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Good understanding of the in-situ stress and rock mass strength or their relative magnitudes is essential to underground geomechanical practices. A prevailing technique for estimating the maximum horizontal stress is the integrated method combining stress polygon, borehole breakout, and drilling-induced tensile fracture. This research embraced all these elements into a nondimensionalized chart to interpret the triangle relation among the in-situ stress, rock mass strength, and failure observation for an underground cylindrical opening at any depth. One of the potential applications of the nondimensionalized chart was shown via the filed observations from a testing tunnel in the Canadian underground research laboratory. An approaching method of untangling two correlated stress components was also proposed to constrain the in-situ stress ratio under the framework of Coulomb frictional failure theory. It is proved to be highly efficient and considerably accurate by implementing the approaching method using the failure observations from the KTB borehole in German. [ABSTRACT FROM AUTHOR]

Heisenberg's uncertainty principle is originally represented by the tradeoff relations between the error of measuring an observable A and the consequential disturbance to the value of another observable B with respect to a particular quantum state. Many different error-disturbance tradeoff relations have been proposed and experimentally tested in various scenarios. The error-disturbance uncertainty relations is incorporated into multiparameter estimation theory recently, but they are all the tradeoff relations between the errors of measuring two observables. Here, we derive the error-disturbance tradeoff relations for three observables A, B and C and demonstrate that the new relation is violated, while the relations derived from triple of two observables A and B, A and C and B and C directly are valid by some examples. [ABSTRACT FROM AUTHOR]

Parameter estimation is one of the most important tasks in statistics, and is key to helping people understand the distribution behind a sample of observations. Traditionally, parameter estimation is done either by closed-form solutions (e.g., maximum likelihood estimation for Gaussian distribution) or by iterative numerical methods such as the Newton–Raphson method when a closed-form solution does not exist (e.g., for Beta distribution). In this paper, we propose a transformer-based approach to parameter estimation. Compared with existing solutions, our approach does not require a closed-form solution or any mathematical derivations. It does not even require knowing the probability density function, which is needed by numerical methods. After the transformer model is trained, only a single inference is needed to estimate the parameters of the underlying distribution based on a sample of observations. In the empirical study, we compared our approach with maximum likelihood estimation on commonly used distributions such as normal distribution, exponential distribution and beta distribution. It is shown that our approach achieves similar or better accuracy as measured by mean-square-errors. [ABSTRACT FROM AUTHOR]

Ransomware attacks have emerged as a significant threat to critical data and systems, extending beyond traditional computers to mobile and IoT/Cyber–Physical Systems. This study addresses the need to detect early ransomware behavior when only limited data are available. A major step for training such a detection model is choosing a set of relevant and non-redundant features, which is challenging when data are scarce. Therefore, this paper proposes an incremental mutual information-selection technique as a method for selecting the relevant features at the early stages of ransomware attacks. It introduces an adaptive feature-selection technique that processes data in smaller, manageable batches. This approach lessens the computational load and enhances the system's ability to quickly adapt to new data arrival, making it particularly suitable for ongoing attacks during the initial phases of the attack. The experimental results emphasize the importance of the proposed technique in estimating feature significance in limited data scenarios. Such results underscore the significance of the incremental approach as a proactive measure in addressing the escalating challenges posed by ransomware. [ABSTRACT FROM AUTHOR]

The issue of real-time state estimation in passive target tracking using only bearing measurements is addressed in this research. An algorithm has been developed to detect multiple targets and fuse state vectors when a single target is detected. Target detection is carried out considering the state vectors from two different sensor arrays with various noise variances. The algorithm is evaluated against targets having unique and identical parameters such as range, speed and course. The state vectors are determined using three different filtering techniques, namely, extended Kalman filter (EKF), modified gain bearings-only EKF and unscented Kalman filter. Using the MATLAB software environment, Monte-Carlo simulations are conducted to more precisely assess algorithm performance. [ABSTRACT FROM AUTHOR]

Noise removal is a critical stage in the preprocessing of point clouds, exerting a significant impact on subsequent processes such as point cloud classification, segmentation, feature extraction, and 3D reconstruction. The exploration of methods capable of adapting to and effectively handling the noise in point clouds from real-world outdoor scenes remains an open and practically significant issue. Addressing this issue, this study proposes an adaptive kernel approach based on local density and global statistics (AKA-LDGS). This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and noise points according to the spatial function relationship, which varies with the distance from the points to the center of the LiDAR. The probability density function (PDF) for real points is constructed using a multivariate Gaussian distribution, while the PDF for noise points is established using a data-driven, non-parametric adaptive kernel density estimation (KDE) approach. Experimental results demonstrate that this method can effectively remove noise from point clouds in real-world outdoor scenes while maintaining the overall structural features of the point cloud. [ABSTRACT FROM AUTHOR]

For the functional linear models in which the dependent variable is functional and the predictors are scalar, robust regularization for simultaneous variable selection and regression parameter estimation is an important yet challenging issue. In this paper, we propose two types of regularized robust estimation methods. The first estimator adopts the ideas of reproducing kernel Hilbert space, least absolute deviation and group Lasso techniques. Based on the first method, the second estimator applies the pre-whitening technique and estimates the error covariance function by using functional principal component analysis. Simulation studies are conducted to examine the performance of the proposed methods in small sample sizes. The method is also applied to the Canadian weather data set, which consists of the daily average temperature and precipitation observed by 35 meteorological stations across Canada from 1960 to 1994. Numerical simulations and real data analysis show a good performance of the proposed robust methods for function-on-scalar models. [ABSTRACT FROM AUTHOR]

• Life Cycle Analysis of two materials for spacecraft structures is conducted. • A relationship between fragments generation and materials properties is established. • A novel metric for the evaluation of debris generation upon collision is introduced. • Results are discussed based on terrestrial and orbital impact indicators. Space debris are a primary environmental concern for the sustainability of space mission activities. When considering terrestrial applications, the Life Cycle Assessment represents a widely standardised technique for estimating the environmental impacts of human activities; however, its implementation in the space-related value chain is still in an early stage. In this study, the implications of two alternative materials in the development of a CubeSat structural bus are investigated with reference to the well-established categories for the Life Cycle Impact Assessment. Moreover, the potential impact of these materials on the orbital environment is quantified by the evaluation of the number of generated fragments upon collision for different significant low Earth orbits. The results of this study allow to quantify the impact of alternative design solutions for space systems on the terrestrial environment as well as the degradation of the orbital resources as a result of the space debris generation. [ABSTRACT FROM AUTHOR]

