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Hybrid manufacturing systems utilising raw materials and returned end-of-life products for production are studied. The systems are failure-prone and subject to inventory, market demand and return uncertainties. Thanks to growing environmental and sustainability concerns, the manufacturing sector is currently experiencing a significant growth in the popularity of reverse logistics. However, the practical implementation of production control in such systems is challenging due to return flow uncertainty and variability. To address this challenge, an estimation-based control using the Kalman filter is proposed in this research. The demand and return models employed contain random and deterministic components, with the latter being time-invariant for demand and uncertain with seasonal variations for return. The processing steps used include the estimation of inventory levels and demand and return components, return forecasting allowing cost computation over a long horizon, and the determination of the production and disposal policies adapting to market variations and uncertainties. We classify the systems according to relationships between their production capacity, demand and return ranges. We then present an extensive numerical study of optimal policies for various system classes and show that adaptive policies outperform the conventional ones, thus proving the effectiveness of the proposed production control approach for complex industrially oriented systems. [ABSTRACT FROM AUTHOR]

Extremes in organismal size have broad interest in ecology and evolution because organismal size dictates many traits of an organisms biology. There is particular fascination with identifying upper size extremes in the largest vertebrates, given the challenges and difficulties of measuring extant and extinct candidates for the largest animal of all time, such as whales, terrestrial non-avian dinosaurs, and extinct marine reptiles. The discovery of Perucetus colossus, a giant basilosaurid whale from the Eocene of Peru, challenged many assumptions about organismal extremes based on reconstructions of its body weight that exceeded reported values for blue whales (Balaenoptera musculus). Here we present an examination of a series of factors and methodological approaches to assess reconstructing body weight in Perucetus, including: data sources from large extant cetaceans; fitting published body mass estimates to body outlines; testing the assumption of isometry between skeletal and body masses, even with extrapolation; examining the role of pachyostosis in body mass reconstructions; addressing method-dependent error rates; and comparing Perucetus with known physiological and ecological limits for living whales, and Eocene oceanic productivity. We conclude that Perucetus did not exceed the body mass of todays blue whales. Depending on assumptions and methods, we estimate that Perucetus weighed 60-70 tons assuming a length 17 m. We calculated larger estimates potentially as much as 98-114 tons at 20 m in length, which is far less than the direct records of blue whale weights, or the 270 ton estimates that we calculated for body weights of the largest blue whales measured by length.

Abstract–This article proposes a novel model in the family zero-inflated (ZI) binary models which we named it a ZI Logit Probit (ZILP) model. This model can be employed to analyze the binary data that has an exorbitant number of zero counts. In the scope of this work, we first present the general formula, connected functions, and estimating equation (EE) of the ZILP model. We next rely on some popular regularity conditions to present theory of large-sample for this model. To have many numerical proofs, multiple simulations and a real medical data set were executed in this study. We perform the number of infected blood cells (IBC) data set to test the effectiveness and stability of the maximum likelihood estimation (MLE) method in estimating the parameters for the ZILP model. The results obtained in the analysis of actual medical data are significant and important in practice. The results indicated that smoking status would have no effect on the number of IBCs, however, gender would more or less have an effect on the number of IBCs. Finally, some discussions and conclusions are given in this article. [ABSTRACT FROM AUTHOR]

The probability of ruin of an insurance company is one of the main risk measures considered in risk theory, and the problems of its calculation and approximation have attracted much attention. Statistical estimations have been developed on the ruin probability in infinite time for insurance loses from heavy-tailed distributions. However, these estimation suffer heavily from under-coverage or have a robustness problem. We therefore need another method for estimating the probability of ruin in infinite time for heavy-tailed losses. In this paper, we introduce a robust estimator of the infinite-time probability of ruin for such distributions. Our methodology is based on extreme value theory, which offers adequate statistical results for such distributions. Our approach is based on a sensitive distribution known as the t-Hill estimator (t-score or score moment estimation) for the index of any tail distribution and introduced in Fabiáan and Stehlík. We establish their asymptotic normality, and through a simulation study, illustrate their behaviour in terms of absolute bias and mean squared error. The simulation results show that our estimators perform well and that they are fairly robust to outliers. [ABSTRACT FROM AUTHOR]

There is a lack of point process models on linear networks. For an arbitrary linear network, we consider new models for a Cox process with an isotropic pair correlation function obtained in various ways by transforming an isotropic Gaussian process which is used for driving the random intensity function of the Cox process. In particular, we introduce three model classes given by log Gaussian, interrupted, and permanental Cox processes on linear networks, and consider for the first time statistical procedures and applications for parametric families of such models. Moreover, we construct new simulation algorithms for Gaussian processes on linear networks and discuss whether the geodesic metric or the resistance metric should be used for the kind of Cox processes studied in this paper. [ABSTRACT FROM AUTHOR]

