Selection Method Research Articles

Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors

ObjectiveSystemic lupus erythematosus (SLE) is an autoimmune disease characterised by a loss of immune tolerance, affecting multiple organs and significantly impairing patients’ health and quality of life. While hereditary elements...

An effective inliers selection strategy for high-precision image feature matching

ABSTRACT Seeking a reliable inliers selection method to image feature matching is a fundamental and important task in image processing and robotic vision. To improve the precision of feature matching on putative matches with heavy outliers, we propose a new strategy to select the inliers from the rough feature correspondence-set. Firstly, we construct the inliers selection problem as a concise mathematical model with linear time complexity based on the local K-nearest neighbour structure preserving (KNNSP). Then, to improve the robustness of this model, we design a threshold decrease strategy for this model to iteratively solve the optimal inliers set. Extensive experiments on various image pairs demonstrate that the proposed method does not need prior information and can be directly used for rigid or non-rigid image matching problems. In addition, compared with other typical image matching algorithms, our method ensures competitive performance in precision and recall.

Determination of Hormonal Growth Promotants in Beef Using Liquid Chromatography-Tandem Mass Spectrometry.

Hormonal growth promotants (HGPs) are a class of pharmaceutical agents commonly administered to cattle in the United States to improve growth rates of the animal, alter behavior, or to improve the desired characteristics of retail cuts of meat. There is a concern that low residual concentrations of HGPs may remain in tissue after slaughter, and consumption of tissues containing these compounds may increase the risk of adverse health outcomes, including cancer. Sensitive and selective methods are necessary to assess exposure of HGPs by populations that consume meat products from animals that may have been administered HGPs. A liquid chromatography-tandem mass spectrometry method was developed and validated to detect the low-level presence of HGPs including estradiol, testosterone, estradiol benzoate, melengestrol, melengestrol acetate, progesterone, testosterone propionate, trenbolone, trenbolone acetate, and α-zearalanol in retail cuts of meat following a liquid-liquid extraction using a high pH solution with 30-50× less mass of meat required as compared to similar approaches. Good chromatographic performance and sensitivity was achieved utilizing ammonium fluoride as a mobile phase additive without the need for derivatization. Validation parameters including accuracy, precision, recovery, matrix effects, limits of detection, limits of quantitation, linear range, and stability were determined. The limits of detection ranged from 0.1 to 1.0 ng/g, depending on the compound, with adequate accuracy and precision without the need for extensive sample preparation approaches.

Losengram: an effective demodulation frequency band selection method for rolling bearing fault diagnosis under complex interferences

Abstract Resonance demodulation is one of the most effective methods for rolling bearing fault diagnosis. However, the selection of the proper demodulation frequency band (DFB) has always been considered as a substantial challenge. Although many popular DFB selection methods have been developed, such as fast Kurtogram (FK), Protrugram, and Autogram, they would suffer unsatisfactory performance degradation when encountering random impulsive noise or cyclostationary noise. Therefore, this paper proposes a novel DFB selection method called Losengram to address this problem. In the proposed method, a robust sub-band indicator, localized square envelope spectrum kurtosis, is designed to evaluate the fault information in a sub-band. With this indicator, the interferences of random impulsive noise and cyclostationary noise could be suppressed well. Besides, in order to circumvent the various adverse effects incurred by the utilization of a multi-rate finite impulse response filter bank, a frequency-domain sub-band filtering strategy is presented to filter the divided sub-bands in a 1/3-binary tree structure. The effectiveness of the proposed method is tested on both simulated and experimental signals, and the results show that it has a superior performance than the FK, Protrugram, as well as Autogram.

Optimization of a disposable pipette tips extraction (DPX) for the analysis of psychoactive substances in sweat specimens using Design of Experiments.

Novel psychoactive substances (NPS) continue to emerge in the marketplace and are often found as substances in traditional illicit drug materials, and users are often unaware of the presence of other drugs. The proper identification and confirmation of the exposure to a drug is made possible when a biological specimen is collected and tested. Sweat is an alternative biological matrix of great interest for clinical, and forensic analysis. One of the reasons is attributed to its expanded drug detection window, enabling a greater monitoring capacity, and provision of information on prospective drug use. However, the concentrations of drugs in sweat samples are often low, which requires highly sensitive and selective methods. Disposable pipette tips extraction (DPX) is a new miniaturized solid phase extraction technique capable of efficiently extracting analytes from biological specimens, providing high recoveries, and requiring minimized solvent use. This study describes the development and optimization of two methods for the extraction of basic and neutral psychoactive substances from sweat samples using GC-MS and Design of Experiments (DoE). The following extraction parameters were optimized by DoE techniques: sample volume, elution solvent volume, washing solvent volume, sample aspiration time, elution solvent aspiration time, and number of cycles performed, including the elution step. It was possible to design a simple extraction protocol that provided optimized recoveries for both basic and neutral compounds. The sum of analyte areas increased at a rate of 54.7% for compounds of basic character and 39.2% for compounds of neutral character. Therefore, our results were satisfactory, demonstrating that DPX can be successfully used for extracting the target drugs from sweat samples.

Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies

Efficiency, low-power consumption, and real-time processing in embedded machine learning implementations are critical, particularly for models deployed in environments with large-scale data processing and resource-constrained environments. This paper investigates the application of approximate computing techniques as a viable solution to reduce computational complexity and optimize machine learning models, focusing on two widely used supervised machine learning models: k-nearest neighbors (KNN) and support vector machines (SVM). Although many studies compare machine learning classification techniques, the combined use of optimization strategies remains underexplored. Specifically, the combined utilization of feature selection, sampling, quantization, precision scaling, and relaxation methods for the purpose of optimizing and acquiring training and validation data is underexplored, particularly within the context of medical diagnosis datasets. In this paper, we propose a framework that uses data-level approximate computing techniques, including by diverse sampling strategies, precision scaling, quantization, and feature selection methods, to evaluate the impact of these techniques on the computational efficiency and accuracy of KNN and SVM models. Experimental results demonstrate that with careful application of approximate computing strategies, especially in critical applications such as medical diagnosis, it is possible to achieve considerable gains in efficiency while maintaining acceptable levels of accuracy. The combined application of these methods by selecting 3 features and quantizing the data values to 8 levels, then applying random sampling with 30% reductions and scaling the precision at 5 bits resulted in reductions of 87.5% in computation, 76.9% in memory usage, and 17% in delay, without any degradation in accuracy, as validated by tenfold cross-validation, Training Data validation, and full dataset validation. This study confirms the potential of approximate computing to optimize machine learning workflows, making it particularly suitable for applications with limited computational resources. The source code is publicly available online https://github.com/AyadMDalloo/DatalvlAxC.

A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection.

The methods used to detect epileptic seizures using electroencephalogram (EEG) signals suffer from poor accuracy in feature selection and high redundancy. This problem is addressed through the use of a novel multi-domain feature fusion and selection method (PMPSO). Discrete Wavelet Transforms (DWT) and Welch are used initially to extract features from different domains, including frequency domain, time-frequency domain, and non-linear domain. The first step in the detection process is to extract important features from different domains, such as frequency domain, time-frequency domain, and non-linear domain, using methods such as Discrete Wavelet Transform (DWT) and Welch. To extract features strongly correlated with epileptic classification detection, an improved particle swarm optimization (PSO) algorithm and Pearson correlation analysis are combined. Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest (RF) and XGBoost classifiers are used to construct epileptic seizure detection models based on the optimized detection features. According to experimental results, the proposed method achieves 99.32% accuracy, 99.64% specificity, 99.29% sensitivity, and 99.32% score, respectively. The detection performance of the three classifiers is compared using 10-fold cross-validation. Surpassing other methods in detection accuracy. Consequently, this optimized method for epilepsy seizure detection enhances the diagnostic accuracy of epilepsy seizures.

From ideological uniformity to thematic diversity. Some reflections on the modern historiography of the New Economic Policy

Importance. Analysis of modern historiography of the New Economic Policy makes it possible to assess the state of research on this science topic, identify the strengths and weaknesses of the historiographic process, and define promising areas of research. It is believed that the conducted analysis will help young researchers update their original scientific research.Materials and Methods. The methodological approach to the analysis of literature, due to the presence of a vast number of publications, which makes a detailed historiographic analysis almost impossible, is based on a selection method, when the most authoritative publications are analysed, the assessments and conclusions of which have determined the angle of subsequent research. The second method is to focus on specific research areas that are clearly manifested in the modern historiographic process.Results and Discussion. Modern historiography of the New Economic Policy is extensive both in thematic diversity and the degree of study of research issues. The study process continues; the New Economic Policy topics are far from exhausted and remain attractive to researchers. The re search process becomes methodologically multifaceted, more multidimensional in terms of sources, and interdisciplinary in content. The result of the modern historiographic process can be considered the development of a more realistic position in assessing the historical model of the New Economic Policy with all its elements.Conclusion. The results of the study make it possible to give a realistic picture of the state of the modern historiographic process, identify new methodological approaches to the study of the New Economic Policy problems, expand the thematic diversity and pay attention to the problems in this historiographic segment.

