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This article presents a new set of estimators designed to estimate the finite population variance of a study variable in two-phase sampling. These estimators utilize the information about extreme values and ranks of an auxiliary variable. Through a first-order approximation, we investigate the properties of these estimators, including biases and mean squared errors (MSEs). Furthermore, a comprehensive simulation study is conducted to assess their performance and validate our theoretical insights. Results demonstrate that our proposed class of estimators performs better in terms of percent relative efficiency (PRE) across various simulation scenarios compared to existing estimators. In addition, in the application section, we utilize three data sets to further validate the performance of our proposed estimators against conventional unbiased variance estimators, ratio and regression estimators, as well as other existing methods. [ABSTRACT FROM AUTHOR]

This paper presents several nonparametric estimators for the Sharma–Taneja–Mittal entropy measure of a continuous random variable with known support, utilizing spacing, a local linear model, and a kernel function. The properties of these estimators are discussed. Their performance was also examined through real data analysis and Monte Carlo simulations. In the Monte Carlo experiments, the proposed Sharma–Taneja–Mittal entropy estimators were employed to create a test of goodness-of-fit under the standard uniform distribution. The suggested test statistics demonstrate strong performance, as evidenced by a comparison of their power with that of other tests for uniformity. Finally, we examine a classification issue in the recognition of patterns to underscore the significance of these measures. [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]

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]

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]

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

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

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

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

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

The sustainability of the transport sector is targeted by various policies adopted by the European Union, and their impact must be constantly monitored in order to maximize the desired objective. This paper, through a two-stage investigation, aims to present a systemic approach of the sustainability dimensions in transport and to introduce an innovative technique to analyze the interdependencies between them. In the first stage, relevant indicators were selected from the Eurostat database for the content of four dimensions: economic, environmental, social and technological. The robustness of the developed dimensions was assessed and validated through a confirmatory factor analysis. In the second stage, a Gaussian graphical model was estimated as a technique integrating graphical and statistical modeling to identify complex structures of linkages between variables (as components of each dimension of sustainability). The structure of the network clearly highlights the dependence of transport on fossil fuel consumption as the main determinant of pollution in the sector (CO2 emissions). In addition, the central role of railways in decarbonizing transport is highlighted, in contrast to the limited, and isolated at one end of the network, role of electric vehicles. The findings support that affordability of this new technology plays an important role in its impact on zero-emission transition. Concentrating on the period 2013–2022, at EU27 level, the results are relevant in the context of decarbonization policies, offering useful insights both for future research and policy makers. [ABSTRACT FROM AUTHOR]

This paper concerns with the existence of radial solutions of the biharmonic elliptic equation ▵ 2 u = f (| x | , u , | ∇ u | , ▵ u) in an annular domain Ω = { x ∈ R N : r 1 < | x | < r 2 } ( N ≥ 2 ) with the boundary conditions u | ∂ Ω = 0 and ▵ u | ∂ Ω = 0 , where f : [ r 1 , r 2 ] × R × R + × R → R is continuous. Under certain inequality conditions on f involving the principal eigenvalue λ 1 of the Laplace operator − ▵ with boundary condition u | ∂ Ω = 0 , an existence result and a uniqueness result are obtained. The inequality conditions allow for f (r , ξ , ζ , η) to be a superlinear growth on ξ , ζ , η as | (ξ , ζ , η) | → ∞ . Our discussion is based on the Leray–Schauder fixed point theorem, spectral theory of linear operators and technique of prior estimates. [ABSTRACT FROM AUTHOR]

First-order algorithms have long dominated the training of deep neural networks, excelling in tasks like image classification and natural language processing. Now there is a compelling opportunity to explore alternatives that could outperform current state-of-the-art results. From the estimation theory, the Extended Kalman Filter (EKF) arose as a viable alternative and has shown advantages over backpropagation methods. Current computational advances offer the opportunity to review algorithms derived from the EKF, almost excluded from the training of convolutional neural networks. This article revisits an approach of the EKF with decoupling and it brings the Fully Decoupled Extended Kalman Filter (FDEKF) for training convolutional neural networks in image classification tasks. The FDEKF is a second-order algorithm with some advantages over the first-order algorithms, so it can lead to faster convergence and higher accuracy, due to a higher probability of finding the global optimum. In this research, experiments are conducted on well-known datasets that include Fashion, Sports, and Handwritten Digits images. The FDEKF shows faster convergence compared to other algorithms such as the popular Adam optimizer, the sKAdam algorithm, and the reduced extended Kalman filter. Finally, motivated by the finding of the highest accuracy of FDEKF with images of natural scenes, we show its effectiveness in another experiment focused on outdoor terrain recognition. [ABSTRACT FROM AUTHOR]

