An\'alise e modelagem de jogos digitais: Relato de uma experi\^encia educacional utlizando PBL em um grupo multidisciplinar

Traditional software engineering education generally emphasizes strict collaboration and technical skills However active teaching strategies where students actively engage with the material transitioning from passive observers to active manipulators of realworld tools have shown effectiveness in software engineering The evolving market demands new skills in the context of digital transformation presenting challenges such as modeling complex business scenarios and navigating the interconnections between people systems and technologies Shifting from conventional software engineering instruction to active methodologies like ProblemBased Learning PBL has proven to bring realworld market challenges and realities into the classroom This article details an experience from the Digital Games Analysis and Modeling course in the Digital Games Masters program at Pontifical Catholic University of Sao Paulo It covers the discussed concepts case study rolebased work method and steps of the meetings We also present examples of outcomes like requirement diagrams context diagrams use case diagrams class diagrams interviews and others that contributed to the Game Design Document GDD These were created by each group during the meetings alongside their game prototypes Additionally a discussion on the developed capabilities is includedComment: in Portuguese languag

Flower Visitation by Bees, Wasps and Ants: Revealing How a Community of Flower-Visitors Establish Interaction Networks in a Botanical Garden

The Hymnoptera order includes several flower-visiting insects (e.g. ants, bees, and wasps) and the coexistence of many different species in the same community can generate interspecific competition. Notwithstanding shared communities, research which evaluates how these taxonomic groups influence a whole community of flower-visiting Hymenoptera is lacking. Moreover, abiotic factors can also impact these floral visits, because each organism responds differently to climatic variations. The goal of this study is to evaluate abiotic factors, specifically relative air humidity and air temperature, which may be able to impact the number and the frequency of interactions between hymenopterans and flowers and to assess the composition and niche organization, by making use of interaction networks, of the entire community of flower-visiting Hymenoptera at the botanical garden of the Universidade Federal Rural do Rio de Janeiro. For the duration of a year, we took samples in that botanical garden, compartmentalizing the collections temporally in accordance with the time of the insects’ shift (morning or afternoon). We observed a positive influence of air temperature on the number of ant interactions and visits. It is also possible to observe that most of these interaction networks exhibited a nested and non-modular pattern and an average level of network specialization. In addition, bees stood out as the species with the highest frequency of visits and with the most generalist behavior. This study demonstrates how a botanical garden can sustain a diverse community of floral visiting Hymenoptera in an urban environment and why it consists in an important tool for biodiversity conservation

The impact of pain and nocturnal cramps on sleep quality in Charcot Marie Tooth disease: a case-control study

Introduction: Charcot-Marie-Tooth disease is an inherited neuropathy that presents two main forms - type 1 and type 2 -, differentiated by the speed of the nervous conduction. Our goal was to assess sleep in Charcot-Marie-Tooth disease and its relationship with pain perception and nocturnal cramps. Material and Methods: This was a case-control study. The case group was composed of 10 volunteers diagnosed with the type 1 and 23 with the type 2. The control group was composed of 22 individuals from the same family matched by age and gender. Volunteers underwent clinical screening to assess the presence of nocturnal cramps and filled the brief pain inventory, the Chalder fatigue scale, the Epworth sleepiness scale, and the Pittsburgh sleep quality index. Sleep was evaluated by actigraphy. Results: Type 2 patients presented a more severe perception of pain and fatigue, more time spend awake after sleep onset, and had lower sleep efficiency. The individuals who reported nocturnal cramps also had worse perception of pain, reduced sleep latency, and increased sleep fragmentation. Conclusion: The Charcot-Marie-Tooth type 2 was related with worse sleep quality, perception of pain, and fatigue and these parameters were negatively related

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost