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In this work, we explored the growth regime of (100) β-Ga2O3 homoepitaxial films on substrates with different miscut angles (1°, 2°, and 4°) in the MOVPE system. Under a low O2/Ga ratio growth condition, step-flow growth of (100) β-Ga2O3 homoepitaxial films can be maintained up to 3 μm on substrates with different miscut angles. Moreover, the results reveal that the growth rate decreases slightly with decreasing miscut angles, which matches estimation of the Burton–Cabrera–Frank theory and can be explained by the model of adsorption–desorption. By comparing the miscut-dependent growth rates, we give experimental evidence on the fundamental difference between the growth of (100) β-Ga2O3 films by MOVPE and MBE. In addition, a transport model is proposed to explain the desorption process in terms of the boundary layer and the kinetic resistance. [ABSTRACT FROM AUTHOR]

Small radius Bdot measurements of electrical current delivered by pulsed power devices are routinely compromised by electrode/convolute plasmas endemic to multiterawatt transmission lines. Inferences of delivered current unaffected by these issues have recently been obtained by numerically optimizing consistency between model predictions and local experimental velocimetry data, but these are only unique for shockless velocity profiles. Here, we describe a more general Bayesian method capable of inferring current despite the presence of shocks. Additionally, we describe uncertainty estimates and use of the technique on experimental data. This technique is the first to provide uncertainty estimates on the full current trace delivered to an inertial confinement fusion target. [ABSTRACT FROM AUTHOR]

In this work, we study the quantum Fisher information on the Heisenberg model in presence of the Dzyaloshinskii–Moriya interaction, basing on the multi-parametric estimation strategy in quantum metrology. First, we employ the density matrix vectorization method with a tool of the quantum Fisher information matrix through quantum Cramer–Rao bound. Second, we show that the multi-parameter quantum Cramer–Rao bound for simultaneous and individual estimation of the temperature and Dzyaloshinskii–Moriya interaction strongly relies on the concepts of the quantum Fisher information matrix. We confirm also the simultaneous strategy that provides a high precision than the individual. [ABSTRACT FROM AUTHOR]

In this paper, a quick convergence adaptive structure is proposed for trajectory tracking of an articulated type robotic manipulator using the Hardware in the Loop (HIL) simulation technique. A novel Nonlinear Quick Convergence Adaptive Sliding Backstepping Control (QCASBC) algorithm is implemented on a C2000 real-time controller board. The performance of the proposed control algorithm is inspected concerning the sliding mode PID (SM-PID) control technique to estimate its correlation with the proposed algorithm. The experimental part of the HIL simulation tests has been carried out on a simulated model of a three-link serial robot manipulator. A dynamic model of the robotic manipulator has been developed using Matlab Simulink software and its performance is analyzed using the HIL technique via a C2000 real-time controller for tracking the desired trajectory. Results show that the speed of convergence while tracking the desired trajectory of the manipulator is better in the proposed algorithm. It is also estimated that the positional error of the QCASBC algorithm is superior to the SM-PID control algorithm. The observed average angle error is improved by 14% and the average response time error is improved by 11% by using the proposed QCASBC algorithm compared to the SM-PID approach. [ABSTRACT FROM AUTHOR]

AbstractSpatial processes observed in various fields, such as climate and environmental science, often occur at large-scale and demonstrate spatial nonstationarity. However, fitting a Gaussian process with a nonstationary Matérn covariance is challenging, as it requires handling the complexity and computational demands associated with modeling the varying spatial dependencies over large and heterogeneous domains. Previous studies in the literature have tackled this challenge by employing spatial partitioning techniques to estimate the parameters that vary spatially in the covariance function. The selection of partitions is an important consideration, but it is often subjective and lacks a data-driven approach. To address this issue, in this study, we utilize the power of Convolutional Neural Networks (ConvNets) to derive subregions from the nonstationary data by employing a selection mechanism to identify subregions that exhibit similar behavior to stationary fields. We rely on the ExaGeoStat software for large-scale geospatial modeling to implement the nonstationary Matérn covariance for large-scale exact computation of nonstationary Gaussian likelihood. We also assess the performance of the proposed method with synthetic and real datasets at large-scale. The results revealed enhanced accuracy in parameter estimations when relying on ConvNet-based partition compared to traditional user-defined approaches. [ABSTRACT FROM AUTHOR]

This systematic review aimed to evaluate the potential of deep learning algorithms for converting low-dose Positron Emission Tomography (PET) images to full-dose PET images in different body regions. A total of 55 articles published between 2017 and 2023 by searching PubMed, Web of Science, Scopus and IEEE databases were included in this review, which utilized various deep learning models, such as generative adversarial networks and UNET, to synthesize high-quality PET images. The studies involved different datasets, image preprocessing techniques, input data types, and loss functions. The evaluation of the generated PET images was conducted using both quantitative and qualitative methods, including physician evaluations and various denoising techniques. The findings of this review suggest that deep learning algorithms have promising potential in generating high-quality PET images from low-dose PET images, which can be useful in clinical practice. [ABSTRACT FROM AUTHOR]

