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This article investigates the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning. A key aspect of the research is the development of accurate moisture content prediction models. The study compares the performance of various machine learning models, and XGBoost emerges as the most promising model, demonstrating superior accuracy in predicting moisture content based on factors like soil properties, cement content, and ultrasonic pulse velocity (UPV). The study employs SHAP (SHapley Additive exPlanations) to understand how these features influence the model's predictions. UPV is the most significant factor affecting predicted moisture content, followed by cement content and soil properties like uniformity coefficient. Also, the study explores the possibility of using a reduced set of features for moisture content prediction. They demonstrate that a combination of UPV, cement content, and uniformity coefficient can achieve good accuracy, highlighting the potential for practical applications where obtaining all data points might be challenging. [ABSTRACT FROM AUTHOR]

Class imbalance is one of many problems of customer churn datasets. One of the common problems is class overlap, where the data have a similar instance between classes. The prediction task of customer churn becomes more challenging when there is class overlap in the data training. In this research, we suggested a hybrid method based on tabular GANs, called CTGAN-ENN, to address class overlap and imbalanced data in datasets of customers that churn. We used five different customer churn datasets from an open platform. CTGAN is a tabular GAN-based oversampling to address class imbalance but has a class overlap problem. We combined CTGAN with the ENN under-sampling technique to overcome the class overlap. CTGAN-ENN reduced the number of class overlaps by each feature in all datasets. We investigated how effective CTGAN-ENN is in each machine learning technique. Based on our experiments, CTGAN-ENN achieved satisfactory results in KNN, GBM, XGB and LGB machine learning performance for customer churn predictions. We compared CTGAN-ENN with common over-sampling and hybrid sampling methods, and CTGAN-ENN achieved outperform results compared with other sampling methods and algorithm-level methods with cost-sensitive learning in several machine learning algorithms. We provide a time consumption algorithm between CTGAN and CTGAN-ENN. CTGAN-ENN achieved less time consumption than CTGAN. Our research work provides a new framework to handle customer churn prediction problems with several types of imbalanced datasets and can be useful in real-world data from customer churn prediction. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are the most widely used as energy storage devices in electric mobility applications. However, due to complex electrochemical processes of battery degradation, it is challenging to predict accurately the battery end-of-life (EOL) to ensure their reliability, safety, and extended usage. In this context, the introduction of machine learning techniques can provide relevant solutions based on data collection and analysis. Indeed, we compared in this study the prediction performance of numerous machine learning approaches that predict if the battery EOL bypasses a predefined threshold. Based on the variation of different indicators during the first several hundred cycles, such as charge and discharge capacity, internal resistance, and energy efficiency, extensive numerical tests have been executed and compared in terms of accuracy score, precision score, recall score, etc. All the studied machine learning approaches are trained and validated using an open-access database of 124 commercial lithium iron phosphate/graphite cells cycled under different fast-charging conditions. As a result, the classification prediction performance score reached up to 98.74% depending on the percentage of data and cycles used for training and validation as well as the predefined EOL threshold. The comparative results can be used to improve the existing health-aware energy management strategy by taking the state-of-health (SOH) of batteries into consideration. Overall, the presented research findings are relevant to battery system reliability and safety engineering. [ABSTRACT FROM AUTHOR]

This study aims to optimize the performance of CrN coatings deposited on WC cutting tools for machining Ti6Al4V alloy, where the formation of built-up edge (BUE) is a prevalent and critical issue. In-house CrN coatings were developed using the PVD (Physical Vapor Deposition) process, with variations in deposition parameters including nitrogen gas pressure, bias voltage, and coating thickness. A comprehensive experimental approach encompassing deposition, characterization, and machining performance evaluation was employed to identify the optimal deposition conditions. The results indicated that CrN coatings deposited at a nitrogen gas pressure of 4 Pa, a bias voltage of −50 V, and a thickness of 1.81 µm exhibited superior performance, significantly reducing BUE formation and tool wear. These optimized coatings demonstrated enhanced properties, such as a higher elastic modulus and a lower coefficient of friction, which contributed to improved tool life and machining performance. Comparative studies with commercial CrN coatings revealed that the in-house developed coatings outperformed the commercial variants by approximately 65% in tool life, owing to their superior mechanical properties and reduced friction. This research highlights the potential of tailored CrN coatings for advanced machining applications and emphasizes the importance of optimizing deposition parameters to achieve high-performance tool coatings. [ABSTRACT FROM AUTHOR]

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications. [ABSTRACT FROM AUTHOR]

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the scheduled timeline. In this paper, the Naive Bayes algorithm was used to establish a novel rig state. Training data, consisting of a large set of rules, was generated based on drilling experts' recommendations. This dataset was then employed to build a Naive Bayes classifier capable of emulating the cognitive processes of skilled drilling engineers and accurately recognizing the actual drilling operation from surface data. The developed model was used to process high-frequency drilling data collected from three wells, aiming to derive the Key Performance Indicators (KPIs) related to each drilling crew's efficiency and quantify the ILT during the drilling connections. The obtained results revealed that the established rig state excelled in automatically recognizing drilling operations, achieving a high success rate of 99.747%. The findings of this study offer valuable insights for drillers and rig supervisors, enabling real-time visual assessment of efficiency and prompt intervention to reduce ILT. [ABSTRACT FROM AUTHOR]

Aluminium alloys are widely employed across numerous thanks to their superior combination of strength, lightweight characteristics and corrosion resistance. In recent years, Friction stir processing (FSP) has come up as a promising technique for machining aluminium alloys, enabling greater control over microstructural evolution and mechanical properties. This paper provides an in-depth exploration the impact of process parameters on the microstructure and mechanical properties of aluminium alloys processed by FSP. The study focuses on the systematic investigation of the influence of the principle process parameters, including rotational speed, machining speed and applied pressure, on the microstructure and resulting mechanical performance of FSP machined aluminium alloys. Furthermore, the mechanical properties of FSP treated aluminium alloys are measured by a comprehensive set of tests, including tensile tests, hardness measurements and fracture tests. The relationship between process parameters and the mechanical behaviour of the alloys is analysed in detail to identify the optimum conditions for achieving the desired properties. Findings show that process parameters plays a significant role in defining the microstructure and mechanical properties of alloys. [ABSTRACT FROM AUTHOR]

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower's distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower's distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings. [ABSTRACT FROM AUTHOR]

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally. [ABSTRACT FROM AUTHOR]

The common machining technologies for difficult-to-machine materials do not remarkably ensure acceptable efficiency and precision in bulk materials cutting. High-energy abrasive water injection jet (AWIJ) treatment can cut diverse materials, even multi-layer composites characterized by divergent properties, accurately cutting complex profiles and carrying them out in special circumstances, such as underwater locations or explosion hazard areas. This work reports research on the AWIJ machining quality performance of X22CrMoV12-1 high-alloy steel. The response surface method (RSM) was utilized in modeling. The most influencing process control parameters on cut kerf surface roughness—abrasive flow rate, pressure, and traverse speed—were tested. The result is a mathematical model of the process in the form of a three-variable polynomial. The key control parameter affecting the cut slot roughness turned out to be the traverse speed. In contrast, pressure has a less significant effect, and the abrasive mass flow rate has the slightest impact on the cut slot roughness. Under the optimal conditions determined as a result of the tests, the roughness of the intersection surface Sq does not exceed 2.3 μm. Based on the ANOVA, we confirmed that the model fits over 96% appropriately with the research outcomes. This method reduces the computations and sharply determines the optimum set of control parameters. [ABSTRACT FROM AUTHOR]

Very low earth orbit (VLEO) spacecraft have become an attractive proposition with obvious advantages in various missions, including communication and ground observation. Higher requirements for precise orbit decay prediction (PODP) technology are requested, which provides accurate state references for orbit maintenance and necessary database for space situational awareness. The effectiveness of the traditional orbital prediction method for PODP is limited by inaccurate estimation of the spacecraft's physical parameters and space environments. Generalization performance of machine learning techniques (MLTs) is blocked by the universal challenge known as catastrophic forgetting, resulting in limited improvement on PODP. In this study, a method of expandable orbit decay propagator (EODP) for spacecraft PODP in VLEO, based on model‐agnostic MLTs, is proposed. The plasticity of the proposed model is discussed, which originates from the uncertainty of neural network (NN) parameters. The proposed method overcomes the negative effects of uncertain physical parameters and complex space environments. The model achieves at least a 70% improvement in accuracy compared to the high‐precision orbital propagator (HPOP) and presents a novel approach for the future implementation of machine learning–based methods in the field of orbit prediction. [ABSTRACT FROM AUTHOR]

Probability-arisen models play a considerable role in preparing a crucial stage for decision-making concerning reliability, engineering, and more closely related scenarios. Bearing in mind the consequential roles of probability-arisen models, we introduce and implement a new probabilistic model that has arisen by using the sine function, namely, the sine very flexible Weibull (SVF-Weibull) distribution. The proposed SVF-Weibull distribution is a result of a combination of the very flexible Weibull distribution with the sine-based strategy. For the SVF-Weibull distribution, point estimates are obtained. The assessment of the point estimates of the SVF-Weibull distribution is done via a simulation study. Finally, the consequential role of the SVF-Weibull distribution, illustrated by considering reliability and music engineering data sets. Furthermore, we implement some machine learning tools for predicting the reliability and music engineering data sets. The performances of the machine learning tools are assessed across many hidden variables. Our findings suggest that the artificial neural network method is more optimal than other methods for predicting the reliability and music engineering data sets. [ABSTRACT FROM AUTHOR]

This study compares the calculation of snow water equivalent (SWE) using machine learning algorithms with the conventional degree-day method. The study uses machine learning techniques such as LASSO, Random Forest, Support Vector Machines, and CatBoost. It proposes an innovative use of feature engineering (FE) to improve the accuracy and robustness of SWE predictions by machine learning intended for interpolation, extrapolation, or imputation of missing data. The performance of machine learning approaches is evaluated against the traditional degree-day method for predicting SWE. The study emphasizes and demonstrates gains when modeling is enhanced by transforming basic, raw data through feature engineering. The results, verified in a case study from the mountainous region of Slovakia, suggest that machine learning, particularly CatBoost with feature engineering, shows better results in SWE estimation in comparison with the degree-day method, although the authors present a refined application of the degree-day method by utilizing genetic algorithms. Nevertheless, the study finds that the degree-day method achieved accuracy with a Nash–Sutcliffe coefficient of efficiency NSE = 0.59, while the CatBoost technique enhanced with the proposed FE achieved an accuracy NSE = 0.86. The results of this research contribute to refining snow hydrology modeling and optimizing SWE prediction for improved decision-making in snow-dominated regions. [ABSTRACT FROM AUTHOR]

Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stage. Hence, clinical records may play a vital role to diagnose this disease at the correct stage for suitable medication to cure it. To detect lung cancer an accurate prediction method is needed which is significantly reliable. In the digital clinical record era with advancement in computing algorithms including machine learning techniques opens an opportunity to ease the process. Various machine learning algorithms may be applied over realistic clinical data but the predictive power is yet to be comprehended for accurate results. This paper envisages to compare twelve potential machine learning algorithms over clinical data with eleven symptoms of lung cancer along with two major habits of patients to predict a positive case accurately. The result has been found based on classification and heat map correlation. K-Nearest Neighbor Model and Bernoulli Naive Bayes Model are found most significant methods for early lung cancer prediction. [ABSTRACT FROM AUTHOR]

Optimization techniques play a crucial role in improving the performance of machine learning applications. However, traditional techniques may not be suitable for addressing complex problems that involve numerous parameters requiring adaptation. This paper presents a novel variant of the gradient-based optimizer (GBO) and investigates the effectiveness of integrating the harmony search (HS) algorithm to enhance the GBO algorithm. The HS algorithm offers several advantages, including fast convergence and fewer adjustable parameters. The proposed method, called GBOHS, is evaluated through three experiments. The first experiment focuses on solving global optimization problems, while the second experiment assesses its ability to select the most relevant features using sixteen benchmark feature selection datasets. The final experiment applies the GBOHS method to solve five real engineering problems. The results are compared against those of well-known optimization algorithms using various performance measures, including fitness function value and classification accuracy. The findings demonstrate that the proposed GBOHS method achieves high accuracy and consistently outperforms the compared methods across all the experiments, demonstrating its promising effectiveness. [ABSTRACT FROM AUTHOR]

This study explores the performance of gradient boosting methods in bankruptcy prediction for a highly imbalanced dataset. We developed different heterogenous ensemble models based on three popular gradient boosting methods—XGBoost, LightGBM, and CatBoost. Our ensemble models were optimized using the cross‐validation method and the results of the hold‐out test sets showed that the optimized ensemble models not only outperform their base learners, but also improve the state‐of‐the‐art benchmark results on the same dataset. Interestingly, we observed that the data oversampling technique that is commonly used to address the class imbalance issue had an adverse impact on our ensemble models' performance. This indicates that our models are robust to the imbalanced dataset problem that typically degrades the classification performance of machine learning models. [ABSTRACT FROM AUTHOR]

Coatings play a crucial role in the functionality of vacuum chambers in particle accelerators, serving a dual goal by efficiently facilitating pumping and mitigating electron cloud effects. However, their impact on the surface impedance of the chamber walls raises concerns, potentially affecting the machine performance and imposing limitations on achievable energies and currents. Therefore, an electromagnetic characterization is essential for a comprehensive study of accelerator structures, particularly in the context of the next-generation machines where the demand for extremely short particle bunches accentuates the importance of evaluating material responses in the very-high-frequency region. We present a technique for probing the sub-THz response of coating materials by measuring pulsed signals passing through a specifically designed waveguide, in which is placed a slab with the deposited material under test. The proposed methodology allows for a comprehensive exploration of the electromagnetic properties of the most used technical surfaces (substrate plus coatings) in accelerators under realistic conditions, providing valuable insights into their behavior in the sub-THz frequency range. The experimental data of three different Non-Evaporable Getter coating samples, prepared on a copper substrate at the CERN deposition facilities under different sputtering conditions, are discussed. The findings contribute to a deeper understanding of the complex interactions between coatings and accelerator structures, with the aim of optimizing performance and efficiency in the evolving landscape of particle acceleration technologies. The limitations and advantages of the technique are also reported. [ABSTRACT FROM AUTHOR]

Crop models are instrumental in simulating resource utilization in agriculture, yet their complexity necessitates extensive calibration, which can impact the accuracy of yield predictions. Machine learning shows promise for enhancing yield estimations but relies on vast amounts of training data. This study aims to improve the pakchoi yield prediction accuracy of simulation models. We developed a stacking ensemble learning model that integrates three base models—EU-Rotate_N, Random Forest Regression and Support Vector Regression—with a Multi-layer Perceptron as the meta-model for the pakchoi dry matter yield prediction. To enhance the training dataset and bolster machine learning performance, we employed the EU-Rotate_N model to simulate daily dry matter yields for unsampled data. The test results revealed that the stacking model outperformed each base model. The stacking model achieved an R² value of 0.834, which was approximately 0.1 higher than that of the EU-Rotate_N model. The RMSE and MAE were 0.283 t/ha and 0.196 t/ha, respectively, both approximately 0.6 t/ha lower than those of the EU-Rotate_N model. The performance of the stacking model, developed with the expanded dataset, showed a significant improvement over the model based on the original dataset. [ABSTRACT FROM AUTHOR]

Beans are the main direct source of protein consumed by humans in the world and their productivity is directly linked to nitrogen. The short crop cycle imposes the need for fast methodologies for N quantification. In this work, we evaluated the performance of four machine learning algorithms in nitrogen estimation using NIR spectroscopy, comparing predictions between complete spectral data and only intervals obtained with the variable importance in projection (VIP). Doses of 0, 50, 100, and 150 kg ha−1 of N were applied and leaf reflectance was collected. Weka software was used to test the algorithms. The selection of the most effective spectral zones was made with the variable importance in projection (VIP). The intervals of 700–740 nm and 983–995 nm were considered the most important for the study of nitrogen. More efficient predictions were verified for RF and KNN models (R2 = 0.89, RMSE = 2.23 g kg−1; and R2 = 0.80, RMSE = 2.89 g kg−1, respectively) when only the most important spectral regions were included. The efficiency of nitrogen prediction based on NIR reflectance combined with machine learning was verified, which can serve as an important tool in precision agriculture. [ABSTRACT FROM AUTHOR]

The Ti-5Al-5V-5Mo-3Cr (Ti-5553) alloy is a relatively novel difficult-to-cut material with limited machinability and tool life analysis available in the literature, and hence requires further investigation. This study focuses on the machining and tribological performance of Ti-5553 under high-speed finish turning (150 m/min, 175 m/min, and 200 m/min) via novel mono/bi-layered PVD-coated WC tools. A base AlTiN coating is used as the reference monolayer coating, with AlCrN, diamond-like ta-C, and TiAlSiN coatings each deposited on top of a base AlTiN coating, totaling four separate coated tools (one monolayer and three bi-layer). Tool life, cutting forces, workpiece surface quality, and tribological chip analysis are among the subjects of investigation in this study. Overall, the AlTiN/AlCrN coated tool outperformed all the other combinations: an improvement of ~19% in terms of tool life in reference to the base AlTiN coating when averaging across the three speeds; lowest surface roughness values: ~0.30, 0.33, and 0.64 µm; as well as the lowest chip back surface roughness values: ~0.80, 0.68, and 0.81 µm at 150, 175, and 200 m/min, respectively. These results indicate that the AlTiN/AlCrN coating is an excellent candidate for industrial applications involving high-speed machining of Ti-5553. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel optimization algorithm called the OX optimizer, inspired by oxen animals, which are characterized by their great strength. The OX optimizer is designed to address the challenges posed by complex, high-dimensional optimization problems. The design of the OX optimizer embodies a fundamental symmetry between global and local search processes. This symmetry ensures a balanced and effective exploration of the solution space, highlighting the algorithm's innovative contribution to the field of optimization. The OX optimizer has been evaluated on CEC2022 and CEC2017 IEEE competition benchmark functions. The results demonstrate the OX optimizer's superior performance in terms of convergence speed and solution quality compared to existing state-of-the-art algorithms. The algorithm's robustness and adaptability to various problem landscapes highlight its potential as a powerful tool for solving diverse optimization tasks. Detailed analysis of convergence curves, search history distributions, and sensitivity heatmaps further support these findings. Furthermore, the OX optimizer has been applied to optimize support vector machines (SVMs), emphasizing parameter selection and feature optimization. We tested it on the NSL-KDD dataset to evaluate its efficacy in an intrusion detection system. The results demonstrate that the OX optimizer significantly enhances SVM performance, facilitating effective exploration of the parameter space. [ABSTRACT FROM AUTHOR]

The circumferential non-uniform tip clearance (CNTC) due to casing out-ofroundness adversely affects the turbine aerodynamic performance due to machining and assembly errors, thermal deformation, and improper active clearance control (ACC), etc. Moreover, the asymmetric computational domain caused by casing out-of-roundness presents difficulties for conventional numerical techniques that consider rotational periodicity. Since previous traditional methods using split computational domains have the disadvantages of high interpolation error and high time cost, an efficient fast-moving mesh (FMM) method based on an algebraic approach is proposed in this paper. This method is first validated by using a single-stage turbine with elliptical casing. The results show that the FMM has the advantages of high accuracy, high efficiency, and easy operation, which helps to solve the CNTC problem quickly in scientific research or engineering applications. Then, the effects of CNTC induced by the elliptical casing on the flow field and aerodynamic performance are investigated by using an in-house code that integrates the FMM method. Finally, the effect of stator row interference on the aerodynamic performance in the turbine stage with an elliptical casing is demonstrated. The results show that different types of elliptical casings have a significant effect on the aerodynamic performance. However, the variation law is not consistent (decreasing by 0.538% or increasing by 0.212%). Importantly, the novel finding of this paper is that this discrepancy is jointly determined by the interaction of multiple secondary flows (passage vortex, scraping vortex, etc.) at different spans, not just related to the variation of the tip leakage vortex (TLV) with tip size. Furthermore, this study is the first to indicate that the stator row interference can mitigate the extent of performance degradation due to elliptical casings by suppressing the development of secondary flows. These results may provide theoretical support for blade tip gap design and can also serve as a reasonable reference for the effective application of ACC in engineering. Finally, low-order harmonic components with high amplitudes are also innovatively found in the rotor row with a CNTC. These components may cause low-engine-order (LEO) resonances that endanger the safe operation of engines. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a novel viscous passive damping solution to mitigate the chatter vibrations issue in the context of turning thin-walled cylindrical shell components for aerospace and other industries. This study involves the use of two different viscous fluids, motor oil and silicone oil, which have viscosities of 102 cSt and 350 cSt, respectively, to fill the in-house developed tube components with the aim of improving machining performance. Fast Fourier Transform (FFT) graphs were studied for chatter analysis, and surface roughness parameters such as average surface roughness (Ra) and mean roughness depth (Rz) were considered for studying the effectiveness of the viscous damping fluids. The results obtained with viscous damping were then compared with an undamped/unfilled tube with the same geometry. The cutting experiments showed that the motor oil reduced the excessive vibrations while silicone oil was able to eliminate them. For the tube with motor oil, the magnitude of the process sound at chatter frequency was reduced by 6.6 times as compared to an unfilled tube, whereas for the tube with silicone oil, the amplitude at chatter frequency was reduced by 14.8 times. Moreover, the surface quality of the tubes with motor oil and silicone oil shows almost equal improvement, indicating the need for future research on the type and amount of viscous fluids for implementing the concept in real cases. [ABSTRACT FROM AUTHOR]

Rolling element bearings are essential components in modern mechanical equipment, providing crucial support for rotating parts. Hybrid ceramic ball bearings, consisting of steel rings and ceramic balls, have gained popularity in high-speed machinery to enhance performance. These bearings offer advantages such as longer fatigue life, improved performance, and higher speeds. Extensive research by scholars has been conducted to promote the wider adoption of hybrid ceramic ball bearings. This paper compiles relevant studies on hybrid ceramic bearings, organizing literature related to their lifetime, arranging literature pertaining to their performance analysis from the perspective of analytical methods, and collating literature on their lubrication techniques from the angle of lubrication methods. This paper covers research on lifetime modeling, fatigue spalling, wear, mechanical and tribological properties, dynamic performance, thermal analysis, temperature considerations, and lubrication techniques of hybrid ceramic ball bearings. The aim is to provide readers and researchers with a comprehensive overview of these innovative bearings. [ABSTRACT FROM AUTHOR]

In this paper, a Dual Winding Flux Modulation Machine (DWFMM) is proposed for variable speed application. The DWFMM is configured by adding windings to the Single Winding Flux Modulation Machine (SWFMM), consisting of a master winding that drives the motor and a slave winding that enables pole changing and performance enhancement. Through pole changing, the DWFMM can operate as two different machines: a Vernier Machine (VM) for varying speeds and torque operations and a Permanent Magnet Synchronous Machine (PMSM). In the VM mode, flux enhancement is applied to improve torque, and in the PMSM mode, Flux Weakening is applied to increase speed. The characteristics of the two different operating modes were analyzed using the Finite Element Method (FEM) to validate the machine's performance. Finally, the DWFMM and SWFMM were designed and compared as variable speed application machines to confirm their suitability and superiority. [ABSTRACT FROM AUTHOR]

In this paper, a fast positioning platform (FPP) is proposed, able to meet simultaneously the requirements of large stroke and high frequency response, developed based on a PZT (piezoelectric actuator) and a quad-parallel flexible mechanism, for application in precision machining. The FPP is driven by a high-stiffness PZT and guided by a flexible hinge-based mechanism with a quad-parallel flexible hinge. The proposed quad-parallel flexible hinge mechanism can provide excellent planar motion capability with high stiffness and good guiding performance, thus guaranteeing outstanding dynamics characteristics. The mechanical model was established, the input and output characteristics of the FPP were analyzed, and the working range (output displacement and frequency) of the FPP was determined. Based on the mechanical model and the input and output characteristics of the FPP, the design method is described for of the proposed FPP, which is capable of achieving a large stroke while responding at a high frequency. The characteristics of the FPP were investigated using finite element analysis (FEA). Experiments were conducted to examine the performance of the FPP; the natural frequency of the FPP was 1315.6 Hz, while the maximum output displacement and the motion resolution of the FPP in a static state were 53.13 μm and 5 nm, respectively. Step response testing showed that under a step magnitude of 50 μm, the stabilization times for the falling and rising edges of the moving platform were 37 ms and 26 ms, respectively. The tracking errors were about ±1.96 μm and ±0.59 μm when the amplitude and frequency of the signal were 50 μm, 50 Hz and 10 μm, 200 Hz, respectively. The FPP showed excellent performance in terms of fast response and output displacement. The cutting test results indicated that compared with the uncontrolled condition, the values of surface roughness under controlled conditions decreased by 23.9% and 12.7% when the cutting depths were 5 μm and 10 μm, respectively. The developed FPP device has excellent precision machining performance. [ABSTRACT FROM AUTHOR]

The purpose of this paper is the design of a potato drip irrigation tape recycling machine that would not require manual assistance or suffer from broken tapes. To clarify the recycling performance of the new potato drip irrigation tape recycling machine under different working parameters, a single-factor performance experiment was conducted. To investigate the reasons for variations in the performance indexes of the machine under different operating parameters, RecurDyn-EDEM coupled simulation experiments were carried out, with the peak height of soil and the maximum bending angle of the drip irrigation tape during the recycling process as the experimental indexes. To determine the optimal working parameter combinations and to clarify the effects of the interaction of various factors on the performance indexes, a response surface experiment was conducted. Following this, an analysis of variance was performed, and a mathematical regression model was established based on the experimental results. The findings revealed that a machine forward speed of 2.4 km/h, a shovel angle of 12°, and a conveyor sprocket speed of 270 r/min resulted in a drip irrigation tape recovery rate of 95.2%, meeting the relevant standards for drip irrigation tape recycling operations. [ABSTRACT FROM AUTHOR]

For manufacturing systems such as hot rolling, where there is no wait in the production process, breaks between adjacent production batches provide "opportunities" for predictive maintenance. With the extensive application of industrial robots, a production machine–robot collaboration mode should be considered in system-level predictive maintenance. The hybrid failure mode of machines and dependencies among machines further elevate the difficulty of developing predictive maintenance schedules. Therefore, a novel system-level predictive maintenance method for the no-wait production machine–robot collaborative maintenance problem (NWPMRCMP) is proposed. The machine-level predictive maintenance optimization model under hybrid failure mode, which consists of degradation and sudden failure, is constructed. Based on this, the system-level maintenance optimization model is developed, which takes into account the economic dependency among machines. The maintenance model with the objective of minimizing the total cost is transformed into a Markov decision process (MDP), and a tailored proximal policy optimization algorithm is developed to solve the resulting MDP. Finally, a case study of a manufacturing system consisting of multiple hot-rolling machines and labeling robots is constructed to demonstrate the effectiveness of the proposed method. The results show that the designed algorithm has good performance and stability. Moreover, the developed strategy maximizes the performance of the machine and thus reduces the total maintenance cost. [ABSTRACT FROM AUTHOR]

Machine learning models' performance in simulating monthly rainfall–runoff in subtropical regions has not been sufficiently investigated. In this study, we evaluate the performance of six widely used machine learning models, including Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), LASSO Regression (LR), Extreme Gradient Boosting (XGB), and the Light Gradient Boosting Machine (LGBM), against a rainfall–runoff model (WAPABA model) in simulating monthly streamflow across three subtropical sub-basins of the Pearl River Basin (PRB). The results indicate that LSTM generally demonstrates superior capability in simulating monthly streamflow than the other five machine learning models. Using the streamflow of the previous month as an input variable improves the performance of all the machine learning models. When compared with the WAPABA model, LSTM demonstrates better performance in two of the three sub-basins. For simulations in wet seasons, LSTM shows slightly better performance than the WAPABA model. Overall, this study confirms the suitability of machine learning methods in rainfall–runoff modeling at the monthly scale in subtropical basins and proposes an effective strategy for improving their performance. [ABSTRACT FROM AUTHOR]

Electrochemical descaling devices have been widely used in the industrial field due to their broad applicability, convenience of operation, and cost-effectiveness. However, there are many factors that affect the descaling performance of electrochemical descaling devices, such as the selection of electrode materials, the shape and layout of the anode and cathode, the voltage and current of electrochemical equipment, the flow rate, temperature, and mineral content. Existing research has primarily focused on the influence of electrode materials and current density on descaling efficiency, while neglecting external factors such as water flow rate and temperature. In order to further explore the internal and external factors affecting the descaling performance of descaling machines, this study constructed an experimental platform for a descaling machine fouling device. Different voltages, currents, water flow rates, and temperatures were studied to assess the descaling efficiency of the descaling machine. The results indicated that under the conditions of a temperature of 30 °C, a flow rate of 0.35 m/s, a voltage of 24 V, and a current of 10 A, the fouling resistance effect of the electrochemical descaling device was optimal. This provides a new perspective for further improving the descaling efficiency of descaling machines and conducting parameter optimization. [ABSTRACT FROM AUTHOR]

Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs' performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties. [ABSTRACT FROM AUTHOR]

The aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine learning (ML) algorithms. In this study, 130 male and 152 female volunteers aged between 6 and 11 years were included. After obtaining demographic information of the participants, skinfold thickness, diameter and circumference measurements, and 20-m sprint performance were determined. The study conducted three distinct experiments to determine the optimal ML technique for predicting outcomes. Initially, the entire feature space was utilized for training the ML models to establish a baseline performance. In the second experiment, only significant features identified through correlation analysis were used for training and testing the models, enhancing the focus on relevant predictors. Lastly, Principal Component Analysis (PCA) was employed to reduce the feature space, aiming to streamline model complexity while retaining data variance. These experiments collectively aimed to evaluate different feature selection and dimensionality reduction techniques, providing insights into the most effective strategies for optimizing predictive performance in the given context. The correlation-based selected features (Age, Height, waist circumference, hip circumference, leg length, thigh length, foot length) has produced a minimum Mean Squared Error (MSE) value of 0.012 for predicting the sprint performance in children. The effective utilization of correlation analysis in the selection of relevant features for our regression model suggests that the features selected exhibit robust linear associations with the target variable and can be relied upon as predictors. [ABSTRACT FROM AUTHOR]

Particle diameters and jet pressure in abrasive water jet (AWJ) are significant jet properties which deserve a better understanding for improving AWJ machining performance. Some influence factors have been verified regarding nozzle wear in abrasive water jet polishing application. A three-dimensional model of a nozzle is established to analyze the influence of internal multi-phase flow field distribution, which is based on Euler-Lagrange methodology. With the increase of jet pressure, the erosion rate decreases; with the increase of the diameter and mass flow rate of the erosion particles, the erosion speed increases as well. When the diameter of the outlet is worn to 1.6 mm, the pressure on the work piece caused by the abrasive water jet increases by more than double compared to the non-worn nozzle; when the diameter of the nozzle outlet is worn to 1.6 mm, the shear force is 2.5 times higher than the shear force when the diameter is 1.0, which means that the jet force is divergent when the diameter is 1.6 mm, and the damage of the work piece is very serious. The obtained results could improve polishing efficiency on the work piece, extend nozzle lifetimes, and guide the future design of AWJ nozzles. [ABSTRACT FROM AUTHOR]

Data missing is a ubiquitous problem in real-world systems that adversely affects the performance of machine learning algorithms. Although many useful imputation methods are available to address this issue, they often fail to consider the information provided by both features and labels. As a result, the performance of these methods might be constrained. Furthermore, feature selection as a data quality improvement technique has been widely used and has demonstrated its efficiency. To overcome the limitation of imputation methods, we propose a novel algorithm that combines data imputation and feature selection to tackle classification problems for mixed data. Based on the mean and standard deviation of quantitative features and the selecting probabilities of unique values of categorical features, our algorithm constructs different imputation models for quantitative and categorical features. Particle swarm optimization is used to optimize the parameters of the imputation models and select feature subsets simultaneously. Additionally, we introduce a legacy learning mechanism to enhance the optimization capability of our method. To evaluate the performance of the proposed method, seven algorithms and twelve datasets are used for comparison. The results show that our algorithm outperforms other algorithms in terms of accuracy and F 1 score and has reasonable time overhead. [ABSTRACT FROM AUTHOR]

Chromophoric dissolved organic matter (CDOM) is a mixture of various types of organic matter and a useful parameter for monitoring complex inland surface waters. Remote sensing has been widely utilized to detect CDOM in various studies; however, in many cases, the dataset is relatively imbalanced in a single region. To address these concerns, data were acquired from hyperspectral images, field reflection spectra, and field monitoring data, and the imbalance problem was solved using a synthetic minority oversampling technique (SMOTE). Using the on-site reflectance ratio of the hyperspectral images, the input variables Rrs (452/497), Rrs (497/580), Rrs (497/618), and Rrs (684/618), which had the highest correlation with the CDOM absorption coefficient aCDOM (355), were extracted. Random forest and light gradient boosting machine algorithms were applied to create a CDOM prediction algorithm via machine learning, and to apply SMOTE, low-concentration and high-concentration datasets of CDOM were distinguished by 5 m−1. The training and testing datasets were distinguished at a 75%:25% ratio at low and high concentrations, and SMOTE was applied to generate synthetic data based on the training dataset, which is a sub-dataset of the original dataset. Datasets using SMOTE resulted in an overall improvement in the algorithmic accuracy of the training and test step. The random forest model was selected as the optimal model for CDOM prediction. In the best-case scenario of the random forest model, the SMOTE algorithm showed superior performance, with testing R2, absolute error (MAE), and root mean square error (RMSE) values of 0.838, 0.566, and 0.777 m−1, respectively, compared to the original algorithm's test values of 0.722, 0.493, and 0.802 m−1. This study is anticipated to resolve imbalance problems using SMOTE when predicting remote sensing-based CDOM. It is expected to produce and implement a machine learning model with improved reliable performance. [ABSTRACT FROM AUTHOR]

In a photoinjector electron source, the initial transverse electron bunch properties are determined by the spatial properties of the laser beam on the photocathode. Spatial shaping of the laser is commonly achieved by relay imaging an illuminated circular mask onto the photocathode. However, the Gibbs phenomenon shows that recreating the sharp edge and discontinuity of the cut profile at the mask on the cathode is not possible with an optical relay of finite aperture. Furthermore, the practical injection of the laser into the photoinjector results in the beam passing through small or asymmetrically positioned apertures. This work uses wavefront propagation to show how the transport apertures cause ripple structures to appear in the transverse laser profile even when effectively the full laser power is transmitted. The impact of these structures on the propagated electron bunch has also been studied with electron bunches of high and low charge density. With high charge density, the ripples in the initial charge distribution rapidly wash‐out through space charge effects. However, for bunches with low charge density, the ripples can persist through the bunch transport. Although statistical properties of the electron bunch in the cases studied are not greatly affected, there is the potential for the distorted electron bunch to negatively impact machine performance. Therefore, these effects should be considered in the design phase of accelerators using photoinjectors. [ABSTRACT FROM AUTHOR]

The effective operation of model-based control strategies in modern energy systems, characterized by significant complexity, is contingent upon highly accurate large-scale models. However, achieving such precision becomes challenging in complex energy systems rife with uncertainties and disturbances. Controlling different parts of the energy system poses a challenge to achieving optimal power system efficiency, particularly when employing model-based control strategies, thereby adding complexity to current systems. This paper proposes a novel model-independent control approach aimed at augmenting transient stability and voltage regulation performance in multi machine energy systems. The approach involves the introduction of an optimized model-free fractional-order-based excitation system stabilizer for synchronous generators in a multi machine energy system. To overcome the limitations associated with complex system model identification, which add degrees of simplification at defined operating conditions and assume the system model remains fixed despite high uncertainty and numerous disturbances, an optimal model-independent fractional-order-based excitation control strategy is introduced. The efficacy of the proposed approach is validated through comparative numerical analyses using the MATLAB/Simulink environment. These simulations were conducted on a two-area, 12-bus multi-machine power system. Simulation results demonstrate that the presented excitation system stabilizer outperforms conventional controllers in terms of transient and small-signal stability. It also suppresses the low-frequency electromechanical oscillations within the multimachine energy system. [ABSTRACT FROM AUTHOR]

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing. [ABSTRACT FROM AUTHOR]

This paper is a comparative study of pole–slot combinations with fractional slot concentrated windings in an outer rotor permanent magnet synchronous generator (ORPMSG) for a hybrid drone system. Fractional slot machines have been studied for automotive applications because of their high performance and simple winding manufacturing, compared with those of integer slot machines. In this study, four pole–slot combinations of ORPMSG with fractional slot concentrated windings were selected for comparison with the performance of the hybrid drone system. Based on the results of a finite element analysis (FEA), the machines were analyzed for cogging torque, back electromagnetic force (BEMF), torque, and electromagnetic loss under the same conditions as the machine specifications. Among the four pole–slot combinations of the ORPMSM, a one pole–slot model of the ORPMSG was selected, considering the performances of the machines. The selected pole–slot model of the ORPMSG was manufactured, and experiments were conducted on the manufactured model to verify the FEA results. Finally, the effectiveness of the comparative study of pole–slot combination with fractional slot concentrated winding in ORPMSM was verified by comparing the FEA and experimental results. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of classification machine learning algorithm performance based on a novel probabilistic confusion matrix concept. The paper develops a theoretical framework which associates the proposed confusion matrix and the resulting performance metrics with the regular confusion matrix. The theoretical results are verified based on a wide variety of real-world classification problems and state-of-the-art machine learning algorithms. Based on the properties of the probabilistic confusion matrix, the paper then highlights the benefits of using the proposed concept both during the training phase and the application phase of a classification machine learning algorithm. [ABSTRACT FROM AUTHOR]

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A simple procedure is established to optimize fundamental design parameters of a high-intensity hadron linac where interparticle Coulomb interaction plays a crucial role. Based on the recently proposed semi-empirical resonance condition, a stability map is constructed which reveals potentially dangerous operating regions in tune space. The map is shown to be consistent with numerical data obtained from more complicated approaches. The effectiveness of the new design scheme is demonstrated through systematic particle-in-cell simulations assuming the most typical structure of an Alvarez-type drift tube linac. The present results suggest that the so-called equipartitioning condition , which has often been taken very seriously in high-intensity linac designs, does not need to be met to guarantee the best machine performance. The basic design concept described here can be applied not only to linacs but also to circular machines. [ABSTRACT FROM AUTHOR]

The rock classification system was initially applied to drill-and-blast tunnels and subsequently adapted for TBM tunnels; however, the majority of these systems primarily focused on rock stability while neglecting considerations of boreability. Compared with conventional tunnels, TBM tunnels are characterized by their rapid tunneling speed and excellent self-stabilization of the rock mass. Therefore, it is imperative to develop a novel rock mass classification system that considers both the tunneling efficiency of TBMs and the required support measures for tunnel construction. This paper introduces a novel rock classification system for TBM tunnels that accurately predicts the construction rate by evaluating the penetration rate and daily utilization, enabling a more precise assessment of daily advancement in tunneling. Firstly, the penetration rate and construction utilization in different rock strata are analyzed based on comprehensive statistics of existing construction data. Consequently, a discriminant matrix for classifying the boreability of rock is derived. Subsequently, employing the Ensemble Classifier method, a regression prediction model for rock boreability classification is established by incorporating input parameters such as thrust, torque, rotational speed, field penetration index, and the uniaxial compressive strength of rock. The validity of the proposed model is verified by comparing predicted machine performance with actual data sets. The proposed method presents a novel approach for predicting the performance of TBM construction. [ABSTRACT FROM AUTHOR]

This study investigates the use of developed machine learning techniques for modeling the performance of AlHayer, Saudi Arabia, wastewater treatment plant (ALWTP). Three physio-chemical characteristics were measured and predicted, including chemical oxygen demand (COD), biological oxygen demand (BOD), and suspended solids (SS), at ALWTP. The pre-evaluation of collected data revealed the effective capabilities of ALWTP for the removal of suspended solids, organic, and nutrient pollutants. To estimate the physio-chemical characteristics of ALWATP, four developed machine learning techniques were evaluated and compared. Logistic regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR) were designed. The evaluation of the proposed models showed RF outperformed other proposed models for estimating COD and SS with accuracy 91 % and 95 % in terms coefficient of determination (R2 ); however, GB was found the best, with accuracy 92 %, for detecting the BOD performance of ALWATP. This indicates ensemble learning models, RF and GB, can be considered a superiority soft solution for estimating physio-chemical characteristics of wastewater treatment plant. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the influence of the shelf ready packaging (SRP) machine performance parameters on the repeatability of generated cardboard boxes' selected geometric features. As part of the research, two batches of packaging were measured. Each batch contained 100 pieces of packaging, manufactured either at a rate of 15 pcs/min or 20 pcs/min, 50 pcs each. Both batches were measured using Creaform's Academia scanner using VXelements 6.2 software. The results were analyzed using GOM Inspect programming. In the first stage, the geometric repeatability of the products was analyzed on the basis of two selected critical features, which were considered to be the internal length and width of the cardboard packaging. These features are responsible for the proper efficiency of the packaging and the possibility of combining cartons during transportation. The results obtained were subjected to statistical analysis. In the second stage of the research, an analysis of surface deviation maps was carried out against a reference element. Our findings allow it to be concluded that increasing the production output of carton packaging (from 15 to 20 pcs/min) did not negatively affect the dimensional accuracy and repeatability of the process. It was also found that the shortening of packaging production time by increasing productivity had a positive effect on the stability of the manufacturing process of hot-glued SRP packaging. [ABSTRACT FROM AUTHOR]

Purpose: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non‐small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strategy based on the risk of recurrence. In this study, we evaluated the performance of both machine learning models using positron emission tomography (PET) and computed tomography (CT) radiomic features for predicting recurrence after SBRT. Methods: Planning CT and PET images of 82 non‐small cell lung cancer patients who performed SBRT at our hospital were used. First, tumors were delineated on each CT and PET of each patient, and 111 unique radiomic features were extracted, respectively. Next, the 10 features were selected using three different feature selection algorithms, respectively. Recurrence prediction models based on the selected features and four different machine learning algorithms were developed, respectively. Finally, we compared the predictive performance of each model for each recurrence pattern using the mean area under the curve (AUC) calculated following the 0.632+ bootstrap method. Results: The highest performance for local recurrence, regional lymph node metastasis, and distant metastasis were observed in models using Support vector machine with PET features (mean AUC = 0.646), Naive Bayes with PET features (mean AUC = 0.611), and Support vector machine with CT features (mean AUC = 0.645), respectively. Conclusions: We comprehensively evaluated the performance of prediction model developed for recurrence following SBRT. The model in this study would provide information to predict the recurrence pattern and assist in making treatment strategies. [ABSTRACT FROM AUTHOR]

In this study, we employed various machine learning models to predict metabolic phenotypes, focusing on thyroid function, using a dataset from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2012. Our analysis utilized laboratory parameters relevant to thyroid function or metabolic dysregulation in addition to demographic features, aiming to uncover potential associations between thyroid function and metabolic phenotypes by various machine learning methods. Multinomial Logistic Regression performed best to identify the relationship between thyroid function and metabolic phenotypes, achieving an area under receiver operating characteristic curve (AUROC) of 0.818, followed closely by Neural Network (AUROC: 0.814). Following the above, the performance of Random Forest, Boosted Trees, and K Nearest Neighbors was inferior to the first two methods (AUROC 0.811, 0.811, and 0.786, respectively). In Random Forest, homeostatic model assessment for insulin resistance, serum uric acid, serum albumin, gamma glutamyl transferase, and triiodothyronine/thyroxine ratio were positioned in the upper ranks of variable importance. These results highlight the potential of machine learning in understanding complex relationships in health data. However, it's important to note that model performance may vary depending on data characteristics and specific requirements. Furthermore, we emphasize the significance of accounting for sampling weights in complex survey data analysis and the potential benefits of incorporating additional variables to enhance model accuracy and insights. Future research can explore advanced methodologies combining machine learning, sample weights, and expanded variable sets to further advance survey data analysis. [ABSTRACT FROM AUTHOR]

Background Objectives Method Results Conclusion Crowdfunding is increasingly favoured by entrepreneurs for online financing. Predicting crowdfunding success can provide valuable guidance for stakeholders. It is a new attempt to evaluate the relative performance of different machine learning algorithms for crowdfunding prediction.This study aims to identify the key factors of crowdfunding, and find the different performance and usage of machine learning algorithms for crowdfunding prediction.We crawled data from MoDian.com, a Chinese crowdfunding platform, and predicted the crowdfunding performance using four machine learning algorithms, which is a new exploration in this area. Most of the existing literature focuses on empirical analysis. This work solves the problem of predicting crowdfunding performance using a dataset with a minimal number of highly contributive features, which has higher accuracy compared to the regression analysis.The experiment results show that feature‐selection‐based machine learning models are effective and beneficial in crowdfunding prediction.Feature selection can significantly improve the prediction performance of the machine learning models. KNN achieved the best prediction results with five features: number of backers, target amount, number of project likes, number of project comments, and sponsor fans. The prediction accuracy was improved by 16%, the precision was improved by 13.23%, the recall was improved by 22.66%, the F‐score was improved by 18.48%, and the AUC was improved by 14.9%. [ABSTRACT FROM AUTHOR]

The classification of imbalanced datasets is a prominent task in text mining and machine learning. The number of samples in each class is not uniformly distributed; one class contains a large number of samples while the other has a small number. Overfitting of the model occurs as a result of imbalanced datasets, resulting in poor performance. In this study, we compare different oversampling techniques like synthetic minority oversampling technique (SMOTE), support vector machine SMOTE (SVM-SMOTE), Border-line SMOTE, K-means SMOTE, and adaptive synthetic (ADASYN) oversampling to address the issue of imbalanced datasets and enhance the performance of machine learning models. Preprocessing significantly enhances the quality of input data by reducing noise, redundant data, and unnecessary data. This enables the machines to identify crucial patterns that facilitate the extraction of significant and pertinent information from the preprocessed data. This study preprocesses the data using various top-level preprocessing steps. Furthermore, two imbalanced Twitter datasets are used to compare the performance of oversampling techniques with six machine learning models including random forest (RF), SVM, K-nearest neighbor (KNN), AdaBoost (ADA), logistic regression (LR), and decision tree (DT). In addition, the bag of words (BoW) and term frequency and inverse document frequency (TF-IDF) features extraction approaches are used to extract features from the tweets. The experiments indicate that SMOTE and ADASYN perform much better than other techniques thus providing higher accuracy. Additionally, overall results show that SVM with 'linear' kernel tends to attain the highest accuracy and recall score of 99.67% and 1.00% on ADASYN oversampled datasets and 99.57% accuracy on SMOTE oversampled dataset with TF-IDF features. The SVM model using 10-fold cross-validation experiments achieved 97.40 mean accuracy with a 0.008 standard deviation. Our approach achieved 2.62% greater accuracy as compared to other current methods. [ABSTRACT FROM AUTHOR]

The dissolved gas analysis of insulating oil in power transformers can provide valuable information about fault diagnosis. Power transformer datasets are often imbalanced, worsening the performance of machine learning-based fault classifiers. A critical step is choosing the proper evaluation metric to select features, models, and oversampling techniques. However, no clear-cut, thorough guidance on that choice is available to date. In this work, we shed light on this subject by introducing new tailored evaluation metrics. Our results and discussions bring fresh insights into which learning setups are more effective for imbalanced datasets. [ABSTRACT FROM AUTHOR]