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Understanding the demography of wildlife populations is a key component for ecological research, and where necessary, supporting the conservation and management of long‐lived animals. However, many animals lack phenological changes with which to determine individual age; therefore, gathering this fundamental information presents difficulties. More so for species that are rare, highly mobile, migratory and those that reside in inaccessible habitats. Until recently, the primary method to measure demography is through labour intensive mark‐recapture approaches, necessitating decades of effort for long‐lived species. Gadfly petrels (genus: Pterodroma) are one such taxa that are overrepresented with threatened and declining species, and for which numerous aspects of their ecology present challenges for research, monitoring and recovery efforts. To overcome some of these challenges, we developed the first DNA methylation (DNAm) demography technique to estimate the age of petrels, using the epigenetic clock of Gould's petrels (Pterodroma leucoptera). We collected reference blood samples from known‐aged Gould's petrels at a long‐term monitored population on Cabbage Tree Island, Australia. Epigenetic ages were successfully estimated for 121 individuals ranging in age from zero (fledgling) to 30 years of age, showing a mean error of 2.24 ± 0.17 years between the estimated and real age across the population. This is the first development of an epigenetic clock using multiplex PCR sequencing in a bird. This method enables demography to be measured with relative accuracy in a single sampling trip. This technique can provide information for emerging demographic risks that can mask declines in long‐lived seabird populations and be applied to other Pterodroma populations. [ABSTRACT FROM AUTHOR]

Reliability growth models are commonly categorized into two primary groups: parametric and non‐parametric models. Parametric models, known as Software Reliability Growth Models (SRGM) rely on a set of hypotheses that can potentially affect the accuracy of model predictions, while non‐parametric models (such as neural networks) can predict the model solely based on training data without any assumptions regarding the model itself. In this paper, we propose several methods to enhance prediction accuracy in software reliability context. More specifically, we, on one hand, introduce two gradient‐based techniques for estimating parameters of classical SRGMs. On the other, we propose methods involving LSTM Encoder–Decoder and Bayesian approximation within Langevin Gradient and Variational inference neural networks. To evaluate our proposed models' performance, we compare them with various neural network‐based software reliability models using three real‐world software failure datasets and utilizing the Mean Square Error (MSE) as a model comparison criterion. The experimental results indicate that our proposed non‐parametric models outperform most classical parametric and non‐parametric models. [ABSTRACT FROM AUTHOR]

This randomised, crossover, sham-controlled study explored the neural basis of source-monitoring, a crucial cognitive process implicated in schizophrenia. Left superior temporal gyrus (STG) and dorsolateral prefrontal cortex (DLPFC) were the key focus areas. Thirty participants without neurological or psychological disorders underwent offline sham and active tDCS sessions with specific electrode montage targeting the left STG and DLPFC. Source-monitoring tasks, reality monitoring (Hear-Imagine), internal source-monitoring (Say-Imagine), and external source monitoring (Virtual–Real) were administered. Paired t-test and estimation statistics was performed with Graphpad version 10.1.0. The Benjamini–Hochberg procedure was employed to control the false discovery rate in multiple hypothesis testing. A significant improvement in internal source monitoring tasks (p = 0.001, Cohen's d = 0.97) was observed, but reality monitoring tasks demonstrated moderate improvement (p = 0.02, Cohen's d = 0.44). The study provides insights into the neural mechanisms of source monitoring in healthy individuals and proposes tDCS as a therapeutic intervention, laying the foundation for future studies to refine tDCS protocols and develop individualized approaches to address source monitoring deficits in schizophrenia. [ABSTRACT FROM AUTHOR]

This paper proposes a semiparametric spatial lag model and develops a Bayesian estimation method for this model. In the estimation of the model, the paper combines Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, random walk Metropolis sampler, and Gibbs sampling techniques to sample all the parameters. The paper conducts numerical simulations to validate the proposed Bayesian estimation theory using a numerical example. The simulation results demonstrate satisfactory estimation performance of the parameter part and the fitting performance of the nonparametric function under different spatial weight matrix settings. Furthermore, the paper applies the constructed model and its estimation method to an empirical study on the relationship between economic growth and carbon emissions in China, illustrating the practical application value of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the existence of positive periodic solutions is studied for Liénard equation with a singularity of repulsive type, x ″ (t) + f (x (t)) x ′ (t) + φ (t) x μ (t) − 1 x γ (t) = e (t) , where f : (0 , + ∞) → R is continuous, which may have a singularity at the origin, the sign of φ (t) , e (t) is allowed to change, and μ, γ are positive constants. By using a continuation theorem, as well as the techniques of a priori estimates, we show that this equation has a positive T-periodic solution when μ ∈ [ 0 , + ∞) . [ABSTRACT FROM AUTHOR]

Stock markets around the world are growing rapidly, and vulnerability created by default risk of option holders has become a serious issue. In such circumstances, this paper suggests the pricing of vulnerable European and barrier options, when the underlying asset and asset held by the option writer follow the geometric Liu process and uncertain mean-reverting process, respectively. According to the uncertainty theory, we present closed-form analytic formulas of these option prices and use the residual method to estimate the model parameters. In order to estimate the model parameters, the genetic algorithm to solve the generalized moment estimation problem derived by the residual method is suggested. From the uncertain hypothesis test, we then evaluate whether the proposed uncertain stock models fit the observed data. Ultimately, based on the estimated parameters, we evaluate the effect of the vulnerable option parameters on its price. [ABSTRACT FROM AUTHOR]