Conventional demand-prediction methods predominantly rely on past user behaviors to predict regular future transportation demands using acquired user preference data. Nevertheless, predicting unforeseen travel demands arising from bad weather or emergency events remains challenging owing to the absence of data on such future contingencies. This study introduces a method to predict travel demand by leveraging search history data, which potentially signal unforeseen travel requirements. We elucidate the correlation between the search count and integrated circuit (IC) card usage on an aggregate level. Subsequently, we propose a two-stage analytical technique to estimate the number of IC card usages based on route-search counts. Our findings demonstrate that the proposed model has superior accuracy, and the route-search count plays a pivotal role in predicting the number of IC card usages, especially unforeseen shifts in demand. [ABSTRACT FROM AUTHOR]

Estimation of the region of attraction of the power system using sum of squares (SOS) programming theory is investigated in this paper. Firstly, the Taylor expansion formula is applied to explore the inherent polynomial structure of the swing equation. The single machine projection equation for a multi-machine system is proposed based on the amplitude characteristic of the rotor angle response in decoupled space. Secondly, the theory of SOS programming originating from the polynomial system is derived, and the theory about estimation of the region of attraction for the differential algebraic system is summarized. Thirdly, the method for estimating the region of attraction for the polynomial swing equation based on a V-S two-stage iterative procedure is proposed. Finally, a single machine infinite bus power system, an IEEE 3-machine power system, and an IEEE 4-machine power system are employed to validate the effectiveness and accuracy of the proposed method and analysis. [ABSTRACT FROM AUTHOR]

Accurate prediction of the Remaining Useful Life (RUL) of components and systems is crucial for avoiding an unscheduled shutdown of production by planning maintenance interventions effectively in advance. For high‐reliability equipment, few complete‐run‐to‐failure trajectories may be available in practice. This constitutes a technical challenge for data‐driven techniques for estimating the RUL. This paper proposes a novel data‐driven approach for fault prognostics using the Long‐Short Term Memory (LSTM) model combined with the Multi‐Head Self‐Attention (MHSA) mechanism. The former is applied to the input signals, whereas the latter is used to extract features from the LSTM hidden states, benefiting from the information from all hidden states rather than utilizing that of the final hidden state only. The proposed approach is characterized by its capability to recognize long‐term dependencies while extracting features in both global and local contexts. This enables the approach to provide accurate RUL estimates in various stages of the equipment's life. The proposed approach is applied to an artificial case study simulated to mimic the realistic degradation behaviour of a heterogeneous fleet of aluminium electrolytic capacitors used in the automotive industry (under variable operating and environmental conditions). Results indicate that the proposed approach can provide accurate RUL estimates for high‐reliability equipment compared to four benchmark models from the literature. [ABSTRACT FROM AUTHOR]

The paper addresses a computational method to simulate more accurate models for population growth with immigration, focusing on integral equations (IEs) featuring a delay parameter in the time variable. The proposed method utilizes Legendre wavelets within the Galerkin scheme as a orthonormal basis. Legendre wavelets are known for their localized functions, offering suitable precision and stability in simulating time-delay biological models. This approach employs the composite Gauss-Legendre (CGL) quadrature rule to compute integrals appeared in the scheme. An error bound analysis demonstrates a convergence rate of order 2 − M k. Additionally, various numerical examples are presented to show the efficiency, accuracy and validate the theoretical error estimate of the novel technique. [ABSTRACT FROM AUTHOR]

Objective: The Mental and Substance Use Disorders Prevalence Study (MDPS) builds upon previous epidemiologic studies to provide estimates of prevalence and treatment rates of mental and substance use disorders among adults aged 18–65 in the U.S. The study background and methods are described. Method: The MDPS employed novel techniques such as the inclusion of household, prison, homeless and state psychiatric hospital populations, a semi‐structured clinical interview administered by trained clinical interviewers to assess disorders, the assessment of both past year and lifetime schizophrenia spectrum disorder (SSD) using full Diagnostic and Statistical Manual 5 criteria, as well as other mental and substance use disorders, and video‐based interviewing. Population specific and combined sample weights were developed to estimate nationally representative prevalence and treatment rates. Results: Data collection was conducted between October 2020 and October 2022 resulting in 5679 clinical interviews. The statistical weighting and analytic plan are described. Weighted response rates and reasons for non‐response are provided for each study population. Conclusions: The MDPS successfully developed and employed novel techniques to estimate the prevalence and treatment rates of mental and substance use disorders in both household and non‐household populations, including some of the most impairing disorders such as SSD. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to estimate the girth of humeral head (GOHead) by analysing the reliability and validity of biepicondylar distance (BED). Materials and Methods: The study was carried out on 66 dry humeruses (36 right and 30 left) at the Ondokuz Mayıs, Ordu and Erciyes Universities, Faculty of Medicine, Departments of Anatomy. The BED and the GOHead were measured, and the related ratio was determined. The validity between the obtained values was analysed by Bland Altman analysis and the reliability by intraclass correlation coefficient (ICC). Results: The BED was measured to be 57.70±5.18 mm, while the GOHead was 132.77±12.17 mm. The relative ratio between the two data was determined to be (RR:2.30). The GOHeadRR (BED x RR) value was defined to be 132.82±12.01 mm. While the results of this study showed agreement in validity according to Bland Altman analysis, an excellent reliability ratio (Cronbach Alpha's: 0.930) was determined in this study. Estimation of the GOHead using the formula (BED x 2.30) shows agreement in terms of validity and reliability. Conclusion: This result we obtained shows that the BED can be used to estimate the GOHead without requiring any imaging method. Based on the results of this study, it is expected to play an important role in reducing the use of radiological materials in GOHead measurements and reducing the radiological footprint. [ABSTRACT FROM AUTHOR]

