Main Important Factors Research Articles

WP1.8 - No, we can’t repair your hernia: An audit to assess pre-operative optimisation for umbilical hernia repair

Abstract Aims The European Hernia Association recently published guidelines which clearly outline the importance of pre-operative optimisation for those undergoing elective umbilical/epigastric hernias. The main factors of importance are: smoking cessation and BMI <35. This audit aimed to identify if we are adequately optimising our patients. Methods We retrospectively evaluated all elective umbilical hernia repairs from 1st January 2022- 31st December 2022 within a district general hospital setting. Clinic letters and pre-operative records were used to ascertain smoking status, BMI and diabetic control (HbA1c). Results 60 umbilical hernias were performed in our district general hospital setting from 1st January 2022- 31st December 2022. Of these patients, 12% (N=7) were known smokers and 13% (N=8) had a BMI of over 35 documented. Additionally, only in 15 cases, was there clear documentation of the patient smoking status. Out of these patients, 9 were subsequently found to be non-smokers and 6 remained smokers despite being given smoking cessation advice. Furthermore, in 10% (N=6) of these cases, there was poor diabetic control (HbA1c > 48), which was not addressed in clinic pre-operatively. Conclusion Patient factors such as smoking, obesity and diabetic control have been shown to significantly increase the rate of post-operative wound infections and complications. Patients are not being adequately optimised for elective procedures which puts them at increased risk of complications and increases the burden on limited NHS resources.

Application of STEEP and Interpretive Structural Modeling in the Design Imagery of Taiwan Public Ceramic Relief Murals

Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature, culture, and architectural wall space, combined with social customs, visual sensibility, and art. It may also become the main axis of ceramic art in the future. Taiwan public ceramic relief murals (PCRM) are most distinctive with the PCRM pioneered by Pan-Hsiung Chu of Meinong Kiln in 1987. In addition to breaking through the limitations of traditional public ceramic murals, Chu leveraged local culture and sensibility. The theme of art gives PCRM its unique style and innovative value throughout the Taiwan region. This study mainly analyzes and understands the design image of public ceramic murals, taking Taiwan PCRM’s design and creation as the scope, and applies STEEP analysis, that is, the social, technological, economic, ecological, and political-legal environments are analyzed as core factors; eight main important factors in the artistic design image of ceramic murals are evaluated. Then, interpretive structural modeling (ISM) is used to establish five levels, analyze the four main problems in the main core factor area and the four main target results in the affected factor area; and analyze the problem points and target points as well as their causal relationships. It is expected to sort out the relationship between these factors, obtain the hierarchical relationship of each factor, and provide a reference basis and research methods.

The Problem-Based Learning Model Assisted with the Ethlaf Application Contains Ethnomathematics to Improve Students' Conceptual Understanding Capability

Mathematics learning plays an important role in the world of education. However, the high demands for mastery of mathematics are not in accordance with current student learning outcomes. This is because learning is direct, so students' ability to understand concepts is low. This research aims to test the average ability of students to understand concepts by applying a problem-based learning model containing ethnomathematics supported by ETHLAF APPS, which is better than applying a direct learning model. This type of research is quantitative, using quasi-experimental methods and a nonequivalent pretest-posttest control group design. The sampling technique uses purposive sampling. This research was carried out in class X of Senior High School. The data collection technique uses tests, and the instrument uses concept understanding test questions. Data analysis includes the normality test, homogeneity test, similarity test, and final data analysis, namely the independent sample t-test. The research results show that the average ability of students to understand concepts when applying the problem-based learning model based on ethnomathematics assisted by ETHLAF APPS is better than when applying the direct learning model. This research concludes that students' ability to understand concepts using the ethnomathematics-based problem-based learning model supported by ETHLAF APPS is better than using direct learning. The ability to understand concepts is the main goal and important factor in learning mathematics.

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important factors that influence the overall organizational performance. This article compares various machine learning algorithms such as XG Boost, Random Forest (RF), Histogram Gradient Boosting Regressor, LGBM Regressor, Ada Boost Regressor, and Gradient Boosting Regressor on the dataset of municipality workers. The study aims to propose a machine learning approach to predict and evaluate the productivity of municipality workers. The evaluation of the overall targeted and actual productivity of each department shows that out of 12 different departments, only 5 departments were able to meet their targeted productivity. A 3D Scatter plot visually displays the incentive given by the department to each worker based on their productivity. The results show that XG Boost performs best in comparison with the other five algorithms, as the value of R Squared is 0.71 and MSE (Mean Squared Error) is 0.01.

