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The fatigue problem for aviation personnel has attracted attention in civil aviation, in order to reduce the risk of civil aviation operation caused by fatigue problems, the development status of aviation personnel fatigue research under the field of civil aviation is analyzed, and the current fatigue research results are sorted out and future development of fatigue detection in civil aviation is put forward. Firstly, the concept of fatigue of aviation personnel and its influencing factors are clarified. Then, the fatigue studies of pilots, air traffic controllers and aircraft maintenance personnel are compared and analyzed from the dimensions of physiological indicators and subjective measurement methods, and the fatigue detection models of pilots and air traffic controllers are sorted out and summarized by combining the computerized detection methods. Finally, the shortcomings and development direction of the current fatigue detection are put forward according to the research. The results show that the objective detection methods are gradually becoming mature, and physiological indicators are widely used in fatigue detection. The selection of indicators for fatigue detection models needs to be further studied, and the accuracy of model identification and fatigue classification needs to be improved, especially the fatigue detection model for maintenance personnel needs to be developed. In the future, passive fatigue caused by automated driving and human-machine function allocation problems in highly automated cockpit environment is the focus of research. [ABSTRACT FROM AUTHOR]

Based on the theory of fluid flow and solid elastic deformation as well as the criterion of the propagation path when hydraulic fracture meets natural fracture, a model for the propagation of hydraulic fracture through layer is established in order to accurately judge the propagation path when hydraulic fracture meets interlayer interface in coal measure composite reservoir fracturing process. The model mainly considers the effect of injection rate and viscosity of fracturing fluid on the propagation of hydraulic fracture through the layer, and gives the critical fracture pressure of the propagation of hydraulic fracture through the layer and along the interface. The propagation behavior of the fracture is judged by comparing the wellbore water pressure with the two critical fracture pressures. The accuracy of the model is verified by comparing the prediction results of the model, the fracturing test data and the on-site fracturing results. The accuracy of the model is verified by comparing the prediction results of the model, the fracturing test data and the on-site fracturing results. The results show that injection rate and fracturing fluid viscosity can directly affect the water pressure in the wellbore, and then change the fracture propagation path. The greater the viscosity between injection rate and fracturing fluid, the more beneficial it is for hydraulic fracture penetration, and the greater the influence of injection rate is. In the process of in-situ fracturing, the purpose of penetrating fracturing can be achieved mainly by increasing the injection rate of fracturing fluid.

In response to the problems of cumulative errors in step size estimation and the large sample size required by traditional deep learning methods in the pedestrian dead reckoning (PDR) based underground personnel positioning system in coal mines, a step size estimation method for underground personnel based on generative adversarial network (GAN) is proposed. The GAN model mainly includes two parts: generative model and discriminative model, both of which are implemented using deep neural networks (DNNs). The generative model aims to generate continuous result distributions (i.e. labels) based on input data. Its output layer uses a linear activation function to preserve the linear features of the network, allowing the model to predict the step size of any personnel during walking. The discriminant model aims to distinguish whether the input data and labels are real labels or labels generated by the generator. Its output layer uses a Sigmoid activation function to achieve binary classification of results. After determining the generative model and discriminant model, the GAN model combines two models for training. By constructing and optimizing the dynamic competition between the generator and discriminator, the generator can learn to generate more realistic and indistinguishable data samples in continuous iterations. The experimental results show that under the same training and testing sets, the average error of the GAN model is 0.14 m, and the standard deviation and root mean square error are both smaller than those of the DNNs model, with the minimum values being 0.74 m. The outdoor test results show that the GAN based underground personnel step estimation method has a minimum error of 3.21% and a maximum error of 4.79% in uphill and downhill scenarios. Compared to uphill and downhill scenarios, the error in playground scenarios is smaller, with a maximum error of 1.91%.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wooden breast barriers the development of broiler industry, and traditional detection methods are time-consuming and inefficient. To investigate the feasibility of the hyperspectral imaging (HSI) technique for the detection of wooden breasts, four different grades of white feather chicken breast were selected and their HSI information of 400~1000 and 1000~2000 nm was collected. After spectral preprocessing and spectral variable selection, partial least squares discriminant (PLS-DA) models and support vector machine (SVM) models were developed based on full wavelength and characteristic spectral variables, as well as their fused HSI data. The results showed that SVM models showed better results than PLS-DA models to discriminate woody grades of chicken breasts. The overall discrimination rates based on the full HSI bands and selected spectral variables in 1000~2000 nm were higher than those of models in 400~1000 nm. Besides, the discrimination models based on fused HSI data of HSI bands and selected spectral variables provided the best results, with the overall discrimination rate of 96.7% for four different woody grades, and the accuracy of the four grades could reach more than 90%. The research results provided technical support for HSI to achieve rapid and non-destructive detection of wooden chicken breasts.

As one of the important food products in our country, the health detection of maize during its growing period has been an important problem in agricultural production. In this paper, the leaves of maize grown under the influence of different factors were taken as the research object, and the ASD spectrometer was used to collect the spectra of the leaves. The derivative (D) of the original spectral data was processed, and the compressed sensing (CS) was introduced to solve the phenomenon that the spectral data after derivative approach to 0 infinitely, the iterative re-weighted least squares (IRLS) data reconstruction method is used to restore the spectral data. Then competitive adaptive re-weighted sampling (CARS) was used to extract the spectral features, and the multi-layer perceptron (MLP) was used to extract the spectral features, in order to identify the factors affecting the poor growth of crops. The accuracy of the D-CS-CARS-MLP model generated in this experiment can be as high as 99%, and the model can be used to identify a variety of factors. After verification, the D-CS-CARS-MLP model has good stability and precision, which provides a new idea and method for monitoring the healthy growth of vegetation. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.

To assess the degree of heavy metal pollution in crops, this study focused on crop leaves subjected to varying levels of heavy metal copper (Cu) stress. After removing outliers and applying smoothing techniques, the spectral data underwent derivative (D) and multiplicative scatter correction (MSC). Competitive adaptive reweighted sampling (CARS) and specific spectral ranges (SSR) were employed to extract the feature bands. Six different combinations of data preprocessing methods were utilized. This model incorporates the semi-supervised learning method, which enables the reduction of time and cost associated with annotating large-scale data. It addresses the identification problem when the labelled data is limited. Finally, the eXtreme Gradient Boosting algorithm (XGBoost) is used to select the best model and discriminate the degree of heavy metal pollution in corn. The D-CARS-XGBoost model achieved an accuracy rate exceeding 98%. [ABSTRACT FROM AUTHOR]

Abstract Objective The differential diagnosis between adult-onset Still’s disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model. Methods All data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1.0.2) in Python (version 3.9.7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python. Results The results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0.9916, ACC: 0.9457, Sens: 0.9556, Spec: 0.9578) and the external validation also achieved satisfactory results (AUC: 0.9800, ACC: 0.9333, Sens: 0.8000, Spec: 1.000). We named this discrimination method AIADSS (AI-assisted discrimination of Still’s disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/ . Conclusion We created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.

Models for maturity discrimination and polyphenol content determination of Aronia melanocarpa (AM) were established to nondestructively determinethe maturityand polyphenol content of AM based on the hyperspectral imaging (HSI) technology. The HSI technology was adopted to collect the image information of Fukangyuan No. 1 AM in different maturity levels, and the Folin-Ciocalteu colorimetric method was employed to determine the polyphenol content. The Monte Carlo method was used to eliminate the outliers. The original image information was preprocessed with the following procedures: Moving average, median filter, normalize, baseline calibration, multiple scattering correction, detrending, and standard normal variate. Meanwhile, the sample set partitioning based on joint x-y distance method was applied to divide the samples. The competitive adaptive reweighting sampling and uninformative variable elimination method were selected to extract the feature wavelengths, based on which the partial least squares model (PLS) and support vector machine (SVM) model were established and compared. The results showed that the UVE-SVM model after multiple scattering correction showed the best performance among all established discriminant models, with the comprehensive recognition rate of 94.62%, Rc2 of 0.9712, and accuracy of 100%. The CARS-SVM model after median filter exhibited the best efficiency in detection of polyphenol content, with the Rc2 of 0.8331. In addition, this work proved that the polyphenol content in AM was visual. This work provided a theoretical basis for the application of HSI technology in berry industry.

In order to identify the counterfeits of Pinellia ternata, hyperspectral technology and machine learning algorithms are used to study Pinellia ternata, Rhizoma Typhonii Flagelliformis and Rhizome Arisaematis. Firstly, the original hyperspectral data was preprocessed by using the wavelet transformation method, and then the characteristic wavelengths were extracted in the full wavelengths by combining principal component analysis (PCA), successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithm, and the classification discriminant model based on the full wavelengths and characteristic wavelengths BP neural network (BP), support vector machine (SVM) and extreme learning machine (ELM) were established. The results showed that the three characteristic wavelengths extraction methods could effectively extract the characteristic wavelengths. Among them, the classification discriminant model based on the feature wavelength extracted by the CARS algorithm had the optimal effects, and the classification recognition rate of the training set and test set samples based on the BP, SVM and ELM discriminant models established by the full wavelengths and the characteristic wavelengths extracted by the CARS algorithm had reached 100%. Finally, the running time of the ELM model based on full wavelengths and characteristic wavelengths was compared, and the results showed that the discriminant model based on characteristic wavelengths ran much shorter than the discriminant model based on full wavelengths. This study laid the foundation for the quality control, counterfeit identification and clinical application of Pinellia ternata medicinal materials. [ABSTRACT FROM AUTHOR]

