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Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future. [ABSTRACT FROM AUTHOR]

In traditional multi-axis machining, the linear tool path generated by computer-aided manufacturing systems is still in a broad tool path format, resulting in discontinuities at the corners, which slow down tool motion, and also trigger tool vibration, reducing machining accuracy and surface quality. In order to ensure the C3 continuity of the tool during the multi-axis machining process, this paper proposes an analytical B-spline local smoothing method with curvature control. The tool position trajectory and tool orientation are both smoothed by a 5th spline curve with 9 control points. Optimal control points are determined for position and orientation splines to achieve C3 continuity at the junctions between the spline and linear segment while preserving the predefined geometric tolerance limits and minimizing the maximum curvature of the inserted B-spline curve. The smoothed tool path is expected to have less curvature fluctuation, which can significantly improve both the kinematic performance of the machine tool and the machining efficiency. Both computer simulations and physical printing experiments are carried out to demonstrate the improvements of our proposed method in motion smoothness and tracking accuracy in multi-axis machining tasks. [ABSTRACT FROM AUTHOR]

Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k-NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing. [ABSTRACT FROM AUTHOR]

To evaluate whether a machine learning classifier can evaluate image quality of maximum intensity projection (MIP) images from F18-FDG-PET scans. A total of 400 MIP images from F18-FDG-PET with simulated decreasing acquisition time (120 s, 90 s, 60 s, 30 s and 15 s per bed-position) using block sequential regularized expectation maximization (BSREM) with a beta-value of 450 and 600 were created. A machine learning classifier was fed with 283 images rated "sufficient image quality" and 117 images rated "insufficient image quality". The classification performance of the machine learning classifier was assessed by calculating sensitivity, specificity, and area under the receiver operating characteristics curve (AUC) using reader-based classification as the target. Classification performance of the machine learning classifier was AUC 0.978 for BSREM beta 450 and 0.967 for BSREM beta 600. The algorithm showed a sensitivity of 89% and 94% and a specificity of 94% and 94% for the reconstruction BSREM 450 and 600, respectively. Automated assessment of image quality from F18-FDG-PET images using a machine learning classifier provides equivalent performance to manual assessment by experienced radiologists. [ABSTRACT FROM AUTHOR]

Fault diagnosis and condition monitoring of synchronous machines running under load is a key determinant of their lifespan and performance. Faults such as broken rotor bars, bent shafts and bearing issues lead to eccentricity faults. These faults if not monitored may lead to repair, replacement and unforeseen loss of income. Researchers who attempted to investigate this kind of machine stopped at characterizing and deduced ways, types and effects of rotor eccentricity fault on the machine inductances using the winding function method. A modified closed-form analytical model of an eccentric synchronus reluctance motor (SynRM) is developed here taking into cognizance the machine dimensions and winding distribution for the cases of a healthy and unhealthy SynRM. This paper reports the study the SynRM under static rotor eccentricity using the developed analytical model and firming up the model with finite element method (FEM) solutions. These methods are beneficial as they investigated and presented the influence of the degrees of static eccentricity on the machine performance indicators such as speed, torque and the stator current and assess the extent to which the machine performance will deteriorate when running with and without load. The results show that static eccentricity significantly affects the machine's performance as the degree of eccentricity increases. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes entropy values and a non-dominant sorting algorithm to identify the candidate with the highest entropy value from the dataset. This is followed by merging the training set with adversarial samples, where a mini-batch of the merged dataset is used to update the dense network parameters. This method can improve the performance of machine learning models, categorization of radiographic images, risk of misdiagnosis in medical imaging, and accuracy of medical diagnoses. To evaluate the efficacy of the proposed model, two datasets, MNIST and COVID, were used with pixel values and without transfer learning. The results showed an increase of accuracy from 0.85 to 0.88 for MNIST and from 0.83 to 0.85 for COVID, which suggests that the model successfully classified images from both datasets without using transfer learning techniques. [ABSTRACT FROM AUTHOR]

Digital twins can improve the level of control over physical entities and help manage complex systems by integrating a range of technologies. The autonomous agricultural machine has shown revolutionary effects on labor reduction and utilization rate in field works. Autonomous vehicles in precision agriculture have the potential to improve competitiveness compared to current crop production methods and have become a research hotspot. However, the development time and resources required in experiments have limited the research in this area. Simulation tools in unmanned farming that are required to enable more efficient, reliable, and safe autonomy are increasingly demanding. Inspired by the recent development of an open-source virtual simulation platform, this study proposed an autoware-based simulator to evaluate the performance of agricultural machine guidance based on digital twins. Oblique photogrammetry using drones is used to construct threedimensional maps of fields at the same scale as reality. A communication format suitable for agricultural machines was developed for data input and output, along with an inter-node communication methodology. The conversion, publishing, and maintenance of multiple coordinate systems were completed based on ROS (Robot Operating System). Coverage path planning was performed using hybrid curves based on Bézier curves, and it was tested in both a simulation environment and actual fields with the aid of Pure Pursuit algorithms and PID controllers. [ABSTRACT FROM AUTHOR]

Accurate prediction of machine RUL plays a crucial role in reducing human casualties and economic losses, which is of significance. The ability to handle spatiotemporal information contributes to improving the prediction performance of machine RUL. However, most existing models for spatiotemporal information processing are not only complex in structure but also lack adaptive feature extraction capabilities. Therefore, a lightweight operator with adaptive spatiotemporal information extraction ability named Involution GRU (Inv-GRU) is proposed for aero-engine RUL prediction. Involution, the adaptive feature extraction operator, is replaced by the information connection in the gated recurrent unit to achieve adaptively spatiotemporal information extraction and reduce the parameters. Thus, Inv-GRU can well extract the degradation information of the aero-engine. Then, for the RUL prediction task, the Inv-GRU-based deep learning (DL) framework is firstly constructed, where features extracted by Inv-GRU and several human-made features are separately processed to generate health indicators (HIs) from multi-raw data of aero-engines. Finally, fully connected layers are adopted to reduce the dimension and regress RUL based on the generated HIs. By applying the Inv-GRU-based DL framework to the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) datasets, successful predictions of aero-engines RUL have been achieved. Quantitative comparative experiments have demonstrated the advantage of the proposed method over other approaches in terms of both RUL prediction accuracy and computational burden. [ABSTRACT FROM AUTHOR]

Accurately characterizing the surface topography of parts is crucial to improve the surface measurement accuracy and analyze the surface contact performance. A method is proposed to separate the morphological characteristics of the actual machined surface based on the layer-by-layer error reconstruction method and the signal-to-noise ratio method during the wavelet transform process, so as to evaluate the contact performance of the different joint surfaces. First, the actual machined surface morphological features are separated by using the wavelet transform method, the layer-by-layer error reconstruction method, and the signal-to-noise ratio method. Second, the reconstructed three-dimensional surface contact model is established by the reverse modeling engineering method. Third, the finite element method is used to analyze the impact of processing methods and surface roughness on contact surface parameters. The result demonstrates that the simplified and efficient three-dimensional reconstructed surface is achieved based on the real machining surface in contrast to other existing approaches. The surface roughness has a more significant influence on contact performance. The contact deformation increases with the increase of surface roughness, while the curves of average contact stress, contact stiffness, and contact area have the opposite trend. [ABSTRACT FROM AUTHOR]

Longitudinal ultrasonic vibration-assisted high-speed dry milling (LUVAHSDM) is adopted to improve the cutting performance and surface quality of Ti-6Al-4V (TC4). Multifaceted comparisons are presented in terms of milling force, milling temperature, tool wear, 3D surface roughness, and residual stress, utilizing AlCrN, AlCrSiN, AlTiN, and AlTiSiN-coated tools. The results show that the maximum milling forces in the feed and longitudinal directions both decreased at first and then increased with the increasing of Al element contents, during which the single edge and average flank wear increased. Therefore, the surface roughness of TC4 becomes more uniform using AlTiN-coated tools, and the residual stress is significantly reduced to 98.57 MPa. Combining the results of milling force, milling temperature, tool wear, 3D surface roughness and residual stress data, it is found that AITiN-coated tools achieve excellent cutting performance and surface quality. The excellent machining performance of coated carbide tools demonstrates the feasibility and effectiveness of applying LUVAHSDM methods. [ABSTRACT FROM AUTHOR]

Thin-walled parts (TWPs) are widely used in aerospace, and their service performance is significantly affected by the machining accuracy. Multi-pass machining is used to machine the poor stiffness TWPs. However, it is difficult to accurately predict the final machining accuracy due to surface topography error propagation and accumulation in multi-pass machining. Therefore, this paper proposes a multi-pass machining accuracy prediction method for TWPs based on dynamic factors (cutting force and stiffness). First, a flexible cutting force prediction model, which considers the axial errors determined by the initial surface topography and part deflection, is proposed. Second, a position-pass-dependent stiffness (PPDS) model is established considering the position dependence of stiffness and multi-pass machining material removal. Finally, combining the two models above, a multi-pass machining accuracy prediction method based on a genetic algorithm–back propagation (GA-BP) neural network is proposed. Experiments under various conditions are carried out to validate the proposed method. The machining accuracy (flatness as an example) is as high as 90.8% using the method in this paper, while it is only 73.9% when the accumulative error is neglected. The proposed method can significantly improve the performance of machining accuracy prediction by revealing the error propagation mechanism and the effect of dynamic factors between multi-pass machining. Furthermore, this also provides a theoretical basis for process parameter optimization and machining accuracy improvement in TWP machining. [ABSTRACT FROM AUTHOR]

The design of machine parts of different sizes and shapes has become relevant in the manufacturing industry which requires five-axis machine tools of high dynamic performance; different machining test pieces have been used to test and reflect the machine tool's performance. The S-shaped is still under development and consideration of which a new test piece better than the S-shaped part has been recommended to be put forward making the NAS979 the only standardized test piece; however, it has some limitations. Hence, this study proposes a new test piece to objectively satisfy the demand for machine tools with higher dynamic performance, which shows much improvement over the standard NAS979 and is the best alternative to the S-shaped test piece, and it combines the geometric and kinematic features of both test pieces. Geometrically, it has non-uniform surface continuity, variable twist angle, and variable curvature; and the cutting tool moves in close and opened angles along the tool path; there is sudden rise and fall of axes' velocity, acceleration, and jerk with much impact during machining which makes the S-cone test piece be machined by only five-axis machine tools with high dynamic performance, and has a better dynamic performance identification effect than the S-shaped test piece based on the trajectory test. Detailed work on the validation of the machine tool's dynamic performance using the S-cone part will be captured next part of this study. [ABSTRACT FROM AUTHOR]

During the service of rotating machinery, the pedestal bolts are prone to looseness due to the vibration environment, which affects the performance of rotating machinery and generate potential safety hazard. To monitor the occurrence and deterioration of the pedestal looseness in time, this paper proposes a data-driven diagnosis strategy for the rotor-bearing system. Firstly, the finite element model of a rotor-bearing system is established, which considers the piecewise nonlinear force caused by pedestal looseness. Taking the vibration response as output and periodic unbalanced force as input, the system's NARX (Nonlinear Auto-Regressive with exogenous inputs) model is identified. Then GALEs (Generalized Associated Linear Equations) are used to evaluate NOFRFs (Nonlinear Output Frequency Response Functions) of the NARX model. Based on the first three-order NOFRFs, the analytical expression of the second-order optimal weighted contribution rate is derived and used as a new health indicator. The simulation results show that compared with the conventional NOFRFs-based health indicator, the new indicator is more sensitive to weak looseness. Finally, a rotor-bearing test rig was built, and the pedestal looseness was performed. The experiment results show that as the degree of looseness increases, the new indicator SRm shows a monotonous upward trend, increasing from 0.48 in no looseness to 0.90 in severe looseness, a rise of 89.7%. However, the traditional indicator Fe2 has no monotonicity, which further verifies the sensitivity of the first three-order NOFRFs-based second-order optimal weighted contribution rate and the effectiveness of the proposed data-driven feature extraction strategy. [ABSTRACT FROM AUTHOR]

