Industry 4.0 is a new industrial paradigm that brings new challenges for workers as they have to actively collaborate with robots in an interconnected environment. The main purpose of this paper is to give a brief overview of how collaborative robots can be used to support human workers in Industry 4.0 manufacturing environments. The use of collaborative robots certainly brings many advantages as these machines enable more efficient product systems by supporting workers with both physical and cognitive tasks, as is the case of exoskeletons. On the other hand, human–robot interaction might also have some risks if human factors considerations are not well thought through throughout the process. Moreover, it becomes clear that the role that humans have been playing so far in a manufacturing environment is rapidly changing. Human workers will have to adapt to these new systems by acquiring and improving a set of skills that have sometime been neglected until nowadays.

In recent years, information has become one of the most important goods for organizations as it brings insights about customer preferences and internal processes that could help to improve organizational performance, decrease costs, improve customer engagement, among other benefits. The consumption of information within an organization has been studied in the literature under several approaches associated to information system success nor information management, but the Information Consumer eXperience (ICX) has not been evaluated following a formally defined methodology. In this work, a methodology to formalize the ICX evaluation process within the organization is proposed. The main goal of this methodology is to improve ICX into the organization by generating recommendations based on information consumers perceptions under a customer experience CX approach. The proposed methodology consists of 3 sequential stages: Characterization, Experimentation and Analysis. In The Characterization Stage an exploratory diagnosis is performed, including the experimental setup planification, consumers behavior exploration, and a preliminary version of the customer Journey Map. The Experimentation Stage is focused on data collection using different instruments such as surveys, interviews, questionnaire, and a mixed qualitative and quantitative instrument to generate data about consumers expectations and perceptions. In the third stage of Analysis, the collected data is analyzed to generate a definitive Customer Journey Map, through quantitative and qualitative data analysis. Our proposed ICX evaluation methodology is the first formally described methodology for information consumer perceptions analysis and experience evaluation. Which could be used to face ICX analysis into any kind of organization that works with information.

In the context of a private organization, this study focuses on the implementation of an agile methodology for assessing Information Consumer Experience (ICX). The objective of this research is to diagnose and apply a simplified version of the methodology within the organizational setting. The proposed agile methodology offers a rapid approach to evaluate ICX in smaller organizations or departments within the organization, intending to enhance it through the generation of recommendations based on information consumer perceptions. This approach centers on Customer Experience (CX). The methodology is divided into three sequential stages: Characterization, Experimentation, and Analysis. In the Characterization stage, information consumers, providers, and the products, systems, or services delivering information are identified. The Experimentation stage focuses on data collection, employing various information collection instruments, as well as both qualitative and quantitative approaches to gather insights into consumer expectations and perceptions. The third stage, Analysis, involves the processing and analysis of the collected data, using both quantitative and qualitative methods to integrate the findings. This study introduces an innovative methodology for evaluating and enhancing the Information Consumer Experience in any organization that manages data and information. The results obtained in this research provide guidance for improving ICX within the organization under study and serve as a resource for future research in the fields of information management and customer experience.

Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

Quinoa (Chenopodium quinoa) and lupin (Lupinus spp.) are ancient nutritious crops cultivated in the Andean region. In addition to basic nutrients, quinoa and lupin contain a variety of bioactive components with potentially beneficial effects on health. The profile and content of bioactive components such as polyphenols, phytosterols, and carotenoids in quinoa grains and lupin beans, and the effect of processing (drying, milling, debittering, germination, kilning) on their nutritional properties and bioactive components are described. Red and black quinoa showed the highest total phenolic and total flavonoid content and the best radical scavenging activities. Recent studies suggest that the pigments of these colored quinoa varieties are betacyanins. In addition to flavonoid glycosides and other more familiar secondary bioactive metabolites, quinoa contains phytoecdosteroids, a class of triterpenoids. In some studies, they have demonstrated insulin-sensitizing, fat-reducing, and fitness-enhancing activities in mammals without inducing androgenic or estrogenic effects. Although the high nutritional quality of lupin has been recognized for decades, not much is known about the content of phytochemicals in this legume. Many of these compounds are bioactive and may exert beneficial effects on health. They include polyphenols, isoflavones, carotenoids, phytosterols, tocopherols, polysaccharides, and peptides with antioxidant, antimicrobial, anticarcinogenic, and anti-inflammatory activities. Processing of the crops to food products has an impact on the nutritional and sensory quality (e.g., germination may improve their nutritional quality even further).

