estimator (MLE), which can be represented as a ratio estimator, with the denominator being the slope estimated from the logistic regression model. However, the MLE can be seriously biased when the sample size is small, a common nature in such studies, or when the dose–response curve is relatively flat (i.e. the slope approaches zero). In this study, we address these issues by developing a novel penalised maximum likelihood estimator (PMLE) that can prevent the denominator of the ratio from being close to zero. Similar to the MLE, the PMLE is computationally simple and thus can be conveniently used in practice. Moreover, with a suitable penalty parameter, we show that the PMLE can (a) reduce the bias to the second order with respect to the sample size and (b) avoid extreme estimates. Through simulation studies and real data applications, we show that the PMLE generally outperforms the existing methods in terms of bias and root mean square error. [ABSTRACT FROM AUTHOR]