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Purpose: The chapter analyses the diversity of the European Union (EU) member states in terms of the transition to a sustainable, green, and just economy.

Need for the study: Sustainable development is an important concern that the EU approaches by aiming to transition to a greener economy, fairly and inclusively. Moreover, the actual context, post-pandemic period and ongoing war at the EU border, makes the social and economic development of the EU countries matter even more in the light of this just transition.

Methodology: The authors relied on a multidimensional approach, using principal component analysis (PCA) to reduce the dimensionality of the dataset. The cluster analysis allowed us to group the countries in such a way that made it possible to identify the best and worst performers, as well as certain patterns that can be the basis for the formulation of good practices and transferable to the states that need more support in the transition to a green economy.

Findings: The results indicated that EU member states can be grouped into five clusters, each needing specific policies to ensure sustainable and inclusive development. Denmark, Finland, and Sweden turned out the best performers, while Estonia, Lithuania, Bulgaria, and Romania proved to have the most unfavourable situation.

Practical implications: The findings provide relevant policy implication insights for policy decision-makers in the field of green economy, economic development, and social policies, with directions for future research.

In the fashion of Martin (2012), we develop an innovative application to a standard, well-grounded methodology to investigate resilience in two critical dimensions: recovery and resistance. Our novel approach allows us to investigate the resilience performance to the 2008 financial crisis within countries of this macro-region according to their shock isolation and absorptive capacities.

By individually estimating six open economy DSGE models within the Central Europe and Baltic macro-region, we identify the business-cycle-volatility drivers for each country. Then, we use the outcome of our six estimates to conduct a principal component analysis to determine structural common characteristics required to explain economic resilience in the CEB macro-region.

In terms of resilience, Central European economies exhibit quite similar paths in terms of recovery, meaning they have similar economic structures. By contrast, Baltic countries behave differently, being outliers in opposite extreme positions. The contrary occurs for resistance: Baltic countries share a similar ranking, whereas Central European economies exhibit substantial differences.

It is important to acknowledge that a limitation of the analysis is that we explicitly consider each country as a stand-alone open economy which are subject to stochastic disturbances. Precisely, we do not model trade or other interactions across countries within the CEB region and with the rest of the world. Consequently, spillover effects in the aftermath of the shock are not accounted for.

We estimate the relative vulnerability or sensitivity of economies within the macro-region to disturbances and disruptions (resistance) and the speed and extent of recovery from such a disruption or recession (recovery). First, we built two different kinds of measures of resilience by aggregating the estimated parameters through non-centered and centered principal component analysis. Then, we use our model to investigate the relation between financial shock and the economic resilience across the region. The approach can be applied to several case studies, parsimoniously.

This paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.

Based on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.

A case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.

The pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

Purpose: This study investigates the role of telemedicine in sustaining healthcare systems in Europe, during the COVID-19 pandemic. It focusses on how telemedicine serves as a strategic response to modern healthcare challenges, emphasising its efficiency, accessibility, and patient-centred nature.

Need for the study: The need for this study arises from the escalating demands on healthcare systems, especially during the COVID-19 pandemic. It aims to understand the adoption of telemedicine practices across European Union (EU) countries and their impact on healthcare sustainability.

Methodology: This study employs hierarchical and K-Means clustering to analyse EU citizens’ attitudes towards teleconsultations during COVID-19. Principal component analysis (PCA) is used for data compression and insight extraction. Data is sourced from Eurofound’s 2020 and 2021 surveys, involving extensive participant responses across the EU.

Findings: The study’s findings reveal significant shifts towards digital healthcare solutions, such as an increase in online consultations and prescriptions. It identifies different patterns of telemedicine use across EU countries, influenced by socioeconomic and geographical factors. These findings offer insights into future healthcare policy and strategy development.

Practical implications: The findings provide valuable insights into the shifts in telemedicine adoption in the EU, highlighting the significance of economic and sociological factors in healthcare trends. This study stresses the importance of customising healthcare strategies to suit the unique needs and digital capabilities of different countries.

This study aims to explore the asymmetric effects of macroeconomic factors on the profitability of large-cap companies in an emerging country like Colombia, using the Morgan Stanley Capital International (MSCI) Colombia index as the basis.

We employ a combination of singular spectrum analysis (SSA) and principal component analysis (PCA) to identify and estimate four key macroeconomic factors that account for approximately 47.8% of Colombia's macroeconomy. These factors encompass indicators related to inflation and cost of living, foreign trade and exchange rate, employment and labor force and trade and production in Colombia. We utilize the distributed lag nonlinear model (DLNM) to analyze the asymmetric relationships between these factors and corporate profitability, considering different scenarios and lags.

Our analysis reveals that there are indeed asymmetric relationships between the identified macroeconomic factors and corporate profitability. These relationships exhibit variability over time and lags, indicating the nuanced nature of their impact on corporate performance.

This study contributes to the existing literature by applying a novel methodology that combines SSA and PCA to identify macroeconomic factors within the Colombian context. Additionally, our focus on asymmetric relationships and their dynamic nature in relation to corporate profitability, using DLNM, adds original insights to the research on this subject.

The purpose of this paper is to define the evaluation criteria for Chinese female lower body shapes and simplify the evaluation process of shapewear, including girdles, shaping pants, etc.

The study utilized a machine learning algorithm based on support vector regression and optimized by a genetic algorithm to construct an evaluation model for the contour beauty of Chinese female lower body shapes. A total of 64 virtual 3D models of women were measured. These models were rated by 42 raters using the Likert 9 psychological scale and data was obtained from 310 female samples.

Eight factors were selected and used to create a regression prediction model. The model achieved an accuracy of 84.7% for the training samples and 86.6% for the test samples.

The model can be utilized to assess the aesthetic appeal of the female lower body and to evaluate the shaping impact of shapewear. The research and evaluation of shapewear for the female lower body are of great significance, particularly in enhancing production efficiency.

The purpose of this study is to construct a robust index and subindices to measure the quality of corporate governance for 266 firms listed in South Africa from 2004 to 2021.

Public information on the compliance of King Code of Good Corporate Governance is used to construct a main index predicated on provisions relating to board characteristics, accounting and auditing and risk management. These categories are transformed into three subindices. All constructs are scored with binary coding and equally weighted.

Cronbach’s alpha test reveals that the index and subindices are highly reliable measures of corporate governance. The principal component analysis supports the construct validity of all measures.

The index is limited to only three corporate governance subcategories and only focuses on South Africa.

These corporate governance indices provide governing authorities, policymakers, investors and other market participants direct information on the quality of corporate governance in South African firms.

As South Africa lacks a formal corporate governance indicator, the development of an appropriate corporate governance index and subindices contributes towards understanding the quality of corporate governance in South African firms. To the best of the authors’ knowledge, this is the first paper to conduct robustness tests on corporate governance indices designed for South African companies.

The present study aims to investigate the relationship between building regulations, urban planning, and perceptions of housing affordability and prices in Prishtina, Kosovo.

A self-report survey with 1,000 respondents, selected through stratified probability sampling, provided the necessary data. Principal component analysis was applied to the questionnaire's internal structure, while regression analysis helped uncover housing affordability and housing prices perception predictors.

The study found that building regulation standards and zoning/land-use regulations reveal positive relationships with housing prices and housing affordability perception. Among these components, building regulations and standards show a stronger connection with housing affordability and price perception in comparison to urban planning and development.

By investigating the relationship between building regulations, urban planning, and housing affordability and price perception in Prishtina, the present research makes a valuable contribution to the existing literature. The findings of this research hold significant implications for policymakers, urban planners, and developers, highlighting the relevance of adopting a well-balanced approach to building regulations and urban planning in order to uphold and maintain housing affordability and understand housing price dynamics.

The novelty of this research originates from the investigation of these relationships within a rapidly urbanizing city context, contributing to a deeper understanding of the complex dynamics between regulatory policies and outcomes in the housing market. Further research should examine additional dimensions and employ longitudinal designs to gain a deeper understanding of the components predicting housing affordability and price perception in Prishtina and similar urban contexts.

