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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]

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]

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]

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]

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 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]

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]

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

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

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

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

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

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

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

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

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

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

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

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

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

In recent years, there has been a lot of interest in the estimation of missing frames in video sequences because of its usage in several critical applications such as medical imaging, manufacturing, security systems, etc. The present study employs the Kalman filtering approach to suggest a technique for estimating missing frames within a video sequence and forecasting forthcoming frames. The Kalman filter model proposed in this study employs a pixel-by-pixel approach to leverage information from both preceding and subsequent video frames for the purpose of predicting the missing frame. The experimental findings demonstrate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

In this paper, we focus on solving the generalized absolute value equations (GAVE). We present a new method named as modulus-based block triangular splitting iteration (MBTS) method based on the block matrix structure resulting from the transformation of the GAVE into two equations. This method is developed by decomposing the matrix into diagonal and triangular matrices, as well as applying a series of suitable combination and modification techniques. The advantage of the MBTS method is that it is not necessary to solve the inverse of the coefficient matrix of the linear equation system during each iteration, which greatly improves the computational speed and reduces its storage requirements. In addition, we present some convergent theorems proving by different techniques and the estimate of the required number of iteration steps. Furthermore, in the accompanying corollaries, we provide some estimations for choosing appropriate parameter values. Finally, we validated the effectiveness and efficiency of our newly developed method through two numerical examples of the GAVE. [ABSTRACT FROM AUTHOR]

The fundamental concept of quantum Fisher information (QFI) evaluating the high sensitivity of a quantum state to parameter variations, plays a crucial role in the realm of parameter estimation theory. Here, we investigate the estimation precision of the mechanical oscillator decay rate in a hybrid optomechanical system by making use of the atomic quantum Fisher information. We also talk about entanglement and try to link this quantum feature with QFI. The considered quantum system contains a Λ -type three-level atom interacting with two optical modes by multi-photon transition in the presence of different sources of the dissipation. Applying the effective non-Hermitian Hamiltonian, we get the explicit form of the state vector of the entire system analytically, with the help of the time-dependent Schrödinger equation. The obtained results show that the dynamics of the atomic QFI and entanglement can suitably be controlled by optomechanical and the atom-field coupling strengths, dissipation parameters and multi-photon transition. It can be seen that decreasing the coefficient of optomechanical coupling as well as the strength of atom-field coupling reduces the amount of QFI. Moreover, it is found that there exists a monotonic relation between the atomic QFI and the quantum entanglement in the absence of atomic decay and optical loss. [ABSTRACT FROM AUTHOR]

The verification of model transformations is important for realizing robust model-driven engineering technologies and quality-assured automation. Many approaches for checking properties of model transformations have been proposed. Most of them have focused on the effective and efficient detection of property violations by contract checking. However, there remains the fault localization step between identifying a failing contract for a transformation based on verification feedback and precisely identifying the faulty rules. While there exist fault localization approaches in the model transformation verification literature, these require the creation and maintenance of test cases, which imposes an additional burden on the developer. In this paper, we combine transformation verification based on symbolic execution with spectrum-based fault localization techniques for identifying the faulty rules in DSLTrans model transformations. This fault localization approach operates on the path condition output of symbolic transformation checkers instead of requiring a set of test input models. In particular, we introduce a workflow for running the symbolic execution of a model transformation, evaluating the defined contracts for satisfaction, and computing different measures for tracking the faulty rules. We evaluate the effectiveness of spectrum-based analysis techniques for tracking faulty rules and compare our approach to previous works. We evaluate our technique by introducing known mutations into five model transformations. Our results show that the best spectrum-based analysis techniques allow for effective fault localization, showing an average EXAM score below 0.30 (less than 30% of the transformation needs to be inspected). These techniques are also able to locate the faulty rule in the top-three ranked rules in 70% of all cases. The impact of the model transformation, the type of mutation and the type of contract on the results is discussed. Finally, we also investigate the cases where the technique does not work properly, including discussion of a potential pre-check to estimate the prospects of the technique for a certain transformation. [ABSTRACT FROM AUTHOR]