The objective of this research focuses on the development of a statistical methodology able to answer the question of whether variation in the intake of sulfur amino acids (SAA) affects the metabolic process. Traditional approaches, which evaluate specific biomarkers after a series of preprocessing procedures, have been criticized as not being fully informative, as well as inappropriate for translation of methodology. Rather than focusing on particular biomarkers, our proposed methodology involves the multifractal analysis that measures the inhomogeneity of regularity of the proton nuclear magnetic resonance (1H-NMR) spectrum by wavelet-based multifractal spectrum. With two different statistical models (Model-I and Model-II), three different geometric features of the multifractal spectrum of each 1H-NMR spectrum (spectral mode, left slope, and broadness) are employed to evaluate the effect of SAA and discriminate 1H-NMR spectra associated with different treatments. The investigated effects of SAA include group effect (high and low doses of SAA), depletion/repletion effect, and time over data effect. The 1H-NMR spectra analysis outcomes show that group effect is significant for both models. The hourly variation in time and depletion/repletion effects does not show noticeable differences for the three features in Model-I. However, these two effects are significant for the spectral mode feature in Model-II. The 1H-NMR spectra of the SAA low groups exhibit highly regular patterns with more variability than that of the SAA high groups for both models. Moreover, the discriminatory analysis conducted using the support vector machine and the principal components analysis shows that the 1H-NMR spectra of SAA high and low groups can be easily discriminatory for both models, while the spectra of depletion and repletion within these groups are discriminatory for Model-I and Model-II. Therefore, the study outcomes imply that the amount of SAA is important and that SAA intake affects mostly the hourly variation of the metabolic process and the difference between depletion and repletion each day. In conclusion, the proposed multifractal analysis of 1H-NMR spectra provides a novel tool to investigate metabolic processes. [ABSTRACT FROM AUTHOR]

The study was performed to determine the optimum flushing condition for electrical discharge machining (EDM) of functional material titanium VT6 obtained by plasma cladding with a thermal cycle. Copper is used as an electrode tool (ET) to machine functional materials. The optimum flushing flows are analyzed theoretically by using ANSYS CFX 20.1 software which is also validated by an experimental study. It was observed that while machining the functional materials to adepth of 10 mm or more, the turbulence fluid flow dominates when nozzle angles are 45° and 75°, consequently drastically affecting the quality of flushing and the performance of the EDM. For the highest machining performance, the nozzles should be at an angle of 15° relative to the tool axis. The optimum flushing at deep hole EDM process minimizes the occurrence of debris deposition on tool electrodes, thus facilitating stable machining of functional materials. The adequacy of the obtained models was confirmed experimentally. It has been established that EDM of a hole with a depth of 15 mm, an intense accumulation of sludge, is observed in the processing zone. There arebuild-ups exceeding 3 mm in cross-section after EDM. This build-up leads to a short circuit and a reduction in surface quality and productivity. It has been proven that not correct flushing leads to intensive wear of the tool and a change in its geometric shape and, accordingly, to a decrease in the quality of EDM. [ABSTRACT FROM AUTHOR]

Accurate lithological mapping is a crucial juncture for geological studies and mineral exploration. Hyperspectral data provide the opportunity to extract detailed information about the geology and mineralogy of the Earth's surface. Machine learning (ML) and deep learning (DL) techniques provide an accurate and effective mapping of various types of lithologies in arid and semi-arid regions. This article discusses the use of machine learning algorithms, specifically Support Vector Machines (SVM), one-dimensional Convolutional Neural Network (1D-CNN), random forest (RF), and k-nearest neighbor (KNN), for lithological mapping in a complex area with strong hydrothermal alteration. The study evaluates the performance of the four algorithms in three different zones in the Ameln valley shear zone (AVSZ) area at eastern Kerdous inlier, Moroccan western Anti-Atlas. The results demonstrated that 1D-CNN achieved the best classification results for most lithological units. Additionally, the LK-SVM demonstrated good mapping results compared to the other SVM models, as well as RF and KNN. Our study concludes that the combination of the CNN and HyMap data can provide the most accurate lithologic mapping for the three selected region, with an overall accuracy of ~95%. However, this study highlights the challenges in identifying different lithological units using remotely sensed data due to spectrum similarities induced by similar chemical and mineralogical compositions. This study emphasizes the importance of carefully considering and evaluating ML and DL methods for lithological mapping studies, then recommends the high-resolution hyperspectral data and DL models for accurate results. The implications of this study would be fascinating to exploration geologists for Mineral Prospectivity Mapping (MPM), especially in selecting the most appropriate techniques for highly accurate mineral mapping in metallogenic provinces. [ABSTRACT FROM AUTHOR]

In this study, we develop a rule-based method for the detection of seizures in electroencephalogram (EEG) signals. The proposed method is based on the observations that EEG seizure can be modeled either as a train of impulses or as the summation of harmonically related frequency-modulated chirps. For detecting spike-train-type seizures, the proposed method estimates group delay-based features, whereas to detect seizures modeled as the summation of harmonically related frequency-modulated chirps, the instantaneous frequency-related features are extracted. Experimental results indicate that the proposed method achieves better performance than the machine learning approach in terms of total accuracy and sensitivity. [ABSTRACT FROM AUTHOR]

Database and data structure research can improve machine learning performance in many ways. One way is to design better algorithms on data structures. This paper combines the use of incremental computation as well as sequential and probabilistic filtering to enable "forgetful" tree-based learning algorithms to cope with streaming data that suffers from concept drift. (Concept drift occurs when the functional mapping from input to classification changes over time). The forgetful algorithms described in this paper achieve high performance while maintaining high quality predictions on streaming data. Specifically, the algorithms are up to 24 times faster than state-of-the-art incremental algorithms with, at most, a 2% loss of accuracy, or are at least twice faster without any loss of accuracy. This makes such structures suitable for high volume streaming applications. [ABSTRACT FROM AUTHOR]

Deviation from the traction path affects the operation of composting machines that work in an offset manner and reduces their performance. In this article, a compost mixing machine behind a tractor, which deviates from the movement path during operation, was investigated and evaluated. The equations governing the performance of the extraction machine and the effective parameters in the optimal performance of the device were determined and their effect on the deviation of the device was investigated. The results of this research show that by changing the parameters of the machine without increasing its weight in order to reduce the deviation from the movement path, it is possible to significantly increase the balance of the machine and work efficiency and reduce the deviation from the movement path. In this investigation, it was found that by changing several parameters such as the longitudinal and transverse distance of the composting machine, the friction coefficient, etc., the deviation angle of the machine decreases from 17 degrees to less than 4 degrees. Based on the theory presented in this article, the device was designed, built and used in Gulab Zahra Kerman Company, and its performance is satisfactory, and the practical review of each of the parameters presented in this article will be presented in other articles. [ABSTRACT FROM AUTHOR]

Over the past decades, we have witnessed an immense expansion in the arsenal and performance of machine learning (ML) algorithms. One of the most important fields that could benefit from these advancements is biomedical science. To streamline the training and evaluation of binary classifiers, we constructed a universal and flexible ML framework that uses tabular biomedical data as input. Our framework requires the input data to be provided as a comma-separated values file, in which rows correspond to subjects and columns represent different features. After reading the content of this file, the framework enables the users to perform outlier detection, handle missing values, rescale features, and tackle class imbalance. Then, hyperparameter tuning, feature selection, and internal validation are performed using nested cross-validation. If an additional dataset is available, the framework also provides the option for external validation. Users may also compute SHapley Additive exPlanations values to interpret the individual predictions of the model and identify the most important features. Our ML framework was implemented in Python (version 3.9), and its source code is freely available via GitHub. In the second part of this paper, we also demonstrate the usage of the framework through a case study from the field of cardiovascular imaging. The proposed ML framework enables the efficient training and evaluation of binary classifiers on tabular biomedical data. We hope our framework will serve as a useful resource for both learning and research purposes and will promote further innovation. [ABSTRACT FROM AUTHOR]

In modern machine tool design, precision is an important index to characterize machine tool performance and precision allocation has become a key task. Since the middle of the twentieth century, precision allocation methods using optimization technology to balance manufacturing cost and precision level have gradually developed, but most methods mainly take the cost minimization as the goal to optimize the precision allocation. As the precision and manufacturing costs are a pair of factors to be comprehensively considered, balance between them is needed to meet different design requirements. This paper proposes a comprehensive optimization method to trade-off between precision and cost. A multi-object precision allocation optimization model aiming at minimizing fuzzy manufacturing cost and comprehensive precision of machine tool is constructed. A multi-object optimization algorithm to solve the model is designed, combining the multi-objective gray wolf optimization algorithm with multi-objective decision analysis method Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A case study based on a large-scale hobbing machine shows that the comprehensive optimization of manufacturing cost and machining precision is realized by using the proposed multi-object precision allocation optimization method. [ABSTRACT FROM AUTHOR]

Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets. However, the direct application of traditional deep learning algorithms, such as Convolutional Neural Networks (CNNs), is limited as they are designed for regular Euclidean data like 2D grids and 1D sequences. In contrast, graph-structured data are in a non-Euclidean form. Graph Neural Networks (GNNs) are specifically designed to handle non-Euclidean data and make predictions based on connectivity rather than spatial structure. Real-life graph data can be broadly categorized into two types: spatially-invariant graphs, where the link structure between nodes is independent of their spatial positions, and spatially-variant graphs, where node positions provide additional information about the graph's properties. However, there is limited understanding of the effect of spatial variance on the performance of Graph Neural Networks. In this study, we aim to address this issue by comparing the performance of GNNs and CNNs on spatially-variant and spatially-invariant graph data. In the case of spatially-variant graphs, when represented as adjacency matrices, they can exhibit Euclidean-like spatial structure. Based on this distinction, we hypothesize that CNNs may outperform GNNs when working with spatially-variant graphs, while GNNs may excel on spatially-invariant graphs. To test this hypothesis, we compared the performance of CNNs and GNNs under two scenarios: (i) graphs in the training and test sets had the same connectivity pattern and spatial structure, and (ii) graphs in the training and test sets had the same connectivity pattern but different spatial structures. Our results confirmed that the presence of spatial structure in a graph allows for the effective use of CNNs, which may even outperform GNNs. Thus, our study contributes to the understanding of the effect of spatial graph structure on the performance of machine learning methods and allows for the selection of an appropriate algorithm based on the spatial properties of the real-life graph dataset. [ABSTRACT FROM AUTHOR]

Real-time condition monitoring and fault diagnosis of spindle bearings are critical to the normal operation of the matching machine tool. In this work, considering the interference of random factors, the uncertainty of the vibration performance maintaining reliability (VPMR) is introduced for machine tool spindle bearings (MTSB). The maximum entropy method and Poisson counting principle are combined to solve the variation probability, so as to accurately characterize the degradation process of the optimal vibration performance state (OVPS) for MTSB. The dynamic mean uncertainty calculated using the least-squares method by polynomial fitting, fused into the grey bootstrap maximum entropy method, is utilized to evaluate the random fluctuation state of OVPS. Then, the VPMR is calculated, which is used to dynamically evaluate the failure degree of accuracy for MTSB. The results show that the maximum relative errors between the estimated true value and the actual value of the VPMR are 6.55% and 9.91%, and appropriate remedial measures should be taken before 6773 min and 5134 min for the MTSB in Case 1 and Case 2, respectively, so as to avoid serious safety accidents that are caused by the failure of OVPS. [ABSTRACT FROM AUTHOR]

The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second-most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model's accuracy in heart disease prediction. When compared to the other five models, the random forest and k-nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k-nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. Six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for accuracy, precision, recall, and F measure are used to evaluate models. [ABSTRACT FROM AUTHOR]

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models' application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472. [ABSTRACT FROM AUTHOR]

