Linear Discriminant Research Articles

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.

Battery Condition Monitoring of Quadrotor UAV Using Machine Learning Classification Algorithm

Unmanned aerial vehicle flight performance and efficiency rely on various factors. Flight instabilities can happen due to malfunctions inside the system and disturbances from the external environment. Battery status plays a significant role in healthy flight conditions. A weak battery will affect the performance of propellers and motors, and the presence of wind disturbance can contribute towards inefficient flying capabilities. Therefore, investigation of fault at the early stage is crucial to maintain the great performance of the UAV. This paper aims to investigate the best prediction system from the existing machine learning algorithm such as Decision Tree (DT), Linear Discriminant (LD), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Neural Network (NN) to classify the battery condition of the quadrotor by extracting the features from the displacement time series dataset. By using recorded flight data, it will be statistically analyzed to extract the flying condition features. The extracted features are the Euclidian distance (ED), speed, acceleration, Periodogram Power spectral density (PSD) and Fast Fourier Transform (FFT) of the signal. The result shows that the two best classifier algorithms are the Decision Tree and Neural Network models with training accuracy of 98% and 93% in Set A and B, respectively.

Comparison of a linear discrimination function and artificial neural networks approach to discriminate the seismic events in Ankara (Turkey)

In this study, natural and blast-induced ground vibrations (namely earthquakes and quarry blasts) in Ankara (Turkey) were analysed and distinguished from each other. A total of 156 digitized vertical component velocity seismograms of seismic events of 2009-2014 with Md≤3.5 were obtained from the Lodumlu broadband station (LOD) controlled by Boğaziçi University, Kandilli Observatory, and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center (BU-KOERIRETMC). We examined the following variables: the ratio of the highest amplitude value of the S-wave and the highest amplitude value of the P-wave (Amplitude ratio) of vertical component velocity seismograms, the power ratio (Complexity), the logarithmical value of the amplitude of the S-wave of the seismogram (Log S), and total signal duration of the seismogram. It is the first time that natural and blast-induced ground vibrations were separated from each other using the Fisher’s Linear Discriminate Analysis (FLDA) technique and Artificial Neural Networks (ANNs) approach together in Ankara (The capital of Turkey). Ninety-two (59%) of the 156 seismic events studied were identified as earthquakes and sixty-four (41%) of them were described as blast-induced ground vibrations. Then the obtained accuracy percentage results of three pairs of some variables were compared. The amplitude ratio versus complexity and the amplitude ratio versus total signal duration had a high classification accuracy determination coefficient for the LOD dataset (94% for the FLDA technique and 100% for ANNs approach). ANNs approach is more successful than the FLDA technique.

An efficient diagnosis of heart disease using optimized cross-layer Densenet121 pyramid mutual attention network

Heart disease significantly impacts global mortality rates, underscoring the need for improved early-stage diagnosis through deep learning algorithms to enhance prognosis accuracy and prevent mortality. However, challenges such as missing values, class imbalance, redundant features and faulty predictions persist in heart disease diagnosis. To address these issues, this research proposes an Optimized Cross-layer Densenet121 Pyramid mutual attention Network (OCDPN) to improve prediction accuracy. Initially, a Hybrid Bag-boost K-means Synthetic Minority Oversampling TEchnique (HBK-SMOTE) is employed to eliminate missing values and handle class imbalance issues. Subsequently, a Two-layer Angle Kernel Linear Discriminant Extreme Learning (TK-LDE) method extracts features and the Hybrid Binary Aquila Beluga whale Optimization (HBABO)selects the most significant features. Finally, the selected features are classified using Fick's law algorithm to achieve high accuracy. The OCDPN model, comprising several dense layers and a softmax classifier, with Fick's law optimization, enhances feature understanding and classification performance. The model's efficiency is assessed using the Framingham Heart Study (FHS), Statlog (Heart), Cleveland Heart Disease (CHD) and Z-Alizadeh Sani datasets. The introduced technique demonstrates outstanding performance, achieving accuracies of 98.2%, 99.8%, 99.2% and 98.5%, respectively, for the considered datasets. These outcomes highlight the effectiveness of the introduced method in predicting heart diseases.

Evolution of Key Oxygen-Sensing Genes Is Associated with Hypoxia Tolerance in Fishes.

Low dissolved oxygen (hypoxia) is recognized as a major threat to aquatic ecosystems worldwide. Because oxygen is paramount for the energy metabolism of animals, understanding the functional and genetic drivers of whole-animal hypoxia tolerance is critical to predicting the impacts of aquatic hypoxia. In this study, we investigate the molecular evolution of key genes involved in the detection of and response to hypoxia in ray-finned fishes: the prolyl hydroxylase domain (PHD)-hypoxia-inducible factor (HIF) oxygen-sensing system, also known as the EGLN (egg-laying nine)-HIF oxygen-sensing system. We searched fish genomes for HIFA and EGLN genes, discovered new paralogs from both gene families, and analyzed protein-coding sites under positive selection. The physicochemical properties of these positively selected amino acid sites were summarized using linear discriminants for each gene. We employed phylogenetic generalized least squares to assess the relationship between these linear discriminants for each HIFA and EGLN and hypoxia tolerance as reflected by the critical oxygen tension (Pcrit) of the corresponding species. Our results demonstrate that Pcrit in ray-finned fishes correlates with the physicochemical variation of positively selected sites in specific HIFA and EGLN genes. For HIF2A, two linear discriminants captured more than 90% of the physicochemical variation of these sites and explained between 20% and 39% of the variation in Pcrit. Thus, variation in HIF2A among fishes may contribute to their capacity to cope with aquatic hypoxia, similar to its proposed role in conferring tolerance to high-altitude hypoxia in certain lineages of terrestrial vertebrates.

Targeted FT-NIR and SERS Detection of Breast Cancer HER-II Biomarkers in Blood Serum Using PCB-Based Plasmonic Active Nanostructured Thin Film Label-Free Immunosensor Immobilized with Directional GNU-Conjugated Antibody.

This work describes our recent PCB-based plasmonic nanostructured platform patent (US 11,828,747B2) for the detection of biomarkers in breast cancer serum (BCS). A 50 nm thin gold film (TGF) was immersion-coated on PCB (i.e., PCB-TGF) and immobilized covalently with gold nanourchin (GNU) via a 1,6-Hexanedithiol (HDT) linkage to produce a plasmonic activated nanostructured thin film (PANTF) platform. A label-free SERS immunosensor was fabricated by conjugating the platform with monoclonal HER-II antibodies (mAb) in a directional orientation via adipic acid dihydrazide (ADH) to provide higher accessibility to overexpressed HER-II biomarkers (i.e., 2+ (early), 3+ (locally advanced), and positive (meta) in BCS. An enhancement factor (EF) of 0.3 × 105 was achieved for PANTF using Rhodamine (R6G), and the morphology was studied by scanning electron microscopy (SEM) and atomic force microscope (AFM). UV-vis spectroscopy showed the peaks at 222, 231, and 213 nm corresponding to ADH, mAb, and HER-II biomarkers, respectively. The functionalization and conjugation were investigated by Fourier Transform Near Infrared (FT-NIR) where the most dominant overlapped spectra of 2+, 3+, and Pos correspond to OH-combination of carbohydrate, RNH2 1st overtone, and aromatic CH 1st overtone of mAb, respectively. SERS data were filtered using the filtfilt filter from scipy.signals, baseline corrected using the Improved Asymmetric Least Squares (isals) function from the pybaselines.Whittaker library. The results showed the common peaks at 867, 1312, 2894, 3026, and 3258 cm-1 corresponding to glycine, alanine ν (C-N-C) assigned to the symmetric C-N-C stretch mode; tryptophan and α helix; C-H antisymmetric and symmetric stretching; NH3+ in amino acids; and N-H stretch primary amide, respectively, with the intensity of Pos > 3+ > 2+. This trend is justifiable considering the stage of each sample. Principal Component Analysis (PCA) and Linear Discrimination Analysis (LDA) were employed for the statistical analysis of data.