The development and validation of two uncomplicated, selective, and non-extraction-based visible spectrophotometric methods was carried out for the quantification of a tricyclic antidepressant amitriptyline hydrochloride as pharmaceutical tablets as well as in its pure form. The methods rely on the formation of ion pair complexes between amitriptyline base and two acidic dyes, bromothymol blue and bromocresol purple, which were subsequently measured at 410 and 400 nm, respectively. The developed methods included numerous reaction variables that were examined and improved. Beer's law is applicable under optimal conditions for the bromothymol blue and bromocresol purple methods, respectively, over concentrations from 0.5 to 12.5 μg mL−1 and from 0.5 to 10.0 μg mL−1 amitriptyline hydrochloride. The computed values of molar absorptivity were 1.79 × 104 and 2.32 × 104 L.mol−1.cm−1 for bromothymol blue and bromocresol purple methods, respectively, with Sandell's sensitivity values of 0.0155 and 0.0119 μg cm−2. Both detection limits and quantification limits were computed and determined to be 0.55 and 1.67 μg mL−1 for bromothymol blue method, and 0.49 and 1.49 μg mL−1 for bromocresol purple method, respectively. The developed methods were successfully applied to estimate the concentration of amitriptyline hydrochloride in bulk form and commercial pills. Therefore, these methods demonstrate promising potential for testing this drug in labs for quality control. Spectrophotometric quantification of amitriptyline hydrochloride in pharmaceutical tablets: method development and validation [ABSTRACT FROM AUTHOR]

Electric vehicles need continuous monitoring of the energy supply all the time to provide the user with accurate information about the available energy. However, an increase in the power consumed by the monitoring system may cause a significant decrease in the vehicle’s autonomy. This letter presents a monitoring and estimation system for an electric car with a hybrid energy supply based on supercapacitors and batteries based on an embedded system capable of monitoring the general current, the current from the battery system, the voltage in the battery system, the voltage in the supercapacitor system, estimating the value of the state of charge from batteries, and showing the measurements to the user with an OLED display. The proposed system performs similarly in the estimation but decreases the energy consumption by 99.9% concerning the laboratory systems. [ABSTRACT FROM AUTHOR]

Digitalization and automation in the agricultural sector enable the enhancement of production processes, leading to increased yields. Specifically, the medications administration or complementary treatments in animals often prove to be a demanding task for human operators. This letter introduces an embedded system prototype that facilitates monitoring the level of capsules coverage in troughs through image processing. The suggested system enables an innovative antiparasitic treatment using biological control agents. The prototype utilizes a Raspberry Pi 3B as the platform to execute the developed image processing algorithm. The obtained results successfully demonstrate the algorithm’s accurate functionality estimating capsules coverage within the troughs. [ABSTRACT FROM AUTHOR]

This research centers on the application of the discrete-time Taylor–Fourier transform (DTTFT) algorithmic implementation for phasor estimation on a field-programmable gate array board. The system employs a finite impulse response structure of a digital Taylor–Fourier filter to extract amplitude and phase information. The hardware description utilizes a multiply accumulator architecture with only forty embedded 9-bit multiplier elements, achieving an 18-bit input–output resolution. Performance assessment involves signal analysis through FPGA-in-the-loop simulation in MATLAB/Simulink. Findings demonstrate that the DTTFT-based phasor estimator can be effectively characterized using VHDL code and implemented on an Intel D2-115 board. [ABSTRACT FROM AUTHOR]

This letter introduces the design of an estimator for parameters of Middleton Class A noise using its canonical formula and classical numerical methods. The main focus is to acquire parameters to characterize communication channels in intelligent systems or those based on cognitive paradigms. A comprehensive analysis of the first-order characteristics of the Middleton Class A noise model is conducted to establish the foundational understanding necessary for developing the presented estimator model, named impulsive noise estimator with minimization methods (INEMM). Subsequently, the method is introduced, substantiated, and compared to various established estimators concerning precision and complexity. Results show a distinct advantage in terms of overall performance. [ABSTRACT FROM AUTHOR]

This letter focuses on the field-programmable gate array (FPGA) implementation of a Class A Middleton noise estimator, aiming to enhance its efficiency and performance. The inherent algorithm of the estimator undergoes strategic enhancements, leveraging median approximations. This endeavor leads to the development of a more streamlined and expeditious architecture. The research not only introduces the refined architecture but also conducts a comparative analysis of its attributes. The outcomes of this investigation show the benefits of algorithmic optimization, as the execution times achieved in hardware significantly surpass those attainable through software-based implementation. This underscores the practicality of the algorithmic refinement and also the notable advantages of the FPGA-based execution in terms of computational speed. [ABSTRACT FROM AUTHOR]

Due to the emergence of social networking platforms, a large number of users around the world are being part and partial of this platform. At a fraction of the time users on social media are communicating digital files in the form of text, video, images, voice and music which ultimately generates big data. The matter of interest is to estimate precisely the average file size at time duration (occasion). The time may hours or days or months. This paper presents a sample-based methodology to deal with mean size estimation of digital communication content spreading on a social media platform. An estimator is suggested using a random sample from big data and its properties are derived. A simulation method is suggested that computes the confidence interval (CI) for the prediction of précised range of digital file size. The proposed method produces an optimal confidence interval at the suitable choice of constant. These estimated confidence intervals can be used for developing α-control charts for constant monitoring of the growth in file size in social media storage at the data centre. If the growth of mean digital file size crosses the upper limit then additional storage infrastructure is needed at the administration level of the social media site. One can generate machine learning algorithms proposed method for monitoring the growth of average digital file size over time duration. [ABSTRACT FROM AUTHOR]

This paper deals with estimating the underlying parameter of a length-biased inverse Gaussian distribution, when the observations are prone to length-biased sampling. Length-biased sampling occurs when the observations of smaller lengths or dimensions are neglected from the sample. We focus on a particular type of sequential fixed-accuracy confidence interval for estimation purposes. This method proves to be both time and cost efficient as one is able to perform the estimation using an optimal number of observations according to some set criteria. We discuss the applicability of our proposed method with regards to estimating the cluster size of the "Eastern Tropical Pacific Dolphins", which are often vulnerable to length biasedness. [ABSTRACT FROM AUTHOR]

Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and estimating causal effects. In this paper, we review the potential outcomes framework with a focus on potential outcomes notation to define individual and average causal effects. We then show how three canonical assumptions, Unconfoundedness, Positivity, and Consistency, may be used to identify average causal effects. The identification results motivate methods for estimating causal effects in practice, which include model-based estimators, such as regression, inverse probability weighting, and doubly robust estimation, and procedures that target covariate balance, such as matching and stratification. Examples and discussion are grounded in the context of a running example of a study aimed at assessing the causal effect of receipt of special education services on 5th grade mathematics achievement in school-aged children. Practical considerations for education research are discussed. [ABSTRACT FROM AUTHOR]

Estimating parameters of functional ARMA, GARCH and invertible processes requires estimating lagged covariance and cross‐covariance operators of Cartesian product Hilbert space‐valued processes. Asymptotic results have been derived in recent years, either less generally or under a strict condition. This article derives upper bounds of the estimation errors for such operators based on the mild condition Lp‐m‐approximability for each lag, Cartesian power(s) and sample size, where the two processes can take values in different spaces in the context of lagged cross‐covariance operators. Implications of our results on eigen elements and parameters in functional AR(MA) models are also discussed. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the cognitive and affective factors associated with cross-cultural differences in arithmetic tasks. Methods: A total of 404 third- and fourth- graders were recruited from China and Italy to complete exact arithmetic, arithmetic estimation and cognitive tasks (i.e., short-term memory, executive functions, and fluid reasoning). Their mathematical anxiety was also measured. Results: The results showed that Chinese children performed better than Italian children in both arithmetic tasks and in shifting task. Italian children performed better in visuospatial updating task and reported higher levels of mathematical anxiety than their Chinese peers. Multi-group path analyses showed that the patterns of relations among cognitive factors (i.e., short-term memory, inhibition and shifting), mathematical anxiety, and arithmetic performance were similar across groups. The only exception was that visuospatial updating uniquely predicted arithmetic estimation for Chinese but not for Italian children. Conclusions: Chinese children outperformed their Italian peers in the exact arithmetic task, likely due to the greater emphasis on arithmetic fluency in Chinese mathematics education, both in schools and at home. They also had a slight advantage than Italian peers in the arithmetic estimation task. The unique link between updating and arithmetic estimation found in Chinese children but not Italian children suggests that, although arithmetic estimation is not emphasized in the curricula of either country, instruction and practice in exact arithmetic may enhance Chinese children's efficiency in solving arithmetic estimation problems. [ABSTRACT FROM AUTHOR]

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference. [ABSTRACT FROM AUTHOR]

Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations. [ABSTRACT FROM AUTHOR]

ABSTRACT The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post‐baseline ‘intercurrent’ events (IEs) are to be handled. In late‐stage clinical trials, it is common to handle IEs like ‘treatment discontinuation’ using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation. [ABSTRACT FROM AUTHOR]

There are good reasons to perform a randomized controlled trial (RCT) even in early phases of clinical development. However, the low sample sizes in those settings lead to high variability of the treatment effect estimate. The variability could be reduced by adding external control data if available. For the common setting of suitable subject-level control group data only available from one external (clinical trial or real-world) data source, we evaluate different analysis options for estimating the treatment effect via hazard ratios. The impact of the external control data is usually guided by the level of similarity with the current RCT data. Such level of similarity can be determined via outcome and/or baseline covariate data comparisons. We provide an overview over existing methods, propose a novel option for a combined assessment of outcome and baseline data, and compare a selected set of approaches in a simulation study under varying assumptions regarding observable and unobservable confounder distributions using a time-to-event model. Our various simulation scenarios also reflect the differences between external clinical trial and real-world data. Data combinations via simple outcome-based borrowing or simple propensity score weighting with baseline covariate data are not recommended. Analysis options which conflate outcome and baseline covariate data perform best in our simulation study. [ABSTRACT FROM AUTHOR]

Metric regularity is widely concerned since its important applications in optimization and control theory. For promoting the application of metric regularity, it is valuable to study the estimation of the neighborhood which makes the regularity hold. However, it seems that no result has been established about this issue. This paper investigates the estimation of the neighborhood of metric regularity for quadratic functions. The main result gives the expression of the neighborhood of metric regularity for a kind of convex quadratic functions. [ABSTRACT FROM AUTHOR]

This paper proposes a new probability model called as Maxwell-Boltzmann-Exponential (MBE) distribution. The MBE distribution arises as a mixture distribution of the Maxwell-Boltzmann and exponential distributions. The statistical properties of the distributions are studied and obtained in closed-form expressions. Three methodologies are assessed and compared for the estimation of parameters in the MBE distribution. The MBE regression model is defined, with the proposed regression model being an alternative to the gamma regression model for response variables that are extremely right-skewed and bimodal. Two real data sets are used to demonstrate the applicability of the proposed models against the existing models. [ABSTRACT FROM AUTHOR]

