Discrimination Method Research Articles

Evaluation of 1064 nm Raman for meat and bone meal species discrimination: From raw form to chemical and physical components

The 1064 nm Raman spectroscopy technique was employed to characterize raw, bone fragments (BF), meat fragments (MF), lipid, and residue of meat and bone meal (MBM) samples. Employing three chemometric algorithms, species discrimination models were optimized for the aforementioned sample types, indicating that the presence of BF favored discriminant analysis for poultry samples, yielding a classification error of 0.000. The species discrimination model constructed using Raman spectra combined with PLS-DA algorithm for residue samples was found to be overall optimal, with classification error for four species samples all below 0.061. A detailed analysis of the BF related spectral variables contributing significantly to PLS-DA discriminant analysis was conducted. Finally, this study established the complementarity of Raman spectroscopy in species discrimination analysis from both a physical and chemical components perspective, laying the theoretical groundwork for the development of a high-precision 1064 nm Raman spectroscopic analysis method for MBM species discrimination.

Experimental investigation of the radial structure of energetic particle driven GAM in TCV

Abstract High amplitude energetic particle geodesic acoustic mode (EGAM) oscillations driven by Neutral Beam (NBI) injected in the direction counter to the toroidal plasma current are observed in the TCV tokamak. The modes appear at frequencies close to the geodesic acoustic mode (GAM) frequency, corresponding to the mode radii calculated without plasma elongation corrections. The spatial structure of the EGAM density oscillation is analyzed using multichannel Soft X-Ray (SXR) and broadband light emission diagnostics. The analysis of spatiotemporal emission data shows a non-rotating structure of the density oscillations. The non-rotating mode discrimination is the additional new capability of the multichannel spatiotemporal SXR data analysis technique in TCV. We present the discrimination method of assessing the standing character of the EGAM wave. The structure of EGAM density oscillations in TCV is consistent with the theoretical GAM poloidal structure, namely a m=1 standing wave with density oscillations amplitude proportional to sin(θ), where the poloidal angle θ is measured from the equatorial plane. The poloidal structure of magnetic the field oscillations is analyzed using a poloidal array of magnetic coils. The structure of the EGAM-induced magnetic field oscillations in TCV is a m=2 standing wave. Time evolution of EGAMs suggests that a nonlinear EGAM chirping is observed. The chirping depends on the EGAM radial location, which varies as the NBI deposition is varied through a vertical shift of the plasma magnetic axis. The chirping disappears at the plasma periphery. A fast periodic radial shift of the EGAMs radial location is also observed to occur during the single chirp.

The diagnostic efficacy of seven autoantibodies in early detection of ground-glass nodular lung adenocarcinoma

BackgroundPersistent ground-glass nodules (GGNs) carry a potential risk of malignancy, however, early diagnosis remained challenging. This study aimed to investigate the cut-off values of seven autoantibodies in patients with ground-glass nodules smaller than 3cm, and to construct machine learning models to assess the diagnostic value of these autoantibodies.MethodsIn this multi-center retrospective study, we collected peripheral blood specimens from a total of 698 patients. A total of 466 patients with ground-glass nodular lung adenocarcinoma no more than 3cm were identified as a case group based on pathological reports and imaging data, and control group (n=232) of patients consisted of 90 patients with benign nodules and 142 patients with health check-ups. Seven antibodies were quantified in the serum of all participants using enzyme-linked immunosorbent assay (ELISA), and the working characteristic curves of the subjects were plotted to determine the cut-off values of the seven autoantibodies related ground-glass nodular lung adenocarcinoma early. Subsequently, the patients were randomly divided into a training and test set at a 7:3 ratio. Eight machine-learning models were constructed to compare the diagnostic performances of multiple models. The model performances were evaluated using sensitivity, specificity, and the area under the curve (AUC).ResultsThe serum levels of the seven autoantibodies in case group were significantly higher than those in the control group (P < 0.05). The combination of the seven autoantibodies demonstrated a significantly enhanced diagnostic efficacy in identifying ground-glass nodular lung adenocarcinoma early when compared to the diagnostic efficacy of the autoantibodies when used respectively. The combined diagnostic approach of the seven autoantibodies exhibited a sensitivity of 84.05%, specificity of 91.85%, and AUC of 0.8870, surpassing the performance of each autoantibody used individually. Furthermore, we determined that Sparrow Search Algorithm-XGBoost (SSA-XGBOOST) had the best diagnostic performance among the models (AUC=0.9265), with MAGEA1, P53, and PGP9.5 having significant feature weight proportions.ConclusionsOur research assessed the diagnostic performance of seven autoantibodies in patients with ground-glass nodules for benign-malignant distinction, and the nodules are all no more than 3cm especially. Our study set cut-off values for seven autoantibodies in identifying GGNs no more than 3cm and constructed a machine learning model for effective diagnosis. This provides a non-invasive and highly discriminative method for the evaluation of ground-glass nodules in high-risk patients.