This paper presents the exponentiated alpha-power log-logistic (EAPLL) distribution, which extends the log-logistic distribution. The EAPLL distribution emphasizes its suitability for survival data modeling by providing analytical simplicity and accommodating both monotone and non-monotone failure rates. We derive some of its mathematical properties and test eight estimation methods using an extensive simulation study. To determine the best estimation approach, we rank mean estimates, mean square errors, and average absolute biases on a partial and overall ranking. Furthermore, we use the EAPLL distribution to examine three real-life survival data sets, demonstrating its superior performance over competing log-logistic distributions. This study adds vital insights to survival analysis methodology and provides a solid framework for modeling various survival data scenarios. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Duchenne muscular dystrophy (DMD) is a genetic neuromuscular disorder that leads to mobility loss and life‐threatening cardiac or respiratory complications. Quantitative ultrasound (QUS) envelope statistics imaging, which characterizes fat infiltration and fibrosis in muscles, has been extensively used for DMD evaluations.Notably, changes in muscle microstructures also result in acoustic attenuation, potentially serving as another crucial imaging biomarker for DMD. Expanding upon the reference frequency method (RFM), this study contributes to the field by introducing the robust RFM (RRFM) as a novel approach for ultrasound attenuation imaging in DMD.The RRFM algorithm was developed using an iterative reweighted least squares technique. We conducted standard phantom measurements with a clinical ultrasound system equipped with a linear array transducer to assess the improvement in attenuation estimation bias by RRFM. Additionally, 161 DMD patients, included in both a validation dataset (n = 130) and a testing dataset (n = 31), underwent ultrasound scanning of the gastrocnemius for RRFM‐based attenuation imaging. The diagnostic performances for ambulatory functions and discrimination between early and late ambulatory stages were evaluated and compared with those of QUS envelope statistics imaging (involving Nakagami distribution, homodyned K distribution, and entropy values) using the area under the receiver operating characteristic curve (AUROC).The results indicated that the RRFM method more closely matched the actual attenuation properties of the phantom, reducing measurement bias by 50% compared to conventional RFM. The AUROCs for RRFM‐based attenuation imaging, used to discriminate between early and late ambulatory stages, were 0.88 and 0.92 for the validation and testing datasets, respectively. These performances significantly surpassed those of QUS envelope statistics imaging (p < 0.05).Ultrasound attenuation imaging employing RRFM may serve as a sensitive tool for evaluating the progression of ambulatory function deterioration, offering substantial potential for the health management and follow‐up care of DMD patients. [ABSTRACT FROM AUTHOR]

Precise measurement of magnetic fields is essential for various applications, such as fundamental physics, space exploration, and biophysics. Although recent progress in quantum engineering has assisted in creating advanced quantum magnetometers, there are still ongoing challenges in improving their efficiency and noise resistance. This study focuses on using symmetric graph state resources for quantum magnetometry to enhance measurement precision by analyzing the estimation theory under time-homogeneous and time-inhomogeneous noise models. The results show a significant improvement in estimating both single and multiple Larmor frequencies. In single Larmor frequency estimation, the quantum Fisher information spans a spectrum from the standard quantum limit to the Heisenberg limit within a periodic range of the Larmor frequency, and in the case of multiple Larmor frequencies, it can exceed the standard quantum limit for both noisy cases. This study highlights the potential of graph state-based methods for improving magnetic field measurements under noisy environments. [ABSTRACT FROM AUTHOR]

Fish age and growth data are crucial for understanding vital species' traits and their populations. Boarfish is a pelagic species widely distributed in the Northeast Atlantic that is one of the most discarded non-commercial species on the Portuguese coast. Due to its potential valorisation as a new fishery in the Northeast Atlantic, boarfish has become object of interest in several published studies. However, since no age standard protocol has been implemented for boarfish, studies have used a variety of different otolith preparation techniques to estimate age, leading to contradicting and discrepant results. This research aims to consolidate biological insights into boarfish age and growth, lay the foundation for a standardized ageing protocol and serve as a benchmark study for the Portuguese west coast before commercial exploitation. Through the comparison of two otolith ageing methodologies, this study recommends using whole otoliths as the best method, revealing a maximum age of 15 years. The growth pattern estimated, indicated a biphasic growth pattern, with a faster initial growth rate that slows down at 2.4 years, the moment of change, when energy is allocated for sexual maturation. This finding was corroborated by a maturity ogive and analysis of fish size and otolith length relationship. [ABSTRACT FROM AUTHOR]

Hydrogen fuel holds promise for clean energy solutions, particularly in onboard applications such as fuel cell vehicles. However, the development of efficient hydrogen storage systems remains a critical challenge. This study addresses this challenge by exploring the potential of high-strength novel materials, including glass, to maximize onboard hydrogen storage capacity. A mathematical approach was employed to evaluate the feasibility and efficacy of various high-strength materials for hydrogen storage. This study focused on capillary arrays as a promising storage medium and utilized mathematical modeling techniques to estimate the storage capacity enhancement achievable with different materials. The analysis revealed significant variations in storage capacity enhancements in different high-strength novel materials, with glass having promising results. Glass-based materials demonstrated the potential to meet or exceed US Department of Energy (DOE) targets for both gravimetric and volumetric hydrogen storage capacities in capillary arrays. By leveraging a mathematical approach, this study identified high-strength novel materials, including glass and polymers, capable of substantially improving onboard hydrogen storage capacity: 29 wt.% with 40 g/L for quartz glass and 25 wt.% with 38 g/L for Kevlar compared to 5.2 wt.% with 26.3 g/L from a conventional type IV tank. These findings underscore the importance of material selection in optimizing hydrogen storage systems and provide valuable insights for the design and development of next-generation hydrogen storage technologies for onboard applications. [ABSTRACT FROM AUTHOR]