Fractional uncertain differential equations are used for forecast in this paper. It is crucial to provide an accurate estimation method since fractional uncertain differential equations hold memory effects and parameter estimation strongly depends on the accuracy of numerical schemes. A generalized moment estimation method is developed by the famous L 1 numerical scheme. Adam optimization algorithm is adopted in the minimum optimization problem and the obtained optimal parameters can pass the uncertain hypothesis test. Compared with the uncertain differential equations, the fractional one holds more parameter freedom degrees and captures long-term interactions of observed data. Finally, the applications to aftershock frequency and pharmacokinetics are provided as examples. It can be concluded that fractional uncertain differential equations lead to the higher forecast accuracy which also provides some potential works on "fractional deep learning" under uncertain environments. [ABSTRACT FROM AUTHOR]

This paper presents a novel, cost-effective technique for estimating the Doppler effect in the time domain using a single pulse and subsequently leveraging the precise Doppler value to recover the radar waveform. The proposed system offers several key advantages over existing techniques, including the ability to calculate the target speed without any frequency ambiguity and the ability to detect a wide range of target speeds. These two features are not available in any existing techniques, including the conventional moving target detection (MTD) processor. To ensure improved accuracy and robust estimation, the system employs ensemble and iterative techniques by recursively and efficiently reducing the Doppler residues from the signal. Furthermore, the proposed system demonstrates effective signal recovery of a well-known bi-phase code shape at low signal-to-noise ratios in just a few iterations. The performance evaluation of the new algorithm demonstrates its practicability and its superiority over traditional radar systems. Implementation on software-defined radio (SDR) reveals that the proposed system excels in Doppler estimation and signal recovery at low SNRs, demonstrating promising results. [ABSTRACT FROM AUTHOR]

This work presents a stochastic framework for the construction of rainfall intensity–time scale–return period relationships, which was applied in the recent regionalization of design rainfall curves over the Greek territory, described in a companion paper. The methodology outlined herein builds upon a widely-used mathematical framework, which has been recently revisited and upgraded, and incorporates two different versions: (a) a theoretically consistent stochastic model applicable for rainfall intensity over any scale of interest; and (b) a simplified version valid over small scales, which makes parameter estimation easier. Special attention is given to the presentation of the simplified version, which suffices for most engineering tasks. Parameter estimation approaches are presented in detail, including the K-moments framework that allows for reliable high-order moment estimation and handling of bias due to spatiotemporal dependence. [ABSTRACT FROM AUTHOR]

Introduction Injury epidemiology research with military populations typically utilizes data obtained through medical chart review (MCR) or injury self-reports (ISRs). MCR data will not capture musculoskeletal injury (MSKI) data for which medical care was not sought, which is common during military recruit training. Injury self-report is affected by issues with recall, especially for MSKIs perceived as less severe. U.S. Marine Corps (USMC) recruits participate in an intense 13-week recruit training program during which they are susceptible to MSKIs. The purpose of the current analysis was to utilize a novel statistical method, the capture-recapture (CRC) technique, to account for the undercounting inherent in MSKI data sources and estimate the ascertainment-corrected cumulative incidence of MSKIs during USMC recruit training. Materials and Methods Data for the current study were derived from a larger study, the USMC Gender-Integrated Recruit Training Study, which was initiated to provide data-driven recommendations to increase gender integration in USMC recruit training. The estimated cumulative incidence of MSKIs during 13-weeks of USMC recruit training was calculated from the 2 sources of MSKI data (MCR, ISR) and using CRC analysis. Medical charts were reviewed to extract data about MSKIs that occurred during recruit training. Self-reported MSKI data for the same period were obtained from recruits at the end of recruit training. MSKIs were classified according to their anatomical location and type. The Chapman modification of the Lincoln-Peterson estimator was utilized to conduct the CRC analysis. Results Medical chart review and ISR MSKI data were available for 464 USMC recruits (age: 19.1 ± 1.9 years; gender: men 70.0%). The observed 13-week cumulative incidence of MSKI in the sample was 21.8% in the MCR and 28.4% in the ISR, while the CRC incidence was much higher (62.0%). The MCR and ISR ascertainment were 35.1% and 45.9%, respectively, while the overall ascertainment or completeness of MSKI data when 2 sources were used was moderate (65.0%). When stratified by MSKI anatomical location, the overall ascertainment varied by anatomical location of the MSKI. It was highest for lower extremity MSKIs (64.8%), but lower for upper extremity (38.9%) and spine (33.3%) MSKIs. The overall ascertainment also varied by MSKI type; it was highest for sprain (55.1%), followed by strain (54.8%), and the pain/spasm/ache (43.3%). Conclusions This was the first study to utilize the CRC technique to access the ascertainment-corrected incidence of MSKIs among USMC recruits. There was significant undercounting in both sources of the data analyzed, and the extent of undercounting varied by both MSKI anatomical location and type. When 2 sources of data were utilized simultaneously, the percent of CRC-estimated MSKIs observed from 2 sources of data was more complete. There is a need for further application of the CRC technique to MSKI data in military populations to provide a more complete assessment of MSKIs. Identification of modifiable factors that influence completeness of MSKI data obtained during military recruit training is also warranted. [ABSTRACT FROM AUTHOR]