Ridge Estimation (RE) is a widespread method to overcome the problem of collinearity defining a class of estimators depending on the non-negative scalar parameter k. A great number of papers focus on the estimation of this biasing parameter. Traditionally, the mean squared error criterion is used to compare the performance of the different proposed estimators. However, the minimization of the mean squared error (MSE) does not always guarantee the mitigation of collinearity, meaning it is possible, for example, to obtain a variance inflation factor (VIF) higher than 10 for the k that minimizes the MSE. In this paper, we propose the VIF criteria to select the biased ridge parameter. A Monte Carlo simulation is presented with results that support this idea. Also, two real life empirical applications are used to illustrate the contribution of this paper. [ABSTRACT FROM AUTHOR]

In the presence of fixed threshold effects, the least squares (LS) estimator of the threshold parameter poses challenges for statistical inference due to its nonstandard limiting distribution, which also presents challenges for bootstrap methods. To address this issue, we propose a novel estimator: a two-step smoothed gradient least squares (SGLS) estimator. Our proposed method achieves a normal limiting distribution for the threshold parameter with minimal efficiency loss compared to the LS estimator. Furthermore, our modified bootstrap method significantly enhances computational efficiency, leading to improved bootstrap confidence intervals (CIs) for the threshold parameter compared to asymptotic CIs. Our method is validated through a small Monte Carlo study and demonstrated with an empirical application. [ABSTRACT FROM AUTHOR]

Weight estimation is essential in the care of ill children when a weight cannot be obtained. This is particularly important for children with medical complexity, who are at higher risk for adverse drug events. Our objective was to compare the accuracy of different methods of weight estimation in children and stratify by the presence of medical complexity. We performed a retrospective cross-sectional study of children (<18 years) seen in the emergency department (ED) or ambulatory clinic from January 1, 2013 to December 31, 2022 at a tertiary academic pediatric center. We compared the performance of nine age-based formulae and two length-based methods using metrics of mean average error (MAE), root mean square error (RMSE), and agreement within 10% and 20% of measured weight. We additionally evaluated the approaches stratified by body mass index (BMI) and the presence of medical complexity. Of 361,755 children (median age 8.2 years, IQR 2.5–14.2 years; 51.5% male), 59,283 (16.4%) were seen in the ED. Length was measured or available in 21,330 (36.0%) patients in the ED and 293,410 (97%) patients in clinics. The Broselow tape outperformed all methods, with 50.7% estimates within 10% of measured weight, 80.0% estimates within 20% of measured weight, the lowest MAE (2.5 kg), and lowest RMSE (4.5 kg). The Antevy formula was the most accurate age-based formula, with 49.2% estimates within 10% of measured weight, 80.1% estimates within 20% of measured weight, MAE of 2.8 kg, and RMSE of 4.7 kg. Estimates became less accurate as BMI and estimated weight increased for all methods. Among children with medical complexity (14.1%), the Broselow tape consistently outperformed age-based formulae, with 47.7% estimates within 10% of measured weight, 77.1% estimates within 20% of measured weight, MAE of 2.6 kg, and RMSE of 5.4 kg. The Antevy formula remained the most accurate age-based method among children with medical complexity. The Broselow tape predicted weight most accurately in this large sample of children, including among those with medical complexity. The Antevy formula is the most accurate age-based method for pediatric weight estimation. [ABSTRACT FROM AUTHOR]

Interface bonding between asphalt layers plays a vital role in ensuring the proper functionality of pavement structures. Interlayer Shear Strength (ISS) is recognized as an indicator quantifying the interface bonding quality. Consequently, accurate evaluation and prediction of the ISS is imperative in determining the performance of asphalt pavement structures. By conducting laboratory experiments and employing machine learning (ML) techniques, this research aims to predict and assess the ISS in asphalt pavement. In this regard, the classification and regression trees (CART) model was proposed based on measured data collected from laboratory experiments. Three experimental factors of curing temperature, normal stress, and tack coat application rate were selected as variables. The findings showed that the developed CART model explained over 98% of the experimental data in a relatively short period. The curing temperature was found to have the most significant influence on the ISS, followed by normal stress and tack coat dosage. Moreover, a parametric analysis of the interaction effects of input parameters on the ISS revealed that higher curing temperature and lower normal stress reduced the ISS. In contrast, a high tack coat application rate and low normal stress corresponded to a lower ISS of the asphalt pavement. The outcomes of this study could pave the way for the realization of a reliable and efficient design of interlayer bonding between asphalt pavement layers. [ABSTRACT FROM AUTHOR]

This paper investigates the practicality of an autonomous and resilient positional navigation using corner cube reflectors on the lunar surface. The proposed approach stands as an efficient navigation solution when the current system, e.g. GPS, is unavailable due to being attacked, jammed or spoofed by a third party. The paper provides a complete setup of the mathematical models using a nonlinear least squares algorithm and covariance analysis. A study is done that determines how the blockage of the Earth affects the proposed solution’s ability to perform positional navigation. Numerical simulations are presented to show the accuracy, performance and practicality of the proposed solution. The results of the proposed solution are compared with the results of an Earth-based ground station system that uses radio frequency to track the given satellite. The results show that the lunar reflector-based system generates more accurate results than the Earth-based ground station system. [ABSTRACT FROM AUTHOR]

Recent research syntheses have identified several potentially high-leverage teaching strategies for improving low-attaining secondary school students' learning of mathematics. These strategies include the structured use of representations and manipulatives and an emphasis on derived facts and estimation. This paper reports on 70 semi-structured interviews conducted with low-attaining students in Years 9–10 (ages 13–15) in England. The interviews addressed the students' perceptions of learning mathematics and the teaching strategies that they experienced and believed were most helpful. Many students reported rarely using number lines, not spontaneously estimating answers and being unfamiliar with derived facts. During the interviews, with minimal direction, students often showed that they were well able to make use of these strategies; however, they did not report making spontaneous use of them independently. We conclude that many of the most well-evidenced and recommended strategies to support low-attaining students in mathematics appear to be unfamiliar and unvalued, and we discuss how this might be addressed. [ABSTRACT FROM AUTHOR]

Modern science is dependent on imaging on the nanoscale, often achieved through processes that detect secondary electrons created by a highly focused incident charged particle beam. Multiple types of measurement noise limit the ultimate trade-off between the image quality and the incident particle dose, which can preclude useful imaging of dose-sensitive samples. Existing methods to improve image quality do not fundamentally mitigate the noise sources. Furthermore, barriers to assigning a physically meaningful scale make the images qualitative. Here, we introduce ion count-aided microscopy (ICAM), which is a quantitative imaging technique that uses statistically principled estimation of the secondary electron yield. With a readily implemented change in data collection, ICAM substantially reduces source shot noise. In helium ion microscopy, we demonstrate 3x dose reduction and a good match between these empirical results and theoretical performance predictions. ICAM facilitates imaging of fragile samples and may make imaging with heavier particles more attractive. [ABSTRACT FROM AUTHOR]

AbstractA novel model for count data is proposed in this work. This new model is briefly called the Zero-Inflated Probit Bell (ZIPBell) model. The ZIPBell model can be used to simulate and analyze traditional count data and can be used well in the simulation and analysis of count data with the phenomenon that the frequency for zero occupies a very high percentage. In the scope of this study, we first provide the general formula, functions or related equations for the ZIPBell model. The results of asymptotic inferences for the ZIPBell model are also given in this work. Maximum likelihood estimator (MLE) is used to check its performance of the proposed model. According to the numerical illustrations via several simulation researches and the analysis of a practical data set, it has been seen that the MLE method has offered correct and reliable inferences. Finally, the inferences and conclusions of this study are mentioned. [ABSTRACT FROM AUTHOR]

In recent years, the modeling of time-to-events has emerged as a highly promising and dynamic research area. This field has witnessed a surge of research studies dedicated to developing novel statistical methodologies aimed at effectively handling time-to-event phenomena. These studies are motivated by the increasing recognition of the importance of time-related factors in various fields such as medicine, epidemiology, finance, and engineering. Researchers have been actively engaged in proposing innovative approaches to address the complexities associated with time-to-event data. The overarching goal is to enhance our understanding of event occurrence and duration, enabling more accurate predictions and informed decision-making. This research encompasses a wide range of topics, including survival analysis, reliability modeling, and event prediction. The motivation behind these research efforts stems from the need to overcome traditional limitations in time-to-event analysis and to explore new avenues for modeling and interpretation. By introducing advanced statistical techniques, researchers seek to capture the intricate dynamics of event processes, considering factors such as censoring, competing risks, and time-varying covariates. The proliferation of research studies in this domain reflects a collective effort to push the boundaries of statistical modeling and analysis, paving the way for more comprehensive and robust methodologies. As researchers continue to delve deeper into the intricacies of time-to-event data, the impact of these advancements extends to diverse applications, ultimately fostering innovation and progress across interdisciplinary fields. This paper adopts and implements a new statistical approach to propose a family of flexible distributions, namely, a new generalized- O family of distributions. For the newly obtained family, certain mathematical properties such as identifiability, quantile function, r th non-central moment, Lorenz curve, incomplete moments, and the expression of the Bonferroni curve are obtained. Furthermore, an extension of the Weibull model is introduced using the newly developed approach, namely, a new generalized Weibull model. The parameters of the new generalized version of the Weibull model are estimated by adopting a well-known estimation approach. Finally, a data set consists of sixty (60) observations representing the times of the survival of some patients infected by the COVID-19 epidemic is analyzed to illustrate the new generalized Weibull model. [ABSTRACT FROM AUTHOR]

Kalman filter (KF) is a widely used navigation algorithm, especially for precise positioning applications. However, the exact filter parameters must be defined a priori to use standard Kalman filters for coping with low error values. But for the dynamic system model, the covariance of process noise is a priori entirely undefined, which results in difficulties and challenges in the implementation of the conventional Kalman filter. Kalman Filter with recursive covariance estimation applied to solve those complicated functional issues, which can also be used in many other applications involving Kalaman filtering technology, a modified Kalman filter called MKF-RCE. While this is a better approach, KF with SAR tuned covariance has been proposed to resolve the problem of estimation for the dynamic model. The data collected at (x: 706,970.9093 m, y: 6,035,941.0226 m, z: 1,930,009.5821 m) used to illustrate the performance analysis of KF with recursive covariance and KF with computational intelligence correction by means of SAR (Search and Rescue) tuned covariance, when the covariance matrices of process and measurement noises are completely unknown in advance. [ABSTRACT FROM AUTHOR]