Rapid identification of seed vitality plays key roles in the cultivation of the agricultural and forestry crops. This study discusses the use of compose a specular like technology, the computer algorithm and the feasibility of the physical system identification under different osmanthus seed vigor, in order to improve the ability to recognize. Two varieties of Osmanthus seeds (JinQiGui and RiXiangGui) were artificially aged and then hyperspectral data were collected. Multivariate scattering correction (MSC) and competitive adaptive reweighted sampling algorithm (CARS) were used for spectral preprocessing and feature wavelength selection, respectively. The extreme learning machine (ELM) and k-nearest neighbor (KNN) were used to establish the spectral discriminant model, and convolutional neural network was used in the computer image discriminant model. When MSC+CARS is combined with the above Discriminative model, nearly 100% recognition can be achieved with fewer bands. Compared with machine learning model, image-depth learning model can get higher model accuracy for different vigor JQG and RXG without complex preprocessing. These results indicate that hyperspectral imaging technology can effectively distinguish different vigor of Osmanthus fragrans seeds based on computer technology and physical system. Combining deep neural networks with image information is of great importance for research and development of portable high precision seed vitality spectral imagers. [ABSTRACT FROM AUTHOR]

Astragali Radix is widely used because of its dual use in medicine and food, and its quality evaluation is of great importance. In this study, a pseudo‐targeted metabolomics approach based on scheduled multiple reaction monitoring was developed, and a total of 114 compounds with good linearity, sensitivity, and reproducibility were selected for relative quantification, and the chemical differences between Astragali Radix of different growth patterns were further compared by chemometric analysis. With the help of multivariate and univariate analysis, 26 differential compounds between wild/semi‐wild Astragali Radix and cultivated Astragali Radix were determined. Then five marker compounds were screened out by lasso regression, and further verified by systematic clustering, random forest, support vector machine, and logistic regression. In addition, malonyl‐substituted flavonoids showed relatively higher content in wild/semi‐wild Astragali Radix. Thus, the malonyl substitution was characteristic for flavonoids in wild/semi‐wild Astragali Radix. In conclusion, the application of pseudo‐targeted metabolomics and various statistical methods could offer multi‐dimensional information for the holistic quality evaluation of Astragali Radix. [ABSTRACT FROM AUTHOR]

This paper extends the criterion of the misclassification ratio of discriminant model and presents a new selection method of discriminant model. For selecting the discriminant model, this method establishes the rule of misclassification degree ratio through misclassification ratio of the discriminant model and misclassification degree of the samples. To test the effect of this method, this work uses seven UCI data sets. Numerical experiments on these examples indicate that this method has certain rationality and has a better effect to select a discriminant model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research Purpose. The study aimed to develop a discriminant model for cricketers on the basis of physiological & anthropometric variables. Material and Methods. The study included sixty female seniors BCCI board players representing five different states with mean age 23.4 ± 2.03, mean height 152.1 ± 3.44, and mean weight 52.4 ± 4.21. A multivariate technique was used to predict the cricket performance by classifying the players into batsmen and pace bowlers on the basis of selected physiological & anthropometrical variables. Results. All the assumptions were positively full-filled (Shapiro-Wilk test p > 0.05 and F = 8.121, p = 0.264 for Box’sM test) for applying discriminant analysis to develop the model. A total of eleven performance variables were initially selected for the study and after applying the stepwise statistical technique of discriminant analysis, the model selected certain variables, namely Muscle Mass (1.311), Fat (-0.182) & Shoulder Diameter (0.292) and showed its effectiveness as the Eigenvalue for the fit model was 0.848. Conclusion. A discriminant function F1 = -29.531 + (1.311 × Muscle Mass) + (-0.182 × Fat) + (0.292 × Shoulder Diameter) was developed. The percentage of total variation explained by the model was 71.9%. A total of 93.2% of the observations were correctly classified using the proposed discriminant model.

