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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]

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]

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 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]

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]

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 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]

Quantum annealing machines are next-generation computers for solving combinatorial optimization problems. Although physical simulations are one of the most promising applications of quantum annealing machines, a method how to embed the target problem into the machines has not been developed except for certain simple examples. In this study, we focus on a method of representing real numbers using binary variables, or quantum bits. One of the most important problems for conducting physical simulation by quantum annealing machines is how to represent the real number with quantum bits. The variables in physical simulations are often represented by real numbers but real numbers must be represented by a combination of binary variables in quantum annealing, such as quadratic unconstrained binary optimization (QUBO). Conventionally, real numbers have been represented by assigning each digit of their binary number representation to a binary variable. Considering the classical annealing point of view, we noticed that when real numbers are represented in binary numbers, there are numbers that can only be reached by inverting several bits simultaneously under the restriction of not increasing a given Hamiltonian, which makes the optimization very difficult. In this work, we propose three new types of real number representation and compared these representations under the problem of solving linear equations. As a result, we found experimentally that the accuracy of the solution varies significantly depending on how the real numbers are represented. We also found that the most appropriate representation depends on the size and difficulty of the problem to be solved and that these differences show a consistent trend for two annealing solvers. Finally, we explain the reasons for these differences using simple models, the minimum required number of simultaneous bit flips, one-way probabilistic bit-flip energy minimization, and simulation of ideal quantum annealing machine. [ABSTRACT FROM AUTHOR]

Wood fuel from the agroforestry sector is one of the main strategies cited by the EU for reducing energetic dependance on foreign markets. Its sustainability, both economic and environmental, can be improved through the optimization of harvesting and chipping operations. This study was focused on the dynamic and energetic balance of the chipping phase carried out by a chipper operated by the power-take-off (PTO) of a medium-power tractor. Both machines were equipped with sensors for real-time monitoring of fuel consumption, PTO torque and speed, trunk diameter and working time during the comminution of 61 poplar trees grown in a medium rotation coppice system. The data analysis was carried out on the entire dataset (about 29,000 records) without considering their belonging to different trees. By means of proper data ordinations, it has been possible to define all the intervals in which the chipping stopped (e.g., between two trees) and to exclude them from the intervals of actual chipping. This has allowed forcomputation of operative and actual working time, as well as of the basic power required to operate the chipper and the power for actual chipping. Subsequently, the parameter values observed during actual chipping were related to the cutting diameters measured at the same instant. Subsequently, the dataset was divided according to seven diameter classes, and, for each class, the descriptive statistical indices of working time, work productivity, CO2 emissions, energy requirement and fuel consumption were calculated. Eventually, the correlation between the variations in trunk diameter and other parameters was verified both on the whole dataset and based on the class average values. The analysis made it possible to identify the conditions of greatest efficiency for the chipper. More generally, the method could help to increase the accuracy of measurements aimed at characterizing the performance of chippers from the point of view of dynamic energy requirements as well as in relation to different wood species. [ABSTRACT FROM AUTHOR]

Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples. [ABSTRACT FROM AUTHOR]

Mastitis has a substantial impact on the dairy industry across the world, causing dairy producers to suffer losses due to the reduced quality and quantity of produced milk. A further problem, related to this issue, is the excessive use of antibiotics that leads to the development of resistance in different bacterial strains. The growing consumer awareness oriented toward food safety and rational use of antibiotics has promoted the search for new methods of early identification of cows that may be at risk of developing the disease. Subclinical mastitis does not cause any visible changes to the udder or milk, and therefore it is more difficult to detect than clinical mastitis. The collection of large amounts of data related to milk performance of cows allows using machine learning (ML) methods to build models that could be used for classifying cows into healthy and at risk of subclinical mastitis. The data used for the purpose of this study included information from routine milk recording procedures. The dataset consisted of 19,856 records of 2,227 Polish Holstein-Friesian cows from 3 herds. The authors decided to use the approach of building ensemble ML models, in particular bagging, boosting, stacking, and super-learner models, and comparing them for accuracy of identification of disease-affected cows against single ML models based on the support vector machines, logistic regression, Gaussian Naive Bayes, k-nearest neighbors, and decision tree algorithms. The models were trained and evaluated based on the information recorded for herd 1 and using an 80:20 train-test split ratio according to animal ID (to avoid data leakage). The information recorded for herds 2 and 3 was only used to evaluate on unseen data models developed using the herd 1 dataset. Among the single ML models, the support vector machines model was found to be the most accurate in predicting subclinical mastitis at subsequent test day when used both for the training set (mean F 1 -score of 0.760) and the testing sets containing data for herds 1, 2, and 3 (F 1 -score of 0.778, 0.790, and 0.741 respectively). The gradient boosting model was found to be the best performing model among the ensemble ML models (F 1 -score of 0.762, 0.779, 0.791, and 0.723 for the training set and the testing sets, respectively). The super-learner model, featuring the most advanced design and logistic regression in the meta layer, achieved the highest mean F 1 -score of 0.775 during the cross validation; however, it was characterized by a slightly worse prediction accuracy of the testing sets (mean F 1 -score of 0.768, 0.790, and 0.693 for herds 1, 2 and 3 respectively). The study findings confirm the promising role of ensemble ML methods, which were found to be slightly superior with respect to most of the single ML models. [ABSTRACT FROM AUTHOR]