Generative models have recently received a lot of attention. However, a challenge with such models is that it is usually not possible to compute the likelihood function, which makes parameter estimation or training of the models challenging. The most commonly used alternative strategy is called likelihood-free estimation, based on finding values of the model parameters such that a set of selected statistics have similar values in the dataset and in samples generated from the model. However, a challenge is how to select statistics that are efficient in estimating unknown parameters. The most commonly used statistics are the mean vector, variances, and correlations between variables, but they may be less relevant in estimating the unknown parameters. We suggest utilizing Tukey depth contours (TDCs) as statistics in likelihood-free estimation. TDCs are highly flexible and can capture almost any property of multivariate data, in addition, they seem to be as of yet unexplored for likelihood-free estimation. We demonstrate that TDC statistics are able to estimate the unknown parameters more efficiently than mean, variance, and correlation in likelihood-free estimation. We further apply the TDC statistics to estimate the properties of requests to a computer system, demonstrating their real-life applicability. The suggested method is able to efficiently find the unknown parameters of the request distribution and quantify the estimation uncertainty. [ABSTRACT FROM AUTHOR]

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation. [ABSTRACT FROM AUTHOR]

Hispanic‐Serving Community Colleges enroll and educate a large share of Hispanic postsecondary students. Due to work trends and demographic changes, Hispanic and other historically underrepresented students play a crucial role in shaping the next generation of Science, Technology, Engineering, and Math (STEM) scholars and practitioners. We use data from the National Center for Education Statistics and a difference‐in‐differences technique to estimate the association between federal capacity building grants aimed at improving STEM degree attainment outcomes and associate degree completion. We find receipt of a Title III grant was associated with about a 30% increase in the total number of STEM associate degrees awarded. Further, the grants nearly doubled the number of STEM associate degrees awarded to Hispanic students, on average. This positive relationship is driven by institutions receiving more than one grant. We conclude by discussing these findings and providing federal and institutional recommendations. [ABSTRACT FROM AUTHOR]

We characterize the full classes of M -estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evaluation with the theory of M -estimation. This novel characterization result allows us to leverage existing results on loss functions known from the literature on forecast evaluation in estimation theory. We exemplify advantageous implications for the fields of robust, efficient, equivariant and Pareto-optimal M -estimation. [ABSTRACT FROM AUTHOR]

The paper shows how to estimate the three parameters of the generalized exponential Rayleigh distribution by utilizing the three estimation methods, namely, the moment employing estimation method (MEM), ordinary least squares estimation method (OLSEM), and maximum entropy estimation method (MEEM). The simulation technique is used for all these estimation methods to find the parameters for the generalized exponential Rayleigh distribution. In order to find the best method, we use the mean squares error criterion. Finally, in order to extract the experimental results, one of object oriented programming languages visual basic. net was used. [ABSTRACT FROM AUTHOR]

Distinctive low diversity foraminiferal and ostracod faunas in Holocene mud from a stratigraphic drillhole through Rangitoto Volcano (∼600 years old) indicate a highly sheltered, shallow-marine environment existed near the middle of the exposed entrance to Waitemata Harbour, Auckland, for at least 7000 years prior to Rangitoto's eruption. Modern Analogue Technique estimates for these faunas indicate they accumulated at low tidal to shallow subtidal depths (0–11 m). The most similar analogue faunas lived subtidally in the extreme shelter inside the breached Onepoto maar lagoon, Auckland. Therefore, we infer the possible existence of a pre-7500 year-old maar crater and surrounding tuff ring buried beneath Rangitoto Volcano. This Holocene mud occurs in the drillhole 24–10 m lower than where the microfaunas indicate it accumulated. It is inferred to have slid into the adjacent harbour channel, along with 7 m of basaltic tuff and a piece of lava flow, near the start of the second phase of Rangitoto's eruption. [ABSTRACT FROM AUTHOR]

Landslides constitute recurrent natural hazards worldwide with greater socioeconomic impacts in low-income places. Landslide susceptibility studies are carried out to determine the potential for landslides to occur in a region, to predict possible risks in the future and to integrate this information into sustainable planning and management strategies. Slip-type landslides occur in limited areas of less than 1% of the Asi watershed in Southern Türkiye. Therefore, meaningful estimations cannot be made with classical statistical models. So, using the maximum entropy approach (MaxEnt), a technique for estimating based on a small number of observations, a landslide susceptibility assessment was constructed. The association between the locations where landslides are detected and environmental variables is modeled using the best probability density in the maximum entropy technique. The analyses were repeated with three different random selection methods using two-thirds of the landslide dataset to analyse it and one-third as validation data. The accuracy and precision of the obtained models were evaluated with the area under the receiver operating characteristic curves and success prediction curves. Hence, we performed a susceptibility assessment based on a maximum entropy method in the 7893 km² Asi watershed. The area under the receiver operating characteristic curves was 0.76 as a result, and 72% of the landslides occurred in areas with a total area of 28% that were high and extremely high susceptible areas. The determination of the landslides that will develop in the region over time and the susceptibility assessments to be repeated will contribute to the more effective planning of mitigation studies. This method can be applied in other watersheds with localized landslide processes that affect human activities. Landslide susceptibility studies are a fundamental tool for understanding landslide risks at a regional scale and predicting future hazards. These studies have a central role in regional planning, infrastructure management, crisis response, and the formulation of sustainable development strategies. [ABSTRACT FROM AUTHOR]

