Selection Method Research Articles

Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy.

Among patients with epilepsy, 30-40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms.

Psychometric properties of computerized adaptive testing for chronic obstructive pulmonary disease patient-reported outcome measurement.

Computerized adaptive testing (CAT) is an effective way to reduce time, repetitious redundancy, and respond burden, and has been used to measure outcomes in many diseases. This study aimed to develop and validate a comprehensive disease-specific CAT for chronic obstructive pulmonary disease (COPD) patient-reported outcome measurement. The discrimination and difficulty of the items from the modified patient-reported outcome scale for COPD (mCOPD-PRO) were analyzed using item response theory. Then the initial item, item selection method, ability estimation method, and stopping criteria were further set based on Concerto platform to form the CAT. Finally, the reliability and validity were validated. The item discrimination ranged from 1.05 to 2.71, and the item difficulty ranged from - 3.08 to 3.65. The measurement reliability of the CAT ranged from 0.910 to 0.922 using random method, while that ranged from 0.910 to 0.924 using maximum Fisher information (MFI) method. The content validity was good. The correlation coefficient between theta of the CAT and COPD assessment test and modified Medical Research Council dyspnea scale scores using random method was 0.628 and 0.540 (P < 0.001; P < 0.001) respectively, while that using MFI method was 0.347 and 0.328 (P = 0.007; P = 0.010) respectively. About 11 items (reducing by 59.3%) on average were tested using random method, while about seven items (reducing by 74.1%) on average using MFI method. The correlation coefficient between theta of the CAT and mCOPD-PRO total scores using random method was 0.919 (P < 0.001), while that using MFI method was 0.760 (P < 0.001). The comprehensive disease-specific CAT for COPD patient-reported outcome measurement is well developed with good psychometric properties, which can provide an efficient, accurate, and user-friendly measurement for patient-reported outcome of COPD.

A Tri-Objective Method for Bi-Objective Feature Selection in Classification.

Minimizing the number of selected features and maximizing the classification performance are two main objectives in feature selection, which can be formulated as a bi-objective optimization problem. Due to the complex interactions between features, a solution (i.e., feature subset) with poor objective values does not mean that all the features it selects are useless, as some of them combined with other complementary features can greatly improve the classification performance. Thus, it is necessary to consider not only the performance of feature subsets in the objective space, but also their differences in the search space, to explore more promising feature combinations. To this end, this paper proposes a tri-objective method for bi-objective feature selection in classification, which solves a bi-objective feature selection problem as a tri-objective problem by considering the diversity (differences) between feature subsets in the search space as the third objective. The selection based on the converted tri-objective method can maintain a balance between minimizing the number of selected features, maximizing the classification performance, and exploring more promising feature subsets. Furthermore, a novel initialization strategy and an offspring reproduction operator are proposed to promote the diversity of feature subsets in the objective space and improve the search ability, respectively. The proposed algorithm is compared with five multiobjective-based feature selection methods, six typical feature selection methods, and two peer methods with diversity as a helper objective. Experimental results on 20 real-world classification datasets suggest that the proposed method outperforms the compared methods in most scenarios.

Getting the Right Clones in an Automated Manner: An Alternative to Sophisticated Colony-Picking Robotics

In recent years, the design–build–test–learn (DBTL) cycle has become a key concept in strain engineering. Modern biofoundries enable automated DBTL cycling using robotic devices. However, both highly automated facilities and semi-automated facilities encounter bottlenecks in clone selection and screening. While fully automated biofoundries can take advantage of expensive commercially available colony pickers, semi-automated facilities have to fall back on affordable alternatives. Therefore, our clone selection method is particularly well-suited for academic settings, requiring only the basic infrastructure of a biofoundry. The automated liquid clone selection (ALCS) method represents a straightforward approach for clone selection. Similar to sophisticated colony-picking robots, the ALCS approach aims to achieve high selectivity. Investigating the time analogue of five generations, the model-based set-up reached a selectivity of 98 ± 0.2% for correctly transformed cells. Moreover, the method is robust to variations in cell numbers at the start of ALCS. Beside Escherichia coli, promising chassis organisms, such as Pseudomonas putida and Corynebacterium glutamicum, were successfully applied. In all cases, ALCS enables the immediate use of the selected strains in follow-up applications. In essence, our ALCS approach provides a ‘low-tech’ method to be implemented in biofoundry settings without requiring additional devices.