Aims/Introduction: Machine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed the best and whether muscle strength provided any incremental benefit in identifying undiagnosed diabetes in Chinese adults. Methods: This cross‐sectional study enrolled 4,482 eligible participants from eight provinces in China, who were randomly divided into the training dataset (n = 3,586) and the testing dataset (n = 896). Muscle strength was assessed by handgrip strength and the number of chair stands in the 30‐s chair stand test. An oral glucose tolerance test was used to ascertain undiagnosed diabetes. The areas under the curve (AUCs) were calculated accordingly and compared with each other. Results: Of the included participants, 233 had newly diagnosed diabetes. All the four machine learning algorithms, which were developed based on nonlaboratory parameters, showed acceptable discriminative ability in identifying undiagnosed diabetes (all AUCs >0.70), with the ANN approach performing the best (AUC 0.806). Adding handgrip strength or the 30‐s chair stand test to this approach did not increase the AUC further (P = 0.39 and 0.26, respectively). Furthermore, compared with the New Chinese Diabetes Risk Score, the ANN approach showed a larger AUC in identifying undiagnosed diabetes (Pcomparison < 0.01), regardless of the addition of handgrip strength or the 30‐s chair stand test. Conclusions: The ANN approach performed the best in identifying undiagnosed diabetes in Chinese adults; however, the addition of muscle strength might not improve its efficacy. [ABSTRACT FROM AUTHOR]

A novel analytical model based on the generalized ubiquitiformal Sierpinski carpet is proposed which can more accurately obtain the normal contact stiffness of the grinding joint surface. Firstly, the profile and the distribution of asperities on the grinding surface are characterized. Then, based on the generalized ubiquitiformal Sierpinski carpet, the contact characterization of the grinding joint surface is realized. Secondly, a contact mechanics analysis of the asperities on the grinding surface is carried out. The analytical expressions for contact stiffness in various deformation stages are derived, culminating in the establishment of a comprehensive analytical model for the grinding joint surface. Subsequently, a comparative analysis is conducted between the outcomes of the presented model, the KE model, and experimental data. The findings reveal that, under identical contact pressure conditions, the results obtained from the presented model exhibit a closer alignment with experimental observations compared to the KE model. With an increase in contact pressure, the relative error of the presented model shows a trend of first increasing and then decreasing, while the KE model has a trend of increasing. For the relative error values of the four surfaces under different contact pressures, the maximum relative error of the presented model is 5.44%, while the KE model is 22.99%. The presented model can lay a solid theoretical foundation for the optimization design of high-precision machine tools and provide a scientific theoretical basis for the performance analysis of machine tool systems. [ABSTRACT FROM AUTHOR]

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms. [ABSTRACT FROM AUTHOR]

This research aimed to optimize the configuration of an indented cylinder rice separator using response surface methodology (RSM). The study employed the Box-Behnken design (BBD) with three center points to investigate the impact of sieve slope, trough angle, and sorting duration on efficiency and loss. The results demonstrated that all three factors significantly influenced the performance of the machine. By increasing the sieve slope and sorting duration while adjusting the trough angle closely to zero, the loss percentage decreased significantly, leading to improved sorting efficiency. A quadratic model was employed to determine the optimal conditions for the studied factors. The analysis revealed that a 2-degree sieve slope, a -0.1 scale of the trough angle, and a sorting duration of 120 seconds achieved a minimum loss of 0.42% and a maximum efficiency of 97.29%. Validation experiments confirmed that the actual performance closely matched the predicted values. This optimized condition provides practical guidelines for implementing the studied factors in a continuous type of indented cylinder rice separator. [ABSTRACT FROM AUTHOR]

Separation flow in a curved duct is a common phenomenon in engineering applications, and it highly contributes to the performance of fluid machinery. Accurate prediction of curved duct flows using the computational fluid dynamics method remains a challenge due to the limitations of turbulence modeling. Hence, the high-fidelity method of the delayed detached eddy simulation (DDES) approach is employed to simulate the U-duct flow with a Reynolds number of 105. The DDES results are compared with experimental data from the study by Monson et al. (1990) and analyzed in detail. The Q-criterion is defined to analyze the vortex structures and study the mechanism in the flow separation region. Discussions are made on turbulence characteristics, including turbulence energy spectra, helicity density, and turbulence anisotropy in the U-duct flow. Results indicate that the regions near the wall and within flow separation are highly anisotropic. The turbulence near the wall region is in a two-dimensional state, and the turbulence within the flow separation region is in a "rod-like" state. [ABSTRACT FROM AUTHOR]

The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an application capable of generating the conductor distribution for different machine configurations (single or poly-phase, single or double layer, integral or fractional slots, full or shortened pitch, with the presence of empty slots, etc.) the curves of magnetomotive force and the end-winding leakage inductance associated with the winding are computed. Taking as an optimal winding the one that presents, simultaneously, less harmonic distortion of the magnetomotive force and less leakage inductance, optimization by multi-objective particle swarm was used to obtain the optimal electrical machine configuration and the results are presented. [ABSTRACT FROM AUTHOR]

Interior permanent magnet synchronous machines (IPMSMs) driven with a square-wave control (i.e., six-step, block, or 120° control), known commonly as brushless direct current (BLDC) drives, are used widely due to their high power density and control simplicity. The advance firing (AF) angle is employed to achieve improved operation characteristics of the drive. The AF angle is, in general, applied to compensate for the commutation effects. In the case of an IPMSM, the AF angle can also be adjusted to exploit reluctance torque. In this paper, a detailed study was performed to understand its effect on the drive's performance in regard to reluctance torque. Furthermore, a multi-objective optimization of the machine's cross-section using neural network models was conducted to enhance performance at a constant AF angle. The reference and improved machine designs were evaluated in a system-level simulation, where the impact was considered of the commutation of currents. A significant improvement in the machine performance was achieved after optimizing the geometry and implementing a fixed AF angle of 10°. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to modeling the velocity-time curve in 100m sprinting by integrating machine learning algorithms. It critically addresses the limitations of traditional speed models, which often require extensive and intricate data collection, by proposing a more accessible and accurate method using fewer variables. The research utilized data from various international track events from 1987 to 2019. Two machine learning models, Random Forest (RF) and Neural Network (NN), were employed to predict the velocity-time curve, focusing on the acceleration phase of the sprint. The models were evaluated against the traditional exponential speed model using Mean Squared Error (MSE), with the NN model demonstrating superior performance. Additionally, the study explored the correlation between maximum velocity, the time of maximum velocity occurrence, the duration of the maximum speed phase, and the overall 100m sprint time. The findings indicate a strong negative correlation between maximum velocity and final time, offering new insights into the dynamics of sprinting performance. This research contributes significantly to the field of sports science, particularly in optimizing training and performance analysis in sprinting. [ABSTRACT FROM AUTHOR]

The present study aims to extend the life of tungsten carbide (WC) cutting tools utilized in face milling operations of Ti-6Al-4V by applying hard protective coatings developed using the cathodic-arc physical vapor deposition (CAPVD) technique. Box-Behnken design with 15 experimental runs and response surface methodology were used to mathematically model the relationships between input parameters and output responses to determine the optimal cutting parameters resulting in maximum material removal rate and minimum tool wear. Additionally, the performance of uncoated and coated carbide tools was assessed under dry and wet conditions to identify the best machining conditions. Intriguingly, the tool life of an uncoated WC tool in dry conditions was up to threefold more than that in wet conditions. The tool life of the coated tools was 33% to 166% longer than that of their uncoated counterparts. An analysis of their performance revealed an inverse relationship between the amount of Al in the coating and the tool life. The results from sliding wear tests suggest that Al-deficient coatings have a low coefficient of friction. This study sheds light on the effectiveness of CAPVD-coated carbide tools for improving the tool life during face milling of Ti-6Al-4V. [ABSTRACT FROM AUTHOR]

Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. In this study, we attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI) and a machine learning model. Videos of the right shoulder of 10 healthy volunteers (10 males, mean age 37.7 years, mean height 168.3 cm, mean weight 72.7 kg, mean BMI 25.6) were recorded and processed into 10,608 images. Parameters were created using the coordinates measured from the posture estimation AI, and these were used to train the machine learning model. The measured values from the smartphone's angle device were used as the true values to create a machine learning model. When measuring the parameters at each angle, we compared the performance of the machine learning model using both linear regression and Light GBM. When the pose estimation AI was trained using linear regression, a correlation coefficient of 0.971 was achieved, with a mean absolute error (MAE) of 5.778. When trained with Light GBM, the correlation coefficient was 0.999 and the MAE was 0.945. This method enables the estimation of internal and external rotation angles from a direct-facing position. This approach is considered to be valuable for analyzing motor movements during sports and rehabilitation. [ABSTRACT FROM AUTHOR]

Five‐phase permanent magnet synchronous machine (PMSM) can operate under open‐circuit faults with the appropriate current regulations. However, the traditional current regulations based on the five‐phase half‐bridge inverter have to satisfy the constraint of zero neutral point current, which limits the fault‐tolerant performance of the machine. Thus, this paper investigates the current regulation based on the five‐phase six‐leg (FPSL) inverter, which relieves the zero‐neutral point current limitation, and both current regulations considering the maximum torque (MT) and minimum copper loss (MC) are derived under different open‐circuit conditions. The cost of the system has increased, but with the new regulations, better fault‐tolerant operation capability of the machine can be obtained under all kinds of open‐circuit faults. Moreover, with the introduction of the neutral point current, the machine can work under triple‐phase open‐circuit faults. In conclusion, the proposed control scheme based on the FPSL inverter expands the fault‐tolerant operation capability of the five‐phase PMSM. The experiment results prove the effectiveness of the proposed current regulation on increasing average torque or reducing copper loss under fault‐tolerant operation. [ABSTRACT FROM AUTHOR]

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree—are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process. [ABSTRACT FROM AUTHOR]

Nomex honeycomb composites (NHCs) are commonly used in various industrial sectors such as aerospace and automotive sectors due to their excellent material properties. However, when machining this type of structure, problems can arise due to significant cutting forces and unwanted cell vibrations. In order to remedy these shortcomings, this study proposes to integrate RUM (rotary ultrasonic machining) technology, which consists of applying ultrasonic vibrations along the axis of rotation of the cutter. To fully understand the milling process by ultrasonic vibrations of the NHC structure, a 3D numerical finite element model is developed using Abaqus/Explicit software. The results of the comparative analysis between the components of the simulated cutting forces and those from the experiment indicate a close agreement between the developed model and the experimental results. Based on the developed numerical model, this study comprehensively analyzes the influence of the ultrasonic vibration amplitude on various aspects, such as stress distribution in the cutting zone, chip size, the quality of the machined surface and the components of the cutting force. Ultimately, the results demonstrate that the application of ultrasonic vibrations leads to a reduction of up to 50% in the components of the cutting force, as well as an improvement in the quality of the machined surface and a reduction in the size of chips. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

Objective: This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. Background: Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent's actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences. Methods: A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity. Results: Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants' approach to the intersection and not their departure. Conclusion: Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver's perception of similarity, and (b) understand what elements of the driving style govern subjective similarity. [ABSTRACT FROM AUTHOR]

Clarifying the relationship between the man–machine environment and its impact on the tunnel wall drilling task performance (TWDTP) is crucial for enhancing the task performance. Based on a questionnaire survey, indicators of the man–machine environment that affect the TWDTP were proposed in this study, and exploratory factor analysis and a structural equation model were employed to examine the potential factors influencing the task performance and their degrees of influence. By comparing the discrepancy between the perceived performance and importance, the satisfaction of potential factors was evaluated, and the priority order for optimizing these factors was determined by considering the degree of influence and dissatisfaction. The results of survey data analysis based on actual tunnel drilling operation scenarios indicated that tools had the greatest impact on the TWDTP, followed by the quality of the physical environment, while human factors had the least influence on the task performance. Convenient functional maintenance is the key to improving the TWDTP, along with enhancing the quality of the working environment. Once these main aspects are optimized, it is important to consider additional factors such as availability of spare tools, efficient personnel organization, man–tool matching, and safety and health assurance. This research approach provides significant guidance in understanding the relationships between the man–machine environmental factors affecting the performance of complex engineering tasks and identifying key influencing factors, thus providing essential insights for optimizing the TWDTP. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi. [ABSTRACT FROM AUTHOR]

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests. [ABSTRACT FROM AUTHOR]

Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing the number of surface electromyography (sEMG) electrodes or adding other sensing mechanisms. While many prescribed myoelectric hands still adopt two-electrode sEMG systems, detailed studies on signal processing and classification performance are still lacking. In this study, nine able-bodied participants were recruited to perform six typical hand actions, from which sEMG signals from two electrodes were acquired using a Delsys Trigno Research+ acquisition system. Signal processing and machine learning algorithms, specifically, linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines (SVM), were used to study classification accuracies. Overall classification accuracy of 93 ± 2%, action-specific accuracy of 97 ± 2%, and F1-score of 87 ± 7% were achieved, which are comparable with those reported from multi-electrode systems. The highest accuracies were achieved using SVM algorithm compared to LDA and KNN algorithms. A logarithmic relationship between classification accuracy and number of features was revealed, which plateaued at five features. These comprehensive findings may potentially contribute to signal processing and machine learning strategies for commonly prescribed myoelectric hand systems with two sEMG electrodes to further improve functionality. [ABSTRACT FROM AUTHOR]

Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model's robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model's predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system's sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes. [ABSTRACT FROM AUTHOR]

Machine learning analyses within the realm of oral cancer outcomes are relatively underexplored compared to other cancer types. This study aimed to assess the performance of machine learning algorithms in identifying oral cancer patients, utilizing microRNA expression data. In this study, we implemented this approach using a panel of oral cancer-associated microRNAs sourced from standard incisional biopsy specimens to identify cases of oral squamous cell carcinomas (OSCC). For the model development process, we used a dataset comprising 30 OSCC and 30 histologically normal epithelium (HNE) cases. We initially trained a logistic regression prediction model using 70 percent of the dataset, while reserving the remaining 30 percent for testing. Subsequently, the model underwent hyperparameter tuning resulting in enhanced performance metrics. The hyperparameter-tuned model exhibited high accuracy (0.894) and ROC AUC (0.898) in predicting OSCC. Testing the model on cases of potentially malignant disorders (OPMDs) revealed that leukoplakia with mild dysplasia was predicted as having a high risk of progressing to OSCC, emphasizing machine learning's advantage over histopathology in detecting early molecular changes. These findings underscore the necessity for further refinement, incorporating a broader set of variables to enhance the model's predictive capabilities in assessing the risk of oral potentially malignant disorders. [ABSTRACT FROM AUTHOR]

In this paper, we develop a method for the estimation of airgap spatial variation from the harmonics that appear in the stator's current under static eccentricity conditions. We move beyond the detection of the eccentricity fault and endeavour to quantify the severity of this fault. To achieve this, we formulate the problem as a mathematical inverse problem and attempt to solve it in the Bayesian framework using the so-called Bayesian approximation error approach. The solution takes the form of an algorithm capable of estimating the parameter(s) that characterize the spatial variation of the airgap length from noisy and limited data. Furthermore, it can quantify the uncertainty associated with the results as a byproduct of the Bayesian analysis approach employed in this paper. The results demonstrate that with each iteration of the algorithm, estimation accuracy improves, and the uncertainty of the estimated results reduces. • Bayesian approximation error analysis shows promise in monitoring electrical machines with eccentric rotors. • The paper applies Bayesian inversion for online estimation of airgap length in induction machines with eccentric rotors. • The study estimates static eccentricity ratio from limited data on harmonics of the machine line currents. • This paper gives an analytical method for calculating all inductances of induction machines under static eccentricity. [ABSTRACT FROM AUTHOR]

With the widespread application of computer numerical control (CNC) machine tools in high-precision manufacturing, their machining accuracy has garnered significant attention. Thermal errors generated during machining processes are one of the primary factors affecting accuracy. Although thermal error compensation technologies have been extensively researched and implemented in practice to improve machine accuracy, existing methods still face limitations in the dynamic thermal behavior analysis and adaptability in practical applications. This paper delves into the thermal error compensation technologies for CNC machine tools, exploring measurement, prediction, and compensation methods. Firstly, it enhances the accuracy and efficiency of measurements by optimizing the layout of temperature measurement points through a detailed analysis of the mechanisms of thermal error generation. Secondly, it introduces a prediction framework based on digital twin technology to accurately simulate and predict the thermal behavior of machine tools. Lastly, it employs an optimized back propagation neural network (BPNN) for intelligent modeling of thermal errors, thereby improving the prediction accuracy and response speed. These studies not only aid in improving the design and operation of machine tools but also provide theoretical and technical support for high-precision machining. [ABSTRACT FROM AUTHOR]