The purpose of this work is to study the relationship between the fabric’s mechanical properties such as tear strength (T_S), breaking strength (B_S) and cloth’s dimensional stability (Sh), particularly, after industrial launderings (stone wash, enzyme wash, mixed wash and rinse). Hence, we select the most interrelationships using the principal component analysis (PCA) technique. In this study, the treatments of finishing garments during washing are the important parameters influencing the cloth’s dimensional and the fabric’s mechanical properties. To improve the obtained results, the selected significant inputs are also analyzed within their influence on shrinkage. The polynomial regression model relating the tear strength and the shrinkage of denim fabric proves the effectiveness of the PCA method and the obtained findings.

To investigate the matter, the type of washing, and their contributions to shrinkage, four types of fabrics manufactured into pants were used. These fabrics differ not only by their basis weights (medium and heavy weight fabrics) but, also by their compositions (within and without elastane) and their thread count (warp and weft yarn count, twist and density. To evaluate significant results, a factorial design analysis based on an experimental design was established. The choice of these treatments, as well as their design mode, led us to make a complete factorial experimental design.

According to the results, the prediction of shrinkage behavior as a function of the process washing input parameters seems significant and useful in our experimental design of interest. As a consequence, it was also concluded that after these input parameters, we can find the relationship between the shrinkage (Sh_warp and Sh_weft) and the mechanical properties such as tear strength (T_Swarp and T_Sweft) and breaking strength (B_Swarp and B_Sweft). Thanks to the PCA, it is very easy to reduce the number of the influent output parameters, and knowing these significant parameters, the prediction of mechanical properties knowing the shrinkage of denim garment, during the process of washing seems successful and can undoubtedly help industrial to minimize the poor workmanship of the finishing quality.

This study is very interesting for finishing denim garments. The shrinkage is very important for correcting measures in sewing, considering that a high shrinkage may cause the cancellation of the fit from the client. This type of defect cannot be repaired in the major part of the cases and causes a big loss for the company, moreover the mechanical properties. For this reason, analyzing the value of shrinkage before starting the production cycle is of great importance to apply the right balance to the pattern. The model of predicting the mechanical properties behaviors as a function of the shrinkage denim garment leads manufacturers to eliminate the test of mechanical properties that remain as destructive tests. Moreover, according to the results obtained, it may be concluded that prediction is still accurate through the shrinkage test which is an inevitable test. Even though, these results can bring a huge gain for the garment wash industries.

This work presents the first study predicting a relationship between the mechanical properties and denim garment shrinkage, applying the PCA technique to minimize the all-output parameters that are not significant or correlated with each other. Besides, it deals with the relationship developed between the fabric’s mechanical properties such as tear strength (T_S), breaking strength (B_S) and cloth’s dimensional stability (Sh), particularly, after industrial launderings (stone wash, enzyme wash, mixed wash and rinse). Moreover, it is notable to mention that the originality of this study is to let to the garment wash industries to save in production time of orders and also in quality.

The aim of this study is to investigate, in the Turkish market, the elements on the front and back labels of wine bottles depicting the characteristics of the wines and their impact on the purchasing decisions of wine consumers in Turkey.

The questionnaire consisted of 24 items that used a five-point Likert scale. Data were collected through a self-administered online survey completed by 340 participants. Following exploratory factor analysis with principal component analysis and varimax rotation, the number of factors was reduced to six: front and back-label consumer experiences, front label design elements, intrinsic cue information, back-label contents, storage/flavor profile and health warnings. The final section of the questionnaire presented five digitally designed front and back labels and asked participants to indicate their preferences.

The results indicated that reading/checking the front and back labels on wine bottles was more important than all other factors. Turkish consumers generally prefer traditional front label designs, while female consumers are more open to contemporary designs compared to male consumers. Back-label designs with more information are generally more acceptable, although female consumers with good wine knowledge may not consider food pairing, storage, or service information.

This research was designed only based on the elements of the front and back labels. The research should expand to include packaging characteristics such as bottle shape, bottle closure, price, and situational use.

The findings of this study offer valuable insights for Turkish wine producers aiming to enhance their marketing strategies by customizing their wine label designs to better align with the market.

This study is the preliminary study to investigate Turkish consumers purchasing decisions based on the front- and back-label characteristics by using both verbal and visual elements.