Tsunami simulations require powerful computational resources to be performed efficiently. Although the modern graphics processing units (GPUs) allow the acceleration of this kind of simulations, they can still last many minutes or even hours for simulations which have to deal with very high spatial resolutions or simulation times. In this paper, we propose a method to predict the alert or inundation level of a tsunami generated by an earthquake using deep learning methods. In particular, we train multilayer perceptron (MLP) neural networks for predicting the alert level due to a tsunami at given coastal locations. Ensemble methods are used to improve the predictions of the neural networks. Tsunamis caused by ruptures of several fault segments at different time instants, application to real events, probabilistic forecasting and comparison with other machine learning algorithms are also addressed. Results on realistic scenarios confirm that good accuracies are obtained. The inference times of the trained networks and ensembles are also very low, lasting less than one second to predict the results of thousands of simulations. The proposed method could be used in a tsunami early warning system along with the application of scaling laws. Plain Language Summary: In tsunami early warning systems (TEWS) it is crucial to perform fast predictions of the risk of a tsunami in coastal areas in order to take appropriate measures before the arrival of the tsunami. Currently, tsunami simulations may take many minutes or even hours despite using modern computational resources. In this work we propose a method based on deep learning techniques to predict the alert level of a tsunami generated by an earthquake at given coastal locations in a fraction of a second. This method could be used in TEWS along with techniques to estimate the values of some parameters of the earthquake. Application to real events and probabilistic predictions are also addressed. Key Points: A deep learning method to predict different tsunami alert levels is proposedTsunamis caused by the rupture of multiple fault segments at different time instants are supportedApplication to real events with probabilistic predictions are addressed [ABSTRACT FROM AUTHOR]

In this research, we establish the novel sin-G class, an innovative and comprehensive class of trigonometric distributions centered on the sine function. A sin-G class analytical approach comprises calculations for the probability density function and reliability indicators. Various estimation methods were presented to estimate the specified model parameters. Applying randomly created datasets and the provided estimating techniques, the behavior of these variables was investigated. Then, the focus is directed toward a specific member of this class, the novel sin-Lomax distribution, which is established with the Lomax distribution serving as the baseline. Exploring the innovative sin-Lomax distribution's inferential characteristics, the constructed distribution is then fitted to real datasets, demonstrating the actual application of the Sin-G class. [ABSTRACT FROM AUTHOR]

We provide a theoretical estimation of the relaxation time for the kinetic theoretical representation of homogeneous, isotropic, and stationary turbulent flow. The basic approach depends on the construction of a balance of fluctuation and dissipation between the relaxation process and the self-consistently generated fluctuating force. The kinetic theory representation generalizes the modeling of turbulence so that turbulent viscosity as well as all higher order transport coefficients are determined by the relaxation time. The resulting value of the relaxation time gives an estimation of the turbulent viscosity with the coefficient value comparable with those from most of the representative turbulence models, which have been obtained empirically or semi-empirically. [ABSTRACT FROM AUTHOR]

The implementation of ultra-wideband technology for accurate indoor location in industrial and public environments has recently been the basis of numerous research studies. Wireless sensor networks must take localization into consideration. Localization processes improve in various ways when mobility is established. As research progresses on range-free localization with UWB, enhancements in methods and optimal techniques need to be evaluated for the present scenario. Within a single localization zone, they focus is on accuracy. The objective of work is to improve the location accuracy by decreasing localization error with hybrid optimal techniques and algorithms. However, many large-scale applications will benefit from technologies developed for line-of-sight conditions. TDOA (Time Difference of Arrival) is a method for calculating the distance between two or more radio transmitters and one receiver. Distance vector routing protocols measure distance by the number of routers a packet has to pass through; one router counts as one hop. These protocols send the entire routing table to each neighbor every 30 s. DV-Hop method has been adopted for the present work, and hybridization has been considered as a means of enhancing the results. Accuracy with initial measurement techniques lies in 2D and 3D with Kalman filter linked with DV-Hop to reduce initial errors. Localization error in nodes' dynamic momentum was observed, and the deployment of beacons in the range-free selected area was estimated with an improved technique. Ensemble method (EM)-PSO-based optimization with hybrid HOP algorithms is proposed in the present work. The results of the present approach show a significant difference in localization error reduction compared to conventional DV-HOP. Finally, an error below 2% is observed using multiple iterations, and a variation of 15% is observed compared with other state-of-the-art existing techniques. Using hybrid algorithms, this increased accuracy can then be used to reduce the localization error in network for the application users in range-free conditions. [ABSTRACT FROM AUTHOR]

Introduction: body composition (BC) analysis is an essential element in clinical nutritional practice. Bioelectrical impedance is one of the most widely used techniques for estimating BC. Several approaches have been proposed to reduce the margin of error it presents, ensuring its application in all populations. One of them is the use of impedance vectors, using tolerance ellipses. Objective: to compare the sample vectors with the Italian population and to determine specifi c tolerance ellipses for the Colombian university population. Materials and Methods: observational cross-sectional study in 608 university students. Participation was at convenience and voluntary, between February 2022 and March 2023. BIVA 2002 software was used to calculate the tolerance ellipses and BIVA confi dence to compare the vectors of the ellipses with the Italian population using Hotelling's T2 test, which was considered as signifi cant at p < 0.05. Results: R/H and Xc/H measurements were higher in females (420.75 ± 56.012 O/m vs 308.7508 ± 41.81 O/m) and (46.15 ± 5.79 O/m vs 39.44 ± 5.01 O/m), respectively. The impedance vectors were positioned over the upper quadrants of the RXc plot, evidencing signifi cant differences in the distribution of the composition vectors between samples. Conclusions: the vectors of the Colombian university students were different from the reference population, so it was necessary to determine the specifi c ellipses. [ABSTRACT FROM AUTHOR]

In this paper, we draw on recent research on so-called Fermi problems and situate the fundamental principles underlying this type of tasks and their use from a task design perspective. We use the models and modelling perspective on teaching and learning to elaborate on aspects related to the design of single-use, as well as sequences of, Fermi problems. In addition, we discuss a framework (called the Fermi problem activity template [FPAT] framework) for supporting the design and use of Fermi problems to facilitate students' learning within particular mathematics content areas and/or aimed at particular concepts or higher-order thinking skills. We also illustrate how the FPAT framework can be used to (i) facilitate interdisciplinary collaborations with other subjects such as the social sciences, but in particular with the other STEM subjects; and (ii) support teachers in adapting and implementing Fermi problems in their classrooms. [ABSTRACT FROM AUTHOR]