The skills dimension in the talent acquisition processes of Greek private schools

This paper explores the processes involved in talent acquisition practices, including recruitment, selection, and professional development within the context of private schools in Greece. Unlike the standardized recruitment and selection processes observed in the Greek public educational sector, which assess formal qualifications alongside social criteria, the private sector in Greece adopts a talent acquisition approach primarily based on job-matching theory. The theoretical section of the paper examines the divergence in talent acquisition processes between the Greek public and private educational sectors. In the empirical research section, the study presents findings from qualitative research conducted through semi-structured interviews with 15 managers from private schools in Northern Greece. Thematic analysis was employed for data analysis, with convenience sampling utilized as the sampling method. The research aims to capture managers' perspectives on selection methods, training practices, and professional development initiatives for educational staff. The findings highlight the emphasis placed on soft skills during the recruitment process, the diverse blend of skills and qualifications that form the occupational profile demanded and the significant role of managers in facilitating the professional growth of educational staff.

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis.

The effects of a risk-based approach to tax examinations: evidence from a tax pilot programme in Tanzania

AbstractTechnical assistance and increased use of ICT in tax administrations hold promise for greater revenue collection. Yet, the evidence on how these activities work in the real-world circumstances of developing countries is scant. The paper attempts to fill this gap by evaluating an intervention undertaken jointly by the Finnish and Tanzanian revenue administrations. The programme introduced a new risk-based case selection method for enhancing the effectiveness of firm tax examinations in Tanzania complemented by supplementary staff training and developing management practices. Our results, stemming from a difference-in-differences analysis and administrative data from the Tanzanian Revenue Authority, indicate that the intervention increased the corrected amount of taxable income by approximately 20% during the first year of the new approach.

The SRG/eROSITA All-Sky Survey: X-ray beacons at late cosmic dawn

The Spektrum-Roentgen-Gamma (SRG)/extended Roentgen Survey with an Imaging Telescope Array (eROSITA) All-Sky Survey (eRASS) is expected to contain $ quasars that emitted their light when the universe was less than a billion years old, that is, at $z&gt;5.6$. By selection, these quasars populate the bright end of the active galactic nuclei (AGN) X-ray luminosity function, and their space density offers a powerful demographic diagnostic of the parent super-massive black hole (SMBH) population. Of the $ 400$ quasars that have been discovered at $z&gt;5.6$ to date, less than 15&lt;!PCT!&gt; have been X-ray detected. We present a pilot survey to uncover the elusive X-ray luminous end of the distant quasar population. We have designed a quasar selection pipeline based on optical, infrared and X-ray imaging data from DES DR2, VHS DR5, CatWISE2020 and the eRASS (up to its four-pass cumulative version, eRASS:4). The core selection method relies on SED template fitting. We performed optical follow-up spectroscopy with the Magellan /LDSS3 instrument for the redshift confirmation of a subset of candidates. We have further obtained a deeper X-ray image of one of our candidates with Chandra ACIS-S. We report the discovery of five new quasars in the redshift range $5.6 &lt; z &lt; 6.1$. Two of these quasars are detected in eRASS and are, therefore, X-ray ultra-luminous by selection. We also report the detection of these quasars at radio frequencies. The first one is a broad absorption line quasar, which shows significant, order-of-magnitude X-ray dimming over 3.5 years, corresponding to six months in the quasar rest frame. The second X-ray detected quasar is a jetted source with compact morphology. We show that a blazar configuration is likely for this source, making it one of the most distant blazars known to date. With our pilot study, we demonstrate the power of eROSITA as a discovery machine for luminous quasars in the epoch of reionization. The X-ray emission of the two eROSITA detected quasars are likely to be driven by different high-energetic emission mechanisms, a diversity which we will further explore in a future systematic full-hemisphere survey.