To find the most effective way of contradiction between engineering construction and land destruction. Based on the catastrophe progression method, the suitability of land reclamation into cultivated land in coalmining areas is studied, and a comprehensive evaluation model is established. First of all, land reclamation suitability is divided into four levels; appropriate, moderate, less appropriate, and unsuitable, and 11 parameters are used as evaluation indicators. Secondly, Matlab software is used to generate 800 total samples between the arrays corresponding to each level of standards according to the normal distribution principle, of which 600 are used as training samples to establish the mutation progression criterion, and 200 are used as test samples to verify the reliability of the criterion, and this criterion is verified through an engineering example. Finally, the established catastrophe progression model is used to comprehensively evaluate the suitability of land reclamation to cultivated land in a coal mining area in Shaodong county. The verification results show that the sample mutation progression method can accurately and quantitatively describe the level attribute of the evaluation index eigenvalue, and can better quantitatively and comprehensively evaluate the suitability of land reclamation. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and aimsThe relationship of non-alcoholic fatty liver disease (NAFLD) with the atherogenic index of plasma (AIP) is unclear. This study aims to detect the association between AIP and NAFLD, compare the discriminative power of AIP with other lipid parameters for NAFLD, and establish a discriminant model using physical examination data.MethodsParticipants aged over 20 years who underwent routine physical examination in Beijing Chaoyang Hospital from April 2016 to August 2020 were included. We categorized subjects based on hepatic ultrasound results and analyzed the association between NAFLD risk and AIP, conventional plasma lipids, remnant cholesterol (RC), triglyceride and glucose (TyG) index, and other atherogenic indices (n = 112,200) using logistic regression, restricted cubic spline regression, and receiver operating characteristic curve.ResultsOut of the 112,200 subjects, 30.4% had NAFLD. The body weight index, plasma glucose, conventional lipids, TyG index, AIP, atherogenic coefficient (AC), and coronary risk index (CRI) were significantly higher, while HDL-C was lower (p < 0.001) in patients with NAFLD than those without NAFLD (all p < 0.001). Compared with conventional lipids, RC, TyG index, AC, and CRI, AIP had a stronger correlation with the risk of NAFLD (OR 6.71, 95% CI 6.23–7.22, p < 0.001) after adjusting confounders and presented a non-linear dose–response relationship (p < 0.0001). The optimal cut-off value of AIP was 0.05 and the area under the curve (AUC) was 0.82 (95% CI: 0.81–0.82) with high sensitivity and specificity. The AUC of the simplified three-variable NAFLD discriminant model was 0.90 in both the training set and the validation set.ConclusionAIP was significantly associated with NAFLD and showed superior discriminative performance to other lipid parameters. These findings might help screen NAFLD in high-risk individuals and reduce the prevalence of NAFLD.

Background: Based on the etiology, membranous nephropathy (MN) can be categorized into idiopathic membranous nephropathy (IMN) and secondary membranous nephropathy. Malignancy-associated membranous nephropathy (MMN) is a common type of secondary MN. Its incidence is only second to that of lupus nephritis. As the treatment and prognosis of MMN differ significantly from those of other MNs, the identification of MMN is crucial for clinical practice. The purpose of this study was to develop a model that could efficiently discriminate MMN, to guide more precise selection of therapeutic strategies. Methods: A total of 385 with IMN and 62 patients with MMN, who were hospitalized at the First Affiliated Hospital of Zhengzhou University between January 2017 and December 2020 were included in this study. We constructed a discriminant model based on demographic information and laboratory parameters for distinguishing MMN and IMN. To avoid an increased false positivity rate resulting from the large difference in sample numbers between the two groups, we matched MMN and IMN in a 1:3 ratio according to gender. Regression analysis was subsequently performed and a discriminant model was constructed. The calibration ability and clinical utility of the model were assessed via calibration curve and decision curve analysis. Results: We constructed a discriminant model based on age, CD4+ T cell counts, levels of cystatin C, albumin, free triiodothyronine and body mass index, with a diagnostic power of 0.860 and 0.870 in the training and test groups, respectively. The model was validated to demonstrate good calibration capability and clinical utility. Conclusion: In clinical practice, patients demonstrating higher scores after screening with this model should be carefully monitored for the presence of tumors in order to improve their outcome. [ABSTRACT FROM AUTHOR]

To ensure effective competition for global resources, countries around the world are creating an international image (nation brands), on which their influence in the international arena depends. This brand is influenced by politics, governance and socio-economic development. The attractiveness of nation brands is a necessary condition for the competitiveness of the state. Based on the components of a significant measure of a country's positive reputation of the Global Soft Power Index (GSPI) 2022 for 120 countries, a discriminant model was built to determine the significant factors that affect the level of perception of the state's competitiveness and assess geopolitical risks. [ABSTRACT FROM AUTHOR]

A hyperspectral imaging system covering 400 - 1000 nm spectral range was applied for vigour detection of waxy maize seeds after artificial aging. After spectral pre-processing, the characteristic wavelength was selected by uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and random frog (RF) methods. The moisture, starch, protein, and fat contents were measured for each grade of seed, and these values were correlated with the spectrum. Finally, the vitality detection model was established by least squares support vector machine (LS-SVM), extreme learning machine (ELM), and random forest (RF). The prediction sets exhibited high classification accuracy (> 99%) for 115 features. The model constructed from the bands significantly correlated with chemical composition (CC), and was better than the classic feature selection methods. The overall results indicated that hyperspectral imaging could be a potential technique to assess seed vigour. [ABSTRACT FROM AUTHOR]