The acquisition of seismic exploration data in remote locations presents several logistical and economic criticalities. The irregular distribution of sources and/or receivers facilitates seismic acquisition operations in these areas. A convenient approach is to deploy nodal receivers on a regular grid and to use sources only in accessible locations, creating an irregular source–receiver layout. It is essential to evaluate, adapt, and verify processing workflows, specifically for near-surface velocity model estimation using surface-wave analysis, when working with these types of datasets. In this study, we applied three surface-wave techniques (i.e., wavelength–depth (W/D) method, laterally constrained inversion (LCI), and surface-wave tomography (SWT)) to a large-scale 3D dataset obtained from a hard-rock site using the irregular source–receiver acquisition method. The methods were fine-tuned for the data obtained from hard-rock sites, which typically exhibit a low signal-to-noise ratio. The wavelength–depth method is a data transformation method that is based on a relationship between skin depth and surface-wave wavelength and provides both S- and P-wave velocity (Vs and Vp) models. We used Poisson's ratios estimated through the wavelength–depth method to constrain the laterally constrained inversion and surface-wave tomography and to retrieve both Vs and Vp also from these methods. The pseudo-3D Vs and Vp models were obtained down to 140 m depth over an area of approximately 900 × 1500 m 2. The estimated models from the methods matched the geological information available for the site. A difference of less than 6 % was observed between the estimated Vs models from the three methods, whereas this value was 7.1 % for the retrieved Vp models. The methods were critically compared in terms of resolution and efficiency, which provides valuable insights into the potential of surface-wave analysis for estimating near-surface models at hard-rock sites. [ABSTRACT FROM AUTHOR]

Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyze the bivariate data on related survival times (e.g., matched pairs experiments, twin or family data), the shared frailty models were suggested. Shared frailty models are used despite their limitations. To overcome their disadvantages correlated frailty models may be used. In this paper, we introduce the correlated compound Poisson frailty models based on reversed hazard rate with three different baseline distributions namely, the generalized log-logistic type I, the generalized log-logistic type II and the modified inverse Weibull. We introduce the Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. We also apply the proposed models to the Australian twin data set and a better model is suggested. [ABSTRACT FROM AUTHOR]

We provide a formal statistical theory of consistent estimation of the set of all arbitrage portfolios that meet the description of being a stochastic arbitrage opportunity. Two empirical likelihood ratio tests are developed: one for the null that a given arbitrage portfolio is qualified, and another for the alternative that the portfolio is not qualified. Apart from considering generalized concepts and hypotheses based on multiple host portfolios, the statistical assumption framework is also more general than in earlier studies that focused on special cases with a single benchmark portfolio. Various extensions and generalizations of the theory are discussed. A Monte Carlo simulation study shows promising statistical size and power properties for testing the null, for representative data dimensions. The results of an empirical application illustrate the importance of selecting a proper blocking structure and moment estimation method. [ABSTRACT FROM AUTHOR]

Introduction: N‐3 polyunsaturated fatty acids (PUFAs) supplementation has been reported to have an impact on flow‐mediated dilatation (FMD), a conventionally used clinical technique for estimating endothelial dysfunction. However, its proven effects on endothelial function are unclear. This systematic review and meta‐analysis were conducted to evaluate the effects of n‐3 PUFAs supplementation on FMD of the brachial artery. Method: This study was performed following the PRISMA guidelines. To identify eligible RCTs, a systematic search was completed in PubMed/Medline, Scopus and Web of Science using relevant keywords. A fixed‐ or random‐effects model was utilized to estimate the weighted mean difference (WMD) and 95% confidence interval (95% CI). Results: Thirty‐two studies (with 35 arms) were included in this meta‐analysis, involving 2385 subjects with intervention duration ranging from 4 to 48 weeks. The pooled meta‐analysis demonstrated a significant effect of omega‐3 on FMD (WMD = 0.8%, 95% CI = 0.3–1.3, p =.001) and heterogeneity was significant (I2 = 82.5%, p <.001). Conclusion: We found that n‐3 PUFA supplementation improves endothelial function as estimated by flow‐mediated dilatation of the brachial artery. [ABSTRACT FROM AUTHOR]

Negative bias in mark‐recapture abundance estimators due to heterogeneity in detection (capture) probability is a well‐known problem, but we believe most biologists do not understand why heterogeneity causes bias and how bias can be reduced. We demonstrate how heterogeneity creates dependence and bias in mark‐recapture approaches to abundance estimation. In comparison, heterogeneity, and hence estimator bias, is not as problematic for distance sampling and mark‐resight methods because both techniques estimate detection probabilities based on a known quantity. We show how the introduction of a known number of individuals planted into a study population prior to a mark‐recapture survey can reduce bias from heterogeneity in detection probability. We provide examples with simulation and an analysis of motion‐sensitive camera data from a study population of introduced eastern wild turkeys (Meleagris gallopavo silvestris) of known size with a subset of telemetered birds. In choosing a method for abundance estimation, careful consideration should be given to assumptions and how heterogeneity in detection probability can be accommodated for each application. [ABSTRACT FROM AUTHOR]

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R2 = 0.835, RMSE = 14.357; for LAI, R2 = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training. [ABSTRACT FROM AUTHOR]

The b‐value in earthquake magnitude‐frequency distribution quantifies the relative frequency of large versus small earthquakes. Monitoring its evolution could provide fundamental insights into temporal variations of stress on different fault patches. However, genuine b‐value changes are often difficult to distinguish from artificial ones induced by temporal variations of the detection threshold. A highly innovative and effective solution to this issue has recently been proposed by van der Elst (2021, https://doi.org/10.1029/2020jb021027) by means of the b‐positive estimator, which is based on analyzing only the positive differences in magnitude between successive earthquakes. Here, we demonstrate the robustness of the estimator, which remains largely unaffected by detection issues due to the properties of conditional probability. We illustrate that this robustness can be further improved by considering positive differences in magnitude, not only between successive earthquakes but also between different pairs of earthquakes. This generalized approach, defined as the "b‐more‐positive estimator," enhances efficiency by providing a precise estimate of the b‐value while including a larger number of earthquakes from an incomplete catalog. However, our analysis reveals that the accuracy of the b estimators diminishes when earthquakes below the completeness threshold are included in the catalog. This leads to the paradoxical observation that greater efficiency is achieved when the catalog is more incomplete. To address this, we introduce the "b‐more‐incomplete estimator," where the b‐more‐positive estimator is applied only after artificially filtering the instrumental catalog to make it more incomplete. Our findings show the superior efficiency of the b‐more‐incomplete method. Plain Language Summary: Earthquake magnitudes can vary widely, and the b‐value is a common metric used to measure the frequency of earthquakes with large versus small magnitudes. In addition, the b‐value could serve as an indicator of the stress state of different fault patches, making it a valuable tool in earthquake research. However, since small earthquakes are often obscured by previous larger ones, determining whether changes in the b‐value are genuine or simply caused by detection problems can be challenging. To address this issue, a new approach called the b‐positive estimator has been recently developed. The method only considers positive changes in magnitude between successive earthquakes. In this study, we confirm that the b‐positive estimator is a powerful and effective technique to estimate the b‐value and is largely unaffected by issues related to detecting earthquakes. We extend the method by considering positive differences in magnitude, encompassing not only successive earthquakes but also different pairs of earthquakes. In particular we show that the b‐positive estimator is more efficient when the catalog is more incomplete. This allows us to develop modifications to the b‐positive method providing a more efficient tool to monitor the b‐value in ongoing seismic sequences. Key Points: Conditional probability detecting consecutive earthquakes makes positive magnitude difference distribution weakly affected by incompletenessThe b‐positive estimator can be enhanced by including more earthquake pairs, not only consecutive onesThe b‐positive estimator can be enhanced by making the catalog more incomplete [ABSTRACT FROM AUTHOR]

