Selection Method Research Articles

Digital twin method for intelligent lifting of cable structures under multi-collaborative mode

In order to ensure the high efficiency and quality consistency of cable structure in each construction stage, this study proposes a digital twin method for intelligent lifting of cable structure under multi-collaborative mode. Firstly, a construction and operation methods of the digital twin logic model are formed. In the logical model, a relationship between the transmission and iteration of construction information in the collaborative process is defined. In order to realize the adaptability of working conditions, the coupling relationship between construction step and structural elevation is established. A cable structure lifting analysis model driven by an improved long short-term memory neural network is proposed, which effectively solves the problem of poor convergence of the traditional finite element method. An automatic time window selection method is formed for the neural network. An adaptive moment estimation method is used for neural network parameter optimization, which improves the efficiency and accuracy of the analysis. Taking the lifting process of a cable truss structure test model as an example, a digital twin collaborative construction platform is established to verify the effectiveness and feasibility of the research method. The results show that the digital twin logic model realizes the process collaboration, scene collaboration and data collaboration, and significantly improves the stability of construction quality and the linkage of construction process. Through the improved long short-term memory neural network, the coupling relationship between the construction step and the structural elevation is established, which accurately guides the whole construction process.

The Influence of Environmental, Social, Governance and Capital Structure Performance on Financial Performance

This study aims to analyze the influence of Enviromental, Social and Governance (ESG), and capital structure on financial performace. This type of research is quantitative research with secondary data sources. The population in this study is financial sector companies listed on the Indonesia Stock Exchange (IDX) in 2018-2022. The sample selection method used in this study is purposive sampling wich undergoes an outlier process, so that the final sample obtained is 10 companies. The result of the study shows that silmutaneously the variables of Enviromental, Social and Governance (ESG) and capital structure have an effect on financial performance. The result show that companies had high ESG scores and low capital structures can improve their financial performance. Then partially the Enviromental, Social and Governance (ESG) variables have an influence on financial performance. The result show that companies with high ESG scores have the potential to improve the company’s financial performance. Furthermore, the capital structure variable has no effect on financial performance. The result show that Bank companies with a large amount of debt cannot affect the company’s financial performance.

Robust batch-to-batch optimization with global sensitivity analysis for microbial fermentation processes under model-plant mismatch

This paper studies the optimization of product yield for the microbial fermentation process in the presence of model-plant mismatch. The typical “two-step” optimization method involves modifying the model by adjusting its parameters and subsequently utilizing the updated model to optimize product output. However, due to estimability and overfitting problems, it is often impractical to update all available parameters. Therefore, we propose a robust batch-to-batch optimization method with global sensitivity analysis. First, the parameters to be identified are determined through global sensitivity analysis. Then, the robust batch-batch optimization method is employed to optimize the yield of fermentation products. Compared with previous parameter selection methods, global sensitivity analysis considers the complex correlation between parameters, allowing for a more comprehensive evaluation of the impact of multiple parameters and their interactions on the model. Furthermore, compared with previous studies, the robust batch-to-batch optimization method assigns weights to each variable in the identification objective function based on experience and model output variance, significantly reducing the uncertainty of the next optimal batch run. Simultaneously, the method is robust to the uncertainty in initial conditions, ensuring a more stable process when running in large batches. A penicillin fermentation case study verifies the convergence and robustness of the method.

Synthesis of non-canonical amino acids through dehydrogenative tailoring.

Amino acids are essential building blocks in biology and chemistry. While nature relies on a small number of amino acid structures, chemists desire access to a vast scope of structurally diverse analogs1-3 The selective modification of amino acid side-chain residues represents an efficient strategy to access non-canonical derivatives of value in chemistry and biology. While semi-synthetic methods leveraging the functional groups found in polar and aromatic amino acids have been extensively explored, highly selective and general approaches to transform unactivated C-H bonds in aliphatic amino acids remain less developed4,5 Here, we disclose a stepwise dehydrogenative method to convert aliphatic amino acids into structurally diverse analogs. The key to the success of this approach lies in the development of a selective catalytic acceptorless dehydrogenation method driven by photochemical irradiation, which provides access to terminal alkene intermediates for downstream functionalization. Overall, this strategy enables the rapid synthesis of new amino acid building blocks and suggests possibilities for the late-stage modification of more complex oligopeptides.

