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In a multi-state manufacturing system, preventive maintenance is performed on other machines once the performance of a machine falls below a threshold state. Many investigations have been performed on the maintenance of manufacturing systems. However, studies on preventive maintenance for manufacturing systems that account for the different costs involved are limited. This paper focuses on the analysis of multi-state machines in a manufacturing system considering different costs. Subsequently, a cost-based preventive maintenance prioritisation method for multi-state machines is proposed. Based on the buffer capacity, three maintenance policies on the machines are discussed. Different machine failures lead to different maintenance priorities. The real-time buffer capacity greatly influences the selection of machines for preventive maintenance. The set of the optimal machines for preventive maintenance is determined based on the constraints of machine running time and different preventive maintenance costs. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

AbstractObjectiveDesignStudy sampleResultsConclusionsTo compare the performance of a selection of machine learning algorithms, trained to label peaks I, III, and V of the auditory brainstem response (ABR) waveform. An additional algorithm was trained to provide a confidence measure related to the ABR wave latency estimates.Secondary data analysis of a previously published ABR dataset. Five types of machine learning algorithm were compared within a nested k-fold cross-validation procedure.A set of 482 suprathreshold ABR waveforms were used. These were recorded from 81 participants with audiometric thresholds within normal limits.A convolutional recurrent neural network (CRNN) outperformed the other algorithms evaluated. The algorithm labelled 95.9% of ABR waves within ±0.1 ms of the target. The mean absolute error was 0.025 ms, averaged across the outer validation folds of the nested cross-validation procedure. High confidence levels were generally associated with greater wave-labelling accuracy.Machine learning algorithms have the potential to assist clinicians with ABR interpretation. The present work identifies a promising machine learning approach, but any algorithm to be used in clinical practice would need to be trained on a large, accurately labelled, heterogeneous dataset and evaluated in clinical settings in follow-on work. [ABSTRACT FROM AUTHOR]

In this research paper, we explored the predictive capabilities of three different models of Polynomial Regression (PR), Extreme Gradient Boosting (XGB), and LASSO to estimate the density of supercritical carbon dioxide (SC-CO2) and the solubility of niflumic acid as functions of the input variables of temperature and pressure. The optimization of hyperparameters for these models is achieved using the innovative Barnacles Mating Optimizer (BMO) algorithm. For SC-CO2 density estimation, PR exhibits remarkable accuracy, showing an R-squared value of 0.99207 for data fitting. XGB performs admirably with an R2 of 0.92673, while LASSO model demonstrates good predictive ability, showing an R2 of 0.81917. Furthermore, we assess the models' performance in predicting the solubility of niflumic acid. PR exhibits excellent predictive capabilities with an R2 of 0.96949. XGB also delivers strong performance, yielding an R-squared score of 0.92961. LASSO performs well, achieving an R-squared score of 0.82094. The results indicated promising performance of machine learning models and optimizer in estimating drug solubility in supercritical CO2 as the solvent applicable for pharmaceutical industry. [ABSTRACT FROM AUTHOR]

Rock abrasivity plays an important role in the machine design, construction scheduling, and budgeting of TBM projects. Establishing several faster and simpler estimation equations for the Cerchar Abrasivity Index (CAI) of rocks is, therefore, very important. This study investigated the correlation between the CAI and mechanical properties of rock, rock mass classification parameters, and machine performance. A TBM construction database including 159 tunnel sections is established. Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis (0.66 < R2 < 0.76). In this process, a normalized specific energy is proposed to evaluate the machine performance. The results show that the rock compressive strength and brittleness index are the most dependent parameters to explain CAI, and the estimated rock mass strength also indicates a close correlation. In addition, the contribution of a rock mass classification system and machine performance index for estimating CAI cannot be ignored. Finally, the estimation performance of the developed equations is compared and evaluated, and a new method for selecting an optimal model based on ranking is proposed. Since the input parameters of the proposed equations can be readily available at the project planning stage, they are very practical for TBM designers, tunnel designers, and contractors. Highlights: The correlations between the CAI and rock mechanical properties, rock mass classification parameters, and machine performance are investigated. A normalized specific energy index is proposed for assessing the performance of rocks excavation by TBMs. Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis. A new method for selecting optimal estimation model based on ranking is presented. [ABSTRACT FROM AUTHOR]

Machine learning methods have gained much attention in health sciences for predicting various health outcomes but are scarcely used in pharmacoepidemiology. The ability to identify predictors of suboptimal medication use is essential for conducting interventions aimed at improving medication outcomes. It remains uncertain whether machine learning methods could enhance the identification of potentially inappropriate medication use among older adults compared with traditional methods. This study aimed to (1) to compare the performances of machine learning models in predicting use of potentially inappropriate medications and (2) to quantify and compare the relative importance of predictors in a population of community-dwelling older adults (>65 years) in the province of Québec, Canada. We used the Québec Integrated Chronic Disease Surveillance System and selected a cohort of 1 105 295 older adults of whom 533 719 were potentially inappropriate medication users. Potentially inappropriate medications were defined according to the Beers list. We compared performances between 5 popular machine learning models (gradient boosting machines, logistic regression, naive Bayes, neural networks, and random forests) based on receiver operating characteristic curves and other performance criteria, using a set of sociodemographic and medical predictors. No model clearly outperformed the others. All models except neural networks were in agreement regarding the top predictors (sex and anxiety-depressive disorders and schizophrenia) and the bottom predictors (rurality and social and material deprivation indices). Including other types of predictors (eg, unstructured data) may be more useful for increasing performance in prediction of potentially inappropriate medication use. • Potentially inappropriate medications are commonly used in older adults. Being a woman and mental diseases are known to be associated with potentially inappropriate medications use. • When compared with logistic regression, machine learning models did not increase performance for prediction of potentially inappropriate medication. Models ranked predictors similarly, except neural networks. • Our findings reveal that in the specific context of predicting potentially inappropriate medications using medical and administrative databases, classical models yield competitive results. [ABSTRACT FROM AUTHOR]

Optimizing text preprocessing and text classification algorithms is an important, everyday task in large organizations and companies and it usually involves a labor-intensive and time-consuming effort. For example, the filtering and sorting of a large number of electronic mails (emails) are crucial to keeping track of the received information and converting it automatically into useful and profitable knowledge. Business emails are often unstructured, noisy, and with many abbreviations and acronyms, which makes their handling a challenging procedure. To overcome those challenges, a two-step classification approach is proposed, along with a two-cycle labeling procedure in order to speed up the labeling process. Every step incorporates a heuristic classification approach to assign emails to predefined classes by comparing several classification and text vectorization algorithms. These algorithms are compared and evaluated using the F1 score and balanced accuracy. The implementation of the proposed algorithm is demonstrated in a shipbroker agent operating in Greece with excellent performance, improving organization and administration while reducing expenses. [ABSTRACT FROM AUTHOR]

Currently, there are demands for machine tools with higher dynamic performance as a result of their high machining accuracy and efficiency in designing complex parts in the manufacturing industry. The motion delays in each of the motion axes cause dynamic tracking errors or tool tracking errors which greatly affect the surface quality of machined parts; hence, tuning the dynamic parameters like the position gains of the motion axis causing the defect is as good as eliminating the error source rather than tuning the parameters of all the motion axes. This work proposes a decoupling method to identify the particular motion axis that greatly affects or causes the tool tracking error by using the ISO BK3 kinematic test, and this involves orientation contour error estimation and motion axis error computation based on inverse kinematics of the machine tool transformation. An experiment was carried out on an industrial machine tool with a tilting rotary table to verify the simulation results; the feed servo system model of the motion axes was constructed using MATLAB Simulink tools. The results obtained show that the tool tracking errors are greatly influenced by some dynamic parameters like the feedrate and the position gains emanating from a particular motion axis with dynamic deficiency, and the proposed decoupling method robustly identifies the individual motion axes greatly affecting the dynamic tracking error which makes it very relevant to tune only the dynamic parameters of those particular motion axes instead tuning all. Moreover, this method is simple and robust, and its implementation can help improve the dynamic performance of machine tools in the manufacturing industry. [ABSTRACT FROM AUTHOR]

This review paper provides a comprehensive overview of machine vision pose measurement algorithms. The paper focuses on the state-of-the-art algorithms and their applications. The paper is structured as follows: the introduction in provides a brief overview of the field of machine vision pose measurement. Describes the commonly used algorithms for machine vision pose measurement. Discusses the factors that affect the accuracy and reliability of machine vision pose measurement algorithms. Summarizes the paper and provides future research directions. The paper highlights the need for more robust and accurate algorithms that can handle varying lighting conditions and occlusion. It also suggests that the integration of machine learning techniques may improve the performance of machine vision pose measurement algorithms. Overall, this review paper provides a comprehensive overview of machine vision pose measurement algorithms, their applications, and the factors that affect their accuracy and reliability. It provides a valuable resource for researchers and practitioners working in the field of computer vision. [ABSTRACT FROM AUTHOR]

Titanium and its new-generation alloys are widely employed in high-end applications due to their special properties. Modern industries frequently use micro – Electro Discharge Machining (μ-EDM), a non-conventional machining technique that is especially useful for processing hard to machine materials like titanium alloys. μ-EDM can produce intricate shapes with excellent dimensional precision. The main emphasis of this work is an experimental analysis of the μ-EDM of TNZT alloy (Ti-35Nb-7Zr-5Ta) employing a tungsten carbide (WC) electrode. The results showed that with the increase in capacitance (C) 10nF to 100nF at Voltage (V) 80 V, the machining performance parameters such as material removal rate (MRR), overcut (OC), crater area, surface microhardness, and surface roughness increase by 6.38, 1.52, 4.66, 1.07, and 3.29 times, respectively. However, the circularity error of the µ-hole increases by 1.81 times. This study intends to show how the two key machining factors (C & V) influence significant machining performance parameters. [ABSTRACT FROM AUTHOR]

Background: The active functionalities of RNA are recognized to be heavily dependent on the structure and sequence. Therefore, a model that can accurately evaluate a design by giving RNA sequence-structure pairs would be a valuable tool for many researchers. Machine learning methods have been explored to develop such tools, showing promising results. However, two key issues remain. Firstly, the performance of machine learning models is affected by the features used to characterize RNA. Currently, there is no consensus on which features are the most effective for characterizing RNA sequence-structure pairs. Secondly, most existing machine learning methods extract features describing entire RNA molecule. We argue that it is essential to define additional features that characterize nucleotides and specific sections of RNA structure to enhance the overall efficacy of the RNA design process. Results: We develop two deep learning models for evaluating RNA sequence-secondary structure pairs. The first model, NU-ResNet, uses a convolutional neural network architecture that solves the aforementioned problems by explicitly encoding RNA sequence-structure information into a 3D matrix. Building upon NU-ResNet, our second model, NUMO-ResNet, incorporates additional information derived from the characterizations of RNA, specifically the 2D folding motifs. In this work, we introduce an automated method to extract these motifs based on fundamental secondary structure descriptions. We evaluate the performance of both models on an independent testing dataset. Our proposed models outperform the models from literatures in this independent testing dataset. To assess the robustness of our models, we conduct 10-fold cross validation. To evaluate the generalization ability of NU-ResNet and NUMO-ResNet across different RNA families, we train and test our proposed models in different RNA families. Our proposed models show superior performance compared to the models from literatures when being tested across different independent RNA families. Conclusions: In this study, we propose two deep learning models, NU-ResNet and NUMO-ResNet, to evaluate RNA sequence-secondary structure pairs. These two models expand the field of data-driven approaches for learning RNA. Furthermore, these two models provide the new method to encode RNA sequence-secondary structure pairs. [ABSTRACT FROM AUTHOR]

The hard part turning process used to finish the cylindrical surfaces has some critical machining responses like tool wear with the lower quality of the machined surfaces. Minimum quantity lubrication (MQL)-based machining environment is currently considered an advanced cooling and lubrication technique. Cutting speed, feed rate, and cutting depth are considered input parameters, and the TaguchiL27 orthogonal array (OA) has been adopted as the design of the experiment. In this context, average surface roughness (Ra), tool flank wear (VBc), cutting temperature (T∘C), and chip morphology have been selected as measured outputs. To improve the machining performance of hardened steel, a sustainable MQL (flank+rake directions) cooling system has been employed at the cutting zone. Iron–aluminum LRT 30 was considered as the base fluid for the hard turning of AISI 4340 steel by PVD (AlTiN) and CVD (TiN/TiCN/Al2O3) coating cutting inserts. The primary effects of process parameters and their influences on the measured outputs have been discussed. The dual jet MQL-based hard turning was performed at three levels of cutting speeds (80, 170, and 260m/min), feed rate (0.5, 0.1, and 0.15mm/rev), and cutting depth (0.2, 0.3, and 0.4mm) with an air pressure of 5bar and mist flow rate 50ml/h. The findings showed that PVD-coated carbide tools outperformed CVD-coated tools. The model of the measured outputs of both tools has been developed using second-order regression analysis. Utilizing ANOVA analysis, all the predictive models were observed to be significant and acceptable with larger than 90% of R2 value. [ABSTRACT FROM AUTHOR]

Hyperparameters play a critical role in analyzing predictive performance in machine learning models. They serve to strike a balance between overfitting and underfitting of research-independent features to prevent extremes. Manual tuning and automated techniques are employed to identify the optimal combination and permutation to achieve the best model performance. This study explores the pursuit of the best fit through various hyperparameters. Following Logistic Regression analysis, this research compared Random Forest, Randomized search, Grid search, Genetic, Bayesian, and Optuna machine learning model tuning for the best accuracy of prediction the student The model accuracy was further assessed using confusion matrices and Receiver Operating Characteristic—Area Under the Curve (ROC-AUC) curves for student grade classification. The genetic algorithm's recommended hyperparameter tuning yielded the highest accuracy (82.5%) and AUC-ROC score (90%) for student result classification. Manual tuning with an estimator of 300, criterion entropy, max features of sqrt, and a minimum sample leaf of 10 achieved an accuracy of 81.1%, which closely resembled the performance randomized search cross-validation algorithm. The default random forest model scored the least accuracy (78%). However, this manual tuning process took a lesser time (3.66 s) to fit the model while grid search CV tuned 941.5 s. Hence, this research made significant contributions to optimizing various machine learning models using a range of hyperparameters for grade classification. [ABSTRACT FROM AUTHOR]

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]

A variety of bionic vacuum suckers with papillae structures of different parameters were developed to improve the attachment performance of vacuum suckers. The oral sucker of the suckermouth catfish served as the bionic prototype and bionic suckers were made by taking the diameter of papillae, the number of single rows of papillae, the center distance between adjacent rows of papillae, and the distance between the outermost row of papillae and the edge as influencing factors. The single-factor tests and response surface tests were carried out respectively with the growth rates of vertical and horizontal attachment forces as the response values. Twenty-five groups of bionic suckers and one group of standard suckers were fabricated and tested for attachment forces. The experimental results revealed that the bionic suckers had higher vertical and horizontal attachment forces than standard suckers and that their maximum growth rates for these forces were 5.16% and 59.87%, respectively. This suggested that the bionic suckers had better attachment performance. The significant orders of the factors affecting response values were established and their interaction was also analyzed. Besides, the optimal parameter combination of papillae structures of the bionic suckers with optimal vertical and horizontal attachment properties was obtained, respectively. Finally, the influence mechanism of papillae structures on the attachment properties of bionic suckers was analyzed. The studies can serve as a guide for the application of bionic suckers in agricultural production areas such as fruit picking and handling, and the improvement of attachment performance of associated machinery. [ABSTRACT FROM AUTHOR]

This study presents a two-dimensional analytical method for fast optimization, taking into consideration the influence of the eddy current in a magnet and iron loss within a coaxial magnetic gear. Subdomain modeling was utilized to obtain vector potentials in the air-gap, magnet, and modulation regions by solving Maxwell's equations. After that, the magnet, rotor, and modulation losses were predicted and then compared using a finite element method simulation within three topologies with gear ratios ranging from five to six. The authors improved the machine performance, specifically the torque density, by employing a multi-objective function with particle swarm optimization. The flux density obtained using subdomain modeling in just 0.5 s benefits the optimization process, resulting in a torque-density optimal model after around 3 h. A 3/19/16 prototype targeting a low-speed, high-torque, permanent generator application was fabricated to verify the analytical and simulation results. [ABSTRACT FROM AUTHOR]

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations of sea fog occurrence and compares the performance of three machine learning (ML) models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—in predicting visibility associated with sea fog in the Beibu Gulf. The results show that sea fog occurs more frequently during the nighttime than during the daytime, primarily due to day-night differences in air temperature, specific humidity, wind speed, and wind direction. To predict visibility associated with sea fog, these variables, along with temperature-dew point differences ( T a − T d ), pressure (p), month, day, hour, and wind components, were used as feature variables in the three ML models. Although all the models performed satisfactorily in predicting visibility, XGBoost demonstrated the best performance among them, with its predicted visibility values closely matching the observed low visibility in the Beibu Gulf. However, the performance of these models varies by station, suggesting that additional feature variables, such as geographical or topographical variables, may be needed for training the models and improving their accuracy. [ABSTRACT FROM AUTHOR]

