With the complication of the business environment of commercial economic activity, competition intensifies, which threatens the bankruptcy of enterprises, the prevention of which requires quality monitoring and timely identification of crises using methods of comprehensive assessment and analysis of economic security. This research is aimed at conducting component-by-component and, as a result, integrated assessment of the state of economic security of the enterprise. Methodological research tools include analysis of the main components, causation and vector regression modeling. A resource-functional security model is developed (which consists of partial indicators and components of economic security of business) and a resource-functional approach to calculations is also applied. Using the data of the expert survey, the values of indicators of structural components of economic security of the enterprise are determined. Using the resource-functional approach, the integrated values of sub-indices and the integral values of the general level of economic security of the enterprise are calculated. According to the results of the assessment, it is established that the integrated level of economic security of the enterprise is 7.04 (sufficient level of security). However, the components of economic security identified critically low values, namely - the financial component (0.452), the information component (0.554), the institutional and legal component (0.647). The results of the study are of practical value for the development of technological schemes - algorithms for strengthening the financial, informational and institutional and legal security of the enterprise, making sound (using economic and mathematical tools) management decisions to ensure the trajectory of sustainable economic development.

Natural organic pigments such as carotenoids, betalains, anthocyanins, and carminic acid are notably found as safer food preservatives compared to other harmful synthetic chemicals. Due to glycosylation and acylation, betalains exhibit a broad-spectrum antimicrobial functionality with protection against degenerative diseases. Thus, betalains have been investigated as a potential bacterial inhibitor for food preservative applications. Initially, 36 betalain derivatives have been taken for primary screening using molecular docking. Afterward, the top ten ligands are taken for further study and analysis. The results of Prediction of Activity Spectrum of Substances (PASS) assured the antibacterial capabilities of betalains, and Lipinski's rule-of-five ensures the acceptability of the selected ligands as antibacterial inhibitors. The bacterial pathogens, such as C. botulinum (3FIE), E. coli (2ZWK), and S. typhi (3UU2) are selected for molecular docking by these betalain pigments. Furthermore, ADMET investigations and QSAR studies are performed to check insights into the bacterial inhibition process. Most active and common binding sides were observed at GLY159, ASN165, and SER166 for C. botulinum, at ASP8, LYS40, and TRP50 for E. coli; and at ARG37, GLN5, and ARG74 for S. typhi. The present study clearly shows an excellent insight towards the invention of plant-based new organic inhibitors to face the challenges of bacterial-resistant common food preservatives.

The article reports on the workability of two P92 steels having a chromium content of 8.29 and 9.48 wt%. Constitutive equations were used to calculate material parameters describing the hot deformation flow stress. Hot deformation tests were conducted using the Gleeble® 3500 thermomechanical facility. Test conditions were: temperature of 850-1000°C and strain rate of 0.1-10s-1 to a strain of 0.5. The flow stress curve results show that dynamic recovery was the only softening mechanism. A comparative study of the two steels revealed that Cr content had a marginal significance on the flow stress behaviour. Constitutive analysis results of the material parameters were: a stress exponent of 9.0 (P92-A), and 11.0 (P92-B), while the activation energy was 369 kJmol-1 (P92-A), and 472 kJmol-1 (P92-B). A brief explanation of the material parameter results is in this article. A flow stress model was developed to predict the flow stress behaviour of the two P92 steels investigated. The results show that the model accurately predicts the flow stress at all the deformation conditions applied. The statistical parameters showed a good correlation between the predicted and the experimental data. Therefore, this model can be used to develop metal forming schedules for industrial applications.

Demand elasticity is the sensitivity of changes in the number of goods demanded by consumers due to changes in the price of goods. This paper compares the price elasticity of demand with and without memory effect using fractional-order derivatives. This study is designed using the development theory of fractional derivatives for the economic field in determining the price elasticity of demand. The result of numerical simulation using the value of α and p indicated that the price elasticity of demand with memory effect is more accurate than without the memory effect. Furthermore, this study concluded that the price elasticity of demand does not only depend on the latest price (current price) but changes in all prices from a specific time interval. The findings of this study suggest future studies can examine the phenomenon of market equilibrium using fractional-order derivatives.

This study considers a sequential batching problem with a minimum quantity commitment (MQC) constraint in N-level non-exclusive agglomerative hierarchical clustering structures (AHCSs). In this problem, batches are created for item types included in clusters according to the sequence of the levels in a given AHCS such that the MQC constraint as well as the maximum and minimum batch size requirements are satisfied, simultaneously. The MQC constraint ensures that more items than a committed minimum quantity must be batched at a level before items not batched at the level are sent to the next level. We apply the MQC constraint to control effectively the degree of heterogeneity (DoH) in the batching results. We developed a sequential batching algorithm for minimizing the total processing cost of items using properties identified to find better solutions of large-sized practical problems. Results of computational experiments showed that the developed algorithm found very good solutions quickly and the heuristic algorithm could be used in various practical sequential batching problems with the MQC constraint such as input lot formations in semiconductor wafer fabrication facilities, determination of truckloads in delivery service industry, etc. Also, we found some meaningful insights that dense cluster, small batch size, and tight MQC constraint are effective in reducing the total processing cost. Additionally, small batch size with loose MQC constraint seem to be helpful to reduce the DoH in the batching results. Finally, we suggested that the density of cluster, batch size, and MQC tightness should be determined simultaneously because of interactions among these factors.

