Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Exploring the Impact of Model Complexity on Laryngeal Cancer Detection.

Background: Laryngeal cancer accounts for a third of all head and neck malignancies, necessitating timely detection for effective treatment and enhanced patient outcomes. Machine learning shows promise in medical diagnostics, but the impact of model complexity on diagnostic efficacy in laryngeal cancer detection can be ambiguous. Methods: In this study, we examine the relationship between model sophistication and diagnostic efficacy by evaluating three approaches: Logistic Regression, a small neural network with 4 layers of neurons and a more complex convolutional neural network with 50 layers and examine their efficacy on laryngeal cancer detection on computed tomography images. Results: Logistic regression achieved 82.5% accuracy. The 4-Layer NN reached 87.2% accuracy, while ResNet-50, a deep learning architecture, achieved the highest accuracy at 92.6%. Its deep learning capabilities excelled in discerning fine-grained CT image features. Conclusion: Our study highlights the choices involved in selecting a laryngeal cancer detection model. Logistic regression is interpretable but may struggle with complex patterns. The 4-Layer NN balances complexity and accuracy. ResNet-50 excels in image classification but demands resources. This research advances understanding affect machine learning model complexity could have on learning features of laryngeal tumor features in contrast CT images for purposes of disease prediction.

Ensemble of vision transformer architectures for efficient Alzheimer's Disease classification.

Transformers have dominated the landscape of Natural Language Processing (NLP) and revolutionalized generative AI applications. Vision Transformers (VT) have recently become a new state-of-the-art for computer vision applications. Motivated by the success of VTs in capturing short and long-range dependencies and their ability to handle class imbalance, this paper proposes an ensemble framework of VTs for the efficient classification of Alzheimer's Disease (AD). The framework consists of four vanilla VTs, and ensembles formed using hard and soft-voting approaches. The proposed model was tested using two popular AD datasets: OASIS and ADNI. The ADNI dataset was employed to assess the models' efficacy under imbalanced and data-scarce conditions. The ensemble of VT saw an improvement of around 2% compared to individual models. Furthermore, the results are compared with state-of-the-art and custom-built Convolutional Neural Network (CNN) architectures and Machine Learning (ML) models under varying data conditions. The experimental results demonstrated an overall performance gain of 4.14% and 4.72% accuracy over the ML and CNN algorithms, respectively. The study has also identified specific limitations and proposes avenues for future research. The codes used in the study are made publicly available.

Machine learning model predicting factors for incisional infection following right hemicolectomy for colon cancer.

BACKGROUND AND AIM: Colorectal cancer is a prevalent malignancy worldwide, and right hemicolectomy is a common surgical procedure for its treatment. However, postoperative incisional infections remain a significant complication, leading to prolonged hospital stays, increased healthcare costs, and patient discomfort. Therefore, this study aims to utilize machine learning models, including random forest, support vector machine, deep learning models, and traditional logistic regression, to predict factors associated with incisional infection following right hemicolectomy for colon cancer. METHODS: Clinical data were collected from 322 patients undergoing right hemicolectomy for colon cancer, including demographic information, preoperative chemotherapy status, body mass index (BMI), operative time, and other relevant variables. These data are divided into training and testing sets in a ratio of 7:3. Machine learning models, including random forest, support vector machine, and deep learning, were trained using the training set and evaluated using the testing set. RESULTS: The deep learning model exhibited the highest performance in predicting incisional infection, followed by random forest and logistic regression models. Specifically, the deep learning model demonstrated higher area under the receiver operating characteristic curve (ROC-AUC) and F1 score compared to other models. These findings suggest the efficacy of machine learning models in predicting risk factors for incisional infection following right hemicolectomy for colon cancer. CONCLUSIONS: Machine learning models, particularly deep learning models, offer a promising approach for predicting the risk of incisional infection following right hemicolectomy for colon cancer. These models can provide valuable decision support for clinicians, facilitating personalized treatment strategies and improving patient outcomes.

Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity in multi-layered aquifers spanning the entire Mekong Delta (MD) region using machine learning (ML) models based on an in situ dataset and using three indicators (Cl-, pH, and HCO3-). We applied nine different decision tree-based models and evaluated their prediction performances. The models were trained using 13 input variables: weather (2), hydrogeological conditions (4), water levels (3), groundwater usage (2), and relative distance from water sources (2). Subsequently, by employing model interpretation techniques, we quantified the significance of factors within the model prediction. Performance evaluations of the ML models demonstrated that the Extra Trees model exhibited superior performance and demonstrated generalization capabilities in predicting Cl- concentration, whereas the Bagging and Random Forest models outperformed the other models in predicting pH and HCO3- concentration. The coefficients of determination were determined to be 0.94, 0.67, and 0.78 for Cl-, pH, and HCO3-, respectively Additionally, the model interpretation effectively identified significant factors that depended on the target variables and aquifers. In particular, salinity indicators and aquifers that were strongly influenced by the artificial usage of groundwater were identified. Therefore, our research, which provides accurate spatial predictions and interpretations of groundwater salinity in the MD, has the potential to establish a foundation for formulating effective groundwater management policies to control groundwater salinization.

Using machine learning methods to investigate the impact of age on the causes of death in patients with early intrahepatic cholangiocarcinoma who underwent surgery.

BACKGROUND: The impact of age on the causes of death (CODs) in patients with early-stage intrahepatic cholangiocarcinoma (ICC) who had undergone surgery was analyzed in this study. METHODS: A total of 1555 patients (885 in the older group and 670 in the younger group) were included in this study. Before and after applying inverse probability of treatment weighting (IPTW), the different CODs in the 2 groups were further investigated. Additionally, 7 different machine learning models were used as predictive tools to identify key variables, aiming to evaluate the therapeutic outcome in early ICC patients undergoing surgery. RESULTS: Before (5.92 vs. 4.08 years, P < 0.001) and after (6.00 vs. 4.08 years, P < 0.001) IPTW, the younger group consistently showed longer overall survival (OS) compared with the older group. Before IPTW, there were no significant differences in cholangiocarcinoma-related deaths (CRDs, P = 0.7) and secondary malignant neoplasms (SMNs, P = 0.78) between the 2 groups. However, the younger group had a lower cumulative incidence of cardiovascular disease (CVD, P = 0.006) and other causes (P < 0.001) compared with the older group. After IPTW, there were no differences between the 2 groups in CRDs (P = 0.2), SMNs (P = 0.7), and CVD (P = 0.1). However, the younger group had a lower cumulative incidence of other CODs compared with the older group (P < 0.001). The random forest (RF) model showed the highest C-index of 0.703. Time-dependent variable importance bar plots showed that age was the most important factor affecting the 2-, 4-, and 6-year survival, followed by stage and size. CONCLUSIONS: Our study confirmed that younger patients have longer OS compared with older patients. Further analysis of the CODs indicated that older patients are more likely to die from CVDs. The RF model demonstrated the best predictive performance and identified age as the most important factor affecting OS in early ICC patients undergoing surgery.

Machine Learning Models to Classify Shiitake Mushrooms (Lentinula edodes) According to Their Geographical Origin Labeling.

The shiitake mushroom has gained popularity in the last decade, ranking second in the world for mushrooms consumed, providing consumers with a wide variety of nutritional and healthy benefits. It is often not clear the origin of these mushrooms, so it becomes of great importance to the consumers. In this research, different machine learning algorithms were developed to determine the geographical origin of shiitake mushrooms (Lentinula edodes) consumed in Korea, based on experimental data reported in the literature (δ13C, δ15N, δ18O, δ34S, and origin). Regarding the origin of shiitake in three categories (Korean, Chinese, and mushrooms from Chinese inoculated sawdust blocks), the random forest model presents the highest accuracy value (0.940) and the highest kappa value (0.908) for the validation phase. To determine the origin of shiitake mushrooms in two categories (Korean and Chinese, including mushrooms from Chinese inoculated sawdust blocks in the latter ones), the support vector machine model is chosen as the best model due to the high accuracy (0.988) and kappa (0.975) values for the validation phase. Finally, to determine the origin in two categories (Korean and Chinese, but this time including the mushrooms from Chinese inoculated sawdust blocks in the Korean ones), the best model is the random forest due to its higher accuracy value (0.952) in the validation phase (kappa value of 0.869). The accuracy values in the testing phase for the best selected models are acceptable (between 0.839 and 0.964); therefore, the predictive capacity of the models could be acceptable for their use in real applications. This allows us to affirm that machine learning algorithms would be suitable modeling instruments to determine the geographical origin of shiitake.

Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics.

Introduction: Accurate prediction of tumor dynamics following Gamma Knife radiosurgery (GKRS) is critical for optimizing treatment strategies for patients with brain metastases (BMs). Traditional machine learning (ML) algorithms have been widely used for this purpose; however, recent advancements in deep learning, such as autoencoders, offer the potential to enhance predictive accuracy. This study aims to evaluate the efficacy of autoencoders compared to traditional ML models in predicting tumor progression or regression after GKRS. Objectives: The primary objective of this study is to assess whether integrating autoencoder-derived features into traditional ML models can improve their performance in predicting tumor dynamics three months post-GKRS in patients with brain metastases. Methods: This retrospective analysis utilized clinical data from 77 patients treated at the "Prof. Dr. Nicolae Oblu" Emergency Clinic Hospital-Iasi. Twelve variables, including socio-demographic, clinical, treatment, and radiosurgery-related factors, were considered. Tumor progression or regression within three months post-GKRS was the primary outcome, with 71 cases of regression and 6 cases of progression. Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. The study further explored the impact of incorporating features derived from autoencoders, particularly focusing on the effect of compression in the bottleneck layer on model performance. Results: Traditional ML models achieved accuracy rates ranging from 0.91 (KNN) to 1.00 (Extra Trees). Integrating autoencoder-derived features generally enhanced model performance. Logistic Regression saw an accuracy increase from 0.91 to 0.94, and SVM improved from 0.85 to 0.96. XGBoost maintained consistent performance with an accuracy of 0.94 and an AUC of 0.98, regardless of the feature set used. These results demonstrate that hybrid models combining deep learning and traditional ML techniques can improve predictive accuracy. Conclusion: The study highlights the potential of hybrid models incorporating autoencoder-derived features to enhance the predictive accuracy and robustness of traditional ML models in forecasting tumor dynamics post-GKRS. These advancements could significantly contribute to personalized medicine, enabling more precise and individualized treatment planning based on refined predictive insights, ultimately improving patient outcomes.

Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple points and non-point sources. Monitoring chlorophyll-a (Chl-a) concentrations, a proxy for algal biomass is essential for assessing the trophic status of the river and managing its ecological health. This study aimed to improve the accuracy and reliability of Chl-a estimation in the Nakdong River using machine learning models (MLMs) and simultaneous use of multiple remotely sensed datasets. This study compared the performances of four MLMs: multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: (1) two remotely sensed datasets (Sentinel-2 and Landsat-8), (2) standalone Sentinel-2, and (3) standalone Landsat-8. The results showed that the MLP model with multiple remotely sensed datasets outperformed other MLMs with 0.43 - 0.86 greater in R2 and 0.36 - 5.88 lower in RMSE. The MLP model demonstrated the highest performance across the range of Chl-a concentrations and predicted peaks above 20 mg/m3 relatively well compared to other models. This was likely due to the capacity of MLP to handle imbalanced datasets. The predictive map of the spatial distribution of Chl-a generated by MLP well captured the areas with high and low Chl-a concentrations. This study pointed out the impacts of imbalanced Chl-a concentration observations (dominated by low Chl-a concentrations) on the performance of MLMs. The data imbalance likely led to MLMs poorly trained for high Chl-a values, producing low prediction accuracy. In conclusion, this study demonstrated the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. These findings would provide valuable insights into utilizing MLMs effectively for Chl-a monitoring.

Pathogenesis and potential diagnostic biomarkers of atrial fibrillation in Chinese population: a study based on bioinfor-matics. / åŸºäºŽç”Ÿç‰©ä¿¡æ¯å­¦æŽ¢è®¨ä¸­å›½äººç¾¤å‘ç”Ÿå¿ƒæˆ¿é¢¤åŠ¨çš„æœºåˆ¶åŠæ½œåœ¨ç”Ÿç‰©æ ‡å¿—ç‰©.