This paper focused on the decision and regulation for the production-warehousing-selling (P-W-S) model. A novel event-triggered mechanism (ETM) was meticulously developed to determine when to impose control, alongside the development of the corresponding impulsive strategy. By applying the input-to-state stability (ISS) theory, some quantitative relationships between system parameters and ETM were integrated into the estimation of the state of the P-W-S model. It was shown that under the designed ETM and impulsive strategy, the warehouse was able to autonomously adjust inventory levels based on factory production efficiency and market selling trends, and hence the quantity of goods could be maintained within a reasonable range, avoiding excessive inventory while adequately meeting market requirements. At last, an example with numerical simulations was presented to validate our results. [ABSTRACT FROM AUTHOR]

The present DOA estimation approach based on sparse Bayesian learning has two major shortcomings: high algorithm complexity and large estimate errors. These two flaws are mostly the result of an excessive number of complex-valued matrix inversion operations and incorrect grid partitioning in the EM stage of sparse Bayesian learning (SBL). To overcome the aforementioned issues, we propose a three-procedure, low-complexity DOA estimate technique based on SBL. First, the roots of the estimated covariance are used to complete the real-valued conversion and generate the received signal matrix, which includes the virtual array steering vector. Second, a novel iterative method for immobile spots is devised using the probability distribution function of the noise variance. Finally, iterations are completed utilizing dynamic grid approaches to improve DOA estimation accuracy. The simulation findings reveal that the proposed technique significantly speeds up DOA estimation and, to a lesser extent, improves estimation accuracy. [ABSTRACT FROM AUTHOR]

Measurement of transcutaneous bilirubin (TcB) is a non-invasive, widely used technique to estimate serum bilirubin (SB). However, its reliability in multiethnic populations during and after phototherapy is still controversial even in covered skin. The aim of this study was to determine the reliability of TcB in covered (cTcB) and exposed (eTcB) skin during and after phototherapy in a multiethnic population of term and preterm neonates according to Neomar's neonatal skin color scale. Prospective, observational study comparing SB and TcB. We determined SB when clinically indicated and, at the same time, measured cTcB under a photo-opaque patch and eTcB next to it with a jaundice meter (Dräger JM-105TM). All dyads TcB-SB were compared, both globally and according to skin color. We obtained data from 200 newborns (color1: 44, color2: 111, color3: 41, color4: 4) and compared 296 dyads TcB/SB. Correlation between cTcB and SB is strong during (0.74–0.83) and after (0.79–0.88) phototherapy, both globally and by color group. The SB-cTcB bias depends on gestational age during phototherapy and on skin color following phototherapy. The correlation between eTcB and SB during phototherapy is not strong (0.54), but becomes so 12 h after discontinuing phototherapy (0.78). Conclusions: Our study supports the reliability of cTcB to assess SB during and after phototherapy, with differences among skin tones after the treatment. The use of cTcB and Neomar's scale during and mainly after phototherapy may help reduce the number of blood samples required. What is Known: • Controversies exist on the reliability of jaundice meters during and after phototherapy in covered skin. Only a few studies have analyzed their accuracy in multiethnic populations, but none has used a validated neonatal skin color scale. What is New: • We verified correlation between serum and transcutaneous bilirubin in covered skin in a multiethnic population depending on skin color based on our own validated neonatal skin color scale during and after phototherapy. [ABSTRACT FROM AUTHOR]

Population‐adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head‐to‐head trials are unavailable. Three commonly used PAIC methods include matching‐adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta‐regression (ML‐NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user‐friendly R Shiny application Shiny‐MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny‐MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny‐MAIC application, enabling a user‐friendly approach conducting MAIC for healthcare decision‐making. The Shiny‐MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/. [ABSTRACT FROM AUTHOR]

Mesocarnivores are major components of carnivore assemblages, and they play important roles in structuring communities and regulating community dynamics. In Southwest China, many apex predators have been extirpated, and this has potentially resulted in the ecological release of mesocarnivores. Estimating the sizes of mesocarnivore populations is challenging. We used DNA derived from hairs and spatial capture–mark–recapture techniques to estimate the population size of masked palm civets (Paguma larvata) in Laohegou Nature Reserve in the Minshan Mountains of Sichuan Province, China. In the fall of 2014 and the spring of 2015, we collected 144 and 230 hair samples, respectively, at 30 baited stations. We used 16S rRNA fragments, microsatellite genotyping, and sexing to determine that 191 samples were derived from 44 masked palm civet individuals (24 males and 20 females). Using spatially explicit capture–recapture analysis, we estimated that there were 82 ± 13 masked palm civets in the study area, with a density of 1.7 individuals/km2. This is the first study to estimate the population size of masked palm civets in the wild. Our data provide important new information on the density of masked palm civets. [ABSTRACT FROM AUTHOR]