Utilizing non-overlap windings has emerged as a favourable choice for minimizing electrical machine manufacturing costs, among other benefits. Nevertheless, it is widely acknowledged that these windings exhibit a notable level of harmonic contents in the resultant magnetomotive force, which can detrimentally impact machine performance, particularly in terms of torque ripple. In the context of wind energy conversion, maintaining low torque ripple is an essential and demanding prerequisite. Medium-speed wind generators present a good trade-off between high energy yield and low gearbox ratios. So far, medium-speed non-overlap winding wound-rotor synchronous generator (WRSG) technologies have been limited to 10/12 and the less common 16/18 pole/slot combinations. In this study, the analysis of a high-pole number combination (24/27 pole/slots) non-overlap WRSG is carried out to theoretically and comparatively predict the electromagnetic and radial force mechanical stress performance analysis with the 16/18 machine with a phase-shifted non-overlap winding (PSW), at 5 MW power level. The study, which is founded on the finite element analysis (FEA) technique, shows that the 24/27 machine exhibits comparable average torque and torque ripple, lower core losses and significantly reduced radial forces compared to the 16/18 PSW-WRSG. However, the 16/18 PSW-WRSG has a 50% reduction in the radial forces compared to the conventional 16/18 non-overlap winding. Experimental vibration analysis of a 3 kW 16/18 WRSG test machine confirms the radial force and vibration reduction in the phase-shifted winding. [ABSTRACT FROM AUTHOR]

In the process of high-yield cultivation of greenhouse cut flower, high labour consuming and lacking of operational standardization stand out as significant issues. As an important agronomic technique for seedlings rooting, cuttage is a key link in the planting process and an important measure to increase agricultural income, accounting for 70% of the total labor cost. A carnation cuttage robot system was designed to integrate with the grasping-cuttage multifunction for the carnations' automatic cuttage. Taking carnation cuttings as the research object, according to the agronomic requirements under high-yield cultivation models and the materials characteristics of carnation cuttings, an auxiliary cuttage end-effector operation mode and a grasping-cuttage coordinated motion planning model are proposed. It realized high-efficient cuttage operation by the combination of cuttings gripping and auxiliary cuttage. A multi-objective identification and localization model was constructed based on the YOLOv5s deep learning network and the hand-eye coordinate system mapping relationship to meet the needs of target detection. Additionally, a kinematic model was built for the cuttage robot, and an improved MD-H (modified denavit-hartenberg) rule based inverse kinematics modeling method and time optimal trajectory planning algorithm are proposed. A multi-objective cuttage points traversal simulation method was proposed to verify that the inverse kinematics modeling method based on improved MD-H rule can effectively reduce the joint mutation rate and solution time of inverse kinematics. In the framework of Ubuntu 18.04 and ROS-melodic, Moveit-gazebo is used to carry out the joint simulation of time optimal trajectory planning, and the validity and efficiency of time optimal trajectory planning algorithm are verified. Finally, the key technologies of the cuttage robot were integrated to develop the standardized and automated cuttage workflow. We constructed a comprehensive test bench for the cuttage robot to evaluate the localization error and machine performance. The localization average error in X and Y directions during the grasping operation were 2.33 mm and 2.38 mm, the cuttage operation had an average localization error in X, Y and Z directions were 2.21 mm, 2.25 mm and 2.05 mm, respectively. On average, it took 14.787 s in total to operation per plant. The time of recognizing and grasping, cuttage were 6.803 s and 5.104 s, accounting for 46.01% and 34.52% of the total working time respectively. During the test of the performance of cuttage robot, the average qualified rate of cuttage depth and the vertical degree of cuttage were 88% and 92% respectively. The average rate of missing cuttage and root damage were 5% and 2% respectively. The cuttage robot met the basic requirements of standardized cuttage operations. Thus, the study fills gaps in the literature on mechanized cuttage of carnation in China and provides a theoretical basis for standardized seedlings cuttage. The main factors limiting the cuttage efficiency of the robot include: 1) The greater the difference in the shape of the cuttings on the conveyor belt, the longer the detection operation time will be, thus restricting the efficiency of the grasping operation; 2) In order to ensure the safety and accuracy of the robot, it needs to complete two complex actions of grasping and cuttage, so it takes more time to complete the calculation, which affects the overall efficiency of cuttage. The irregular shape of carnation cuttings, the unstable posture and the occlusion of stems and leaves are the main reasons that affected the success rate of visual detection and led to missing cuttage. Future research should be carried out on the attitude estimation algorithm for disordered cuttings to achieve the disordered grasping of multi-objective and multi-pose cuttings. The researches on other crops are needed to further improve the performance and applicability of the robot. Therefore, it can provide more comprehensive theoretical support for the high-efficient and standardized operation of cuttage robot. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Design Setting Participants Main Outcome Measures Results Conclusion Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.An observational cohort study.Queen Elizabeth University Hospital.Patients who were referred via the USOC pathway between January 2019 and May 2021.Predicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.The classic statistical method of ordinal logistic regression worked best on the data, achieving an AUC of 0.6697 and balanced accuracy of 0.641. The demographic features describing recreational drug use history and living situation were the most important variables alongside the red flag symptom of a neck lump.Further studies should aim to collect larger samples of malignant and pre‐malignant patients to improve the class imbalance and increase the performance of the machine learning models. [ABSTRACT FROM AUTHOR]

