Z-score Method Research Articles

Optimizing asphalt mix design through predicting volumetric properties using interpretable machine learning

Traditional asphalt mix design methods primarily rely on experience, consuming immense time and resources. Currently, advanced machine learning (ML) method can accelerate the asphalt mix design procedure. However, historical data on asphalt mix design exhibit high variability, and the outputs of ML models are often challenging to interpret. To address these issues, this study selectively utilized outlier detection algorithms for data preprocessing and employed interpretable ML to predict and explain the volumetric properties of the mixtures, achieving an automated and rapid mix design method. A database (1525 sets of data) of volumetric properties was established based on experimental data of the engineering site. Z-score, KDE (kernel density estimation) and LOF (local outlier factor) methods were employed to detect outliers of the database. Three ML models were trained to predict the volumetric properties of mixture (air void and voids in mineral aggregate), based on material proportion, gradation morphological and material performance features. The SHAP method was used to interpret the extreme gradient boosting (XGB) model, quantifying feature importance, and aiding in decision-making. Finally, an asphalt mix design framework was proposed. The results indicate that the combination of KDE and LOF methods for outlier detection enabled the machine learning model to achieve optimal performance. The XGB model had the best prediction effect on volumetric properties. According to the SHAP method results, the influence of the critical features on the results was explained by the physical laws. The method proposed effectively reducing the design time and costs. The feasibility of the proposed design method was proved by the case study.

Impact of Health and Non-health Inputs on Health Outcomes: A Comparative Study of South Asia, Southeast Asia, BRICS and ASEAN Regions

This study assessed the impact of health and non-health factors on crude death rate, infant mortality rate and life expectancy at birth across 21 developing and emerging countries. Utilising panel regression and Z-score methods, it identified two country clusters based on the relationship between health input augmenting factors and health outcomes. Findings revealed variations in health outcomes among emerging countries like the Russian Federation, Brazil, China, and India compared to some developing countries. The study emphasised the significant role of health input augmenting factors and noted varied influences of socio-economic, environmental and health factors on health outcomes. Additionally, a notably stronger elasticity of health outcomes was observed concerning non-health determinants. JEL: I12; I13; I15

Abstract PO1-07-02: Sensitive tumor detection, accurate quantification, and ER subtyping using low-pass methylome of liquid biopsy samples from patients (pts) with metastatic breast cancer (mBC)