This study focuses on the precise model estimation for a position control problem actuated by a shape memory alloy (SMA) wire. Because the hysteresis characteristic of SMA introduces complexities in system modelling and adds degrees of freedom, a model with reduced order is implemented for controller design. This model is online updated by calculating a complementary term from the measured data to compensate for the SMA actuator dynamics and other parametric uncertainties. The position controller, derived from the formulated reduced‐order model, adapts itself to real conditions and is cost‐effective due to the use of only displacement sensor. The saturation of the control input is modelled within the structure of a constrained optimization problem solved by Karush–Kuhn–Tucker theorem. The boundedness of mean and covariance of tracking error and its derivative is demonstrated by stochastic analysis. The experimental results conducted on a platform incorporating a SMA wire show the efficiency of the proposed system in precisely controlling the position by admissible voltage range. The comparative results with a sliding mode controller indicate higher accuracy for the proposed controller to reduce the effect of uncertainties. [ABSTRACT FROM AUTHOR]

We describe a new species of Discostella from Guiyang based on light and scanning electron microscopic observations, as well as molecular analysis. This species is morphologically distinguished from other species by a relatively smooth and slightly recessed central area with marginal striae of unequal lengths forked near the margin. Marginal fultoportulae and a single rimoportula are situated between every two costae or on the costae. Both morphological characters and the concatenated data of rbcL and SSU rDNA indicate that Discostella guiyangensis is a unique and new species. According to divergence time estimation analysis, Discostella guiyangensis is the oldest species among the Discostella species sequenced currently. In addition, we complement molecular data of Discostella asterocostata and Discostella stelligera. [ABSTRACT FROM AUTHOR]

Since 2007, the Yellow Sea green tide has broken out every summer, causing great harm to the environment and society. Although satellite remote sensing (RS) has been used in biomass research, there are several shortcomings, such as mixed pixels, atmospheric interference, and difficult field validation. The biomass of green tide has been lacking a high-precision estimation method. In this study, high-resolution unmanned aerial vehicle (UAV) RS was used to quantitatively map the biomass of green tides. By utilizing experimental data from previous studies, a robust relationship was established to link biomass to the red-green-blue floating algae index (RGB-FAI). Then, the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements. Results show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas. The study provided an effective complement to the traditional satellite RS, as well as high-precision quantitative techniques for decision-making in disaster management. [ABSTRACT FROM AUTHOR]

The performance of Doppler velocity logs (DVLs) in terms of velocity estimate error is directly linked to the geometry of the beam and the pulse transmitted. Beyond a specific transmitted bandwidth, the phase‐shift beamformer can introduce significant errors in velocity estimation. To delineate the operating mechanism of phase‐shift errors within a phased array of acoustic DVLs, the correlation between bottom echo and velocity distribution, in conjunction with the power‐weighted function, was initially examined predicated on spectral estimation theory. Subsequently, numerical and analytical models of the Gaussian‐shaped Doppler spectrum were formulated. The models are employed to evaluate the velocity estimation inaccuracies attributed to phase shifts in extant DVLs, and the comparative results with field experiments corroborate the model's efficacy in forecasting errors. The theoretical findings evaluate the performance limitations of the current phased array transducer design and provide insights for developing new designs. Pool experimental results show that this design effectively reduces the velocity estimation error caused by phase shift under static conditions and in the presence of Doppler frequencies to a level of almost complete elimination of the error compared to conventional configurations. [ABSTRACT FROM AUTHOR]

We investigate information processing in the stochastic process driving stock's volatility (volatility discovery). We apply fractionally cointegration techniques to decompose the estimates of the market-specific integrated variances into an estimate of the common integrated variance of the efficient price and a transitory component. The market weights on the common integrated variance of the efficient price are the volatility discovery measures. We relate the volatility discovery measure to the price discovery framework and formally show their roles on the identification of the integrated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and in-fill asymptotics. The empirical application is in line with our theoretical results, as it reveals that trading venues incorporate new information into the stochastic volatility process in an individual manner and that the volatility discovery analysis identifies a distinct information process than that based on the price discovery analysis. [ABSTRACT FROM AUTHOR]

As enterprises face increasing competitive pressures, financial crises can significantly impact on their capital operations, potentially leading to operational difficulties and, ultimately, market exclusion. Consequently, many enterprises have begun to utilize financial early-warning systems to guide and control risks. Currently, there is neither a universal nor comprehensive enterprise financial risk management model in China, nor a unified classification standard for enterprise financial risk management levels. This article takes financial data on A-share listed companies in 2020 as the data sample, including those with special treatment (represented by ST) or non-ST status. We establish an independent indicator system within the framework of profitability, solvency, operational capability, development potential, shareholders' retained earnings, cash flow, and asset growth. The model is constructed employing the factor–logistic fusion algorithm. The factor part addresses the issue of collinearity among risk indicators, and the logistic part presents the results in probabilistic form, enhancing the interpretability of the model. The prediction accuracy of this model exceeds 89%. Finally, by applying the principles of interval estimation theory to statistical hypothesis testing, we categorize the risk levels into Grade A, representing significant risk; Grade B, representing moderate risk; Grade C, representing minor risk; and Grade D, representing no risk. This article aims to provide a comprehensive definition of a universal financial risk management early-warning model applicable to all enterprises in China. [ABSTRACT FROM AUTHOR]

