Modeling the Business Environment of an Energy Holding in the Formation of a Financial Strategy

This article consists of the development of a set of methodological provisions concerning the identification of the features of the influence of the business environment on the effectiveness of the implementation of the company’s financial strategy and the development of a system for its adaptation to the conditions of a dynamic external environment. The purpose of this article is to build an economic and mathematical model to identify the main elements of the business environment that affect the company’s strategy, the formation of methods for evaluating the effectiveness of the implementation of a financial strategy taking into account such influence. The author’s contribution consists in the development of an effective financial algorithmic strategy of the energy holding, considering the influence of the environmental factors. Hypothesis: the use of mathematical models of the business environment will increase the efficiency of energy holding management in the field of finance and investments. The scientific novelty of this article lies in the development of an algorithm that allows for obtaining an integral assessment of the impact of external and internal factors of the energy holding’s business environment on its financial strategy using taxonomy methods, multidimensional statistical analysis and cluster and discriminant models. Results: the authors have developed a model of the influence of the energy holding’s business space, which allows improving the interaction of financial flows within the holding and obtaining an optimal distribution of financial resources, taking into consideration the dynamic factors of the company’s external environment.

Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry in Combination with Chemometrics for Detecting the Age of Chinese Liquor (Baijiu)

This paper proposes the combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and chemometrics as a method to detect the age of Chinese liquor (Baijiu). Headspace conditions were optimized through single-factor optimization experiments. The optimal sample preparation involved diluting Baijiu with saturated brine to 15% alcohol by volume. The sample was equilibrated at 70 °C for 30 min, and then analyzed with 200 μL of headspace gas. A total of 39 Baijiu samples from different vintages (1998–2019) were collected directly from pottery jars and analyzed using HS-GC-IMS. Partial least squares regression (PLSR) analysis was used to establish two discriminant models based on the 212 signal peaks and the 93 identified compounds. Although both models were valid, the model based on the 93 identified compounds discriminated the ages of the samples more accurately according to the goodness of fit value (R2) and the root mean square error of prediction (RMSEP), which were 0.9986 and 0.244, respectively. Nineteen compounds with variable importance for prediction (VIP) scores > 1, including 11 esters, 4 alcohols, and 4 aldehydes, played vital roles in the model established by the 93 identified compounds. Overall, we determined that HS-GC-IMS combined with PLSR could serve as a rapid and accurate method for detecting the age of Baijiu.

Discriminant models of the possibility of occurrence and course of psoriasis in men of the general group and different somatotypes depending on the characteristics of anthropo-somatotypological indicators

Significant prevalence and multifactorial occurrence of psoriasis are the main reasons why this disease has been studied for years by scientists in the field of dermatology. Finding tools to predict the occurrence and severity of this disease is one of the key unrealized areas of modern medicine in the field of skin diseases. The purpose of the study is to build and analyze discriminant models of the possibility and features of psoriasis course in Ukrainian men without and taking into account the somatotype, depending on the structure and size of the body. Anthropometric and somatotypological examination of 82 practically healthy and 100 patients with mild and severe psoriasis was performed. Construction of discriminant models of the possibility of occurrence and features of psoriasis depending on anthropo-somatotypological indicators is performed in the license package “Statistica 5.5”. It was found that men of the general group and representatives of the mesomorphic somatotype can reliably interpret the obtained classification indicators both between healthy and patients with psoriasis of different course, and between men with psoriasis of mild and severe course (correctness 84.1% of cases, statistics Wilks’ Lambda=0.074, р<0.001 in the general group, correctness 83.6% of cases, statistics Wilks’ Lambda=0.077, р<0.001 in mesomorphic somatotype). In men of endo-mesomorphic somatotype, a reliable interpretation of the obtained classification indicators is possible only between healthy and psoriatic men (correctness 84.6%, statistics Wilks’ Lambda=0.027, р<0.001). Discriminant models in men of the general group include body diameters and SFT (44.4% each) and the fat component of body weight (11.1% each); in men of mesomorphic somatotype – body diameters (57.1%), SFT (28.6%) and body surface area (14.3%); in men of endo-mesomorphic somatotype – body diameters (60.0%) and SFT on the thigh and the height of the finger anthropometric point (20.0% each). The greatest contribution to discrimination in men of the general group and representatives of the mesomorphic somatotype is made by shoulder width, and in men of endo-mesomorphic somatotype – shoulder width, interspinous and intercristal distances. The results obtained, especially in the division of men into somatotypes, indicate a high genetic predisposition to psoriasis.

Creating a Disaster Chain Diagram from Japanese Newspaper Articles Using Mechanical Methods

A new method for creating a chain diagram of events that occur during disasters by extracting causal knowledge from Japanese newspaper articles and designing a causal network is proposed herein. Machine learning discriminant models were created for both conventional cue phrases and succession expressions with causation to extract causal sentences. We found that causal sentences can be extracted with a certain degree of accuracy from disaster articles. We were also able to create a causal network using sentences as nodes and links. The chain diagram using our new method extracted events and causal knowledge that were unavailable in a disaster chain diagram designed using conventional methods.