Abstract Background The presence and amount of blood circulating tumor DNA (Tumor Content, TC) is emerging as a clinically relevant factor in many cancers, including BC. In the metastatic setting, a commonly used tumor tissue agnostic approach involves analyzing Copy Number Alterations (CNA) from low-pass whole-genome sequencing (lpWGS) by ichorCNA. However, with a lower limit of detection of 3%, it is unsuitable for low TC applications, such as monitoring disease in response to treatment. Here we report on the use of low-pass whole genome bisulfite sequencing (lpWGBS) coupled with a novel tool, METER (METhylation analyzER), to overcome current limitations of lpWGS, enabling sensitive TC detection, accurate quantification, and ER subtyping from mBC liquid biopsies. Methods METER is a computational tool to analyse TC exploiting BC-specific Differentially Methylated Sites (DMS) and Regions (DMR) in lpWGBS data. To define BC DMSs and DMRs, Rocker-meth was applied to the WGBS profiles of 30 BC tissue (BASIS dataset) and 23 healthy donor cell free DNA (cfDNA) (Fox-Fisher 2021). METER consists of three modules: 1) METER-quant, a DMS-based quantifier of TC; 2) METER-detect, a DMR-based z-score method to classify samples as TC+ (METER+) or TC- (METER-); 3) METER-subtype, to infer ER status via Robust Partial Correlation applied to ER+ vs. ER- BC DMR. We generated lpWGBS data (coverage 0.5-1X) of 135 cfDNA samples from 58 pts (45 ER+, 13 ER-, 55 (95%) up to 3 previous lines of treatment; before starting treatment (T0, n=58), after the first cycle (T1, n=25), and at progression (T2, n=22)) and 30 from healthy individuals as controls. ichorCNA using sensitive parameters (allowing normal fraction up to 0.99) was used to estimate reference TC. A leave-one-out strategy applied to the control samples was used to estimate the false discovery rate (FDR) of METER-detect classification. METER+/- pts were tested for association with OS and PFS using the log-rank test and compared with ichorCNA detection (TC by ichorCNA above or below 3%, ichorCNA+/-). Results TC of 105 cfDNA BC samples by METER-quant was concordant with estimates from the state-of-the-art tool ichorCNA (R >0.90, p< 1e-10). In 22 pts with complete longitudinal data, METER-quant median TC of 0.05 at T0 (IQR 0.02-0.12), 0.02 at T1 (0.01-0.07), and 0.06 at T2 (0.03-0.11) were obtained, with significant difference observed for T0 vs T1 and T2 vs T1 (paired Wilcoxon p< 0.05). Across all time points, METER-detect classified 43% of the n=42 ichorCNA- samples as METER+. The reliability and prognostic performance of METER and ichorCNA detection classification were then compared using clinical outcome data. Based on T0 data, METER+ pts had significantly worse OS than METER- (HR=4.2 CI=2.0-8.9, p< 0.001). This effect was stronger than using ichorCNA (HR=2.3 CI=1.3-4.0, p=0.006). Worse PFS at T0 was observed for METER+ compared with METER- pts (HR=3.7 CI=1.8-7.9, p< 0.001), while no significant association was observed for ichorCNA+ pts (HR=1.4 CI=0.8-2.5, p=0.2). Of note, ichorCNA-/METER+ pts (N=11) at T0 had worse PFS compared with ichorCNA-/METER- (N=11) (HR=3.7 CI 1.4-9.7, p=0.005). Considering the 73 METER+ and 53 samples with METER-quant >5%, METER-subtype showed an accuracy of 0.84 (CI=0.73-0.92) and 0.94 (CI=0.83-0.99) respectively in classifying ER status based on IHC. Discussion lpWGBS enables tumor tissue agnostic analysis and concurrent investigation of complementary molecular features, including CNA and DNA methylation patterns. In a small and heterogeneous cohort, METER showed comparable performance to state-of-the-art tools in terms of TC quantification (ichorCNA) and ER subtyping (IHC), while offering enhanced sensitivity and ensuring a low FDR. METER showed promising prognostic stratification capabilities, particularly for patients with low TC. Validation in an extended prospective cohort is currently ongoing. Citation Format: Marta Paoli, Agostina Nardone, Francesca Galardi, Chiara Biagioni, Dario Romagnoli, Francesca De Luca, Gian Marco Franceschini, Ilenia Migliaccio, Marta Pestrin, Giuseppina Sanna, Emanuela Risi, Luca Livraghi, Erica Moretti, Laura Biganzoli, Luca Malorni, Francesca Demichelis, Matteo Benelli. Sensitive tumor detection, accurate quantification, and ER subtyping using low-pass methylome of liquid biopsy samples from patients (pts) with metastatic breast cancer (mBC) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-02.

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization

Background and purposeMagnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs). Materials and methodsTwo T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance. ResultsApplying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization. ConclusionsTo develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.

Model Altman Z-Score Untuk Menganalisis Kejadian Kebangkrutan Perusahaan Peternakan Broiler X Di Kabupaten Malang

The purpose of this study was to determine the causes of bankruptcy in broiler farms using the Altman Z-Score method using financial statements for the 2017-2019 period. This research was conducted from August to September 2021, this research was conducted by conductibf interviews with the Company Owner. The type of research used is descriptive quantitative research, namely by collecting, classifying, analyzing and interpreting the data obtained from the company so that it can provide an overview of the actual situation. The method used by researchers for data collection is by interview and documentation. The results showed how to overcome bankruptcy based on financial data, the company had total debt which was one of the causes of the company going bankrupt and another reason the company closed because in 2019 the price of chicken decreased. Factors causing the decline in broiler prices in 2019 were due to oversupply from broiler chickens, oversupply from Day Old Chicks (DOC), a decrease in people's purchasing power and distribution channels that were disrupted due to the issue of the cartel mafia.