Mechanism of action and selection of endoscopic bariatric therapies for treatment of obesity.

Endoscopic bariatric therapies (EBTs) are minimally invasive and safe procedures with favorable weight loss outcomes in obesity treatment. We aimed to present the weight loss mechanism of action of EBTs and an individualized selection method for patients with obesity. We searched PubMed, Medline, Scopus, Embase, and Google Scholar databases for studies on the topic from databases inception to July 1, 2023, written in English. We focused on EBTs potential mechanism of action to induce weight loss. We also present an expert opinion on a novel selection of EBTs based on their mechanism of action. EBTs can result in weight loss through variable mechanisms of action. They can induce earlier satiation, delay gastric emptying, restrict the accommodative response of the stomach, decrease caloric absorption, and alter the secretion of gastrointestinal hormones. Selecting EBTs may be guided through their mechanism of action by which patients with abnormal satiation may benefit more from tissue apposition devices and aspiration therapy while patients with fast gastric emptying may be better candidates for intragastric devices, endoscopic anastomosis devices, and duodenal mucosal resurfacing. Consequently, the selection of EBTs should be guided by the mechanism of action which is specific to each type of therapy.

A Novel Hierarchical Clustering Sequential Forward Feature Selection Method for Paddy Rice Agriculture Mapping Based on Time-Series Images

Timely and accurate mapping of rice distribution is crucial to estimate yield, optimize agriculture spatial patterns, and ensure global food security. Feature selection (FS) methods have significantly improved computational efficiency by reducing redundancy in spectral and temporal feature sets, playing a vital role in identifying and mapping paddy rice. However, the optimal feature sets selected by existing methods suffer from issues such as information redundancy or local optimality, limiting their accuracy in rice identification. Moreover, the effects of these FS methods on rice recognition in various machine learning classifiers and regions with different climatic conditions and planting structures is still unclear. To overcome these limitations, we conducted a comprehensive evaluation of the potential applications of major FS methods, including the wrapper method, embedded method, and filter method for rice mapping. A novel hierarchical lustering sequential forward selection (HCSFS) method for precisely extracting the optimal feature set for rice identification is proposed. The accuracy of the HCSFS and other FS methods for rice identification was tested with nine common machine learning classifiers. The results indicated that, among the three FS methods, the wrapper method achieved the best rice mapping performance, followed by the embedded method, and lastly, the filter method. The new HCSFS significantly reduced redundant features compared with eleven typical FS methods, demonstrating higher precision and stability, with user accuracy and producer accuracy exceeding 0.9548 and 0.9487, respectively. Additionally, the spatial distribution of rice maps generated using the optimal feature set selected by HCSFS closely aligned with actual planting patterns, markedly outperforming existing rice products. This research confirms the effectiveness and transferability of the HCSFS method for rice mapping across different climates and cultivation structures, suggesting its enormous potential for classifying other crops using time-series remote sensing images.

Advanced Fuzzy-Based Decision-Making: The Linear Diophantine Fuzzy CODAS Method for Logistic Specialist Selection

This study addresses the inherent uncertainty in human decision-making by leveraging fuzzy sets, which were introduced to better capture the imprecision associated with human thoughts and judgments. As an extension to traditional fuzzy sets, the Linear Diophantine Fuzzy Set (LDFS) was developed, offering a more flexible approach by relaxing existing limitations on grade values. The LDFS has found applications across various fields, demonstrating its versatility and effectiveness. In this study, we explore the application of the Linear Diophantine Fuzzy Set within the framework of the Combinative Distance-based Assessment (CODAS) method. The CODAS method stands out because it incorporates Euclidean and Taxicab distances. Decision-making should consider not only the direct distances between ideal solutions and alternatives but also the indirect distances. The foremost objective of this research is to propose a novel approach by integrating the CODAS method with the LDFS to address complex decision-making problems characterized by uncertainty and imprecision. To illustrate the practical utility of the proposed method, we apply it to a numerical example involving the selection of a logistics specialist, a critical decision in emergency logistics optimization. The research also includes a case study on a logistics specialist, highlighting the practical application of the methods discussed. Furthermore, this study provides a comprehensive sensitivity analysis of the parameters' weights to evaluate the robustness and reliability of the proposed method. The results highlight the effectiveness of the LDF CODAS method in making informed and reliable decisions under conditions of uncertainty, paving the way for future applications in other decision-making scenarios.

