Selection Method Research Articles

Selective Extraction of Terbium Using Functionalized Metal–Organic Framework-Based Solvent-Impregnated Mixed-Matrix Membranes

Advancements in membrane separation techniques will expand the applications and requirements for highly specialized, inventive, efficient, and resistant separation materials. The selective separation of rare earth elements (REEs) is one of the expanding applications of membrane-based techniques, as their use is becoming more widespread. Membrane techniques are becoming increasingly desired as environmentally friendly, straightforward methods for treating wastewater and separating metals. For the separation of REEs, an innovative impregnated mixed-matrix membrane (IMMM) technique was developed in this study. It provides a selective, efficient, and reusable method that is suitable for industrial applications. Terbium was selectively adsorbed from other REEs using organophosphorus IMMM with a loading capacity of 113.2 mg/g in 3 h and was reused three times without destroying the initial membrane. Solvent impregnation is thought to offer specific chelation sites that are selective for terbium separation.

Achieving optimum balance between mechanical properties and gloss of acrylonitrile–styrene–acrylate resin through control of rubber particle size distribution

AbstractPreparation of acrylonitrile–styrene–acrylate (ASA) with high gloss and superior impact properties is still challenging. Here, we developed a new strategy for the preparation of ASA resins by a selective redox initiation method based on a semi‐continuous emulsion polymerization technique. A series of ASA core‐shell impact modifiers were firstly obtained by grafting styrene (St) and acrylonitrile (AN) onto poly(n‐butyl acrylate) (PBA) seeded latexes of different sizes. The ASA core‐shell impact modifier was then used to toughen the styrene–acrylonitrile copolymer (SAN). The synergistic effects between the single particle size of the rubber phase and different particle sizes were systematically investigated, and the mechanical properties of SAN‐toughened resins at various particle sizes of the rubber phase were analyzed. The results revealed that toughened SAN with small particle sizes possessed better gloss, and the impact strength of the doped small‐particle toughened SAN was much higher than that of the single particle size toughened SAN. The ASA graft powders synthesized from large particle PBA (particle size 316 nm) and small particle PBA (particle size 99 nm), respectively, were mixed at a 50/50 ratio and blended with SAN resin, a gloss level reaching 95, and an impact performance of 275 J/m values almost twice those of a single particle size toughened SAN.Highlights Acrylonitrile–styrene–acrylate resins with an impact strength of 275 J/m and gloss of 95 were prepared. The balance between mechanical properties and gloss of acrylonitrile–styrene–acrylate resin was obtained. Polymerization was initiated at low temperature.

A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems

Feature selection is a crucial data preprocessing technique that effectively reduces the dataset size and enhances the performance of machine learning models. Evolutionary computation (EC) based feature selection has become one of the most important parts of feature selection methods. However, the performance of existing EC methods significantly decrease when dealing with datasets with thousands of dimensions. To address this issue, this paper proposes a novel method called importance-guided particle swarm optimization based on MLP (IGPSO) for feature selection. IGPSO utilizes a two stage trained neural network to learn a feature importance vector, which is then used as a guiding factor for population initialization and evolution. In the two stage of learning, the positive samples are used to learn the importance of useful features while the negative samples are used to identify the invalid features. Then the importance vector is generated combining the two category information. Finally, it is used to replace the acceleration factors and inertia weight in original binary PSO, which makes the individual acceleration factor and social acceleration factor are positively correlated with the importance values, while the inertia weight is negatively correlated with the importance value. Further more, IGPSO uses the flip probability to update the individuals. Experimental results on 24 datasets demonstrate that compared to other state-of-the-art algorithms, IGPSO can significantly reduce the number of features while maintaining satisfactory classification accuracy, thus achieving high-quality feature selection effects. In particular, compared with other state-of-the-art algorithms, there is an average reduction of 0.1 in the fitness value and an average increase of 6.7% in classification accuracy on large-scale datasets.

Comparativa de diferentes modelos radiómicos para la clasificación de lesiones adrenales indeterminadas diagnosticadas de forma incidental en TC con contraste

Purpose: A comparison of different machine learning models to discriminate adrenal incidentalomas by CT studies was performed. Methods: Sixty-two different features were obtained from a sample of 61 incidentalomas using the free license software LIFEx and 19 radiomic studies were performed with different models and feature selection methods to obtain the most efficient determination of possible malignancy. For all of them, four cross-validation methods were evaluated. Adenoma contouring was performed in duplicate by different radiologists evaluating all models in both groups. Results: ROC AUC between 0.42 (0.09-0.81) and 0.92 (0.63-1.00), and accuracy of the models between 0.63 (0.43-0.79) and 0.94 (0.82-1.00). The best-performing model was the balanced logistic regression applied to the 14 features with an intraclass coefficient greater than 0.9, with which accuracy of 0.94 (0.74-1.00), ROC AUC of 0.917 (0.63-1.00), benign recall of 0.92 (0.65-1.00) and malignant recall of 1.00 (0.71-1.00) were obtained. Conclusions: The evaluation and validation of different models has allowed us to obtain an efficient radiomic model for the discrimination of adrenal incidentalomas

AI4ACEIP: A Computing Tool to Identify Food Peptides with High Inhibitory Activity for ACE by Merged Molecular Representation and Rich Intrinsic Sequence Information Based on an Ensemble Learning Strategy.

Hypertension is a common chronic disorder and a major risk factor for cardiovascular diseases. Angiotensin-converting enzyme (ACE) converts angiotensin I to angiotensin II, causing vasoconstriction and raising blood pressure. Pharmacotherapy is the mainstay of traditional hypertension treatment, leading to various negative side effects. Some food-derived peptides can suppress ACE, named ACEIP with fewer undesirable effects. Therefore, it is crucial to seek strong dietary ACEIP to aid in hypertension treatment. In this article, we propose a new model called AI4ACEIP to identify ACEIP. AI4ACEIP uses a novel two-layer stacked ensemble architecture to predict ACEIP relying on integrated view features derived from sequence, large language models, and molecular-based information. The analysis of feature combinations reveals that four selected integrated feature pairs exhibit enhancing performance for identifying ACEIP. For finding meta models with strong abilities to learn information from integrated feature pairs, PowerShap, a feature selection method, is used to select 40 optimal feature and meta model combinations. Compared with seven state-of-the-art methods on the source and clear benchmark data sets, AI4ACEIP significantly outperformed by 8.47 to 20.65% and 5.49 to 14.42% for Matthew's correlation coefficient. In brief, AI4ACEIP is a reliable model for ACEIP prediction and is freely available at https://github.com/abcair/AI4ACEIP.

Development and Validation of an Analytical Procedure for Determining Convallatoxin in Medicinal Products Containing Cardiac Glycosides of Lily of the Valley