Predicting the classification of high vowel sound by using artificial neural network: a study in forensic linguistics

<span lang="EN-US">One of the tasks in forensic linguistics, especially forensic phonetics, is evaluating the speech sounds in the recordings. The speech evaluation aims at identifying and verifying speakers to predict if the sound were spoken by the suspect or not. The common problem in the task is determining which acoustic features of the speech sounds are reliable for the speaker identification and verification. The purpose of this research is studying formant frequencies to predict high vowel sounds /i/, and /u/ by using artificial neural network (ANN). Using three various normalization methods (i.e., softmax, z-score and sigmoid), we utilized multilayer perceptron on backpropagation ANN with the architectural models of 4-5-2, 4-10-2 and 4-20-2. The results show that the z-score normalization method provides higher accuracy than the other two in all formations and the 4-10-2 formation has shown the highest accuracy (92.26%).</span>

The impact of financial ratios on financial distress in the transportation sector

The aim of this study was to assess the impact of various financial ratios on the financial instability of companies, with particular emphasis on the profitability ratio of Return on Assets (ROA), liquidity ratio of Current Ratio (CR), activity ratio of Total Assets Turnover (TATO), and solvency ratio of Debt to Assets Ratio (DAR). The study employs secondary data and selective selection to choose a sample of thirteen transport companies listed on the Indonesia Stock Exchange (IDX) between 2020 and 2022. The study utilised multiple linear regression analysis methods, employing SPSS as the testing programme. The Altman Z-Score method was employed to prioritise the financial distress component. The results indicated that the liquidity ratio, a component of the activity ratio, had a positive impact on financial distress, suggesting that more liquidity might be associated with increased financial hardship. Conversely, the utilisation of leverage was discovered to have an adverse impact on financial distress, indicating that elevated levels of leverage may reduce the likelihood of financial hardship for the selected entities. This study offers useful insights into the intricate relationship between particular financial statistics and the financial distress situation of organisations. It provides a detailed comprehension of how the interplay between liquidity, leverage, and activity dynamics affects the overall financial health of transport companies in the Indonesian stock market.

Analisis Laporan Keuangan Untuk Mengukur Kinerja Keuangan Pt. Kimia Farma pada Bursa Efek Indonesia

This research aims to see how the financial performance of PT. Kimia Farma uses liquidity and profitability ratio analysis for the 2017-2021 period, and whether PT. Kimia Farma is experiencing potential bankruptcy based on the Altman Z-Score model for the 2017-2021 period. This research uses quantitative descriptive methods. The type of data used by researchers is quantitative data and qualitative data obtained from www.idx.id. Data collection takes the form of literature study and documentation. Bankruptcy prediction calculations use the Altman Z-Score Model and profitability ratios ROA, ROE, and Net Profit Margin. The research results show that the Liquidity Ratio value of PT. Kimia Farm Ajika is measured by the Current Ratio which is in accordance with industry standards, however the company's quick ratio is still below the industry standard value. Profitability value of PT. Kimia Farma is still below industry standards due to the company's lack of minimizing costs which causes the company's profits to decrease. In calculating bankruptcy using the Altman Z-Score method, the company can be said to be good because it is still above the standard value. There are recommendations that can be used as input for PT. Kimia Farma, Tbk must further increase working capital in order to create and increase profits which will have an impact on the increase in shares and must minimize the company's operational costs.

Analisis Prediksi Kebangkrutan Perusahaan Asuransi Kerugian Di Indonesia

The application of the bankruptcy model using the Altman Z-score model is carried out to predict financial difficulties by using balance sheet analysis which can reflect the company's financial performance. Insurance companies are needed to minimize risks and uncertainties for futureprotection. Altman's Z-score model is analyzed through the calculation results if it is smaller than <1.1 it is concluded that the company is at high risk, if the calculation results are 1.1-2.6 it is concluded that the company is prone to bankruptcy, and if the calculation result is> 2.6 itcan be said that the company is healthy. So that the probability of bankruptcy can be estimated using the Z- score Altman method. This modeling can be used by insurance companies in making decisions. (MMG) Keywords: Bankruptcy, Financial Performance, Altman's Z-score Method

Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease.