Thyroid hormones (T3 and T4) are essential for the development and function of the mammalian central nervous system (CNS). For this reason, controlling the availability of thyroid hormones to neural cells is an essential step. This is probably why this is a complex process with several levels of regulation, with the ultimate goal of controlling the appropriate spatial and temporal levels of T3, the nuclear active thyroid hormone, in the CNS. The mechanisms responsible for an appropriate thyroid hormone availability and action during foetal brain development include the expression of thyroid hormone receptors, to which T3 binds to mediate its actions by regulating the expression of T3-target genes; transplacental passage of thyroid hormones to the foetus; expression of transmembrane transporter proteins with the ability to transport thyroid hormones across cell membranes and expression of deiodinase enzymes that can activate (T4 to T3 conversion) or inactivate thyroid hormone action. Lack of thyroid hormone action during brain development can lead to several brain impairments, which functional consequences depend on the timing and the cause for thyroid hormone deficiency.

The fields of artificial intelligence and bio-inspired robotics have proven to cross several other fields of expertise including Cognitive Neuroscience. Here, we review principles of interaction between a natural (or artificial) organism and the environment where it lives. Then we ask whether such structural coupling shapes the way it behaves. For instance, how the sensory processing of the external world controls actions, and finally, behavior? We remind the main sources of inspiration for bio-inspired robotics and relate them to currently active fields of research like Embodiment and Enaction. These latter concepts are illustrated by examples of recent researches on two main aspects: (i) bio-inspired algorithms processing sensory signals coming from the outer world and (ii) bio-inspired controllers based on human behavior and physiology. Finally, we include an example of a bio-inspired robot controller design based on the concepts here exposed.

The bridges reinforcement is an expensive activity. In this line, new cheap ways to fix bridges have been developed. One of them is the bridges reinforcement through conversion of the cable-stayed arch. The main aim is minimizing the distances between the tensions of each lengthwise position between the original bridge and the bridge with the reinforcement by the cable-stayed arch. For that, it is necessary to know the order that the tension must be applied. In this paper, we resolve this problem by using a recent metaheuristic called Crow Search Algorithm which is based on the intelligent behavior of crows, this approach is inspired by the feature of crows to hide their excess food. The obtained results are compared other approximate techniques by using a well-known statistical test. Promising results reveal that this new algorithm is competitive to solve the proposed problem.

The problem existing in bridges that collapse due to undermining of the piers that sustain it, as well as collapses due to hydraulic action, generate high costs that can be reduced, making the repair of its structures. It is possible to reinforce them by modification, incorporating cable-stayed arches, and tensioning of hangers to support them. In this paper, a new approach is presented to solve the problem effectively, using a modern metaheuristic based on nature called monarch butterfly optimization it works imitating the way of migration, who uses the monarch butterflies. The results obtained are compared with those provided by black hole algorithm through the use of a known statistical test.

Data and knowledge management has taken on a fundamental role in decision-making processes into organizations in recent years. The consumption of information within the organization is present in most of the operational and administrative processes of the organization. Therefore, information consumption also takes on importance in the design and application of organizational strategy initiatives. The Information Consumer Experience (ICX) can be considered as a particular case of Customer eXperience (CX), involving expectations, satisfactions and pains of information consumers within the organization. ICX analyzes the interaction between employees or departments and the information provider organization or departments, through their information products, systems and services offered. The main objective of this work is to analyze ICX within an organization by conducting an exploratory analysis in order to make a diagnosis based on three fundamental elements: organizational departments involved, participants (Sample) and information products, systems or services used by information consumers. As results of this analysis, consumers’ needs were identified and consumers profiles were generated, which lead us to elaborate a Customer Journey Map that described the different points of contact and the perceptions of consumers. Finally, the greatest pains of consumers can be identified, related to: (1) high complexity for information searching, (2) better-known information systems preference over the more specialized ones, and (3) the isolated Information distribution across departmental unit hides the overview state of the department.

Abstract In Chile the experiment shows that candidate gender typically is not a strong component of leadership templates for either male or female participants, yet when gender is significant women candidates are favored (to be a deputy and in feminine policy areas). The study also shows that women are evaluated as capable in masculine policy areas. Coalition, in contrast, regularly has a significant effect on candidate evaluations, and coalitions receive higher scores on policies associated with their issue ownership. Findings that women fit the leader image of Chilean urban young adults are notable given the long history of limited election of women to congress, paired with a woman president for eight years. These findings are also important given the context of political protest in the years surrounding the experiment, with protests often having young women as “on the street” leaders.

