›Ich, Christine‹ enthält Auszüge aus ihrem ›Buch von den Wechselfällen des Schicksals‹ (1402/03) sowie aus ihrer Prosaschrift ›Christines Vision‹ (1405) – autobiografische Lebensdokumente mit detaillierten Informationen zu ihrer Herkunft und Familie und zum Leben in Paris am Hofe von König Charles V. im Zeichen einer extrem launischen Fortuna. Von den Höhen einer glücklichen Jugend und Ehe stürzt Christine ab in Armut und Vereinsamung und gibt uns einen sehr anschaulichen Bericht von finanziellen Nöten und Lebenskrisen. Zugleich erfahren wir, wie sie diese überwindet, sich durch Berührung mit dem Zauberstab der Fortuna in einen ›Mann‹ verwandelt, ihr Leben mutig meistert, sich autodidaktisch weiterbildet, wie sie in selbstgewählter Einsamkeit zu schreiben beginnt – und eine erfolgreiche europäische Schriftstellerin wird.

Isolamento social, quarentena, são estressores que podem contribuir com o surgimento de sofrimento psíquico, conduzindo ao uso de medicamentos psicotrópicos.

Technological advances in generative artificial intelligence (GAI) have increased the relevance of pedagogical conversational agents (PCAs) like chatbots to support students. Despite their potential for learning as a service, PCAs are rarely used by educational institutions. They often neglect the benefits of group learning, like collaborative problem- solving, and insufficiently address learners’ motivational challenges in digital environments. To tackle these issues, we designed and evaluated a gamified PCA for information systems (IS) education with collaborative and competitive group learning capabilities. We investigated the effects of collaborative and competitive elements in PCA design using a 2x2 experiment (n = 120). Our findings show that collaborative game elements foster learners’ motivation and highlight the PCA’s role as a group learning moderator. This study contributes new knowledge on GAI in IS education, meaningful gamification in the PCA context, and computer-supported collaborative learning.

INTRODUÇÃO: A pandemia do COVID-19 ocasiona perturbações psicológicas que afetam a capacidade de enfrentamento de toda a sociedade.

Probabilities calculated in the probability and contingency fields, explaining the concept of Bayes' theorem.

Ants often face injuries during foraging, or interspecific competition, elevating infection risk and mortality among the wounded. To avoid this, ants engage in wound care on injured nestmates as a form of social immunity. In this study, we show that Cataglyphis nodus desert ants perform differentiated wound care behavior, depending on wound location and state. Leg-injured ants received significantly more wound care than antenna-injured ants. However, leg wounds with induced infections received barely any wound care from nestmates, leading to similar levels of mortality in isolation and inside the nest. Instead, such leg-infected ants were mainly found outside of the nest. Infections of antennal wounds showed no change in the level of wound care, nor increased mortality. Our results suggest that the level of wound care in ants can be flexibly adjusted to the perceived mortality risk of injuries. Leg injuries pose a greater risk of infection and mortality compared to antennal injuries, likely because of the larger wound area and increased vascular circulation, necessitating intensive prophylactic care to prevent infection. This study is the first to show wound care in Cataglyphis ants, despite their short lifespan, and offers significant insights into social immunity mechanisms.

The US healthcare system relies on trustworthy and effective data exchange. Yet its data flows are impaired by pervasive conflicting interests and incentives and the lack of authentic data, leading to persistent inefficiencies. By ensuring authentic data, decentralized identity (ID) presents a potential solution. However, information systems (ISs) have historically shown high rates of unsuccessful deployment in healthcare. Since formative use case evaluation frameworks can enhance deployment success rates, this paper conducted a participatory action research study to develop and test an evidence-based, theory-informed, and practical framework for assessing healthcare use cases' amenability to decentralized ID. The framework serves healthcare organizations as a decision-support tool in choosing suitable use cases for decentralized ID and planning corresponding initiatives. For researchers, the socio-technical factors that determine use cases's amenability to decentralized ID enhance their understanding of this novel IS and the socio-technical change it brings about.

OpenAI has published “A Student’s Guide to Writing with ChatGPT”. In this article, I review their advice and offer counterpoints, as a university researcher and teacher. After addressing each of OpenAI’s 12 suggestions, I conclude by mentioning the ethical, cognitive and environmental issues that all students should be aware of before deciding to use or not use ChatGPT. I also answer some of the more critical feedback at the end of the post.

Use of steel fibers in cement-based composites has become popular due to possibility of using that material as total or partial replacement of conventional reinforcement, as in floor slabs or tunnel cover. This reinforcement aims to improve composites properties with regard, mainly, to cracks propagation. It’s known that concrete has high compressive strength and durability, but its toughness is low, being considered a fragile material. The performance of the fiber as stress bridge depends on several factors, among them bond strength between fiber and matrix. Interfacial Transition Zone (ITZ) is one in which first cracks that lead to material failure appear. Some researches with mineral additives, such as rice husk ash (RHA), are aimed at ITZ densification. RHA has a high content of amorphous silica, which gives it high potential for its pozzolanic activity. That RHA characteristic is able to potentiate new hydration products formation, especially C-S-H, giving a higher density to cement matrix and pores and voids reduction. Research’s objective was to evaluate the effect of cement substitution by RHA, aiming to densify cement matrix to point where the adhesion stress with steel fiber was higher than in conventional mixture. For this, control specimens were produced and 15% of the cement replaced by RHA. Through the single fiber pullout test it was possible to measure the adhesion stress between steel fiber and cement matrix. Results indicate an increase in the adhesion strength with a long curing time, which confirms that the RHA hydration occurs late in relation to cement.