A group of algorithms for estimating the current intensity (CI) of typhoons, which use infrared and microwave sensor-based images as the input of the algorithm because it is more skilled than each algorithm separately, are used to create a technique to estimate the typhoon intensity which is known as SATCON. In the current study, an effort was undertaken to assess how well the SATCON approach performed for estimating typhoon intensity throughout the West Pacific basin from year 2017 to 2021. To do this, 26 typhoons over the West Pacific basin were analysed using the SATCON-based technique, and the estimates were compared to the best track parameters provided by the Regional Specialized Meteorological Centre (RSMC), Tokyo. The maximum sustained surface winds (Vmax) and estimated central pressures (ECP) for various 'T' numbers and types of storm throughout the entire year, as well as during the pre-monsoon (March–July) and post-monsoon (July–February) seasons, have been compared. When compared to weaker and very strong typhoons, the ability of the SATCON algorithm to estimate intensity is determined to be rather excellent for mid-range typhoons. We demonstrate that SATCON is more effective in the post-monsoon across the West Pacific basin than in the pre-monsoon by comparing the algorithm results. [ABSTRACT FROM AUTHOR]

Most studies which deal with natural populations require a reliable and convenient way of age estimation. However, even rough aging of live individuals is often a real challenge. In this study, we develop a radiographic method for age estimation in Ellobius talpinus, a promising model species for population and behavioral ecology. Using portable X-ray equipment, we radiographed wild, non-sedated animals from the population that had been subjected to extensive mark-recaptures for 3 years. Two molar metrics strongly dependent on age and easy to measure on radiographs were selected: the lengths of the synclinal folds of the 1st upper and 1st lower molars. No influence of sex on the molar condition age dynamics was found. Discriminant function analysis based on molar condition and date of radiography in 86 animals of known age classes assigned X-ray images to three age classes (young of the year, yearlings, and 2 years or older) with an accuracy of 99%. Leave-one-out cross-validation yielded 97% correct assignments. All age estimates for 52 repeatedly radiographed individuals were consistent across images. The analysis of the repeated X-ray images obtained from the same animals showed that the 1st lower molars change faster in the first summer of life than later whereas the change rate of the 1st upper molars decreases little throughout life. We propose the X-ray technique as a useful alternative to direct skull and dental morphometry for age estimation of wild small mammals, saving the investigator's time and lives of animals. [ABSTRACT FROM AUTHOR]

Effective planning and managing medical waste necessitate a crucial focus on both the public and private healthcare sectors. This study uses machine learning techniques to estimate medical waste generation and identify associated factors in a representative private and a governmental hospital in Bahrain. Monthly data spanning from 2018 to 2022 for the private hospital and from 2019 to February 2023 for the governmental hospital was utilized. The ensemble voting regressor was determined as the best model for both datasets. The model of the governmental hospital is robust and successful in explaining 90.4% of the total variance. Similarly, for the private hospital, the model variables are able to explain 91.7% of the total variance. For the governmental hospital, the significant features in predicting medical waste generation were found to be the number of inpatients, population, surgeries, and outpatients, in descending order of importance. In the case of the private hospital, the order of feature importance was the number of inpatients, deliveries, personal income, surgeries, and outpatients. These findings provide insights into the factors influencing medical waste generation in the studied hospitals and highlight the effectiveness of the ensemble voting regressor model in predicting medical waste quantities. [ABSTRACT FROM AUTHOR]