The phenotypic evaluation of root traits in soybeans presents challenges in breeding due to its high cost and the requirement for experimental plot destruction. Establishing relationships between aerial and root traits is crucial, given the relative ease of phenotypic evaluations for aerial traits. Therefore, this study aims to utilize the canonical correlation technique to estimate latent variables, subsequently employing GBLUP for the genomic prediction of the root traits (length, volume, surface area, and dry mass) using phenotypic information from aerial part traits (hypocotyl diameter and dry mass). Our results demonstrate the effectiveness of the technique in predicting the root part, even when not directly evaluated. The agreement observed between the top 10% of individuals selected based on the canonical variable and each root trait individually was considered moderate or substantial. This enables the simultaneous selection of genotypes based on both trait groups, providing a valuable approach for soybean breeding programs. [ABSTRACT FROM AUTHOR]

Economic theory argues that foreign direct investment (FDI) and external debt are expected to enhance economic growth in any given economy. Consequently, this study (i) investigated the relationship between foreign direct investment, external debt servicing, and economic growth in Nigeria; (ii) investigated how foreign direct investment and external debt impact Nigeria's economic growth; and (iii) analyzed the direction of causality among the three macroeconomic variables. Descriptive statistics, time series autoregressive distributive lag, and robust Granger causality tests were adopted as the estimating techniques. The results showed that from 2011 to 2022, Nigeria's FDI continued to decline, Nigeria's external debt servicing continued to grow on an upward trajectory, and the growth of the GDP has been meandering. ARDL analysis results confirmed that the lag of FDI and current exchange rate exert positive effects on current economic growth in Nigeria, with a 1% increase in FDI, current external debt, and current exchange rate increasing growth by 1.49%, 1.58%, and 0.02%, respectively. Results from the Granger causality showed that FDI and external debt do Granger cause GDP in Nigeria. Policymakers should focus on prudent debt management practices and strive to reduce domestic debt levels. [ABSTRACT FROM AUTHOR]

Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation. [ABSTRACT FROM AUTHOR]

Utilizing a turnkey approach to deliver a construction project entails significant risks from the contractor's perspective. Essentially, the owner awaits project completion without commitments regarding additional expenditures incurred by the contractor during the project's duration. This paper specifically focuses on estimating and analyzing the contingency value for residential turnkey projects in Saudi Arabia. The contingency value across the project's life cycle is estimated using six Artificial Neural Network (ANN) models, which are compared to identify the best-trained network according to project complexity, contingency factor, and contingency impact during the project phases. The output layer provides the contingency factor percentages for each project phase. A 13-story reinforced concrete (RC) residential building established in one of Saudi Arabia's cities was selected to implement the developed methodology. The contingency estimation, performed using @Risk 7.5 and NeuralTools 7.5, was determined to be 11.34% and was distributed across the five phases of the project's life cycle: 0.30% for predesign, 0.99% for design, 2.61% for preconstruction, 6.33% for construction, and 1.12% for postconstruction. Furthermore, it was found that the estimated contingency varies based on project complexity, which is 7.20% for low complexity, 8.16% for medium complexity, 9.41% for complicated, and 11.34% for very complicated projects. Historical data and peer review approaches are employed to validate the results, both of which are endorsed by professionals in this field. This paper highlights two main contributions: Firstly, it significantly enhances risk management by facilitating a comprehensive understanding and systematic analysis of risks, thus improving the contractors' ability to mitigate potential negative impacts on projects. Secondly, it supports more informed decision-making through the use of advanced techniques to estimate and analyze contingency values. These contributions are critical for contractors engaged in Saudi construction projects, particularly those involving residential buildings. [ABSTRACT FROM AUTHOR]