Summary: As a consequence of continuing demand for additional capacity and higher quality data transmission, channel coding as a key component of a digital communication network has progressed from a classical single pair, point‐to‐point information theoretic application to a class of coding techniques with diverse parameters. The heterogeneous requirements of B5G/6G networks technology have made flexibility of code parameters the main desired feature while designing channel codes. Owing to this, channel coding techniques have evolved to support enabling applications that depend on different factors such as latency, reliability, energy efficiency and throughput. We review and discuss the application of new techniques, modifications in the design and construction of modern channel coding schemes, including block, convolutional, rateless and space‐time codes. Further, the error correction performance of mainstream channel decoders for LDPC, polar and turbo codes is compared and analysed for their attainable error rate. The aim of this study is to highlight the adaptability of the discussed coding schemes for the forthcoming cellular network landscape. This article also addresses the implementational challenges of high throughput, low latency and machine type communication scenarios. Moreover, some recent studies exploring the role of machine learning for optimisation of B5G/6G networks are classified and discussed. Concluding, research areas pertaining to the scalability of error control coding for next generation networking are addressed. [ABSTRACT FROM AUTHOR]

ABSTRACT From early in the coronavirus disease 2019 (COVID‐19) pandemic, there was interest in using machine learning methods to predict COVID‐19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing‐RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS‐CoV‐2 infection status and extensive study participant meta‐data. This allowed us to rigorously assess state‐of‐the‐art machine learning techniques to predict SARS‐CoV‐2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks. [ABSTRACT FROM AUTHOR]

ABSTRACT The high tensile strength and high resistance of nickel‐based superalloy 686 against high temperatures and corrosion rates have made it a widely used in important applications such as the aerospace industry, high pollution‐ and corrosion‐resistance equipment manufacturing and petrochemical industry. Therefore, the machining of this advanced alloy with its unique properties is extremely important and can be challenging. Significant increase in input parameters levels, reduction of machining costs, improvement of surface and subsurface properties and clean production are among the issues that should be considered in dealing with Inconel 686 turning operations. Simultaneous application of advanced tools such as polycrystalline diamond (PCD) and polycrystalline cubic boron nitride (PCBN) and optimised minimum quantity lubrication (MQL) method and evaluating the results obtained with a wide range of output parameters related to machining process performance and tribological properties can be proposed as an innovation and a solution to this problem in this article. This study analyses several output parameters with different speeds and feeds to evaluate the effect of cutting insert type on machining process performance and tribological properties. The output parameters include tool wear, residual stress, cutting zone temperature, surface smoothness, machining forces and workpiece surface defects. The results indicated that using the optimised MQL method reduces the size of lubricant droplets and increases the surface covered by cooling. With these changes, the performance of the machining process and the parameters related to the surface integrity increase significantly. Among the parameters associated with the performance of the machining process, the PCD tool reduces the cutting zone temperature by 23%, the tool wear by 19% and the machining forces by 18% compared to the PCBN tool. In the parameters related to surface integrity, this method reduces the residual stress by 19% and the surface roughness by 9% compared to the PCBN tool. From the production index perspective, the PCD tool can significantly increase the cutting speed and feed rate, reducing production time and costs. [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]

The use of biomass as a source of energy has been identified to be energy intensive, involving high handling costs. However, pelletization reduces the bulk density of biomass, thereby reducing the handling costs and enhancing ease of use. This study modified and evaluated an existing hand-operated fish feed pelleting machine. The parts of the machine that were redesigned were the hopper and the power transmission unit. Corncob was used to evaluate the modified machine using the die hole diameter (5, 6 and 7 mm) and the binder quantity (0, 2.5 and 5 wt%) as factors. The average results obtained for machine efficiency, throughput, pellet length and bulk density were 58.83%, 4.24 kg/h, 15.51 mm and 0.160 g/cm3, respectively. The die hole diameter had a significant effect on the pellet length only. The binder quantity had a significant effect on machine efficiency, throughput and pellet length. Machine efficiency and throughput decreased as the quantity of binder increased, and the pellet length increased with the increasing quantity of binder. [ABSTRACT FROM AUTHOR]

Food web robustness is a critical aspect of ecosystem stability and has been extensively studied in ecology. However, the potential of machine learning techniques in predicting food web robustness and the identification of key network structure indicators have not been fully explored. We compared the suitability of different machine learning methods and assessed the relative importance of network structure indicators for predicting the robustness of food webs. We utilized a variety of food web datasets spanning different ecosystems to calculate network structure indicators, which include average distance (AD), betweenness centrality (BC), directional connectivity (C), closeness centrality (CC), diameter (D), degree centrality (DC), edge betweenness centrality (EBC), number of links (L), linkage density (LD), and number of nodes (N). We then compared the performance of machine learning methods, including artificial neural network (ANN), random forest (RF), least absolute shrinkage and selection operator (LASSO), and decision tree (DT), and evaluated the relative importance of network structure indicators on robustness predictions. The results demonstrate that the RF model has the best performance (MAE = 0.0178, RMSE = 0.0263, R2 = 0.9063). Meanwhile, the CC indicator has a significant impact in predicting robustness of food webs. It is suggested that both the RF model and the CC indicator should be considered seriously in predicting food web robustness. This research elucidates the differential outcomes when various machine learning methodologies and indicators are employed to predict the robustness of food webs. It significantly enhances our understanding by demonstrating the precise capability of machine learning models in forecasting the robustness of food webs. [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]