Context: Dyslipidemia is related to fatty liver disease (FLD), whose relationship with remnant lipoprotein cholesterol (RLP-C), a component of blood lipids, remains unclear. Objective: To clarify the correlation between RLP-C and the occurrence and severity of FLD and establish an FLD discriminant model based on health check indicators. Methods: Retrospective study of participants who underwent health check-up in the First Affiliated Hospital of China Medical University (Shenyang, China) between January and December 2019. We categorized participants according to liver ultrasound results and analyzed the correlation between RLP-C and occurrence of FLD (n = 38 885) through logistic regression, restricted cubic spline, and receiver operating characteristic curve. We categorized the severity of FLD according to the control attenuation parameter and analyzed the correlation between RLP-C and FLD severity through multiple logistic regression; only males were included (n = 564). Results: The adjusted OR (aOR) per SD between RLP-C and FLD was 2.33 (95% CI 2.21- 2.46, P < .001), indicating a dose–response relationship (P < .0001). The optimal cut-off value of RLP-C was 0.45 mmol/L and the area under the curve (AUC) was 0.79. The AUC of the 8-variable model was 0.89 in both the training and the validation sets. FLD severity was related to the level of RLP-C (aOR per SD = 1.29, 95% CI 1.07-1.55, P = .008). Conclusion: RLP-C has a strong positive correlation with FLD occurrence and FLD severity. These results may help clinicians identify and implement interventions in individuals with high FLD risk and reduce FLD prevalence. [ABSTRACT FROM AUTHOR]

This study develops statistical and machine learning models based on discriminant analysis, logistic regression analysis, and a support vector machine (SVM) to predict the occurrence of radiation fog in Japan. The selection of a suitable set of explanatory variables for the models was made using the Akaike information criterion (AIC). The accuracies of the three models were measured and compared. To determine the optimum combination of explanatory variables, temperature, humidity, wind speed, precipitation, sunshine, and visibility data were considered. Based on the root mean square error (RMSE) and AIC values, the best combination of variables was found to include: the presence of precipitation, mean wind speed during the night, minimum temperature during the night, the amount of temperature cooling during the night, the minimum humidity during the previous day, and visibility at 18:00. A comparison of the predictive accuracies of the three models using the selected variable combination showed that the discriminant model produced a critical success index (CSI, or threat score) of 22.5, while the logistic regression model and the SVM model both produced better CSI results—with scores of 33.8 and 38.2, respectively. [ABSTRACT FROM AUTHOR]

The article is devoted to substantiating the choice of financial indicators for discriminant and neural network models for diagnosing the economic security of innovative and active enterprises in the field of telecommunications in conditions of uncertainty of the internal and external environment. The importance of assessing the economic security of the system at any level is unquestionable, such assessments are the basis for decision-making not only on ensuring economic security, but also opportunities for system development, determination of necessary resources, creation and use of system reserves, evaluating the effectiveness of the economic security system and the activities of the relevant division of the enterprise. It is established that due to a completely different tool base, the available approaches to assessing the economic security of the enterprise do not compete with each other. We can only talk about the different degree of development of approaches and their dissemination. It shows the importance of taking into account the phenomenon of “uncertainty” in the economic activity of innovative-active enterprises, which is closely related to the concept of “economic risk”, since any economic or economic activity of an enterprise is characterized by incompleteness of information about the presence of patterns, unpredictability of many economic phenomena and processes, the influence of a large number of interrelated and difficult to identify factors. The latest methodological foundations for assessing the economic security of innovative and active enterprises, which are based on the tools of Fuzzy Logic and neural networks, are proposed and shown. The fuzzy set method makes it possible to use the data selected for assessing the economic security of enterprises in their dynamics, which makes it possible to take them into account when making strategic management decisions. In addition, the use of modern analytical platforms allows for deep data preprocessing in order to check them for multicoleniarity, exclude random data, which takes into account the uncertainty of input and output variables as much as possible.

Abstract This study develops statistical and machine learning models based on discriminant analysis, logistic regression analysis, and a support vector machine (SVM) to predict the occurrence of radiation fog in Japan. The selection of a suitable set of explanatory variables for the models was made using the Akaike information criterion (AIC). The accuracies of the three models were measured and compared. To determine the optimum combination of explanatory variables, temperature, humidity, wind speed, precipitation, sunshine, and visibility data were considered. Based on the root mean square error (RMSE) and AIC values, the best combination of variables was found to include: the presence of precipitation, mean wind speed during the night, minimum temperature during the night, the amount of temperature cooling during the night, the minimum humidity during the previous day, and visibility at 18:00. A comparison of the predictive accuracies of the three models using the selected variable combination showed that the discriminant model produced a critical success index (CSI, or threat score) of 22.5, while the logistic regression model and the SVM model both produced better CSI results—with scores of 33.8 and 38.2, respectively.