In longitudinal vibration-assisted milling (LVAM), the tool flute is retracted from the bottom of the machined surface along the axial/longitudinal direction. Nevertheless, there is still a limitation in the knowledge of LVAM during Ti-6Al-4 V cutting regarding the cutting forces pattern in analysis using finite element (FE) simulations and analytical prediction comparatively. This study developed an FE model of the cutting force using ABAQUS and a mathematical model of the cutting force using MATLAB. The FE model was developed based on the Johnson–Cook plastic deformation and the Johnson–Cook damage model. The analytical prediction was developed based on the fluctuated undeformed axial chip thickness and the influence of the longitudinal vibration on the undeformed chip thickness. According to this comparative study, the analytical prediction of the cutting force coded in MATLAB has remarkable similarity with the FE simulation model cutting force. The experimental cutting forces were also carried out to validate both the FE simulation model and the mathematical model. According to the comparison results in the LVAM, the FE simulation model has higher quantitative error than that in the mathematical model, with the highest error in the mathematical model being about 7.146% and the highest error in the numerical model being about 9.913%. In addition, a comparative study between LVAM and CM in terms of the experimental cutting temperature, machined surface morphology, and chip morphology was also carried out. In evidence, LVAM provides better machining performance than CM. [ABSTRACT FROM AUTHOR]

To improve the efficiency of government-funded research and development (R&D) programs for small and medium enterprises, it is necessary to make the process of selecting beneficiary firm objective. We aimed to develop machine learning models to predict the performances of individual R&D projects in advance, and to present an objective method that can be utilized in the project selection. We trained our models on data from 1771 R&D projects conducted in South Korea between 2011 and 2015. The models predict the likelihood of R&D success, commercialization, and patent applications within 5 years of project completion. Key factors for predicting the performances include the research period and area, the ratio of subsidy to research budget, the firm's region and venture certification, and the average debt ratio of the industry. Our models' precisions were superior to qualitative expert evaluation, and the machine learning rules could be explained theoretically. We presented a methodology for objectively scoring new R&D projects based on their propensity scores of achieving the performances and balancing them with expert evaluation scores. Our methodology is expected to contribute to improving the efficiency of R&D investment by supplementing qualitative expert evaluation and selecting projects with a high probability of success. [ABSTRACT FROM AUTHOR]

Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area. [ABSTRACT FROM AUTHOR]

A twin bounded support vector machine (TBSVM) is a phenomenon of symmetry that improves the performance of the traditional support vector machine classification algorithm. In this paper, we propose an improved model based on a TBSVM, called a Welsch loss with capped L 2 , p -norm distance metric robust twin bounded support vector machine (WCTBSVM). On the one hand, by introducing the capped L 2 , p -norm metric in the TBSVM, the problem of the non-sparse output of the regularization term is solved; thus, the generalization and robustness of the TBSVM is improved and the principle of minimizing the structural risk is realized. On the other hand, a bounded, smooth, and non-convex Welsch loss function is introduced to reduce the influence of noise, which further improves the classification performance of the TBSVM. We use a half-quadratic programming algorithm to solve the model non-convexity problem caused by Welsch loss. Therefore, the WCTBSVM is more robust and effective in dealing with noise compared to the TBSVM. In addition, to reduce the time complexity and speed up the convergence of the algorithm, we constructed a least squares version of the WCTBSVM, named the fast WCTBSVM (FWCTBSVM). Experimental results on both UCI and artificial datasets show that our model can show better classification performance on classification problems. [ABSTRACT FROM AUTHOR]

The effect of a machine feeding rate (FR; 1, 1.5 and 2 Mg/h) and/or three levels of selected fineness degree (FD; 3, 5 and 7 mm) on machine performance, pellet physical parameters, required energy and production cost of three main types of broilers diets were examined in this experiment. The examined broiler diets were formulated to meet the Ross 308 strain requirements. A complete factorial design (3x3x3) was used to identify the effects of studied factors on the pellet mill machine and pellet production. The obtained results indicated that the pellet mill productivity significantly (p<0.001) improved through increased pellet mill feeding rate level. In addition, the machine pelleting efficiency was found to be significantly affected by all studied variables and their interactions. While the total power consumption of the machine showed no variations under the impact of the tested factors or with any of their combinations. Regarding the pellet physical quality indices, all broiler diets with all selected FD and lower FR had the maximum durability and bulk density levels. Furthermore, lower feeding rates were associated with higher hardness degrees. The lowest production costs were substantially correlated with high FR and intermediate FD (5 mm). Furthermore, production costs were determined to be reduced in finisher broiler diets under different feeding rates. Moreover, manufacturing costs of finisher broiler meals were observed to decrease in several feeding rates. Overall, these findings indicate the capabilities of producing high-quality pellets and reducing the needed production costs by optimizing feeding rates to 2 Mg/h and 2 mm fineness in broiler diets. [ABSTRACT FROM AUTHOR]

The article presents studies on the impact of the significant process parameters of a paper machine with a Yankee cylinder on its production capacity and heat energy consumption for drying the paper web. Parameters such as machine speed, web moisture content before and after pressing, parameters of steam supplied to the cylinder and parameters of hot air flowing from the nozzles of the hood were analyzed. The study's results were used to optimize production to improve the energy efficiency and performance of the machine. In order to use the possible methods of improving the production capacity and heat energy consumption, the parameters of the production process were measured and the basic indicators characterizing the operation of the machine were calculated in the Yankee cylinder–dryer hood system. The correct functioning of the machine components and the possibility of their modernization were also analyzed. Technological and construction changes introduced based on the research results made it possible to increase the production capacity by 10% and to reduce the consumption of heat energy per 1 ton of produced paper by 16.3%. The article presents a description of changes in the technology of paper production and modernization of the tissue machine made in the years 2013–2022. [ABSTRACT FROM AUTHOR]

Machining accuracy is an important index for evaluating the performance of machine tools. To improve the machining accuracy and efficiency of an internal thread grinding machine and reduce the cost, this paper carries out a forward design of the tolerance of the tool motion axis based on the required precision of the workpiece. Using the parameters of a nut raceway, a calculation model of a sand profile under ideal conditions is established under the principle of spiral surface machining and gear meshing. The tolerance criteria are used to evaluate the machining accuracy through multi-body system theory, Monte Carlo simulation, and least squares fitting. Global sensitivity analysis based on variance is used to identify the key factors influencing error. Finally, statistical analysis is completed with the Monte Carlo method to obtain the reliability of machining accuracy. The geometric design is optimized according to reliability to ensure that the tolerance zone meets the required machining accuracy. The feasibility of this method is verified by designing 30 error tolerance zones for an SCS-180 T grinder. [ABSTRACT FROM AUTHOR]