The equivalent model method was used to calculate the theoretical value of ecosystem service, and the regional characteristic coefficient was selected for value correction. Combined with the analysis of the ecological sensitivity and per capita ecological value of the research area, the problems of difficult transformation of ecological service value and low market recognition in current practice were discussed. The result showed that the revised value of one standard unit equivalent ecological service in the study area in 2020 was 6 180. 02 yuan / hm², with a total value of about 15. 982 billion yuan. The service value of different land ecosystems varied greatly, in which the highest value per unit area reached 776 272. 05 yuan / hm², followed by wetland 321 484. 50 yuan / hm² . From the perspective of different ecological functions, the contribution rates of regulatory services, supply services, support services and cultural services were 84. 08%, 8. 24%, 5. 75% and 1. 93%, respectively; the per capita value of ecological value was about 12 618. 96 yuan, with different differences; the regional average ecological sensitivity index was 0. 79<1, indicating that ecological service value was sensitive to the regional development stage. [ABSTRACT FROM AUTHOR]

Accurately estimating knee joint angle during walking from surface electromyography (sEMG) signals can enable more natural control of wearable robotics like exoskeletons. However, challenges exist due to variability across individuals and sessions. This study evaluates an attention-based deep recurrent neural network combining gated recurrent units (GRUs) and an attention mechanism (AM) for knee angle estimation. Three experiments were conducted. First, the GRU-AM model was tested on four healthy adolescents, demonstrating improved estimation compared to GRU alone. A sensitivity analysis revealed that the key contributing muscles were the knee flexor and extensors, highlighting the ability of the AM to focus on the most salient inputs. Second, transfer learning was shown by pretraining the model on an open source dataset before additional training and testing on the four adolescents. Third, the model was progressively adapted over three sessions for one child with cerebral palsy (CP). The GRU-AM model demonstrated robust knee angle estimation across participants with healthy participants (mean RMSE 7 degrees) and participants with CP (RMSE 37 degrees). Further, estimation accuracy improved by 14 degrees on average across successive sessions of walking in the child with CP. These results demonstrate the feasibility of using attention-based deep networks for joint angle estimation in adolescents and clinical populations and support their further development for deployment in wearable robotics. [ABSTRACT FROM AUTHOR]

In this paper, an on-line closed-loop identification of mainly mass and inertia of an under-actuated aerial vehicle, namely a quadrotor acting as an aerial manipulator, is presented. Being treated as a rigid body, only one set of eighteen- or six tri-axial linear accelerometers is used to facilitate such estimation. Force and torque disturbances acting upon the vehicle during identification are also estimated which will refine the overall estimation quality of the unknown parameters. Namely, recursive linear and nonlinear least squares methods are used to obtain initial and refined estimations, respectively. Random disturbances are introduced to the closed-loop system to ensure enough excitation of the overall system. Issues related to identifiability, stability and performance are discussed. Since the proposed method depends on general kinematical and dynamical analyses of motion, it is claimed that the presented method will be also applicable to a wider range of, mainly, aerial vehicles. [ABSTRACT FROM AUTHOR]

When data exhibit a high frequency of small to medium values and a low frequency of large values, fitting a classical distribution might fail. This is why spliced models defined from different distributions on distinct intervals are proposed in the literature. In contrast to the intensive study of two-spliced distributions, the case with more than two components is scarcely approached. In this paper, we focus on three-spliced distributions and on their ability to improve the modeling of extreme data. For this purpose, we consider a popular insurance data set related to Danish fire losses, to which we fit several three-spliced distributions; moreover, the results are compared to the best-fitted two-spliced distributions from previous studies. [ABSTRACT FROM AUTHOR]

Count time series exhibiting seasonal patterns are frequently encountered in practical scenarios. For example, the number of hospital emergency service arrivals may show seasonal behavior (Moriña et al. 2011 Stat Med 30:3125–3136). Numerous models have been proposed for the analysis of seasonal count time series with an unbounded support, yet seasonal patterns in bounded count time series, which are sometimes suffered in environmental science such as the number of monthly rainy-days and air quality level data, have not received formal attention. The contribution of this article lies in coping with the modeling challenges associated with seasonal count time series with a bounded support, which is beneficial for enhancing the applicability of environmental science data. This is achieved by introducing a seasonal structure and seasonally varying model parameters into the first-order binomial autoregressive (BAR(1)) model (McKenzie 1985 J Am Water Resour Assoc 21:645–650). The probabilistic and statistical properties, marginal distribution and some special cases of the proposed model are studied. Estimation of model parameters is conducted using the Yule-Walker, conditional least squares and maximum likelihood methods. The asymptotic normality of the estimators is also presented. To demonstrate the utility of our model in environmental data, applications are carried out on the monthly number of rainy-days in two Russian cities. [ABSTRACT FROM AUTHOR]

Software maintenance is a challenging and laborious software management activity, especially for open‐source software. The bugs reports of such software allow tracking maintenance activities and were used in several empirical studies to better predict the bug resolution effort. These reports are known for their large size and contain nonrelevant instances that need to be preprocessed to be suitable for use. To this end, instance selection (IS) has been proposed in the literature as a way to reduce the size of the datasets, while keeping the relevant instances. The objective of this study is to perform an empirical study that investigates the impact of data preprocessing through IS on the performance of bug resolution prediction classifiers. To deal with this, four IS algorithms, namely, edited nearest neighbor (ENN), repeated ENN, all‐k nearest neighbors, and model class selection, are applied on five large datasets, together with five machine learning techniques. Overall, 125 experiments were performed and compared. The findings of this study highlight the positive impact of IS in providing better estimates for bug resolution prediction classifiers, in particular using repeated ENN and ENN algorithms. [ABSTRACT FROM AUTHOR]