An integral step in an ultra-wideband localization network installation is determining the positions of the fixed infrastructure nodes, the anchors. This process is time-consuming and usually requires specialized equipment. Additionally, it is difficult to achieve scalability, as any change or addition in the network requires a redetermination of the affected anchors. One can automate this process by utilizing the distance-measuring capabilities of the network infrastructure and employing a distributed position estimation algorithm, such as the consensus subgradient (CSG) algorithm. Yet, the CSG suffers from scalability issues due to high problem dimensionality and data-sharing bottlenecks in practical applications. Consequently, implementation in embedded devices is difficult. In this article, we propose a modification of this algorithm, the neighborhood CSG, which aims toward embedded implementation by local reduction of the problem dimensions without hindering the precision of the original CSG algorithm or its convergence rate. [ABSTRACT FROM AUTHOR]

Abstract–In the regression analysis, ordinary least square techniques is commonly used. However, the data's outcomes may be untrustworthy if there is an outliers in it. In order to deal with the outliers problem, robust quantile regression methods have been frequently presented as alternatives to OLS for a long time. In this article, primarily a exponential ratio-type estimators is suggested. After that, robust quantile regression estimators are proposed, that is a useful strategy. The application of robust quantile regression empowered the efficiency of the estimators especially for outliers in the data. The MSE equations of the various estimators are computed and compared to OLS approaches. Numerical illustration and simulations studies are performed to support our theoretical findings. [ABSTRACT FROM AUTHOR]

In the reliability analysis of the landslide-stabilizing piles system (LSP), the most commonly used uncertainty models of geotechnical parameters mainly include the homogeneity model (HM) and unconditional random field (UCRF). However, neither of these models considers the existence of borehole data, which may lead to overestimation of the degree of site uncertainty. Based on borehole data in situ, the conditional random field (CRF) of the shear strength of the sliding surface was established through random field theory and the Kriging estimation method. By combining the limit equilibrium method (LEM) and Monte Carlo simulation (MCS), a novel procedure was introduced that can calculate the stability coefficient and design thrust of pile stabilization at the same time. By considering multiple failure modes comprehensively, a calculation flowchart for the failure probability of the LSP was proposed. The spatial layout parameters of pile stabilization, including the pile location, pile spacing, and pile length, significantly impact the calculation results. Compared with using HM and UCRF, the failure probability of the LSP may be lower when using CRF. Based on the allowable failure probability, feasible design solutions were provided. Further comparisons were made on the number of feasible solutions under these three uncertainty models. The research results in this paper might provide some assistance for the optimization of pile stabilization. [ABSTRACT FROM AUTHOR]

The fractal formalisms are well known for providing new understandings regarding the geometrical, spatial, and temporal behaviour of seismicity. Particularly, the fractal dimensions give information about the seismic events self‐organization and self‐similarity. On the other hand, the Gutenberg–Richter value, known as the b‐value, has shown through the years to give handy information regarding the statistical distribution of earthquakes, on‐site physical parameters, and geomechanical inputs. The Gutenberg–Richter value (b) and the capacity and correlation fractal dimensions, (D0 and D2), of the spatial distribution of earthquake hypocentres interact mathematically for micro‐ and macro‐events. From this interaction, it is possible to obtain new insights into the fracture network development and the microseismicity source characterization in terms of single fractures, fault planes, or densely fractured volumetric spaces. Here we show this interaction for the open‐source Decatur CO2 project seismicity catalogue, comparing it with the results obtained for a natural earthquake catalogue of Illinois, in the United States. The fractal dimension D0 is calculated using two different methodologies: box‐counting and correlation integral partitioning. This last method is also used to calculate D2. The results presented in this study allow us to describe how the fracture network geometry influences the earthquake complexity. Together with the calculation of the b‐value, we present clear indications which show that seismicity recorded in the Illinois tectonic environment partially follows the Aki relationship D0 ∼ 2b, which is not the case for induced events. In addition, the induced earthquake dataset shows that D2 > D0, an anomalous behaviour in terms of the fractal formalisms. All these facts might be used to establish spatial fracture network control techniques and seismicity‐type distinctions in CO2 injection sites located in highly active tectonic areas, respectively. [ABSTRACT FROM AUTHOR]

Several numerical simulations of fluid flow were performed using the Lattice Boltzmann method and image processing techniques to estimate the effective properties of 2-D porous rocks. The effective properties evaluated were the physical characteristics that allow fluid flow including the effective porosity, permeability, tortuosity, and average throat size to determine the storage and transport of fluids in porous rocks. The permeability was compared using the Darcy model simulation and the empirical Kozeny–Carman Equation. The results showed that the Lattice Boltzmann method and image processing techniques can estimate the effective parameters of porous rocks. Furthermore, there was a good correlation between permeability and parameters such as effective porosity, tortuosity, and average throat size. The Darcy model simulation revealed a gamma distribution in the permeability, while the empirical Kozeny–Carman Equation showed a log-normal distribution. [ABSTRACT FROM AUTHOR]