• A random forest model was built for screening thalassemia carrier from low HbA 2 case. • The efficiency of the random forest model was better than 7 published formulae. • The model may detect thalassemia carriers from the Chinese, including pregnant women. Since screening of α-thalassemia carriers by low HbA 2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA 2 patients. Laboratory data of 1213 patients with low HbA 2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae. The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA 2 , and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set. Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA 2 cases. [ABSTRACT FROM AUTHOR]

The difficult economic situation in Ukraine today is the main motivating factor for studying problems and developing possible solutions. Since Ukraine is an agrarian country, in our research we justified the need to take into account risk factors in the agricultural sector by diagnosing the financial situation using models to assess the likely crisis and bankruptcy. The article highlights the advantages and disadvantages of using discriminant models developed by both foreign and domestic scientists. The main drawback is the maladaptation to the industry factor. The shortcomings of the diagnosis of financial state for agricultural enterprises are also identified. In order to develop measures to prevent bankruptcy, recommendations for improving the financial state of agricultural enterprises on the example of Lviv region cluster analysis was used. The article states that the need to diagnose the financial condition of the enterprise is systematic and requires a comprehensive assessment using various methods, techniques and methods of analysis, as it is one of the most important characteristics of the results of agricultural enterprises, which is determined by the interaction of all components of the financial relations of the enterprise, the totality of all production and economic factors. The combination of financial indicators and risk assessment, which have the greatest impact on the life of agricultural enterprises, thanks to a comprehensive approach will increase the accuracy of assessing the prospects of the financial state of agricultural enterprises. [ABSTRACT FROM AUTHOR]

Place of origin has an important influence on walnut quality and commercial value, which results in the requirement of rapid geographical traceability method. Thus, a method for geographical origin identification of walnuts on the basis of nutritional quality of walnuts from China was conducted. The concentrations of 43 phytochemical components were analyzed in walnut samples from five different walnut-producing regions of China. Based on 14 chemical markers selected by the Random Forest method from these phytochemical components, a new discriminant model for geographical origin was built, with the corresponding correct classification rate of 99.3%. In addition, the quantitative quality differences of walnuts from five regions were analyzed, with values of 0.17–1.43. Moreover, the top three chemical markers for the geographical origin discriminant analysis were Mo, V, and stearic acid, with contribution rates of 26.8%, 18.9%, and 10.9%, respectively. This study provides a potentially viable method for application in food authentication. [ABSTRACT FROM AUTHOR]

Background. Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches. Methods. The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning. Results. In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74. Conclusions. Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches. [ABSTRACT FROM AUTHOR]

The article is devoted to the substantiation of the choice of financial indicators for discriminant and neural network models for diagnosing the financial condition of innovative active enterprises and determining the probability of their bankruptcy, as well as the construction of these models based on a study of the financial condition of 36 enterprises. The modern imperative of the successful development of the domestic economy is its transition to the rails of innovative development. This process is impossible without competent distribution of financial resources by business entities. In this regard, especially important is the question regarding the development of new approaches and methods for the assessment of readiness of enterprises for implementation of innovation activities due to which investors or, indeed, the state itself will be able to determine the amount of financial resources which is necessary for the development and implementation of new technologies, products or services. It is shown the importance of researching the financial condition of Ukrainian enterprises that are engaged in innovations, since their innovative activity is almost entirely financed by own means. With the aid of Deductor analytical platform, a discriminant model for assessing the financial situation and the probability of bankruptcy for innovative enterprises was built. The neural network model, which together with the analysis "if-then" gives an adequate forecast of the financial state of enterprises engaged in innovation activity, was substantiated and built. Five financial ratios (X1, X2, X3, X4 and X5) are selected and calculated for the analysis of the financial condition of 36 enterprises. For all the studied enterprises (both bankrupt and those against which bankruptcy proceedings were not initiated), the satisfactory forecast was for 30 out of 36 enterprises (83.33%), unsatisfactory for 2 enterprises (5.56%), in the gray zone there were 4 enterprises (11.11%). It is shown that the built neural network model provides forecasts of the financial condition of enterprises and the probability of their bankruptcy at a level significantly higher than discriminant models. The neural network model takes into account the specifics of domestic economic activity of enterprises, because it is built on the basis of financial data of Ukrainian enterprises.