Standalone University Campuses’ Outdoor Spaces: A case of the German International University, Egypt

This paper investigates the nature of university campuses’ outdoor spaces and some of the main important aspects and factors that contribute in achieving a successful campus. Through referring to several related researches, observing and mapping, an analysis and study of the German International University has been conducted. These have highlighted different dynamics and complexities. As it is located in a still not yet urbanized area, the German International University is considered one of the leading projects in its surrounding. A new concept was introduced, namely the “standalone” campus, accommodating all needed services, which are missing in its surrounding. This new concept is considered a solution for universities with similar circumstances and nature.

Involving prediction of dynamic modulus in asphalt mix design with machine learning and mechanical-empirical analysis

Dynamic modulus (E∗) plays a dominant role in comprehensively capturing the mechanical behavior of asphalt mixture. Many researchers tried to consider E∗ as a performance indicator for a mix design stage, but the cost of time and labor for experimentally measuring the E∗ is high. Moreover, establishing harmonized E∗ criterion is unrealistic. To involve E∗ in an asphalt mix design, this paper improved the Superpave volumetric mixture design via controlling the performance (rutting) of mixtures, which were estimated by incorporating machine learning (ML) predictive models of E∗ into the mechanical-empirical approach. The data from the concatenation of the original E∗ database and volumetric properties data extracted from the NCHRP 9–19 report were involved. The different ML models including Support Vector Regression (SVR), kernel ridge regression (KRR), artificial Neural Networks (ANN), Gaussian process regression (GPR), gradient boosting (GB), and eXtreme gradient boosting (XGBoost) were optimized, trained and tested, and their performances were compared to empirical Witczak’s predictive equations. To illustrate the improved asphalt mix design, the case study was presented. Candidate asphalt mixtures were prepared by mixing the selected gradation and different asphalt content and subject to a series of E∗ predictions using the best ML model, which were then used to calculate the permanent deformation of these mixtures with elastic layered analysis and transfer functions in MATLAB. The results show that among all the ML models, XGBoost to predict the E∗ at actual scale has the largest prediction accuracy (R2 = 0.9867), and its prediction accuracy is significantly higher than Witczak’s equations. Feature importance analysis suggested that no matter which scale of E∗ is predicted, the main important factors are test conditions and asphalt binder properties. The relationship between asphalt contents and estimated rutting by the proposed procedure agrees with the engineering experience, which suggests the feasibility of the proposed asphalt mix design procedure.

Integrated Transcriptomic and Metabolomic Analyses Uncover the Differential Mechanism in Saline-Alkaline Tolerance between Indica and Japonica Rice at the Seedling Stage.

Saline-alkaline stress is one of the major damages that severely affects rice (Oryza sativa L.) growth and grain yield; however, the mechanism of the tolerance remains largely unknown in rice. Herein, we comparatively investigated the transcriptome and metabolome of two contrasting rice subspecies genotypes, Luohui 9 (abbreviation for Chao2R under study, O. sativa ssp. indica, saline-alkaline-sensitive) and RPY geng (O. sativa ssp. japonica, saline-alkaline-tolerant), to identify the main pathways and important factors related to saline-alkaline tolerance. Transcriptome analysis showed that 68 genes involved in fatty acid, amino acid (such as phenylalanine and tryptophan), phenylpropanoid biosynthesis, energy metabolism (such as Glycolysis and TCA cycle), as well as signal transduction (such as hormone and MAPK signaling) were identified to be specifically upregulated in RPY geng under saline-alkaline conditions, implying that a series of cascade changes from these genes promotes saline-alkaline stress tolerance. The transcriptome changes observed in RPY geng were in high accordance with the specifically accumulation of metabolites, consisting mainly of 14 phenolic acids, 8 alkaloids, and 19 lipids based on the combination analysis of transcriptome and metabolome. Moreover, some genes involved in signal transduction as hub genes, such as PR5, FLS2, BRI1, and NAC, may participate in the saline-alkaline stress response of RPY geng by modulating key genes involved in fatty acid, phenylpropanoid biosynthesis, amino acid metabolism, and glycolysis metabolic pathways based on the gene co-expression network analysis. The present research results not only provide important insights for understanding the mechanism underlying of rice saline-alkaline tolerance at the transcriptome and metabolome levels but also provide key candidate target genes for further enhancing rice saline-alkaline stress tolerance.