We study a method for calculating the utility function from a candidate of a demand function that is not differentiable, but is locally Lipschitz. Using this method, we obtain two new necessary and sufficient conditions for a candidate of a demand function to be a demand function. The first concerns the Slutsky matrix, and the second is the existence of a concave solution to a partial differential equation. Moreover, we show that the upper semi-continuous weak order that corresponds to the demand function is unique, and that this weak order is represented by our calculated utility function. We provide applications of these results to econometric theory. First, we show that, under several requirements, if a sequence of demand functions converges to some function with respect to the metric of compact convergence, then the limit is also a demand function. Second, the space of demand functions that have uniform Lipschitz constants on any compact set is compact under the above metric. Third, the mapping from a demand function to the calculated utility function becomes continuous. We also show a similar result on the topology of pointwise convergence. [ABSTRACT FROM AUTHOR]

Purpose: Despite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria's economic performance during the 1996–2021 period. Design/methodology/approach: Various estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality. Findings: The bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth. Originality/value: Existing studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended. [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]

Sparse joint shift (SJS) was recently proposed as a tractable model for general dataset shift which may cause changes to the marginal distributions of features and labels as well as the posterior probabilities and the class-conditional feature distributions. Fitting SJS for a target dataset without label observations may produce valid predictions of labels and estimates of class prior probabilities. We present new results on the transmission of SJS from sets of features to larger sets of features, a conditional correction formula for the class posterior probabilities under the target distribution, identifiability of SJS, and the relationship between SJS and covariate shift. In addition, we point out inconsistencies in the algorithms which were proposed for estimating the characteristics of SJS, as they could hamper the search for optimal solutions, and suggest potential improvements. [ABSTRACT FROM AUTHOR]

Purpose: This study seeks to estimate union wage effect in the public and private sectors of Ghana, respectively. It also seeks to ascertain whether the union wage effect in the two sectors varies. Design/methodology/approach: The authors use data from the Ghana Living Standards Survey 6 (GLSS 6, 2012/2013) and Ghana Labour Force Survey (GLFS, 2015). In terms of estimation technique, the authors employ the Blinder–Oaxaca decomposition technique to estimate union wage effect in public and private sectors, respectively. Findings: The findings indicate that union wage effect in the public sector is positive and higher relative to that of the private sector. Practical implications: The findings imply that strict enforcement of Section 82 of Labour Act 2003 (Act 651) will curb the political influence of public sector unions over their employer (Government). Originality/value: This research paper has not been presented to any journal for publication and it is the authors' original work. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0045 [ABSTRACT FROM AUTHOR]

In this paper, we provide a comprehensive overview of the recent developments in the field of fractional parabolic equations, with a primary focus on the underlying ideas and techniques. These results have appeared in a series of previous literatures, mainly in [30] [92] [29] [32] [97]. However, in these articles, the ideas were usually submerged in detailed calculations. What we are trying to do here is to highlight these ideas and illustrate the inner connections among them, so that the readers can see the whole picture and quickly grasp the essence of these useful methods and hence will be able to apply them to solve a variety of other problems in this area.Actually, many techniques and estimates in fractional elliptic equations can be modified and then be applied to investigate the corresponding parabolic fractional problems. We will clearly explain how to make such transitions in relevant sections.If you find it challenging to grasp the main ideas and essences of [30], [92], [29], [32], and [97], we suggest using this paper as a primer. Beginning with this work will provide you with a solid conceptual framework, making it much easier to navigate and comprehend the additional details in the aforementioned articles. [ABSTRACT FROM AUTHOR]

Risk assessment and categorization of terrorist attacks can assist enhance awareness of terrorism and give crucial information support for anti-terrorism efforts. This study utilizes quantitative approaches for the risk assessment and categorization of terrorist attacks. A total of 210,454 terrorist attacks that occurred worldwide from 1970 through 2020 were collected in the Global Terrorism Database, and 22 indicators related to the risk of terrorist attacks were selected. Then, the moment estimation theory and four comprehensive evaluation models were utilized to identify the top 10 riskiest terrorist attacks in the world. Furthermore, the five clustering analysis methods and three evaluation criteria were performed for the risk categorization of terrorist attacks, and the visual analysis was carried out using the kernel density estimation method. The research results have identified the top 10 riskiest global terrorist attacks, which were led by the September 11 terrorist attack event, along with their downward counterfactual events. The spatial distribution of global terrorist attack risk is primarily composed of four "turbulent cores" in the region of Central Asia, Middle East & North Africa, South Asia, and Central America & Caribbean. This study also provided insights and recommendations for anti-terrorism efforts. It has realized the risk assessment and categorization of terrorist attacks, aiding in the swift identification of its risk levels, and holds immense significance for safeguarding global national security and societal stability under new circumstances. [ABSTRACT FROM AUTHOR]

In this paper we focus on two aspects. First we study the initial-value problem of the Timoshenko-Fourier system with memory-type dissipation. By using the method of energy estimate combined with the technique of Fourier analysis, we obtain the decay estimates of solutions to the problem. Then we deal with the open problem proposed by N. Mori and S. Kawashima in the paper [18] entitled "Decay property of the Timoshenko-Cattaneo system" from the point view of energy estimate. [ABSTRACT FROM AUTHOR]