The surface integrity of machined workpiece is an important index to evaluate the service performance of parts. In order to improve the service performance and machining efficiency of titanium alloy, the concept of variable distribution density micro-textured ball end milling cutter is proposed based on texture effect. Firstly, based on the ball end milling model of titanium alloy, a variable density micro-texture model is established in the chip contact region. Secondly, a milling titanium alloy test platform was built to reveal the change law and mechanism of micro-texture parameters on the machined surface integrity. Finally, based on the hierarchical structure weight method and fuzzy comprehensive evaluation theory, the integrity of the machined surface is comprehensively evaluated and the micro-texture parameters are optimized. The results show that the variable distribution density micro-texture has a positive effect on improving the machining quality of titanium alloy. The micro-texture in the bonding zone can reduce the tool-chip contact area, improve the friction state, increase the heat dissipation area, and reduce the plastic deformation of titanium alloy. The micro-texture in the slip zone can improve the chip curl and promote the stability of the system. In addition, reasonable micro-texture parameters can obtain better surface quality of the workpiece. [ABSTRACT FROM AUTHOR]

There are many state-of-the-art algorithms presented in the literature that perform very well on some evaluation data but are not studied with the data properties on which they are applied; therefore, they could have low performance on data with other characteristics. In this paper, the results of comprehensive research regarding the prediction with the frequently applied AdaBoost algorithm on real-world sensor data are presented. The chosen dataset has some specific characteristics, and it contains error and failure data of several machines and their components. The research aims to investigate whether the AdaBoost algorithm has the capability of predicting failures, thus providing the necessary information for monitoring and condition-based maintenance (CBM). The dataset is analyzed, and the principal characteristics are presented. Performance evaluations of the AdaBoost algorithm that we present show a prediction capability below expectations for this algorithm. The specificity of this study is that it indicates the limitation of the AdaBoost algorithm, which could perform very well on some data, but not so well on others. Based on this research and some others that we performed, and actual research from worldwide studies, we must outline that the mathematical analysis of the data is especially important to develop or adapt algorithms to be very efficient. [ABSTRACT FROM AUTHOR]

Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the response of new drug combinations. However, most existing models have been tested only within a single study, and these models cannot generalize across different datasets due to significantly variable experimental settings. Here, we thoroughly assessed the transferability issue of single-study-derived models on new datasets. More importantly, we propose a method to overcome the experimental variability by harmonizing dose–response curves of different studies. Our method improves the prediction performance of machine learning models by 184% and 1367% compared to the baseline models in intra-study and inter-study predictions, respectively, and shows consistent improvement in multiple cross-validation settings. Our study addresses the crucial question of the transferability in drug combination predictions, which is fundamental for such models to be extrapolated to new drug combination discovery and clinical applications that are de facto different datasets. A machine learning-based method improves the transferability of drug combination predictions across datasets from studies with variable experimental settings, such as the number of doses and dose ranges tested. [ABSTRACT FROM AUTHOR]

Background: Machine-learning models are susceptible to external influences which can result in performance deterioration. The aim of our study was to elucidate the impact of a sudden shift in covariates, like the one caused by the Covid-19 pandemic, on model performance. Methods: After ethical approval and registration in Clinical Trials (NCT04092933, initial release 17/09/2019), we developed different models for the prediction of perioperative mortality based on preoperative data: one for the pre-pandemic data period until March 2020, one including data before the pandemic and from the first wave until May 2020, and one that covers the complete period before and during the pandemic until October 2021. We applied XGBoost as well as a Deep Learning neural network (DL). Performance metrics of each model during the different pandemic phases were determined, and XGBoost models were analysed for changes in feature importance. Results: XGBoost and DL provided similar performance on the pre-pandemic data with respect to area under receiver operating characteristic (AUROC, 0.951 vs. 0.942) and area under precision-recall curve (AUPR, 0.144 vs. 0.187). Validation in patient cohorts of the different pandemic waves showed high fluctuations in performance from both AUROC and AUPR for DL, whereas the XGBoost models seemed more stable. Change in variable frequencies with onset of the pandemic were visible in age, ASA score, and the higher proportion of emergency operations, among others. Age consistently showed the highest information gain. Models based on pre-pandemic data performed worse during the first pandemic wave (AUROC 0.914 for XGBoost and DL) whereas models augmented with data from the first wave lacked performance after the first wave (AUROC 0.907 for XGBoost and 0.747 for DL). The deterioration was also visible in AUPR, which worsened by over 50% in both XGBoost and DL in the first phase after re-training. Conclusions: A sudden shift in data impacts model performance. Re-training the model with updated data may cause degradation in predictive accuracy if the changes are only transient. Too early re-training should therefore be avoided, and close model surveillance is necessary. [ABSTRACT FROM AUTHOR]

Ti6Al4V alloy has been widely used in aerospace and medical fields due to its advantages in superb high strength-to-weight ratio and biocompatibility. However, the hard machinability of Ti6Al4V alloy seriously affects the surface integrity and performance of components. This paper adopts a hybrid process of picosecond laser ablation and micro-milling to fabricate a high-quality U-shape groove on a Ti6Al4V alloy surface. Material removal mechanisms under a single micro-milling process and hybrid process are obtained through experiments and simulations. Picosecond laser ablation with material removal is utilized to produce different textures on the workpiece surface prior to the micro-milling process, and the effect of surface textures on the subsequent micro-milling process is revealed. Furthermore, simulation results show that surface textures ablated by picosecond laser can directly reconstruct the axial cutting depth of micro-milling, thus converting continuous chip forming into an intermittent process and effectively reducing the cutting force. This study offers a promising process to effectively improve the machining performance of hard-to-machine materials. [ABSTRACT FROM AUTHOR]