Parts-Per-Million Level Loading Cyclometalated Ru(II)-NHC Catalyzed Selective Oxidation of Olefins to Carbonyls.

Oxidative cleavage of olefins is a useful reaction in organic synthesis. The most well-known catalytic system is the osmium based Lemieux-Johnson catalyst, which generally requires high catalyst loading and tends to suffer from rapid overoxidation to produce the acid predominantly. Hence, the development of a mild, general, and selective method toward the oxidative cleavage of alkenes to carbonyl compounds is highly desired. In this work, a highly efficient ruthenium-based catalyst for olefin oxidation has been demonstrated by employing a fused π-conjugated imidazo[1,5-a]quinoxaline (ImQx) based NHC ligand with bidentate C(carbanion) CNHC motif. Strong C-donor ligands, paired with a rigid backbone and ruthenium redox activity, provided exceptionally high catalytic activity and a long lifetime for olefin oxidation. Complex showed high catalytic activity and a long lifetime, TONs are several million. The catalyst tolerates numerous functional groups and can be applicable to challenging biomass, natural products, sugar, amino acids, and fatty acid-derived substrates. Based on kinetic studies, thermodynamic activation parameters, and DFT study, the mechanistic finding demonstrated that [3+2] cycloaddition reaction is the key step in the oxidation process. The use of the by-product NaIO3 in the catalytic efficiency has been disclosed for the first time.

Advanced Predictive Modeling for Hepatitis C Diagnosis Using Machine Learning

The hepatitis C virus (HCV) virus, which has infected nearly fifty million individuals across the world, has recently been receiving significant attention. With the advent of artificial intelligence, there is an increasing effort to predict the HCV by utilizing machine learning models and training such models on relevant datasets. These data-driven models can aid doctors in the early detection of Hepatitis C infection. In this study, using datasets from the National Center for Health Statistics, we aim to develop a reliable model for predicting patients with positive hepatitis C tests. The most significant challenge in predicting such diseases using machine learning methods is finding an appropriate technique to address extremely imbalanced datasets, which arise from the smaller number of patients compared to healthy individuals in real-world datasets. We utilized real-world datasets and employed different machine learning algorithms, along with various methods for data balancing and feature selection. Unlike previous studies, which lacked this level of comprehensiveness, we utilized diverse feature selection and sampling methods such as Recursive Feature Elimination, Analysis of Variance (ANOVA) feature selection, Correlation matrix, synthetic minority over-sampling technique (SMOTE), BorderlineSMOTE, support vector machine synthetic minority over-sampling technique (SVMSMOTE), and Adaptive Synthetic Sampling (ADASYN) in conjunction with different machine learning algorithms including Random Forest, Decision Tree, XGBoost, AdaBoost, and logistic regression. We are the first to develop such a comprehensive study for the prediction of the HCV. Our findings suggest that we can predict patients infected by the HCV with a reasonable recall score of 0.86. This recall is achieved through the application of a model using the AdaBoost algorithm and the ADASYN method for balancing the dataset. The model's performance, especially on the validation dataset, was slightly better when we used ANOVA feature selection. Feature importance analysis conducted with ANOVA feature selection and Recursive Feature Elimination indicates that four to five features are the most prominent in our research analysis to predict patients with positive Hepatitis C tests.

Estimation and group-feature selection in sparse mixture-of-experts with diverging number of parameters

Mixture-of-experts provide flexible statistical models for a wide range of regression (supervised learning) problems. Often a large number of covariates (features) are available in many modern applications yet only a small subset of them is useful in explaining a response variable of interest. This calls for a feature selection device. In this paper, we present new group-feature selection and estimation methods for sparse mixture-of-experts models when the number of features can be nearly comparable to the sample size. We prove the consistency of the methods in both parameter estimation and feature selection. We implement the methods using a modified EM algorithm combined with proximal gradient method which results in a convenient closed-form parameter update in the M-step of the algorithm. We examine the finite-sample performance of the methods through simulations, and demonstrate their applications in a real data example on exploring relationships in body measurements.

Targeted Chiral Metabolomics of D-Amino Acids: Their Emerging Role as Potential Biomarkers in Neurological Diseases with a Focus on Their Liquid Chromatography-Mass Spectrometry Analysis upon Chiral Derivatization.