Tunnel boring machine (TBM) performance prediction in mechanized tunneling is an essential factor for selecting an appropriate excavation machine, tunnel design, and safe construction. To implement safe mechanized excavation, it is important to accurately assess and predict the range of machine driving parameters, especially the machine rate of penetration (ROP); this can reduce the cost of TBM repairs due to the abrasion of disc cutters and cutterhead and also has a positive effect on the post-construction period. This study focuses on predicting the ROP of TBMs passing through metamorphic rocks during deep excavation and under a complex geotechnical situation. For this purpose, three fuzzy-based models of the Mamdani fuzzy inference system (MFIS), adaptive neuro-fuzzy inference system (ANFIS), Takagi Sugeno fuzzy model (TSF), as well as linear and non-linear regression models were developed. Historical tunnels were used to compile 189 data points (151 for training and 37 for testing). In the dataset, three parameters, including uniaxial compressive strength (UCS), cutterhead rotational speed per minute (RPM), and thrust force (TF), were considered effective parameters on the TBM's ROP. According to the findings, the suggested models provided satisfactory and consistent accuracy. Moreover, the results demonstrated that the forecasted values correlate rather well with the measured ones. The proposed algorithms can be considered for use in similar ground and tunneling conditions (metamorphic rocks with low-average strength). It is worth noting that this study has the potential to drastically cut down on tunneling uncertainties and makes fuzzy inference systems a robust algorithm for planning mechanized tunneling. Highlights: Prediction of TBM performance in complex geological conditions. Forecasting TBM performance in deep tunnels passing through metamorphic rocks. Presenting an empirical model for calculating the TBM performance based on statistical analysis. Detailed analysis of fuzzy-based techniques potential for TBM performance prediction. Examining the models' accuracy with several loss functions and statistical indices. [ABSTRACT FROM AUTHOR]

Multilevel regression and post-stratification (MRP) is a popular use of hierarchical models in political science. Multiple papers have suggested that relying on machine learning methods can provide substantially better performance than traditional approaches that use hierarchical models. However, these comparisons are often unfair to traditional techniques as they omit possibly important interactions or nonlinear effects. I show that complex ("deep") hierarchical models that include interactions can nearly match or outperform state-of-the-art machine learning methods. Combining multiple models into an ensemble can improve performance, although deep hierarchical models are themselves given considerable weight in these ensembles. The main limitation to using deep hierarchical models is speed. This paper derives new techniques to further accelerate estimation using variational approximations. I provide software that uses weakly informative priors and can estimate nonlinear effects using splines. This allows flexible and complex hierarchical models to be fit as quickly as many comparable machine learning techniques. [ABSTRACT FROM AUTHOR]

We develop a binary classifier to evaluate whether the rank condition (RC) is satisfied or not for the Common Correlated Effects (CCE) estimator. The RC postulates that the number of unobserved factors, m, is not larger than the rank of the unobserved matrix of average factor loadings, ϱ. When this condition fails, the CCE estimator is inconsistent, in general. Despite its importance, to date this rank condition could not be verified. The difficulty lies in the fact that factor loadings are unobserved, such that ϱ cannot be directly determined. The key insight in this article is that ϱ can be consistently estimated with existing techniques through the matrix of cross-sectional averages of the data. Similarly, m can be estimated consistently from the data using existing methods. Thus, a binary classifier, constructed by comparing estimates of m and ϱ, correctly determines whether the RC is satisfied or not as (N , T) → ∞. We illustrate the practical relevance of testing the RC by studying the effect of the Dodd-Frank Act on bank profitability. The RC classifier reveals that the rank condition fails for a subperiod of the sample, in which case the estimated effect of bank size on profitability appears to be biased upwards. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the use of a novel technique to estimate the symmetrical placement of percutaneous bone‐anchored hearing systems (BAHS) with a guide‐marker in patients undergoing bilateral surgery with this device. Study Design: Prospective cohort study. Methods: A guide‐marker and anatomical landmarks were used to estimate the implant placement and transferred to the contralateral ear in 12 subjects eligible for bilateral BAHS surgery. To investigate the bilateral symmetry, preoperative tri‐dimensional (3D) computed tomography (CT) image reconstruction was used to compare the distances between the mandibular condyle and implant placement estimation (mandible‐implant distance) in both the right and left ears of the subjects. Results: The guide‐marker could be used to estimate the bilateral implant placement in all subjects included in this study, simply and easily, including one subject with craniofacial malformation. The mean mandible‐implant distances were 5.37 and 5.38 cm, in the right and left ears of the subjects, respectively, and no differences were observed between them, thereby indicating optimal bilateral symmetry. Conclusion: The use of the guide‐marker proved to be an effective tool to provide symmetrical placement of bilateral BAHS. We propose a novel method employing a simple guide‐marker and tracing based on symmetrical anatomical landmarks to achieve precise placement and optimal symmetry and which may be easily adopted in the surgical routine of BAHS. Level of Evidence: 3. [ABSTRACT FROM AUTHOR]