Earthquake hazard mapping assesses and visualizes seismic hazards in a region using data from specific points. Conducting a seismic hazard analysis for each point is essential, while continuous assessment for all points is impractical. The practical approach involves identifying hazards at specific points and utilizing interpolation for the rest. This method considers grid point spacing and chooses the right interpolation technique for estimating hazards at other points. This article examines different point distances and interpolation methods through a case study. To gauge accuracy, it tests 15 point distances and employs two interpolation methods, inverse distance weighted and ordinary kriging. Point distances are chosen as a percentage of longitude and latitude, ranging from 0.02 to 0.3. A baseline distance of 0.02 is set, and other distances and interpolation methods are compared with it. Five statistical indicators assess the methods. Ordinary kriging interpolation shows greater accuracy. With error rates and hazard map similarities in mind, a distance of 0.14 points seems optimal, balancing computational time and accuracy needs. Based on the research findings, this approach offers a cost-effective method for creating seismic hazard maps. It enables informed risk assessments for structures spanning various geographic areas, like linear infrastructures. [ABSTRACT FROM AUTHOR]

Liquid-phase adsorption technology has been widely applied to address environmental problems related to the removal of pollutants from aqueous streams. Simple and effective methods for determining mass transfer parameters, including intra-particle and fluid-to-solid film resistances, are crucial for designing adsorption processes. The efficient simplified diffusion technique (ES technique), based on a completely mixed batch reactor (CMBR), is proposed in this study to address these needs. In this study, we compare three diffusivity (Ds) determination methods: the rigorous diffusion technique (R technique), the simplified diffusion technique (S technique), and the ES technique. Although the simulation results from the R technique are excellent, it is a very complicated and time-consuming approach that is not convenient for practical use. The S technique provides a much simpler approach, but its results are only valid in cases where the contribution of fluid film resistance is negligible (Biot number > 40). The ES technique proposed in this study can overcome those limitations. The estimation errors of the ES technique are significantly smaller than that of the S technique when compared with the R technique. The proposed ES technique would be very useful for field applications to determine diffusivity for aqueous adsorption systems. [ABSTRACT FROM AUTHOR]

Long-term solar wind sequence forecasting is essential for understanding the influence of the solar wind on celestial settings, predicting variations in solar wind parameters, and identifying patterns of solar activity. The intrinsic erratic temporal features of solar wind datasets present significant challenges to the development of solar wind factor estimate techniques. In response to these challenges, we present MoCoformer, a novel model based on the Transformer model in deep learning that integrates the Multi-Mode Decomp Block and Mode Independence Attention. The Multi-Mode Decomp Block employs an optimized version of variational mode decomposition technology to flexibly handle irregular features by adaptively decomposing and modeling the impact of sudden events on the temporal dynamics, enhancing its ability to manage non-stationary and irregular features effectively. Meanwhile, the Mode Independence Attention module computes attention independently for each mode, capturing the correlation between sequences and mitigating the negative impact of irregular features on time series prediction. The experimental results on solar wind datasets demonstrate that MoCoformer significantly outperforms current state-of-the-art methods in time series forecasting, showcasing superior predictive performance. This underscores the resilience of MoCoformer in handling the intricate, irregular temporal characteristics of solar wind data, rendering it a valuable instrument for enhancing the understanding and forecasting of solar wind dynamics. [ABSTRACT FROM AUTHOR]

Featured Application: This work is intended to provide additional approaches for analyzing and interpreting dam monitoring data. Pore water pressure (PWP) response is significant for evaluating the earth dams' stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R2), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R2 = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models' robustness and the hyperparameter's influence on the performance of the prediction model. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field. [ABSTRACT FROM AUTHOR]

The determination of a mobile terminal's position with high accuracy and ubiquitous coverage is still challenging. Global satellite navigation systems (GNSSs) provide sufficient accuracy in areas with a clear view to the sky. For GNSS-denied environments like indoors, complementary positioning technologies are required. A promising approach is to use the Earth's magnetic field for positioning. In open areas, the Earth's magnetic field is almost homogeneous, which makes it possible to determine the orientation of a mobile device using a compass. In more complex environments like indoors, ferromagnetic materials cause distortions of the Earth's magnetic field. A compass usually fails in such areas. However, these magnetic distortions are location dependent and therefore can be used for positioning. In this paper, we investigate the influence of elementary structures, in particular a sphere and a cylinder, on the achievable accuracy of magnetic positioning methods. In a first step, we analytically calculate the magnetic field around a sphere and a cylinder in an outer homogeneous magnetic field. Assuming a noisy magnetic field sensor, we investigate the achievable positioning accuracy when observing these resulting fields. For our analysis, we calculate the Cramér–Rao lower bound, which is a fundamental lower bound on the variance of an unbiased estimator. The results of our investigations show the dependency of the positioning error variance on the magnetic sensor properties, in particular the sensor noise variance and the material properties, i.e., the relative permeability of the sphere with respect to the cylinder and the location of the sensor relative to the sphere with respect to the cylinder. The insights provided in this work make it possible to evaluate experimental results from a theoretical perspective. [ABSTRACT FROM AUTHOR]

