For decades, researchers have experimented with the possibility that machines can equal human linguistic capabilities. Recently, advances in the field of natural language processing (NLP) as well as a substantial increase in available naturally occurring linguistic data on social media platforms have made more advanced methodologies such as sentiment analysis (SA) gain substantial momentum on contemporary applications. This document compiles what the authors consider to be some of the most important concepts related to SA, as well as techniques and processes necessary for the various stages of its implementation. Furthermore, specific applications related to the extraction and classification of social media data using novel SA techniques are presented and quantified, with an emphasis on those used for the identification of mental health degradation during the COVID-19 pandemic. Finally, the authors present several conclusions highlighting the most prominent benefits and drawbacks of the methods discussed, followed by a brief discussion of possible future applications of certain methods of interest.

Solar energy is in high demand due to its environmental benefits and economic potential; however, concerns remain about the total impact it holds. In 2020, for Spain, Castilla-La Mancha was the second autonomous community with the highest photovoltaic energy production. Thus, a systematic review on 15 large-scale PV solar energy projects was carried out to assess the industry impacts, through environmental impact assessment (EIA), within the Autonomous Community of Castilla—La Mancha. An estimation of these impacts from a pre-operational approach is presented, based on primary energy needs and emissions discarded during its life cycle due to the manufacture, operation, and recycling of the photovoltaic modules. Based on both the life cycle assessment (LCA) and EIA, the approaches were compared with the results obtained. The obtained results suggest that determining the actual impacts of power plants in this region could provide justified information for the public administration and technicians in the measures for the installation and operation of PV plants and the future benefits of renewable solar technologies. Furthermore, the results indicate the possibility to recognize the relationship between the size of the plant and a high generation capacity, with a shorter time to pay for emissions from the manufacture and recycling of panels, suggesting that it is around 1.66–2.08 years for the Castilla-La Mancha region.

Rapid economic growth, expanding populations and increasing prosperity are driving up demand for energy, water and food, especially in developing countries. To understand the energy consumption of a country, we used the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) approach. The MuSIASEM is an innovative approach to accounting that integrates quantitative information generated by distinct types of conventional models based on different dimensions and scales of analysis. The main objective of this work is to enrich the MuSIASEM approach with information from multivariate methods in order to improve the efficiency of existing models of sustainability. The Biplot method permits the joint plotting, in a reduced dimension of the rows (individuals) and columns (variables) of a multivariate data matrix. We found, in the case study of Ecuador, that the highest values of the Exosomatic Metabolic Rate (EMR) per economic sector and Economic Labor Productivity (ELP) are located in the Productive Sector (PS). We conclude that the combination of the MuSIASEM variables with the HJ-Biplot allows us to easily know the detailed behavior of the labor productivity and energy consumption of a country.

To understand the sustainability problem for Panama’s metropolitan area, its urban metabolism was investigated. A way to evaluate its current state was obtained by estimating a sustainable indicator based on the Green City Index. With the abstraction of the identified problems, the biomimetic strategy “problem-based approach” was carried out, where different pinnacles of nature were selected as a reference for the design of regenerative solutions. These were inspired by the understanding of the living world and how to include ecosystems in urban designs. Therefore, a framework was proposed for positive generation and natural solutions in cities to take advantage of the regenerative potential in Panama City. Using ecosystem services, a set of indicators were developed to measure regeneration over the years at the city scale. The results indicate that from the 11 selected pinnacles, 17 solutions inspired in nature were proposed to regenerate cities. Consequently, a SWOT analysis was realized along with a questionnaire by experts from different fields. The findings obtained show that the feasible solutions were: arborization, green facades, solar roofs, e-mobility, green corridors, bicycle lanes, sidewalks, and biofilters. This research represents a step towards creating and developing regenerative cities, thus improving the quality of life of living beings and ecosystems present in society.

Coastal areas are attractive for human settlements because they allow easy access to benefits like food, security, and fishing. These aquatic ecosystems are supported by photosynthetic organisms that constitute the base of the food web. The term “Harmful Algal Bloom” (HAB) refers to the excessive proliferation of some taxa of these microorganisms reaching harmful levels to humans and other organisms. Biotoxins produced by these HABs could be transferred to the food chain and are the best-documented impact that HABs have on humans. The location and abundance of the HAB species producing the toxin is a good indicator of a possible human health hazard. The aim of this study was to monitor potentially harmful benthic/epibenthic microalgae in Punta Galeta, Panama over a 15-month period. The 3 main microalgae found were 2 dinoflagellates from the genera Prorocentrum and Ostreopsis and 1 diatom from the genus Coscinodiscus. Sampling made with both natural and artificial substrates yielded similar overall abundance patterns; however, for the macroalgae samples, there appeared to be significant host preferences for Ostreopsis and Coscinodiscus. Physicochemical measures taken at the site were found to fall within previously reported growth parameters for the microalgae found in the study.

Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish-language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale.