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.

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.

Study of brown sugar grade discrimination method based on electrochemical scanning techniques

Study of brown sugar grade discrimination method based on electrochemical scanning techniques

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.

HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation.

Medical image segmentation has been significantly advanced with the rapid development of deep learning (DL) techniques. Existing DL-based segmentation models are typically discriminative; i.e., they aim to learn a mapping from the input image to segmentation masks. However, these discriminative methods neglect the underlying data distribution and intrinsic class characteristics, suffering from unstable feature space. In this work, we propose to complement discriminative segmentation methods with the knowledge of underlying data distribution from generative models. To that end, we propose a novel hybrid diffusion framework for medical image segmentation, termed HiDiff, which can synergize the strengths of existing discriminative segmentation models and new generative diffusion models. HiDiff comprises two key components: discriminative segmentor and diffusion refiner. First, we utilize any conventional trained segmentation models as discriminative segmentor, which can provide a segmentation mask prior for diffusion refiner. Second, we propose a novel binary Bernoulli diffusion model (BBDM) as the diffusion refiner, which can effectively, efficiently, and interactively refine the segmentation mask by modeling the underlying data distribution. Third, we train the segmentor and BBDM in an alternate-collaborative manner to mutually boost each other. Extensive experimental results on abdomen organ, brain tumor, polyps, and retinal vessels segmentation datasets, covering four widely-used modalities, demonstrate the superior performance of HiDiff over existing medical segmentation algorithms, including the state-of-the-art transformer- and diffusion-based ones. In addition, HiDiff excels at segmenting small objects and generalizing to new datasets. Source codes are made available at https://github.com/takimailto/HiDiff.

Active Learning for Deep Visual Tracking.

Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restrictive in practice, because manually labeling such a large number of training samples is time-consuming and prohibitively expensive. In this article, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNN model. Under the guidance of active learning, the tracker based on the trained deep CNN model can achieve competitive tracking performance while reducing the labeling cost. More specifically, to ensure the diversity of selected samples, we propose an active learning method based on multiframe collaboration to select those training samples that should be and need to be annotated. Meanwhile, considering the representativeness of these selected samples, we adopt a nearest-neighbor discrimination method based on the average nearest-neighbor distance to screen isolated samples and low-quality samples. Therefore, the training samples' subset selected based on our method requires only a given budget to maintain the diversity and representativeness of the entire sample set. Furthermore, we adopt a Tversky loss to improve the bounding box estimation of our tracker, which can ensure that the tracker achieves more accurate target states. Extensive experimental results confirm that our active-learning-based tracker (ALT) achieves competitive tracking accuracy and speed compared with state-of-the-art trackers on the seven most challenging evaluation benchmarks. Project website: https://sites.google.com/view/altrack/.

Information enhanced slow feature analysis integrated with prior fault data for sensitive monitoring of chemical processes

As an effective feature extraction method, slow feature analysis (SFA) has been successfully applied in the domain of chemical process monitoring. However, as an unsupervised method, the basic SFA ignores the key information carried by prior fault data so that many complicated faults cannot be sensitively detected. To address this issue, an enhanced SFA method, termed Information Enhanced SFA (IE-SFA), is proposed by integrating available fault discriminant information to achieve more sensitive detection of complicated chemical process faults. The proposed method builds a primary-auxiliary SFA modeling framework. On the one hand, the normal training data are analyzed to establish the primary SFA model. On the other, prior fault data are integrated to develop an auxiliary monitoring model for providing extra fault-sensitive information. In the auxiliary model, a sample-variable weighted Fisher discriminant analysis method is performed on normal data, fault data, and their respective slow feature components, thereby extracting fault-sensitive features for enhancing the fault detection capability. For full-view fault monitoring, the Bayesian fusion strategy is used to fuse the outcomes from both primary and auxiliary models. The applications on a benchmark Tennessee Eastman chemical process and a three-phase flow process demonstrate that the proposed IE-SFA method provides superior fault detection performance compared to the basic SFA method.

Development of [formula omitted]/[formula omitted]/[formula omitted] discrimination with the phoswich detector using improved charge comparison method

Both pulse shape discrimination methods based on temporal difference characteristic and phoswich detectors are well-known and effective for particles discrimination, so the combination and further improvement are significant for better performance. In this paper, a phoswich detector combining YAG(Ce) and CsI(Tl) was constructed for α/β/γ discrimination using various techniques including two machine learning algorithms and an improved charge comparison method that further exploited the difference in the decay rate of pulse tail. The detector was designed with Geant4, simulating the suitable sizes of scintillators, and a high-speed digitizer was used for the collection of pulses that were from three radioactive sources. The results among the α (β) and γ rays had the figure of merit values greater than 2, simultaneously with an optimal 1.68 of the improved method that had more than twice the performance comparing the original 0.69 for α/β identification, which showed the detector is able to discriminate α, β and γ rays in mixed radiation field effectively.