Pipelines serve as a crucial infrastructure for long-distance and cost-efficient transportation of oil and gas resources. The safety and reliability of oil and gas pipelines crossing mountainous areas, however, are susceptible to rainfall-induced landslides. Landslide-induced pipeline leakage or fracture may result in the release of oil and gas, causing resource waste and environmental contamination as well as casualties. To date, user-friendly and comprehensive methods for early warning about pipeline safety issues under landslides are scarce. The present study develops a novel early warning degree (EWD)-based safety early warning framework for oil and gas pipelines subjected to landslides, including three monitoring indexes of disaster-inducing bodies, disaster-causing bodies, and disaster-bearing bodies. The use of an improved fuzzy decision-making trial and evaluation laboratory (DEMATEL) method determines the weight of each index. The hybrid application of fuzzy comprehensive evaluation approach and three-dimensional (3D) space method is used to develop the 3D early warning matrix. The matrix finally integrates an improved radar chart technique to estimate EWD-based early warning intervals. The proposed framework has been implemented through a case study on an in-service pipeline, demonstrating its capability to improve the safety of oil and gas pipelines traversing landslide-prone areas, and enhance the resilience of pipelines against landslides. [ABSTRACT FROM AUTHOR]

In this study, thermogravimetric and thermo-kinetic analysis of sugarcane bagasse pith (S.B.P.) were performed using a robust suite of experiments and kinetic analyses, along with a comparative evaluation on the thermo-kinetic characteristics of two other major sugarcane residues, namely sugarcane straw (S.C.S.) and sugarcane bagasse (S.C.B.). The thermogravimetric analysis evaluated the pyrolysis behavior of these residues at different heating rates in a nitrogen atmosphere. The Kissinger, advanced non-linear isoconversional (ANIC), and Friedman methods were employed to obtain effective activation energies. Moreover, the compensation effect theory (CE) and combined kinetic analysis (CKA) were used to determine the pre-exponential factor and pyrolysis kinetic model. Friedman's method findings indicated that the average activation energies of S.C.S., S.C.B., and S.B.P. are 188, 170, and 151 kJ/mol, respectively. The results of the ANIC method under the integral step Δα = 0.01 were closely aligned with those of the Friedman method. The CKA and CE techniques estimated ln(f(α)Aα) with an average relative error below 0.7%. The pre-exponential factors of S.C.S., S.C.B., and S.B.P. were in the order of 1014, 1012, and 1011 (s−1), respectively. From a thermodynamic viewpoint, positive ∆G* and ∆H* results provide evidence for the non-spontaneous and endothermic nature of the pyrolysis process, indicating the occurrence of endergonic reactions. [ABSTRACT FROM AUTHOR]

In this paper, statistical inferences for the parameters, reliability, and failure rate functions (as parameters) of the exponentiated Kies distribution are accomplished using maximum likelihood and both Lindley and Markov chain Monte Carlo techniques, under adaptive Type-II progressive censoring. Depending on the Fisher information matrix and the delta method, we find the approximate confidence intervals for the parameters and the reliability and failure rate functions, respectively. Furthermore, the samples generated from Markov chains Monte Carlo are used to generate credible intervals for the parameters, reliability, and failure rates functions. Symmetric and asymmetric loss functions are also used to find Bayes estimators using Markov chain Monte Carlo and Lindley techniques. Comparisons between the two techniques of evaluation for different estimators under different sizes of data sets are conducted. Finally, a real-life example is applied to clarify the results of the used techniques. [ABSTRACT FROM AUTHOR]

The occurrence and regularity of macrofungal fruitbody formation are influenced by meteorological conditions; however, there is a scarcity of data about the use of machine-learning techniques to estimate their occurrence based on meteorological indicators. Therefore, we employed an artificial neural network (ANN) to forecast fruitbody occurrence in mycorrhizal species of Russula and Amanita, utilizing meteorological factors and validating the accuracy of the forecast of fruitbody formation. Fungal data were collected from two locations in Western Hungary between 2015 and 2020. The ANN was the commonly used algorithm for classification problems: feed-forward multilayer perceptrons with a backpropagation algorithm to estimate the binary (Yes/No) classification of fruitbody appearance in natural and undisturbed forests. The verification indices resulted in two outcomes: however, development is most often studied by genus level, we established a more successful, new model per species. Furthermore, the algorithm is able to successfully estimate fruitbody formations with medium to high accuracy (60–80%). Therefore, this work was the first to reliably utilise the ANN approach of estimating fruitbody occurrence based on meteorological parameters of mycorrhizal specified with an extended vegetation period. These findings can assist in field mycological investigations that utilize sporocarp occurrences to ascertain species abundance. [ABSTRACT FROM AUTHOR]

Parameter estimation is one of key issues in the study of uncertain differential equations. In this paper, one novel estimation method named the estimating function technique of uncertain differential equations based on uncertain integrals is proposed, which is the first method based on uncertain integrals appeared in the literature. Compared to existing methods, our approach fully utilizes information from both the parameters and observed data. In certain settings, previous methods can be viewed as special cases of our technique. One numerical example with different transform functions is provided to illustrate our method and the efficiency is shown by residual analysis. Additionally, a case study on Alibaba stock prices is included to demonstrate the application of our method. The paper concludes with final remarks and recommendations for future research. [ABSTRACT FROM AUTHOR]

Since the 1990s, the number of lions (Panthera leo) has declined by about 43% across African range states. Reliable lion population estimates can contribute to effective management and inform local and international conservation policies. Though many survey methods have been used to estimate lion abundance, an assessment of their usefulness and effectiveness is lacking.We reviewed and identified available methods used to estimate lion populations, then investigated their feasibility, cost‐effectiveness and suitability to achieve primary objectives.We identified 12 methods in two categories: direct (minimum counts, call‐in surveys, capture–recapture, opportunistic sightings, remote cameras, distance‐based and aerial counts) and indirect (track counts, secondary information, prey‐based, genetic samples and roar counts).We identified 48 studies (57%) in which lion population estimation was not repeatable. Additionally, 38% did not provide any measure of precision. About 28% of the studies were conducted specifically for management purposes, while remaining studies focused on population estimates and dynamics.Because data from these studies have influenced local management and global lion conservation policies, we recommend basing future policies and management on repeatable, precise and accurate population estimates supported by appropriate and cost‐effective experimental designs. In most cases, these estimates are obtained through actual lion counts derived from direct observation methods including call‐in, capture–recapture and remote camera surveys or through reliable animal signs such as genetic samples. [ABSTRACT FROM AUTHOR]

