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

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

Lithium-ion batteries are becoming more popular due to their superior performance like high power density, long lifespan, broad operating range of temperatures, quick charging capabilities, and low self-discharge. The implementation of a Battery Management System (BMS) is crucial in order to guarantee the secure and optimal functioning of electric vehicle batteries. BMS monitors, controls, and maintains the health and performance of rechargeable batteries by monitoring the State of Charge (SoC), State of Health (SoH), and State of Temperature (SoT). SoC quantifies the amount of energy stored in a battery at a certain moment, and it is employed to approximate the remaining distance that may be covered. SoH describe the overall health or state of a rechargeable battery. It reveals how well a battery performs in comparison to its original state. SoT estimation keeps the battery temperature within a safe range, allowing it to reach a higher age and safety, which is critical for battery reliability. In this paper SoC, SoH, SoT estimation models for a lithium-ion battery have been developed using an improved EP-based R110-BLSTM approach. The Emperor Penguin based Residual Network-110 incorporated Bidirectional Long-Short Term Memory (EP-based R110-BLSTM) is ideal for estimating SoC, SoH, and SoT and features great accuracy, a quick estimation speed, and strong generalization capabilities. However, Extreme Learning Machine performance is heavily reliant on proper feature extraction. In order to enhance estimate performance by extracting the best features, Simulated-Annealing-based Golden Eagle optimization is utilized. Electric vehicle drive cycles are used to test the model's resilience to temperature changes. MATLAB 2018b software is used in the execution of this research. The results demonstrate that the proposed model performs better in terms of accuracy and has lower SoC, SoH, and SoT error rates than existing models. A thorough comparison between the recommended model and existing methods is also made, further demonstrating the proposed model's superiority. [ABSTRACT FROM AUTHOR]

Induction machines are widely utilized in the industry due to their sturdy construction nature and relatively simple maintenance. For the design, control, optimization, and failure analysis procedures of induction machines, equivalent circuit parameters are required. These parameters can be estimated using a variety of techniques, such as traditional testing, experimental research, analytical methods, programming, and machine learning algorithms. However, the aforementioned methods have limitations; experimental methods are laborious and time-consuming, analytical methods require complicated and iterative computations for each machine, and programming algorithms involve writing of lengthy command sequences. The article concentrates on a high precision estimation method based on neural network algorithms to avoid complex calculations, thus, minimizing time and effort, and eliminating errors. In this context, it is aimed to provide a greater reliability compared to studies using small amounts of datasets and to investigate earlier methods, which are not studied vastly, for estimating the parameters of induction machines. In this regard, three distinct artificial neural networks were applied to a large dataset consisting of 1164 machines with power output ranging from 4 to 900 kW and belonging to 7 different manufacturers. RMSE values of 0.0125 were attained in parameter estimation, and artificial neural networks produced encouraging results. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been made in the field of non-autoregressive machine translations. However, the accuracy of non-autoregressive models still lags behind their autoregressive counterparts. This discrepancy can be attributed to the abundance of repetitive tokens in the target sequences generated by non-autoregressive models. In this study, we delve into this phenomenon and propose a novel approach to train a non-autoregressive model using unlikelihood loss. We evaluate our method on three widely used benchmark tasks. The experimental results demonstrating that our proposed approach significantly reduces the number of repetitive tokens while improving the overall performance of non-autoregressive machine translations. Compared to the baseline model "Mask-Predict", the average number of repetitions on IWSLT 14 DE → EN valid set is reduced from 0.48 to 0.17, resulting in a remarkable 62% decrease. [ABSTRACT FROM AUTHOR]

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs. [ABSTRACT FROM AUTHOR]

Accurate reference evapotranspiration (ET0) estimation has an effective role in reducing water losses and raising the efficiency of irrigation water management. The complicated nature of the evapotranspiration process is illustrated in the amount of meteorological variables required to estimate ET0. Incomplete meteorological data is the most significant challenge that confronts ET0 estimation. For this reason, different machine learning techniques have been employed to predict ET0, but the complicated structures and architectures of many of them make ET0 estimation very difficult. For these challenges, ensemble learning techniques are frequently employed for estimating ET0, particularly when there is a shortage of meteorological data. This paper introduces a powerful super learner ensemble technique for ET0 estimation, where four machine learning models: Extra Tree Regressor, Support Vector Regressor, K-Nearest Neighbor and AdaBoost Regression represent the base learners and their outcomes used as training data for the meta learner. Overcoming the overfitting problem that affects most other ensemble methods is a significant advantage of this cross-validation theory-based approach. Super learner performances were compared with the base learners for their forecasting capabilities through different statistical standards, where the results revealed that the super learner has better accuracy than the base learners, where different combinations of variables have been used whereas Coefficient of Determination (R2) ranged from 0.9279 to 0.9994 and Mean Squared Error (MSE) ranged from 0.0026 to 0.3289 mm/day but for the base learners R2 ranged from 0.5592 to 0.9977, and MSE ranged from 0.0896 to 2.0118 mm/day therefore, super learner is highly recommended for ET0 prediction with limited meteorological data. [ABSTRACT FROM AUTHOR]

Evaluating the ability of a classifier to make predictions on unseen data and increasing it by tweaking the learning algorithm are two of the main reasons motivating the evaluation of classifier predictive performance. In this study the behavior of Balanced A C 1 — a novel classifier accuracy measure — is investigated under different class imbalance conditions via a Monte Carlo simulation. The behavior of Balanced A C 1 is compared against that of several well-known performance measures based on binary confusion matrix. Study results reveal the suitability of Balanced A C 1 with both balanced and imbalanced data sets. A real example of the effects of class imbalance on the behavior of the investigated classifier performance measures is provided by comparing the performance of several machine learning algorithms in a churn prediction problem. [ABSTRACT FROM AUTHOR]

Forecasting the volume of water allocated to drinking water supply (DWS) and irrigation is strategic for efficient and effective planning and management of water mobilized by reservoir dams. The objective of this study is to simulate the total volume allocated (TVA) of water maintaining the minimum level of the reservoir and preventing spillovers. However, accurate and reliable simulation of TVA for optimum use of water resources cannot be achieved without precise and highly performing models. Therefore, this research has examined and compared three machine learning (ML) algorithms namely; random forest regression (RFR), support vector regression (SVR) and multi-layer perceptron neural network (MLPNN), using a database, of eight operating variables at the daily time step, collected over eight years (2009- 2017) at the Zit Emba dam (ZED) reservoir, in Algeria. Seven input combinations were considered and analyzed to find the best input variables for simulating TVA. The results indicate that although all the models, with five inputs, are adequate for modeling TVA, the performance of the RFR is better than the other models giving the correlation coefficient of 0.920, the root mean square error 0.006 hm3, the mean absolute error 0.003 hm3, and the Nash–Sutcliffe efficiency of 0.847. The findings demonstrate that the three ML algorithms are all promising tools for simulating TVA from the reservoir. Accordingly, the accuracy and efficiency of these models emphasize on their importance to be considered in reservoir planning and management. [ABSTRACT FROM AUTHOR]

Tunnel boring machine (TBM) performance prediction in mechanized tunneling is an essential factor for selecting an appropriate excavation machine, tunnel design, and safe construction. To implement safe mechanized excavation, it is important to accurately assess and predict the range of machine driving parameters, especially the machine rate of penetration (ROP); this can reduce the cost of TBM repairs due to the abrasion of disc cutters and cutterhead and also has a positive effect on the post-construction period. This study focuses on predicting the ROP of TBMs passing through metamorphic rocks during deep excavation and under a complex geotechnical situation. For this purpose, three fuzzy-based models of the Mamdani fuzzy inference system (MFIS), adaptive neuro-fuzzy inference system (ANFIS), Takagi Sugeno fuzzy model (TSF), as well as linear and non-linear regression models were developed. Historical tunnels were used to compile 189 data points (151 for training and 37 for testing). In the dataset, three parameters, including uniaxial compressive strength (UCS), cutterhead rotational speed per minute (RPM), and thrust force (TF), were considered effective parameters on the TBM's ROP. According to the findings, the suggested models provided satisfactory and consistent accuracy. Moreover, the results demonstrated that the forecasted values correlate rather well with the measured ones. The proposed algorithms can be considered for use in similar ground and tunneling conditions (metamorphic rocks with low-average strength). It is worth noting that this study has the potential to drastically cut down on tunneling uncertainties and makes fuzzy inference systems a robust algorithm for planning mechanized tunneling. Highlights: Prediction of TBM performance in complex geological conditions. Forecasting TBM performance in deep tunnels passing through metamorphic rocks. Presenting an empirical model for calculating the TBM performance based on statistical analysis. Detailed analysis of fuzzy-based techniques potential for TBM performance prediction. Examining the models' accuracy with several loss functions and statistical indices. [ABSTRACT FROM AUTHOR]

Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that ML models trained on the augmented data consistently achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment. [ABSTRACT FROM AUTHOR]

What dataset features affect machine learning (ML) performance has primarily been unknown in the current literature. This study examines the impact of tabular datasets' different meta-level and statistical features on the performance of various ML algorithms. The three meta-level features this study considered are the dataset size, the number of attributes and the ratio between the positive (class 1) and negative (class 0) class instances. It considered four statistical features for each dataset: mean, standard deviation, skewness and kurtosis. After applying the required scaling, this study averaged (uniform and weighted) each dataset's different attributes to quantify its four statistical features. We analysed 200 open-access tabular datasets from the Kaggle (147) and UCI Machine Learning Repository (53) and developed ML classification models (through classification implementation and hyperparameter tuning) for each dataset. Then, this study developed multiple regression models to explore the impact of dataset features on ML performance. We found that kurtosis has a statistically significant negative effect on the accuracy of the three non-tree-based ML algorithms of the Support vector machine (SVM), Logistic regression (LR) and K-nearest neighbour (KNN) for their classical implementation with both uniform and weighted aggregations. This study observed similar findings in most cases for ML implementations through hyperparameter tuning, except for SVM with weighted aggregation. Meta-level and statistical features barely show any statistically significant impact on the accuracy of the two tree-based ML algorithms (Decision tree and Random forest), except for implementation through hyperparameter tuning for the weighted aggregation. When we excluded some datasets based on the imbalanced statistics and a significantly higher contribution of one attribute compared to others to the classification performance, we found a significant effect of the meta-level ratio feature and statistical mean and standard deviation features on SVM, LR and KNN accuracy in many cases. Our findings open a new research direction in understanding how dataset characteristics affect ML performance and will help researchers select appropriate ML algorithms for a possible optimal accuracy outcome. [ABSTRACT FROM AUTHOR]

In this research, the optical properties of the PVP: ZnTiO3 nanocomposite are studied using the spectroscopic ellipsometry technique. The preparation procedure of the ZnTiO3 nanocomposite is explained in detail. The absorbance/transmittance, surface morphology, structural information, chemical identification, and surface topography of the ZnTiO3 nanocomposite is studied using UV–Vis spectroscopy, field-emission scanning electron microscopy, Raman spectroscopy, Fourier transform infra-red, and atomic force microscopy, respectively. The ellipsometry method is used to obtain the spectra of the real and imaginary parts of the dielectric function and refractive index in the photon energy range of 0.59–4.59 eV. Moreover, using two machine learning algorithms, namely artificial neural network and support vector regression methods, the ellipsometric parameters ψ and Δ are analyzed and compared with non-linear regression. The error and accuracy of each three methods, as well as the time required for their execution, are calculated to compare their suitability in the ellipsometric data analysis. Also, the absorption coefficient was used to determine the band gap energy of the ZnTiO3 nanocomposite, which is found to be 3.83 eV. The second-energy derivative of the dielectric function is utilized to identify six critical point energies of the prepared sample. Finally, the spectral-dependent optical loss function and optical conductivity of the ZnTiO3 nanocomposite are investigated. [ABSTRACT FROM AUTHOR]

Electrochemical machining (ECM) is a contemporary electrochemical energy-based machining practice that has widely been attempted for the productive processing of a vast range of typical engineering materials, namely, composites and difficult-to-process advanced materials. In the present work, an attempt has been made to critically review and report the previously investigated studies and research in the domain of machining numerous materials with electrochemical machining and its other allied methods. This work also deeply explores the various theoretical, experimental, modeling-based, and optimization-related research studies carried out in the broad domain of ECM for finding out the most effective and conclusive outcomes with the overall process improvement with the support and quality selection of machine, tool materials, electrolyte flow, and some other operating parameters. While shifting from macro- to micro-electrochemical machining, numerous investigations have been reported in the literature that have been targeted to enhance the overall machining performance and the measures. Among the numerous acquirable modifications in assisted ECM as employed in research and developments and industries, vibration-assisted ECM has been revealed as a more sturdily established ECM variation as compared to other explored versions such as abrasive Jet-ECM, magnetic-assisted ECM, and laser-assisted ECM. Furthermore, the in-depth discussion about jet-assisted ECM and wire ECM has also been reported, along with the considerations of several impactful process inputs. The concluding section of this article explores the several developments made in wire and micro-ECM processes and layouts the issues and directions for further research. [ABSTRACT FROM AUTHOR]

Streamflow prediction is an important matter for the water resources management and the design of hydraulic structures that can be built on rivers. Recently, it has become a widely studied research field where data obtained from stream gauge stations can be utilized for creating estimating models by resorting to different methods such as machine and deep learning techniques. In this study, we performed monthly streamflow predictions by using the following data-driven methods of machine learning: linear regression, support vector regression, random forest and deep learning (DL) models to compare the performances of ML's and DL's techniques. A general workflow that can be applied to similar regions is presented. An estimating model containing six-input combinations and time-lagged streamflow data is improved by means of the autocorrelation function (ACF) and partial autocorrelation function (PACF). Furthermore, moving average is used as a smoothing technique to make the dataset more stable and reduce the effects of noise data. A comparative evaluation has been conducted to determine the performances of the above-mentioned methods. In this study, we proposed four different DL models and compared them with existing techniques. For the comparison of the results, we used evaluation criteria such as Nash–Sutcliffe efficiency (NSE), mean square error (MSE) and percent bias (PBIAS). The experimental results indicate that our bidirectional gated recurrent units (BiGRU) model outperforms both ML algorithms and existing solutions with 0.971 NSE, 0.001 MSE and − 1.536 PBIAS scores. [ABSTRACT FROM AUTHOR]

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a 21.16 % reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools. [ABSTRACT FROM AUTHOR]

Prior to further processing, completed questionnaires must be screened for the presence of careless respondents. Different people will respond to surveys in different ways. Some take the easy path and fill out the survey carelessly. The proportion of careless respondents determines the survey's quality. As a result, identifying careless respondents is critical for the quality of obtained results. This study aims to explore the characteristics of careless respondents in survey data and evaluate the predictive power and interpretability of different types of data and indices of careless responding. The research question focuses on understanding the behavior of careless respondents and determining the effectiveness of various data sources in predicting their responses. Data from a three-month web-based survey on participants' personality traits such as honesty-humility, emotionality, extraversion, agreeableness, conscientiousness and openness to experience was used in this study. Data for this study was taken from Schroeders et al.. The gradient boosting machine-based prediction model uses data from the answers, time spent for answering, demographic information on the respondents as well as some indices of careless responding from all three types of data. Prediction models were evaluated with tenfold cross-validation repeated a hundred times. Prediction models were compared based on balanced accuracy. Models' explanations were provided with Shapley values. Compared with existing work, data fusion from multiple types of information had no noticeable effect on the performance of the gradient boosting machine model. Variables such as "I would never take a bribe, even if it was a lot", average longstring, and total intra-individual response variability were found to be useful in distinguishing careless respondents. However, variables like "I would be tempted to use counterfeit money if I could get away with it" and intra-individual response variability of the first section of a survey showed limited effectiveness. Additionally, this study indicated that, whereas the psychometric synonym score has an immediate effect and is designed with the goal of identifying careless respondents when combined with other variables, it is not necessarily the optimal choice for fitting a gradient boosting machine model. [ABSTRACT FROM AUTHOR]

Probabilistic computing has been introduced to operate functional networks using a probabilistic bit (p-bit), broadening the computational abilities in non-deterministic polynomial searching operations. However, previous developments have focused on emulating the operation of quantum computers similarly, implementing every p-bit with large weight-sum matrix multiplication blocks and requiring tens of times more p-bits than semiprime bits. In addition, operations based on a conventional simulated annealing scheme required a large number of sampling operations, which deteriorated the performance of the Ising machines. Here we introduce a prime factorization machine with a virtually connected Boltzmann machine and probabilistic annealing method, which are designed to reduce the hardware complexity and number of sampling operations. From 10-bit to 64-bit prime factorizations were performed, and the machine offers up to 1.2 × 108 times improvement in the number of sampling operations compared with previous factorization machines, with a 22-fold smaller hardware resource. [ABSTRACT FROM AUTHOR]

There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorithms and classification algorithms was trained and tuned using a ten-fold cross validation, then AUC was computed. This procedure was repeated three times per dataset. Best overall performances were obtained with JMI and JMIM as feature selection algorithms and random forest and linear regression models as classification algorithms. The choice of the classification algorithm was the factor explaining most of the performance variation (10% of total variance). The choice of the feature selection algorithm explained only 2% of variation, while the train-test split explained 9%. [ABSTRACT FROM AUTHOR]

This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS. [ABSTRACT FROM AUTHOR]

In this study, the performance of machine learning methods was examined in terms of predicting the science education achievement scores of the students who took the exam for the next term, PISA 2018, and the science average scores of the countries, using PISA 2015 data. The research sample consists of a total of 67,329 students who took the PISA 2015 exam from 13 randomly selected countries (Brazil, Chinese Taipei, Dominican Republic, Estonia, Finland, Hungary, Italy, Japan, Lithuania, Luxembourg, Peru, Singapore, Türkiye). In this study, multiple linear regression, support vector regression, random forest, and extreme gradient boosting (XGBoost) machine learning algorithms were used. For the machine learning process, a randomly determined part from the PISA-2015 data of each country researched was divided as training data and the remaining part as testing data to evaluate model performance. As a result of the research, it was determined that the XGBoost algorithm showed the best performance in estimating both PISA-2015 test data and PISA-2018 science academic achievement scores in all researched countries. Furthermore, it was determined that the highest PISA-2018 science achievement scores of the students who participated in the exam, estimated by this algorithm, were in Luxembourg (r = 0.600, RMSE = 75.06, MAE = 59.97), while the lowest were in Finland (r = 0.467, RMSE = 79.38, MAE = 63.24). In addition, the average PISA-2018 science scores of the countries were estimated with the XGBoost algorithm, and the average science scores calculated for all the countries studied were estimated with very high accuracy. [ABSTRACT FROM AUTHOR]

This study conducted a comprehensive analysis of multiple supervised machine learning models, regressors and classifiers, to accurately predict diamond prices. Diamond pricing is a complex task due to the non-linear relationships between key features such as carat, cut, clarity, table, and depth. The analysis aimed to develop an accurate predictive model by utilizing both regression and classification approaches. To preprocess the data, the study employed various techniques. The work addressed outliers, standardized the predictors, performed median imputation of missing values, and resolved multicollinearity issues. Equal-width binning on the cut variable was performed to handle class imbalance. Correlation-based feature selection was utilized to eliminate highly correlated variables, ensuring that only relevant features were included in the models. Outliers were handled using the inter-quartile range method, and numerical features were normalized through standardization. Missing values in numerical features were imputed using the median, preserving the integrity of the dataset. Among the models evaluated, the RF regressor exhibited exceptional performance. It achieved the lowest root mean squared error (RMSE) of 523.50, indicating superior accuracy compared to the other models. The RF regressor also obtained a high R-squared ( R 2 ) score of 0.985, suggesting it explained a significant portion of the variance in diamond prices. Furthermore, the area under the curve with RF classifier for the test set was 1.00 (100 %) , indicating perfect classification performance. These results solidify the RF's position as the best-performing model in terms of accuracy and predictive power, both in regression and classification. The MLP regressor showed promising results with an RMSE of 563.74 and an R 2 score of 0.980, demonstrating its ability to capture the complex relationships in the data. Although it achieved slightly higher errors than the RF regressor, further analysis is needed to determine its suitability and potential advantages compared to the RF regressor. The XGBoost Regressor achieved an RMSE of 612.88 and an R 2 score of 0.972, indicating its effectiveness in predicting diamond prices but with slightly higher errors compared to the RF regressor. The Boosted Decision Tree Regressor had an RMSE of 711.31 and an R 2 score of 0.968, demonstrating its ability to capture some of the underlying patterns but with higher errors than the RF and XGBoost models. In contrast, the KNN regressor yielded a higher RMSE of 1346.65 and a lower R 2 score of 0.887, indicating its inferior performance in accurately predicting diamond prices compared to the other models. Similarly, the Linear Regression model performed similarly to the KNN regressor, with an RMSE of 1395.41 and an R 2 score of 0.876. The Support Vector Regression model showed the highest RMSE of 3044.49 and the lowest R 2 score of 0.421, indicating its limited effectiveness in capturing the complex relationships in the data. Overall, the study demonstrates that the RF outperforms the other models in terms of accuracy and predictive power, as evidenced by its lowest RMSE, highest R 2 score, and perfect classification performance. This highlights its suitability for accurately predicting diamond prices. The study not only provides an effective tool for the diamond industry but also emphasizes the importance of considering both regression and classification approaches in developing accurate predictive models. The findings contribute valuable insights for pricing strategies, market trends, and decision-making processes in the diamond industry and related fields. [ABSTRACT FROM AUTHOR]

Singularities in five-axis machining are a series of positions where the exact motion of the rotary axis becomes unpredictable or incalculable. In the neighborhood of a singular point, machining may cause unstable movements of drive axes and deteriorative dynamic performance of machine tools. In this paper, we proposed a closed-loop inverse kinematics method to solve accurately the five-axis joints' parameters around singular points. To achieve this purpose, the damped Jacobian pseudoinverse algorithm is introduced and the machine joints' velocities are calculated directly by the velocities of the five-axis cut points. The joints are then obtained by integrating the corresponding velocities. Furthermore, a feedback control method is applied to reduce the integrating error. In the end, simulations and experiments' results demonstrated the effectiveness of the closed-loop inverse kinematics method. [ABSTRACT FROM AUTHOR]

Probabilistic computing has been introduced to operate functional networks using a probabilistic bit (p-bit), broadening the computational abilities in non-deterministic polynomial searching operations. However, previous developments have focused on emulating the operation of quantum computers similarly, implementing every p-bit with large weight-sum matrix multiplication blocks and requiring tens of times more p-bits than semiprime bits. In addition, operations based on a conventional simulated annealing scheme required a large number of sampling operations, which deteriorated the performance of the Ising machines. Here we introduce a prime factorization machine with a virtually connected Boltzmann machine and probabilistic annealing method, which are designed to reduce the hardware complexity and number of sampling operations. From 10-bit to 64-bit prime factorizations were performed, and the machine offers up to 1.2 × 108 times improvement in the number of sampling operations compared with previous factorization machines, with a 22-fold smaller hardware resource. [ABSTRACT FROM AUTHOR]

Correctly predicting up and down trends for stock prices is of immense important in the financial market. To further improve the prediction performance, in this paper we introduce five penalties: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation and minimax concave penalty to logistic regressions with 19 technical indicators, and propose the five penalized logistic regressions to predict up and down trends for stock prices. Firstly, we translate the five penalized logistic log-likelihood functions into the five penalized weighted least squares functions and combine them with the tenfold cross-validation method to calculate the solution path to parameter estimators. Secondly, we combine the binomial deviation with cross-validation error as a risk measure to choose an appropriate tuning parameter for the penalty functions and apply the training set and the coordinate descent algorithm to obtain parameter estimators and probability estimators. Thirdly, we employ the testing set and the chosen optimal thresholds to construct two-class confusion matrices and receiver operating characteristic curves to assess the prediction performances to the five regressions. Finally, we compare the proposed five penalized logistic regressions with logistic regression, support vector machine and artificial neural network and found that the minimax concave penalty logistic regression performs the best in terms of the prediction performance to up and down trends for Google's stock prices. Therefore, in this paper we propose the five new prediction methods to improve the prediction accuracy of stock returns and bring economic benefits for investors. [ABSTRACT FROM AUTHOR]

Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine learning models in the presence of limited labeled data. Although many self-supervised learning methods have been suggested before, they have failed to exploit the unique characteristics of genomic data. Therefore, we introduce Self-GenomeNet, a self-supervised learning technique that is custom-tailored for genomic data. Self-GenomeNet leverages reverse-complement sequences and effectively learns short- and long-term dependencies by predicting targets of different lengths. Self-GenomeNet performs better than other self-supervised methods in data-scarce genomic tasks and outperforms standard supervised training with ~10 times fewer labeled training data. Furthermore, the learned representations generalize well to new datasets and tasks. These findings suggest that Self-GenomeNet is well suited for large-scale, unlabeled genomic datasets and could substantially improve the performance of genomic models. Self-GenomeNet, a self-supervised learning technique, is trained by predicting unlabeled reverse-complement genome sequences of different lengths and improves the performance of models substantially when a limited amount of labeled data is available. [ABSTRACT FROM AUTHOR]

In automated fiber placement (AFP), the abrupt variations of orientations affect layup quality and kinematic performance. This paper proposes a quaternion-based orientation smoothing method constrained by the Domain of Admissible Orientation (DAO) to solve this problem. The DAO is the orientation domain attached to each orientation and defined by the interference avoidance and layup process constraints. All the placement orientations are interpolated into a quaternion curve, and the quaternion curve is smoothed by minimizing its energy function. The energy function is presented in three-dimensional (3D) space, so to facilitate this minimization, the DAO is also transformed into 3D space to constrain the orientation. Then, a spline shortcut generation (SPSG) algorithm is proposed to geometric smooth the curve by generating a spline passing through all DAOs. After that, a Resampling method nonlinearly optimizes the energy function by sampling the curve parameters to redefine the layup orientation within the DAO. Experiments show that the method effectively smooths the orientation trajectory under layup constraints and improves the kinematic performance of APF machines. [ABSTRACT FROM AUTHOR]

Introduction: Epidemiological studies show that breast cancer is the most common cancer in women in the world. Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. The goal can be achieved by using large-scale breast cancer data with the machine learning models Methods: This paper proposes a new intelligent approach using an optimized ensemble classifier for breast cancer diagnosis. The classification is done by proposing a new intelligent Group Method of Data Handling (GMDH) neural network-based ensemble classifier. This method improves the performance of the machine learning technique by using a Teaching–Learning-Based Optimization (TLBO) algorithm to optimize the hyperparameters of the classifier. Meanwhile, we use TLBO as an evolutionary method to address the problem of appropriate feature selection in breast cancer data. Results: The simulation results show that the proposed method has a better accuracy between 7 and 26% compared to the best results of the existing equivalent algorithms. Conclusion: According to the obtained results, we suggest the proposed algorithm as an intelligent medical assistant system for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

In this work, a highly carbonated liquid has been utilized as a dielectric medium in the EDM process. The effect of the dielectric medium has been investigated and then, finally, compared with EDM oil. The outcomes have demonstrated that discharge energy density has escalated to fierce-extent, thus, removing a voluminous amount of material from the workpiece. The results also emphasize that the dielectric medium has a viscosity (8.6 CS) higher than EDM oil (2.16 CS) is helpful for higher MRR. The debris particle has remained suspended in the dielectric medium due to its highly viscous properties leading to high MRR without any flushing technique. The process parameters, pulse-on-time, and peak current have dramatically influenced the EDM process. Further, the surface morphology has also been investigated. Results have also revealed that at a low peak current value, the MRR has increased by 155.55%, whereas, at a low value of pulse-on-time, MRR has decreased by 28.25%. At a high peak current value, MRR has increased by 217.55%, whereas, at a high value of pulse-on-time, MRR has increased by 324.05%. The highly carbonated liquid dielectric has considered the most suitable dielectric medium for high discharge energy. Micro-crack density and size have increased with MRR because of highly concentrated discharge energy. The recast layer thickness has increased with the dielectric medium's viscosity because of improper molten metal flushing, and the molten metal re-solidifies on the same surface. [ABSTRACT FROM AUTHOR]

The applications of fused deposition modelling (FDM) are stretched widely by making functional parts. Metal oxides-based polymer nano composites have great potential to develop those functional parts. The intention of this work is to recognize the appropriate metal oxide filler and level of its loading in composite filament which is compatible for FDM by analyzing their tensile and metal flow properties. Filaments were synthesized by taking acrylonitrile butadiene styrene (ABS) as common matrix material and two metal oxides such as copper oxide (CuO) and cobalt oxide (Co3O4) were taken as fillers with 0.5, 1.0, 1.5 and 2.0 wt.% of loading. The composite filaments were fabricated using a single screw extruder after compounding in a twin screw extruder. Totally, nine filaments of diameter 1.75±0.1 mm were extruded and all the samples were characterized by tensile strength and metal flow index measures. An increased pattern of tensile strength and decreased trend of % elongation (%EL) and metal flow index (MFI) values were noticed for increased filler loading for both fillers. When considering ABS as a reference material, CuO shows better results compared to Co3O4. Even though Co3O4 shows high tensile strength, its %EL and MFI values were not good. At 1 wt% loading of CuO, tensile strength increased up to 46 %, MFI and % elongation values were reduced slightly as 19 % and 20 % from ABS. Further addition of nanoparticles improved tensile strength, but %EL and MFI values decreased drastically. Hence, the promising filler and filler loading were concluded as CuO with 1 wt% loading, which may be considered as suitable material for making blades and impellers in hydro dynamic machinery to improve their performance. [ABSTRACT FROM AUTHOR]

For the drilling of micro-holes using micro-EDM technology, it is not easy for debris to be removed from the narrow gap. Aiming at this issue, a relative three-dimensional ultrasonic vibration (RTDUV)-assisted micro-EDM method was proposed to effectively improve the debris removal rate and micro-hole machining performance. The RTDUV consists of circular electrode vibration and vertical workpiece vibration. To better analyze the impact of RTDUV assistance on flow field distribution and debris removal, a flow field simulation was conducted. Micro-hole drilling experiments were conducted to investigate the machining performance with RTDUV assistance. From the simulation results, it is known that RTDUV assistance effectively accelerates debris removal in micro-hole machining, and the debris removal rate with RTDUV assistance is higher than that with the assistances electrode vibration (EV) and workpiece vibration (WV). Compared with the case without vibration assistance, EV, WV, and RTDUV assistances improved the consistency accuracy of the arrayed micro-hole (10 × 10) entrance by 24.8%, 21%, and 33.8% respectively, and the inlets of micro-holes have better morphologies under RTDUV assistance. The machining efficiency when using RTDUV assistance has been improved by 19.5%. The advantage of RTDUV assisted micro-EDM is that the lateral and underside gaps are in an alternating state, which greatly promotes the removal of discharge debris in the discharge gap and increases the renewal rate of the gap dielectric. Consequently, the reduction in processing stability caused by the high concentration of gap debris is avoided to some extent. The proposed novel ultrasonic vibration assistance method is prospective to further enhance the performance of micro-EDM. [ABSTRACT FROM AUTHOR]

There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorithms and classification algorithms was trained and tuned using a ten-fold cross validation, then AUC was computed. This procedure was repeated three times per dataset. Best overall performances were obtained with JMI and JMIM as feature selection algorithms and random forest and linear regression models as classification algorithms. The choice of the classification algorithm was the factor explaining most of the performance variation (10% of total variance). The choice of the feature selection algorithm explained only 2% of variation, while the train-test split explained 9%. [ABSTRACT FROM AUTHOR]

Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpected performance of the learned model in deployment. The issue in which the training and test data inputs follow different probability distributions while the input–output relationship remains unchanged is referred to as covariate shift. In this paper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariate shift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function of the input data to assess the classifier's performance per domain region. Distributional changes were simulated in a two-dimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on the experimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing the lowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the results reveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that the models exhibit high bias toward the region with high density in the input space domain of the training samples. [ABSTRACT FROM AUTHOR]

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a 21.16 % reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools. [ABSTRACT FROM AUTHOR]

Prior to further processing, completed questionnaires must be screened for the presence of careless respondents. Different people will respond to surveys in different ways. Some take the easy path and fill out the survey carelessly. The proportion of careless respondents determines the survey's quality. As a result, identifying careless respondents is critical for the quality of obtained results. This study aims to explore the characteristics of careless respondents in survey data and evaluate the predictive power and interpretability of different types of data and indices of careless responding. The research question focuses on understanding the behavior of careless respondents and determining the effectiveness of various data sources in predicting their responses. Data from a three-month web-based survey on participants' personality traits such as honesty-humility, emotionality, extraversion, agreeableness, conscientiousness and openness to experience was used in this study. Data for this study was taken from Schroeders et al.. The gradient boosting machine-based prediction model uses data from the answers, time spent for answering, demographic information on the respondents as well as some indices of careless responding from all three types of data. Prediction models were evaluated with tenfold cross-validation repeated a hundred times. Prediction models were compared based on balanced accuracy. Models' explanations were provided with Shapley values. Compared with existing work, data fusion from multiple types of information had no noticeable effect on the performance of the gradient boosting machine model. Variables such as "I would never take a bribe, even if it was a lot", average longstring, and total intra-individual response variability were found to be useful in distinguishing careless respondents. However, variables like "I would be tempted to use counterfeit money if I could get away with it" and intra-individual response variability of the first section of a survey showed limited effectiveness. Additionally, this study indicated that, whereas the psychometric synonym score has an immediate effect and is designed with the goal of identifying careless respondents when combined with other variables, it is not necessarily the optimal choice for fitting a gradient boosting machine model. [ABSTRACT FROM AUTHOR]

Milling force is one of the key output variables related to milling efficiency and accuracy. However, the time-consuming identification of milling forces cannot be evaluated online for machining performance during the milling process. In this paper, a novel millisecond-level method is developed to identify the milling forces online using acceleration signals. Firstly, the milling force identification problem is transformed into an ill-posed problem in the time-domain convolution framework. Next, the advantages of the regularization method for the ill-posed problem are analyzed. Then, the properties of the transfer matrix and the regularization operator are used to reduce the milling force identification time. Lastly, the proposed method is verified with several sets of milling experiments, and the results show that the accuracy of milling force identification is acceptable and the identification time is less than 10 ms. [ABSTRACT FROM AUTHOR]

There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies. [ABSTRACT FROM AUTHOR]

Smart industry paradigms are encouraged for automated and error-less manufacturing and job shop processing with the Industrial Internet of Things. The smart manufacturing environment employs independent machines and devices for providing cost-effective outcomes. The independent operating nature of the machines/devices is lured easily through the external hijackers that degrade the production outcome. By considering the real-time smart industrial process, this paper introduces Performance-focused Process Transaction Framework. The main motive of this work is to ensure secure job transactions in the smart industry with reduced task failures and improved production efficiency. The machines' performance is observed and measured based on the previous job completion rate and successive allocation intervals. Reward-based learning is induced in this framework for monitoring the operation state of the machine/device. The centralized controller makes use of the rewards and states for deciding the allocation without adversary interruption. In this job transaction process, the machine's adversary behavior is vital in determining production efficiency. The adverse behaving nature of the devices is detected in an early stage for reducing task failures. Further, the proposed framework's performance is verified using the metrics like true negative rate, adversary impact, processing time, and task allocation rate. [ABSTRACT FROM AUTHOR]