Research on Irrigation Grade Discrimination Method Based on Semantic Segmentation

As one of China’s major grain crops, wheat has a high demand for water resources, making it susceptible to drought stress. Traditional irrigation evaluation methods are often based on experience and rule-based calculations, which struggle to cope with complex environmental factors and dynamic changes in crop needs. With technological advancements, deep learning-based research methods, characterized by their strong data-driven analytical capabilities, are expected to improve the accuracy of evaluation results. This paper focuses on the irrigation demand assessment of winter wheat farmland, aiming to explore a new regional-scale irrigation demand assessment method based on deep learning. By establishing samples of different irrigation evaluation levels, this study seeks to better meet the requirements of irrigation demand assessment. For the problem of regional-scale irrigation-level discrimination, the Convolutional Network Attention(CONAT) module was proposed to optimize the backbone network structure of the Mask2Former model. To tackle issues related to data imbalance and underfitting across certain categories, a loss function tailored for imbalanced sample distributions was implemented, accompanied by enhancements to the training scheme. By contrasting this refined model with alternative methods for discriminating irrigation levels, the viability of this approach was showcased.

Accompanying Hemoglobin Polymerization in Red Blood Cells in Patients with Sickle Cell Disease Using Fluorescence Lifetime Imaging.

In recent studies, it has been shown that fluorescence lifetime imaging (FLIM) may reveal intracellular structural details in unstained cytological preparations that are not revealed by standard staining procedures. The aim of our investigation was to examine whether FLIM images could reveal areas suggestive of polymerization in red blood cells (RBCs) of sickle cell disease (SCD) patients. We examined label-free blood films using auto-fluorescence FLIM images of 45 SCD patients and compared the results with those of 27 control persons without hematological disease. All control RBCs revealed homogeneous cytoplasm without any foci. Rounded non-sickled RBCs in SCD showed between zero and three small intensively fluorescent dots with higher lifetime values. In sickled RBCs, we found additionally larger irregularly shaped intensively fluorescent areas with increased FLIM values. These areas were interpreted as equivalent to polymerized hemoglobin. The rounded, non-sickled RBCs of SCD patients with homogeneous cytoplasm were not different from those of the erythrocytes of control patients in light microscopy. Yet, variables from the local binary pattern-transformed matrix of the FLIM values per pixel showed significant differences between non-sickled RBCs and those of control cells. In a linear discriminant analysis, using local binary pattern-transformed texture features (mean and entropy) of the erythrocyte cytoplasm of normal appearing cells, the final model could distinguish between SCD patients and control persons with an accuracy of 84.7% of the patients. When the classification was based on the examination of a single rounded erythrocyte, an accuracy of 68.5% was achieved. Employing the Linear Discriminant Analysis classifier method for machine learning, the accuracy was 68.1%. We believe that our study shows that FLIM is able to disclose the topography of the intracellular polymerization process of hemoglobin in sickle cell disease and that the images are compatible with the theory of the two-step nucleation. Furthermore, we think that the presented technique may be an interesting tool for the investigation of therapeutic inhibition of polymerization.

Environmental dependence of geometrical efficiency for the scintillation cell in radon and thoron measurement.

222Rn is recognized as a matter of international concern for human health risk. Because 220Rn as well as 222Rn coexist in the natural environment, thoron sometimes influences the experiment for radon measurement. It is important to measure radon and thoron separately to evaluate the risk of the exposure to 222Rn. As a discriminative measurement method for 222Rn and 220Rn, a simple technique with a single scintillation cell is well known. However, in recent years, the influence of atmospheric environment on the geometrical efficiency of the scintillation cell has not yet been investigated. In this study, environmental dependence of geometrical efficiency for the scintillation cell in 222Rn and 220Rn measurement was investigated using the Lucas type scintillation cell and Monte Carlo particle simulation. It was found that the influence of temperature and pressure on the geometrical efficiencies were larger than that of relative humidity.

Propagation of traveling wave solution of the strain wave equation in microcrystalline materials

Abstract This study focuses on the propagation behavior of traveling wave solution in microcrystalline materials using the polynomial complete discriminant system method. By establishing a complete discriminant system, we systematically analyze the formation and evolution process of traveling wave solution in microcrystalline materials. Specifically, we apply the cubic polynomial extension to the strain wave equation to obtain more accurate analytical solutions. Additionally, two-dimensional, three-dimensional, and contour plots are generated to visually illustrate the characteristics of the obtained solutions, facilitating a more intuitive understanding of their physical significance. These findings not only help reveal the propagation mechanism of traveling wave solution but also provide a theoretical foundation for the application of microcrystalline materials.

Discrimination analysis of quantitative indicators of understanding the written form of deaf children's communication through the use of types of words

In deaf students, there is no contextual understanding and recognition of word types through linguistic competence testing compared to the hearing population, because 67.10% do not understand, and 10.00% of deaf children partially understand the contextual application of word types in a written text task. The aim of the study is to determine the distance of language discourse between deaf and hearing respondents and to establish a discrimination factor that can be used for practical purposes in a classification sense, with the aim of determining the priority of linguistic education and language elements of deaf children in relation to their lagging behind hearing children. The study was conducted on a sample of 140 respondents. The first subsample of respondents, the experimental group consisted of 70 deaf students, and the second subsample, the control group of 70 hearing students, of the same chronological age and gender. The measuring instrument “Test of comprehension of the written form of expression” was applied. The collected data were processed using the discriminant analysis method. The results of the study showed that the discrimination factor is in the sequence of the use of words, adjectives, exclamations and adverbs. Nouns and prepositions have a negative correlation, which points to the fact that these four types of words are in direct implication with nouns and prepositions, and represents information that these four types of words must be more represented in the educational materials of written expression of deaf children. The results of the study also open up a series of questions focused on the quality of the educational processes of deaf children, as well as the level of lag in written communication compared to hearing children. The results of the study can influence the raising of general rehabilitation procedures to a higher level of responsibility in education centers where deaf children are educated.

DIE-CDK: A Discriminative Information Enhancement Method With Cross-Modal Domain Knowledge for Fine-Grained Ship Detection

DIE-CDK: A Discriminative Information Enhancement Method With Cross-Modal Domain Knowledge for Fine-Grained Ship Detection

Identification of apple watercore based on ConvNeXt and Vis/NIR spectra

This paper proposes a method for discriminating between normal apples and watercore apples using visible/near-infrared spectroscopy technology, which combines Gramian Angular Fields (GAF) encoding technology and the ConvNeXt deep learning network. The existing feature extraction methods for visible/near-infrared spectroscopy data do not perform deeper information mining on the extracted features, which results in the quality of the established model being entirely determined by the extracted features. Additionally, the process of building a visible/near-infrared spectroscopy data classification model is complex and time-consuming, and the accuracy of the established model is not high. To address these issues, the experimental visible/near-infrared spectroscopy data of apples was first transformed into two-dimensional images using Gramian Angular Summation Fields (GASF) and Gramian Angular Difference Fields (GADF) with sizes of 64, 128, 256, and 512. These images were then input into the ConvNeXt network, and the performance of different encoding methods and sizes was compared. The results showed that, under the conditions provided in this paper, the GADF encoding method with a size of 256 achieved the highest classification accuracy of 98.48%. Next, ResNet, EfficientNet, and RegNet deep learning networks were selected to classify the encoded images under the same conditions. The results above indicate that the apple variety discrimination method based on GAF encoding technology and ConvNeXt network combined with visible/near-infrared spectroscopy technology can achieve deep information mining of visible/near-infrared spectroscopy data and provide a relatively simple method for establishing qualitative classification models of visible/near-infrared spectroscopy. This method has a relatively excellent discrimination effect between normal apples and watercore apples.

Van der Waals interactions between nonpolar alkyl chains and polar oxide surfaces prevent catalyst deactivation in aldehyde gas sensing

Catalysis-based electrical sensing of volatile organic compounds on metal oxide surfaces is a powerful method for molecular discrimination. However, catalyst deactivation caused by the poisoning of catalytic sites by analytes and/or catalyzed products remains a challenge. This study highlights the underestimated role of van der Waals interactions between hydrophobic aliphatic alkyl chains and hydrophilic ZnO surfaces in mitigating catalyst deactivation during aliphatic aldehyde sensing. By immobilizing octadecylphosphonic acid (ODPA) on ZnO nanowire sensors, recovery times for nonanal detection are significantly reduced without compromising sensitivity. Temperature-programmed measurements demonstrate a reduction in desorption temperature of carboxylates on ODPA-modified ZnO to below 150 °C, whereas carboxylates on bare ZnO remain above 300 °C, indicating a significant decrease in catalyst deactivation. Density functional theory calculations reveal that accumulated van der Waals interactions between alkyl chains and ZnO surfaces significantly contributed to adsorption molecular kinetics. IR spectroscopy using deuterated self-assembled monolayers (SAMs) reveals conformational changes of alkyl chains within the SAMs caused by aldehyde adsorption, supporting the suggested adsorption kinetics. A model is proposed based on the dynamic surface-covering by alkyl chains destabilizes catalytically oxidized carboxylic acids.

Designing broadband cross-polarization conversion metasurfaces using binary particle swarm optimization algorithm

Owing to their disadvantages of broadband discontinuity, infinite value, or incapability of distinguishing cross-polarization conversions from co-polarization conversions, the traditional linear polarization discrimination methods relating to the phase difference Δφ, the ellipticity angle β, or the axial ratio are inapplicable to the software-based optimal design of broadband cross-polarization conversion metasurfaces (CPMs) consisting of subblocks. Therefore, we introduce the pseudo ellipticity angle β' to characterize the cross-polarization conversion. And based upon the coding metasurfaces and β', this work first employs the Python-programmed binary particle swarm optimization algorithm to accomplish the optimal design of reflective broadband CPMs. Then, the obtained CPM is further simplified by reprocessing the relatively isolated subblocks, which results in the final CPM. The simulated results show that the final CPM realizes the broadband cross-polarization conversion with β' ≥ 0.4π and a relative bandwidth of 36.1 % from 11.66 GHz to 16.79 GHz. Moreover, the measured results are in excellent agreements with the simulated ones, which validates the proposed design method. It advances the broadband CPMs design from manual to automated method.

Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99–100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.