Primary Stability Assessment of Conical Implants in Under-Prepared Sites: An In Vitro Study in Low-Density Polyurethane Foams

Bone characteristics, the implant macrogeometry, and the drilling technique are considered the main important factors to obtain a good implant primary stability (PS). Indeed, although it is known that implant placement in poor bone sites increases the possibility of implant failure, several surgical procedures have been proposed to improve PS, such as site under-preparation. Hence, this in vitro study aimed to evaluate the insertion torque (IT), removal torque (RT), and resonance frequency analysis (RFA) of conical implants (3.3 and 4 × 13 mm) placed in under-prepared sites on 10 and 20 pounds per cubic foot (PCF) density polyurethane sheets (simulating a D3 and D2 bone, respectively) with and without a cortical sheet of 30 PCF in density (corresponding to a D1 bone). After using ANOVA or Kolmogorov–Smirnov test to elaborate data, the resulting IT and RT values were directly proportional to the polyurethane block densities (e.g., the lowest and highest IT values were 8.36 ± 0.52 Ncm in the 10 PCF density sheet and 46.21 ± 0.79 Ncm in the 20 PCF density sheet + cortical for 4 × 13 mm implants) and increased with the increasing amount of site under-preparation (the highest results for both implants were found with a 2.2 mm under-preparation, showing a significantly higher IT with a p < 0.05 compared with others, especially in the highest-density sheets). Both implants inserted in the 20 PCF density block + cortical with all under-preparation protocols exhibited significantly higher RFA values (p < 0.05–0.0001) compared with the corresponding ones in the 10 PCF block. Moreover, 3.3 × 13 mm implants showed the same results comparing the 20 PCF block and the 10 PCF block + cortical. In conclusion, in this in vitro study using low-density polyurethane blocks, the under-preparation of the implant insertion sites was shown to be effective in increasing implants’ PS.

Impacts of Meteorological Parameters on Tropospheric Ozone Concentrations in Çanakkale

Meteorological factors have a significant impact on the formation and transport of tropospheric ozone. In addition to the meteorological factors, solar radiation and associated chemical reactions, topography, and pollution sources also affect the ozone concentration levels (O3). In this study, the influences of the meteorological factors (temperature, wind speed, relative humidity, solar radiation) on O3 were investigated for Çanakkale throughout the 2014-2018 period. It was found that O3 concentration levels in summer season are linked with the high humidity and, especially high temperature. The statistical analysis indicated that O3 concentrations at Çan district (SE Çanakkale) were positively correlated with temperature during summer and autumn seasons (r=0.53). Similarly, a statistically significant positive correlation was found at Lapseki station of Çanakkale during autumn (r=0.48), spring (r=0.41), and summer months (r=0.40), respectively. On the other hand, relative humidity (RH) displayed negative correlations with O3 at Çan and Lapseki stations. In addition, negative correlation was observed between nitrogen oxides (NOX) and O3 values measured at Lapseki station. The main important factors affecting ozone concentration levels in Çanakkale were found as local meteorological conditions and long-range transportation of pollutants from anthropogenic sources (e.g. vehicle, industrial emissions, volatile organic compounds (VOCs) emitted from solvents and industry).

Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise.

BackgroundMachine learning (ML) is a type of artificial intelligence (AI) and has been utilized in clinical research and practice to construct high-performing prediction models. Hidden blood loss (HBL) is prevalent during the perioperative period of spinal treatment and might result in a poor prognosis. The aim of this study was to develop a ML-based model for identifying perioperative HBL-related risk factors in patients with thoracolumbar burst fracture (TBF).MethodsIn this study, single-central TBF patients were chosen. The medical information on patients, including clinical characteristics, laboratory indicators, and surgery-related parameters, was extracted. After comparing various ML model algorithms, we selected the best model with high performance. The model was validated using the internal validation set before performing recursive feature elimination (RFE) to determine the importance of HBL-related risk factors. The area under the receiver operating characteristic (AUC) curve, accuracy (ACC), sensitivity, and specificity were reported as critical model measures for evaluating predictive performance.ResultsIn this study, 62 (38.5%) of the 161 TBF patients were positive for HBL. There was a significant statistical difference in age, body mass index (BMI), diabetes, hypertension, Beta (percentage of vertebral restoration), duration of operation, and other pre-operative laboratory indicators between the HBL-positive and HBL-negative groups. Nine ML-based models were built and validated, with the Random Forest model having the greatest AUC in both the training set (0.905) and internal validation set (0.864). Furthermore, following RFE, age, duration of operation, Beta, pre-operative fibrinogen (Fib), and activated partial thromboplastin time (APTT) were identified as the five main important risk factors in patients with TBF during the perioperative period.ConclusionIn this study, we built and validated ML algorithms for an individualized prediction of HBL-related risk factors in the perioperative period of TBF. The importance of HBL-related risk factors could be determined, which contributes to clinicians' decision-making and improves perioperative management.

Evaluate sustainable human resource management in the manufacturing companies using an extended Pythagorean fuzzy SWARA-TOPSIS method

Today, organizations realized the importance of sustainability in concern with their business activities. They found out that they should focus on other aspects such as environmental and social and economic sustainability. Lately, Sustainable Human Resource Management (SHRM) is introduced as an instrument to indicate that HRM can affect the sustainable development and resource preservation of organizations. Despite that, SHRM and its related subjects are still in an emerging phase. The present study aims to propose a comprehensive approach to examine the main important factors of SHRM. To do so, a survey approach with the literature review interviewsview with experts is carried out to classify, rank, and evaluate the key SHRM factors in the manufacturing companies in Ecuador. In order to assess and prioritize the factors and the alternatives, this paper introduces a new approach using an integrated Stepwise Weight Assessment Ratio Analysis (SWARA)- Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method on the Pythagorean fuzzy sets (PFSs) setting called the PF-SWARA-TOPSIS method. To do this, we extend the SWARA method to identify and rank the SHRM factors by using PFSs. Afterward, to assess and prioritize the alternatives, this study utilizes the TOPSIS approach under PFSs. Next, to show the efficacy and applicability of the developed framework, a real case study of the SHRM problem is discussed on PFSs. Furthermore, this study performs a sensitivity investigation over diverse sets of criteria weights to illustrate the advantages of the proposed methodology. The investigation results demonstrate that the green work-life balance was ranked as the first factor by following corporate social responsibility, green employee relations, and business process redesign factors. Finally, the results of this study found that the developed approach has high efficacy and capability to handle the SHRM problem in manufacturing companies.

Comparison of property price development in regions affected by mining with other regions of the CR

The property market has experienced a sharp increase in prices recently, which indicates a different nature of the Covid 19 downturn of the economy compared with usual economic crises. This paper identifies key features of the property market which may help understand why the property market performs differently from other markets. A number of factors play an important role in shaping real estate prices, some of which can and cannot be influenced. The quality of the environment is often one of the main important factors, which is also presented by air pollution values. Apart from transport, waste incineration and thermal power plants, the most common causes of air pollution include the mining industry's activities. We aim to explain why property prices do not converge across regions, but prices in expensive regions experience an even stronger price dynamics, a recent phenomenon. We argue that the price dynamics have been predominantly demand-driven in a market with an unelastic and lacking supply of new properties. The real estate market was also significantly affected by the monetary and fiscal policies of individual states. It has been found that money, which has been invested on a large scale in all sectors of the economy in order to limit the negative effects of pandemic measures, has often found its way to investing in assets of all kinds. As a result, real estate prices are rising sharply, especially in richer areas with minimal unemployment. This article aims to offer a deeper insight into the relationship between flats/house prices and variables that proved to be significantly correlated to prices in regional decomposition. Finally, we suggest ways for sustainable future development of the property market.

The evolutionary trends of health inequality among elderly Chinese people and influencing factors of these trends from 2005 to 2017.

Reducing health inequality and ensuring national health equity have become issues of great concern to all countries in the world. This paper based on the ordered Probit model and concentrated index decomposition method, analysed the influencing factors and evolution trend of health inequality among the elderly with high age in China from 2005 to 2017. The study found that in 2005-2017, the self-rated health distribution of the elderly with high age in China showed an obvious inverted "U" shape, with the proportion of general and relatively healthy being the largest, while the proportion of unhealthy and very healthy was lower. Lifestyle, family income, and age were the main important factors to expand health inequality. Therefore, encouraging the elderly with high age to develop good living habits and narrowing the income gap of the elderly are conducive to solving the health inequality of the elderly with high age and achieving the goals of active ageing and healthy ageing.