The development of three simple, precise, efficient, and accurate spectrophotometric techniques that manipulate ratio spectra is undertaken to measure Ivabradine and Carvedilol simultaneously in bulk and tablet formulation. Applying mathematical calculations like the ratio derivative, first-order ratio derivative and ratio derivative subtraction method has been used for determination of CVD and IBD. For Carvedilol, the calibration curve is linear between concentrations of 40–65 µg/mL and 50–81.25 µg/mL for Ivabradine. These strategies have been put into practice and used for analyzing marketed pharmaceutical formulations. Further, the outliers were statistically assessed by using Grubb’s test and found, as null hypothesis cannot be rejected. The suggested approach was evaluated using four green evaluation approaches with which these tools found an excellent green outcome. Altogether, the proposed method was found to be a simple, sensitive, accurate and eco-friendly method for the analysis of drugs that have overlapping properties in the UV spectrum.

In this article, we analyse whether the management structure of a company plays a role in the sustainability of companies. More specifically, we study the impact of occupied outside directors, outside directors sitting on several boards of directors, on the environmental, social and governance (ESG) performance of the company. We collect information about board characteristics, information about the board and management from MSCI ESG Research and financial information from Compustat. The study collects data based on panel data, which ranges from 2014 to 2020. The final sample consists of 550 US companies over a five-year period and contains 3850 firm-year observations. The study finds a positive relationship between busy outside directors and ESG performance. Busy outside directors have a positive impact not only on the overall ESG score, but also on individual ESG components. The environmental score is most affected by busy external directors, while the governance score appears to be little affected. Contrary to the theory that busy outside directors are overly engaged and degrade the fixed value, the findings support the theory that busy outside directors improve a company's sustainability performance because of their engagement, experience and the ESG performance.

Recently, due to their extraordinary strength-to-weight ratio and multi-functional applications, three-dimensional woven fiberglass sandwich structures have become a well-received topic by researchers and manufacturers. Nevertheless, using light and foam absorber materials as injected fillers within sandwich cores can improve their overall mechanical performance and, in particular, their fracture toughness behavior. This study evaluates the fracture toughness of three-dimensional woven fiberglass sandwich panels filled with natural nano-structured zeolite/polyurethane foams injected between their parallel panels. The Single-Edge Notched Bend test was carried out to understand the effect of the injected foam on the mode-I fracture toughness response. It is demonstrated that the polyurethane foam reinforced with natural nano-structured zeolite particles highly improved the fracture toughness of sandwich core panels. It was found that the presence of vertical glass yarns within the sandwich panel gallery resulted in a significantly higher toughness compared with typical sandwich panels of no reinforcing vertical columns confirmed by the crack propagation and observed failure mode. The SEM and EDX analyses were used to better understand the correlations amongst the specimen morphology, the cracks behavior, and the toughness exhibited by the fabricated specimens.

In this research article, four chromophores based on benzoxazine as the electron donor and tricyanovinyl dihydrofuran (TCF) as the electron acceptor have been designed to investigate the nonlinear optical (NLO) response. The geometric and electronic structures, absorption spectra, NBO analysis, and nonlinear optical response have been calculated by employing density functional theory (DFT) at PBEPBE/6-31G (d,p). The new design of chromophores has been proposed by the structural modification of π-spacers/conjugated systems. The DFT and TD−DFT computations at CAM−B3LYP/6−31G (d,p) have been performed to shed light on the influences of structural modification on the NLO properties. The absorption wavelength in different organic solvents, polarizability (α), and hyperpolarizability (β) are all determined. A strong NLO response indicates that this family of organic compounds with a D-π-A structure exhibits large first hyperpolarizability βtot values, these values are much greater than ones of urea. This theoretical model may be used to design other chromophores for usage in electro-optics.

Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a vital task to rescue patients’ lives from bad consequences and provide them with better healthcare. Machine learning methods have been proven to be accurate in predicting dementia in the early phases of the disease. The prediction of dementia depends heavily on the type of collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic Resonance Imaging (MRIs). Other biological features such as age and gender can also help in the diagnosis of dementia. Although many studies use machine learning for predicting dementia, we could not reach a conclusion on the stability of these methods for which one is more accurate under different experimental conditions. Therefore, this paper investigates the conclusion stability regarding the performance of machine learning algorithms for dementia prediction. To accomplish this, a large number of experiments were run using 7 machine learning algorithms and two feature reduction algorithms namely, Information Gain (IG) and Principal Component Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted in 7×9 + 7×7= 112 experiments. In each experiment, various classification evaluation data were recorded. The obtained results show that among seven algorithms the support vector machine and Naïve Bayes are the most stable algorithms while changing the selection threshold. Also, it was found that using IG would seem more efficient than using PCA for predicting Dementia. These promising results open the door to a new era of early prognosis of Alzheimer’s Disease and Related Dementias (ADRD).