INTRODUCTION. The safe use of lily-of-the-valley medicinal products requires adequately controlling the content of cardiac glycosides. The State Pharmacopoeia of the Russian Federation stipulates that the content of cardiac glycosides in lily-of-the-valley medicinal products should be quantified using bioassays and spectrophotometry. However, the determination of cardiac glycosides needs more accurate and selective physicochemical methods, such as high-performance liquid chromatography (HPLC).AIM. This study aimed to develop and validate an HPLC analytical procedure for the quantitative determination of cardiac glycosides (convallatoxin) in lily-of-the-valley herbal medicinal products.MATERIALS AND METHODS. The study examined the Lily-of-the-Valley Tincture, Zelenin Drops, Valocormid, Carniland®, and Lily-of-the-Valley Extract Reference Standard samples. The content of cardiac glycosides was determined by comparison with a reference standard for convallatoxin by HPLC and spectrophotometry. The samples were prepared as outlined in Monograph 3.4.0003.18 Lily-of-the-Valley Herb Tincture of the State Pharmacopoeia of the Russian Federation. Mixed cardiac glycosides were separated on a Luna 5 µm C18(2) column by gradient elution with 0.1% orthophosphoric acid in water and acetonitrile. The analysis was performed using an autoinjector with sample cooling to 5 °C.RESULTS. The developed analytical procedure met the acceptance criteria for specificity, intermediate precision, linearity (correlation coefficient of 0.99985), and repeatability (relative standard deviation of convallatoxin measurements of 1.61%). The analytical procedure is suitable for the quantitative determination of convallatoxin in lily-of-the-valley herbal medicinal products because it produces reliable and repeatable results.CONCLUSIONS. The authors developed a highly sensitive and selective HPLC analytical procedure for the quantitative determination of convallatoxin in lily-of-the-valley herbal medicinal products. The content of convallatoxin ranged from 0.012 to 0.018 mg/mL in the liquid active substance Lily-of-the-Valley Tincture, from 0.004 to 0.013 mg/mL in the medicinal product Zelenin Drops, and from 0.005 to 0.007 mg/mL in the medicinal products Carniland® and Valocormid. For the Lily-of-the-Valley Extract Reference Standard, the content of convallatoxin amounted to 0.029 mg/mL.

Conductivity Prediction Method of Solid Electrolyte Materials Based on Pearson Coefficient Method and Ensemble Learning

In order to screen solid electrolytic materials with high ionic conductivity more quickly and accurately, a conductivity prediction method for solid electrolyte materials based on logistic regression model and random forest regression model is constructed. The method first selects 20 characteristic descriptors related to ionic conductivity from material library according to four dimensions: structural stability, metal stability, electronic conductivity and oxidative decomposition stability. The enumeration method and feature selection method are used to combine different features. Then, the dimension reduction of the feature combinations is carried out by Pearson coefficient method, so as to screen the optimal feature combinations. Finally, according to the optimal feature combination and data subset, the constructed random forest ensemble learning model is utilized to predict the ionic conductivity of other solid-state electrolyte materials in the material library, in order to find the solid-state electrolyte materials that can meet the high conductivity. The results indicate that as the number of features in the combination increases, the prediction performance of the model shows a trend of first increasing and then decreasing. When the feature descriptor in the combination is 7, the maximum AUC (Accuracy) value is reached, and the optimal feature in this case contains 7 optimal feature subsets corresponding to feature descriptors, namely the standard deviation of the average adjacency number of Li atoms, the standard deviation of Li-X ion bonds, the average electronegativity of the straight path, the average width of the straight path, the average atomic volume, the average Li-Li bond, and the filling fraction of the sublattice. On the basis of the optimal feature screening, the average prediction accuracy of the logistic regression model is 87.13%, which has a high accuracy. Using the random forest regression model for prediction, the average absolute error and root mean square error obtained are only 0.237 and 0.134, and compared with classical classification methods such as KNN, SVM and Adaboost, they are smaller, which can better predict the ionic conductivity of solid electrolyte materials. This proves that the constructed method can quickly and accurately produce high ionic conductivity materials in solid electrolyte materials, which is worthy of further research and promotion.

COST OPTIMIZATION METHOD FOR INFORMATIONAL INFRASTRUCTURE DEPLOYMENT IN STATIC MULTI-CLOUD ENVIRONMENT

Context. In recent years, the topic of deploying informational infrastructure in a multi-cloud environment has gained popularity. This is because a multi-cloud environment provides the ability to leverage the unique services of cloud providers without the need to deploy all infrastructure components inside them. Therefore, all available services across different cloud providers could be used to build up information infrastructure. Also, multi-cloud offers versatility in selecting different pricing policies for services across different cloud providers. However, as the number of available cloud service providers increases, the complexity of building a costoptimized deployment plan for informational infrastructure also increases. Objective. The purpose of this paper is to optimize the operating costs of information infrastructure while leveraging the service prices of multiple cloud service providers. Method. This article presents a novel cost optimization method for informational infrastructure deployment in a static multicloud environment whose goal is to minimize the hourly cost of infrastructure utilization. A genetic algorithm was used to solve this problem. Different penalty functions for the genetic algorithm were considered. Also, a novel parameter optimization method is proposed for selecting the parameters of the penalty function. Results. A series of experiments were conducted to compare the results of different penalty functions. The results demonstrated that the penalty function with the proposed parameter selection method, in comparison to other penalty functions, on average found the best solution that was 8.933% better and took 18.6% less time to find such a solution. These results showed that the proposed parameter selection method allows for efficient exploration of both feasible and infeasible regions. Conclusion. A novel cost optimization method for informational infrastructure deployment in a static multi-cloud environment is proposed. However, despite the effectiveness of the proposed method, it can be further improved. In particular, it is necessary to consider the possibility of involving scalable instances for informational infrastructure deployment.

Pengaruh Solvabilitas, Profitabilitas, Kepemilikan Institusional, Dewan Komisaris Independen, dan Kepemilikan Publik terhadap Pengungkapan Manajemen Risiko: Studi pada Perusahaan Sub-sektor Perbankan yang Terdaftar di Bursa Efek Indonesia Tahun 2018-2022

The object of this research is a banking sub-sector company listed on the Indonesia Stock Exchange for the period 2018-2022. This study aims to determine the effect of leevrage, profitability, institutional ownership, independent board of commissioners, and public ownership as independent variables and risk management disclosure as the dependent variable. This study used 35 companies, so there were a total of 175 samples used. The study used purposive sampling as a sample selection method used for the selection of certain criteria. This study applied descriptive statistical analysis method and panel data regression analysis. The results of this study show that all variables simultaneously influence risk management disclosure. Meanwhile, leverage, profitability, and independent board of commissioners each have no effect. Meanwhile, institutional ownership and public ownership positively affect risk management disclosure.

Segmentation-based truncated-SVD for effective feature extraction in hyperspectral image classification

ABSTRACT Remote sensing hyperspectral images (HSIs) are rich sources of information about land cover captured across hundreds of narrow, contiguous spectral wavelength bands. However, using the entire original HSI for practical applications can lead to suboptimal classification accuracy. To address this, band reduction techniques, categorized as feature extraction and feature selection methods, are employed to enhance classification results. One commonly used feature extraction approach for HSIs is Principal Component Analysis (PCA). However, PCA may fall short of capturing the local and specific characteristics present in the HSI data. In this paper, we introduce two novel feature extraction methods: Segmented Truncated Singular Value Decomposition (STSVD) and Spectrally Segmented Truncated Singular Value Decomposition (SSTSVD) to improve classification performance. Segmentation is carried out based on highly correlated bands’ segments and spectral bands’ segments within the HSI data. Our study evaluates and compares these newly proposed methods against classical feature extraction methods, including PCA, Incremental PCA, Sparse-PCA, Kernel PCA, Segmented-PCA (SPCA), and Truncated Singular Value Decomposition (TSVD). We perform this analysis on three distinct HSI datasets, namely the Indian Pines HSI, the Pavia University HSI, and the Kennedy Space Center HSI, using per-pixel Support Vector Machine (SVM) and Random Forest (RF) classification. The experimental results demonstrate the superiority of our proposed methods for all three datasets. The best-performing feature extraction methods when classification is performed using an SVM classifier are STSVD3 (89.03%), SSTSVD2 (95.55%), and STSVD3 (97.74%) for the Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively. Similarly, for the RF classifier, the best-performing feature extraction methods are SSTSVD4 (88.98%), SSTSVD3 (96.04%), and SSTSVD4 (96.09%) for Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively.

Evaluating the Effectiveness of Traditional Recruitment and Selection Practices in a Technologically Dominated Economy

In the contemporary business environment, there is a need for firms to plan and implement proactive traditional recruitment and selection, especially regarding employees working in the gig economy. However, the poor handling of traditional recruitment and selection processes can significantly affect the quality of employees and their performance in an organisation. This study aimed to evaluate the effectiveness of traditional recruitment and selection practices in a technologically dominated economy. The importance of attracting and selecting a highly skilled workforce in a tight and competitive market has made adopting best practices in recruitment and selection by organisations critical. Firstly, the study highlighted various traditional recruitment and selection methods in organisations. Secondly, the overall recruitment and selection process was identified. Finally, strategies to mitigate the identified challenges faced in the traditional recruitment and selection process were suggested. The study was qualitative, adopting the survey strategy. Purposive was adopted, and hematic was used for data analysis. Findings revealed that traditional recruitment and selection as a practice is important for both employees involved in the gig economy. Importantly, the strategic approach to traditional recruitment and selection practices is vital and should be the cornerstone of any projected recruitment and selection process in a gig economy. Results validate the critical role of the traditional recruitment and selection process in achieving organisational aims and objectives in a technologically dominated economy. The study also proves that collaborative methods are central to the success of any traditional recruitment and selection process. Recommendations affirmed that recruitment and selection should be planned and well implemented. Human resource planning was also measured to be vital and emphasis on significant features of traditional recruitment and selection as a process such as having a place a well and implemented traditional recruitment and selection policy was asserted.

Transcriptome Profiling of Two Camellia japonica Cultivars with Different Heat Tolerance Reveals Heat Stress Response Mechanisms.

Camellia (Camellia japonica) is a semi-shaded plant that is highly vulnerable to heat stress. To investigate the mechanisms underlying heat stress in C. japonica, two C. japonica cultivars, "Xiaotaohong" and "Zhuapolian", which exhibit significant differences in heat tolerance, were selected from four common cultivars. The selection methods included phenotypic observations and physiological index detection, including relative electric conductivity (REC), malondialdehyde (MDA) content, superoxide dismutase (SOD) enzyme activity, relative water content (RWC), and chlorophyll content. RNA-seq analysis yielded 980 million reads and identified 68,455 differentially expressed genes (DEGs) in the two C. japonica cultivars during heat stress compared to the control samples. Totals of 12,565 and 16,046 DEGs were differentially expressed at 16 h and 32 h, respectively, in "Xiaotaohong" during heat stress. In "Zhuapolian", 40,280 and 37,539 DEGs were found at 16 h and 32 h, respectively. KEGG enrichment analysis revealed that both cultivars were enriched in the "plant hormone signal transduction" and "circadian rhythm" pathways at two stages, indicating the critical role these pathways play in the heat stress response. The differences in the tolerance between the two cultivars are likely linked to pathways such as "plant hormone signal transduction", "photosynthesis", and "circadian rhythm". Some members of heat shock proteins (HSPs) are associated with the heat stress response. It is speculated that transcription factor families contributing to the tolerance differences include AP2/ERF, C3H, bHLH, bZIP, and MYB-related with a small number of heat shock factors (HSFs) also induced by the stress. In conclusion, these results reveal the changes in the physiological indices and molecular networks of two C. japonica cultivars under heat stress. This study lays the foundation for the breeding of superior heat-resistant C. japonica cultivars and for further molecular research.

Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods.

Identifying Relevant Covariates in RNA-seq Analysis by Pseudo-Variable Augmentation

AbstractRNA-sequencing (RNA-seq) technology allows for the identification of differentially expressed genes, which are genes whose mean transcript abundance levels vary across conditions. In practice, RNA-seq datasets often include covariates that are of primary interest in addition to a set of covariates that are subject to selection. Some of these covariates may be relevant to gene expression levels, while others may be irrelevant. Ignoring relevant covariates or attempting to adjust for the effect of irrelevant covariates can compromise the identification of differentially expressed genes. To address this issue, we propose a variable selection method that uses pseudo-variables to control the expected proportion of selected covariates that are irrelevant. Our method accurately selects relevant covariates while keeping the false selection rate below a specified level. We demonstrate that our method outperforms existing methods for detecting differentially expressed genes when working with available covariates. Our method is implemented in function of the R package , which is available at www.github.com/ntyet/csrnaseq. The analysis and simulation are available at www.github.com/ntyet/csrnaseq/tree/main/analysis.