Approaches to Sustainable Enterprise Value

Over the last two decades, business has taken a more systematic approach to understanding its impact on all of its stakeholders. Companies are striving to operate in a more sustainable way to improve the environmental, social and economic impact of their activities. Even today, the interest in determining the value of a company or parts of a company has not waned. As the economy continues to evolve, there are reasons and impulses that encourage companies to find out what their value is. The new approach is dependent on re-thinking value creation. There has been a growing understanding that sustainability requires a more comprehensive view of value that includes economic, social and environmental benefits. The aim of this paper is to propose a methodology for determining the sustainable value of an enterprise with respect to the factors influencing its value. This study is based on information that was gathered through an extensive literature review (research publications and research studies; the company’s financial statements with notes) using Internet and research databases and the authors’ own experiences. Methods of analysis, comparison, selection, abstraction, induction, deduction and statistics were used. The main findings of this study are as follows: the stakeholders require a company’s sustainable value; the modification of the EVA method with the sustainable value approach brings a different perspective to the issue of determining the value of an enterprise. In addition to the positives, the proposed methodology has limitations that can be removed or improved with further research.

Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India.

Developing effective strategies to predict areas susceptible to landslides and reducing risk is vital. This involves using ensemble methods to meet the precise prediction and addressing challenges like data limitation. Recent studies have highlighted the potential of using ensemble methods to enhance the prediction of landslide susceptibility maps (LSM). Ensemble methods present a sampling of landslides and non-landslide points from high and low susceptible areas, respectively. Extensive research has explored their application in machine learning processes, particularly in classification-related problems. This study delves into strategies of ensemble as a promising method in future landslide applications. The proposed method was tested considering Kangra district of Himachal Pradesh as study area where three datasets were prepared consisting of presence and absence points. Dataset 1 consisted of initial landslide and randomly generated non-landslide points. In dataset 2, additional landslide points obtained from the very high susceptibility of initial LSM were supplemented with initial landslide data, while the non-landslide points were generated randomly from the study area. Finally, dataset 3 was composed of the landslide points as in dataset 2, and the non-landslide points were obtained from the very low susceptible areas of initial LSM. These datasets are used with random forest (RF) and support vector machine (SVM), thereby preparing six landslide susceptibility maps. To analyze the applicability of the proposed method, we have used metrics such as AUC-ROC, precision, recall, F-score, accuracy and Mathew's correlation coefficient (MCC). The AUC for dataset 1 with SVM and RF is 0.89, which increased to 0.898 and 0.952 for datasets 2 and 3 with SVM and 0.937 and 0.954 with RF. Among all the methods, the precision and recall values were highest for dataset 3 with SVM as well as RF. Hence, based on several accuracy metrics, we conclude that when the landslides and non-landslides samples were sampled from very high and very low susceptible areas respectively, the LSM performed better than all the other methods. Sampling landslides from very high susceptible areas only (dataset 2) does not perform well thereby committing misclassification error. The study demonstrated that the landslide and non-landslide data were obtained from very high and very low susceptibility; the predictive capability of the LSM increased significantly. Thus, the results demonstrate the effectiveness of the proposed ensemble approach in providing precise delineation of landslide zones, facilitating informed decision-making for land and hazard management.