Rice milling industries are seen in large numbers in the eastern and southern parts of India. Like the other industries, this is also going through changes because of the introduction of automation using machine vision technologies. In the consumer market, buyers consider that a good quality rice bag must not contain broken kernels. Considering this commercial aspect, during rice production, the estimation of broken rice kernels mixed with whole rice kernels is an essential grading task in the husking mills. In this paper, we propose a method for monitoring the presence of broken rice kernels in the rice outputs during milling. In our proposed method, during milling, rice kernel samples are captured, and image understanding-based processing is applied to realize broken and whole rice kernels. A whole-grain kernel is distinguishable from a broken-grain kernel in terms of its size, perimeter length, etc. But many times, several kernels get attached to one another during image capturing. These attached kernel groups need to be separated into single kernels so that the shape and size information of kernels may be used to estimate the amount of presence of broken rice kernels. Hence, the proposed method works in two stages. In the first stage, attached rice kernels are accurately separated using geometric analysis of the boundary edges of the attached kernel groups, and then, in the second stage, all individual kernels are clustered into either broken or whole kernel categories based on their shape and size information. The proposed method identifies the broken kernels with accuracy of 98.25 % , 96.54 % , and 98.52 % for Shamashri, Lalswarno, and Basmati rice kernel respectively. Finally, we present statistics of the estimation depicting the percentage of broken and whole rice grains in the sample images. Our test results indicate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Particle swarm optimization approach (PSO) has illustrated promising performance on a variety of optimization problems. The critical issues that must be addressed in PSO research are how to balance conflicts around exploration, exploitation, and reasonably distribute computational resources. In this paper, a neighborhood-aware N states Markovian Switching PSO algorithm (NS-MJPSOloc) is proposed, where we have employed a local search based approach. The neighborhood of PSO algorithm is a single particle itself in the early stages of optimization. The neighborhood will eventually expand to include all particles as the amount of generations increases. The search space is segmented into N potential states according to the size of problem. The value of N is dependent on the dimension of problem space. Every iteration serves a transition probability-based Markovian switching technique to estimate the acceleration coefficients in an adaptable way. The transition probability determines the next state and adequate values for acceleration coefficients. Through adopting a local search strategy while interacting with all other particles in the group, every particle possesses a chance of improving itself. Moreover, new features have been incorporated to accomplish accuracy and stability. The primary contributions of the proposed work are to enhance the topological structure of a recently developed PSO variant by using a local search (loc) mechanism rather than a global (gbest) approach. The purpose of this idea is to develop a more adaptable and less sensitive novel version of PSO that can handle multi-modal and highly challenging problems. The proposed algorithm was then tested using a variety of uni-modal, multi-modal and rotated benchmark functions. The experimental findings show that the proposed NS-MJPSOloc outperformed most of the current versions in terms of accuracy and convergence time, notably for multi-modal problems. [ABSTRACT FROM AUTHOR]

In this paper, the authors consider an approximation to the isentropic planar Magneto-hydrodynamics (MHD for short) equations by a kind of relaxed Euler-type system. The approximation is based on the generalization of the Maxwell law for non-Newtonian fluids together with the Maxwell correction for the Ampère law, hence the approximate system becomes a first-order quasilinear symmetrizable hyperbolic systems with partial dissipation. They establish the global-in-time smooth solutions to the approximate Euler-type equations in a small neighbourhood of constant equilibrium states and obtain the global-in-time convergence towards the isentropic planar MHD equations. In addition, they also establish the global-in-time error estimates of the limit based on stream function techniques and energy estimates for error variables. [ABSTRACT FROM AUTHOR]

Precise computation of evapotranspiration is critical for an efficient irrigation planning and managing agriculture water. This investigation aimed to study the performance of regression techniques and machine learning (ML) techniques in estimating daily reference evapotranspiration (ET0) and compare them to the ET0 computed by the Penman–Monteith (PM-56) technique of "Food and Agriculture Organization" (FAO). The study used the meteorological data of the Pusa Institute, ICAR-IARI, New Delhi, which is located in a semi-arid climatic zone, for a period of 31 years (1990–2020). Six regression techniques were used, including multiple linear regression (MLR), elastic net regression (ENR), ridge regression (RDR), lasso regression (LASSOR), partial least square regression (PLSR), and Poisson regression (POR). Four machine learning models, namely radial basis function (RBF), M5Tree, locally weighted learning (LWL), and gradient boosted tree (GBT), were also evaluated for predicting daily ET0. The M5Tree model outclassed all other models in predicting daily ET0, with a mean absolute error (MAE), mean squared error (MSE), root-mean-squared error (RMSE), R-squared (R2), mean absolute percentage error (MAPE), and Willmott's index (d) of 0.088, 0.018, 0.136, 0.994, 3.073%, and 0.914, respectively, in training of the models. While during testing period, M5Tree model gave MAE, MSE, RMSE, R2, MAPE and d values as 0.114, 0.16, 0.382, 0.949, 3.946% and 0.988, respectively. During training phase, the GBT model reported MAE, MSE, RMSE, R2, MAPE and d as 0.097, 0.021, 0.145, 0.993, 3.257% and 0.998, respectively, which were slightly poor than the M5Tree model. Further, the GBT model showed the best performance during model testing, with MAE, MSE, RMSE, R2, MAPE, and d of 0.126, 0.053, 0.230, 0.982, 4.166%, and 0.995, respectively. Additionally, the POR model performed the worst in predicting daily ET0 values, as evidenced by the prediction error statistics. In conclusion, the developed ET0 model may be utilized to precisely predict ET0 in semi-arid region for efficient irrigation scheduling, especially in the absence of weighing type field lysimeter. [ABSTRACT FROM AUTHOR]