Oriented surface imprinted 96-well microplate-based fluorescent biosensor for glycoprotein detection by boronate affinity sandwich assay

Glycoproteins perform vital functions in numerous biological processes and have important clinical implications. Many glycoproteins have been used as biomarkers and therapeutic targets for disease diagnosis. Due to low concentration of glycoprotein biomarkers and the presence of high-abundance interfering species in biological samples, a selective and sensitive detection method for glycoprotein is essential for real-world applications. In this study, we develop an oriented surface imprinted microplate-based fluorescent biosensor by boronate-affinity sandwich assay (BASA) for the specific, sensitive and high throughput determination of glycoproteins in complex samples. The structure of the BASA is based on sandwich formation between boronate affinity-oriented surface-imprinted microplates, target glycoproteins, and boronate affinity fluorescence probes. The imprinted microplates ensure the high specificity, high affinity and high throughput, while the fluorescence probes, consisting of boronic acid-modified CdTe QDs, provide high sensitivity. The proposed approach could exhibit a wide linear range of 1 ng/mL-105 ng/mL, with a low LOD of 0.528 ng/mL using horseradish peroxidase (HRP) as a model glycoprotein. As compared with traditional “turn off” fluorescent sensor, the developed “turn on” fluorescent sensor provided three orders of magnitude higher sensitivity at least. The fluorescent biosensor achieved average recoveries ranging from 96.8 % to 106.0 % in urine samples.

Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods.

Conventional approaches for establishing bioequivalence (BE) between test and reference formulations using non-compartmental analysis (NCA) may demonstrate low power in pharmacokinetic (PK) studies with sparse sampling. In this case, model-integrated evidence (MIE) approaches for BE assessment have been shown to increase power, but may suffer from selection bias problems if models are built on the same data used for BE assessment. This work presents model averaging methods for BE evaluation and compares the power and type I error of these methods to conventional BE approaches for simulated studies of oral and ophthalmic formulations. Two model averaging methods were examined: bootstrap model selection and weight-based model averaging with parameter uncertainty from three different sources, either from a sandwich covariance matrix, a bootstrap, or from sampling importance resampling (SIR). The proposed approaches increased power compared with conventional NCA-based BE approaches, especially for the ophthalmic formulation scenarios, and were simultaneously able to adequately control type I error. In the rich sampling scenario considered for oral formulation, the weight-based model averaging method with SIR uncertainty provided controlled type I error, that was closest to the target of 5%. In sparse-sampling designs, especially the single sample ophthalmic scenarios, the type I error was best controlled by the bootstrap model selection method.

Performance prediction of sludge volume index of oxygenic photogranule based wastewater treatment system using machine learning algorithms

Oxygenic Photogranulation is a novel biotechnology that treats wastewater without external aeration and produces biomass in dense photogranules with high settling velocities. Oxygenic photogranules (OPG) based wastewater treatment (WWT) faces challenges during scaleup due to its dynamic and complex system variables, making troubleshooting costly. The Machine Learning (ML) approach can address this issue by creating a WWT process simulation. Moreover, traditional mechanistic models do not capture the interaction between input and output features due to their high dimensionality and the non-linear relationship, making them computationally expensive. In this study, the two-stage feature selection (FS) method is studied to enhance the prediction performance of SVI30, a critical operational parameter, to ensure optimal settleability and minimal loss of active photogranules. The optimal feature subsets generated by the two-stage selection method were evaluated using four regression models: decision tree, random forest, gradient boosting, and extreme gradient boosting. Results indicate that, among all regression models, the decision tree performs well having a prediction efficiency of 85 % with the subset of features obtained after Recursive Feature Elimination (RFE) of decision tree features in the second stage. This indicates the effectiveness of the two-stage FS method in identifying the most relevant features for predicting SVI30. The structured approach of FS and model evaluation highlights the potential of ML in addressing complex operational challenges in OPG WWT operations.

PTCOG international survey of practice patterns and trends in utilization of proton therapy for breast cancer

Purpose/objectivesThe indications, techniques, and extent to which proton beam therapy (PBT) is employed for breast cancer are unknown. We seek to determine PBT utilization for breast cancer. Materials/methodsThe Particle Therapy Co-Operative Group (PTCOG) Breast Subcommittee developed an IRB-approved 29-question survey and sent it to breast cancer radiation oncologists at all active PBT centers worldwide in June 2023. Descriptive statistics were used to summarize responses, and comparisons by continent were performed using Fisher’s exact tests. ResultsOf 79 surveys distributed, 28 recipients submitted responses (35 % response rate) representing fifteen U.S., 8 European, and 5 Asian centers (continent response rate 50 %, 38 %, and 18 %, respectively). Overall, 93 % reported treating breast cancer patients with PBT; 13 (50 %) have treated ≥100 breast cancer patients at their center since opening. Most (89 %) have pencil beam scanning technology. Nearly half (46 %) use moderate hypofractionation (15–20 fractions) for regional nodal irradiation and 42 % conventional fractionation (25–30 fractions). More European centers prefer hypofractionation (88 %) vs. Asian (50 %) and U.S. (21 %) centers (p = 0.003). Common patient selection methods were practitioner determination/patient preference (n = 16) and comparative plan evaluation (n = 15). U.S. centers reported the most experience with breast PBT, with 71 % having treated ≥100 breast cancer patients vs. 38 % in Europe and none in Asia (p = 0.001). Of respondent centers, 39 % enrolled ≥75 % of breast PBT patients on a research study. ConclusionUtilization, patient selection methods, and dose-fractionation approaches for breast cancer PBT vary worldwide. These survey data serve as a benchmark from which successor surveys can provide insight on practice pattern evolution.

A new framework for interval wavelength selection based on wavelength importance clustering

BackgroundWavelength selection is one of the key steps in spectral analysis and plays an irreplaceable role in improving model prediction accuracy and computational efficiency. High-dimensional spectral datasets contain substantial irrelevant information and redundant variables. Whereas, at current stage, such problem can be solved by existing abundant wavelength selection methods. However, it is difficult to achieve the balance between strong wavelength interpretability and prediction accuracy by those methods. As a result, there is an urgent need for a new method that can reach the point of balance. Resultswe propose a new framework for wavelength selection based on wavelength importance clustering (WIC) which attempts to establish a hierarchical relationship between wavelength points and attributions of response through a clustering algorithm, consequently, performing combinatorial and filtering to obtain the optimal wavelength combinations. In this paper, a new wavelength selection method (WIC-WRCKF) is constructed based on WIC, and four commonly used wavelength selection methods are selected to be compared with WIC-WRCKF. A large number of experiments are carried out on three publicly available datasets as well, namely, wheat, corn, and tablets. Compared with other methods, WIC-WRCKF has the highest prediction accuracy with high stability on the three datasets, and the number of wavelengths selected is small and highly interpretative, indicating that WIC-WRCKF has a better predictive ability. SignificanceThe wavelength selection method can significantly improve the model prediction accuracy, and the WIC architecture can effectively exploit the essence of the spectral data, which has great potential in the application of wavelength selection.

Model-based causal feature selection for general response types

Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor type I error control) and additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but reflects categories or counts. Here, we develop transformation-model (tram) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses (these model classes, generally, do not allow for identifiability when there is no exogenous heterogeneity). As an invariance test, we propose tram-GCM based on the expected conditional covariance between environments and score residuals with uniform asymptotic level guarantees. For the special case of linear shift trams, we also consider tram-Wald, which tests invariance based on the Wald statistic. We provide an open-source R package tramicp and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients.