Highlights The present work highlights the benefits of matrix strengthening through the inclusion of hard particles within the resin‐impregnated woven Kevlar mat. Aluminium Oxynitride (ALON) particles are added to epoxy resin by 5, 10, and 15 volume percentages. The test coupons were developed through a hand‐lay‐up technique to estimate the low‐velocity impact resistance. The characterization was performed through EDAX and SEM to ensure the presence of the ALON particles and their homogenous distribution respectively. Low‐velocity testing is preferred to assess the capacity of the materials to rebound the incident energy. The damage assessment was made to estimate the material's stiffness. The compression after impact (CAI) was executed to observe the strength of the material after the impact. The post‐CAI micrographic observation reveals the delamination history. The results manifest that the ALON–rich coupon exhibits higher impact resistance to the scale of 33.33% than that of the ALON‐free coupon. The damage assessment and CAI results annunciate the lesser damage and higher compressive strength of ALON‐rich material respectively. The micrographic study studied after the CAI reveals the delamination and failure behavior. Additionally, explicit numerical assessment was conducted to validate the experimental results. A good agreement is attained between the experimental results and numerical predictions. The enriched stiffness of the synthesized material makes it a perfect candidate for structural application where the frequency of impact loading is high. Development of ALON reinforced Kevlar‐Epoxy composites for low‐velocity impact applications. Evaluating the performance of developed composites under low‐velocity impact loading. Determining the optimal volume percentage of ALON in the composites. Studying the fractography of developed composites. Correlation between experimental and numerical studies. [ABSTRACT FROM AUTHOR]

AbstractA simple yet effective way of modeling survival data with cure fraction is by considering Box-Cox transformation cure model (BCTM) that unifies mixture and promotion time cure models. In this article, we numerically study the statistical properties of the BCTM when applied to interval censored data. Time-to-events associated with susceptible subjects are modeled through proportional hazards structure that allows for non-homogeneity across subjects, where the baseline hazard function is estimated by distribution-free piecewise linear function with varied degrees of non-parametricity. Due to missing cured statuses for right censored subjects, maximum likelihood estimates of model parameters are obtained by developing an expectation-maximization (EM) algorithm. Under the EM framework, the conditional expectation of the complete data log-likelihood function is maximized by considering all parameters (including the Box-Cox transformation parameter α) simultaneously, in contrast to conventional profile-likelihood technique of estimating α. The robustness and accuracy of the model and estimation method are established through a detailed simulation study under various parameter settings, and an analysis of real-life data obtained from a smoking cessation study. [ABSTRACT FROM AUTHOR]

In the framework of Baoding Liu's uncertainty theory, some new concepts are introduced and their properties are considered. In particular, regular functions of uncertainty are introduced on an uncountable product of spaces. An analog of the Łomnicki–Ulam theorem from traditional probability theory is obtained. Necessary and sufficient conditions are specified under which a function defined on a Banach space of bounded functions is a distribution function for some uncertain mapping. Some notions of Liu's theory are generalized for uncountably many objects. Examples showing the similarity and the difference between Liu's theory and probability theory are analyzed. An application of Liu's theory to estimation theory is considered with examples. [ABSTRACT FROM AUTHOR]

Quickly determining the metal content in plants and subsequently identifying geochemical anomalies can provide clues and guidance for predicting the location and scale of concealed ore bodies in vegetation-covered areas. Although visible, near-infrared and shortwave infrared (VNIR–SWIR) reflectance spectroscopy at wavelengths ranging from 400 to 2500 nm has been proven by many researchers to be a fast, accurate and nondestructive approach for estimating the contents of copper (Cu), lead (Pb), zinc (Zn) and other metal elements in plants, relatively few studies have been conducted on the estimation of lithium (Li) in plants. Therefore, the potential of applying VNIR–SWIR spectroscopy techniques for estimating the Li content in plants was explored in this study. The Jingerquan Li mining area in Hami, Xinjiang, China, was chosen. Three sampling lines were established near a pegmatite deposit and in a background region, canopy reflectance spectra were obtained for desert plants and Li contents were determined in the laboratory; then, quantitative relationships were established between nine different transformed spectra (including both integer and fractional orders) and the Li content was estimated using partial least squares regression (PLSR). The results showed that models constructed using high-order derivative spectra (with an order greater than or equal to 1) significantly outperformed those based on original and low-order derivative spectra (with an order less than 1). Notably, the model based on a 1.1-order derivative spectrum displayed the best performance. Furthermore, the performance of the model based on the two-layer wavelet coefficients of the 1.1-order derivative spectrum was further improved compared with that of the model based on only the 1.1-order derivative spectrum. The coefficient of determination ( R p r e 2 ) and the ratio of performance to deviation (RPD) for the validation set increased from 0.6977 and 1.7656 to 0.7044 and 1.8446, respectively, and the root mean square error (RMSEpre) decreased from 2.5735 to 2.4633 mg/kg. These results indicate that quickly and accurately estimating the Li content in plants via the proposed spectroscopic analysis technique is feasible and effective; however, appropriate spectral preprocessing methods should be selected before hyperspectral estimation models are constructed. Overall, the developed hybrid spectral transformation approach, which combines wavelet coefficients and derivative spectra, displayed excellent application potential for estimating the Li content in plants. [ABSTRACT FROM AUTHOR]