Communication-based train control (CBTC) system is a typical cyber-physical system with open wireless communication that is vulnerable to attacks. To protect the security of wireless communication in the CBTC system, machine learning-based intrusion detection system (IDS) has been extensively researched. However, the performance of a machine learning-based IDS highly depends on feature design, and the spatial and temporal correlation of network data attributes makes it difficult to design features manually. Meanwhile, this type of IDS can only detect known attacks that are contained in the training dataset and fail to detect new attacks (i.e., zero-day attacks). To cope with the above issue, we propose a novel IDS based on transfer learning for the CBTC system. The proposed IDS leverages an optimized one-dimensional convolutional neural network block and long short-term memory to automatically extract spatial and temporal features from the original data. Furthermore, a knowledge transfer method is utilized to transfer the features to enable zero-day attack detection. We evaluate the proposed IDS on a dataset representing the CBTC system network data. The results show that the proposed IDS can achieve 99.32% accuracy for known attacks and 93.21% average F1-Score for zero-day attacks. [ABSTRACT FROM AUTHOR]

Summary: Permanent magnet synchronous motors (PMSM) are widely employed in the application of electric vehicles (EVs) due to their simplicity of operation. Model predictive current control (MPCC) is an advanced technique used to control the PMSM owing advantages like multi‐variable cost function and good dynamic performance. Cost function of predictive current control (PCC) does not require the flux weighting factor; hence, it is simple in the selection of suitable voltage vector (VV). The conventional PCC (C‐PCC) applied to interior PMSM (IPMSM) contains more torque and flux ripples as it includes only one set of voltage vectors for all speed ranges. In proposed PCC (P‐PCC), the magnitude and location of voltage vectors is to be selected as large and small VVs to reduce torque ripples. The P‐PCC in this article utilizes two set of extended voltage vectors (large and small VVs) based on the applied speed change in dynamic conditions and hence reduces the ripple content. Incorporating maximum torque per ampere (MTPA) control in this article serves the purpose of optimizing the machine performance. By adding MTPA to the proposed method, it is aimed to enhance the machine performance with less ripples and improved torque response. Simulation results are presented for conventional and P‐PCC to highlight the effectiveness and efficacy of the P‐PCC. The P‐PCC is experimentally verified with 3.7 kW PMSM using d‐SPACE controller. [ABSTRACT FROM AUTHOR]

Cutting tool materials are the backbone of machining and play a vital role in the manufacturing industry. Innovation in cutting tools is important for customized and demanding applications. This state-of-the-art review is focused on innovations and future research directions for cutting tools covering i) tool materials/microstructure/property relationships, ii) coatings and their effect on tool performance, iii) cutting edge and functional surface preparation and effect on tool performance, iv) tool geometry for high performance and stable machining considering rapid machining, sustainability, and circularity aspects. The vision is to identify tool material/coating/geometry/functional surface relationships for significant improvement in machining performance. This paper includes perspectives from several research groups with a detailed discussion on current advances, capabilities, and challenges in engineered design of cutting tools, materials, coatings, structures and sets a new agenda for future tooling and research directions. [ABSTRACT FROM AUTHOR]

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The goal of this paper is to provide a point of empirical evidence as to how machine-learning techniques stack-up in their ability to predict consumer choices relative to traditional statistical techniques. We compare a traditional (naïve) multinomial logit to six machine-learning alternatives: learning multinomial logit, random forests, neural networks, gradient boosting, support vector machines and an ensemble learning algorithm. The comparison is done by applying these methods to beer category stock keeping unit (SKU) level panel data. Results show that machine-learning techniques tend to perform better, but not always. Ensemble learning performs best while maintaining an overall high-performance level across all SKU classes, independently of their sample size. This result builds on existing evidence about the benefits of combining multiple prediction techniques over relying on a single best performing model, as conventional wisdom would intuitively make us believe. In general, the better performance of machine learning techniques at predicting product choice should not come as a surprise. At their core, machine learning techniques are designed to augment dimensionality of models and/or scan through orders of magnitude greater model alternatives, relative to the narrower focus of traditional approaches. [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]

Lane changing behavior (LCB) prediction is a crucial functionality of advanced driver-assistance systems and autonomous vehicles. Predicting whether or not the driver of a considered ego vehicle is likely to change lanes in the near future plays an important role in improving road safety and traffic efficiency. Understanding the underlying intentions behind the driver's behavior is an important factor for the effectiveness of assistance and monitoring systems. Machine learning (ML) algorithms have been broadly used to predict this behavior by analyzing datasets of traffic and driving data related to the considered ego vehicle. However, this technology has not yet been widely adopted in commercial products. Further improvements in these algorithms are necessary to enhance their robustness and reliability. In some domains, receiver operating characteristic and precision-recall curves are commonly used to evaluate ML algorithms, not considering the effects of process parameters in the evaluation, while it might be necessary to access the performance of these algorithms with respect to such parameters. This paper proposes the use of deep autoencoders to extract multi-level features from datasets, which can then be used to train an ensemble of classifiers. This allows for taking advantage of high feature-extraction capabilities of deep learning models and improving the final result using ensemble learning techniques. The concept of probability of detection is used in combination with the networks employed here to evaluate which classifiers can detect the correct LCB better in a statistical sense. Applications on data acquired from a driving simulator show that the proposed method can be adopted to improve the reliability of the classifiers, and ensemble ANNs perform best in predicting the upcoming human behavior in this dynamical context earlier than 3 s before the event itself. [ABSTRACT FROM AUTHOR]