ANTHROPOMETRIC AND BIO-IMPEDANSOMETRIC PARAMETERS AS DIAGNOSTIC PREDICTORS IN PATIENTS WITH GASTRIC CANCER

The study of anthropometric and bioimpedance parameters in assessing the physical development of patients helps to clarify the diagnosis, predict the course of the disease, and identify groups of increased risk for the development of the disease. The aim of this study was to identify anthropometric and bioimpedance metrics in patients with stomach cancer. Anthropometric and bioimpedansometric examination of 250 patients with verified gastric cancer, 123 men and 127 women was carried out. As a comparison group, the study used the results of anthropometric and bioimpedance measurements of healthy 221 men and 267 women of the same age in the Krasnoyarsk Territory population. To determine a set of anthropometric and bioimpedansometric variables, allowing to classify the observed people depending on the presence (group of patients with gastric cancer) or the absence of stomach cancer (group of healthy people), the method of discriminant analysis was applied. To test the hypothesis about the homogeneity of the covariance matrices of the compared groups, the multidimensional Box M-criterion was used. The statistical significance of the power of the discriminant function was assessed using the Wilks test. For each discriminant function, the role of its components was assessed by comparing the matrices of total variances and covariances using the F-test. Shoulder diameter for men and women, chest diameter (transverse size) for men and women, waist circumference for men, waist / hip ratio for men and women; lean mass in men, total fluid in men, fat mass in women, phase angle in men and women are statistically significantly different in the observed groups. The developed discriminant models with an accuracy of 75-77% suggest the presence of gastric cancer in patients and can be used in clinical practice at the stage of general medical examination in groups at increased risk of developing the disease.

Features of Modeling the Probability of Bankruptcy Using Discriminant Models with Application in Economic Forensics

The article is aimed at studying the methodologies of forecasting bankruptcy, their application in forensic economic expertise, which will allow to make managerial decisions substantiated from the point of view of financial security of an enterprise and create opportunities for stable functioning and development of the enterprise. All enterprises are affected by negative factors and may find themselves in a crisis situation. That is why the management of enterprise should apply all existing measures to prevent bankruptcy and overcome crisis situations. Any crisis situation can be corrected if you respond to crises in time and form a balanced and adequate management system. Therefore, the use of bankruptcy forecasting methodologies will allow the management of enterprise to identify in advance negative trends in its development. The article analyzed the existing discriminant models for determining the probability of bankruptcy with their application in forensic economic expertise. In modern practice of the financial-economic activities of foreign firms, to assess the probability of bankruptcy, the discriminant models of Altman, Beaver, Taffler, Tishaw and some others received the widest application. It is defined that for a more justified forecast, it is advisable to use several methods at the same time to predict the probability of insolvency (bankruptcy) of enterprise. It is proposed to use a set of models to determine the probability of bankruptcy of enterprise in solving issues of economic forensic expertise, which will significantly increase the degree of probability of the results obtained. The proposed measures can be used not only to diagnose the likelihood of bankruptcy, but also to develop anti-crisis measures.

NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness

In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.

Discriminant Models for the Prediction of Viral Shedding Time and Disease Progression in COVID-19

Background COVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding and disease progression, then develop and validate two prognostic discriminant models. Methods This study included 125 hospitalized patients with COVID-19. 44 parameters were recorded, including age, gender, underlying comorbidities, epidemic features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. F-test and χ2 test were used for feature selection. All models were developed with 4-fold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L2 regularization, prognostic discriminant models were built to predict postponed viral shedding and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models sensitivity and specificity. Results 69 patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Eleven and six demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y1 (postponed viral shedding) = -0.244 + 0.2829x1 (the interval from the onset of symptoms to antiviral treatment) + 0.2306x4 (age) + 0.234x28 (Urea) − 0.2847x34 (Dual-antiviral therapy) + 0.3084x38 (Treatment with antibiotics) + 0.3025x21 (Treatment with Methylprednisolone); y2 (disease progression) = -0.348–0.099x2 (interval from Jan 1st, 2020 to individualized onset of symptoms) + 0.0945x4 (age) + 0.1176x5 (imaging characteristics) + 0.0398x8 (short- term exposure to Wuhan) − 0.1646x19 (lymphocyte counts) + 0.0914x20 (neutrophil counts) + 0.1254x21 (neutrphil/lymphocyte ratio) + 0.1397x22 (C-Reactive Protein) + 0.0814x23 (Procalcitonin) + 0.1294x24 (Lactic dehydrogenase) + 0.1099x29 (Creatine kinase). The output ≥ 0 predicted postponed viral shedding or disease progressing to severe/critical state. These two models yielded the maximum AUROC, and faired best in terms of prognostic performance (sensitivity of 73.3%, 75%, and specificity of 78.6%, 75% for prediction of postponed viral shedding and disease severity, respectively). Conclusion The two discriminant models could effectively predict the postponed viral shedding and disease severity, and be used as early-warning tools for COVID-19.