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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]

Quantum teleportation allows the transmission of unknown quantum states over arbitrary distances. This paper studies quantum teleportation via two non-interacting qubits coupled to local fields and Ornstein Uhlenbeck noise. We consider two different qubit-noise configurations, i.e., common qubit-noise interactions and independent qubit-noise interactions. We introduce a Gaussian Ornstein Uhlenbeck process to take into account the noisy effects of the local external fields. Furthermore, we address the intrinsic behavior of classical fields toward single- and two-qubit quantum teleportation as a function of various parameters. Additionally, using a quantum estimation theory, we study single- and multi-parameter estimation of the teleported state output for single and two-qubit quantum teleportation scenarios. One important application of this work is obtaining more valuable information in quantum remote sensing. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh generation technique based on the parameterization of the junction's geometry, coupled with an advanced physically regularized neural network architecture. Synthetic datasets are generated through stationary 3D Navier–Stokes simulations within immobile boundaries, offering a precise alternative to resource-intensive computations. A comparative analysis of standard grid sampling and Latin hypercube sampling data generation methods is conducted, resulting in datasets comprising 1.1 × 10 4 and 5 × 10 3 samples, respectively. The following two families of feed-forward neural networks (FFNNs) are then compared: the conventional "black-box" approach using mean squared error (MSE) and a physically informed FFNN employing a physically regularized loss function (PRLF), incorporating mass conservation law. The study demonstrates that combining PRLF with Latin hypercube sampling enables the rapid minimization of relative error (RE) when using a smaller dataset, achieving a relative error value of 6 % on the test set. This approach offers a viable alternative to resource-intensive simulations, showcasing potential applications in patient-specific 1D network models of hemodynamics. [ABSTRACT FROM AUTHOR]

In early work we have studied a class of stochastic differential equation (SDE) models, for which the Gompertz and the Bertalanffy-Richards stochastic models are particular cases, to describe individual growth in random environments, and applied it to model cattle weight evolution using real data. We have started to work on these type of models considering that the model parameters are fixed, i.e. the same for all the animals. Aiming to incorporate variability among individuals, we consider that the model parameters can be random variables, resulting in SDE mixed models. In additon, here we consider SDE mixed models, allowing the parameters to be random and propose to incorporate each animal's genetic characteristics considering the transformed animal's weight at maturity to be a function of its genetic values. The main objective is to extend the SDE mixed model to the more realistic case where the individual's genetic value becomes an important component in the estimated growth curve. For the estimation of the model parameters we have used maximum likelihood estimation theory. Estimates and asymptotic confidence intervals of the parameters are presented. A comparison with SDE non-mixed model and SDE mixed model without the inclusion of genetic characteristics is shown with the conclusion that the incorporation of some genetic characteristics in the model parameters improves the estimation of the animal's growth curve. [ABSTRACT FROM AUTHOR]

Estimation of so-called return levels for environmental extremes is of importance for risk assessment. A particular challenge is to find estimates corresponding to long return periods, as uncertainties in the form of confidence intervals became too wide for practical use when applying conventional methodology where large portions of data are not used. A recently proposed technique, the Average Conditional Exceedance Rate (ACER), makes effective use of all available data. For risk analysis related to nuclear infrastructure, usually located along a coastline, extreme sea levels are of concern. We demonstrate, for measurements of the sea level along the Swedish coast at locations close to nuclear power plants, that the methodology results in considerably shorter confidence intervals compared to conventional approaches. [ABSTRACT FROM AUTHOR]

In the present study, an effort to propose and adopt appropriate Ground Motion Prediction Equations (GMPEs) for the Rapid Earthquake Damage Assessment System (REDAS) in the Black Sea basin is attempted. Emphasis of GMPE harmonization in the cross-border areas (CBA) is given. For this reason, two distinct sub-areas are investigated, taking into consideration their seismotectonic regime. One sub-area refers to active shallow crustal earthquakes (Greece-Turkey, CBA) and the other to intermediate-depth and shallow crustal earthquakes (Romania-Moldova, Western Black Sea CBA). Testing and ranking of pre-selected GMPEs has been performed using strong motion data of the broader CBA regions of both sub-areas. The final proposed GMPEs to feed the REDA System may assure the effective estimation of ShakeMaps and—in combination with the appropriate vulnerability curves—reliable near-real-time damage assessment in the cross-border earthquake affected areas. [ABSTRACT FROM AUTHOR]