OBJECTIVES: To explore the pathogenesis and potential biomarkers of atrial fibrillation based on bioinformatics. METHODS: Differentially expressed genes and module genes related to atrial fibrillation were obtained from GSE41177 and GSE79768 databases (a platform using Chinese-origin tissue samples) through differential expression analysis and weighted gene co-expression network analysis, and candidate hub genes were obtained by taking intersections, and hub genes were obtained after gender stratification. Subsequently, functional enrichment analysis and immune infiltration analysis were performed. Four machine learning models were constructed based on the hub genes, and the optimal model was selected to construct a prediction nomogram; the prediction ability of the nomogram was verified using calibration curves and decision curves. Finally, potential therapeutic drugs for atrial fibrillation were screened in the DGIdb database. RESULTS: A total of 67 differentially expressed genes and 65 module genes related to atrial fibrillation were identified, and functional enrichment analysis indicated that the pathogenesis of atrial fibrillation was closely related to inflammatory response, immune response, and immune and infectious diseases. Four hub genes (TYROBP, FCER1G, EVI2B and SOD2) with generalization and two genes specifically expressed in male (PILRA and SLC35G3) and female (HLA-DRA and GATP) patients with atrial fibrillation were obtained after gender-segregated screening. The extreme gradient boosting model had satisfactory diagnostic efficacy, and the nomogram constructed based on the hub genes, male significant variables (PILRA, SLC35G3 and SOD2), and female significant variables (FCER1G, SOD2 and TYROBP) had satisfactory predictive ability. Immune infiltration analysis demonstrated a disturbed immune infiltration microenvironment in atrial fibrillation with a higher abundance of plasma cells, neutrophils, and γδT cells, with a higher abundance of neutrophils in males and resting mast cells in females. Two potential drugs for the treatment of atrial fibrillation, namely, valproic acid and methotrexate, were obtained by database and literature screening. CONCLUSIONS: The pathogenesis of atrial fibrillation is closely related to inflammation and immune response, and the microenvironment of immune cell infiltration of cardiomyocytes in the atrial tissue of patients with atrial fibrillation is disordered. TYROBP, FCER1G, EVI2B and SOD2 serve as potential diagnostic biomarkers of atrial fibrillation; PILRA and SLC35G3 serve as potential specific diagnostic biomarkers of atrial fibrillation in the male population, which can effectively predict the risk of atrial fibrillation development and are also potential targets for the treatment of atrial fibrillation.

Development and validation of machine-learning models of diet management for hyperphenylalaninemia: a multicenter retrospective study.

BACKGROUND: Assessing dietary phenylalanine (Phe) tolerance is crucial for managing hyperphenylalaninemia (HPA) in children. However, traditionally, adjusting the diet requires significant time from clinicians and parents. This study aims to investigate the development of a machine-learning model that predicts a range of dietary Phe intake tolerance for children with HPA over 10 years following diagnosis. METHODS: In this multicenter retrospective observational study, we collected the genotypes of phenylalanine hydroxylase (PAH), metabolic profiles at screening and diagnosis, and blood Phe concentrations corresponding to dietary Phe intake from over 10 years of follow-up data for 204 children with HPA. To incorporate genetic information, allelic phenotype value (APV) was input for 2965 missense variants in the PAH gene using a predicted APV (pAPV) model. This model was trained on known pheno-genotype relationships from the BioPKU database, utilizing 31 features. Subsequently, a multiclass classification model was constructed and trained on a dataset featuring metabolic data, genetic data, and follow-up data from 3177 events. The final model was fine-tuned using tenfold validation and validated against three independent datasets. RESULTS: The pAPV model achieved a good predictive performance with root mean squared error (RMSE) of 1.53 and 2.38 on the training and test datasets, respectively. The variants that cause amino acid changes in the region of 200-300 of PAH tend to exhibit lower pAPV. The final model achieved a sensitivity range of 0.77 to 0.91 and a specificity range of 0.8 to 1 across all validation datasets. Additional assessment metrics including positive predictive value (0.68-1), negative predictive values (0.8-0.98), F1 score (0.71-0.92), and balanced accuracy (0.8-0.92) demonstrated the robust performance of our model. CONCLUSIONS: Our model integrates metabolic and genetic information to accurately predict age-specific Phe tolerance, aiding in the precision management of patients with HPA. This study provides a potential framework that could be applied to other inborn errors of metabolism.

Supervised machine learning for understanding and predicting the status of bistable eukaryotic plankton community in urbanized rivers.

