According to the United Nations, by 2050, about 68% of the world’s population will live in urban areas. This population increase requires environmental resilience and planning ability to reduce the negative environmental impacts associated with growth. In this scenario, life cycle analysis, whose standards were introduced by ISO 14000 series, is an essential tool. From this perspective, smart cities whose concern about environmental sustainability is paramount corroborating SDG 11. This study aims to provide a holistic view of environmental technologies developed by Brazilian inventors, focused on life cycle analysis, which promotes innovation by helping cities build greener, more efficient, resilient, and sustainable environments. The methodology of this article was an exploratory study and investigated the scenario of patents in the life cycle. 209 patent processes with Brazilian inventors were found in the Espacenet database. Analyzing each of the results individually revealed processes related to air quality, solid waste, and environmental sanitation. The review of patent processes allowed mapping of the technological advances linked to life cycle analysis, finding that the system is still little explored and can present competitive advantages for cities.

Knowledge transfer, assimilation, transformation and exploitation significantly impact performing business activities, developing innovations and moving forward to new business models such as transferring to a circular economy. However, organizations’ decisions or willingness to transition to a circular economy are very often also influenced by the external environment. The study aims to determine the influence of the external environment on the transfer from a linear to a circular economy while mediating knowledge assimilation. The quantitative research involved 159 Nordic capital companies operating in Estonia and Lithuania. The survey has been performed by means of the CATI method. The analysis has been done also by applying structural equation modelling (SEM). In order to perform mediation analysis, IBM SPSS and a special PROCESS macro have been used. The study showed that knowledge assimilation partially mediates the relationship between the external environment and the transfer to the circular economy. Hence, the external environment’s direct effect is much more significant than the indirect. The added value of the study also consists in extending the concept of circular economy by including some aspects of absorptive capacity and the external environment.

This study analyzes the dynamic relationships between tourism, gross domestic product (GDP) per capita, exports, imports, and carbon dioxide (CO₂) emissions in five South Asian countries. A VAR-based Granger causality test is performed with time series data from Bangladesh, India, Nepal, Pakistan, and Sri Lanka. According to the results, both bidirectional and unidirectional relationships among tourism, economic growth, and carbon emissions are investigated. Specifically, tourism significantly impacts GDP per capita in Pakistan, Sri Lanka, and Nepal, yet it has no effect in Bangladesh or India. However, the GDP per capita shows a unidirectional relationship with tourism in Bangladesh and India. The unidirectional causal relationship from exports and imports to tourism in the context of India and a bidirectional relationship in the case of Nepal. In Pakistan, it is observed that exports have a one-way influence on tourism. The result of the panel Granger test shows a significant causal association between tourism, economic growth, and trade (import and export) in five South Asian economies. Particularly, there is a bidirectional causal relationship between GDP per capita and tourism, and a significant unidirectional causal relationship from CO₂ emissions, exports, and imports to tourism is explored. The findings of this study are helpful for tourism stakeholders and policymakers in the region to formulate more sustainable and effective tourism strategies.

Nigeria plays important roles in the overall socio-economic development of the entire African continent, including entrepreneurial activities. There is a less focus on the immersion of women and youths in playing participatory roles in digital entrepreneurship and digital technology innovation in order to boost the economic growth of the country. The primary objective of this study is to explore women and youths’ immersion, specifically in connection with digital entrepreneurship and digital technology innovation, for the purpose of fostering the growth of the economy. The methodology employed in this study is Critical Content Analysis (CCA) of cursory literature as an integral part of the qualitative method. The literature was sourced through different databases, such as library sources, journals, and the core collection of Web of Science (WOS), and the collections of studies used for analysis were between 2018 and 2023. The results demonstrated that small and medium enterprises (SMEs) play significant roles in digital entrepreneurship activities in the country. In addition, there are various entrepreneurship programmes in the country, such as the Youth Entrepreneurship Development Programme (YEDP), and there is awareness of the effectiveness and efficiency of digital entrepreneurship. In addition, the result further established that the use of digital technology is an important innovation for the success of digital entrepreneurship in the country. The study further indicated that five factors of women and youths’ immersion in entrepreneurship (perception and opportunities, business performance, digital adoption, skill acquisition, and enabling environment) can boost the growth of the economy in the country. In conclusion, the knowledge and skills of entrepreneurs are major drivers of wealth and job creations, with women and youths playing an active role in the overall entrepreneurship programmes. It is suggested that the stakeholders and actors in entrepreneurship should collaborate to foster the participation of women and youths in entrepreneurship programmes in the country.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.