A Frequency Selecting Method for High-Frequency Communication Based on Ionospheric Oblique Backscatter Sounding

Ionospheric oblique backscatter sounding is an effective means of monitoring the ionosphere which can be used as a frequency selection system to serve HF communication and ensure its quality and stability. But how to obtain effective information from the oblique backscatter ionogram is still a hot issue. Due to this situation, a frequency selecting method for HF communication based on ionospheric oblique backscatter sounding is proposed in this study. After obtaining the ionograms, pattern recognition is used to separate the vertical echoes and the oblique backscatter echoes. Next, the leading edge of the oblique backscatter echoes are extracted, and then a two-dimensional electron density profile can be reconstructed. Then, with the help of ray tracing, the usable frequency range can be estimated. Finally, according to the signal-to-noise ratio reflected by the ionograms, several optimal communication frequencies can be selected. In order to verify this method, oblique ionograms are obtained through oblique sounding experiments to evaluate its accuracy. The result indicates that the usable frequency range and the selected frequencies are in accordance with the echo of the oblique ionogram, so the practicability and accuracy of the method are validated. Eventually, the maximum usable frequencies (MUFs) obtained from oblique backscatter sounding are compared with the MUFs from the oblique sounding ionogram; its Mean Absolute Percentage Error (MAPE) is 7.8% and its root mean squared error (RMSE) is 1.34 MHz.

Effervescence-assisted dispersive liquid-liquid microextraction for the extraction and preconcentration of pesticide residues in fruit juice samples

BackgroundThe widespread use of pesticides for the protection of fruits has resulted in the presence of pesticide residue levels beyond their limit in fruits. This ensures their transfer to processed products, such as juices, posing a threat to human health. Therefore, an efficient and selective method is required for monitoring pesticide residues in fruit juices. Hence, an effervescence-assisted dispersive liquid-liquid microextraction based on solidification of floating organic droplets was developed for the simultaneous extraction of five pesticide residues of different classes. An environmentally green low-density organic solvent, 1-undecanol, was used as extraction solvent. (94) ResultsThe developed method was optimized and validated for quantitative extraction of multiclass pesticide residues at trace levels from fruit juice samples, including apple, pineapple, guava and orange samples. The method showed good linearity in the range of 0.8 to 300 ng mL-1 for all pesticide residues with a regression coefficient ranging from 0.9979 to 0.9997 under the optimized conditions. The LOD and LOQ of the method ranged between 0.03-0.16 ng mL-1 and 0.11-0.52 ng mL-1, respectively, indicating the high sensitivity of the proposed method. Repeatability and reproducibility, in terms of %RSD, were obtained in the range of 1.16-5.50% and 3.12-7.72%, respectively. (101) SignificanceThe developed method exhibited acceptable mean recoveries (%R) in the range of 73.77-113.34 % with %RSDs (n=3) ranging from 1.25 to 7.74% for all the analytes studied. Therefore, the developed method can be used as a selective, sensitive and efficient extraction method for the extraction of multiclass pesticides from fruit juice samples. (52)

Classification of faults in friction stir processed composites using a machine learning and ensemble learning approach

Abstract Aluminium alloy based surface composites with hard reinforcement particles have wide scope in aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing (FSP) with varying parameters are investigated for the faults occurred during fabrication process. It explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano-powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data (vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories (e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Thenafter, various types of feature selection techniques, viz filter, wrapper and embedded methods are implemented to select most relevant features. The selected subset of features from all selection methods used, are applied to different machine learning and ensemble learning classifiers and their performances are evaluated. The optimal combinations of the type of feature selection method and classifier used, are obtained for efficient classification of surface defects in composited formed by FSP. The real time monitoring and defect detection system can be developed in future for the composites developed by FSP using the developed models.