This study investigates the structure of shear-wave velocities (Vs) in the shallow layers of the Oran region, north-west of Algeria, using non-invasive techniques based on ambient vibration arrays. The region has experienced several moderate earthquakes, including the historical Oran earthquake of 1790. Ambient vibration measurements were carried out at 15 sites throughout the study area. Two methods were used: spatial autocorrelation (SPAC) and frequency–wavenumber analysis (f-k), which allowed us to better constrain Rayleigh wave dispersion curves. The inversion of the dispersion curves derived from the f-k analysis allowed for estimating the shear-wave velocity profiles and the Vs30 value at the sites under study. The other important result of the present study is an empirical equation that has been proposed to predict Vs30 in the Oran region. The determination of near-surface shear-wave velocity profiles is an important step in the assessment of seismic hazard. This study has demonstrated the effectiveness of using ambient vibration array techniques to estimate the soil Vs structure. [ABSTRACT FROM AUTHOR]

The inverse problem of reconstructing two space-varying coefficients in a system of one-dimensional (1-d) time-dependent advection-diffusion-reaction (ADR) equations is considered. The ADR system can be used as a water quality model which describes the evolution of the biochemical oxygen demand (BOD) and dissolved oxygen (DO) in a river or stream. The coefficients to be reconstructed represents the effect of the deoxygenation and superficial reaeration processes on the DO and BOD concentration in water. Hölder stability estimates for the coefficients of interest are established using the Carleman estimate technique. [ABSTRACT FROM AUTHOR]

Objectives. Aortic arch replacement is a major surgical intervention. This meta‐analysis aims to combine the international literature on the branch‐first technique to describe its outcomes and offer a comparison to the standard techniques. Methods. PubMed and Cochrane were searched for studies on the branch‐first technique in aortic arch surgery including those comparing this technique with the conventional ones. Mortality, neurological complications, bleeding, and renal replacement therapy were the major endpoints of interest. Subgroup analysis was performed for both single‐ and double‐armed studies. Odds ratios with 95% confidence interval were used as summary estimates. The random‐effects model was used for all outcomes. Results. Ten studies from three countries with a total of 498 patients were included. Proportional analysis of the branch‐first technique estimated mortality of (3%, 95% CI [0.02–0.05]), neurological complications of (8%, 95% CI [0.03–0.13]), re‐exploration for bleeding of (9%, 95% CI [0.05–0.13]), and renal replacement therapy of (9%, 95% CI [0.05–0.12]). This meta‐analysis revealed no significant differences between the branch‐first technique and the conventional technique in mortality (5% vs. 15%, OR = 0.32, 95% CI [0.08–1.18], p = 0.09, I2 = 40%), neurological complications (10% vs. 16%, OR = 0.53, 95% CI [0.24–1.21], p = 0.13, I2 = 0%), or renal replacement therapy (12% vs. 14%, OR = 0.72, 95% CI [0.33–1.59], p = 0.41, I2 = 0%). Conclusions. Branch‐first technique provides a promising alternative approach in aortic arch surgery with no different surgical risks and potentially reduced operative complexity. Randomised controlled trials are needed to further investigate the safety and advantages of this technique. [ABSTRACT FROM AUTHOR]

Motivation Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline. Results We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON uses a Gaussian Mixture Model to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high-quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses. Availability and implementation SEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008. [ABSTRACT FROM AUTHOR]

In this paper, the problem of identifying Multiple-Input-Single-Output (MISO) systems with fractional models from noisy input-output available data is studied. The proposed idea is to use Higher-Order-Statistics (HOS), like fourth-order cumulants (foc), instead of noisy measurements. Thus, a fractional fourth-order cumulants based-simplified and refined instrumental variable algorithm (frac-foc-sriv) is first developed. Assuming that all differentiation orders are known a priori, it consists in estimating the linear coefficients of all Single-Input-Single-Output (SISO) sub-models composing the MISO model. Then, the frac-foc-sriv algorithm is combined with a nonlinear optimization technique to estimate all the parameters: coefficients and orders. The performances of the developed algorithms are analyzed using numerical examples. Thanks to fourth-order cumulants, which are insensitive to Gaussian noise, and the iterative strategy of the instrumental variable algorithm, the parameters estimation is consistent. [ABSTRACT FROM AUTHOR]

Research summary: We conducted a systematic review and meta‐analysis examining whether increasing preventive patrol in large areas reduces crime. Our review included experimental and quasi‐experimental studies that focused on areas such as beats, precincts, or entire jurisdictions and that measured a crime outcome either through official data or surveys. We identified 17 studies to include in our review. We used two methods for assessing study impacts: an approach which identified a primary/general outcome measure and a second approach which used robust variance estimation (RVE) and included all effect sizes across each study. Both approaches showed small crime prevention benefits (RVE: 9% decline; primary/general: 6% decline), but only the RVE model was significant at conventional levels (p < 0.05). There was no significant evidence of displacement. Moderator analyses suggest that as dosage increases so do the crime prevention impacts. In RVE models, preventive patrol was associated with significant reductions in property and violent crime, but nonsignificant increases in drug and disorder offenses. Policy implications: Increasing preventive patrol activities has the potential to reduce crime in large administrative areas. At the same time, existing studies offer little guidance as to how such preventive patrol should be carried out. Deterrence theory, as well as evidence from studies of hot spots policing, suggests that the greatest benefits will be gained from informing patrol efforts about where and when crime occurs. Although more research is needed regarding patrol allocations in large areas, present knowledge suggests that the more such patrols can be targeted at specific places at specific times, the greater will be the crime control benefits. In this context, we argue that police agencies may want to apply a hybrid approach to police patrol, which would include a combination of hot spots policing units and general patrol units informed by data on where crime is concentrated. [ABSTRACT FROM AUTHOR]