We propose a new family of distributions called the Zografos-Balakrishnan type-I heavy-tailed-G (ZBTIHT-G) distributions. A special model of the proposed family, namely Zografos-Balakrishnan type-I heavy-tailed-Weibull (ZBTIHT-W) model is thoroughly studied. Statistical properties of the new family of distributions including, among others, the hazard rate function, quantile function, moments, distribution of order statistics and Rényi entropy are presented. The maximum likelihood method of estimation is used for estimating the model parameters and Monte Carlo simulation is conducted to examine the performance of the estimators of the model parameters. The flexibility and importance of the new family of distributions are demonstrated by means of applications to real data sets. [ABSTRACT FROM AUTHOR]

As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e.g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation. Thus a long-standing challenge of FAU analysis arises from the data scarcity of manual annotations, limiting the generalization ability of trained models to a large extent. Amounts of previous works have made efforts to alleviate this issue via semi/weakly supervised methods and extra auxiliary information. However, these methods still require domain knowledge and have not yet avoided the high dependency on data annotation. This article introduces a robust facial representation model MAE-Face for AU analysis. Using masked autoencoding as the self-supervised pre-training approach, MAE-Face first learns a high-capacity model from a feasible collection of face images without additional data annotations. Then after being fine-tuned on AU datasets, MAE-Face exhibits convincing performance for both AU detection and AU intensity estimation, achieving a new state-of-the-art on nearly all the evaluation results. Further investigation shows that MAE-Face achieves decent performance even when fine-tuned on only 1% of the AU training set, strongly proving its robustness and generalization performance. The pre-trained model is available at our GitHub repository. [ABSTRACT FROM AUTHOR]

Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To save the labeling cost, this paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Then, the prediction inconsistency on the two tasks for the unlabeled samples is used to guide sample selection in active learning for the target task. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning. To our knowledge, this is the first work that utilizes prior knowledge on affective norms and data in a different task to facilitate active learning for a new task, even the two tasks are from different datasets. [ABSTRACT FROM AUTHOR]

Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood for applications in education, healthcare, and entertainment, among others. Deep convolutional neural networks show promising results in classifying facial expressions of adults. However, classifier models trained with adult benchmark data are unsuitable for learning child expressions due to discrepancies in psychophysical development. Similarly, models trained with child data perform poorly in adult expression classification. We propose domain adaptation to concurrently align distributions of adult and child expressions in a shared latent space for robust classification of either domain. Furthermore, age variations in facial images are studied in age-invariant face recognition yet remain unleveraged in adult-child expression classification. We take inspiration from multiple fields and propose deep adaptive FACial Expressions fusing BEtaMix SElected Landmark Features (FACE-BE-SELF) for adult-child expression classification. For the first time in the literature, a mixture of Beta distributions is used to decompose and select facial features based on correlations with expression, domain, and identity factors. We evaluate FACE-BE-SELF using 5-fold cross validation for two pairs of adult-child data sets. Our proposed FACE-BE-SELF approach outperforms transfer learning and other baseline domain adaptation methods in aligning latent representations of adult and child expressions. [ABSTRACT FROM AUTHOR]

In the reconfigurable intelligent surface (RIS) assisted wireless positioning for 6G communication service, an estimation technique is proposed to obtain angle-of-arrival (AOA) parameter under the millimeter-wave channel with IEEE 802. 15.3c standard. It is seen that the proposed AOA estimation technique gives an optimal AOA estimate for wireless positioning with simple reference node and low computation under the millimeter-wave channel of non-line-of-sight (NLOS) and multiple spatial clusters; however, the previously published methods meet a failure in AOA estimation under those channel condition. [ABSTRACT FROM AUTHOR]

Can inefficiency be rational? Excess resources or slack may serve as a buffer against environmental shocks, help decouple organizations, ease planning and implementation, support innovation, and enable effective responses to competitors. Slack may however also be the result of inefficiency. In Bogetoft and Kerstens, Distinguishing useful and wasteful slack, we propose an approach to separate useful and wasteful slack. If an organization can maintain the same levels of output and slack at lower cost, there is wasteful or nonrationalizable spending. We develop ways to measure the extent to which total spending can be rationalized and show how to statistically estimate and test the usefulness of the available slack using bootstrapping. The literature on organization and strategic management suggests that slack in the form of excess resources may be useful. It may, for example, serve as a buffer against environmental shocks, help decouple organizations, ease planning and implementation, support innovation, and enable effective responses to competitors. In contrast, the economic literature tends to view slack as wasteful. When the same products and services can be produced with fewer resources and slack per se is not assigned any value, slack should be eliminated. The aim of this paper is to reconcile these two perspectives. We acknowledge that slack may be both useful and wasteful. The challenge is how to separate the two. Our approach relies on the simple Pareto idea. If an organization can maintain the same levels of output and slack at lower cost, there is wasteful or nonrationalizable spending. We develop ways to measure the extent to which total spending can be rationalized and show how to statistically estimate and test the usefulness of the available slack using bootstrapping. Funding: Financial support from Det Frie Forskningsråd [Grant 9038-00042A] is greatly appreciated. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2415. [ABSTRACT FROM AUTHOR]

