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Multilevel regression and post-stratification (MRP) is a popular use of hierarchical models in political science. Multiple papers have suggested that relying on machine learning methods can provide substantially better performance than traditional approaches that use hierarchical models. However, these comparisons are often unfair to traditional techniques as they omit possibly important interactions or nonlinear effects. I show that complex ("deep") hierarchical models that include interactions can nearly match or outperform state-of-the-art machine learning methods. Combining multiple models into an ensemble can improve performance, although deep hierarchical models are themselves given considerable weight in these ensembles. The main limitation to using deep hierarchical models is speed. This paper derives new techniques to further accelerate estimation using variational approximations. I provide software that uses weakly informative priors and can estimate nonlinear effects using splines. This allows flexible and complex hierarchical models to be fit as quickly as many comparable machine learning techniques. [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]

This editorial summarizes the papers published in issue 17(2) (2022). The first paper evaluates logistic regression and machine-learning methods for predicting firm bankruptcy. The second paper demonstrates that machine learning outperforms existing tools to improve the estimation of regional input–output tables. The third paper investigates whether network centrality depends on the probability that a tie between two nodes is formed, as well as its intensity. The fourth paper sets out a Bayesian estimation technique to estimate a spatial autoregressive multinomial logit model. The fifth paper develops a statistic to test for several misspecification problems in spatial econometric models. The sixth paper compares the prediction accuracy of spatial and non-spatial econometric models explaining the number of tourist arrivals across countries. [ABSTRACT FROM AUTHOR]

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

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

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Effort estimation is a crucial aspect of software development, as it helps project managers plan, control, and schedule the development of software systems. This research study compares various machine learning techniques for estimating effort in software development, focusing on the most widely used and recent methods. The paper begins by highlighting the significance of effort estimation and its associated difficulties. It then presents a comprehensive overview of the different categories of effort estimation techniques, including algorithmic, model-based, and expert-based methods. The study concludes by comparing methods for a given software development project. Random Forest Regression algorithm performs well on the given dataset tested along with various Regression algorithms, including Support Vector, Linear, and Decision Tree Regression. Additionally, the research identifies areas for future investigation in software effort estimation, including the requirement for more accurate and reliable methods and the need to address the inherent complexity and uncertainty in software development projects. This paper provides a comprehensive examination of the current state-of-the-art in software effort estimation, serving as a resource for researchers in the field of software engineering. [ABSTRACT FROM AUTHOR]

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

In 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]

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]

This paper is intended to summarize the research conducted during the first 2 years of the Dragon 5 project 59,332 (geophysical and atmospheric retrieval from Synthetic Aperture Radar (SAR) data stacks over natural scenarios). Monitoring atmospheric phenomena, encompassing both tropospheric and ionospheric conditions, holds pivotal significance for various scientific and practical applications. In this paper, we present an exploration of advanced techniques for estimating tropospheric and ionospheric phase screens using stacks of Synthetic Aperture Radar (SAR) images. Our study delves into the current state-of-the-art in atmospheric monitoring with a focus on spaceborne SAR systems, shedding light on their evolving capabilities. For tropospheric phase screen estimation, we propose a novel approach that jointly estimates the tropospheric component from all the images. We discuss the methodology in detail, highlighting its ability to recover accurate tropospheric maps. Through a series of quantitative case studies using real Sentinel-1 satellite data, we demonstrate the effectiveness of our technique in capturing tropospheric variability over different geographical regions. Concurrently, we delve into the estimation of ionospheric phase screens utilizing SAR image stacks. The intricacies of ionospheric disturbances pose unique challenges, necessitating specialized techniques. We dissect our approach, showcasing its capacity to mitigate ionospheric noise and recover precise phase information. Real data from the Sentinel-1 satellite are employed to showcase the efficacy of our method, unraveling ionospheric perturbations with improved accuracy. The integration of our techniques, though presented separately for clarity, collectively contributes to a comprehensive framework for atmospheric monitoring. Our findings emphasize the potential of SAR-based approaches in advancing our knowledge of atmospheric processes, thus fostering advancements in weather prediction, geophysics, and environmental management. [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]

A significant contribution to the economic growth of a nation comes from the mineral industries. Therefore, the concentration of metallic or nonmetallic minerals in different regions of Earth's crust is important to determine. The present paper studies the grade and thickness estimation of iron and coal deposits, respectively, by applying two-dimensional cellular automata (CAs). Krigging is a popular method for the estimation of mineral resources. However, krigging results in complex mathematical calculations if the number of sample points increases. Here, each cell of the cellular automaton (CA) is represented as a block. Using CAs, the grade values and thickness are estimated in a simpler and faster way. Two-dimensional CAs are used in this paper where the local rule is the ordinary krigging estimator function using the spherical variogram model. The total weight of iron as well as coal is calculated using the CA-based technique. A comparative analysis between the estimated weight of minerals and the actual extracted mineral is also given. [ABSTRACT FROM AUTHOR]

Summary: In recent years, wireless sensor networks (WSNs) have been widely used in various applications. The localization problem has been identified as one of the biggest problems faced by WSNs. The traditional localization techniques may not be able to handle the issues during the scenario to estimate the location of sensor nodes due to anchor mobility, mobile WSNs, latency, energy harvesting, unfavorable environmental states, and many more issues. However, these issues open the door for the amalgamation of machine learning (ML) and optimization techniques with localization techniques. Motivated by the earlier discussion, we explored various ML and optimization techniques to estimate the location coordinates in a sensor network in this paper. Finally, a comparison of existing ML algorithms concerning optimization techniques has been presented, highlighting their improved outcomes. This research offers a detailed survey by exploring the various parameters for location estimation through tabular forms by incorporating ML and optimized localization techniques. A survey of surveys is also presented to identify the key limitations of existing surveys and to introduce the novelty in the comprehensive study done in this paper. A year‐wise evaluation of ML Techniques with localization (2011–2022) is also discussed and presented over various performance parameters, including energy‐efficiency, accuracy, error, and complexity. This discussion concluded that Hybrid Techniques are least explored for using optimized localization machine learning. Further, a summarized discussion of the various comparison tables paves the path for future research in the area of localization in WSN. [ABSTRACT FROM AUTHOR]

In this paper we proposed a class of ratio estimators using auxiliary information for the estimation of population mean under stratified sampling. The proposed estimator in this work is based on the Yadav and Baghel (2021) estimator. The expression of the mean square error for this class of estimators is derived and the performance of the proposed estimator is compared with competing estimators. Numerical illustration of efficiency comparison is also worked out in the paper. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop an understanding of the legal framework for combatting administrative corruption in Jordan and its impact on the Jordanian economy. This paper seeks to answer its key-question which asks whether the existence of a robust legal framework for combatting administrative corruption is able to help to grease the wheels of a slow-moving economy. The study uses 2SLS econometric technique for estimating the relationship between administrative corruption and economic growth. This paper indicates one major conclusion which is the irrevocably annual loss of economic opportunities due to administrative corruption, subsequently, economic progress cannot be achieved without proper administration. The national strategy and the legal frameworks related to anti-corruption are not sufficient enough to grease the wheels for economic growth. Combatting corruption in Jordan is still facing legal and technical obstacles appeared in practice, therefore, more time to surround and prevent corruption is still needed. [ABSTRACT FROM AUTHOR]

In this paper, the problem of predictor-based fast finite-time fuzzy dynamic surface control for the nonlinear systems with strict-feedback structure is considered. Different from the traditional fuzzy dynamic surface control method, the proposed method utilizes prediction errors to update learning parameters for improving fuzzy logic systems learning behaviors in this paper. This technique can estimate the system unknown function smoothly and cannot result in high-frequency oscillations due to the existence of the overlarge adaptive gains. In addition, based on fast finite-time theorem and backstepping control technique, the developed controller can ensure all signals of the closed-loop are bounded at a finite time. Eventually, the illustrative examples are given to validate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

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

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]

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

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]

Non-negative distributions are important tools in various fields. Given the importance of achieving a good fit, the literature offers hundreds of different models, from the very simple to the highly flexible. In this paper, we consider the power–Pareto model, which is defined by its quantile function. This distribution has three parameters, allowing the model to take different shapes, including symmetrical and left- and right-skewed. We provide different distributional characteristics and discuss parameter estimation. In addition to the already-known Maximum Likelihood and Least Squares of the logarithm of the order statistics estimation methods, we propose several additional methods. A simulation study and an application to two datasets are conducted to illustrate the performance of the estimation methods. [ABSTRACT FROM AUTHOR]

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

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

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

In most circumstances, accessing measurements from entire smart metres set in less-voltage grid area for distribution monitoring is not practicable because of less bandwidth as well as excessive delays in accessing Smart Meter monitoring. Estimating the condition of a distribution system can be done using voltage, active and reactive power measurements from smart metres subset. On one hand, increasing number of selected smart metres improves distribution state assessment accuracy, it degrades monitoring data time. It improves the electrical system's dependability and efficiency while also providing security to the system. Smart grid infrastructure is a modified form of smart grid. Smart grid infrastructure is a modified form of the smart grid. It has high-capacity converters, sensing and metering technologies, and automated control to improve efficiency and dependability. The goal of this article is to use smart metering technology to improve the observer of low-voltage distribution grids. The use of a distribution network-based state estimation technique employing smart metre readings for near-real-time monitoring of low-voltage distribution grids is the focus of this research. This paper propose smart metering based on cloud architecture for low voltage distribution networks with distribution network based state estimation algorithm in application of monitoring. The article will discuss the critical role of cloud solution in achieving interoperability, scalability and flexibility as well as enabling integration of various services for distribution system automation. As an example of coordinated operation of diverse distribution grid services by cloud, distributed state estimation technique as well as autonomous network reconfiguration will be demonstrated. • The goal of this article is to use smart metering technology to improve the observability of low-voltage distribution grids. • To deal with estimating grid voltages with reasonable accuracy using a state estimate technique that employs smart meter data. • Here proposed architecture enhanced issue of enhancing low-voltage distribution grids observability using smart metering infrastructure. • The experimental results show comparative analysis for various distributed network types and technique based comparison between existing and proposed technique. [ABSTRACT FROM AUTHOR]

With rapid economic growth and urbanization, the accelerated increase in car ownership has brought massive pressure on urban traffic, and accurate traffic flow prediction information can provide an important basis for urban traffic dynamic planning. The existing methods have problems such as low efficiency, large error, and inability to adapt to short-term traffic changes. To solve the above problems, the CEEMDAN-SE-GWO-LSTM method was proposed in this paper. First, the traffic flow data is processed for outliers and missing values. The Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the traffic flow data, and sample entropy (SE) is used to reconstruct the subsequence, which is used to improve the quality of the input data. Then, the Grey Wolf Optimizer (GWO) is used to optimize the parameters of the long-short-term memory (LSTM) in order to improve the prediction accuracy and prevent the model from falling into a local optimum. Three models are used to compare with the ensemble model proposed in this paper, including back propagation neural network (BPNN), LSTM, and long-short-term memory optimized by Grey Wolf Optimizer (GWO-LSTM). Root mean square error (RMSE) is reduced by 40.9% to 66.7%; R 2 score is improved by 1.5% to 7.1%. The experimental results show that CEEMDAN-SE-GWO-LSTM has a higher prediction accuracy than the existing traffic flow prediction models. Finally, this paper uses the model prediction error to establish an interval prediction model based on the kernel density estimation theory, which enhances the generalization of the model and the practical application value. [ABSTRACT FROM AUTHOR]

The development of traffic state prediction algorithms embedded in intelligent transportation systems is of great importance for improving traffic conditions for drivers and pedestrians. Despite the large number of prediction methods, existing limitations still confirm the need to find a systematic solution and its adaptation to specific traffic data. This paper focuses on the relationship between traffic flow states in different urban locations, where these states are identified as clusters of traffic counts. Extending the recursive Bayesian mixture estimation theory to the Poisson mixtures, the paper uses the mixture pointers to construct the traffic state prediction model. Using the predictive model, the cluster at the target urban location is predicted based on the traffic counts measured in real time at the explanatory urban location. The main contributions of this study are: (i) recursive identification and prediction of the traffic state at each time instant, (ii) straightforward Poisson mixture initialization, and (iii) systematic theoretical background of the prediction approach. Results of testing the prediction algorithm on real traffic counts are presented. [ABSTRACT FROM AUTHOR]

This paper contributes to the literature on capital flight by investigating the relationship between kidnapping rate and capital flight in developing countries. Numerous empirical studies exist on the determinants of capital flight but, surprisingly, none of them have investigated the empirical link between kidnapping and capital flight. To fill this existing void in the literature, this paper utilised a sample of 67 developing countries for the period 2003–2017. Estimates of the GMM technique show that kidnapping rate has a positive and significant impact on capital flight. However, estimations of the marginal differences show that this significant effect remained consistent only in the sample of 'fragile' developing countries. The results remained consistent to alternative measures of capital flight. [ABSTRACT FROM AUTHOR]

The worldwide energy revolution has accelerated the utilization of demand-side manageable energy systems such as wind turbines, photovoltaic panels, electric vehicles, and energy storage systems in order to deal with the growing energy crisis and greenhouse emissions. The control system of renewable energy units and energy storage systems has a high effect on their performance and absolutely on the efficiency of the total power network. Classical controllers are based on integer-order differentiation and integration, while the fractional-order controller has tremendous potential to change the order for better modeling and controlling the system. This paper presents a comprehensive review of the energy system of renewable energy units and energy storage devices. Various papers are evaluated, and their methods and results are presented. Moreover, the mathematical fundamentals of the fractional-order method are mentioned, and the various studies are categorized based on different parameters. Various definitions for fractional-order calculus are also explained using their mathematical formula. Different studies and numerical evaluations present appropriate efficiency and accuracy of the fractional-order techniques for estimating, controlling, and improving the performance of energy systems in various operational conditions so that the average error of the fractional-order methods is considerably lower than other ones. [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]

Copyright of Journal of University of Anbar for Pure Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The goal of this work is to provide an understanding of estimation technology for both linear and nonlinear dynamical systems. A critical analysis of both the Kalman filter (KF) and the extended Kalman filter (EKF) will be provided, along with examples to illustrate some important issues related to filtering convergence due to system modeling. A conceptual explanation of the topic with illustrative examples provided in the paper can help the readers capture the essential principles and avoid making mistakes while implementing the algorithms. Adding fictitious process noise to the system model assumed by the filter designers for convergence assurance is being investigated. A comparison of estimation accuracy with linear and nonlinear measurements is made. Parameter identification by the state estimation method through the augmentation of the state vector is also discussed. The intended readers of this article may include researchers, working engineers, or engineering students. This article can serve as a better understanding of the topic as well as a further connection to probability, stochastic process, and system theory. The lesson learned enables the readers to interpret the theory and algorithms appropriately and precisely implement the computer codes that nicely match the estimation algorithms related to the mathematical equations. This is especially helpful for those readers with less experience or background in optimal estimation theory, as it provides a solid foundation for further study on the theory and applications of the topic. [ABSTRACT FROM AUTHOR]

This comprehensive study reviews the latest and most popular artificial intelligence (AI) techniques utilised for estimating pan evaporation (Ep), an essential parameter for water resource management and irrigation planning. Through an extensive evaluation of 76 papers published between 2006 and 2022, this study analyses the input data categories, time steps, properties, and capabilities of different AI models used for estimating Ep across various regions. The reviewed papers offer partial and comprehensive observations, providing valuable insights for researchers looking to model Ep in similar studies. Furthermore, this study proposes innovative theories and approaches to enhance the efficacy of Ep modelling in the relevant analysis domain. While hybrid AI techniques have gained popularity due to their perceived superiority over standalone deep learning and machine learning approaches, they often pose significant operational and computational challenges for Ep forecasting. As such, the study strongly recommends the use of transformer neural networks for Ep estimation, given their unique architecture and promising performance across various fields. Overall, this study presents a comprehensive and up-to-date overview of the latest AI-based techniques for estimating Ep and highlights the most promising approaches for future research. [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]

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]

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

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

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

This 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]

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]

This paper investigates the estimation of the two unknown parameters and the reliability function of the weighted exponential distribution. It explores Bayesian and non-Bayesian (maximum likelihood) estimation methods when the information available is in the form of fuzzy data. The Newton-Raphson algorithm is used to obtain the maximum likelihood estimates. In Bayes estimation, the symmetric squared error loss function is used. This loss function links equal importance to the losses due to overestimating and underestimating equal magnitude. Lindley approximation procedure in Bayesian estimation theory is used to evaluate the ratio of integrals. A comparative analysis using simulation is carried out to evaluate the performance of the obtained parameters estimators using mean squared error criteria and the performance of the obtained reliability estimators using integrated mean squared error criteria. The simulation results demonstrate that, for different sample sizes, the performance of Bayes estimates surpasses the maximum likelihood, and that all estimators perform consistently. [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 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]

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]

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]

Diagnosing lung diseases accurately and promptly is essential for effectively managing this significant public health challenge on a global scale. This paper introduces a new framework called Modified Segnet-based Lung Disease Segmentation and Severity Classification (MSLDSSC). The MSLDSSC model comprises four phases: "preprocessing, segmentation, feature extraction, and classification." Initially, the input image undergoes preprocessing using an improved Wiener filter technique. This technique estimates the power spectral density of the noisy and original images and computes the SNR assisted by PSNR to evaluate image quality. Next, the preprocessed image undergoes Segmentation to identify and separate the RoI from the background objects in the lung image. We employ a Modified Segnet mechanism that utilizes a proposed hard tanh-Softplus activation function for effective Segmentation. Following Segmentation, features such as MLDN, entropy with MRELBP, shape features, and deep features are extracted. Following the feature extraction phase, the retrieved feature set is input into a hybrid severity classification model. This hybrid model comprises two classifiers: SDPA-Squeezenet and DCNN. These classifiers train on the retrieved feature set and effectively classify the severity level of lung diseases. [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]