The replication crisis in empirical research calls for a more mindful approach to how we apply and report statistical models. For empirical research to have a lasting (policy) impact, these concerns are crucial. In this paper, we present Probabilistic Programming (PP) as a way forward. The PP workflow with an explicit data-generating process enhances the communication of model assumptions, code testing and consistency between theory and estimation. By simplifying Bayesian analysis, it also offers advantages for the interpretation, communication and modelling of uncertainty. We outline the advantages of PP to encourage its adoption in our community. [ABSTRACT FROM AUTHOR]

T he researchers investigate some phenomena by continuously observing physical variables, i.e., time series. Nowadays, the Least-Squares Spectral Analysis (LSSA) technique has been preferred for the analysis of time series to conduct more reliable analysis. This technique uses the least-squares principle to estimate the hidden periodicities in the time series. Based on the previous investigations, LSSA gives more reasonable results in the experimental time series that have disturbing effects such as the datum shifts, linear trend, unequally spaced data and etc. The LSSA method is a unique method that can overcome these problems without preprocessing the original series. However, a practical and user-friendly software package in C programming language is not available for scientific purposes to implement the LSSA method. In this paper, we review the computational scheme of the LSSA method, then a software (LSSASOFT) package in the C programming language is developed in the view of the simplicity of the method and compatibility of all types of data. Finally, LSSASOFT is applied in two sample studies for the determining hidden periods in the synthetic data and sea level observations. Consequently, the numerical results indicate that LSSASOFT is a useful tool that can efficiently predicting hidden periodicity for the experimental time series that have disturbing effects. [ABSTRACT FROM AUTHOR]

Active adaptation to various types of interference from the ground surface is critical for accurately tracking the angles of objects flying at low altitudes. Among the angle-estimation algorithms used in multipath environments, the refined maximum likelihood (ML) technique has been actively investigated. However, it suffers from performance degradation when multiple targets or interference signals exist. In addition, computational complexity increases exponentially as the number of parameters estimated using the refined ML technique increases when multiple targets exist. In this study, we derive an ML estimation formula when two targets are present and propose a refined ML technique for estimating the angles of the two targets by combining the geometric relationship between direct and specular paths with the results of complex envelope estimates using the minimum mean-square-error method. Furthermore, we propose a low-complexity iterative refined ML technique to avoid a two-dimensional grid search. The performance of the proposed algorithm is verified by evaluating its root mean square error and comparing it with those of the conventional refined ML and beam domain ML (BDML). [ABSTRACT FROM AUTHOR]

In sedimentary basins, deep-seated overpressure conditions are frequently encountered. However, the precise origins of these overpressure conditions and the assessment of their formation times have long presented challenges. Previous studies have primarily relied on qualitative approaches to investigate overpressure origins, leading to substantial uncertainties in their findings. Based on theories such as the effective stress law, disequilibrium compaction, equilibrium depth, and nested fluid trapping containers in this paper, a new quantitative methodology is introduced for identifying the disequilibrium-compaction-induced origins of overpressure conditions. Additionally, the formation times of overpressure can be also estimated by nested fluid trapping container theory. This methodology is successfully applied to the northwestern Bozhong subbasin in the Bohai Bay Basin, China. The results indicate that the overpressure within the Dongying Formation of the northwestern Bozhong subbasin is primarily attributed to the disequilibrium compaction of mudstone, because the disequilibrium compaction of mudstone accounts for over 90% of the pressure in sandstone. Furthermore, the overpressure system in this area is not singular but comprises multiple nested relative fluid trapping containers. The application of nested fluid trapping container theory allows for an estimation of the overpressure's formation time, although further validation of these estimates is required. It should be noted that the method proposed in this paper is particularly suited for sedimentary basins with relatively weak tectonic activity. [ABSTRACT FROM AUTHOR]

This paper proposes a robust distributed observer for uncertain systems where some unknown, unstructured, and bounded uncertainties exist in system matrices. In distributed observer, each node includes an observer which directly estimates a part of the system states by its limited measurements and further, the estimation of other states indirectly obtains via exchanging information with neighbouring nodes. It is shown that whole states convergence achieves if the LTI system is observable as well as the network graph is strongly connected. Also, in the proposed distributed observer, by applying a new term in dynamic equation, the robust performance is achieved in dealing with uncertainties in system matrices. The observer gains synthesis is then formulated in terms of linear matrix inequalities. Finally, the simulation results are presented to illustrate the effectiveness of the proposed distributed observer to handle system uncertainties. [ABSTRACT FROM AUTHOR]

Abstract This study focuses on the precise model estimation for a position control problem actuated by a shape memory alloy (SMA) wire. Because the hysteresis characteristic of SMA introduces complexities in system modelling and adds degrees of freedom, a model with reduced order is implemented for controller design. This model is online updated by calculating a complementary term from the measured data to compensate for the SMA actuator dynamics and other parametric uncertainties. The position controller, derived from the formulated reduced‐order model, adapts itself to real conditions and is cost‐effective due to the use of only displacement sensor. The saturation of the control input is modelled within the structure of a constrained optimization problem solved by Karush–Kuhn–Tucker theorem. The boundedness of mean and covariance of tracking error and its derivative is demonstrated by stochastic analysis. The experimental results conducted on a platform incorporating a SMA wire show the efficiency of the proposed system in precisely controlling the position by admissible voltage range. The comparative results with a sliding mode controller indicate higher accuracy for the proposed controller to reduce the effect of uncertainties.