To develop and compare radiomics model and fusion model based on multiple MR parameters for staging liver fibrosis in patients with chronic liver disease. Patients with chronic liver disease who underwent multiparametric abdominal MRI were included in this retrospective study. Multiparametric MR images were imported into 3D-Slicer software for drawing bounding boxes on MR images. By using a 3D-Slicer extension of SlicerRadiomics, radiomics features were extracted from these MR images. The z-score normalization method was used for post-processing radiomics features. The least absolute shrinkage and selection operator method (LASSO) was performed for selecting significant radiomics features. The logistic regression analysis was used for building the radiomics model. A fusion model was built by integrating serum fibrosis biomarkers of aspartate transaminase-to-platelet ratio index (APRI) and the fibrosis-4 index (FIB-4) with radiomics signatures. In the training cohort, AUCs of radiomics and fusion model were 0.707-0.842 and 0.718-0.854 for differentiating different groups. In the testing cohort, AUCs were 0.514-0.724 and 0.609-0.728. For the training cohort, there was no significantdifference of AUCs between radiomics and fusion model (p > 0.05). For the testing cohort, AUCs of fusion model were higher than those of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 (p = 0.011 & 0.042). Radiomics model and fusion model based on multiparametric MRI exhibited the feasibility for staging liver fibrosis in patients with CLD, and APRI and FIB-4 could improve the diagnostic performance of radiomics model in differentiatingF1-3 vs. F4 and F1-2 vs. F4.

Integrated straw-derived biochar utilization to increase net ecosystem carbon budget and economic benefit and reduce the environmental footprint

ContextCrop residues are often returned to the soil to achieve soil carbon (C) sequestration in the agricultural sector, thereby mitigating climate change and soil degradation. However, direct straw incorporation may increase the risks of greenhouse gas (GHG) and reactive nitrogen (Nr) emissions. Objective and methodsTo comprehensively evaluate whether straw-derived biochar is a feasible alternative to straw for increasing grain yields and soil C sequestration while reducing GHG and Nr emissions in cropland, we conducted a two-factor field trial on maize farmland in northwest China during 2019–2021. Factor one comprised three straw incorporation methods (straw removal (T), total straw incorporation (S), and straw-derived biochar incorporation (B; biochar application at a straw/biochar ratio of 33%)) and factor two comprised two nitrogen (N) fertilizer application rates (0 and 300 kg ha–1). The relative benefits of the treatments for the maize productivity, net ecosystem carbon budget (NECB), carbon footprint (CF), nitrogen footprint (NF), and net ecosystem economic benefits (NEEB) were comprehensively evaluated by using the life cycle assessment (LCA) and Z-score methods. ResultsN fertilizer application was the main factor that increased maize productivity, NECB, NF, and NEEB in the maize agroecosystem. Under conventional N application, NECB increased by 244.0% (1647.37 kg C ha–1 yr–1) under SN compared with TN, and heterotrophic respiration (Rh) and N2O emissions increased by 70.3% and 34.9%, respectively. By contrast, BN increased NECB by 295.8% (1996.79 kg C ha–1 yr–1), and decreased Rh and N2O emissions by 8.2% and 20.9%, respectively. Hence, BN reduced the net global warming potential (NGWP) and greenhouse gas intensity (GHGI) of the farmland ecosystems by 56.4% (1609.49 kg CO2-eq ha–1 yr–1) and 46.5%, respectively, compared with SN. Moreover, the grain yield, water use efficiency (WUE) and N use efficiency increased significantly under BN compared with TN. Compared to SN, integrated straw-derived biochar utilization (pre-farm abatement + BN management on the farm) reduced the average CF and NF of the maize agroecosystem by 350.2% (4004.85 kg CO2-eq ha–1 yr–1) and 16.5% (12.68 kg N-eq ha–1 yr–1), respectively, and improved NEEB by $ 624.7 ha–1 yr–1. ConclusionsIntegrated straw-derived biochar utilization may be applied as a sustainable and economical straw alternative to straw for enhancing soil C sinks and reducing GHG and Nr emissions. Implications or significanceIntegrated straw-derived biochar utilization is an economically friendly and sustainable method for straw utilization in dryland maize production, and highly significant for mitigating climate and environmental degradation.