Research on the prediction method for fluvial-phase sandbody connectivity based on big data analysis--a case study of Bohai a oilfield

The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield. Conventional connectivity studies often use methods such as seismic attribute fusion, while the development of contiguous composite sandbodies in this area makes it challenging to characterize connectivity changes with conventional seismic attributes. Aiming at the above problem in the Bohai A Oilfield, this study proposes a big data analysis method based on the Deep Forest algorithm to predict the sandbody connectivity. Firstly, by compiling the abundant exploration and development sandbodies data in the study area, typical sandbodies with reliable connectivity were selected. Then, sensitive seismic attribute were extracted to obtain training samples. Finally, based on the Deep Forest algorithm, mapping model between attribute combinations and sandbody connectivity was established through machine learning. This method achieves the first quantitative determination of the connectivity for continuous composite sandbodies in the Bohai Oilfield. Compared with conventional connectivity discrimination methods such as high-resolution processing and seismic attribute analysis, this method can combine the sandbody characteristics of the study area in the process of machine learning, and jointly judge connectivity by combining multiple seismic attributes. The study results show that this method has high accuracy and timeliness in predicting connectivity for continuous composite sandbodies. Applied to the Bohai A Oilfield, it successfully identified multiple sandbody connectivity relationships and provided strong support for the subsequent exploration potential assessment and well placement optimization. This method also provides a new idea and method for studying sandbody connectivity under similar complex geological conditions.

Vine Copula-Based Classifiers with Applications

AbstractThe vine pair-copula construction can be used to fit flexible non-Gaussian multivariate distributions to a mix of continuous and discrete variables. With multiple classes, fitting univariate distributions and a vine to each class lead to posterior probabilities over classes that can be used for discriminant analysis. This is more flexible than methods with the Gaussian and/or independence assumptions, such as quadratic discriminant analysis and naive Bayes. Some variable selection methods are studied to accompany the vine copula-based classifier because unimportant variables can make discrimination worse. Simple numerical performance metrics cannot give a full picture of how well a classifier is doing. We introduce categorical prediction intervals and other summary measures to assess the difficulty of discriminating classes. Through extensive experiments on real data, we demonstrate the superior performance of our approaches compared to traditional discriminant analysis methods and random forests when features have different dependent structures for different classes.

Study on neutron-gamma discrimination methods based on GMM-KNN and LabVIEW implementation

Study on neutron-gamma discrimination methods based on GMM-KNN and LabVIEW implementation

A Leakage Safety Discrimination Model and Method for Saline Aquifer CCS Based on Pressure Dynamics

The saline aquifer CCS is a crucial site for carbon storage. Safety monitoring is a key technology for saline aquifer CCS. Current CO2 leakage detection methods include microseismic, electromagnetic, and well-logging techniques. However, these methods face challenges, such as difficulties in determining CO2 migration fronts and predicting potential leakage events; as a result, the formulation of test timing and methods for these safety monitoring techniques are somewhat arbitrary. This study establishes a gas–water two-phase seepage model and solves it using a semi-analytical method to obtain the injection pressure and the derivative curve characteristics of the injection well. The pressure derivative curve can reflect the physical properties of the reservoir through which CO2 flows underground, and it can also be used to determine whether CO2 leakage has occurred, as well as the timing and amount of leakage, based on boundary responses. This study conducted sensitivity analyses on eight parameters to determine the impact of each parameter on the bottom-hole pressure and its derivatives, thereby obtaining the influence of its parameters on different flow stages. The research indicates that, when a steady-state flow characteristic appears at the outer boundary, CO2 leakage will occur. Additionally, the leakage location can be determined by calculating the distance from the injection well. This can guide the placement and measurement of safety monitoring methods for saline aquifer CCS. The method proposed in this paper can effectively monitor the timing, location, and amount of leakage, providing a technical safeguard for promoting CCS technology.