In neuroscience research, chiral metabolomics is an emerging field, in which D-amino acids play an important role as potential biomarkers for neurological diseases. The targeted chiral analysis of the brain metabolome, employing liquid chromatography (LC) coupled to mass spectrometry (MS), is a pivotal approach for the identification of biomarkers for neurological diseases. This review provides an overview of D-amino acids in neurological diseases and of the state-of-the-art strategies for the enantioselective analysis of chiral amino acids (AAs) in biological samples to investigate their putative role as biomarkers for neurological diseases. Fluctuations in D-amino acids (D-AAs) levels can be related to the pathology of neurological diseases, for example, through their role in the modulation of N-methyl-D-aspartate receptors and neurotransmission. Because of the trace presence of these biomolecules in mammals and the complex nature of biological matrices, highly sensitive and selective analytical methods are essential. Derivatization strategies with chiral reagents are highlighted as critical tools for enhancing detection capabilities. The latest advances in chiral derivatization reactions, coupled to LC-MS/MS analysis, have improved the enantioselective quantification of these AAs and allow the separation of several chiral metabolites in a single analytical run. The enhanced performances of these methods can provide an accurate correlation between specific D-AA profiles and disease states, allowing for a better understanding of neurological diseases and drug effects on the brain.

Detection of volatile organic compounds in soils (literature review)

Volatile organic compounds (VOC) are major environmental pollutants. Due to their high mobility, they penetrate into all environmental objects, pose an environmental threat and health risks. Getting into the soil, they deteriorate its quality. VOC content requires reliable control. There is presented a review of the literature, including methods of the US Environmental Protection Agency, and regulatory and methodological documents of the Russian Federation regulating methods of selection, storage, preparation and analysis of soil samples for VOC content. The dominant place among methods for monitoring for VOCs belongs to gas chromatography with various types of detectors. For multicomponent analysis of complex objects, gas chromatography with mass selective detection is used due to the wide capabilities provided by the mass detector. A universal mass spectrometry method used in analytical laboratories is electron impact ionization mass spectrometry. For research purposes, modern highly sensitive methods are used – mass spectrometry based on the proton transfer reaction PTR-MS, ion trap mass spectrometry PIT-MS, negative ion ionization mass spectrometry NI-PT-CIMS, time-of-flight mass spectrometry of the transfer reaction proton PTR-TOF-MS. The collection and storage of samples for VOC analysis requires compliance with regulations to prevent both loss of analytes and sample contamination. Sample preparation includes methods such as vacuum and azeotropic distillation, thermal desorption, liquid extraction, various options for static and dynamic headspace analysis.

A biosensor-based phage display selection method for automated, high-throughput Nanobody discovery

Biopanning methods to select target-specific Nanobodies® (Nbs) involve presenting the antigen, immobilized on plastic plates or magnetic beads, to Nb libraries displayed on phage. Most routines are operator-dependent, labor-intensive and often material- and time-consuming. Here we validate an improved panning strategy that uses biosensors to present the antigen to phage-displayed Nbs in a well. The use of automated Octet biolayer interferometry sensors (Sartorius) enables high throughput and precise control over each step. By playing with association and dissociation times and buffer composition, one can efficiently decrease the background of aspecific and low-affinity Nbs, reducing the rounds of panning needed for the enrichment of high-affinity binders. Octet panning also enables the use of unpurified target proteins and unpurified phage from a bacterial culture supernatant. Additionally, downscaling to a 384-well format significantly reduces the amount of protein required. Moreover, enrichment of binders can be quantified by monitoring phage binding to the target by interferometry, omitting additional phage titration steps. Routinely, up to three rounds of Octet panning can be performed in only five days to deliver target-specific binders, ready for screening and characterization using the same Octet instrument.

Feature selection based on consistent granulation

Classification learning from large-scale data is a pivotal and complex task in machine learning. Enhancing classifier performance can be achieved through effective feature selection and construction. However, current feature selection methods often encounter difficulties with large-scale data. Considering granulation is essential for developing accurate and efficient models, we firstly introduce a novel granulation technique, termed consistent granulation in this study. Furthermore, an innovative feature construction method leveraging consistent granulation is proposed to enhance computational speed, scalability, and classification performance of feature selection. Consequently, both time and space complexities are reduced to linear levels relative to sample sizes. Consistent granulation also allows for an accurate representation of the data's topological structure and reduces the parameter set from the interval [0,1] to a finite set, simplifying the search for optimal parameters. Experimental results indicate that our proposed algorithm, FCG, surpasses four classical feature selection algorithms—FARNeMF, HANDI, GBNRS, and RMDPS—in handling datasets with millions of samples and tens of thousands of dimensions. Specifically, on the first fourteen datasets, FCG decreases the average runtime by times of 1029.30 (FARNeMF), 703.54 (HANDI), 1253.78 (GBNRS), and 3.08 (RMDPS), while enhancing average classification accuracy by 3.24% (FARNeMF), 2.74% (HANDI), 5.77% (GBNRS), and 15.5% (RMDPS).