Understanding and predicting the ecological status of urbanized rivers is crucial for their restoration and management. However, the complex and nonlinear nature of ecological responses poses a challenge to the development of predictive models. Here, the study investigated and predicted the status of eukaryotic plankton communities in urbanized rivers by coupling environmental DNA metabarcoding, the alternative stable states theory, and supervised machine learning (SML) models. The results revealed two distinct states of eukaryotic plankton communities under similar environmental conditions: one state was characterized by the enrichment of a diverse phytoplankton population and the high relative abundance of protozoa, whereas the alternative state was characterized by abundant phytoplankton and fungi with an associated risk of algal blooms. Turbidity was identified as a key driver based on the SML model and Mantel test. Potential analysis demonstrated that the response pattern of eukaryotic plankton communities to turbidity was thresholds with hysteresis (Threshold1 = 17 NTU, Threshold2 = 24 NTU). A reduction in turbidity induced a regime shift in the eukaryotic plankton community toward an alternative state associated with a risk of algal blooms. In the prediction of ecological status, both SML models showed excellent performance (R2 > 0.80, RMSE < 0.1, Kappa > 0.70). Additionally, SHapley Additive exPlanations analysis identified turbidity, chlorophyll-a, chemical oxygen demand (COD), ammonia nitrogen and green algae's amplicon sequence variants as crucial features for prediction, with turbidity and COD showing a synergistic effect on ecological status. A framework was further proposed to enhance the understanding and prediction of ecological status in urbanized rivers. The obtained results of this study demonstrated the feasibility of using SML models to predict and explain the ecological status of urbanized rivers with alternative stable states. This provides valuable insights for the application of SML models in the restoration and management of urbanized rivers.

Evaluation and analysis of risk factors for adverse events of the fractured vertebra post-percutaneous kyphoplasty: a retrospective cohort study using multiple machine learning models.

BACKGROUND: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analyze the risk factors of AEFV and develop and select the optimal risk prediction model for AEFV to provide guidance for the prevention of this condition and enhancement of clinical outcomes. METHODS: This work included 383 patients with primary osteoporotic vertebral compression fracture (OVCF) who underwent PKP. The patients were grouped based on the occurrence of AEFV postsurgery, and data were collected. Group comparisons and correlation analysis were conducted to identify potential risk factors, which were then included in the five prediction models. The performance indicators served as basis for the selection of the best model. RESULTS: Multivariate logistic regression analysis revealed the following independent risk factors for AEFV: kissing spine (odds ratio (OR) = 8.47, 95% confidence interval (CI) 1.46-49.02), high paravertebral muscle fat infiltration grade (OR = 29.19, 95% CI 4.83-176.04), vertebral body computed tomography value (OR = 0.02, 95% CI 0.003-0.13, P < 0.001), and large Cobb change (OR = 5.31, 95% CI 1.77-15.77). The support vector machine (SVM) model exhibited the best performance in the prediction of the risk of AEFV. CONCLUSION: Four independent risk factors were identified of AEFV, and five risk prediction models that can aid clinicians in the accurate identification of high-risk patients and prediction of the occurrence of AEFV were developed.

Novel nitrogen-rich hydrogel adsorbent for selective extraction of rare earth elements from wastewater.

Efficient recovery of rare earth elements (REEs) from wastewater is crucial for advancing resource utilization and environmental protection. Herein, a novel nitrogen-rich hydrogel adsorbent (PEI-ALG@KLN) was synthesized by modifying coated kaolinite-alginate composite hydrogels with polyethylenimine through polyelectrolyte interactions and Schiff's base reaction. Various characterizations revealed that the high selective adsorption capacity of Ho (155 mg/g) and Nd (125 mg/g) on PEI-ALG@KLN is due to a combination of REEs (Lewis acids) via coordination interactions with nitrogen-containing functional groups (Lewis bases) and electrostatic interactions; its adsorption capacity remains more than 85 % after five adsorption-desorption cycles. In waste NdFeB magnet hydrometallurgical wastewater, the recovery rate of PEI-ALG@KLN for Nd and Dy can reach more than 93 %, whereas that of Fe is only 5.04 %. Machine learning prediction was used to evaluate adsorbent properties via different predictive models, with the random forest (RF) model showing superior predictive accuracy. The order of significance for adsorption capacity was pH > time > initial concentration > electronegativity > ion radius, as indicated by the RF model feature importance analysis and SHapley Additive exPlanations values. These results confirm that PEI-ALG@KLN has considerable potential in the selective extraction of REEs from wastewater.

Optimal features selection in the high dimensional data based on robust technique: Application to different health database.

Bio-informatics and gene expression analysis face major hurdles when dealing with high-dimensional data, where the number of variables or genes much outweighs the number of samples. These difficulties are exacerbated, particularly in microarray data processing, by redundant genes that do not significantly contribute to the response variable. To address this issue, gene selection emerges as a feasible method for identifying the most important genes, hence reducing the generalization error of classification algorithms. This paper introduces a new hybrid approach for gene selection by combining the Signal-to-Noise Ratio (SNR) score with the robust Mood median test. The Mood median test is beneficial for reducing the impact of outliers in non-normal or skewed data since it may successfully identify genes with significant changes across groups. The SNR score measures the significance of a gene's classification by comparing the gap between class means and within-class variability. By integrating both of these approaches, the suggested approach aims to find genes that are significant for classification tasks. The major objective of this study is to evaluate the effectiveness of this combination approach in choosing the optimal genes. A significant P-value is consistently identified for each gene using the Mood median test and the SNR score. By dividing the SNR value of each gene by its significant P-value, the Md score is calculated. Genes with a high signal-to-noise ratio (SNR) have been considered favorable due to their minimal noise influence and significant classification importance. To verify the effectiveness of the selected genes, the study utilizes two dependable classification techniques: Random Forest and K-Nearest Neighbors (KNN). These algorithms were chosen due to their track record of successfully completing categorization-related tasks. The performance of the selected genes is evaluated using two metrics: error reduction and classification accuracy. These metrics offer an in-depth assessment of how well the selected genes improve classification accuracy and consistency. According to the findings, the hybrid approach put out here outperforms conventional gene selection methods in high-dimensional datasets and has lower classification error rates. There are considerable improvements in classification accuracy and error reduction when specific genes are exposed to the Random Forest and KNN classifiers. The outcomes demonstrate how this hybrid technique might be a helpful tool to improve gene selection processes in bioinformatics.

Use of a structure-borne sound-based in-process sensor system to identify Weld seam irregularities during electron beam welding.

In this publication, an in-process quality assurance method for electron beam welding based on a structure-borne sound emission test for the detection of weld irregularities arising in the process is presented. For this purpose, different sheet materials, i.e., AISI 304, AZ31 and AlMg3, were welded in a butt-joint and the resulting process noises were recorded by means of two acoustic emission sensors specifically designed for structure-borne sound. During the welding experiments, typical irregularities, e.g. incidence points, pore lines and cracks, were deliberately induced. Subsequently, the recorded acoustic signals were examined with regard to defect-specific abnormalities. Various methods in the time and frequency domain as well as pre-trained machine learning models were used to analyze the acoustic emission data. The results show that the investigated irregularities can be identified and distinguished from other process emissions, eventually enabling a robust means of identification for weld seam irregularities and, thus, opening pathways towards cost-effective in-process quality control.

The Language of (Non)Replicable Social Science.

Using publicly available data from 299 preregistered replications from the social sciences, we found that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the article characteristics, study design and results, author information, and replication effort. To understand why, we analyzed the textual differences between replicable and nonreplicable studies. Our findings suggest that the language in replicable studies is transparent and confident, written in a detailed and complex manner, and generally exhibits markers of truthful communication, possibly demonstrating the researchers' confidence in the study. Nonreplicable studies, however, are vaguely written and have markers of persuasion techniques, such as the use of positivity and clout. Thus, our findings allude to the possibility that authors of nonreplicable studies are more likely to make an effort, through their writing, to persuade readers of their (possibly weaker) results.

Assessment of wetland ecosystem health in Rarh Region, India through P-S-R (pressure-state-response) model.

The current study attempted to assess wetland ecosystem health (EH) in the Murshidabad district's Rarh tract using the P-S-R (Pressure-State-Response) model and machine learning (ML) algorithms and validated it with a field-based validation approach as well as conventional validation approaches. To assess the ecosystem's health, 27 metrics were used to monitor the wetlands' pressure, state, and response. All of the models found that 46.1 % of wetlands in strong EH zones have transformed to 11.41 % in relatively fragile EH zones during the previous thirty years, demonstrating a progressive loss of EH quality throughout larger wetland areas. All of the applied models were deemed to be acceptable based on the results of the model validation process, however, the Random Forest (RF) model performed exceptionally well. The deterioration of EH in the wetlands happened due to the rapid expansion of settlement areas and agricultural land. So, the findings of the study deepen our knowledge about EH in the Rarh tract's wetlands, assisting decision-makers in creating sustainable wetland management strategies.