Over the past 5 years due to the changes in the environmental and human lifestyle, several neurodevelopmental disorders are shooting their existence. Out of all, autism prevalence percentage is significant when compared to other neurodevelopmental disorders. To diagnose autism spectrum disorder medical experts, need big medical data and lot of time. Early detection of autism at an early age helps in curing the disorder through medication. Certain machine learning models are developed to predict autism in high-dimensional datasets. Dimensionality reduction is applied to identify predominant features that effect autism. Feature extraction is done by Chi-square test. Feature subsets are extracted using, mutual information and lightgbm. Later, the performances of the machine learning algorithms are analysed to find out suitable classifier that predicts the autism spectrum disorder. Using feature subsets extraction methodologies, the numbers of features are reduced from 802 to 254 features; hence, the performance of Naïve Bayesian and K-nearest neighbour has improved. Decision tree classifier has performed the best in predicting autism spectrum disorder with a testing accuracy of 97.47% and model training time of 23.508 s. [ABSTRACT FROM AUTHOR]

The high-precision machine tool's dynamic, static, and rigid nature directly affects the machining efficiency and surface quality. Static and dynamic analyses are essential for the design and improvement of precision machine to ensure good tool performance under difficult and demanding machining conditions. In this study, the performance of a high-precision machine tool was analyzed using its virtual model created using CAD. Static and model analysis using ANSYS Workbench software was conducted to establish the tool's static deformation and static stiffness. Furthermore, the static and dynamic characteristics of the tool were explored using a finite element modeling approach to study their performance. In particular, the structure, static force, modal, frequency spectrum, and topology optimization of machine tools were primarily analyzed. Using model analysis, we found the first four different frequencies (22.5, 28.9, 40.6, and 47.4 Hz) and vibration type, which suggested of a weak link. Further static structural analysis revealed that the deformation of the spindle was 67.26 μm. An experimental static rigidity analysis was performed, and the experimental deformation values of the tool and spindle were obtained. The static and dynamic characteristics, as well as the accuracy and efficiency of the finite element model, were verified by comparing the data with the finite element analysis (FEA) results. Subsequently, we modified the settings and analysis model to ensure that the analysis results were consistent with the experimental findings. The error between the two results was within 1.56%. For an applied load of 5000 N on the spindle nose, the tool nose transient response was 0.5 s based on transient analysis. Under the condition that the structural deformation is as constant as possible, the lightweight structure may achieve the minimum weight and enhance the natural frequency; thus, the ideal structure will be obtained, and finite element analysis will then be performed. The optimal conditions for topology optimization include a lightweight structure, reduced structural deformation, and increased natural frequency of the structure. The developed method improves structural optimization, increases the ability of the product to be manufactured, and offers designers a variety of price-effective options. [ABSTRACT FROM AUTHOR]

Ultrasonic vibration–assisted electrical discharge machining (UEDM) was studied to improve the machining performance for TC4 micro-holes. First, a computational fluid dynamic (CFD) model of inter-electrode gap for the micro-hole was established using COMSOL Multiphysics, and the effect of ultrasonic amplitude on the distribution of fluid and debris in the inter-electrode gap was analyzed. The experimental study of UEDM for machining performance of TC4 micro-hole under different machining parameters was conducted by using the controlled variable method. Results showed that the ultrasonic vibration of the electrode is beneficial to improve the machining performance for micro-hole. Under the optimal processing parameters, the material removal rate is improved four times, and RTWR and θ are reduced by about 50%. Finally, the neural network and genetic algorithm were used to predict and optimize the experimental results. The average error is 4.1% between the optimal processing parameters [Xn] obtained by the above method and from the experiment. [ABSTRACT FROM AUTHOR]

Twin-field quantum key distribution (TF-QKD) has the advantage of beating the rate-loss limit (PLOB bound) for a repeaterless quantum key distribution (QKD) system. In practice, parameter optimization is of great significance in maximizing the secret key rate. Nevertheless, traditional local search algorithms (LSA) are often time-consuming and limited by the computing capabilities of devices. In this paper, we use the machine learning method instead of LSA to directly predict the optimal parameters for TF-QKD system. Specifically, three neural networks, namely back propagation neural network, radial basis function neural network, and generalized regression neural network, are trained and evaluated. The performance of neural networks and LSA in optimizing parameters is discussed and analyzed in this study. It is proved that the performance of machine learning-based prediction method is comparable to LSA, but the calculation time is shortened by 6 orders of magnitude. Furthermore, a comprehensive comparison of three networks in terms of prediction accuracy and time consumption is conducted, serving as a guide for selecting the most suitable network to optimize parameters in a practical TF-QKD system with different optimization requirements. [ABSTRACT FROM AUTHOR]

Distribution System Operators (DSOs) and Aggregators benefit from novel Energy Generation Forecasting (EGF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between production and consumption. It also aids operations such as Demand Response (DR) management in Smart Grid architecture. This work aims to develop and test a new solution for EGF. It combines various methodologies running EGF tests on historical data from buildings. The experimentation yields different data resolutions (15 min, one hour, one day, etc.) while reporting accuracy errors. The optimal forecasting technique should be relevant to a variety of forecasting applications in a trial-and-error manner, while utilizing different forecasting strategies, ensemble approaches, and algorithms. The final forecasting evaluation incorporates performance metrics such as coefficient of determination ( R 2 ), Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), presenting a comparative analysis of results. [ABSTRACT FROM AUTHOR]

This study aimed to explore the effectiveness of predicting disease activity in patients with inflammatory bowel disease (IBD), using machine learning (ML) models. A retrospective research was undertaken on IBD patients who were admitted into the First Affiliated Hospital of Wenzhou Medical University between September 2011 and September 2019. At first, data were randomly split into a 3:1 ratio of training to test set. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to reduce the dimension of variables. These variables were used to generate seven ML algorithms, namely random forests (RFs), adaptive boosting (AdaBoost), K-nearest neighbors (KNNs), support vector machines (SVMs), naïve Bayes (NB), ridge regression, and eXtreme gradient boosting (XGBoost) to train to predict disease activity in IBD patients. SHapley Additive exPlanation (SHAP) analysis was performed to rank variable importance. A total of 876 participants with IBD, consisting of 275 ulcerative colitis (UC) and 601 Crohn's disease (CD), were retrospectively enrolled in the study. Thirty-three variables were obtained from the clinical characteristics and laboratory tests of the participants. Finally, after LASSO analysis, 11 and 5 variables were screened out to construct ML models for CD and UC, respectively. All seven ML models performed well in predicting disease activity in the CD and UC test sets. Among these ML models, SVM was more effective in predicting disease activity in the CD group, whose AUC reached 0.975, sensitivity 0.947, specificity 0.920, and accuracy 0.933. AdaBoost performed best for the UC group, with an AUC of 0.911, sensitivity 0.844, specificity 0.875, and accuracy 0.855. ML algorithms were available and capable of predicting disease activity in IBD patients. Based on clinical and laboratory variables, ML algorithms demonstrate great promise in guiding physicians' decision-making. [ABSTRACT FROM AUTHOR]

Introduction: There is some debate on the relationship between essential tremor with rest tremor (rET) and the classic ET syndrome, and only few MRI studies compared ET and rET patients. This study aimed to explore structural cortical differences between ET and rET, to improve the knowledge of these tremor syndromes. Methods: Thirty-three ET patients, 30 rET patients and 45 control subjects (HC) were enrolled. Several MR morphometric variables (thickness, surface area, volume, roughness, mean curvature) of brain cortical regions were extracted using Freesurfer on T1-weighted images and compared among groups. The performance of a machine learning approach (XGBoost) using the extracted morphometric features was tested in discriminating between ET and rET patients. Results: rET patients showed increased roughness and mean curvature in some fronto-temporal areas compared with HC and ET, and these metrics significantly correlated with cognitive scores. Cortical volume in the left pars opercularis was also lower in rET than in ET patients. No differences were found between ET and HC. XGBoost discriminated between rET and ET with mean AUC of 0.86 ± 0.11 in cross-validation analysis, using a model based on cortical volume. Cortical volume in the left pars opercularis was the most informative feature for classification between the two ET groups. Conclusion: Our study demonstrated higher cortical involvement in fronto-temporal areas in rET than in ET patients, which may be linked to the cognitive status. A machine learning approach based on MR volumetric data demonstrated that these two ET subtypes can be distinguished using structural cortical features. [ABSTRACT FROM AUTHOR]

In order to predict the thermal deformation of CNC machine tool spindle system more accurately, a method based on multi-source heterogeneous information fusion is proposed. Aiming at the problem that it is difficult to obtain the global information of thermal deformation with a single type of information source, the effective prediction of thermal deformation of spindle system in machining center is realized under the fusion of temperature and vibration signals. First, the combined denoising method of empirical mode decomposition (EMD) and wavelet threshold is used to preprocess the vibration data. Then, the time-domain, frequency-domain, and time-frequency features of vibration signals are extracted, and the feature dimension is reduced based on correlation analysis and kernel principal component analysis (KPCA). After dimensionality reduction, the vibration data and temperature information are fused in the eigenmatrix. The nonlinear prediction of thermal deformation is studied by support vector regression for grid search parameter optimization (GS-SVR) and support vector regression for particle swarm optimization (PSO-SVR) methods. In order to realize the information acquisition of multi-channel temperature, vibration, and verify the effectiveness of the prediction model in the case of information fusion, a specific machining center is taken as the research object and experiments are performed based on multi-channel and heterogeneous signal acquisition. Finally, the prediction results based on different information sources are compared and analyzed. The results show that the thermal deformation of the machine tool obtained by the multi-source heterogeneous information fusion method is consistent with the actual test results. As compared with the predicted performance using only temperature information, the mean square error (MSE) decreased by 0.1663 µm. Therefore, the temperature - vibration information fusion model has higher accuracy in terms of predicting the thermal deformation of the spindle system model. [ABSTRACT FROM AUTHOR]

Background: Measles has high primary reproductive number, extremely infectious and ranked second to malaria in terms of disease burden in Ghana. Owing to the disease's high infectious rate, making early diagnosis based on an accurate system can help limit the spread of the disease. Studies have been conducted to derive models to serve as preliminary tools for early detection. However, these derived models are based on traditional methods, which may be limited in terms of model sensitivity and prediction power. This study focuses on comparing the performance of five machine learning classification techniques with a traditional method for predicting measles patients in Ghana. The study was an analytical cross-sectional design of suspected measles cases in Ghana. Results: The performance of six classifiers were compared and the random forest (RF) model demonstrated better performance among other models. The RF model achieved the highest sensitivity (0.88) specificity (0.96), ROC (0.92) and total accuracy (0.92). Conclusions: Our findings showed that, despite all the six methods had good performance in classifying measles patients, the RF model outperformed all the other classifiers in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting measles in the early stage. [ABSTRACT FROM AUTHOR]

Phasor Measurement Units (PMU) are capable to generate multi-dimensional time series data, which is one of the most important parts for monitoring power system operation. However, various internal and external factors frequently cause the system to generate anomalous data randomly, so we expect to clean and re-fill the raw PMU data with anomalies to provide support for further advanced applications such as situational awareness, early warning, and dispatch control of the system. Existing methods mostly use classical mathematics and traditional machine learning to analyze PMU data, which makes it difficult to identify the pattern changes of the data under multiple operating conditions in power systems. In this paper, we propose a hybrid model named IRFLMDNN, which consists of an improved CART random forest model and a dynamic neural network optimized by the Levenberg Marquardt algorithm for PMU data anomaly detection and adaptive data re-filling, respectively. Experimental results based on the IEEE 39-node 10-machine New England Power System show that the proposed method has accurate and robust anomaly detection and data refilling performance. [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]

Deception is a well-known term that involves acting in a way that it leads another person to believe something that is not true. Deception is a very common occurrence in daily life, and many times it becomes a big problem for national security. To cope with this problem, deception detection has gained a lot of attention recently. In this paper, we have tried to come up with a deceit identification system where we have used electroencephalograph (EEG) data collected by performing a concealed information test. To improve the performance of the system, first we have tried to select the optimal subset of the EEG channels using binary particle swarm optimization and secondly performed support vector machine hyper-parameter optimization using continuous version of PSO. The proposed model is validated using EEG dataset. The performance of the proposed system has resulted in increase of accuracy from 76.98 to 96.45% which is a significant improvement. Also, the proposed approach outperformed in terms of sensitivity, specificity, F1-score and G-measure when compared with state-of-the-art models. [ABSTRACT FROM AUTHOR]