This chapter discusses general patterns of brooding in decapod crustaceans from aquatic to terrestrial environments, addressing behavioral adaptations as well as costs and benefits. Brooding embryos is a common feature among decapods. However, brooding exhibits a wide range of modes that are highly dependent on the environment. Brooding is less common in marine systems, whereas there is a general pattern of extended brooding with terrestrialization. Exceptions are crabs that have invaded land directly via the seashore, i.e. land crabs that have indirect development like their marine ancestors. During terrestrialization, adaption to environmental stressors like desiccation, UV radiation, temperature variability, mechanical support, and osmolality seemed to generally favor decreasing larval development and increasing duration of brood care. Thus, crustaceans developed more complex brooding mechanisms as adaptive responses to the colonization of land (e.g., osmoregulation of the maternal fluids, marsupial fluid, sealed and specialized marsupium, provision of nutritious material, grooming and cleaning, ventilation of the embryo masses). However, clear brooding behaviors are also observed among several marine species (e.g. grooming and cleaning, oxygen provision). The major efforts to characterize general brooding patterns among decapod crustaceans and describe brooding behaviors were not accompanied by comprehensive studies to understand the costs and the benefits of brooding. Several studies have addressed the positive influence of the mother on embryo development, but the efforts to quantify the impact on embryo survival are still limited. This chapter identifies problems that need further consideration to reach a deeper understanding of the evolution of brooding in decapod crustaceans.

This chapter presents an analysis of the main problems and challenges related to teaching and learning Chile's recent history. This analysis relates the authors' experience with theoretical production in the context of a transitional society characterized by post-conflict. The emergence and relevance of the typical matters of teaching recent history are based on a dialogue among disciplinary perspectives including the New Social History, theorizations about memory as an interdisciplinary category, and the materialization of the secondary students' historical learning through their narratives of the past. The discussion of memory and history are contextualized within the Hispano-American context, particularly in the Southern Cone of the continent. The chapter follows a socio-cultural theoretical perspective along with the theoretical contributions from an analysis of historical awareness. The findings describe historical explanations of Chilean students, which allow them to understand the present and recognize intersection points among their narratives and the reference frameworks established by a dominant national curriculum.

Native berries that grow in the southern area of Chile, including the Patagonia, are recognized as foods that exert beneficial effects on health, mainly due to their high content of phenolic compounds. The majority of native berry fruits belong to the Myrtaceae (Murtilla), Berberidaceae (Calafate), Ericaceae (Chaura), Elaeocarpaceae (Maqui), and Rosaceae (Chilean strawberry) families, many of which have been traditionally used by the locals as food and herbal medicines. These berry fruits have a high content of phenolic compounds (especially anthocyanins, responsible for their pink, red, blue, and purple colors) and exhibit very high antioxidant capacity in vitro, and relevant antioxidant and anti-inflammatory effects in vivo. The results of randomized dietary intervention clinical trials with berries are also reported, and relevant mechanisms of protection against the oxidative stress/inflammation damage involved in the development of age-related diseases such as cardiovascular diseases, cancer, diabetes mellitus, and mental disorders, are described.

El Yali is a complex wetlands system composed by more than 14 waterbodies, located in central Chile and delimited by two basins of the most important rivers of the region. Among the waterbodies is a coastal lagoon, some estuaries, artificial wetlands, salt mine and inner lagoons that were coastal lagoons in the past but due to tectonic processes have been moved and raised to their current location. The damming of the river that delimits the system to the south in 1968 cutoff the natural the sedimentary supply to the extensive beach and dunes, leaving the wetlands in a situation of vulnerability before climate change and variability, anthropic pressure, ocean swells and tsunamis. In the present chapter is illustrated the degradation that the wetlands system is suffering, by means of the estimation of tendencies of the long term records available in the zone, antecedents about its natural history, anthropic pressure and natural disasters like earthquakes, tsunamis, ocean swells and ENSO, along with field monitoring that has been carried out with the objective of implementing an ecological restoration. These antecedents show a decrease of precipitations and river discharges, an increase of ambient temperature and sea surface temperature, a rising of sea level and a change of the incident waves.