The inefficiency of the localization process mostly causes delays and operational difficulties in wireless sensor network (WSN) topologies. When localization is suboptimal, the time required for path creation significantly increases, leading to reduced network lifetime and diminished routing efficiency. To address these critical issues, our research focuses on developing a novel procedure that optimizes routing and localization. In this context, localization is the initial step for reaching sensor terminals, followed by activating the routing mechanism. To tackle these issues in Underwater Wireless Sensor Networks (UWSN), the proposed work presents the Elman Bat-based Hello Routing (EBbHR). The primary objective of our research is to locate wireless sensor nodes within the designed UWSN effectively. The process is initiated to achieve this by creating the necessary wireless sensor hubs within the MATLAB Simulink environment. Subsequently, the novel EBbHR function is activated to facilitate the precise localization of all sensor hubs, thereby enhancing the overall data transmission. The success of the designed EBbHR is assessed by considering UWSN factors, and a comparison with current approaches is carried out to show the advancements made by the model.

Knowledge of species' functional traits is essential for understanding biodiversity patterns, predicting the impacts of global environmental changes, and assessing the efficiency of conservation measures. Bats are major components of mammalian diversity and occupy a variety of ecological niches and geographic distributions. However, an extensive compilation of their functional traits and ecological attributes is still missing. Here we present EuroBaTrait 1.0, the most comprehensive and up-to-date trait dataset covering 47 European bat species. The dataset includes data on 118 traits including genetic composition, physiology, morphology, acoustic signature, climatic associations, foraging habitat, roost type, diet, spatial behaviour, life history, pathogens, phenology, and distribution. We compiled the bat trait data obtained from three main sources: (i) a systematic literature and dataset search, (ii) unpublished data from European bat experts, and (iii) observations from large-scale monitoring programs. EuroBaTrait is designed to provide an important data source for comparative and trait-based analyses at the species or community level. The dataset also exposes knowledge gaps in species, geographic and trait coverage, highlighting priorities for future data collection.

Forests are key native habitats in temperate environments. While their structure and composition contribute to shaping local-scale community assembly, their role in driving larger-scale species distributions is understudied. We used detailed forest inventory data, an extensive dataset of occurrence records, and species distribution models integrated with a functional approach, to disentangle mechanistically how species-forest dependency processes drive the regional-scale distributions of nine forest specialist bats in a Mediterranean region in the south of Spain. The regional distribution patterns of forest bats were driven primarily by forest composition and structure rather than by climate. Bat roosting ecology was a key trait explaining the strength of the bat-forest dependency relationships. Tree roosting bats were strongly associated with mature and heterogeneous forest with large trees (diameters\thinspace>\thinspace425 mm). Conversely, and contrary to what local-scale studies show, our results did not support that flight-related traits (wing loading and aspect ratio) drive species distributional patterns. Mediterranean forests are expected to be severely impacted by climate change. This study highlights the utility of disentangling species-environment relationships mechanistically and stresses the need to account for species-forest dependency relationships when assessing the vulnerability of forest specialists towards climate change.

Clustering is a crucial part in the field of data mining, and common clustering methods include divisionbased methods, hierarchy-based methods, density-based methods, and grid-based methods. In order to improve the accuracy of clustering, an optimization study is made mainly for the division-based method FCM clustering, and an FCM clustering method that integrates active learning and principal component analysis (PCA) is proposed. The method first uses principal component analysis to reduce the dimensionality of the data to reduce the computation of electricity data, then trains the sample model by active learning, and introduces the entropy (Entropy) method in the uncertainty sampling method, the larger the entropy means the greater the uncertainty of the sample, and the smaller the entropy means the smaller the uncertainty of the sample, so as to filter the electricity data, and finally the electricity data are clustered by FCM clustering The power data is finally categorized by FCM clustering, and with the proliferation of power data, the power data can be more accurately categorized using this method to achieve the stability of the power grid as well as the utilization rate. Experimental results on three datasets show that this method improves the accuracy of power data clustering by up to 2 percentage points compared to the traditional clustering method without active learning, and achieves good results in each dataset compared to other methods.

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions.

Research over the last 10 years highlights the increasing importance of hybridization between species as a major force structuring the evolution of genomes and potentially providing raw material for adaptation by natural and/or sexual selection. Fueled by research in a few model systems where phenotypic hybrids are easily identified, research into hybridization and introgression (the flow of genes between species) has exploded with the advent of whole-genome sequencing and emerging methods to detect the signature of hybridization at the whole-genome or chromosome level. Amongst these are a general class of methods that utilize patterns of single-nucleotide polymorphisms (SNPs) across a tree as markers of hybridization. These methods have been applied to a variety of genomic systems ranging from butterflies to Neanderthals to detect introgression, however, when employed at a fine genomic scale these methods do not perform well to quantify introgression in small sample windows.