Abstract Routine test of cerebrospinal fluid (CSF), such as glucose concentrations, chloride ion, protein and leukocyte, as well as color, turbidity and clot, were important indicators for intracranial infection. However, there were no models to predict the intracranial infection with these parameters. We collected data of 221 cases with CSF positive-culture and 50 cases with CSF negative culture from January 1, 2016 to December 31, 2018 in the First Affiliated Hospital of Nanchang University, China. SPSS17.0 software was used to establish the model by adopting seven described indicators, and P

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In recent times, feature extraction attracted much attention in machine learning and pattern recognition fields. This paper extends and improves a scheme for linear feature extraction that can be used in supervised multi-class classification problems. Inspired by recent frameworks for robust sparse LDA and Inter-class sparsity, we propose a unifying criterion able to retain the advantages of these two powerful linear discriminant methods. We introduce an iterative alternating minimization scheme in order to estimate the linear transformation and the orthogonal matrix. The linear transformation is efficiently updated via the steepest descent gradient technique. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. We used our proposed method to fine tune the linear solutions delivered by two recent linear methods: RSLDA and RDA_FSIS. Experiments have been conducted on public image datasets of different types including objects, faces, and digits. The proposed framework compared favorably with several competing methods. [ABSTRACT FROM AUTHOR]

Pu-erh tea processed from the sun-dried green tea leaves can be divided into ancient tea (AT) and terrace tea (TT) according to the source of raw material. However, their similar appearance makes AT present low market identification, resulting in a disruption in the tea market rules of fair trade. Therefore, this study analyzed the classification by principal component analysis/hierarchical clustering analysis and conducted the discriminant model through stepwise Fisher discriminant analysis and decision tree analysis based on the contents of water extract, phenolic components, alkaloid, and amino acids, aiming to investigate whether phytochemicals coupled with chemometric analyses distinguish AT and TT. Results showed that there were good separations between AT and TT, which was caused by 16 components with significant (p < 0.05) differences. The discriminant model of AT and TT was established based on six discriminant variables including water extract, (+)-catechin, (−)-epicatechin, (−)-epigallocatechin, theacrine, and theanine. Among them, water extract comprised multiple soluble solids, representing the thickness of tea infusion. The model had good generalization capability with 100% of performance indexes according to scores of the training set and model set. In conclusion, phytochemicals coupled with chemometrics analyses are a good approach for the identification of different raw materials.

This study investigates the features of the development of the discriminant model of diagnostics of the socio-economic potential of health insurance, and the trends in the medical insurance system development in Ukraine. This study uses logistic, statistical and normative methods to assess of the socio-economic potential of health insurance in Ukraine from 2010 to 2017. The empirical result shows that the block assessment of the financial potential of health insurance gives the complete information on the dynamics of individual indicators and trends of the branch. The need for assessment of the socio-economic potential of health insurance in order to improve the efficiency of the public financial management of the health care finance system and the insurance market regulation has been justified.

Image fusion aims at aggregating the redundant and complementary information in multiple original images, the most challenging aspect is to design robust features and discriminant model, which enhances saliency information in the fused image. To address this issue, the authors develop a novel image fusion algorithm for preserving the invariant knowledge of the multimodal image. Specifically, they formulate a novel unified architecture based on non‐subsampled contourlet transform (NSCT). Their method introduces Quadtree decomposition and Bezier interpolation to extract crucial infrared features. Furthermore, they propose a saliency advertising phase congruency‐based rule and local Laplacian energy‐based rule for low‐ and high‐pass sub‐bands fusion, respectively. In this approach, the fusion image could not only combine the local and global features of the source image to avoid smoothing the edge of the target, but also retain the minor scales details and resists the interference noise of the multi‐modal image. Both objective assessments and subjective visions of experimental results indicate that the proposed algorithm performs competitively in both objective evaluation criteria and visual quality. [ABSTRACT FROM AUTHOR]

Routine test of cerebrospinal fluid (CSF), such as glucose concentrations, chloride ion, protein and leukocyte, as well as color, turbidity and clot, were important indicators for intracranial infection. However, there were no models to predict the intracranial infection with these parameters. We collected data of 221 cases with CSF positive-culture and 50 cases with CSF negative culture from January 1, 2016 to December 31, 2018 in the First Affiliated Hospital of Nanchang University, China. SPSS17.0 software was used to establish the model by adopting seven described indicators, and P < 0.05 was considered as statistically significant. Meanwhile, 40 cases with positive-culture and 10 cases with negative-culture were selected to verify the sensitivity and specificity of the model. The results showed that each parameter was significant in the model establishment (P < 0.05). To extract the above seven parameters, the interpretation model C was established, and C = 0.952-0.183 × glucose value (mmol/L) - 0.024 × chloride ion value (mmol/L)- 0.000122 × protein value (mg/L) - 0.0000859 × number of leukocytes per microliter (× 106/L) + 1.354 × color number code + 0.236 × turbidity number code + 0.691 × clot number code. In addition, the diagnostic sensitivity and specificity of the model were 85.0 and 100%, respectively. The combining application of seven physicochemical parameters of CSF might be of great value in the diagnosis of intracranial infection for adult patients. [ABSTRACT FROM AUTHOR]