Prediction and feature analysis of punching shear strength of two-way reinforced concrete slabs using optimized machine learning algorithm and Shapley additive explanations

Punching shear strength (PSS) is an important index for the design and analysis of two-way reinforced concrete slabs. To accurately predict the PSS of two-way reinforced concrete slabs, a hybrid PSO-SVR model, which is the combination of support vector regression (SVR) and particle swarm optimization (PSO) algorithm was employed on 218 datasets with six design parameters as input variables and PSS as output. Moreover, the feature importance and the sensitivity analysis were performed to analyze quantitatively the feature importance and effect of the design parameters on the PSS. The results showed that, compared with the RMSE = 347.349, MAE = 210.019, and R 2 = 0.916 of the original SVR model, the PSO-SVR model reached better prediction performance with RMSE = 187.958, MAE = 135.889, and R 2 = 0.942. Among the six key design parameters, the effective depth of the slab D and the slab thickness H are the two main important factors that can cause large dispersion of the PSS in a stochastic environment and should be given more attention in the design and construction.

Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

A Novel Multi-Modal Network-Based Dynamic Scene Understanding

In recent years, dynamic scene understanding has gained attention from researchers because of its widespread applications. The main important factor in successfully understanding the dynamic scenes lies in jointly representing the appearance and motion features to obtain an informative description. Numerous methods have been introduced to solve dynamic scene recognition problem, nevertheless, a few concerns still need to be investigated. In this article, we introduce a novel multi-modal network for dynamic scene understanding from video data, which captures both spatial appearance and temporal dynamics effectively. Furthermore, two-level joint tuning layers are proposed to integrate the global and local spatial features as well as spatial and temporal stream deep features. In order to extract the temporal information, we present a novel dynamic descriptor, namely, Volume Symmetric Gradient Local Graph Structure ( VSGLGS ), which generates temporal feature maps similar to optical flow maps. However, this approach overcomes the issues of optical flow maps. Additionally, Volume Local Directional Transition Pattern ( VLDTP ) based handcrafted spatiotemporal feature descriptor is also introduced, which extracts the directional information through exploiting edge responses. Lastly, a stacked Bidirectional Long Short-Term Memory ( Bi-LSTM ) network along with a temporal mixed pooling scheme is designed to achieve the dynamic information without noise interference. The extensive experimental investigation proves that the proposed multi-modal network outperforms most of the state-of-the-art approaches for dynamic scene understanding.

Greenhouse gas dynamics in an urbanized river system: influence of water quality and land use.

Rivers act as a natural source of greenhouse gases (GHGs). However, anthropogenic activities can largely alter the chemical composition and microbial communities of rivers, consequently affecting their GHG production. To investigate these impacts, we assessed the accumulation of CO2, CH4, and N2O in an urban river system (Cuenca, Ecuador). High variation of dissolved GHG concentrations was found among river tributaries that mainly depended on water quality and land use. By using Prati and Oregon water quality indices, we observed a clear pattern between water quality and the dissolved GHG concentration: the more polluted the sites were, the higher were their dissolved GHG concentrations. When river water quality deteriorated from acceptable to very heavily polluted, the mean value of pCO2 and dissolved CH4 increased by up to ten times while N2O concentrations boosted by 15 times. Furthermore, surrounding land-use types, i.e., urban, roads, and agriculture, could considerably affect the GHG production in the rivers. Particularly, the average pCO2 and dissolved N2O of the sites close to urban areas were almost four times higher than those of the natural sites while this ratio was 25 times in case of CH4, reflecting the finding that urban areas had the worst water quality with almost 70% of their sites being polluted while this proportion of nature areas was only 12.5%. Lastly, we identified dissolved oxygen, ammonium, and flow characteristics as the main important factors to the GHG production by applying statistical analysis and random forests. These results highlighted the impacts of land-use types on the production of GHGs in rivers contaminated by sewage discharges and surface runoff.

Analysis of Factors Affecting the Readiness of Health Personnel for COVID-19 Patients Treatment in Hospital

BACKGROUND: Many health workers have experienced fatigue due to high workloads with the number of patients who were confirmed positive for COVID-19 with moderate and severe symptoms who were hospitalized. Researchers identified several factors that affect the readiness of health workers in the management of COVID- 19 patients, including knowledge, family status, availability of personal protective equipment (PPE), health status, and reward. Optimal self-preparation is needed to achieve COVID-19 management that is in line with expectations by examining these factors more deeply. AIM: The aim of this study was to identify and knowing factors are affect the readiness of Health personnel for COVID-19 patients’ treatment in hospital. METHODS: This research method uses a descriptive correlation design with a cross-sectional approach aimed at knowing the relationship between variables, where the independent variable and dependent variable are identified at the same time. The number of samples used as many as 34 students of the transfer class who already work as nurses in hospitals. RESULTS: There were two factors that had an influence on the readiness of transfer students in the management of COVID-19 at the hospital, namely, the family status factor and the availability of PPE with p values of 0.036 and 0.007 and three factors that had no effect, namely knowledge, health status, and giving rewards with p > 0.05. CONCLUSION: Family status and the availability of PPE become the main important factors related to readiness of health personnel for patients’ treatment in hospital. Working safely and fully support from the hospital can affect the readiness of the health workers in handling COVID-19 patients.