Gamma-ray spectroscopy (GRS) enables continuous estimation of soil water content (SWC) at the subfield scale with a noninvasive sensor. Hydrological applications, including hyper-resolution land surface models and precision agricultural decision making, could benefit greatly from such SWC information, but a gap exists between established theory and accurate estimation of SWC from GRS in the field. In response, we conducted a robust three-year field validation study at a well-instrumented agricultural site in Nebraska, United States. The study involved 27 gravimetric water content sampling campaigns in maize and soybean and 40K specific activity (Bq kg−1) measurements from a stationary GRS sensor. Our analysis showed that the current method for biomass water content correction is appropriate for our maize and soybean field but that the ratio of soil mass attenuation to water mass attenuation used in the theoretical equation must be adjusted to satisfactorily describe the field data. We propose a calibration equation with two free parameters: the theoretical 40K intensity in dry soil and a, which creates an "effective" mass attenuation ratio. Based on statistical analyses of our data set, we recommend calibrating the GRS sensor for SWC estimation using 10 profiles within the footprint and 5 calibration sampling campaigns to achieve a cross-validation root mean square error below 0.035 g g−1. [ABSTRACT FROM AUTHOR]

The application of statistical estimation theory to Hong–Ou–Mandel interferometry led to enticing results in terms of the detection limit for photon reciprocal delay and polarisation measurement. In the following paper, a fully fibre-coupled setup operating in the telecom wavelength region proves to achieve, for the first time, in common-path Hong–Ou–Mandel-based interferometry, a detection limit for photon phase delay at the zeptosecond scale. The experimental results are then framed in a theoretical model by calculating the Cramer–Rao bound (CRB) and, after comparison with the obtained experimental results, it is shown that our setup attains the optimal measurement, nearly saturating CRB. [ABSTRACT FROM AUTHOR]

Object detection and tracking are pivotal tasks in machine learning, particularly within the domain of computer vision technologies. Despite significant advancements in object detection frameworks, challenges persist in real-world tracking scenarios, including object interactions, occlusions, and background interference. Many algorithms have been proposed to carry out such tasks; however, most struggle to perform well in the face of disturbances and uncertain environments. This research proposes a novel approach by integrating the You Only Look Once (YOLO) architecture for object detection with a robust filter for target tracking, addressing issues of disturbances and uncertainties. The YOLO architecture, known for its real-time object detection capabilities, is employed for initial object detection and centroid location. In combination with the detection framework, the sliding innovation filter, a novel robust filter, is implemented and postulated to improve tracking reliability in the face of disturbances. Specifically, the sliding innovation filter is implemented to enhance tracking performance by estimating the optimal centroid location in each frame and updating the object's trajectory. Target tracking traditionally relies on estimation theory techniques like the Kalman filter, and the sliding innovation filter is introduced as a robust alternative particularly suitable for scenarios where a priori information about system dynamics and noise is limited. Experimental simulations in a surveillance scenario demonstrate that the sliding innovation filter-based tracking approach outperforms existing Kalman-based methods, especially in the presence of disturbances. In all, this research contributes a practical and effective approach to object detection and tracking, addressing challenges in real-world, dynamic environments. The comparative analysis with traditional filters provides practical insights, laying the groundwork for future work aimed at advancing multi-object detection and tracking capabilities in diverse applications. [ABSTRACT FROM AUTHOR]

Sample selection is one of the most important factors in estimating the unknown parameters of distributions, as it saves time, saves effort, and gives the best results. One of the challenges is deciding on a suitable distribution estimate technique and adequate sample selection to provide the best results in comparison with earlier research. The method of moments (MOM) was decided on to estimate the unknown parameters of the Gumbel distribution, but with four changes in the sample selection, which were simple random sample (SRS), ranked set sampling (RSS), maximum ranked set sampling (MRSS), and ordered maximum ranked set sampling (OMRSS) techniques, due to small sample sizes. The MOM is a traditional method for estimation, but it is difficult to use when dealing with RSS modification. RSS modification techniques were used to improve the efficiency of the estimators based on a small sample size compared with the usual SRS estimator. A Monte Carlo simulation study was carried out to compare the estimates based on different sampling. Finally, two datasets were used to demonstrate the adaptability of the Gumbel distribution based on the different sampling techniques. [ABSTRACT FROM AUTHOR]

Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method's effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation. [ABSTRACT FROM AUTHOR]

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

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

Since the beginning of spring 2022, successive landslides have occurred in the eastern pit slope of the Wolong Coal Mine in Qipanjing Town, Otog Banner, Inner Mongolia, which has adversely affected the mine's production safety. This study aims to reveal the deformation patterns and failure mechanisms of landslides. Firstly, this study establishes the stratigraphic structure of the eastern pit slope of the Wolong Coal Mine using extensive field geological surveys combined with unmanned aerial vehicle photography, drilling, and comprehensive physical exploration techniques. Indoor geotechnical tests and microscopic experiments reveal that rock mass typically exhibits the characteristics of expansibility and water sensitivity. Moreover, the mechanical parameters of the rock mass were determined using a combination of the window sampling method, the Geological Strength Index, and the Hoek–Brown strength criterion estimation theory. Finally, this study consolidates the previously mentioned insights and employs FLAC3D (7.0) software to assess the stress–strain characteristics of the excavated slope. The results indicate that the deformation mode of the Wolong open pit coal mine is the toppling failure of soft-hard-interbedded anti-inclined layered rock slopes. The unloading effect and rock expansion-induced softening lead to stress concentration at the slope corners and more substantial deformation, thereby accelerating upper slope deformation. The deformation and destabilization process of landslides is categorized into four stages: the initial deformation stage, the development stage of lateral shear misalignment, the development stage of horizontal tensile-shear damage, and the slip surface development to the preslip stage. This research offers valuable references and engineering insights for future scientific investigations and the prevention of similar slope-related geological hazards. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

In this work, we introduce a novel variance reduction approach utilising the integral formulation of the radiative transfer equation to calculate the radiance in a planar symmetric slab geometry. Due to its integral nature, our method offers a fundamental advantage over well-established variance reduction methods such as the local estimate technique. As opposed to the local estimate procedure, photons add to the overall radiance not only at specific points of interaction but also throughout each consecutive path element; hence, our variance reduction approach can be thought of as an integral local estimate method. This facilitates a substantial enhancement in statistical efficiency, especially in scenarios where only a small number of scattering events or a high attenuation along the detection paths is to be anticipated. To evaluate the overall performance of the integral approach, we incorporated it into a self-developed GPU-accelerated Monte Carlo software, together with a conventional local estimate implementation adapted to slab geometry for a comprehensive comparison. [ABSTRACT FROM AUTHOR]

Recently, many researchers have been studying the visualization of images and the recognition of objects by estimating photons under photon-starved conditions. Conventional photon-counting imaging techniques estimate photons by way of a statistical method using Poisson distribution in all image areas. However, Poisson distribution is temporally and spatially independent, and the reconstructed image has a random noise in the background. Random noise in the background may degrade the quality of the image and make it difficult to accurately recognize objects. Therefore, in this paper, we apply photon-counting imaging technology only to the area where the object is located to eliminate the noise in the background. As a result, it can be seen that the image quality using the proposed method is better than that of the conventional method and the object recognition rate is also higher. Optical experiments were conducted to prove the denoising performance of the proposed method. In addition, we used the structure similarity index measure (SSIM) as a performance metric. To check the recognition rate of the object, we applied the YOLOv5 model. Finally, the proposed method is expected to accelerate the development of astrophotography and medical imaging technologies. [ABSTRACT FROM AUTHOR]

Ratio product estimators have been proposed by several authors for the estimation of the population mean and population variance, but very few authors have proposed ratio product estimators for the estimation of the population coefficient of variation. In this paper, we propose a ratio product estimator for the estimation of the population coefficient of variation. The mean square error of the proposed estimator was obtained up to the first order of approximation using the Taylor series technique. A numerical analysis was conducted, and the results show that the proposed ratio product estimator is more efficient. [ABSTRACT FROM AUTHOR]

In this paper, we suggest a brand new extension of the inverse Lomax distribution for fitting engineering time data. The newly developed distribution, termed the transmuted Topp–Leone inverse Lomax (TTLILo) distribution, is characterized by an additional shape and transmuted parameters. It is critical to notice that the skewness, kurtosis, and tail weights of the distribution are strongly influenced by these additional characteristics of the extra parameters. The TTLILo model is capable of producing right-skewed, J-shaped, uni-modal, and reversed-J-shaped densities. The proposed model's statistical characteristics, including the moments, entropy values, stochastic ordering, stress-strength model, incomplete moments, and quantile function, are examined. Moreover, characterization based on two truncated moments is offered. Using Bayesian and non-Bayesian estimating techniques, we estimate the distribution parameters of the suggested distribution. The bootstrap procedure, approximation, and Bayesian credibility are the three forms of confidence intervals that have been created. A simulation study is used to assess the efficiency of the estimated parameters. The TTLILo model is then put to the test by being applied to actual engineering datasets, demonstrating that it offers a good match when compared to alternative models. Two applications based on real engineering datasets are taken into consideration: one on the failure times of airplane air conditioning systems and the other on the active repair times of airborne communication transceivers. Also, we consider the problem of estimating the stress-strength parameter R = P (Z 2 < Z 1) with engineering application. [ABSTRACT FROM AUTHOR]

Forensic anthropologists have a responsibility to appropriately relay information about a decedent in medicolegal reports and when communicating with the public. The terms 'sex' and 'sex estimation' have been applied with numerous, inconsistent definitions under the guise that sex—a broad, complex concept—can be reduced to a female/male binary. This binary does not reflect biocultural realities and harms those whose bodies do not meet social expectations of maleness or femaleness. The University of Nevada, Las Vegas' Forensic Anthropology and Bioarchaeology Laboratory (UNLV FAB Lab) advocates for the use of the term 'assigned sex at birth' (ASAB) to highlight that binary sex is not biologically inherent to the body, but rather, assigned by society. Additionally, we call for the use of disclaimers in case reports to denote the limitations of ASAB estimation methods, the differentiation between those with mixed trait expression (i.e., indeterminate) and those on whom an ASAB analysis cannot be performed (i.e., unknown), and the included consideration of gender in forensic anthropology research and case reports. Such applications challenge biological normalcy, allowing forensic anthropologists to actively advocate for those whose bodies do not meet biocultural expectations. [ABSTRACT FROM AUTHOR]

For linear optimization problems with a parametric objective, so-called parametric linear programs (PLP), we show that the optimal decision values are, under few technical restrictions, unimodal functions of the parameter, at least in the two-degrees-of-freedom case. Assuming that the parameter is random and follows a known probability distribution, this allows for an efficient algorithm to determe the quantiles of linear combinations of the optimal decisions. The novel results are demonstrated with probabilistic economic dispatch. For an example setup with uncertain fuel costs, quantiles of the resulting inter-regional power flows are computed. The approach is compared against Monte Carlo and piecewise computation techniques, proving significantly reduced computation times for the novel procedure. This holds especially when the feasible set is complex and/or extreme quantiles are desired. This work is limited to problems with two effective degrees of freedom and a one-dimensional uncertainty. Future extensions to higher dimensions could yield a key tool for the analysis of probabilistic PLPs and, specifically, risk management in energy systems. [ABSTRACT FROM AUTHOR]

The design process of a borehole heat exchanger (BHE) requires knowledge of building thermal loads, the expected heat pump's COP and the ground's thermophysical properties. The thermal response test (TRT) is a common experimental technique for estimating the ground's thermal conductivity and borehole thermal resistance. In classic TRT, a constant heat transfer rate is provided above ground to the carrier fluid that circulates continuously inside a pilot BHE. The average fluid temperature is measured, and from its time-dependent evolution, it is possible to infer both the thermal resistance of the BHE and the thermal conductivity of the ground. The present paper investigates the possibility of a new approach for TRT with the continuous injection of heat directly into the BHE's grouting by means of electrical resistance imparted along the entire BHE's length, while local (along the depth) temperature measurements are acquired. This DTRT (distributed TRT) approach has seldom been applied and, in most applications, circulating hot fluid and optical fibers are used to infer depth-related temperatures. The distributed measurements allow the detection of thermal ground anomalies along the heat exchanger and even the presence of aquifer layers. The present paper investigates the new EDDTRT (electric depth-distributed TRT, under patenting) approach based on traditional instruments (e.g., RTD) or one-wire digital sensors. The accuracy of the proposed method is numerically assessed by Comsol Multiphysics simulations. The analysis of the data obtained from the "virtual" EDDTRT confirms the possibility of estimating within 10% accuracy both thermal ground and grout conductivities. [ABSTRACT FROM AUTHOR]