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The objective of this study was to evaluate the ability of milk infrared spectra to predict cow lameness score (LMS) for use as an indicator of cow health on Australian dairy farms, or as an indicator trait for genetic evaluation purposes. The study involved 3,771 cows from 10 farms in Australia. Milk infrared spectra collected during the monthly herd testing were available in all the farms involved in the study. Lameness score was measured once in each herd, within 72 h from a test day, and merged to the closest spectra records. Lameness score was expressed on a scale from 0 to 3, where 0 is assigned to sound cows and scores 1 to 3 are assigned to cows with increased lameness severity. Partial least squares discriminant analysis was used to develop prediction models for classifying sound (score 0) and not-sound cows (i.e., cows walking unevenly, score greater than 0). Discriminant models were tested in a 10-fold random cross-validation process. Milk infrared spectra correctly classified only 57% of the cows walking unevenly and only 59% of the sound cows. When additional predictors (parity, age at calving, days in milk, and milk yield) were included in the prediction model, the model correctly classified 57% of the cows walking unevenly and 62% of the sound cows. The same model applied only to the cows in the first third of lactation correctly classified 66% of the cows walking unevenly and 57% of the sound cows. When the prediction model was used to identify lame cows (scores 2 and 3), only 49% of them were classified as such. These results are considered to be too poor to envisage a practical application of these models in the near future as on-farm tools to provide an indication of LMS. To investigate whether, at this stage, predictions of the LMS could be useful as large-scale phenotypes for animal breeding purposes, we estimated (co)variance components for actual and predicted LMS using 2,670 and 24,560 records, respectively. As the genetic correlation between actual and predicted LMS was not significantly different from zero, predictions of lameness from milk spectra and additional on-farm variables cannot be used, at this stage, as an indicator trait for actual LMS. More research is needed to find better strategies to predict lameness. [ABSTRACT FROM AUTHOR]

• Brain tumor tissue sections were detected by Raman spectroscopy. • Raman spectra combined with multivariate analysis is able to distinguish different types of brain tumors. • Quadratic discriminant analysis (QDA) exhibits a better performance than linear discriminant analysis (LDA) in discriminating different types of brain tumors. Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients. Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors. Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm−1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %. Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy. [ABSTRACT FROM AUTHOR]

The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm−1. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection.

The classification of wheat grain varieties is of great value because its high purity is the yield and quality guarantee. In this study, hyperspectral imaging combined with the chemometric methods was applied to explore and implement the varieties classification of wheat seeds. The hyperspectral images of all the samples covering 874–1734 nm bands were collected. Exploratory analysis was first carried out while using principal component analysis (PCA) and linear discrimination analysis (LDA). Spectral preprocessing methods including standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were introduced, and their effects on discriminant models were studied to eliminate the interference of instrumental and environmental factors. PCA loading, successive projections algorithm (SPA), and random frog (RF) were applied to extract feature wavelengths for redundancy elimination owing to the possibility of existing redundant spectral information. Classification models were developed based on full wavelengths and feature wavelengths using LDA, support vector machine (SVM), and extreme learning machine (ELM). This optimal model was finally utilized to generate visualization map to observe the classification performance intuitively. When comparing with other models, ELM based on full wavelengths achieved the best accuracy up to 91.3%. The overall results suggested that hyperspectral imaging was a potential tool for the rapid and accurate identification of wheat varieties, which could be conducted in large-scale seeds classification and quality detection in modern seed industry. [ABSTRACT FROM AUTHOR]

Research background: Earnings management is a current topic in the world of financial management. It can be considered as a global phenomenon of today’s modern approach to the reporting of accounting information and related accounting decisions of managers, which may affect the overall results of financial statements. Many companies use earnings management as a tool to maintain stable profit growth or prevent “red numbers” from appearing in financial statements that are not beneficial to the company. Purpose of the article: Understanding what earnings management represents and why it is performed is essential for users of a company’s financial statements. However, detecting manipulation in companies is not easy, because Earnings management is successful if it is invisible. Therefore, statistical models are usually used to detect these practices. The aim of this paper is to show that the use of several methods strengthens the results obtained and is more probably to reveal possible manipulation of earnings in companies. Methods: In this study, we used triangulation of methods to detect Earnings management in companies: one of the most frequently used model in this area, Beneish model, but also the model for Slovak companies M-score SVK, which was created under the inspiration of the Beneish model and finally, the model of the company’s propensity score of manipulation. Findings & Value added: The study provides a global view of the possibilities of applying these three models to detect manipulation in the company. The idea of triangulation of methods is based on the consideration that if all the methods detect possible manipulation, it is very likely that it actually happens in the company.