Effective strategy for distinguishing raw and vinegar Schisandrae Chinensis Fructus based on electronic eye and electronic tongue combined with chemometrics.

Schisandrae Chinensis Fructus (SCF), a traditional Chinese medicine, has been used in treating virtual injury and strain since ancient times. The Chinese Pharmacopoeia reveals that SCF includes raw (RSCF) and vinegar-processed (VSCF) decoction pieces. This study developed an effective method combining the electronic eye (e-eye), electronic tongue (e-tongue), and chemometrics to discriminate RSCF and VSCF from the perspective of chemical composition, color, and taste. First, RSCF were collected and processed into VSCF, and their color parameters, e-tongue sensory properties, high-performance liquid chromatography (HPLC) and ultra-HPLC (UPLC) characteristic fingerprints, and nominal ingredients were determined. Multivariate statistical analyses, including principal component, linear discriminant, similarity, and partial least squares discriminant analyses, were conducted. HPLC and UPLC fingerprints were established, demonstrating a > 0.900 similarity. The content determination indicated increased schisantherin A, schisantherin B, and schisandrin A contents in VSCF. The e-eye data demonstrated a > 1.5 total color difference before and after processing ΔE*ab, indicating the significantly changed sample color and appearance before and after processing. The e-tongue technology was used to quantitatively characterize the taste of RSCF and VSCF. The t-test revealed significantly reduced sourness, aftertaste-bitter, and aftertaste-astringent values of SCF after vinegar processing. Principal component and partial least squares discriminant analyses indicated that e-eye and e-tongue realize the rapid RSCF and VSCF identification. The proposed comprehensive strategy of electronic eye and electronic tongue combined with chemometrics demonstrated satisfactory results with high efficiency, accuracy, and reliability. This can be developed into a novel and accurate method for discriminating RSCF and VSCF.

Low-cost multispectral sensor reveals cold chain breaks, meat type, and storage time in chicken meat samples

A portable and low-cost spectrometric system has been proposed to discriminate cold chain (frozen) interruption during the supply chain, as well as, the sample type among four different tested chicken meat and products (nugget, wing, thigh and drumstick) and the storage time elapsed after the cold chain break. To achieve this, an AS7265x sensor chipset was used and controlled by a custom programmed microcontroller through a Qwiic I2C interface. Communication between the user and the sensor setup was made using a custom programmed interface developed using the Python. Frozen chicken samples were thawed under various temperature-time combinations (4, 8, 16, and 24 h at 4 and 24 °C) and then refrozen at -18 °C. The refrozen samples were stored for up to 60 days. Spectral measurements (410–940 nm) were taken before thawing and at 1, 10, 30 and 60 days of storage after refreezing. The spectra obtained were analysed using Principal Component Analysis and outliers were eliminated using Mahalobobis distance. Several classification models (including Decision Tree, Random Forest, AdaBoost, Support Vector Machine, k-Nearest Neighbors, Gaussian-Naïve Bayes, Multilayer Perceptron, Gaussian Process, Linear and Quadratic Discriminant Analysis and Partial Least Squares Discriminant Analysis) were trained where appropriate. The top models with the highest predictive ability were selected and merged to build Soft Voting Classifiers (SVC) for each feature to be predicted. The SVC models correctly classified the samples with 93%, 92% and 83% accuracy for cold chain interruption, chicken meat/product type and storage time after thaw-refreeze cycle, respectively. In addition, a graphical interface was also developed so that it can be used by end users for food safety concerns. The results showed the potential of the developed low-cost sensor to rapidly detect the potential food safety risks due to cold chain breakage and to trace back the problem by predicting the storage time after refreezing.

Performance enhancement of classifiers through Bio inspired feature selection methods for early detection of lung cancer from microarray genes

Gene expression in the microarray is assimilated with redundant and high-dimensional information. Moreover, the information in the microarray genes mostly correlates with background noise. This paper uses dimensionality reduction and feature selection methods to employ a classification methodology for high-dimensional lung cancer microarray data. The approach is enforced in two phases; initially, the genes are dimensionally reduced through Hilbert Transform, Detrend Fluctuation Analysis and Least Square Linear Regression methods. The dimensionally reduced data is further optimized in the next phase using Elephant Herd optimization (EHO) and Cuckoo Search Feature selection methods. The classifiers used here are Bayesian Linear Discriminant, Naive Bayes, Random Forest, Decision Tree, SVM (Linear), SVM (Polynomial), and SVM (RBF). The classifier's performances are analysed with and without feature selection methods. The SVM (Linear) classifier with the DFA Dimensionality Reduction method and EHO feature selection achieved the highest accuracy of 92.26 % compared to other classifiers.