Well logging is a significant method of geological formation description and resource assessment in the exploration and development of oil, natural gas, minerals, groundwater, and sub-surface thermal energy, as well as geotechnical engineering and environmental research. However, the challenging problem of estimating well logging data always exists because well logs can only be measured through a drilling process involving costly and time-consuming field trials. This study provides a brief overview of Deep Learning (DL) models and examines the well-logging issues in estimating missing well-log data. In addition, it discusses a literature review focusing on the application of DL models for well-log estimation. The outcome of this exploratory work necessitates suitable requirements for the design and implementation. [ABSTRACT FROM AUTHOR]

Silicon carbide is a captivating semiconductor material for electrical and electro-optical applications requiring high temperatures. Silicon carbide is a crucial non-oxide ceramic with a wide range of uses in manufacturing. It has special properties like high rigidity and durability, heat and chemical constancy, a high melting point, oxidation resistance, powerful erosion resistance, etc. Due to all of these properties, silicon carbide is the perfect material for high-power, high-temperature electrical devices as well as erosion and cutting purposes. Silicon carbide materials are frequently used in different fields of nuclear materials and semiconductor materials because of their outstanding radiation resistance, thermal conductivity, oxidation resistance, and mechanical strength. This study presents various K-Banhatti entropies, redefines Zagreb entropies, and the atom-bond sum connectivity entropy of the double and strong double graphs of silicon carbide [ S i 2 C 3 - I (r , s) ], and presents a numerical and graphical analysis of these results. The QSPR analysis of silicon carbide is performed for four characteristics Poisson's ratio, shear modulus, Young's modulus and bulk modulus through linear and quadratic regression analysis and found the best prediction. These models provide scientists with a new way of estimating physicochemical properties. [ABSTRACT FROM AUTHOR]

In this research paper, we present an innovative investigation into a novel two parameter semicircular distribution, termed the “stereographic semicircular Erlang distribution,” which is constructed using the inverse stereographic projection (ISP) technique. This distribution serves as advancement over the existing stereographic semicircular exponential distribution. We delve into essential mathematical properties of this distribution and execute a simulation study to estimate its parameter values. Furthermore, we perform an empirical analysis utilizing a dataset comprising posterior corneal curvature measurements extracted from the eyes of 23 patients. This empirical assessment is designed to evaluate the adaptability and potential applicability of the proposed distribution within the realm of ophthalmology in medical science.

This article explores the mathematical and statistical performances and connections of the two well-known ordinary least-squares estimators (OLSEs) and best linear unbiased estimators (BLUEs) of unknown parameter matrices in the context of a multivariate general linear model (MGLM) for regression, both of which are defined under two different optimality criteria. Tian and Zhang [38] once collected a series of existing and novel identifying conditions for OLSEs to be BLUEs under general linear models: On connections among OLSEs and BLUEs of whole and partial parameters under a general linear model, Stat. Probabil. Lett., 112 (2016), 105–112. In this paper, we show how to extend this kind of results to multivariate general linear models. We shall give a direct algebraic procedure to derive explicit formulas for calculating the OLSEs and BLUEs of parameter spaces in a given MGLM, discuss the relationships between OLSEs and BLUEs of parameter matrices in the MGLM, establish many algebraic equalities related to the equivalence of OLSEs and BLUEs, and give various intrinsic statistical interpretations about the equivalence of OLSEs and BLUEs of parameter matrices in a given MGLM using some matrix analysis tools concerning ranks, ranges, and generalized inverses of matrices.

Abstract This paper investigates a novel category of probability distributions and a specific member within this category. We have formulated a new family of trigonometric distributions by utilizing the odds ratio derived from the distribution function of a base distribution. This newly devised distribution family termed the “Sine pie-power odd-G family” of distributions, is constructed through a transformation involving the sine function. The paper presents an overview of the fundamental characteristics inherent to this proposed distribution family. Using the Weibull distribution as a base reference, we have introduced a member belonging to the proposed distribution family. This member demonstrates various hazard functions such as j, reverse-j, increasing, decreasing, or bathtub shapes. The paper examines essential statistical attributes of this newly introduced distribution. The estimation of the distribution’s parameters is carried out via the maximum likelihood estimation method. The accuracy of the parameter estimation procedure is validated through Monte Carlo simulations. The outcomes of these simulations reveal a reduction in biases and mean square errors as sample sizes increase, even for small samples. Two sets of real-engineering data are considered to demonstrate the proposed distribution’s applicability. The performance of the suggested distribution is evaluated using some model selection criteria and goodness-of-fit test statistics. Empirical evidence from these evaluations substantiates that the proposed model outperforms six existing models.

The research is devoted to the estimation the structural complexity of programs. The algorithm of finding cyclomatic routes for program executions is described. By now, two directions of obtaining estimates for the complexity estimates in software modules have been defined: structural and statistical. Both directions connect the value of program complexity with the labor intensity related to their development. The structural complexity of program modules is determined by the number of interacting components, the number and complexity of links between them. The complexity of a program's behavior depends to a large extent on the set of routes through which it is executed. The complexity metric obtained from these positions allows us to determine estimates of the cost of designing the program as a whole, as well as to identify the modules that are likely to contain the most errors, especially the logical ones.