The diagnostic value of a nomogram based on enhanced CT radiomics for differentiating between intrahepatic cholangiocarcinoma and early hepatic abscess.

This study aimed to investigate the value of a CT-enhanced scanning radiomics nomogram in distinguishing between early hepatic abscess (EHA) and intrahepatic cholangiocarcinoma (ICC) and to validate its diagnostic efficacy. Clinical and imaging data on 112 patients diagnosed with EHA and ICC who underwent double-phase CT-enhanced scanning at our hospital were collected. The contours of the lesions were delineated layer by layer across the three phases of CT scanning and enhancement using 3D Slicer software to define the region of interest (ROI). Subsequently, the contours were merged into 3D models, and radiomics features were extracted using the Radiomics plug-in. The data were randomly divided into training (n = 78) and validation (n = 34) cohorts at a 7:3 ratio, using the R programming language. Standardization was performed using the Z-score method, and LASSO regression was used to select the best λ-value for screening variables, which were then used to establish prediction models. The rad-score was calculated using the best radiomics model, and a joint model was constructed based on the rad-score and clinical scores. A nomogram was developed based on the joint model. The diagnostic efficacy of the models for distinguishing ICC and EHA was assessed using receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses. Calibration curves were used to evaluate the reliability and accuracy of the nomograms, while decision curves and clinical impact curves were utilized to assess their clinical value. Compared with the ICC group, significant differences were observed in clinical data and imaging characteristics in the EHA group, including age, centripetal enhancement, hepatic pericardial depression sign, arterial perfusion abnormality, arterial CT value, and arteriovenous enhancement (p < 0.05). Logistic regression analysis identified centripetal enhancement, hepatic pericardial depression sign, arterial perfusion abnormality, arterial CT value, and arteriovenous enhancement as independent influencing factors. Three, five, and four radiomics features were retained in the scanning, arterial, and venous phases, respectively. Single-phase models were constructed, with the radiomics model from the arterial phase demonstrating the best diagnostic efficacy. The rad-score was calculated using the arterial-phase radiomics model, and nomograms were drawn in conjunction with the clinical model. The nomogram based on the combined model exhibited the highest differential diagnostic efficacy between EHA and ICC (training cohort: AUC of 0.972; validation cohort: AUC of 0.868). The calibration curves indicated good agreement between the predicted and pathological results, while decision curves and clinical impact curves demonstrated higher clinical utility of the nomograms. The CT-enhanced scanning radiomics nomogram demonstrates high clinical value in distinguishing between EHA and ICC, thereby enhancing the accuracy of preoperative diagnosis.

The Role of Information Convergence Technology in Reshaping the Multiple Directions of Ideological and Political Education in Colleges and Universities

Abstract The integration of information technology and teaching promotes the reform of teaching in colleges and universities, and this paper extracts students’ learning state in classroom learning based on information fusion technology and explores the remodeling role of Civic and Political Education from multiple dimensions. Face expression recognition is used to extract students’ expressions in classroom learning, and their learning status is evaluated by combining head posture estimation. The z-score method is used to quantify the students’ classroom state data in a dimensionless way, and the learning state data with different scales are transformed into numerical values that can be calculated directly to realize the quantification of learning interest. Four dimensions are examined to explore the remodeling effect of civic and political education: students’ cognitive attention, learning emotions, course acceptance, and information fusion. The results show that the classroom state of student 1 under the information fusion of Civic Education has been in the state of conscientiousness, with cognitive attention greater than 0.25, and the cognitive attention values of students 5 and 6 are at [0.1966,0.2822], which indicates that the cognitive attention of students in the classroom of Civic Education is high. Under the dimension of information fusion, the dimension of information fusion of all 6 students is greater than 0.5, which indicates that students can make connections with other knowledge fusion in the Civic Education classroom.

Стресс реального сектора российских регионов в условиях пандемии и санкций

Recently, the Russian economy has been affected by sanctions and pandemic shocks. Russian regions reacted differently depending on their spatial location and sectoral structure of economies. Using monthly data for 2016-2023, the article assesses the dynamics of stress of regions’ industrial production volume, retail turnover and volume of paid services to the population. A stress index is calculated as a moving difference between the standard deviation and the average growth rate of the indicator compared to the corresponding period of the previous year. An integrated stress index is a simple sum of individual stress indices normalised using the Z-score method within the panel data framework. As a result, time series of individual and integrated stress indices at the national, federal district and regional levels were obtained. The average stress levels of different Russian regions were compared for the entire period and three sub-periods: pre-pandemic, pandemic (from March, 2020 to February, 2022) and post-pandemic/new sanctions. The data revealed a greater and relatively uniform sensitivity of the service sector of Russian regions to the pandemic; various reactions of regional industries to sanctions, causing the diversification effect across the country; greater trade response to pandemic shocks and industry response to new sanctions. On average, the constituent entities of the North Caucasus Federal District turned out to be the most vulnerable to shocks, while the regions of the Siberian Federal District showed the greatest stability. The study demonstrated that sectoral structure and regional income level are significant factors determining the resilience of the regional real economy to pandemic shocks, while spatial location is also important in response to sanctions.

Exploring the Digital Innovation Development Model of Intangible Cultural Heritage in China

Abstract Exploring the innovative development mode of digitalization of China’s intangible cultural heritage, the study focuses on applying and improving the traditional entropy value method in the digital evaluation of intangible cultural heritage, and is dedicated to enhancing the dissemination effect and public cognition of intangible cultural heritage. The study first standardizes the data, including the extreme value processing method, z-score standardization method, etc., and then applies the improved entropy value method for comprehensive evaluation. This paper’s improved entropy value method focuses on the Nature of the evaluation indexes, making the index data more genuinely reflect the relationship between the original values. The study results show that the index of understanding non-heritage culture through the Internet channel is 0.6, significantly higher than other channels. The user experience score of non-heritage culture after digitization increased from 2.48 to 4.43, indicating that digitization technology significantly improved the user experience of non-heritage culture. In addition, digitization technology enhanced NRM cultural heritage’s aesthetic, novelty and social communication. Digitization technology effectively enhances the communication effect and public perception of ICH.

ОЦІНКА КОНКУРЕНТОСПРОМОЖНОСТІ В КОНТЕКСТІ ЗАБЕЗПЕЧЕННЯ ЕКОНОМІЧНОЇ ЕФЕКТИВНОСТІ АГРАРНИХ ПІДПРИЄМСТВ

The article examines the competitiveness of agrarian enterprises in the context of ensuring their economic efficiency. A diagram has been constructed to illustrate the relationship between competitiveness and economic efficiency. A methodology for the assessment of the competitiveness of agrarian enterprises has been developed. The methodology comprises three distinct phases: the identification of key indicators, the normalization of indicators, and the weighted scoring of indicators. The assessment of competitiveness is informed by a number of key indicators, including market share, return on sales, labor productivity, and product quality. These indicators were normalized according to the Z-score method. The weights of each indicator were then determined for the calculation of the general competitiveness index. The indicators of the competitiveness of the studied agrarian enterprises demonstrate an increase in market shares in Ukraine during 2018 – 2022 in TOV Elevator Chysta krynytsia and in PP Imeni Kalashnyka to 0.069% and 0.023%, respectively. In contrast, the market share of VSK Zlahoda decreased in Ukraine to 0.018%. A decrease in return on sales was observed at all three agrarian enterprises under study in 2022 compared to 2018. This was attributed to the challenging economic situation in Ukraine and the global agrarian market. Concurrently, agrarian enterprises have witnessed an increase in labor productivity, which can be attributed to the optimization of the workforce and improvements in production. The competitiveness of the agrarian enterprises under study was evaluated using a weighted scoring method, based on the selected indicators. For the period 2018 – 2022, VSK Zlahoda exhibited the most favorable and unfavorable results. The highest general competitiveness index was 1.44 in 2021, while the lowest was -1.31 in 2019. This situation can be explained by a number of factors, including problems with the stability of revenues and profits, as well as a reduction in the number of workers at the agrarian enterprise. Collectively, these factors have led to a decrease in the market share of the enterprise. The assessment of competitiveness not only compares the state of agrarian enterprises on the market, but also indirectly demonstrates their economic efficiency. A number of ways to increase competitiveness in the context of ensuring the economic efficiency of agrarian enterprises have been identified. Upon achieving an adequate level of economic efficiency, agrarian enterprises will then be able to achieve their business goals and become competitive.