Discrimination of pressed sesame oil: A comparison study of non-targeted UV spectral fingerprints combined with different chemometric methods

This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints. The goal of this study was to provide a reliable tool for assessing the quality of sesame oil. The UV spectra of 51 samples of pressed sesame oil and 420 adulterated samples with refined sesame oil were measured in the range of 200–330 nm. Various classification and prediction methods, including linear discrimination analysis (LDA), support vector machines (SVM), soft independent modeling of class analogy (SIMCA), partial least squares regression (PLSR), support vector machine regression (SVR), and back-propagation neural network (BPNN), were employed to analyze the UV spectral data of pressed sesame oil and adulterated sesame oil. The results indicated that SVM outperformed the other classification methods in qualitatively identifying adulterated sesame oil, achieving an accuracy of 96.15%, a sensitivity of 97.87%, and a specificity of 80%. For quantitative analysis, BPNN yielded the best prediction results, with an R2 value of 0.99, RMSEP of 2.34%, and RPD value of 10.60 (LOD of 8.60% and LOQ of 28.67%). Overall, the developed models exhibited significant potential for rapidly identifying and predicting the quality of sesame oil.

Utilizing plant-based biogenic volatile organic compounds (bVOCs) to detect aflatoxin in peanut plants, pods, and kernels

Mycotoxins and particularly aflatoxin are a concern for peanut growers, processors, and consumers. Aflatoxins are responsible for approximately 25 % of liver cancer cases worldwide, while also costing millions of dollars in lost revenue on an annual basis. Current detection techniques based on high pressure liquid chromatography (HPLC) are time, labor and cost intensive. Peanut product can be held multiple days before testing is complete, costing processors additional revenue loss. Over the past few years, The Georgia Tech Research Institute (GTRI) has been investigating the use of plant-based biogenic volatile organic compounds (bVOCs) for monitoring the status of peanut plants. Initially, GTRI utilized these bVOCs to monitor for heat/drought stress in peanut plants. They quickly saw very promising indications of the ability of bVOCs to monitor other parameters in these plants. More recently, the team has started to investigate the of use of bVOCs to monitor aflatoxin development in plants, pods, and kernels pre- and post-harvest. A field trial for detection of aflatoxin using bVOCs was conducted in August–September of 2020 where three test groups were prepared: plants treated with Aspergillus fungus; plants treated with Afla-Guard (biocontrol agent); plants not treated – acting as a control group. Plant-based bVOCs were collected from the plants before treatment, and once a week post treatment using Stir Bar Sorptive Extraction (SBSE) devices or Twisters®. Each Twister® was then analyzed via gas chromatography–mass spectrometry (GC/MS). Pods from tested plants were harvested and sent to GTRI where bVOCs were collected and analyzed using GC/MS. Several statistical analysis and machine learning techniques were applied to all the collected GC/MS data. It was found using only bVOCs, that Random Forest classification performed well for the analysis of the pod and kernel samples with an F1 score of 0.80. On the other hand, Linear Discriminate Analysis (LDA) was only able to correctly classify 50 % of plant-based samples solely on bVOCs alone, which may be due to training models developed using original labels assuming no cross contamination at the field level. These results indicate the potential for bVOC screening for aflatoxin as an important way to lower impacts to growers, shellers, and consumers.

Anomaly detection in network traffic with ELSC learning algorithm

AbstractIn recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature‐based IDS detects threats. Secondly, anomaly‐based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K‐Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning‐based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%.

