Sustainable development is an integrated blend of energetic, economic, social, environmental, and governmental actors, making it one of the most challenging and subjective aims of the United Nations. A sustainability project that does not consider any of these parameters can generate resistance among different actors of society. In this work, we have demonstrated that the traditional definition of sustainability encompassing economic development, environmental protection, and social justice does not adequately cover large-scale renewable energy projects. We have presented an illuminating case study of solar development in the Yucatan state of Mexico to show that the traditional three-legged stool of sustainable development fails to accommodate local power relations and their role in energy decision-making. This point is made through a substantive and illuminating ethnography of a solar project in the community of San José Tipceh, which involves the importance of energy democracy and understanding power relations in any assessment of sustainable development. These power relations are an important component of sustainable development, and all megaprojects, especially renewable energy projects, require special attention in the governance processes. Through an analysis of an example of implementation of a mega solar project, we argue that the methodology through which the power is exercised is an exercise of great importance because it leads us to a scientific discussion that structures an international reflection on sustainability. The case study consists of mixed methods, including several techniques to deepen the analysis of sociocultural aspects, and tries to present the limits and show the weakness of the sustainability perspective in such megaprojects. It is concluded that environmental justice as a basic element of the new era of renewable energies is limited and can generate social injustices. In the same way, this work shows how power is exercised in the implementation of energy projects and in the absence of equitable comprehensive governance that does not consider the local sociocultural contexts of the communities, resulting in the generation of new powers and permanent conflicts. At the end of this work, a framework to empower local sociocultural contexts for an equitable energy transition is recommended.

The ability of bacteria and archaea to modulate metabolic process, defensive response, and pathogenic capabilities depend on their repertoire of genes and capacity to regulate the expression of them. Transcription factors (TFs) have fundamental roles in controlling these processes. TFs are proteins dedicated to favor and/or impede the activity of the RNA polymerase. In prokaryotes these proteins have been grouped into families that can be found in most of the different taxonomic divisions. In this work, the association between the expansion patterns of 111 protein regulatory families was systematically evaluated in 1351 non-redundant prokaryotic genomes. This analysis provides insights into the functional and evolutionary constraints imposed on different classes of regulatory factors in bacterial and archaeal organisms. Based on their distribution, we found a relationship between the contents of some TF families and genome size. For example, nine TF families that represent 43.7% of the complete collection of TFs are closely associated with genome size; i.e., in large genomes, members of these families are also abundant, but when a genome is small, such TF family sizes are decreased. In contrast, almost 102 families (56.3% of the collection) do not exhibit or show only a low correlation with the genome size, suggesting that a large proportion of duplication or gene loss events occur independently of the genome size and that various yet-unexplored questions about the evolution of these TF families remain. In addition, we identified a group of families that have a similar distribution pattern across Bacteria and Archaea, suggesting common functional and probable coevolution processes, and a group of families universally distributed among all the genomes. Finally, a specific association between the TF families and their additional domains was identified, suggesting that the families sense specific signals or make specific protein-protein contacts to achieve the regulatory roles.

In this work, four artificial intelligence (AI) techniques, based on Artificial Neural Networks, Support Vector Machine (SVM), and Regression Tree Ensembles, were used to estimate the operating temperature of photovoltaic (PV) modules (T_PV). The models' input parameters correspond to experimental measurements of environmental (solar radiation, ambient temperature, relative humidity, wind speed, and wind direction) and operational (power output and tracking system) variables. Several AI models were trained and statistically compared with the measured data using a computational methodology that determines the performance and accuracy of the AI technique. Finally, a global sensitivity analysis was conducted to identify the ability of each technique to reflect the physical coherence of the phenomenon that is under study. It is reported that the four techniques can provide an estimate having a precision of about 93%. On the other hand, the sensitivity analysis demonstrates that all the models cannot correctly interpret the physical interaction of the input parameters with respect to T_PV, where the SVM is reported to be the most appropriate. The results indicate that the proposed methodology is a viable alternative for the estimation of T_PV by AI techniques. This methodology can be implemented as an alternative tool in the development of smart PV module cooling systems to improve its performance and to reduce its operating costs.

This paper presents a fuzzy controller for class of non-linear networked control systems; the varying time delays and packet loss are taken as a variable sampling period of the system. The variable sampling period is estimated using a time stamped and probability density function. A fuzzy model smoothly switches to estimate the system state; the antecedent input is the estimated sampling period and the consequent part is formed by linear models discretized with specific sampling periods. The fuzzy controller generates a control input using the estimated states to ensure system stability for a wide range of sampling periods. A two-degree-of-freedom helicopter is used to show the applicability and effectiveness of the controller with robustness to traffic.

A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.