The λ-exponential family has recently been proposed to generalize the exponential family. While the exponential family is well-understood and widely used, this is not the case yet for the λ-exponential family. However, many applications require models that are more general than the exponential family, and the λ-exponential family is often a good alternative. In this work, we propose a theoretical and algorithmic framework to solve variational inference and maximum likelihood estimation problems over the λ-exponential family. We give new sufficient optimality conditions for variational inference problems. Our conditions take the form of generalized moment-matching conditions and generalize existing similar results for the exponential family. We exhibit novel characterizations of the solutions of maximum likelihood estimation problems, that recover optimality conditions in the case of the exponential family. For the resolution of both problems, we propose novel proximal-like algorithms that exploit the geometry underlying the λ-exponential family. These new theoretical and methodological insights are tested on numerical examples, showcasing their usefulness and interest, especially on heavy-tailed target distributions. [ABSTRACT FROM AUTHOR]

Effort estimation is a crucial aspect of software development, as it helps project managers plan, control, and schedule the development of software systems. This research study compares various machine learning techniques for estimating effort in software development, focusing on the most widely used and recent methods. The paper begins by highlighting the significance of effort estimation and its associated difficulties. It then presents a comprehensive overview of the different categories of effort estimation techniques, including algorithmic, model-based, and expert-based methods. The study concludes by comparing methods for a given software development project. Random Forest Regression algorithm performs well on the given dataset tested along with various Regression algorithms, including Support Vector, Linear, and Decision Tree Regression. Additionally, the research identifies areas for future investigation in software effort estimation, including the requirement for more accurate and reliable methods and the need to address the inherent complexity and uncertainty in software development projects. This paper provides a comprehensive examination of the current state-of-the-art in software effort estimation, serving as a resource for researchers in the field of software engineering. [ABSTRACT FROM AUTHOR]

As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy. Hence, this study aims to introduce a hybrid random forest (RF) technique optimized by global harmony search with generalized opposition-based learning (GOGHS) for forecasting TBM advance rate (AR). Optimizing the RF hyper-parameters in terms of, e.g., tree number and maximum tree depth is the main objective of using the GOGHS-RF model. In the modelling of this study, a comprehensive database with the most influential parameters on TBM together with TBM AR were used as input and output variables, respectively. To examine the capability and power of the GOGHS-RF model, three more hybrid models of particle swarm optimization-RF, genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR. Evaluation of the developed models was performed by calculating several performance indices, including determination coefficient (R²), root-mean-square-error (RMSE), and mean-absolute-percentage-error (MAPE). The results showed that the GOGHS-RF is amore accurate technique for estimating TBMAR compared to the other applied models. The newly-developed GOGHS-RF model enjoyed R² = 0.9937 and 0.9844, respectively, for train and test stages, which are higher than a pre-developed RF. Also, the importance of the input parameters was interpreted through the SHapley Additive exPlanations (SHAP) method, and it was found that thrust force per cutter is the most important variable on TBMAR. The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance. [ABSTRACT FROM AUTHOR]

This paper explores the construction of a model for evaluating the digital ecosystem within a university, with a focus on identifying key factors influencing satisfaction with the implementation of new digital processes in the educational environment. The study employs mathematical methods, specifically factor analysis, to gauge the impact of these digital processes on the overall educational landscape. A questionnaire was designed to collect relevant data, and structural equation modeling, utilizing the asymptotically distribution-free estimation method with Grammian in STATISTICA software, was employed for survey result processing. The proposed model aims to provide insights into the dynamics of a university's digital ecosystem, offering a systematic approach to assess satisfaction levels and comprehend the implications of integrating novel digital processes within the educational framework. Mathematical methods, including factor analysis, add a quantitative dimension to the evaluation process, enabling a comprehensive understanding of the relationships between various factors. The study's methodology ensures a rigorous and systematic analysis of survey data, enhancing the reliability of the findings. The developed model and methodology contribute to advancing our understanding of the digitalization of university environments, providing valuable tools for decision-makers in shaping effective strategies for integrating digital processes in education. The study conducted a survey with 350 participants, including university staff and students. A questionnaire with 17 questions, both open and closed-ended, was developed to collect data. The authors employed structural equation modeling, specifically the asymptotically distribution-free estimation method, for data processing. The study's a posteriori model illustrates the structure of interaction factors influencing satisfaction with the university's digital ecosystem. [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]