Label propagation can rapidly predict the labels of unlabeled objects as the correct answers from a small amount of given label information, which can enhance the performance of subsequent machine learning tasks. Most existing label propagation methods are proposed for static data. However, in many applications, real datasets including multiple feature value types and massive unlabeled objects vary dynamically over time, whereas applying these label propagation methods for dynamic partially labeled hybrid data will be a huge drain due to recalculating from scratch when the data changes every time. To improve efficiency, a novel incremental label propagation algorithm based on neighborhood relation (ILPN) is developed in this paper. Specifically, we first construct graph structures by utilizing neighborhood relations to eliminate unnecessary label information. Then, a new label propagation strategy is designed in consideration of the weights assigned to each class so that it does not rely on a probabilistic transition matrix to fix the structure for propagation. On this basis, a new label propagation algorithm called neighborhood relation-based label propagation (LPN) is developed. For the dynamic partially labeled hybrid data, we integrate incremental learning into LPN and develop an updating mechanism that allows incremental label propagation over previous label propagation results and graph structures, rather than recalculating from scratch. Finally, extensive experiments on UCI datasets validate that our proposed algorithm LPN can outperform other label propagation algorithms in speed on the premise of ensuring accuracy. Especially for simulated dynamic data, the incremental algorithm ILPN is more efficient than other non-incremental methods with the variation of the partially labeled hybrid data. [ABSTRACT FROM AUTHOR]

Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classification (ROC) algorithm to simultaneously optimize fairness and accuracy while safeguarding protected attributes such as age and gender. Our approach seeks a multi-objective optimization solution that balances accuracy, group fairness loss, and individual fairness loss. The framework integrates fairness objectives without relying on a weighted summation method, instead focusing on directly optimizing the trade-offs. Empirical evaluations on publicly available datasets, including German Credit, Adult Income, and COMPAS, reveal several significant findings: the ROC-based approach demonstrates superior performance, achieving an accuracy of 94.29%, an individual fairness loss of 0.04, and a group fairness loss of 0.06 on the German Credit dataset. These results underscore the effectiveness of our framework, particularly the ROC component, in enhancing both the fairness and performance of machine learning models. [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]

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While enhancing accuracy, hyperparameter optimization also led to increased execution time. Visual representations and comprehensive results support the findings, confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease. This research contributes to advancing the understanding and application of machine learning in healthcare, particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to study the effectiveness of various simple machine learning methods in the prediction of bank failures. From a raw dataset of 10,938 US banks during the period of 2000–2020, we find that machine learning approaches do not really outperform the benchmark of conventional statistical method, logistic regression. However, using PCA to retain relevant variance in variables significantly improve the performance of machine learning methods and raise the out-of-sample accuracy of those method to over 70% to over 80%. Of all the machine learning methods used in this paper, the simple KNN seems to be the best model in forecasting bank failure in the United States. [ABSTRACT FROM AUTHOR]

This paper focuses on improving the tracking accuracy for servo systems and increasing the contouring performance of precision machining. The dynamic friction during precision machining is analyzed using the LuGre model. The dynamic and static parameters in the friction model are efficiently and accurately identified using the improved Drosophila swarm algorithm based on cross-mutation. The friction tracking error can be deduced from the friction state space and an expression is derived. To compensate for the tracking error caused by friction, a feedforward compensation control is designed to avoid signal lag in traditional friction controllers. Furthermore, the factors of multi-axis parameter mismatching that impact the machining profile accuracy are analyzed for multi-axis control. An adaptive cross-coupled control-based pre-compensation strategy of contour error is designed to reduce both the tracking error and the contour error. The effectiveness of the proposed method is validated through several experiments, which demonstrate a remarkable improvement in tracking performance and contour accuracy. [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]

Tunneling projects encounter challenges in predicting Rate of Penetration (ROP), often leading to cost overruns. This study introduces a deep learning approach, combining Deep Feed Forward (DFF) and Long-Short Term Memory (LSTM) techniques to enhance the accuracy of ROP prediction. Focused on the Mae Tang - Mae Ngad Project and its geological complexities in massive and highly fractured granite rock conditions, the research aims to improve ROP predictions. The study demonstrates substantial improvements, revealing Root Mean Square Error (RMSE) values of 0.162 (m/h) for DFF and 0.216 (m/h) for LSTM. Notably, the models exhibit enhanced performance in massive rock conditions with an RMSE of 0.110 (m/h), while highly fractured granite shows an RMSE of 0.261 (m/h). These findings underscore the potential for more precise predictions, addressing historical inaccuracies that often lead to cost overruns ranging between 50 and 900 percent. Integrating deep learning techniques proves valuable, offering a pathway for more reliable and cost-effective tunnel construction endeavors. [ABSTRACT FROM AUTHOR]

Dry machining is an environmentally friendly process and minimizes machining costs. Also, using cutting fluids in machining affects the operator's health risks and the surrounding environment. One of the alternatives is surface texture on the coated cutting tool, known as Laser Surface Texturing (LST). In the current research work, Hybrid Textured Tools (HTT) in a combination of dimples and grooves were developed, and the performance of the tools was studied. The experimental findings assert that incorporating HTT during machining operations conclusively leads to a substantial reduction of 47% in the cutting force (Fc), 12% in tool-chip interface temperature (TCT), 26% in surface roughness (Ra), and 30% in flank wear (Vb) in comparison to non-textured tools (NT). HTT implementation can enhance machining performance, improve efficiency, and reduce tool wear, leading to better-quality products. [ABSTRACT FROM AUTHOR]

The global market's need to decrease costs, minimize health and environmental risks, and achieve cleaner manufacturing has resulted in a growing interest toward hard turning in the manufacturing sector recently. Al2SiO5, Sillimanite is an abundantly available ceramic material with superior properties that is naturally available in the coastal areas of the districts of Odisha and Tamil Nadu, India. In this study, the machining performance while using sillimanite (Al2SiO5) sub-micron particles as MQL additives along with environmentally friendly rice bran oil (RBO) was studied while turning AISI D2. The maximum temperatures at the cutting zones are 371.8°C, 352.4°C & 341°C with SiO2, Al2O3 and Al2SiO5 additives MQL, respectively. With Al2SiO5, the maximum flank wear is 49.9 µm which is significantly less than that of other additives SiO2, Al2O3. Sillimanite, an environmentally friendly ceramic particle with superior mechanical properties when used as an additive for MQL provides greener solution for sustainable machining. [ABSTRACT FROM AUTHOR]