Precision medicine is a framework for developing evidence‐based medical recommendations that seeks to determine the optimal sequence of treatments, tailored to all of the relevant, observable patient‐level characteristics. Because precision medicine relies on highly sensitive, patient‐level data, ensuring the privacy of participants is of great importance. Dynamic treatment regimes (DTRs) provide one formalization of precision medicine in a longitudinal setting. Outcome‐weighted learning (OWL) is a family of techniques for estimating optimal DTRs based on observational data. OWL techniques leverage support vector machine (SVM) classifiers in order to perform estimation. SVMs perform classification based on a set of influential points in the data known as support vectors. The classification rule produced by SVMs often requires direct access to the support vectors. Thus, releasing a treatment policy estimated with OWL requires the release of patient data for a subset of patients in the sample. As a result, the classification rules from SVMs constitute a severe privacy violation for those individuals whose data comprise the support vectors. This privacy violation is a major concern, particularly in light of the potentially highly sensitive medical data that are used in DTR estimation. Differential privacy has emerged as a mathematical framework for ensuring the privacy of individual‐level data, with provable guarantees on the likelihood that individual characteristics can be determined by an adversary. We provide the first investigation of differential privacy in the context of DTRs and provide a differentially private OWL estimator, with theoretical results allowing us to quantify the cost of privacy in terms of the accuracy of the private estimators. [ABSTRACT FROM AUTHOR]

Machine learning techniques are potent tools to predict structural failures and forecast future failure patterns. They can provide insights that are not immediately recognizable to human operators, and hence, enhance the maintenance decision-making process, leading to better safety and longevity of structures. This research employed a genetic algorithm based on an identification technique to estimate the location and depth of cracks in circular arches. The arches were made of homogeneous material and had a rectangular cross-section. The study adopted two different procedures for detecting cracks in the circular arches. The first method used natural frequency contours represented in a three-dimensional plot to identify the location and depth of cracks by intersecting a set of natural frequency contours. The second procedure utilized the regression method and an optimization technique to minimize the cost function objective function and determine the crack parameters. The influences of crack location and depth on the vibrational behavior of cracked arches were presented and discussed. Similarly, the results indicate a high level of consistency between the two techniques in predicting both crack size and location. [ABSTRACT FROM AUTHOR]

This paper presents an experimental inverse approach to simultaneously estimate the temperature-dependent thermal properties of AISI 316 stainless steel. The heat flux conducted to the sample was corrected by using a transducer, evaluating contact resistance at microscopic level, and performing experiments under vacuum. Estimates were severely biased when heat flux correction was not applied. Metaheuristics were used to enlarge the search space, which can cover the thermal properties of all known metallic materials, thereby taking advantage of better thermal modeling adequacy. The estimation reliability was shown by comparison with literature data, statistical significance, uncertainty analysis, and inverse heat flux retrieval. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a velocity correction projection method for the Micropolar Navier-Stokes Equations. The velocity correction method are adopted to approximate the time derivative term, stability analysis and error estimation of the first-order semi-discrete scheme are proved. At the same time, the optimal error estimate using the technique of dual norm are obtained. In this way, the divergence free of the velocity u can be conserved. Finally, the numerical results show the method has an optimal convergence order. The numerical results are consistent with our theoretical analysis, and our method is effective. [ABSTRACT FROM AUTHOR]

A technique for estimating the velocity of radio meteors is proposed. The presented method is based on the analysis of the phase of the Fourier spectra of complex amplitudes of signals reflected from meteor trails. The method is very fast compared to the Fresnel transform method, works at low signal-to-noise ratios and is automatic. Approaches for increasing the signal-to-noise ratio and criteria for rejecting the obtained estimates are proposed. An error of 1–10% was achieved, depending on the signal-to-noise ratio (from −5 dB) and the velocity of the meteor. Comparison of meteoroid velocities from Kazan Federal University meteor radar measurements with optical observations showed good agreement of estimates with a slight underestimation of 2–8%, presumably due to the neglect of deceleration. [ABSTRACT FROM AUTHOR]

In this article, the mathematical study of the problem of identifying the space-dependent source term, in transport processes given by an n -dimensional linear parabolic equation, from space-dependent noisy measurements taken at an arbitrary fixed time is conducted. The problem is analytically solved using Fourier techniques, and it is shown that this solution is not stable. Three families of uniparametric regularization operators are proposed to address the instability of the solution. Each of them is designed to compensate for the factor that causes the instability of the inverse operator. Additionally, a selection rule for the regularization parameter is included, and an error bound for each estimation of Hölder type is obtained. Numerical examples of different characteristics are presented to demonstrate the benefits of the proposed strategies. [ABSTRACT FROM AUTHOR]