The principle of maximum entropy is a well-known approach to produce a model for data-generating distributions. In this approach, if partial knowledge about the distribution is available in terms of a set of information constraints, then the model that maximizes entropy under these constraints is used for the inference. In this paper, we propose a new three-parameter lifetime distribution using the maximum entropy principle under the constraints on the mean and a general index. We then present some statistical properties of the new distribution, including hazard rate function, quantile function, moments, characterization, and stochastic ordering. We use the maximum likelihood estimation technique to estimate the model parameters. A Monte Carlo study is carried out to evaluate the performance of the estimation method. In order to illustrate the usefulness of the proposed model, we fit the model to three real data sets and compare its relative performance with respect to the beta generalized Weibull family. [ABSTRACT FROM AUTHOR]

In this paper, we consider a simultaneous modeling of good and bad outputs. We use an input distance function (IDF) with endogenous inputs as well as endogenous bad outputs, which is novel in the literature. Moreover, we model input efficiency to depend on the production of bad outputs which allows us to investigate whether emissions of pollutants (bad outputs) are related to technological performance (technical efficiency). We also model production of each bad output with a spatial structure separately, each depending on production of good outputs, inputs and other exogenous variables. These bad output production functions allow us to estimate both direct and indirect effects of good output on the production of bad outputs, which may be of special interest because they show the cost (to the society) in terms of releasing pollutants to the environment in order to increase production of good outputs. We apply the new technique to a data set on U.S. electric utilities with four bad outputs, three inputs and two good outputs. We used a Bayesian technique to estimate the model which is a system consisting of the input distance function, reduced form equations for each input, dynamics of inefficiency and bad output production technology--separately for each. Empirically, bad outputs are found to affect inefficiency positively. Percentage increases in inefficiency due to a percentage increase in each bad output are found to vary from 0.225% to 0.42%. Energy prices are found to be positively related to inefficiency. From the spatial specifications of bad outputs, we find that the spillover effects of increasing production of good outputs account for the majority of the total effect, indicating that neighborhood effects are more important than own effects. This means, the neighboring utilities played a crucial role indicating "contagion" of practices. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate honey quality based on proline and Brix content using a thermal imaging and machine learning algorithm. The proline, Brix and color properties of twenty honey samples were determined. Proline and Brix values were classified and estimated using the classification and regression tree (CART) algorithm. The mean proline and Brix content in honey samples was 678.83±192.16 mg/kg and 83.2±0.79%, respectively. CART analysis revealed that high proline honey samples had L values above 48.143 and b* values below 35.416. In contrast, honey samples with low Brix values were characterized by L and a* values below 55.860 and 53.660, respectively, and were identified as freshly harvested. The CART algorithm classified the proline and Brix values with an accuracy of 95% and 100%, respectively (p< 0.001). As a result, whitish, bluish, blackish and greenish honeys are of higher quality due to high proline and low Brix content. However, to accurately assess honey quality based on its color traits, comprehensive studies with more honey samples and origin, are required. [ABSTRACT FROM AUTHOR]

This study was carried out to estimate the annual chilling and heat accumulation in Kurdistan region-Iraq using three different models; chilling hours (CH), chilling units (CU) and chilling portions (CP), as well as estimating the accumulated growing degree hours (GDH) using the ChillR Package, Version 0.72.8 based on daily maxima and minima temperatures of 170 locations for 40 years (1983-2022) downloaded from NASA. The results showed that in Kurdistan region, the chilling hours with 7 classes ranged from 335 CH in Shahreban (Diyala) to 1083 CH in Amedi, Deraluk, Dinarte, Kani Masi, and Shiladize (Duhok), and Barzan (Erbil), with an average of 886 CH. The chilling units (CU, Utah Model) with 6 classes ranged from 507 CU (Shahreban, Diyala) to 1135 CU (Hizawa, Duhok), with an average of 927 CU. However, the chilling portions with 7 classes ranged from 30.0 CP in Shahreban to 58.3 CP in Ahmad Awa, Khurmal, Penjwin, Sirwan, and Zalm (Salaymaniyah), with an average of 49.5 CP. Growing degree hours with 7 classes ranged from 1136 GDH Choman, Galala, Haji Omaran and Samilan (Erbil) to 8564 GDH in Shahreban, with an average of 3256 GDH. All models were strongly correlated with location parameters, the highest strong correlations were found between each CP and GDH with AAT. The observations emphasized that both CU and CP models are more accurate than CH model for estimation of chilling and growing degree hours (units) in Kurdistan region-Iraq. [ABSTRACT FROM AUTHOR]

Traditional techniques for estimating the weight of clusters in a winery, generally consist of manually counting the variety of clusters per vine, and scaling by means of the entire variety of vines. This method can be arduous, and costly, and its accuracy depends on the scale of the sample. To overcome these problems, hybrid approaches of computer vision, deep learning (DL), and machine learning (ML) based vineyard yield prediction systems are proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. DL-based approach for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally, a prediction model is proposed to estimate the yield of the entire vineyard. The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system with grape cluster segmentation pixel accuracy up to 94.81% and yield prediction accuracy up to 99.58%. [ABSTRACT FROM AUTHOR]

Smooth non-parametric quantile function estimators on basis of symmetric kernels exhibit boundary bias due to spillover near the edges. An improved non-parametric estimator of a quantile function under simple random sampling without replacement is proposed, based on a multiplicative bias corrected distribution function. There is no spillover around the edges with our new quantile estimator. The proposed quantile estimator’s asymptotic properties are investigated. The suggested method is compared to existing estimators using real data set findings, demonstrating the improved performance. [ABSTRACT FROM AUTHOR]