Prospects for mineral biofortification of wheat: classical breeding and agronomy

Low intake of micro- and macroelements and vitamins in food negatively affects the health of more than two billion people around the world provoking chronic diseases. For the majority of the world’s population, these are soft and durum wheats that provide beneficial nutrients, however their modern high-yielding varieties have a significantly depleted grain mineral composition that have reduced mineral intake through food. Biofortification is a new research trend, whose main goal is to improve the nutritional qualities of agricultural crops using a set of classical (hybridization and selection) methods as well and the modern ones employing gene/QTL mapping, bioinformatic analysis, transgenesis, mutagenesis and genome editing. Using the classical breeding methods, biofortified varieties have been bred as a part of various international programs funded by HarvestPlus, CIMMYT, ICARDA. Despite the promise of transgenesis and genome editing, these labor-intensive methods require significant investments, so these technologies, when applied to wheat, are still at the development stage and cannot be applied routinely. In recent years, the interest in wheat biofortification has increased due to the advances in mapping genes and QTLs for agronomically important traits. The new markers obtained from wheat genome sequencing and application of bioinformatic methods (GWAS, meta-QTL analysis) has expanded our knowledge on the traits that determine the grain mineral concentration and has identified the key gene candidates. This review describes the current research on genetic biofortification of wheat in the world and in Russia and provides information on the use of cultivated and wild-relative germplasms to expand the genetic diversity of modern wheat varieties.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient’s health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network’s edge. The system’s performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model’s performance empirically in real-world IoMT scenarios.

Tryptophan 2,3-dioxygenase-positive matrix fibroblasts fuel breast cancer lung metastasis via kynurenine-mediated ferroptosis resistance of metastatic cells and T cell dysfunction.

Tumor metastasis is a major threat to cancer patient survival. The organ-specific niche plays a pivotal role in tumor organotropic metastasis. Fibroblasts serve as a vital component of the metastatic microenvironment, but how heterogeneous metastasis-associated fibroblasts (MAFs) promote organotropic metastasis is poorly characterized. Here, we aimed to decipher the heterogeneity of MAFs and elucidate the distinct roles of these fibroblasts in pulmonary metastasis formation in breast cancer. Mouse models of breast cancer pulmonary metastasis were established using an in vivo selection method of repeated injections of metastatic cells purified from the mouse lung. Single-cell RNA-sequencing (scRNA-seq) was employed to investigate the heterogeneity of MAFs. Transgenic mice were used to examine the contribution of tryptophan 2,3-dioxygenase-positive matrix fibroblasts (TDO2+ MFs) in lung metastasis. We uncovered 3 subtypes of MAFs in the lung metastatic microenvironment, and their transcriptome profiles changed dynamically as lung metastasis evolved. As the predominant subtype, MFs were exclusively marked by platelet-derived growth factor receptor alpha (PDGFRA) and mainly located on the edge of the metastasis, and T cells were enriched around MFs. Notably, high MF signatures were significantly associated with poor survival in breast cancer patients. Lung metastases were markedly diminished, and the suppression of T cells was dramatically attenuated in MF-depleted experimental metastatic mouse models. We found that TDO2+ MFs controlled pulmonary metastasis by producing kynurenine (KYN), which upregulated ferritin heavy chain 1 (FTH1) level in disseminated tumor cells (DTCs), enabling DTCs to resist ferroptosis. Moreover, TDO2+ MF-secreted chemokines C-C motif chemokine ligand 8 (CCL8) and C-C motif chemokine ligand 11 (CCL11) recruited T cells. TDO2+ MF-derived KYN induced T cell dysfunction. Conditional knockout of Tdo2 in MFs diminished lung metastasis and enhanced immune activation. Our study reveals crucial roles of TDO2+ MFs in promoting lung metastasis and DTCs' immune evasion in the metastatic niche. It suggests that targeting the metabolism of lung-specific stromal cells may be an effective treatment strategy for breast cancer patients with lung metastasis.