Do OPEC+ policies help predict the oil price: A novel news-based predictor.

The OPEC+, composed of the Organization of the Petroleum Exporting Countries (OPEC) and non-OPEC oil-producing countries, exerts considerable influence over the global crude oil market. However, existing literature lacks a comprehensive application of this factor in oil price forecasting, primarily due to the complexity of measuring such policy evolutions. To address this research gap, this study develops a news-based OPEC+ policy index based on text mining methods for comprehensive analysis and forecasting of the oil price. First, by crawling and mining news headlines related to OPEC+ production decisions, a dynamic and high-frequency (weekly) OPEC+ policy index is established. Second, the linear and nonlinear relationship between the proposed OPEC+ policy index and the WTI crude oil futures price is thoroughly examined, assessing the potential predictive power of the index in explaining the movements of the crude oil price. Third, the forecasting efficacy of the constructed index on the oil price is rigorously evaluated across eight econometric and machine learning models. Key findings include: (1) The proposed weekly OPEC+ policy index demonstrates strong concordance with OPEC+ production change decisions, exhibiting notable peaks and troughs corresponding to OPEC+ Ministerial Meetings. (2) The relationship analysis demonstrates a strong linear and nonlinear association between the proposed OPEC+ policy index and the crude oil price. (3) For oil price prediction, models incorporating our proposed OPEC+ policy index demonstrate superior performance compared to models without this index. In particular, the index exhibits a more significant predictive effect within three-week forecasting horizons and performs exceptionally well during periods of pandemic and the Russia-Ukraine conflict. In addition, the OPEC+ policy index also exhibits a significant predictive effect on the daily crude oil price and natural gas price, further confirming the robust and powerful forecasting capability of this index within the energy system.

Advancing Emergency Department Triage Prediction With Machine Learning to Optimize Triage for Abdominal Pain Surgery Patients.

BACKGROUND: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use machine learning models to predict emergency surgical abdominal pain patients in triage, and then compare their performance with conventional Logistic regression models. METHODS: Using 38 214 patients presenting with acute abdominal pain at Zhongnan Hospital of Wuhan University between March 1, 2014, and March 1, 2022, we identified all adult patients (aged ≥18 years). We utilized routinely available triage data in electronic medical records as predictors, including structured data (eg, triage vital signs, gender, and age) and unstructured data (chief complaints and physical examinations in free-text format). The primary outcome measure was whether emergency surgery was performed. The dataset was randomly sampled, with 80% assigned to the training set and 20% to the test set. We developed 5 machine learning models: Light Gradient Boosting Machine (Light GBM), eXtreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Random Forest (RF). Logistic regression (LR) served as the reference model. Model performance was calculated for each model, including the area under the receiver-work characteristic curve (AUC) and net benefit (decision curve), as well as the confusion matrix. RESULTS: Of all the 38 214 acute abdominal pain patients, 4208 underwent emergency abdominal surgery while 34 006 received non-surgical treatment. In the surgery outcome prediction, all 4 machine learning models outperformed the reference model (eg, AUC, 0.899 [95%CI 0.891-0.903] in the Light GBM vs. 0.885 [95%CI 0.876-0.891] in the reference model), Similarly, most machine learning models exhibited significant improvements in net reclassification compared to the reference model (eg, NRIs of 0.0812[95%CI, 0.055-0.1105] in the XGBoost), with the exception of the RF model. Decision curve analysis shows that across the entire range of thresholds, the net benefits of the XGBoost and the Light GBM models were higher than the reference model. In particular, the Light GBM model performed well in predicting the need for emergency abdominal surgery with higher sensitivity, specificity, and accuracy. CONCLUSIONS: Machine learning models have demonstrated superior performance in predicting emergency abdominal pain surgery compared to traditional models. Modern machine learning improves clinical triage decisions and ensures that critically needy patients receive priority for emergency resources and timely, effective treatment.