Titanium alloys are frequently utilised in applications that call for high strength at high temperatures as well as excellent mechanical resistance. This alloy has excellent mechanical properties that enable improved performance from titanium alloys. In the current research, Taguchi L25 orthogonal array of experiments were conducted first, and MCDM techniques and ANNOVA were utilised to forecast the influencing machining factors. From MCDM three methods (SAW, VIKOR & TOPSIS) prediction algorithms which rank one for experiment no. 2. Experiment results shows that experiment no. 02 gives optimum/best results in terms of Tool life and surface roughness. While the process parameter has values cutting speed = 80 rpm, Feed rate = 018 rev/mm, Depth of cut = 0.12 mm, and Rake angle = 14 degrees. From ANOVA outcome for surface finish it is noticed that the process parameter rake angle is having Rank 01 for all levels. Thus rake angle is major influencing parameter to achieve a better quality of surface roughness. While in case of flank wear it is crystal clear that the process parameter DOC is having Rank 01 for all levels. Thus DOC is most influencing parameter to achieve lesser flank wear and better quality of tool life. [ABSTRACT FROM AUTHOR]

The conventional machining of the widely used nickel-based superalloy Inconel 625 is challenging since it exhibits high strength at elevated temperatures. Cryogenic machining is an effective cooling technique to extract the heat developed during machining and enhance the machining performance of superalloys by eliminating the high-temperature related machining obstacles. The maximum machining performance of cryogenic machining can be achieved by optimising the input parameters considered for the study. This work attempts to improve the cryogenic machining performance of Inconel 625 using grey relational analysis. Multiple responses that are having the most significance on the performance of the machining such as main cutting force, tool wear and surface roughness were considered to optimise the process. The experiments were designed on the basis of the orthogonal array. Taguchi's L9 design with three levels of cutting speed, feed and depth of cut was considered for conducting the experiments. A cutting speed of 60 m/min, feed of 0.05 mm/rev and depth of cut of 0.2 mm were obtained as the optimal parameters. The analysis of modes of wear and morphology of chips was also conducted using SEM imaging to understand the wear mechanisms and machining performance at the optimal level under cryogenic cooling. [ABSTRACT FROM AUTHOR]

Globally, the COVID-19 pandemic has aggravated the problem of depression. In the literature on depression detection, predictive capacity of multi-modal biomarkers like speech, facial and language characteristics were combined to improve the performance of Machine Learning (ML) models. The existing techniques have the limitation of not using the feature based weighting on multimodal data items in supervised learning models. The aim of the study is to use correlation based weighting mechanism between the feature and label in supervised learning to build an automatic multimodal depression detection system. We constructed a dataset of audio-visual recordings that consists of depressed and non-depressed subjects, to address this limitation by proposing a novel two-stage multimodal method for classification. The first stage attention weight is found using the Pearson correlation coefficients. Then these attention weights are combined with the multimodal feature vector, to use in the second stage. The resultant feature vector is experimented with three state-of-the-art classifiers i.e. Support Vector Machine (SVM), Decision Tree (DT) and K-Nearest Neighbour (KNN) classifiers, for predictions. Overall performance measures of proposed model are: accuracy 87.3%, precision 83.3%, recall 77%, and f1-score 80% which showed its effectiveness. Also outcomes of this proposed model were compared with the outcomes of existing state-of-the-art methods, such as baseline base classifiers and feature-level fusion method. Further, the proposed model is also evaluated applying to a widely used benchmarking dataset i.e. Distress Analysis Interview Corpus (DAIC) in depression detection. Results showed that combination of heterogeneous classifiers improves performance when classifying depressed and non-depressed subjects. [ABSTRACT FROM AUTHOR]

Accurate prediction of fatal events in car accidents has significant health management implications. This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance. By implementing these techniques on car accident data, health organizations can identify and forecast a fatal event, enabling more efficient and effective allocation of limited health resources. Concurrently, enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions. Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents, ultimately contributing to improved road safety and better management of health resources. Moreover, the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction. Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes. [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]

This study utilized ion implantation to modify the material properties of silicon carbide (SiC) to mitigate subsurface damage during SiC machining. The paper analyzed the mechanism of hydrogen ion implantation on the machining performance of SiC at the atomic scale. A molecular dynamics model of nanoscale cutting of an ion-implanted SiC workpiece using a non-rigid regular tetrakaidecahedral diamond abrasive grain was established. The study investigated the effects of ion implantation on crystal structure phase transformation, dislocation nucleation, and defect structure evolution. Results showed ion implantation modification decreased the extension depth of amorphous structures in the subsurface layer, thereby enhancing the surface and subsurface integrity of the SiC workpiece. Additionally, dislocation extension length and volume within the lattice structure were lower in the ion-implanted workpiece compared to non-implanted ones. Phase transformation, compressive pressure, and cutting stress of the lattice in the shear region per unit volume were lower in the ion-implanted workpiece than the non-implanted one. Taking the diamond abrasive grain as the research subject, the mechanism of grain wear under ion implantation was explored. Grain expansion, compression, and atomic volumetric strain wear rate were higher in the non-implanted workpiece versus implanted ones. Under shear extrusion of the SiC workpiece, dangling bonds of atoms in the diamond grain were unstable, resulting in graphitization of the diamond structure at elevated temperatures. This study established a solid theoretical and practical foundation for realizing non-destructive machining at the atomic scale, encompassing both theoretical principles and practical applications. [ABSTRACT FROM AUTHOR]