Bayesian hierarchical hypothesis testing in large-scale genome-wide association analysis.

Variable selection and large-scale hypothesis testing are techniques commonly used to analyze high-dimensional genomic data. Despite recent advances in theory and methodology, variable selection and inference with highly collinear features remain challenging. For instance, collinearity poses a great challenge in genome-wide association studies involving millions of variants, many of which may be in high linkage disequilibrium. In such settings, collinearity can significantly reduce the power of variable selection methods to identify individual variants associated with an outcome. To address such challenges, we developed a Bayesian hierarchical hypothesis testing (BHHT)-a novel multiresolution testing procedure that offers high power with adequate error control and fine-mapping resolution. We demonstrate through simulations that the proposed methodology has a power-FDR performance that is competitive with (and in many scenarios better than) state-of-the-art methods. Finally, we demonstrate the feasibility of using BHHT with large sample size (n∼ 300,000) and ultra dimensional genotypes (∼ 15 million single-nucleotide polymorphisms or SNPs) by applying it to eight complex traits using data from the UK-Biobank. Our results show that the proposed methodology leads to many more discoveries than those obtained using traditional SNP-centered inference procedures. The article is accompanied by open-source software that implements the methods described in this study using algorithms that scale to biobank-size ultra-high-dimensional data.

Метод автоматичного виділення прямокутної зони об'єктів зображення

T The automatic selection method for rectangle bounding of image objrcts exposed on the film is described, to optimize the image's size and increase the decoding efficiency. The image processing algorithm has been developed to improve the efficiency of grouping individual elements into a single structure. The simulation was carried out to prove the effectiveness of the proposed method. The main tool that has been used for developing the application is the open-source library of functions and algorithms of computer vision, image processing, and numerical algorithms of general-purpose OpenCV.

A hybrid feature selection method for text classification using a feature-correlation-based genetic algorithm

A hybrid feature selection method for text classification using a feature-correlation-based genetic algorithm

Recent Advances in Aluminum Alloy Surface Treatment Technology and Bonding Properties

Aluminum alloys are widely used in lightweight automotive structures due to their excellent properties. To deeply explore the development of surface bonding technology, aluminum alloy is selected as the object, and current research status of aluminum alloy surface treatment methods is reviewed. The adhesion mechanism during joint preparation, the method of adhesive selection, and the bonding process are summarized. This overview discusses the impact of different surface treatment processes on aluminum alloy joints from two perspectives: substrate characteristics and joint failure modes. It examines how these processes affect surface roughness, surface morphology, surface contact angle, surface free energy, surface chemical composition, and bonding performance. Additionally, it looks ahead to key directions for future research on adhesive joint performance. The results indicate that surface treatment increases the surface roughness of aluminum alloys, reduces the contact angle, and improves surface wettability. Moreover, chemical elements or functional groups that enhance adhesion are introduced on the surface, improving the bonding capability between the adhesive and the substrate. Compared to single‐surface treatment methods, hybrid treatment methods significantly enhance the surface characteristics of aluminum alloys and are expected to become a primary focus for future research on bonded joint performance.

Automatic Feature Selection for Atom-Centered Neural Network Potentials Using a Gradient Boosting Decision Algorithm.

Atom-centered neural network (ANN) potentials have shown high accuracy and computational efficiency in modeling atomic systems. A crucial step in developing reliable ANN potentials is the proper selection of atom-centered symmetry functions (ACSFs), also known as atomic features, to describe atomic environments. Inappropriate selection of ACSFs can lead to poor-quality ANN potentials. Here, we propose a gradient boosting decision tree (GBDT)-based framework for the automatic selection of optimal ACSFs. This framework takes uniformly distributed sets of ACSFs as input and evaluates their relative importance. The ACSFs with high average importance scores are selected and used to train an ANN potential. We applied this method to the Ge system, resulting in an ANN potential with root-mean-square errors (RMSE) of 10.2 meV/atom for energy and 84.8 meV/Å for force predictions, utilizing only 18 ACSFs to achieve a balance between accuracy and computational efficiency. The framework is validated using the grid searching method, demonstrating that ACSFs selected with our framework are in the optimal region. Furthermore, we also compared our method with commonly used feature selection algorithms. The results show that our algorithm outperforms the others in terms of effectiveness and accuracy. This study highlights the significance of the ACSF parameter effect on the ANN performance and presents a promising method for automatic ACSF selection, facilitating the development of machine learning potentials.