РОЛЬ ЦІНОВОЇ СТРАТЕГІЇ В РОЗВИТКУ ПІДПРИЄМСТВ ПИВНОЇ ГАЛУЗІ

Introduction. Within the article, the importance of the pricing strategy in market economy is proved. Trends in the pricing strategy in the beer industry are revealed. The main problems affecting the beer industry development in Ukraine are substantiated. The research has a theoretical and cognitive nature and contains an analytical part. Practical principles of using the proposed general pricing strategy are demonstrated on the example of Obolon PrJSC. Financial statements of Obolon PrJSC for 2016-2020 were used as official data. The purpose of the article is to study the role of the pricing strategy in the beer companies’ development and develop proposals to improve their efficiency. In accordance with the set goal, the following tasks were solved: peculiarities of the pricing strategy of beer industry enterprises were studied, and its role was determined; proposals to increase their efficiency were developed; economic substantiation of the offered directions on the example of Obolon PrJSC was carried out. Materials and methods. Within the research, critical analysis of thematic literature; similar and comparative analysis to identify the features of the pricing strategy of the beer industry; deductive and inductive reasoning by developing the proposals to improve the effectiveness of the pricing strategy; meta-analysis and comparative analysis in economic justification of the measures to improve the pricing strategy of enterprises are used by the author. Results and discussion. A scientific and methodological approach to the development of the overall pricing strategy of beer industry enterprises is offered, which includes a sequence of stages of the pricing strategy implementation with the definition of its main components and important tactical factors. The project on the production of a composite paper bottle is developed, and the economic effect from its production on the example of the definite enterprise is calculated. It is proved that its introduction will allow the company to strengthen its leading position in the domestic market and enter new markets, reduce the negative impact on the environment and extend the range of its customers. The economic effect of the introduction of the system of return of deposit containers (return of empty containers for a small amount of money) is proposed and calculated, which allows on the one hand to reduce its costs and on the other – increase consumer responsibility for proper handling of empty containers and earn extra income. Conclusions. The pricing policy of the beer industry is characterized by the following features: formation of a pricing strategy depending on the principles, general goals, and objectives of the enterprise development for a certain period; considering the peculiarities of pricing of different sales channels; gradual expansion of the product range depending on the consumers’ needs; orientation of the enterprise management on conducting an active pricing policy. During the research, a general pricing strategy was developed, which determines all possible pricing strategies and pricing tactics for beer industry enterprises. Tactical measures to increase the efficiency of the pricing strategy are proposed: change in the utilization technology of by-products; development of containers from paper raw materials; introduction of the deposit container return system.

Fibrin clot properties in coronary artery disease: new determinants and prognostic markers.

Despite improved diagnosis and treatment options, coronary artery disease (CAD) is still the leading cause of mortality and morbidity worldwide. Established risk factors such as smoking, hypercholesterolemia, and hypertension only partly explain the pathophysiology of CAD. Besides the well-known role of platelets in atherosclerosis and arterial thrombus formation, reduced endogenous fibrinolytic activity may play a key role in CAD formation and progression. Thus, biomarkers of fibrinolysis may be future CAD risk markers. In this review, we provide an overview of regulators of fibrinolysis and the main factors of importance to fibrin clot formation including coagulation factor XIII, thrombin, and fibrinogen. We summarize markers of altered fibrinolysis and current laboratory methods applied in clinical practice and research. We present today’s evidence on fibrin clot properties in patients with stable CAD or acute coronary syndrome compared with healthy individuals and the significance of altered fibrinolysis as a risk for coronary thrombotic disease. In conclusion, we found evidence that altered fibrin clot properties and impaired fibrinolysis appear to contribute significantly to the thromboembolic risk in CAD patients. Therefore, more research is crucial in order to clarify whether modulation of the fibrinolytic system may pave the way for improved treatment of CAD.