Heteroscedastic Gaussian Process Regression for material structure–property relationship modeling

Uncertainty quantification is a critical aspect of machine learning models for material property predictions. Gaussian Process Regression (GPR) is a popular technique for capturing uncertainties, but most existing models assume homoscedastic aleatoric uncertainty (noise), which may not adequately represent the heteroscedastic behavior observed in real-world datasets. Heteroscedasticity arises from various factors, such as measurement errors and inherent variability in material properties. Ignoring heteroscedasticity can lead to lower model performance, biased uncertainty estimates, and inaccurate predictions. Existing Heteroscedastic Gaussian Process Regression (HGPR) models often employ complicated structures to capture input-dependent noise but may lack interpretability. In this paper, we propose an HGPR approach that combines GPR with polynomial regression-based noise modeling to capture and quantify uncertainties in material property predictions while providing interpretable noise models. We demonstrate the effectiveness of our approach on both synthetic and physics-based simulation datasets, including mechanical properties (effective stress) of porous materials. We also introduce an approximated expected log predictive density method for model selection, which eliminates the need for retraining the model during leave-one-out cross-validation, allowing for efficient hyperparameter tuning and model evaluation. By capturing heteroscedastic behavior, enhancing interpretability, and improving model selection, our approach contributes to the development of more robust and reliable machine learning models for material property predictions, enabling informed decision-making in material design and optimization.

SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion

- Accurate state of health (SOH) of batteries is a crucial prerequisite for ensuring the safety and stable operation of electric vehicles. However, existing conventional prediction methods do not consider the influence of early-stage capacity variations on full-cycle SOH with extensive test data. This paper develops a SOH early prediction method of lithium-ion batteries based on voltage interval selection and features fusion. To identify the battery SOH curves with high similarity under identical charge-discharge conditions, a double correlation based early-stage SOH similarity analysis method is presented. To minimize the amount of voltage training data collected during feature extraction, a voltage interval selection method considering sampling time and features correlation is introduced. Meanwhile, a feature fusion method combining entropy weight and correlation factor is used to reduce the impact of redundant health feature information. To address the problems of low accuracy and computational inefficiency using the least squares support vector machine (LSSVM) model, a grey wolf optimizer-LSSVM-adaptive boosting model is developed for battery SOH prediction. The experimental results show that the coefficients of determination remain above 0.98, indicating high SOH prediction accuracy using the developed method. Compared to other methods, the mean absolute errors based on the developed method are maintained below 1.5 %.

Determination of Possible Biomarkers for Predicting Well-Differentiated Thyroid Cancer Recurrence by Different Ensemble Machine Learning Methods

Objective: Well-differentiated thyroid cancer (WDTC) is the most common thyroid malignancy and although it is curable, the risk of recurrence is high. In this study, classification algorithms based on clinicopathologic features of WDTC patients were used to determine the possible of recurrence in WDTC and to evaluate potential predictive factors, and possible biomarkers based on the optimal model were identified. Method: In this study, open access data on 383 patients with WDTC, 108 with recurrence and 275 without recurrence, were used. In order to predict recurrence in WDTC patients, features were selected using recursive feature elimination variable selection method among features and classification was performed with two ensemble learning methods (Random Forest, Adaboost). Results: Two different ensemble learning models used to classify recurrence in WDTC were Random Forest with an accuracy of 0.957, sensitivity of 0.889, specificity of 0.978, positive predictive value of 0.923, negative predictive value of 0.967, Matthews correlation coefficient of 0.878, G-mean of 0.945, F1-score of 0.906, and accuracy of 0.940, sensitivity of 0.889, specificity of 0.955, positive predictive value of 0.857, negative predictive value of 0.966, Matthews correlation coefficient of 0.833, G-mean of 0.910, F1-score of 0.873. Conclusion: According to variable importance based on the Random Forest, the 5 possible clinical biomarkers for predicting WDTC recurrence are Response, Risk, Node, Tumor, and age. In the light of these findings, patient management and treatment planning can be organized.