Analysis of gasoline quality by ATR-FTIR spectroscopy with multivariate techniques

In this paper, chemometric methods were used for exploratory analysis, categorization, and quantification of gasoline fuel using Fourier Transform Infrared Spectroscopy (FTIR) data. During exploratory analysis, Principal Component Analysis (PCA) was employed, and to categorise the gasoline samples, Support Vector Machine Classification (SVMC), Linear Discrimination Analysis (LDA), and Partial Least Squares Discriminant Analysis (PLS-DA) were used. The concentration of Benzene, Methyl Tert-butyl Ether (MTBE), and Public Distribution System (PDS) Kerosene were determined using the Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Machine Regression (SVMR). All of the chemometric models had 100% accuracy and high R-square and RMSEC significance values. The SVM classification techniques were identified as a suitable choice for both classifying oxygenated and adulterated samples among all approaches. Both PLSR and PCR can also be suitable choices for quantification when dealing with high dimensional data. As a result, the research reported in this study has the potential to become a significant tool for improving gasoline sample discrimination and as a resource for quality control in the petroleum industry.

Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model

The degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and performance. This study explores an acoustic-based method to classify propeller faulty conditions in Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAV). The main objective is to emphasize the difference between classifier models developed using different battery-level flight data. The sound generated by VTOL UAV provides valuable information about the flight performance, essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classification algorithms-Medium Tree (MT), Linear Support Vector Machine (LSVM), and Linear Discriminant (LD), to classify propeller failures of VTOL UAVs. Datasets are collected from three simulated propeller faulty conditions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. The Mel Frequency Cepstral Coefficients technique is implemented in MATLAB (R2020a) to extract valuable features from the recorded sound signals. Extracted features from high and low-battery flights are utilized to develop classification models. Classifiers' performance is analyzed to compare the difference between selected models developed using high and low-battery flight data. The accuracy was measured with other samples to test the robustness of classification models. LSVM and MT classification models developed using high-battery flight data produce better accuracy than low-battery flight data in the training and testing phases. LD classification model developed using high-battery flight data produces better accuracy than low-battery flight data in the testing phase only. These results show that battery degradation can affect the performance of the VTOL UAV faulty classification algorithm.

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data.

Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings-8 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature-humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.

Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies

This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For instance, on the TIMIT babble noise dataset (120 speakers), feature fusion achieves a remarkable speaker identification accuracy of 92.7%, while various feature optimization techniques combined with K nearest neighbor (KNN) and linear discriminant (LD) classifiers result in a speaker verification equal error rate (SV EER) of 0.7%. Notably, this study achieves a speaker identification accuracy of 93.5% and SV EER of 0.13% on the TIMIT babble noise dataset (630 speakers) using a KNN classifier with feature optimization. On the TIMIT white noise dataset (120 and 630 speakers), speaker identification accuracies of 93.3% and 83.5%, along with SV EER values of 0.58% and 0.13%, respectively, were attained utilizing PCA dimension reduction and feature optimization techniques (PCA-MPA) with KNN classifiers. Furthermore, on the voxceleb1 dataset, PCA-MPA feature optimization with KNN classifiers achieves a speaker identification accuracy of 95.2% and an SV EER of 1.8%. These findings underscore the significant enhancement in computational speed and speaker recognition performance facilitated by feature optimization strategies.

A new sub-class linear discriminant for miniature spectrometer based food analysis

The well-known and extensively studied Linear Discriminant Analysis (LDA) can have its performance lowered in scenarios where data is not homoscedastic or not Gaussian. That is, the classical assumptions when LDA models are built are not applicable, and consequently LDA projections would not be able to extract the needed features to explain the intrinsic structure of data and for classes to be separated. As with many real word data sets, data obtained using miniature spectrometers can suffer from such drawbacks which would limit the deployment of such technology needed for food analysis. The solution presented in the paper is to divide classes into subclasses and to use means of sub classes, classes, and data in the suggested between classes scatter metric. Further, samples belonging to the same subclass are used to build a measure of within subclass scatterness. Such a solution solves the shortcoming of the classical LDA. The obtained results when using the proposed solution on food data and on general machine learning datasets show that the work in this paper compares well to and is very competitive with similar sub-class LDA algorithms in the literature. An extension to a Hilbert space is also presented; and the kernel version of the presented solution can be fused with its linear counter parts to yield improved classification rates.