Astrometric Jitter as a Detection Diagnostic for Recoiling and Slingshot Supermassive Black Hole Candidates

Supermassive black holes (SMBHs) can be ejected from their galactic centers due to gravitational wave recoil or the slingshot mechanism following a galaxy merger. If an ejected SMBH retains its inner accretion disk, it may be visible as an off-nuclear active galactic nucleus (AGN). At present, only a handful of offset AGNs that are recoil or slingshot candidates have been found, and none have been robustly confirmed. Compiling a large sample of runaway SMBHs would enable us to constrain the mass and spin evolution of binary SMBHs and study feedback effects of displaced AGNs. We adapt the method of varstrometry—which was developed for Gaia observations to identify off-center, dual, and lensed AGNs—in order to quickly identify off-nuclear AGNs in optical survey data by looking for an excess of blue versus red astrometric jitter. We apply this to the Pan-STARRS1 3π Survey and report on five new runaway AGN candidates. We focus on ZTF18aajyzfv: a luminous quasar offset by 6.7 ± 0.2 kpc from an adjacent galaxy at z = 0.224, and conclude after Keck LRIS spectroscopy and comparison to ASTRID simulation analogs that it is likely a dual AGN. This selection method can be easily adapted to work with data from the soon-to-be commissioned Vera C. Rubin Telescope Legacy Survey of Space and Time (LSST). LSST will have a higher cadence and deeper magnitude limit than Pan-STARRS1, and should permit detection of many more runaway SMBH candidates.

A Q-learning-based multi-population algorithm for multi-objective distributed heterogeneous assembly no-idle flowshop scheduling with batch delivery

Distributed heterogeneous factory environments have become the mainstream in real-world manufacturing enterprises. Scheduling the assembly no-idle flowshops in such distributed heterogeneous environments is significant for practitioners. This problem takes into account the heterogeneity between different factories and the batch delivery process. To minimize inventory and tardiness costs of this new problem, a novel Q-learning-based multi-population multi-objective evolutionary algorithm is proposed. The algorithm incorporates five problem-specific properties to design the solution representation and calculate objectives. To obtain high-quality initial dual populations, a collaborative initialization technique combining four NEH heuristics and a completely random approach is proposed. To enhance algorithm’s search efficiency, a Q-learning-based selection method is developed to coordinate four job-related and four product-related operators. The dual-cooperation strategy is introduced to improve the global search capability. Experimental results demonstrate the effectiveness of all the designed components, indicating that the proposed algorithm outperforms five existing algorithms. It is well-suited for addressing real-world distributed heterogeneous scheduling scenarios by the proposed algorithm.

Integrating temporal deep learning models for predicting screen-out risk levels in hydraulic fracturing

Amid the transformative shift in global energy structures, the exploitation and utilization of shale gas, an essential unconventional natural gas resource, have drawn widespread attention from both industrial and academic circles. However, screen-out incidents during hydraulic fracturing operations pose significant obstacles to extraction efficiency and safety. Traditional prediction methods, which rely on empirical estimations and simplified models, are deficient in accuracy and real-time applicability. Addressing this, our study introduces a novel deep learning ensemble integrating Gated Recurrent Units (GRU), Transformer, and One-Dimensional Convolutional Neural Networks (1D-CNN) for precise screen-out prediction. This approach markedly improves predictive accuracy by efficiently processing time-series data and capturing the complex dynamics of fracturing processes. Furthermore, the application of the correlation coefficient method and random forest algorithm for feature selection optimizes model input and further enhances prediction accuracy and operational efficiency. Our comparative analysis demonstrates the model’s superiority, achieving an F1 score of 0.951 and a loss of 0.430, clearly surpassing traditional and other deep learning methods. This integration of advanced neural architectures and feature selection techniques not only advances screen-out prediction but also yields practical insights for optimizing shale gas extraction strategies and enhancing safety.