In this paper, we employ parametrization techniques within the framework of f (T) gravity to investigate the deceleration parameter (DP), a key quantity characterizing the universe's expansion dynamics. By analyzing the DP, we gain valuable insights into the nature of cosmic constituents and their impact on the universe's evolution. We utilize a combination of observational data, including 31 Cosmic Chronometers (CC) measurements, 1048 Type Ia Supernovae (SNIa), 17 Baryon Acoustic Oscillation (BAO) measurements, Cosmic Microwave Background (CMB), 162 Gamma Ray Bursts (GRB), and 24 observations of compact radio quasars (Q). We employ the Markov chain Monte Carlo (MCMC) sampling technique to estimate the best-fit range of model parameters. Cosmological and cosmographic parameters are investigated, and their implications in the context of cosmology have been discussed. We also analyze the statefinder and Om diagnostic to gain deeper insights into the universe's behavior. Furthermore, we conduct statistical analyses to compare our model with the standard Λ CDM model. Our investigation also includes the study of physical parameters, providing comprehensive insights into the cosmological behavior within the f (T) gravity framework. The results and comparisons presented in this work contribute to a deeper understanding of the universe's dynamics and provide valuable implications for the cosmological model under consideration. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) is a high-resolution technique to estimate the parameters of dielectric permittivity (ϵ) and electrical conductivity (σ) and identify the structure of the subsurface for Ground Penetrating Radar (GPR) application. However, permittivity and conductivity parameters can be coupled in bi-parameter GPR inversion. This coupling effect leads to the crosstalk in the FWI result. To solve this problem, we propose a novel approach to use the multi-scale FWI with the hybrid regularization method, which combines Tikhonov and total-variation (TV) regularizers that simultaneously invert the ϵ and the σ parameters, which improve the inversion accuracy and reduce the crosstalk effect. The multi-scale strategy uses the Wiener filtering to process the GPR data in different frequency ranges. Then, the low frequencies signal updates the bottom part and subsequently increases the frequencies to invert for the shallow areas. The Tikhonov regularization stabilizes the reconstruction of the smoothly varying background part. In contrast, Total Variation (TV) regularization can recover the large contrasts associated with the LNAPL model. The new Tikhonov-TV (TT) regularization can mitigate the crosstalk caused by the parameter coupling effect. Numerical tests with typical GPR models demonstrate that the proposed multi-scale TT-FWI strategy can effectively eliminate the crosstalk and improve the reconstruction accuracy when the model parameters have a different structure. [ABSTRACT FROM AUTHOR]

This paper proposes a quick and straightforward technique for estimating the insertion loss (IL) of earplugs measured on an acoustical test fixture (ATF) using a commercial impedance tube. In this method, the earplug's acoustic properties (i.e., its transmission loss and the reflection coefficient of its medial surface) are determined from its transfer matrix measured using the three-microphones impedance tube method modified here for the current application. The IL is then estimated using a one-dimensional analytical model of open and occluded earcanals based on the wavefield decomposition theory. The method is evaluated numerically and experimentally from 50 Hz to 6.5 kHz. The numerical study allows for verifying the accuracy of the proposed approach in comparison to a simplified model of an ATF earcanal excited by normal incidence plane wave and diffuse field excitation. The experimental evaluation, which involves six earplugs (including five commercially available ones) representing various earplug families, demonstrates the accuracy of the estimation method, yielding results with a maximum difference of 3 dB compared to ATF measurements, on average, among the tested earplugs. [ABSTRACT FROM AUTHOR]

The present work deals with the wet tropospheric delay estimation using the different models for navigation signals over the Indian region. Results from various mathematical models are analysed for saturation vapour pressure, partial vapour pressure, and wet delay estimation. The delay is estimated for four places in India which are spread at different geographical locations. It is observed that the Wexeler model is more suitable for saturation vapour estimation. It shows that the difference between 0.00 to 0.25 mb with reference values throughout the year. Saturation vapour pressure which is derived using the Wexeler model is further used for partial vapour pressure estimation. Four different models were used to estimate partial vapour pressure and Zyuring model is found more suitable among all. The partial vapour pressure values vary from 5 mb to 25 mb at different altitudes. The results of the Zyuring model is further used to derive wet tropospheric delay. It is found that Hopfield model provides difference of up to 4 mm in tropospheric delay, better than any other seven models results which are used for wet delay estimation. Tropospheric wet delay is observed more in daytime as compared to nighttime. From the analysis, it is found that the Hopfield model is more suitable for wet delay estimation and can be used for wet delay corrections in navigation signals for accurate position estimation. [ABSTRACT FROM AUTHOR]