Estimating the health status is a crucial step in learning about the health of hypersonic vehicles beforehand. The estimation results can be used to detect abnormal states and provide data reference for fault diagnosis. However, certain conventional neural network-based estimate techniques rely heavily on data and have limited model interpretability, which challenges the accuracy of the estimation results. This research aims to address the problems of data dependency and model interpretability in estimation models. In this study, a block interpretable neural network model with constraints on the trajectory and attitude equations is established. On the basis of the interpretable neural network model, two health status estimation methods are proposed: one that is unsupervised and the other that is supervised. Additionally, in the supervised health status estimate approach, an FC-LN-Mish structure is created to fit the relationship between the fault residual and the fault state parameters. The results indicate that the proposed estimation methods can fit the system mechanism relationship more accurately, improve the model interpretability, reduce data dependency, and ensure high estimation efficiency and precision. The FC-LN-Mish structure can reduce the missed detection rate and false detection rate to some extent, and perform better than other models under the low fault deviation degree. In conclusion, the interpretable neural network model-based observers accurately observe the health status parameters of rudders and RCS, reduce data dependence and data processing costs, and have better performance under high uncertainty interference. It provides effective method for online health estimation. ● An interpretable neural network model is established for hypersonic vehicles. ● Unsupervised parameter observer is designed based on interpretable model. ● It provides an effective method for fault detection under incomplete data sets. ● It reduces the missed detection rate and false detection rate under the less data. [ABSTRACT FROM AUTHOR]

Objective: Much of the fish biodiversity in the southeastern USA is found in lotic systems, but assessment of fish community structure in large nonwadeable rivers has lagged compared to streams. Sampling designs that adequately estimate species richness are a prerequisite to creating bioassessment metrics but thus far has not been attempted in highly biodiverse rivers in the southeastern USA east of the Mississippi River. Methods: Fish communities in four nonwadeable rivers across Alabama spanning catchments of 2046–57,415 km2 were sampled using electrofishing in summer and fall of 2015 and 2016. All rivers were sampled during day; two rivers were also sampled at night to assess diel changes in species accumulation. Result: In total, 55,239 fishes were collected across rivers, representing 106 species in 24 families. Species collected continued to increase as sampling increased in all river‐season combinations, rarely displaying an asymptote. Species generally accrued more quickly and accumulation curves showed more tendency to asymptote in fall than summer in all but the largest river. Species accumulated faster at night than during the day in both rivers during summer but was similar between diel periods in fall. On average, 95% of the species observed were collected in eight subreaches in fall but not until nine during summer; however, observed number of species was generally one to six species lower than mean estimates of true species richness. Sampling efforts collected approximately similar species assemblages across transects, years, season, and diel periods. Extrapolation of species accumulation curves suggested that 6–17 km of sampling distance would be required to collect all species present. Conclusion: Results of this study demonstrate that assessing fish communities in species nonwadeable rivers requires an inordinate amount of sampling effort, which may be unattainable for many agencies. Further study is warranted to develop innovative solutions to overcome these logistical challenges. Impact statementAgencies often use standard monitoring techniques to estimate species richness, diversity, and biotic condition of aquatic communities in wadeable streams, but this has rarely been attempted in nonwadeable rivers. Here we assess the ability of boat electrofishing techniques to estimate species richness in four Alabama rivers characterized by high species diversity. Biologists can use these results to better plan for future monitoring efforts in these larger, diverse systems. [ABSTRACT FROM AUTHOR]

Autonomous optical navigation is one of the most promising techniques to estimate the position and velocity of a spacecraft in Earth orbit without the supports of Earth-based tracking stations. To improve the navigation performance, this paper presents a novel autonomous optical navigation method, where a star camera on the spacecraft is utilized to measure the line-of-sight (LOS) directions of a number of non-cooperative space targets, whose position vectors are supposed to be not precisely known in advance, and an improved Q-learning based extended Kalman filter (IQEKF) is developed to obtain the accurate motion state estimate of both the spacecraft and the space targets based on the LOS direction measurements. The main advantage of the presented method is that the LOS directions of the space targets can be acquired with high-accuracy by using the state-of-the-art star camera, such that the superior navigation accuracy is achievable. In addition, the whole motion state of the spacecraft, such as position, velocity, and attitude, can be obtained with the star camera, in the case that the space targets and the stars are observed simultaneously. The high performance of the presented autonomous navigation method is illustrated through a representative simulation of a medium Earth orbit (MEO) satellite. Furthermore, the simulation results indicate that the IQEKF yields more accurate solutions than the traditional navigation filtering algorithms. [ABSTRACT FROM AUTHOR]