Assessing model selection techniques for distributions use in hydrological extremes in the presence of trimming and subsampling

A suitable probability distribution is required to quantify and estimate hydraulic structure design for risk evaluation and management. The inability of model selection criteria to differentiate, in some cases, among candidate distributions used in the analysis of hydrological extremes is often criticised. This study verifies, with the aid of model selection techniques, the potential utility of trimming and subsampling in distinguishing between candidate distributions, which might not be feasible using the traditional goodness of fit method alone, when samples available are small. The performance of the proposed method is evaluated through its application to real and simulated yearly peak rainfall datasets. The proposed approach is then compared with several standard model selection techniques. Results show that the model selection techniques with the aid of subsampling are effective in identifying the true parent distribution for the untrimmed samples given a two-parameter distribution; contrarily, they are inefficient where a distribution with a three-parameter is the parent distribution. However, as trimming is introduced, all model selection methods recognise the true parent distribution for a three-parameter distribution. Overall, utilising trimming and subsampling with the aid of model selection methods yields promising outcomes in the analysis of hydrological extreme frequencies. Drawing from the results of numerical simulation and examination of observed data, the use of trimming and subsampling can be a viable tool in differentiating among candidate distributions used in the investigation of hydrological extreme frequencies.

Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data

This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value.

Comparison of the Accuracy and Reliability of Instrumental Shade Selection Devices and Visual Shade Selection: An in Vitro Study.

New shade-matching devices are available for clinical use, but their accuracy and reliability have not been investigated. This in vitro study compared the accuracy and reliability of a shade-matching colorimeter (Optishade), a spectrophotometer (VITA Easyshade V), and visual shade matching in simulated clinical conditions. Shade matching was conducted with two digital devices; (Optishade and Vita Easyshade) and the visual shade selection method, with a new 16-tab Vita Classical shade guide. Shade tabs were placed within a gingival matrix and measurements were taken from the central area of each shade tab enclosed in a black box to simulate the oral cavity. Visual assessments were conducted independently by two examiners. For the reliability assessment, each shade tab was measured 20 times by each examiner. Accuracy was calculated as a percentage of the matching readings with the actual shade per each tab. Data were analyzed with the one-way ANOVA test (α = 0.05) and Pairwise Comparison (Bonferroni Post hoc Test) to assess accuracy. The reliability evaluation was conducted by comparison between both examiners using mean and standard deviation; by a two-tailed t-test for the independent samples for all the three groups included in the study. The shade matching accuracy of Optishade was 97.50%, Vita Easyshade 89.38%, and visual method 62.19%. Statistically, high significant difference in accuracy (p < 0.001) was found when comparing both Optishade and VITA Easyshade with the visual method. Additionally, there was statistically significant difference in accuracy (p < 0.05) when Optishade was compared to Vita Easyshade. The reliability of the Optishade was 99.6% and Vita Easy shade was 99.38%. Statistically, showed no statistically significant difference in the reliability of shade match obtained within the same group when comparing the readings of Examiner 1 and Examiner 2 (p > 0.05). The colorimeter demonstrated the highest accuracy, followed by the spectrophotometer, and the visual shade selection method. Both digital methods investigated in this study showed comparable reliability. The results of this study emphasize the clinical advantage of digital shade-matching devices over visual shade-matching. The colorimeter offers high accuracy for shade matching and should be considered to use for shade matching especially with restorations in the esthetic zone.

A New Hybrid Model for Heart Disease Prediction Using Machine Learning Algorithms Optimized by Modified Whale Optimization Algorithm

Heart disease is a kind of cardiovascular disease (CVD) which globally considered death's number one cause. Data Science plays an important role in processing large data amounts in the healthcare domain. There are several problems that could hinder appropriate cardiac monitoring, including limited of medical dataset, lack of depth analysis, and feature selection. In this paper, we exploit the method of Fast Correlation-Based Feature Selection (FCBF) for filtering extra features for developing heart disease classification quality. Next, we implement the classification according to various algorithms of classification like Decision tree, Naïve Bayes, Logistic Regression, K-Nearest Neighbour (KNN), Random Forest, Support Vector Machine (SVM), and a Multilayer Perception which is optimized with the modified whale optimization algorithm evaluation version named Modified Whale Optimization Algorithm (MWOA). The proposed model optimized with FCBF as well as MWOA obtain an 83.26% accuracy score by Logistic Regression.