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This study aimed to evaluate the sulfur hexafluoride (SF6) tracer technique for estimating methane (CH4) emissions in dual-flow continuous culture systems (DFCCS). In experiment 1 (Exp1), fermenters were filled with water, and known CH4 concentrations (0, 1.35, 2.93, or 4.43 g/d) were injected using permeation tubes with SF6 release rates (3.30 or 9.65 mg/d). Headspace gas was collected using canisters, and the SF6 technique estimated CH4 recovery. Experiment 2 (Exp2) involved a DFCCS fermentation trial with ruminal fluid from three Holstein cows, testing diets with soybean meal or its partial replacement (50%) by Chlorella or Spirulina. Headspace gas was collected at intervals post-feeding. Standard curves for SF6 and CH4 quantification were inadequate for DFCCS samples, with the CH4:SF6 ratio differing from standards, indicating the data needs further SF6 release rate evaluation. In Exp1, a high correlation (r = 0.97) was found between infused and calculated CH4, indicating good repeatability. Low and high SF6 rates performed similarly at low CH4 infusion, but high SF6 overestimated CH4 at high infusion. Exp2 showed CH4 emissions irrespective of SF6 rate and indicated reduced CH4 emissions and increased NDF degradation with algae-containing diets. Further evaluation of the SF6 tracer technique is warranted for DFCCS. [ABSTRACT FROM AUTHOR]

This article constructs a new two‐level explicit/implicit numerical scheme in an approximate solution for the two‐dimensional time fractional mobile/immobile advection‐dispersion problem. The stability and error estimates of the proposed technique are deeply analyzed in the L∞(0,T;L2)$$ {L}^{\infty}\left(0,T;{L}^2\right) $$‐norm. The developed approach is less time consuming, fourth‐order in space and temporal accurate of order O(k2−λ2)$$ O\left({k}^{2-\frac{\lambda }{2}}\right) $$, where k$$ k $$ is the time step and λ$$ \lambda $$ denotes a positive parameter less than 1. This result shows that the two‐level explicit/implicit formulation is faster and more efficient than a large class of numerical schemes widely discussed in the literature for the considered problem. Numerical experiments are performed to verify the theoretical studies and to demonstrate the efficiency of the new numerical method. [ABSTRACT FROM AUTHOR]

Amassive market penetration of electric vehicles (EVs) associated with non-negligible energy consumption and environmental issues has imposed a big challenge on evaluating electrical power distribution and related transportation facilities improvement in response to the largescale EV charging service need. Strategical deployment of EV charging stations including location and determination ofnumber of slowcharging stations and fast charging stationshas become an emerging concern and one of the most pressing needs in planning. This paper conducts a comprehensive survey of EV charging demand and distribution models with consideration of realistic driver behaviors impacts. This is currently a shortage in academic literature, but indeed has drawn practical attention in the strategic planning process. To address the need, this paper presents an in-depth literature review of relevant studies that have identified different types of EV charging facilities, needs or concerns that are considered into EV charging demand and distribution modeling, alongside critical impacting factor identification,mathematical relationshipsof the contributing factorsandEVchargingdemand and distribution modeling. Key findings from the current literature are summarized with strategies for optimized plan of charging station deployments (i.e., location and related number of charging station), in an attempt to provide a valuable reference for interested readers. [ABSTRACT FROM AUTHOR]

Analysing and mitigating errors in production processes is a primary objective of companies in the automotive sector. Unfortunately, due to inaccurate or partially missing information, comparing errors is often very difficult, resulting from the experts' provision of incomplete pairwise comparison matrices. In the literature, several techniques have been developed to complete such matrices. These techniques merely estimate what the decision makers or experts would have entered according to known entries. In this article, we propose a new methodology based on the stochastic multi-objective acceptability analysis; we apply it to vary the missing entries of the pairwise comparison matrix, thus providing the probability that an alternative/criterion will attain a given rank. This approach gives a complete view of the possible outcomes because it represents all possible decision maker mindsets. We present a case study carried out in a multinational automotive industry where we apply our methodology for evaluating errors in the production process. [ABSTRACT FROM AUTHOR]

In manufacturing industries, reliability analysis of cutting tools is of paramount importance, as their frequent failures may result in enhanced downtime of production lines, leading to reduced throughput, enhanced process cycle times, and low profits. There are numerous factors that govern the desired operations of cutting tools, e.g., tool cutting speed, feed, depth of cut, and many others. Existing literature on cutting tools' reliability estimation emphasizes mainly three variables, as mentioned earlier while neglecting other important factors. Including a greater number of factors in the process of estimating reliability increases the number of covariates, hence rendering the data acquisition costlier and estimation models highly complex. This work initially utilizes Analytical Hierarchy Process (AHP) to assess the importance of various factors that are responsible for the cutting tool's performance, followed by the reliability estimation of the cutting tools using proportional hazards model (PHM) considering the four "critical to reliability" factors as obtained through AHP as covariates. The proposed method also helps in determining the relationship of these sub-factors with the hazard rate and reliability of the cutting tools. Experimental results are then used to verify the model's predictions through response surface methodology (RSM) and Weibull fit. Furthermore, the paper also presents a proposed technique to estimate the required number of cutting tools for one machine per day and the number of job completions that can be an essential takeaway for various industries. Thus, this research paper proposes an integrated AHP-RSM-PHM based approach for a comprehensive reliability analysis of cutting tools. [ABSTRACT FROM AUTHOR]

Diameter estimates for Kähler metrics are established which require only an entropy bound and no lower bound on the Ricci curvature. The proof builds on recent PDE techniques for L∞$L^\infty$ estimates for the Monge–Ampère equation, with a key improvement allowing degeneracies of the volume form of codimension strictly greater than one. As a consequence, we solve the long‐standing problem of uniform diameter bounds and Gromov–Hausdorff convergence of the Kähler–Ricci flow, for both finite‐time and long‐time solutions. [ABSTRACT FROM AUTHOR]