Influence of polymorphisms on the phenotype of TLR1, TLR4 and TLR9 genes and their association with cervical cancer: Bioinformatics prediction analysis and a case-control study

Susceptibility to cervical cancer has been associated with Toll-like receptors (TLRs), which is an important component of innate immunity. According to previous studies, polymorphisms in TLRs genes can affect immune response pathways and lead to the development of cervical cancer. The present study aims to evaluate the functionality of polymorphisms in TLR1, TLR4 and TLR9 genes and their associations with cervical cancer. To identify the functionality of polymorphisms, we used the following tools: MUpro, ChimeraX, SNP2TFBS and GTEx. A case-control study including 57 cases (11 High-grade Intraepithelial Lesion - HSIL and 46 cervical cancer) and 67 clinically healthy controls was conducted in the Brazilian population. Polymorphisms genotyping was performed by real-time PCR, using TaqMan probes, using the allelic discrimination method. Bioinformatics prediction showed that the TLR1 rs4833095 [NM_003263.4 (TLR1):c.743T>C (p.Asn248Ser)] and TLR4 rs4986790 [NM_138554.5 (TLR4):c.896A>G (p.Asp299Gly)] polymorphisms alter the structure and stability of their respective proteins. TLR9 rs187084 [NM_017442.3(TLR9):c.-1486A>G] polymorphism seems to affect the THAP1 binding site and modify gene expression. In the case-control study, the c.743TC heterozygous genotype of the rs4833095 SNP in the TLR1 gene was associated with an increased risk for HSIL/cervical cancer. No association of TLR4 rs4986790 and TLR9 rs187084 SNPs with HSIL/cervical cancer was found in the studied population. Allelic combination CAG (rs4833095/ rs4986790/ rs187084) increased the risk of cervical cancer. In conclusion, the present study identified that polymorphisms in TLRs genes can affect the phenotype of their respective genes and contribute to the development of HSIL or cervical cancer.

Parameter estimation-based discrimination method for osteoporosis stage with ultrasound bone densitometer

This paper proposes a new method for predicting the stage of osteoporosis by estimating bone-density parameters using an ultrasonic-bone densitometer. Using the developed ultrasonic bone densitometer, the ultrasonic signal passing through the phalanx of the finger was measured, and the measured signal was preprocessed to generate input data for parameter estimation. The Nakagami probability-density function was used for modeling this data, and optimal bone-density parameters were estimated using an optimization problem – to predict the stage of osteoporosis. The proposed method was verified through in vitro and in vivo experiments. In phantom experiments, cubes with different materials (ABS plastic, PLA plastic, Nylon) were used, and each cube had a different density through internal density so that it could mimic bones. The experiments were conducted using them and the results were compared. The experimental results confirmed that there was a correlation between the density and estimated parameters for all three materials. In the in vivo experiment, the osteoporosis stages were classified as normal, osteopenia, and osteoporosis, and the bone-density parameters were estimated for the participants of each group. Thus, a basis for predicting the stage of osteoporosis was created, and data from new participants were collected and verified as test data. As a result, normal and abnormal groups (osteopenia and osteoporosis) were predicted.

Low Energy Neutron-induced Charged-particle (Z) (LENZ) instrument development with a focus on pulse shape discrimination for low-energy charged particles

To study neutron-induced charged-particle reactions with high precision, the Low Energy Neutron-induced Charged-particle (Z) instrument (LENZ) was developed at the Los Alamos Neutron Science Center. For the interest of measuring (n,p) and (n, α) reactions simultaneously, Pulse Shape Discrimination methods were investigated to identify different charged particles in the energy range of 3 - 20 MeV and improve signal-to-background ratios using Double-sided Silicon Strip Detectors and waveform digitizers. The risetime and pulse shape properties of detected charged particles were characterized for various silicon detector’s thickness with different orientations. During the post-processing of waveforms, we implemented different digital filters for effective particle identifications and improved energy- and timing- resolutions. We validated the optimized digital filters and pulse shape analyses, via measurements with 228Th and 229Th calibration sources, proton-induced reactions on a 7LiF target, and neutron-induced reactions on CH2, Ta2O5, 58Ni, and 6LiF/59Ni targets at the time-of-flight facility, LANSCE. The summary of effective thresholds and Figure of Merits on separating different charged particles is reported.