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This paper highlights the need for studies that explore the experiences of entrepreneurs based in a family business setting who are considering retirement.

This paper is based on a concise review of the literature.

Retirement planning and subsequent succession management is a significant event in a family business and is an issue that requires research that captures and interprets the perspectives of the different stakeholders.

This paper synthesizes existing research on retirement in an entrepreneurial context, with an emphasis on family business and proposes three key areas of research.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

Higher education institutions (HEIs) around the world are engaged in internationalisation efforts. Yet internationalisation per se is associated with significant pressures on the environment and environmental resources, which need to be addressed. This study aims to assess the opportunities, benefits and challenges associated with the internationalisation of universities at a global level.

A total of 27 relevant case studies were extracted from the literature to illustrate how HEIs worldwide are ensuring sustainability in their internationalisation efforts.

Through case studies of international HEIs, the study lists the opportunities, benefits and challenges associated with the internationalisation of universities at a global level and some of the measures that may be deployed to reduce the environmental impacts of their international activities.

This study provides a welcome contribution to the literature because it outlines some of the works taking place at universities, where matters related to sustainable development are considered against a background of internationalisation efforts.

Based on profound information lacking in compiled information materials, the risks of losing knowledge related to the values of traditional woodworking processes are increasing. The purpose of this article is to collect and structure diverse marking tool data into a comprehensive, understandable and clear design schematic view, which serves as a basis for the accumulation and preservation of diverse marking objects and shows woodworking marking tool relation in the group and subgroup levels.

A method for marking tools structuring and analysis are described, including breaking down a set of objects into groups of marking objects, and assigning one or more attributes to the parcelled objects by arranging them into hierarchic levels. Research is based on marking tools used by carpenters, joiners and woodcarvers mainly in the Baltic region.

Collected data, object analyses and comparison within-group and subgroup levels are based on written and visual sources, museum and museum funds visits, and participation in the local craftsmen events. The created structure is expandable in group and subgroup levels. The most comprehensive way for object structuring is chosen as a base to reveal a diversity of the objects.

Structure schemes of woodworking marking tools are important in scientific, educative and cultural levels based on their wide range and use. Aggregated information of the woodworking tools serves as a base for existing tool studies and improvement, new tool and wood product creation as well as complements the structure of the upcoming woodworking hand tool database and book.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

This study aims to investigate how organizational culture (OC) and transformational leadership (TL) affect corporate social responsibility (CSR) performance (environmental performance and social performance) and financial performance (FP) in the context of the Italian manufacturing sector. Grounded in resource-based view theory, this study explores how these factors influence sustainable firm performance.

Data gathered from 260 employees were analyzed to examine the multidimensional aspects of CSR, encompassing social and environmental sustainability.

The findings highlight the pressing need for sustainable firm performance in the existing environment, supporting the hypothesis that firms achieve sustainable and FP through the recognition of TL and OC. Moreover, a positive and significant relationship between CSR performance and FP was established, underscoring the strategic importance of integrating CSR initiatives into core business practices. This study offers valuable insights for both academia and firms, providing theoretical and practical implications that underscore the importance of cultivating a robust OC to drive performance enhancements.

This study is novel because it is one of the first, to the best of the author’s knowledge, to analyze the relationships between TL, OC and performance components associated with CSR.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

The purpose of this study is to determine whether the Ottoman Empire’s net interest payments and foreign debt were sustainable or not in terms of their burden on budget revenues, using the method of historical econometric analysis.

In this study, the period between 1847 and 1882 of the Ottoman Empire is analyzed for sustainability analysis. Within the framework of the study, unit root tests and econometric analysis methods frequently used in the literature were used to analyze the sustainability of public debt. In the econometric analysis, in addition to various unit root tests, current econometric analysis methods, in particular Fourier expansion, were also used.

The results of econometric analyses showed that the burden of interest payments and foreign debt on the budget of the Ottoman state was unsustainable. This situation clearly shows the reason for the official bankruptcy of the Ottoman Empire, which was declared in 1875.

Although this study reveals the bankruptcy process of an important structure such as the Ottoman Empire in the historical process through econometric analyses, it also gives a very important message to today’s states. Accordingly, today’s state policies and decision-making mechanisms should take these results into account and strive to make the burden of public interest payments sustainable. It is believed that the study will shed light on the public finance policies of today’s states by drawing lessons from the collapse process of the Ottoman state.

Unlike the historical assessments in the literature on the decline of the Ottoman Empire, this study presents a cliometric approach by applying current econometric analysis techniques to past historical data. The study explains the unsustainability of the Ottoman Empire’s interest payments and external debt burden in the period under consideration in a way that, to the best of the authors’ knowledge, has not been done before.

The study investigates the relationship between hospital environmental factors and the well-being of geriatric in-patients. It aims to identify the impact of architectural design on comfort, safety, privacy and stress levels experienced by elderly patients during their hospital stays.

Employing a mixed-methods approach, the research assesses the experiences of 100 geriatric in-patients across various hospital types through surveys, observational checklists and state anxiety measurements. The methodology involves examining architectural features, patient perceptions and correlations among environmental variables and patient experiences. Statistical analyses, including correlations and chi-square tests, were employed to discern associations between environmental variables and patient experiences.

The research identified key architectural features significantly impacting geriatric patients' experiences. Factors such as sturdy beds, furniture quantity, lighting conditions, proximity to facilities and ward occupancy levels were found to influence spatial, sensory and social comfort. Notably, proximity to facilities and control over the immediate environment were crucial for self-control and safety perceptions. Privacy, highly valued by patients, correlated with the presence of curtains and ward occupancy. Moreover, patient stress levels exhibited correlations with autonomy, privacy and ward occupancy.

This research offers significant insights into the criticality of specific architectural elements in enhancing comfort and reducing stress for geriatric in-patients. These findings hold substantial value for healthcare facility design, emphasizing the need to prioritize certain design aspects to promote the well-being of elderly patients during hospitalization.