Rumor detection is a recent and quite active topic of multidisciplinary research due to its evident impact on society, which can even result in physical harm to people. Despite its broad definition and scope, most existing research focuses on Twitter, as it makes the problem a bit more tractable from the point of view of information gathering, processing, and further retrieval of social network features. This paper presents an empirical study of novel machine learning ensembles for the rumor classification task on Twitter. As it has been observed that certain neural models perform better for specific veracity labels, we present a study on how the combination of such classifiers in different kinds of ensemble results in a new classifier that has better performance. Using benchmark data, we evaluate three groups of models (two groups of deep neural networks and a control group of classical machine learning methods). In addition, we study the performance of three ensemble strategies: Bagging, Stacking, and Simple Soft Voting. After varying several parameters of the models, such as the number of hidden units and dropout, among others experimental factors, our study shows that the LSTM, Stacked LSTM (S-LSTM), Recurrent Convolutional Neural Networks (RCNN), and Bidirectional Gated Recurrent Unit (Bi-GRU) yields the best results reaching an accuracy of 0.93 and an average precision of 0.97.

Big Data and Artificial Intelligence (AI) confer substantial advancements across diverse sectors of society, education included. However, it is imperative to extend the discourse surrounding these interventions to align with ethical research principles, data usage, and prevailing legislation. Consequently, this study endeavors to scrutinize scholarly literature spanning the interval between 2011 and 2022, concentrating on the ethical facets recommended for inquiries that meld big data and artificial intelligence within educational contexts. Employing a bibliographic research approach, the inquiry was conducted on the CAPES Periodicals Portal, utilizing descriptors such as “ethics,” “big data,” “artificial intelligence,” and “education.“ Out of a corpus of 84 articles, nine were incorporated into this research subsequent to the application of inclusion and exclusion criteria. These works encompass both empirical and theoretical contributions, interlinking big data and AI with educational settings. Researchers employ documentary sources, questionnaires, and their individual pedagogical experiences for data collection. Methodologically, these studies lean towards techniques such as descriptive content analysis, descriptive statistical analysis, confirmatory factor analysis, and textual data mining for data analysis. With regard to ethical principles spotlighted in the studies, salient themes include responsibility, transparency, reliability, and privacy. The outcomes suggest that crucial lacunae still exist, particularly concerning aspects like informed consent and other pivotal ethical protocols.

The search for improved teaching methods and a more personalized education has been a constant challenge in the educational field. Learning Analytics (LA), the measurement, collection, analysis, and reporting of data about students and their contexts, has emerged as a promising approach to understand and optimize learning environments. This paper focuses on identifying the expectations of high school teachers in the southern region of Santa Catarina, Brazil, regarding the use of LA. Through a questionnaire distributed to high school teachers, the study investigated their perceptions and opinions about the application of LA in their pedagogical practices. The results indicate that teachers have positive expectations regarding the potential impact of LA on improving teaching and learning. They understand the concept of LA and believe that the feedback provided by the system should be presented in a clear and accessible format. Furthermore, the study reveals that teachers expect support and guidance from educational institutions in accessing and interpreting analytical results. They emphasize the importance of proper training for all stakeholders involved in the implementation of LA. Overall, the study highlights the teachers’ positive attitudes towards utilizing LA as a powerful tool to enhance education and promote students’ academic and professional development in the southern region of Santa Catarina.

Social networks are used every day to report daily events, although the information published in them many times correspond to fake news. Detecting these fake news has become a research topic that can be approached using deep learning. However, most of the current research on the topic is available only for the English language. When working on fake news detection in other languages, such as Spanish, one of the barriers is the low quantity of labeled datasets available in Spanish. Hence, we explore if it is convenient to translate an English dataset to Spanish using Statistical Machine Translation. We use the translated dataset to evaluate the accuracy of several deep learning architectures and compare the results from the translated dataset and the original dataset in fake news classification. Our results suggest that the approach is feasible, although it requires high-quality translation techniques, such as those found in the translation’s neural-based models.

In this article we propose S2C, a strategy-to-code methodological approach to integrate organisational, business process, and information system modelling levels to support strategic alignment in software development. Through a model-driven approach and under the Conceptual-Model Programming paradigm, the proposal supports the semi-automatic generation of working software, as well as traceability among the modelling levels. Via a working example, we illustrate how strategic definitions can be traced into specific software components by the integration of three modelling methods: Lite*, for modelling strategic reaction to external influences, Communication Analysis, for business process modelling, and the OO-Method, for modelling the conceptual schema of the information system. We discuss how this approach not only supports strategic alignment, but fosters the elicitation of business process performance measurement requirements, as well as its relevance considering the business and code alignment of the most recent enterprise architecture and agile software development initiatives.