Modality-specific impacts of distractors on visual and auditory categorical decision-making: an evidence accumulation perspective.

Our brain constantly processes multisensory inputs to make decisions and guide behaviors, but how goal-relevant processes are influenced by irrelevant information is unclear. Here, we investigated the effects of intermodal and intramodal task-irrelevant information on visual and auditory categorical decision-making. In both visual and auditory tasks, we manipulated the modality of irrelevant inputs (visual vs. auditory vs. none) and used linear discrimination analysis of EEG and hierarchical drift-diffusion modeling (HDDM) to identify when and how task-irrelevant information affected decision-relevant processing. The results revealed modality-specific impacts of irrelevant inputs on visual and auditory categorical decision-making. The distinct effects on the visual task were shown on the neural components, with auditory distractors amplifying the sensory processing whereas visual distractors amplifying the post-sensory process. Conversely, the distinct effects on the auditory task were shown in behavioral performance and underlying cognitive processes. Visual distractors facilitate behavioral performance and affect both stages, but auditory distractors interfere with behavioral performance and impact on the sensory processing rather than the post-sensory decision stage. Overall, these findings suggested that auditory distractors affect the sensory processing stage of both tasks while visual distractors affect the post-sensory decision stage of visual categorical decision-making and both stages of auditory categorical decision-making. This study provides insights into how humans process information from multiple sensory modalities during decision-making by leveraging modality-specific impacts.

Nondestructive evaluation of harvested cabbage texture quality using 3D scanning technology

Current nondestructive methods for evaluating the texture quality of leafy vegetables have limitations due to their complicated leafy structure. In this study, a promising solution was proposed using 3D scanning technology to nondestructively assess the texture quality of leafy vegetables, and the harvested cabbages were chosen as the experimental samples. The cabbages were scanned to extract the morphological traits, especially for surface features of vein distribution. Results demonstrated that morphological traits exhibited better correlations with texture indices compared to traditional compression features. Texture indices were well predicted based on the XGBoostR algorithm with high R2 values of over 0.89 and low RMSE values. The texture quality of cabbages at different harvesting times analyzed by linear discrimination analysis also showed well-discriminative results with an accuracy exceeding 98.3%. These results successfully indicated that 3D scanning technology was effective in evaluating the texture quality of cabbages, showcasing its potential in the nondestructive texture evaluation of leafy vegetables.

Typology of Parent-to-Child Emotions: A Study of Japanese Parents of a Foetus up to a 12-Year-Old Child.

Background: Emotions are the fundamental origin of parent-child bonding, which is measurable by the Scale for Parent-to-Child Emotions (SPCE) based on the theories of basic and self-conscious emotions. Methods: This study is based on the data from a cross-sectional study that we previously reported. The data consist of fathers and mothers who had a child/children, whose eldest child's age was at the foetal stage up to 12 years old, and were recruited via the Internet (N = 4600). A series of cluster analyses using factor scores (theta[Ө]s) of all domains of the SPCE were conducted. After the clusters emerged, the fathers and mothers allocated to each cluster were compared by the child's age stage. The validation of the classifications was also conducted using ANOVAs and chi-squared tests. A discriminant function analysis was conducted. Results: The participant mothers and fathers were classified into Cluster 1 (Lack of Bonding Emotions, n = 509), Cluster 2 (Bonding Disorder, n = 1471), Cluster 3 (Ambivalent Bonding Emotions, n = 1211), and Cluster 4 (Positive Bonding, n = 1409). Across the four clusters, there were no differences in the age of the parents or the gender of the child. During the second trimester, mothers made up the majority of Cluster 4 (Positive Bonding), totalling 81 cases (37.5%), whereas fathers made up the majority of Cluster 2 (Bonding Disorder), totalling 126 cases (60.0%). The three linear discriminants (LDs) well predicted the four clusters, and their functions showed cross validation. Conclusions: The typology of the SPCE is helpful to understand individual differences in terms of parental emotional bonding.