The overall motion performance of mechatronic machines depends heavily on proper knowledge of the machine's characteristics, among which the inertia. Moreover, the inertia of most multi-body mechanisms, for example, reciprocating slider-crank mechanisms, varies as a function of the machine position, thereby challenging optimal control. Nevertheless, the inertia's variation is sometimes not known accurately enough or changes over time due to, e.g. a production process or premature wear, forcing an online control/correction of the inertia profile. Therefore, earlier, the author developed a new computational-friendly online method based on a frequency-specific magnitude response to estimate the machine's position-dependent load side inertia, assuming good knowledge of all other machine properties. Yet, to guarantee accuracy, the maximum machine speed during estimation had to be strongly reduced, resulting in an undesirable longer motion time. Therefore, in this work, the failure mechanism of the earlier proposed estimator is analysed and overcome by intelligently separating estimation and motion signals. As a result, the estimator is no longer limited by the machine's motion speed but rather by a maximum in the inertia variation per signal excitation period and, thus, time. Moreover, given this limiting variation, a guideline was established to evaluate the new method's estimation quality. Finally, both in simulation and on a physical machine, by taking advantage of the newly proposed method, the minimal required estimation time is reduced significantly (up to 50% on the machine) without diminishing the estimation accuracy. [ABSTRACT FROM AUTHOR]

One of the most important subjects in statistics is the theory of estimation. In this paper, we consider the generalized Bayes shrinkage estimator of the mean vector for multivariate normal distribution with the unknown covariance matrix under balanced quadratic loss. Also, we find the minimax and admissible estimator of the mean vector based on the generalized Bayes estimator. We obtain Stein's unbiased risk estimator (SURE) threshold with a generalized Bayes shrinkage estimator and we find the wavelet shrinkage generalized Bayes SURE. Finally, we present a simulation study to test the validity of the generalized Bayes SURE and the concrete compressive strength dataset is given to test the efficiency of this estimator in denoising. [ABSTRACT FROM AUTHOR]

Good understanding of the in-situ stress and rock mass strength or their relative magnitudes is essential to underground geomechanical practices. A prevailing technique for estimating the maximum horizontal stress is the integrated method combining stress polygon, borehole breakout, and drilling-induced tensile fracture. This research embraced all these elements into a nondimensionalized chart to interpret the triangle relation among the in-situ stress, rock mass strength, and failure observation for an underground cylindrical opening at any depth. One of the potential applications of the nondimensionalized chart was shown via the filed observations from a testing tunnel in the Canadian underground research laboratory. An approaching method of untangling two correlated stress components was also proposed to constrain the in-situ stress ratio under the framework of Coulomb frictional failure theory. It is proved to be highly efficient and considerably accurate by implementing the approaching method using the failure observations from the KTB borehole in German. [ABSTRACT FROM AUTHOR]

Heisenberg's uncertainty principle is originally represented by the tradeoff relations between the error of measuring an observable A and the consequential disturbance to the value of another observable B with respect to a particular quantum state. Many different error-disturbance tradeoff relations have been proposed and experimentally tested in various scenarios. The error-disturbance uncertainty relations is incorporated into multiparameter estimation theory recently, but they are all the tradeoff relations between the errors of measuring two observables. Here, we derive the error-disturbance tradeoff relations for three observables A, B and C and demonstrate that the new relation is violated, while the relations derived from triple of two observables A and B, A and C and B and C directly are valid by some examples. [ABSTRACT FROM AUTHOR]

This paper presents a semi-parametric modeling technique for estimating the survival function from a set of right-censored time-to-event data. Our method, named pseudo-value regression trees (PRT), is based on the pseudo-value regression framework, modeling individual-specific survival probabilities by computing pseudo-values and relating them to a set of covariates. The standard approach to pseudo-value regression is to fit a main-effects model using generalized estimating equations (GEE). PRT extend this approach by building a multivariate regression tree with pseudo-value outcome and by successively fitting a set of regularized additive models to the data in the nodes of the tree. Due to the combination of tree learning and additive modeling, PRT are able to perform variable selection and to identify relevant interactions between the covariates, thereby addressing several limitations of the standard GEE approach. In addition, PRT include time-dependent effects in the node-wise models. Interpretability of the PRT fits is ensured by controlling the tree depth. Based on the results of two simulation studies, we investigate the properties of the PRT method and compare it to several alternative modeling techniques. Furthermore, we illustrate PRT by analyzing survival in 3,652 patients enrolled for a randomized study on primary invasive breast cancer. [ABSTRACT FROM AUTHOR]

This paper examines the phenomenon of residential segregation in Berlin over time using a dynamic clustering analysis approach. Previous research has examined the phenomenon of residential segregation in Berlin at a high spatial and temporal aggregation and statically, i.e. not over time. We propose a methodology to investigate the existence of clusters of residential areas according to migration background, age group, gender, and socio-economic dimension over time. To this end, we have developed a sequential mixed methods approach that includes a multivariate kernel density estimation technique to estimate the density of subpopulations and a dynamic cluster analysis to discover spatial patterns of residential segregation over time (2009-2020). The dynamic analysis shows the emergence of clusters on the dimensions of migration background, age group, gender and socio-economic variables. We also identified a structural change in 2015, resulting in a new cluster in Berlin that reflects the changing distribution of subpopulations with a particular migratory background. Finally, we discuss the findings of this study with previous research and suggest possibilities for policy applications and future research using a dynamic clustering approach for analyzing changes in residential segregation at the city level. [ABSTRACT FROM AUTHOR]

Conventional demand-prediction methods predominantly rely on past user behaviors to predict regular future transportation demands using acquired user preference data. Nevertheless, predicting unforeseen travel demands arising from bad weather or emergency events remains challenging owing to the absence of data on such future contingencies. This study introduces a method to predict travel demand by leveraging search history data, which potentially signal unforeseen travel requirements. We elucidate the correlation between the search count and integrated circuit (IC) card usage on an aggregate level. Subsequently, we propose a two-stage analytical technique to estimate the number of IC card usages based on route-search counts. Our findings demonstrate that the proposed model has superior accuracy, and the route-search count plays a pivotal role in predicting the number of IC card usages, especially unforeseen shifts in demand. [ABSTRACT FROM AUTHOR]

The implementation of ultra-wideband technology for accurate indoor location in industrial and public environments has recently been the basis of numerous research studies. Wireless sensor networks must take localization into consideration. Localization processes improve in various ways when mobility is established. As research progresses on range-free localization with UWB, enhancements in methods and optimal techniques need to be evaluated for the present scenario. Within a single localization zone, they focus is on accuracy. The objective of work is to improve the location accuracy by decreasing localization error with hybrid optimal techniques and algorithms. However, many large-scale applications will benefit from technologies developed for line-of-sight conditions. TDOA (Time Difference of Arrival) is a method for calculating the distance between two or more radio transmitters and one receiver. Distance vector routing protocols measure distance by the number of routers a packet has to pass through; one router counts as one hop. These protocols send the entire routing table to each neighbor every 30 s. DV-Hop method has been adopted for the present work, and hybridization has been considered as a means of enhancing the results. Accuracy with initial measurement techniques lies in 2D and 3D with Kalman filter linked with DV-Hop to reduce initial errors. Localization error in nodes' dynamic momentum was observed, and the deployment of beacons in the range-free selected area was estimated with an improved technique. Ensemble method (EM)-PSO-based optimization with hybrid HOP algorithms is proposed in the present work. The results of the present approach show a significant difference in localization error reduction compared to conventional DV-HOP. Finally, an error below 2% is observed using multiple iterations, and a variation of 15% is observed compared with other state-of-the-art existing techniques. Using hybrid algorithms, this increased accuracy can then be used to reduce the localization error in network for the application users in range-free conditions. [ABSTRACT FROM AUTHOR]