To address the issues of poor anti-noise performance of second-order statistics and low estimation accuracy in previous joint underdetermined blind source separation (JUBSS) methods, we propose a novel JUBSS method based on the dependence between different data sets and the advantages of cross third-order cumulant in resisting distributed noise. The method involves several steps. Firstly, we calculate the cross third-order cumulant of multiple whitening data sets with different delays. Then, we stack several third-order cumulants into fourth-order tensors. Next, we decompose the fourth-order tensor using Canonical Polyadic through weight nonlinear least squares, which allows us to estimate the mixed matrix. Finally, depending on the independence of source signals, we propose a matrix diagonalization method to recover the source signal. Experiments demonstrate that the method effectively suppresses the influence of Gaussian noise and performs well in underdetermined, positive and overdetermined cases and produces a better performance than various common approaches. Specifically, for the 3 × 4 mixed model with signal-to-noise ratio of 20 dB, the average relative error is − 14.48 dB, the average similarity coefficient is 0.92 and the signal-to-interference ratio is 24.84 dB. [ABSTRACT FROM AUTHOR]

The electron beam-induced current technique (EBIC) developed at CEA-Leti has been a valuable tool for studying the transport properties of minority carriers in HdCdTe. Indeed, previous work has demonstrated the use of this technique for estimating diffusion length from the exponential decay of EBIC as a function of junction distance, as well as direct measurement of the modulation transfer function (MTF) of small pixel pitch diodes. In this work, a modulated electron beam and a lock-in amplifier are used to measure the variation in current and phase shift with the scan distance to the junction. This work is focused on the estimation of the minority carrier lifetime from the linear evolution of the phase shift as a function of junction distance. [ABSTRACT FROM AUTHOR]

Understanding source rocks' organic content and thermal maturity is crucial in assessing their hydrocarbon potential. To address this, our study focused on developing an accurate artificial neural network (ANN) model for estimating total organic carbon (TOC) content and a complete set of pyrolysis parameters from conventional well logs. The accuracy of the ANN-based technique in estimating TOC content was found to be significantly higher (correlation coefficient of 0.95) compared to the results obtained using Passey's method (correlation coefficient of 0.44). Additionally, the ANN model provided highly accurate predictions for the pyrolysis parameters S1, S2, S3, and Tmax, with correlation coefficients of 0.85, 0.90, 0.86, and 0.93, respectively. The study focused on the Abu Gabra Formation in the Hamra field, and the ANN data analysis revealed that the source rock in this area is of fair to good quality. The assessment of kerogen type indicated a mixed kerogen type II and type III, suggesting the potentiality for oil and gas generation. The predicted parameters further confirmed that the Abu Gabra source rock is thermally mature and capable of generating indigenous hydrocarbons. The results of the ANN-based modeling were consistent with laboratory measurements, demonstrating the reliability of the predictions for comprehensive source rock evaluation using well logs. [ABSTRACT FROM AUTHOR]

This investigation deals with two new subclasses of analytic and bi-univalent functions defined by Bernoulli polynomial. In this paper, coefficient estimation and Fekete-Szego problems are solved for these newly defined function subclasses. In addition, certain remarks are indicated for the subclasses of bi-starlike and bi-convex functions. [ABSTRACT FROM AUTHOR]

Abstract In recent years, growth in environmental awareness and increasing non‐renewable energy costs have augmented the interest in hydropower generation as a renewable energy. The volume of water stored in a reservoir system is crucial in hydropower production and has deep uncertainty. The subject of this investigation is the estimation of the amount of future hydropower generation in reservoir systems, some of which have multi‐purpose use. This investigation was carried out in Turkey at Atatürk, Keban, Karakaya and Demirköprü Dams. In the estimation of the hydropower generation for future, the decision matrices were devised and run separately for each reservoir system. In the verification of the validity of the model, the amounts of energy generated in reality in the last three years, which were not included in the modelling process, were used for the reservoirs investigated. In the verification process, when the model results and the amounts of energy produced in reality are compared, similar satisfactory results were obtained which are compatible with the properties of the regions where the reservoirs were built. It is expected that the proposed model will provide a decision support tool especially in multi‐objective planning of constrained water resources.

Summary: Cloud computing has become popular for training deep‐learning (DL) models, avoiding the costs of acquiring and maintaining on‐premise systems. SageMaker is a cloud service that automates the execution of DL workloads. Its features include automatic hyperparameter optimization and use of spot instances. Nonetheless, it does not assist in selecting the right instance type for a workload. In public clouds, rent price depends on the configuration of the chosen instance type. Advanced and faster instances are typically more expensive, but not always the best choice. To select the optimal instance type, users must compare the workload's relative performance (and hence cost) on several candidates. Building on the execution profiles of multiple DL applications, we model the performance and cost of training DL applications on SageMaker and propose a lightweight technique to estimate these at low temporal and monetary cost. This method is a performance proxy that can be used to replace more expensive performance measurement procedures. So, it could speed up any technique that relies on such measurements. We show how it can help cloud customers seeking suitable instance types to train DL models, and that it can accurately predict the performance of different instance types when training these models on SageMaker. [ABSTRACT FROM AUTHOR]