Tungsten alloy has excellent performance and has been widely used in military, aerospace and nuclear energy, and other cutting-edge industries. However, tungsten alloy has large hardness, high strength, and poor plastic deformation ability, which resulted in high cutting force and serious tool wear during the cutting process, leading to low surface quality of the workpiece after molding. Therefore, it is of great significance to strengthen the research on tungsten alloy cutting technology to promote the development of tungsten alloy application. Firstly, the research progress of tungsten alloy cutting process technology has been systematically reviewed, and the current status of cutting parameters optimization, new cutting methods and devices, and cutting fluid technology have been emphatically reviewed. Secondly, the types of tungsten alloy cutting tools, the relevant tool micro-texture, and tool coatings technology have been briefly described, and the composite cutting technology such as cryogenic cutting, electroplastically assisted cutting, and ultrasonic vibration assisted cutting and the effect of cutting performance prediction technology on tungsten alloy machining performance have been summarized. Finally, the development prospect of tungsten alloy cutting technology has been prospected. [ABSTRACT FROM AUTHOR]

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( F y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of F y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones. [ABSTRACT FROM AUTHOR]

Meningioma is the most common primary tumor of the central nervous system (CNS). Individualized treatment strategies should be formulated for the patients according to the WHO (World Health Organization) grade. Our aim was to investigate the effectiveness of various machine learning and traditional statistical models in predicting the WHO grade of preoperative patients with meningioma. Patients diagnosed with meningioma after surgery in West China Hospital and Shangjin Hospital of Sichuan University from 2009 to 2016 were included in the study cohort. As the training cohort (n = 1975), independent risk factors associated with high-grade meningioma were used to establish the Nomogram model. which was validated in a subsequent cohort (n = 1048) from 2017 to 2019 in our hospital. Logistic regression (LR), XGboost, Adaboost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) models were determined using F1 score, recall, accuracy, the area under the curve (ROC), calibration plot and decision curve analysis (DCA) were used to evaluate the different models. Logistic regression showed better predictive performance and interpretability than machine learning. Gender, recurrence history, T1 signal intensity, enhanced signal degree, peritumoral edema, tumor diameter, cystic, location, and NLR index were identified as independent risk factors and added to the nomogram. The AUC (Area Under Curve) value of RF was 0.812 in the training set, 0.807 in the internal validation set, and 0.842 in the external validation set. The calibration curve and DCA (Decision Curve Analysis) indicated that it had better prediction efficiency of LR than others. The Nomogram preoperative prediction model of meningioma of WHO II and III grades showed effective prediction ability. While machine learning exhibits strong fitting ability, it performs poorly in the validation set. [ABSTRACT FROM AUTHOR]

Titanium alloys are extensively used in various industries due to their excellent corrosion resistance and outstanding mechanical properties. However, titanium alloys are difficult to machine due to their low thermal conductivity and high chemical reactivity with tool materials. In recent years, there has been increasing interest in the use of titanium components produced by additive manufacturing (AM) for a range of high-value applications in aerospace, biomedical, and automotive industries. The machining of additively manufactured titanium alloys presents additional machining challenges as the alloys exhibit unique properties compared to their wrought counterparts, including increased anisotropy, strength, and hardness. The associated higher cutting forces, higher temperatures, accelerated tool wear, and decreased machinability lead to an expensive and unsustainable machining process. The challenges in machining additively manufactured titanium alloys are not comprehensively documented in the literature, and this paper aims to address this limitation. A review is presented on the machining characteristics of titanium alloys produced by different AM techniques, focusing on the effects of anisotropy, porosity, and post-processing treatment of additively manufactured Ti-6Al-4V, the most commonly used AM titanium alloy. The mechanisms resulting in different machining performance and quality are analysed, including the influence of a hybrid manufacturing approach combining AM with conventional methods. Based on the review of the latest developments, a future outlook for machining additively manufactured titanium alloys is presented. [ABSTRACT FROM AUTHOR]

Due to COVID-19, the researching of educational data and the improvement of related systems have become increasingly important in recent years. Educational institutions seek more information about their students to find ways to utilize their talents and address their weaknesses. With the emergence of e-learning, researchers and programmers aim to find ways to maintain students' attention and improve their chances of achieving a higher grade point average (GPA) to gain admission to their desired colleges. In this paper, we predict, test, and provide reasons for declining student performance using various machine learning algorithms, including support vector machine with different kernels, decision tree, random forest, and k-nearest neighbors algorithms. Additionally, we compare two databases, one with data related to online learning and another with data on relevant offline learning properties, to compare predicted weaknesses with metrics such as F1 score and accuracy. However, before applying the algorithms, the databases need normalization to meet the prediction format. Ultimately, we find that success in school is related to habits such as sleep, study time, and screen time. More details regarding the results are provided in this paper. [ABSTRACT FROM AUTHOR]

Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal with the problem, they have yet to consider how to select a proper treatment, especially when uncertainty levels are high. Applying rough-fuzzy theory to the imbalanced data learning problem could be a promising research direction that generates the synthetic data and removes the outliers. The proposed work identifies the positive, boundary, and negative regions of the target set using the rough set theory and removes the objects in the negative region as outliers. It also explores the positive and boundary regions of the rough set by applying the fuzzy theory to generate the samples of the minority class and remove the samples of the majority class. Thus the proposed rough-fuzzy approach performs both oversampling and undersampling to handle the imbalanced class problem. The experimental results demonstrate that the novel technique allows qualitative and quantitative data handling. [ABSTRACT FROM AUTHOR]

In this paper, a fractal-fractional model of the impact stress on the crusher drum is established by using He's fractal derivative and the fluid-solid coupling vibration equation. The two-scale transform is used to obtain its solution, which can be used to improve the safety performance of beating machines. [ABSTRACT FROM AUTHOR]

Hepatitis C is a liver infection caused by the hepatitis C virus (HCV). Due to the late onset of symptoms, early diagnosis is difficult in this disease. Efficient prediction can save patients before permeant liver damage. The main objective of this study is to employ various machine learning techniques to predict this disease based on common and affordable blood test data to diagnose and treat patients in the early stages. In this study, six machine learning algorithms (Support Vector Machine (SVM), K-nearest Neighbors (KNN), Logistic Regression, decision tree, extreme gradient boosting (XGBoost), artificial neural networks (ANN)) were utilized on two datasets. The performances of these techniques were compared in terms of confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), and the area under the curve (AUC) to identify a method that is appropriate for predicting this disease. The analysis, on NHANES and UCI datasets, revealed that SVM and XGBoost (with the highest accuracy and AUC among the test models, >80%) can be effective tools for medical professionals using routine and affordable blood test data to predict hepatitis C. [ABSTRACT FROM AUTHOR]

