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This work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.

Since 2014, the CGIAR research program on Climate Change, Agriculture and Food Security has collaborated with different stakeholders to implement climate change adaptation approaches and practices in critical locations in the Central American Dry Corridor. A new university course for professionals in the Dry Corridor aims to scale these approaches and practices. This article presents the core elements of the course, summarizes the main results, and offers recommendations for future editions. It was observed that the different trajectories and experiences of professionals participating in the first edition of the course facilitated a lively exchange of knowledge, the integration of local experiences in teaching, and the integration of learning in follow up proposals of governmental and non-governmental organizations. The following aspects should be central in future course editions: diversity of participants, adaptation of didactic strategies to the needs of different users, and follow-up support, as well as the integration of course concepts and practices in the actions of different organizations. The latter requires strong organizational commitment.

Two models were developed to simulate energy flows in a mangrove area of A. germinans and A. bicolor in the Bay of Panama, considering the importance of these areas in CO2 fixation. The first model (black box) consisted of the use of artificial neural networks for estimation, using meteorological data and energy flows calculated by the Eddy Covariance method for model training. The second model (grey box) used the RC circuit theory, considering a non-steady state model for the flow of water from the ground to the atmosphere. A methodology was developed to reduce the uncertainty of the data collected by the sensors in the field. The black box model managed to predict the fluxes of latent heat (R2 > 0.91), sensible heat (R2 > 0.86), CO2 (R2 > 0.88), and the potential of water in the air (R2 > 0.88) satisfactorily, while the grey box model generated R2 values of 0.43 and 0.37, indicating that it requires further analysis regarding the structuring of the equations and parameters used. The application of the methodology to filter the data improved the effectiveness of the model during the predictions, reducing the computational capacity necessary for the resolution of the iterations.

Passive solutions for more energy-efficient buildings are critical to improving our odds in the current energy crisis. This work focuses on assessing the thermal performance of different envelope construction layouts in a tropical climate through proposed indicators regarding the thermal mass degree (TMD) and insulation degree (ID). For this, a numerical study was performed for a reference building (RB) in Panama City and validated with the electricity consumption bills. Behavioral and sensitivity analyses were employed to identify critical heat gains and the most important envelope constructions, resulting in the layouts of the roof and external walls. Optimization analyses were performed to find adequate layouts to reduce the discomfort hours. Different roofs, external walls, internal partition layouts, and glazing types were evaluated. Results indicated that the adequate envelope configuration is a roof layout with low TMD and ID, along with wall layouts with high TMD and low ID.

STEM (science, technology, engineering, and math) has taken center stage as a priority policy agenda for Qatar’s leadership. At present, STEM stands as a fundamental catalyst for Qatar’s sustainable economic, environmental, human, and social development goals, as is outlined in the Qatar National Vision 2030. The aim of this exploratory study was to investigate the determinants of students’ interest in pursuing Science, Technology, Engineering, and Mathematics (STEM) studies and eventual careers in Qatar. This study used a survey involving a representative sample of a total of 425 students from public (government-funded) middle schools in the country. Data for this research were gathered using a survey distributed to students in grades 7, 8, and 9. Guided by the Social Cognitive Theory, a survey was implemented with a view to investigating the intrinsic and extrinsic factors likely to contribute to student STEM educational and career interest. Two main statistical tests were carried out: independent sample t-tests and one way ANOVA. Results derived from the study reveal that gender, nationality, and parental education and occupation served as predictors of student interest in a STEM degree or profession. The results derived from this study have important implications for STEM-related fields of study and career.

In the last decade, great strides have been made to increase access to education and enrollment rates at all levels (primary, secondary, and higher education). Actions such as promoting equal access to education, preventing school dropout, and establishing free education, among other actions proposed by organizations such as UNESCO, seek to guarantee the universality of education. This is undoubtedly a great challenge, and to address it, it is necessary to characterize how students face the learning process, considering factors such as autonomy, competencies, and their own basic psychological needs, looking for effective learning models based on students’ needs in a changing world, where ICTs play a relevant role. This research analyzes learning models, motivation, self-determination, and technologies used for learning achievement in higher engineering education. The study will highlight relevant initiatives and the most commonly used technological tools. It will also identify learning models that seek to link emotional intelligence, self-determination, cognitive skills, and ICT. The PRISMA protocol guided the study, finding 132 relevant articles to be analyzed and proposing best practices in using learning models, strategies, and ICT to foster students’ intrinsic motivation.

In order to improve environmental performance, the participation of effective and transparent citizens and governments that help counteract corruption in environmental matters is crucial. In this sense, this work focuses on exploring relationships between e-participation, e-government, the corruption index and environmental performance indicators. To this end, a sample comprising 116 countries from varying geographic regions is used in conjunction with indicators of environmental performance, e-participation, e-government and the corruption index. Through the use of the HJ-biplot and STATIS multivariate statistical techniques, it will be possible to observe the role that these variables play in countries’ behavioural patterns with respect to environmental performance. The results show a correlation between the indicator ‘perception of corruption’ and environmental performance; therefore, the lower the level of corruption, the higher the environmental performance index. We conclude that countries that exhibit more e-participation, lower levels of corruption and better level income are more likely to follow policies and programmes aimed at achieving better environmental performance.