Background: Although the COVID-19 pandemic claimed a great deal of lives, it is still unclear how it affected mortality in low- and lower-middle-income countries (LLMICs). This review summarized the available literature on excess mortality during the COVID-19 pandemic in LLMICs, including methods, sources of data, and potential contributing factors that might have influenced excess mortality. Methods: We conducted a systematic review and meta-analysis on excess mortality during the COVID-19 pandemic in LLMICs in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines We searched PubMed, Embase, Web of Science, Cochrane Library, Google Scholar, and Scopus. We included studies published from 2019 onwards with a non-COVID-19 period of at least one year as a comparator. The meta-analysis included studies reporting data on population size, as well as observed and expected deaths. We used the Mantel–Haenszel method to estimate the pooled risk ratio with 95% confidence intervals. The protocol was registered in PROSPERO (ID: CRD42022378267). Results: The review covered 29 countries, with 10 countries included in the meta-analysis. The pooled meta-analysis included 1,405,128,717 individuals, for which 2,152,474 deaths were expected, and 3,555,880 deaths were reported. Calculated excess mortality was 100.3 deaths per 100,000 population per year, with an excess risk of death of 1.65 (95% CI: 1.649, 1.655, p < 0.001). The data sources used in the studies included civil registration systems, surveys, public cemeteries, funeral counts, obituary notifications, burial site imaging, and demographic surveillance systems. The primary techniques used to estimate excess mortality were statistical forecast modelling and geospatial analysis. One out of the 24 studies found higher excess mortality in urban settings. Conclusion: Our findings demonstrate that excess mortality in LLMICs during the pandemic was substantial. However, estimates of excess mortality are uncertain due to relatively poor data. Understanding the drivers of excess mortality, will require more research using various techniques and data sources. [ABSTRACT FROM AUTHOR]

This study investigated the effect of government expenditure on real growth in ECOWAS countries. This paper used panel cointegration techniques to examine the impact of government expenditure on economic growth for a sample of 15 ECOWAS countries between 1999 and 2021. The study uses the POLS, FMOLS, and DOLS techniques for estimating four models. The study supports the view that government expenditure positively affects real economic growth in ECOWAS countries. However, we also found that higher control of corruption improves the effectiveness and efficiency of government expenditure in promoting economic growth. Furthermore, a higher incidence of conflict minimizes the effectiveness and efficiency of government expenditure in promoting economic growth. The finding suggests that a well-managed government can contribute positively to economic growth. The finding that government expenditure positively affects real growth in ECOWAS countries suggests that a well-managed government can contribute positively to economic growth. This finding is helpful for policymakers in ECOWAS countries interested in improving their countries' economic growth. [ABSTRACT FROM AUTHOR]

Leaf Area Index (LAI) remains one of the most important forest structural attributes, as accurate estimation of LAI is crucial for predicting the growth of different species. Using the Partial Least Squares Regression (PLS-R) algorithm, this study investigated three image processing techniques to determine the best technique for estimating LAI using WorldView-3 imagery in the Midlands, KwaZulu-Natal province of South Africa. The PLS-R texture ratios model achieved the highest accuracy of R2 = 0.70, RMSE = 1.21 (2.32% of the mean measured LAI) and R2 = 0.72, RMSE = 1.26 (2.41% of the mean measured LAI) for the wet and dry seasons, respectively. This was followed by the PLS-R single texture band model that produced an accuracy of R2 = 0.65, RMSE = 1.35 (2.58% of the mean measured LAI) and R2 = 0.67, RMSE = 1.32 (2.52% of the mean measured LAI) for the wet and dry seasons, respectively. The PLS-R model using a combination of vegetation indices had the lowest estimation accuracy of R2 = 0.59, RMSE = 1.38 (2.64% of the mean measured LAI) and R2 = 0.60, RMSE = 1.40 (2.67% of the mean measured LAI) for the wet and dry seasons, respectively. The results of this study provided evidence that image texture ratios can be used to estimate LAI effectively. [ABSTRACT FROM AUTHOR]

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation. [ABSTRACT FROM AUTHOR]

AbstractBias-adjusted three-step latent class (LC) analysis is a popular technique for estimating the relationship between LC membership and distal outcomes. Since it is impossible to randomize LC membership, causal inference techniques are needed to estimate causal effects leveraging observational data. This paper proposes two novel strategies that make use of propensity scores to estimate the causal effect of LC membership on a distal outcome variable. Both strategies modify the bias-adjusted three-step approach by using propensity scores in the last step to control for confounding. The first strategy utilizes inverse propensity weighting (IPW), whereas the second strategy includes the propensity scores as control variables. Classification errors are accounted for using the BCH or ML corrections. We evaluate the performance of these methods in a simulation study by comparing it with three existing approaches that also use propensity scores in a stepwise LC analysis. Both of our newly proposed methods return essentially unbiased parameter estimates outperforming previously proposed methods. However, for smaller sample sizes our IPW based approach shows large variability in the estimates and can be prone to non-convergence. Furthermore, the use of these newly proposed methods is illustrated using data from the LISS panel. [ABSTRACT FROM AUTHOR]