Prediction of deep low permeability sandstone seismic reservoir based on CBAM-CNN

The prediction of sand body is a key focus in evaluating reservoir distribution, playing a crucial role in oil and gas exploration and development. To clarify the development characteristics of the sand body in the Huangyan structural belt of the Xihu Sag and effectively identify the hidden channel, a new approach is proposed. This approach incorporates a Convolutional Block Attention Module (CBAM) into a Convolutional Neural Network (CNN). Well logging and seismic data were used to predict the distribution of sand body in the lower section of the Huagang Formation (H12). Firstly, based on known sand-stratum ratio data and well-side seismic data, the seismic attributes that are sensitive to the reservoir response are preferred through correlation analysis, which are used to construct a Polynomial Linear Regression (PLR) model to obtain the preliminary sand distribution prediction results. Secondly, the grid is divided according to this result. Then, three sample selection methods, namely proportional, fixed-range, and random, are used to construct three sample sets in conjunction with the geologic model guide. Finally, CBAM-CNN, CNN, Random Forest (RF), Support Vector Machines (SVM), and Backpropagation Neural Network (BPNN) are utilized for sand body prediction. The results indicate that when using the same model, the sample set selected by the fixed-range selection method yielded the best prediction outcome. When using the same sample set, the CBAM-CNN model outperformed the others. Among various strategies, training the CBAM-CNN model with the sample set derived from the fixed-range selection method led to the highest test set R2 of 0.913. The results of the sand body distribution indicate that fine river channels are more continuous, and reservoir boundaries are clearer, significantly outperforming other schemes. This outcome can serve as a guide for further reservoir delineation.

A novel parallel feature rank aggregation algorithm for gene selection applied to microarray data classification

Microarray data often comprises numerous genes, yet not all genes are relevant for predicting cancer. Feature selection becomes a crucial step to reduce the high dimensionality in these kinds of data. While no single feature selection method consistently outperforms others across diverse domains, the combination of multiple feature selectors or rankers tends to produce more effective results compared to relying on a single ranker alone. However, this approach can be computationally expensive, particularly when handling a large quantity of features. Hence, this paper presents a parallel feature rank aggregation that utilizes borda count as the rank aggregator. The concept of vertically partitioning the data along feature space was adapted to ease the parallel execution of the aggregation task. Features were selected based on the final aggregated rank list, and their classification performances were evaluated. The model’s execution time was also observed across multiple worker nodes of the cluster. The experiment was conducted on six benchmark microarray datasets. The results show the capability of the proposed distributed framework compared to the sequential version in all the cases. It also illustrated the improved accuracy performance of the proposed method and its ability to select a minimal number of genes.

GWAS as a tool for detecting SNPs in cattle to study their relationship to reproduction, productivity, growth, behavior, diseases

The fundamental goal of animal husbandry is the cost-effective production of human food from healthy animals, which includes production, reproduction. The method of whole-genome association study (GWAS) is actively used in various fields, including agriculture. Genome-wide association analyzes were generated as an identifier for genomic variations associated with economically significant traits in different livestock species. This method of genomic selection provides new priorities for improving the productive and reproductive qualities of livestock.The purpose of this review article is a comprehensive analysis of the current state of GWAS in cattle, focusing on the identification of SNPs associated with reproduction, productivity, growth, behavior and genetically determined diseases. The scope of the article covers the study of GWAS results worldwide, both in dairy and beef cattle, with special emphasis on the identification of candidate genes, QTL and genome regions related to the direction of productivity. Additionally, the organization of this review will include a classification of GWAS results based on the specific traits studied, providing a comprehensive overview of the genetic determinants of reproduction, growth, behavior, and disease traits in cattle.

Authentication of apples (Malus × domesticaborkh.) according to geographical origin, variety and production method using 1H NMR spectroscopy and random forest

When buying apples, there are several characteristics that are important to the consumer. These include taxonomic variety, which influences the desired flavor and the allergenicity, and, increasingly, whether the cultivation is regional and organic. In this study, we analyzed 217 apple samples with 1H NMR spectroscopy and verified the authentication of the apples in terms of country of origin, local origin within Germany, taxonomic variety and whether they were organically produced. The obtained random forest models showed classification accuracies ranging from about 73 % for the discrimination of seven taxonomic varieties to 88.5 % for the separation of German and non-German samples. The results demonstrate that 1H NMR spectroscopy is promising for comprehensive authentication of apples. This is achieved by using different parts of the NMR fingerprint depending on the desired outcome, which was confirmed by the application of variable selection methods and the comparison of the selected variables.