Analysis Financial Distress Potential in Asean Industrial Companies Using Altman Z-Score and Springate Methods

This research aims to analyze the potential for financial distress in the industrial sector companies across five ASEAN countries, using the Altman Z-score (1993) and Springate (1978) methods. Subsequently, these estimations are utilized to determine the error rate in classifying companies into specific groups, such as failed, safe, and grey-area companies. To measure the significance of differences among these groups, an independent sample t-test is employed. The bankruptcy analysis results from the Altman Z-Score and Springate methods within the industrial sector companies across ASEAN countries (Indonesia, Malaysia, Philippines, Singapore, and Thailand) in 2018 until 2020 indicate varying levels of financial distress.

Financial Distress Analysis using Altman Z-Score and Zmijewski Methods in Oil and gas sector listed on the IDX in the 2018-2021 period

Based on the research results obtained that in the Altman Z-Score method in 2018 (APEX), (ARTI), (ENRG), (ESSA), (MTFN), and (SURE), (WOWS), experienced financial distress, while according to Zmijewski the company was non distressed. In the Altman Z-Score method in 2019 the company (APEX), (ARTI), (ELSA), (ENRG), (ESSA), (MEDC), (MITI), (PKPK), (RUIS) and (SURE) are experiencing financial distress, while according to Zmijewski the company is non distressed. In the Altman Z-Score method in 2020 the companies (ARTI), (ESSA), (MEDC), and (MITI) experienced financial distress, while according to Zmijewski the company was non-distress. In the Altman Z-Score method in 2021 the companies (ARTI), (ESSA), (MEDC), and (MTFN) experienced financial distress, while according to Zmijewski the companies were non-distressed. so that based on the results of testing and discussion in this study, the advice given is for companies that are bankrupt, management companies must find the best way to improve business performance as soon as possible, and advice that can be given to investors, namely investors must be more selective in choosing companies to invest in, especially those with good financial performance. So that investors can invest their funds properly.

Impact of Digitalization on Global Sustainable Development Across Countries

This study develops an economic development index, a social development index, an environmental sustainability index, and an information and communication technology index to explore the comparison of 34 countries in global sustainable development (GSD) and its promoting indicators, and digitalization. The composite Z-score method is used to create the above-mentioned indexes between 2000 - 2021. The statistical values of these indexes are used to explicate the performance of these countries in all components of sustainable development and digitalization. The correlation coefficients among the mentioned indicators are also estimated to measure associations among them. The regression coefficients of explanatory variables with GSD and digitalization are also estimated using a log-linear regression model. There is reported a high diversity in digitalization, economic development (ED), social development (SD), environmental sustainability (ES), and GSD across countries. GSD is not possible without ED, SD, and ES. Digitalization has a positive impact on sustainable, social, and economic development. Digital technology helps to increase GSD, while GSD seems favorable to increasing digitalization. Digitalization improves as ED, SD, and GSD increase. While digitalization has a negative impact on ES. Green innovation, green and appropriate technologies, green entrepreneurship, and technological advancement would be supportive to increase digitalization and GSD.