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In this paper, we considered the condition number theory of a new generalized ridge regression model. The explicit expressions of different types of condition numbers were derived to measure the ill-conditionness of the generalized ridge regression problem with respect to different circumstances. To overcome the computational difficulty of computing the exact value of the condition number, we employed the statistical condition estimation theory to design efficient condition number estimators, and the numerical examples were also given to illustrate its efficiency. [ABSTRACT FROM AUTHOR]

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method. [ABSTRACT FROM AUTHOR]

Aiming at the robustness issue in high-speed trains (HSTs) operation control, this article proposes a model-free adaptive control (MFAC) scheme to suppress disturbance. Firstly, the dynamic linearization data model of train system under the action of measurement disturbance is given, and the Kalman filter (KF) based on this model is derived under the minimum variance estimation criterion. Then, according to the KF, an anti-interference MFAC scheme is designed. This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance. Finally, the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor. The proposed control algorithm can effectively suppress the measurement disturbance, and obtain smaller tracking error and larger signal to noise ratio with better applicability. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a methodology for the implementation of edge intelligence for predicting solar panel performances on wireless sensor nodes. The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper. [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]

Background: Numerous observational studies have investigated the risk of prostate cancer (PCa) in patients diagnosed with Parkinson's Disease (PD). However, the existence of a definitive association remains uncertain. Methods: Systematic searches were performed on PubMed, Web of Science, Scopus, and Google Scholar for studies published up to October 1, 2023. For Mendelian randomized (MR) causal inference, we employed pooled data from the IPDGC and PRACTICAL Consortium. The inverse variance weighted (IVW) method served as the principal technique for estimating odds ratios (ORs) and 95% confidence intervals (CIs) for the associations under investigation. Results: Cumulative analysis of nine studies revealed no significant association between patients diagnosed with PD and the subsequent incidence of PCa ([relative ratio] RR = 0.89, 95%CI = 0.73 to 1.08, P = 0.237). However, subgroup analyses indicated a reduced occurrence of PCa in Caucasian patients with PD (RR = 0.81, 95%CI = 0.69 to 0.95, P = 0.011). MR analyses failed to establish a significant link between increased genetic susceptibility to PD and the risk of PCa (IVW OR = 1.025, 95%CI = 0.997 to 1.054, P = 0.082). Sensitivity analyses further corroborated the robustness of these results. Conclusion: Both observational meta-analysis and MR analysis based on genetic variation do not support an association between PD patients and the subsequent risk of PCa. Further research is warranted to unravel the potential underlying mechanisms linking these two diseases. [ABSTRACT FROM AUTHOR]

Water and sand leakage (WSL) disasters frequently occur in underground engineering such as tunnels and foundation pits. The appearance of leakage port destroys the stable soil structure and leads to the leakage of soil particles in large quantities under the action of water flow and gravity, especially in gap-graded sandy soil layer. Therefore, model experiments on the WSL of gap-graded sandy soil with different grain size distributions are conducted by self-designed WSL simulation device. Moreover, the applicability of Peck curve and ellipsoid theory in estimation of the volume loss rate and influence radius of WSL in sandy soils is explored. Results show that GGS1 containing 20% coarse particles fails to form soil arch with 80.7% of sand leaked. However, GGS2 and GGS3 containing 40% and 60% coarse particles form soil arches in WSL with 13.5% and 10.9% of sand particles leaked, respectively. Moreover, the soil surface settlement decreases, while the settlement influence radius increases, as the content of coarse particles increases. The natural filter channel formed in sandy soils with higher coarse particle content is longer and wider. Finally, theoretical results are consistent with experimental results, which verifies the applicability of Peck formula and ellipsoid theory in the evolution of WSL. [ABSTRACT FROM AUTHOR]

Accurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relying solely on a single rainfall–runoff model may not provide reliable predictions. To address this challenge, over the years, researchers have developed and applied hydrologic forecast merging (HFM) techniques that combine multiple models or ensembles to enhance forecast accuracy, reduce uncertainty, and increase confidence in the forecast. This review summarizes the progress in HFM techniques since the early 1990s and covers developments and applications in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. The findings indicate that while HFM techniques outperform individual models regarding forecasting efficiency, their performance across applications is not uniform. Among the different methods, Bayesian model averaging (BMA) is the most popular due to its ability to reduce uncertainty and provide accurate and reliable forecasts in deterministic and probabilistic simulations. With their application simplicity, regression techniques are also robust and efficient as they perform competitively well across different model-merging applications. While specific techniques, such as model-dependent weighted averaging and neural network methods, effectively reduce forecast uncertainty, there is still room for improving forecast accuracy across different lead times. Future research can focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, like simulating low flows in seasonally dry catchments. [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]