Tunnel boring machine (TBM) performance prediction in mechanized tunneling is an essential factor for selecting an appropriate excavation machine, tunnel design, and safe construction. To implement safe mechanized excavation, it is important to accurately assess and predict the range of machine driving parameters, especially the machine rate of penetration (ROP); this can reduce the cost of TBM repairs due to the abrasion of disc cutters and cutterhead and also has a positive effect on the post-construction period. This study focuses on predicting the ROP of TBMs passing through metamorphic rocks during deep excavation and under a complex geotechnical situation. For this purpose, three fuzzy-based models of the Mamdani fuzzy inference system (MFIS), adaptive neuro-fuzzy inference system (ANFIS), Takagi Sugeno fuzzy model (TSF), as well as linear and non-linear regression models were developed. Historical tunnels were used to compile 189 data points (151 for training and 37 for testing). In the dataset, three parameters, including uniaxial compressive strength (UCS), cutterhead rotational speed per minute (RPM), and thrust force (TF), were considered effective parameters on the TBM's ROP. According to the findings, the suggested models provided satisfactory and consistent accuracy. Moreover, the results demonstrated that the forecasted values correlate rather well with the measured ones. The proposed algorithms can be considered for use in similar ground and tunneling conditions (metamorphic rocks with low-average strength). It is worth noting that this study has the potential to drastically cut down on tunneling uncertainties and makes fuzzy inference systems a robust algorithm for planning mechanized tunneling. Highlights: Prediction of TBM performance in complex geological conditions. Forecasting TBM performance in deep tunnels passing through metamorphic rocks. Presenting an empirical model for calculating the TBM performance based on statistical analysis. Detailed analysis of fuzzy-based techniques potential for TBM performance prediction. Examining the models' accuracy with several loss functions and statistical indices. [ABSTRACT FROM AUTHOR]

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

In this study, thermogravimetric and thermo-kinetic analysis of sugarcane bagasse pith (S.B.P.) were performed using a robust suite of experiments and kinetic analyses, along with a comparative evaluation on the thermo-kinetic characteristics of two other major sugarcane residues, namely sugarcane straw (S.C.S.) and sugarcane bagasse (S.C.B.). The thermogravimetric analysis evaluated the pyrolysis behavior of these residues at different heating rates in a nitrogen atmosphere. The Kissinger, advanced non-linear isoconversional (ANIC), and Friedman methods were employed to obtain effective activation energies. Moreover, the compensation effect theory (CE) and combined kinetic analysis (CKA) were used to determine the pre-exponential factor and pyrolysis kinetic model. Friedman's method findings indicated that the average activation energies of S.C.S., S.C.B., and S.B.P. are 188, 170, and 151 kJ/mol, respectively. The results of the ANIC method under the integral step Δα = 0.01 were closely aligned with those of the Friedman method. The CKA and CE techniques estimated ln(f(α)Aα) with an average relative error below 0.7%. The pre-exponential factors of S.C.S., S.C.B., and S.B.P. were in the order of 1014, 1012, and 1011 (s−1), respectively. From a thermodynamic viewpoint, positive ∆G* and ∆H* results provide evidence for the non-spontaneous and endothermic nature of the pyrolysis process, indicating the occurrence of endergonic reactions. [ABSTRACT FROM AUTHOR]

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

The occurrence and regularity of macrofungal fruitbody formation are influenced by meteorological conditions; however, there is a scarcity of data about the use of machine-learning techniques to estimate their occurrence based on meteorological indicators. Therefore, we employed an artificial neural network (ANN) to forecast fruitbody occurrence in mycorrhizal species of Russula and Amanita, utilizing meteorological factors and validating the accuracy of the forecast of fruitbody formation. Fungal data were collected from two locations in Western Hungary between 2015 and 2020. The ANN was the commonly used algorithm for classification problems: feed-forward multilayer perceptrons with a backpropagation algorithm to estimate the binary (Yes/No) classification of fruitbody appearance in natural and undisturbed forests. The verification indices resulted in two outcomes: however, development is most often studied by genus level, we established a more successful, new model per species. Furthermore, the algorithm is able to successfully estimate fruitbody formations with medium to high accuracy (60–80%). Therefore, this work was the first to reliably utilise the ANN approach of estimating fruitbody occurrence based on meteorological parameters of mycorrhizal specified with an extended vegetation period. These findings can assist in field mycological investigations that utilize sporocarp occurrences to ascertain species abundance. [ABSTRACT FROM AUTHOR]

Parameter estimation is one of key issues in the study of uncertain differential equations. In this paper, one novel estimation method named the estimating function technique of uncertain differential equations based on uncertain integrals is proposed, which is the first method based on uncertain integrals appeared in the literature. Compared to existing methods, our approach fully utilizes information from both the parameters and observed data. In certain settings, previous methods can be viewed as special cases of our technique. One numerical example with different transform functions is provided to illustrate our method and the efficiency is shown by residual analysis. Additionally, a case study on Alibaba stock prices is included to demonstrate the application of our method. The paper concludes with final remarks and recommendations for future research. [ABSTRACT FROM AUTHOR]

Primary components of sodium cooled fast reactors get contaminated due to activity transport that occurs from core to out-of-core through coolant which causes activity burden to the personnel during operation and maintenance. To minimize the MAN-REM issues it is necessary to scavenge 54Mn, 60Co and 65Zn which are of major radiological concern from primary sodium using a compact device. Experimental studies were performed at various durations and temperatures with nickel as single trap material to trap the radionuclides simultaneously and its uptake was estimated. Trapping efficiency of porous and non-porous Ni at 773 K was estimated using radiotracer technique. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical analysis in discrete time for a multi-tier weak radiofrequency (RF) signal estimation process with N simultaneous signals. Discrete time dynamic sampling is introduced and is shown to provide the capability to extract signal parameter values with increased accuracy compared with accuracy of estimates obtained in prior work. This paper advances phase measurement approaches by proposing discrete time dynamic sampling which our paper shows offers the desirable capability for more accurate weak signal parameter estimates. For N = 2 simultaneous signals with a strong signal at 850 MHz and a weak signal at 855 MHz, the results show that dynamically sampling the instantaneous frequency at 24 times the Nyquist rate provides weak signal frequency estimates that are within 1.7 × 10 - 5 of the actual weak signal frequency and weak signal amplitude estimates that are within 428 PPM of the actual weak signal amplitude. Results are also presented for situations with N = 2 simultaneous 5G signals. In one case, the strong signal is 3950 MHz, and the weak signal is 3955 MHz; in the other case the strong case is 5950 MHz, and the weak signal is 5955 MHz. The results for these cases show that estimates obtained with dynamic sampling are more accurate than estimates provided using a single sample rate of 65 MSPS. This work has promising applications for weak signal parameters estimation using instantaneous frequency measurements. [ABSTRACT FROM AUTHOR]

Dairy industry wastewater can be considered as an important source of pollution due to its high amounts and pollutant concentrations. Anaerobic treatment is seen as a suitable alternative over aerobic treatment which requires huge aeration systems. Biochemical methane potential (BMP) testing is a widely applied technique for estimating the performance of anaerobic digesters and still has no clear alternative. In the study, the biochemical methane potential change was investigated by mixing dairy wastewater with different co-substrates (cattle manure, chicken manure and slaughterhouse wastewater) at different rates. The highest biogas potential per gram of chemical oxygen demand added (CODadded) was determined as 574 mLbiogas in a mixture of 74% dairy wastewater + 2% chicken manure + 24% slaughterhouse wastewater inoculated with granular sludge. The highest methane potential was determined as 340 mLCH4 in the same co-substrate mixture inoculated with anaerobic sludge. In recent years, mathematical modeling offers an alternative to BMP tests and many different models are used for this purpose. In the study, six different mathematical models were used to simulate the BMP results, and the highest correlation coefficient in almost all mixtures ranged from 0.900 to 0.997 with the Modified Gompertz equation and Fitzhugh models. [ABSTRACT FROM AUTHOR]

This paper presents a study on developing a chemical vapor deposition film thickness prediction model for semiconductor IC manufacturing. Traditional methods for measuring layer thickness are time-consuming and labor-intensive, prompting the exploration of machine-learning techniques for virtual measurement. These techniques estimate layer thickness directly from production process data, enabling real-time monitoring of product quality and maintaining process efficiency. This study used the Harris Hawks optimization algorithm (HHO) to perform feature selection on deposition process data and constructed a predictive model using the selected feature parameters. Four machine learning models, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), are evaluated using metrics such as mean squared error (MSE), mean absolute percentage error (MAPE), and R-squared ( R 2 ) on a test set. Results demonstrate that the CatBoost model outperforms the others with an MSE of 353.55, MAPE of 0.246% and R 2 of 0.796, highlighting its superior prediction accuracy in estimating film thickness. However, the integrated tree model used in this study is complex, and its prediction results cannot be directly interpreted, making the model less credible. To address this issue, the SHAP method was introduced for interpretable analysis of the model. Overall, this study shows that the use of machine learning models, such as the CatBoost model, combined with SHAP analysis, significantly improves the credibility of prediction results on deposited film thickness in IC fabrication. Moreover, the timely adjustment of production equipment parameters can be achieved to reduce process deviations and improve IC yield. [ABSTRACT FROM AUTHOR]