After applying high-frequency vibration of micro-displacement to the tool of the common turning method, it has a significant positive effect on improving the machinability of the machined material, but the high-frequency vibration amplitude will make the tool produce a serious plowing effect and impact effect; it will not only shorten the tool life but also affect the processing force and surface roughness. As one of the main factors affecting the plowing effect of three-drive ultrasonic elliptical vibration-assisted turning, the contribution of tool geometry to the machining performance of three-drive ultrasonic elliptical vibration-assisted turning was investigated. It is considered that the tool nose radius, tool sharp angle, and clearance angle all have an influence on the processing force and surface roughness, and among them, the effect trend of the tool clearance angle on the two is obviously different from the tool nose radius and sharp angle. As one of the main factors affecting the impact effect of three-drive ultrasonic elliptical vibration-assisted turning, the effect trend of workpiece geometry (length-diameter ratio) on the processing force and surface roughness formed by three-drive ultrasonic elliptical vibration-assisted turning was determined. It is found that the processing force fluctuates greatly when the three-drive ultrasonic elliptical vibration-assisted turning is used to process workpieces with large length-diameter ratios, and the average processing force and surface roughness also tend to increase. The study also found that under the same cutting conditions, the machining performance and machining system stability of three-drive ultrasonic elliptical vibration-assisted turning are better than common turning. However, the influence of the workpiece geometry on the surface roughness and processing force of three-drive ultrasonic elliptical vibration-assisted turning is more significant than that of common turning. [ABSTRACT FROM AUTHOR]

In this paper, the Hunger Games Search (HGS) optimization algorithm is used to estimate the electrical parameters of the induction machine steady state model. Induction machine nameplate data is used as input to the proposed algorithm. The performance of the proposed method is confil'med by comparing the output characteristics obtained by estimating the motor parameters including torque, current and power factor in the steady state model of the induction machine with the values provided by the manufacturer. In addition, by evaluating and comparing the results of the proposed method with the results of previous research, it is shown that the proposed algorithm is a very effective and accurate method for the acceptable estimation of induction machine parameters. [ABSTRACT FROM AUTHOR]

Success in the industrial sector is compromised by diverse conditions such as imperfect product production, manufacturing line interruptions, and unscheduled maintenance. The precise use of common practices in production environments is an available solution to eliminate some of these issues. Applying a warm-up period in a manufacturing process is adequate and cost-effective for almost all companies. It improves the equipment's productivity and helps the manufacturing line generate fewer defective products. Even though several inventory management studies have included a warm-up phase in their models, its use in economic production quantity (EPQ) models remains largely unexplored. Adding a warm-up phase to the production cycle minimizes maintenance expenses and defective products and increases the machine's performance. In this study, the dependency between the machine downtime and the warm-up length is examined for the first time. The warm-up time depends on the machine's off-state period: if the machine has a longer operation timeout, then a longer warm-up period is needed. The model includes a function to model the warm-up time relative to the machine downtime and two types of defective products: scrapping and reworking items. The study is concluded with some numerical examples, a sensitivity analysis, and some management insights related to the EPQ. [ABSTRACT FROM AUTHOR]

Salvage logging is increasing in Central Europe because of the growth of severe meteorological events, and timber harvesting in these conditions is challenging in terms of both productivity performances and safety of the operations. In recent years, with the increase of natural calamities, several researchers studied machinery productivity performances regarding salvage logging carried out by ground-based systems. In fact, a common post-disturbance management approach is salvage logging which consists of the widespread removal of damaged trees. In this research, system productivity and the cost of salvage logging are analysed in European beech stands affected by wet snow. The accretion of heavy wet snow poses the greatest risk to forests in the Northern Hemisphere. This type of snow attaches more effectively to tree crowns and branches when temperatures are close to freezing at the time of precipitation. As a result, trees may break or bend and may be uprooted when the soil is unfrozen. This study has been implemented to evaluate the productivity and cost-effectiveness of extraction in salvage logging deployed with a skidder in beech stands affected by two different types of wet snow damage. The results show that the productivity of the four-wheel-drive cable skidder, despite operating in salvage cutting with a removal intensity of 10%, is 14.73 m3·SMH-1, similar to skidder performances in 'ordinary' cuttings. Skidder's productive time was 86% of the scheduled time, whereas the delays were due to organisational reasons, mechanical delays, and adverse weather conditions. The mean travel speed of the cable skidder obtained in this study is close to the results obtained from other studies on similar machines. The costs per unit are lower than effective cost consumptions for the other cable skidders and agricultural tractors, adapted for skidding operated in hardwood salvage logging. Therefore, under the given conditions, the operation of the fourwheel-drive cable skidder is viable from a silvicultural, technical, and economic point of view in the salvage operation logging. [ABSTRACT FROM AUTHOR]

A cost-effective harvesting method and equipment for small-scale farms is essential to ensure the viability and sustainability of their operations. This study aims to retrofit and test a pull-type combine harvester for effective and efficient operations in small grains. A 1960s-era functional combine harvester was retrofitted with a stripper header rotor in place of the sickle bar cutting system to increase the harvesting capacity. Preliminary field testing found that the original header auger and feeder house could not handle the changed crop composition at an increased capacity and did not properly convey the grain from the auger to the feeder house and onto the threshing system. Therefore, redesign modifications were conducted for the auger and feeder house to help increase the efficiency and capacity required to use the stripper header. Various design concepts were proposed, analyzed, and prototyped in this study. The machine performance of the material-conveying efficiency of the wheat-material-other-than-grain mixture was evaluated at various material throughput rates. The results showed that the auger shim and feeder house paddle redesign increased the efficiency from 80.68% to 98.56%, as compared to the original machine configuration. The cost-effective and high-performance pull-type combine harvester proposed in this study has a high potential in solving the bottleneck problem of local production of small grains by small-scale farming operations. [ABSTRACT FROM AUTHOR]