A comprehensive electrochemical analysis revealing the surface oxidation behavior difference between pyrite and arsenopyrite

This study systematically investigated the surface oxidation behavior differences between pyrite and arsenopyrite under neutral and alkaline pH conditions using various electrochemical measurements. These measurements include open circuit potential (OCP), cyclic voltammetry (CV), potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), and Mott-Schottky analyses. Key electrochemical parameters have been obtained. The OCP, oxidation potential (Eox), and charge transfer resistance (Rct) of arsenopyrite and pyrite declined as the solution pH increased, but oxidation current density (jox) and carrier concentration (ND) increased, suggesting that a high solution pH is thermodynamically and dynamically favorable for the oxidation of both pyrite and arsenopyrite. Compared to pyrite, arsenopyrite has a higher Fermi level, lower OCP and Eox, a larger jox, and a smaller Rct at the same pH conditions, suggesting that arsenopyrite oxidation has a higher thermodynamic preference and occurs at a faster rate than pyrite oxidation under neutral and alkaline pH conditions. This comprehensive study enhances our understanding of the oxidation difference between pyrite and arsenopyrite and offers important guidance for the pyrite-arsenopyrite flotation separation when using the selective surface oxidation method.

Phase quantification analysis in cement-blended slag system with the PONKCS method

The Partial Or No Known Crystal Structure (PONKCS) method, an XRD analysis technique, is widely employed for phase quantification in cement-blended supplementary cementitious materials (SCMs). This study emphasized unsolved factors in applying this method to SCMs blends, including obtaining accurate SCM structure information, accounting for chemical composition variation, and identifying suitable C–S–H profiles. Ground granulated blast furnace slag was chosen as the representative SCM. Results show that four fundamental parameter (FP) type peaks were sufficient to index slag pseudo-structure without Pawley fitting. Slag weight fraction quantification was minimally impacted by slag pseudo-structures from slag with SiO2 content variations up to 3.03%. The C–S–H profile, with negligible clinker residue and a Lorentzian crystallite size of approximately 6.9 nm, was found suitable for PONKCS method application. Combining selective dissolution method with PONKCS method improved its accuracy. This study enhances improving quantification precision and extending the applications of PONKCS method in variable SCMs systems.

The credibility assessment of Twitter/X users based organization objectives by heterogeneous resources in big data life cycle

Social network data, such as Twitter/X, is of Big Social Data type. Big social data describes people's social behaviors and interactions. They have high business value for decision-making in organizations. However, because of the anonymous nature of social network users, their credibility is ambiguous. Credibility expresses the accuracy and value of big social data. Despite extensive research on the credibility of big social data, most methods have not paid sufficient attention to the important dimensions of their assessment, including user expertise based on topic, selecting social network features, and labeling them. Furthermore, these methods cannot manage the time, high volume, and speed of big social data. To address these issues, this paper presents a novel model for assessing the credibility of Twitter/X users by integrating Twitter/X with Google Scholar. The model automatically defines users' credibility labels using Google Scholar. Machine learning feature selection methods also select features that affect the credibility of Twitter/X users based on the topic. This study uses Google Scholar and the BerTopic algorithm for effective topic modeling on Twitter/X. The model considers unrelated data management, dynamic user credibility, and organizing activities based on the Big Data lifecycle. Finally, using Linear Regression, Support Vector Regression, K-Nearest Neighbor, Random Forest, Classification and Regression Trees algorithms, the model predicts the credibility of Twitter/X users and proves that it performed better than similar models through Classification and Regression Trees. In addition, the model is generalizable for all organizational purposes due to the integration of heterogeneous resources and feature selection methods.