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Semi-autogenous grinding (SAG) mills play a pivotal role in the grinding circuit of mineral processing plants. Accurate prediction of SAG mill throughput as a crucial performance metric is of utmost importance. The potential of applying genetic programming (GP) for this purpose has yet to be thoroughly investigated. This study introduces an enhanced GP approach entitled multi-equation GP (MEGP) for more accurate prediction of SAG mill throughput. In the new proposed method multiple equations, each accurately predicting mill throughput for specific clusters of training data are extracted. These equations are then employed to predict mill throughput for test data using various approaches. To assess the effect of distance measures, four different distance measures are employed in MEGP method. Comparative analysis reveals that the best MEGP approach achieves an average improvement of 10.74% in prediction accuracy compared with standard GP. In this approach, all extracted equations are utilized and both the number of data points in each data cluster and the distance to clusters are incorporated for calculating the final prediction. Further investigation of distance measures indicates that among four different metrics employed including Euclidean, Manhattan, Chebyshev, and Cosine distance, the Euclidean distance measure yields the most accurate results for the majority of data splits.

In mineral processing plants, grinding is a crucial step, accounting for approximately 50 percent of the total mineral processing costs. Semi-autogenous grinding mills are extensively employed in the grinding circuit of mineral processing plants. Maximizing SAG mill throughput is of significant importance considering its profound financial outcomes. However, the optimum process parameter setting aimed at achieving maximum mill throughput remains an uninvestigated domain in prior research. This study introduces a hybrid intelligent framework leveraging expert knowledge, machine learning techniques, and evolutionary algorithms to address this research need. In this study, we utilize an extensive industrial dataset comprising 36743 records and select relevant features based on the insights of industry experts. Following the removal of erroneous data, a comprehensive evaluation of 17 diverse machine learning models is undertaken to identify the most accurate predictive model. To improve the model performance, feature selection and outlier detection are executed. The resultant optimal model, trained with refined features, serves as the objective function within three distinct evolutionary algorithms. These algorithms are employed to identify parameter configurations that maximize SAG mill throughput while adhering to the working limits of input parameters as constraints. Notably, our analysis revealed that CatBoost, as an ensemble model, stands out as the most accurate predictor. Furthermore, differential evolution emerges as the preferred optimization algorithm, exhibiting superior performance in both achieving the highest mill throughput predictions and ensuring robustness in predictions, surpassing alternative methods.

Epithelial-mesenchymal transition (EMT) is a fundamental process for embryonic development during which epithelial cells acquire mesenchymal characteristics, and the underlying mechanisms confer malignant features to carcinoma cells such as dissemination throughout the organism and resistance to anticancer treatments. During the past decades, an entire class of molecules, called non-coding RNA (ncRNA), has been characterized as a key regulator of almost every cellular process, including EMT. Like protein-coding genes, ncRNAs can be deregulated in cancer, acting as oncogenes or tumor suppressors. The various forms of ncRNAs, including microRNAs, PIWI-interacting RNAs, small nucleolar RNAs, transfer RNA-derived RNA fragments, long non-coding RNAs, and circular RNAs can orchestrate the complex regulatory networks of EMT at multiple levels. Understanding the molecular mechanism underlying ncRNAs in EMT can provide fundamental insights into cancer metastasis and may lead to novel therapeutic approaches. In this review, we describe recent advances in the understanding of ncRNAs in EMT and provide an overview of recent ncRNA applications in the clinic.

As social media continues to grow rapidly, the prevalence of harassment on these platforms has also increased. This has piqued the interest of researchers in the field of fake detection. Social media data, often forms complex graphs with numerous nodes, posing several challenges. These challenges and limitations include dealing with a significant amount of irrelevant features in matrices and addressing issues such as high data dispersion and an imbalanced class distribution within the dataset. To overcome these challenges and limitations, researchers have employed auto-encoders and a combination of semi-supervised learning with a GAN algorithm, referred to as SGAN. Our proposed method utilizes auto-encoders for feature extraction and incorporates SGAN. By leveraging an unlabeled dataset, the unsupervised layer of SGAN compensates for the limited availability of labeled data, making efficient use of the limited number of labeled instances. Multiple evaluation metrics were employed, including the Confusion Matrix and the ROC curve. The dataset was divided into training and testing sets, with 100 labeled samples for training and 1,000 samples for testing. The novelty of our research lies in applying SGAN to address the issue of imbalanced datasets in fake account detection. By optimizing the use of a smaller number of labeled instances and reducing the need for extensive computational power, our method offers a more efficient solution. Additionally, our study contributes to the field by achieving an 81% accuracy in detecting fake accounts using only 100 labeled samples. This demonstrates the potential of SGAN as a powerful tool for handling minority classes and addressing big data challenges in fake account detection., Comment: needed more investigation o final results

This study concentrates on clustering problems and aims to find compact clusters that are informative regarding the outcome variable. The main goal is partitioning data points so that observations in each cluster are similar and the outcome variable can be predicated using these clusters simultaneously. We model this semi-supervised clustering problem as a multi-objective optimization problem with considering deviation of data points in clusters and prediction error of the outcome variable as two objective functions to be minimized. For finding optimal clustering solutions, we employ a non-dominated sorting genetic algorithm II approach and local regression is applied as prediction method for the output variable. For comparing the performance of the proposed model, we compute seven models using five real-world data sets. Furthermore, we investigate the impact of using local regression for predicting the outcome variable in all models, and examine the performance of the multi-objective models compared to single-objective models.

Objectives Rates of novel coronavirus disease 2019 (COVID-19) infections have rapidly increased worldwide and reached pandemic proportions. A suite of preventive behaviours have been recommended to minimize risk of COVID-19 infection in the general population. The present study utilized an integrated social cognition model to explain COVID-19 preventive behaviours in a sample from the Iranian general population. Design The study adopted a three-wave prospective correlational design. Methods Members of the general public (N = 1,718, Mage = 33.34, SD = 15.77, male = 796, female = 922) agreed to participate in the study. Participants completed self-report measures of demographic characteristics, intention, attitude, subjective norm, perceived behavioural control, and action self-efficacy at an initial data collection occasion. One week later, participants completed self-report measures of maintenance self-efficacy, action planning and coping planning, and, a further week later, measures of COVID-19 preventive behaviours. Hypothesized relationships among social cognition constructs and COVID-19 preventive behaviours according to the proposed integrated model were estimated using structural equation modelling. Results The proposed model fitted the data well according to multiple goodness-of-fit criteria. All proposed relationships among model constructs were statistically significant. The social cognition constructs with the largest effects on COVID-19 preventive behaviours were coping planning (? = .575, p < .001) and action planning (? = .267, p < .001). Conclusions Current findings may inform the development of behavioural interventions in health care contexts by identifying intervention targets. In particular, findings suggest targeting change in coping planning and action planning may be most effective in promoting participation in COVID-19 preventive behaviours. Statement of contribution What is already known on this subject? Curbing COVID-19 infections globa, Special Section Article.

The main goal of this thesis is to improve the cardiovascular health monitoring by developing a novel model-based blind system identification approach. This research lies on the core idea that the central aortic blood pressure (BP) waveform can be estimated from as few as two non-invasive circulatory signals. To achieve this goal, first, we formulated a physiological model for the class of multi-channel systems with non-invasive BP measurements and expressed it as a blind system identification problem. We verified this model for estimating the central blood pressure waveform from pulse volume records (PVR) signals from arm and leg, collected from 10 human subjects. The results showed that the proposed approach could estimate central aortic blood pressure waveform accurately. The average root-mean-squared error associated with the central aortic blood pressure waveform was 4.1 mmHg while the average errors associated with central aortic systolic and pulse pressures were 2.4 mmHg and 2.0 mmHg respectively. Afterward, we compared this method with a population-based technique to calculate cardiovascular risk predictors. First, we used the same approach to estimate the central blood pressure waveform from two non-invasive peripheral waveforms and then, calculated cardiovascular risk predictors. Experimental results obtained from 164 human subjects with a wide blood pressure range showed that this approach could estimate cardiovascular risk predictors accurately. Further analysis showed that the suggested approach outperformed a generalized transfer function regardless of the degree of pulse pressure amplification, but especially in high and low amplification ranges. Finally, a new closed-loop approach to input de-convolution in coprime multi-channel systems based on state estimation techniques is proposed. This approach is based on the idea that the unknown input signal in a multi-channel system may be regarded as a state variable to be estimated from multiple output sig