Metaphoric Perceptions of Adolescents towards the Concept of Corona Virus (COVID-19)

This research was carried out to examine the metaphoric perceptions of adolescents regarding the concept of coronavirus (COVID-19).For this purpose, 60 adolescents (33 girls, 27 boys) studying at different grade levels of three high schools at different socio-economic levels determined by maximum diversity sampling from high schools in Erzincan city center were included in the research.With the questionnaireform developed for adolescents, “Coronavirus (COVID-19) ……is similar. Because………." was asked.The data obtained were analyzed by content analysis method. In this context, categories suitable for metaphors were created and the consistency of the categories with metaphors and descriptions was determined. Eight metaphors that are not suitable for the subject of the study are excluded from the study. In line with the results obtained, it was determined that adolescents developed 49 different metaphors and these metaphors were collected under eight categories. It can be said that the metaphoric perceptions of adolescents regarding the concept of coronavirus (COVID-19) are generally negative.

The Difficulties the Caregivers of Cancer Patients Experience in the Covid-19 Pandemic

Purpose This is a qualitative study using interpretive phenomenological analysis to determine the difficulties experienced by caregivers of cancer patients during the Covid-19 pandemic. Methods Fourteen cancer patients were interviewed between 20 December 2020 and 20 January 2021 using maximum diversity sampling. The data were collected using semi-structured interview forms, one-on-one interviews, and online interview technique. Results The experiences of caregivers of cancer patients regarding the Covid 19 pandemic were presented under four themes, namely emotional problems, economic problems, neglect of health, and behavioural responses associated with Covid 19”. Conclusions It was found that caregivers of cancer patients were emotionally affected, faced economic difficulties, neglected their own health, and developed behavioural responses to the risk of Covid-19 during the pandemic.

Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are rare and usually limited to the work they extend upon. This paper's goal is to conduct an experimental study on four recent deep learning procedural level generators for Sokoban to explore their strengths and weaknesses. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models' quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are rare and usually limited to the work they extend upon. This paper's goal is to conduct an experimental study on four recent deep learning procedural level generators for Sokoban to explore their strengths and weaknesses. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models' quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are either nonexistent or limited to the works they extend upon. This paper’s goal is to conduct an experimental study on four recent deep learning procedural level generation methods for Sokoban (size = 7 × 7) to explore their strengths and weaknesses and provide insights for possible research directions. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models’ quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are either nonexistent or limited to the works they extend upon. This paper’s goal is to conduct an experimental study on four recent deep learning procedural level generation methods for Sokoban (size = 7 × 7) to explore their strengths and weaknesses and provide insights for possible research directions. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models’ quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

Ant diversity sampling in the Brazilian Amazon: a comparison of litter collection and pitfall trapping

Introduction: Ants in tropical forests are a hyper-diverse group that plays important ecological roles. Several studies on tropical forests have used different sampling protocols to capture soil ants, making it difficult to compare responses and patterns of diversity between studies. Thus, research that compares different well-structured and standardized sampling methodologies to adequately estimate the richness of ant species in tropical forests is necessary. Objective: In this study, we examined the combination of catches with pitfall traps with and without baits and litter collections for soil ant sampling. Methods: In the fall traps, we use two baited (sardines and bananas) and one non-baited. For the manual collections (litter sampling), the litter and only the topsoil of the loose soil were collected. Results: We found that traps containing sardine baits collected a greater abundance of ants, whereas non-baited traps collected a greater richness of ant species. On the other hand, litter collections captured the largest number of exclusive species, presenting a different species composition from the pitfall traps (with and without baits). In general, baited traps showed greater abundance in more degraded locations, while manual collections and unbaited pitfalls captured more individuals in preserved environments. Conclusions: Our results provide evidence that, for accurate sampling of soil ant assemblages in tropical forests, the use of different methodologies is necessary to capture a greater diversity of species because the methods differ in effectiveness according to habitat.

Special Issue: Breeding Towards Agricultural Sustainability - Invited

Wheat yields are stagnating around the world and new sources of genes for resistance or tolerances to abiotic traits are required. In this context, the tetraploid wheat wild relatives are among the key candidates for wheat improvement. Despite of its potential huge value for wheat breeding, the tetraploid GGAtAt genepool is largely neglected. Understanding the population structure, native distribution range, intraspecific variation of the entire tetraploid GGAtAt genepool and its domestication history would further its use for wheat improvement. We report the first comprehensive survey of genomic and cytogenetic diversity sampling the full breadth and depth of the tetraploid GGAtAt genepool. We show that the extant GGAtAt genepool consists of three distinct lineages. We provide detailed insights into the cytogenetic composition of GGAtAt wheats, revealed group-, and population-specific markers and show that chromosomal rearrangements play an important role in intraspecific diversity of T. araraticum. We discuss the origin and domestication history of the GGAtAt lineages in the context of state-of-the-art archaeobotanical finds. We shed new light on the complex evolutionary history of the GGAtAt wheat genepool. We provide the basis for an increased use of the GGAtAt wheat genepool for wheat improvement. The findings have implications for our understanding of the origins of agriculture in southwest Asia.

Investigating the Virtualized Cultural Heritage of Piri Reis with Social Participation and Map Literacy Skills

In this study, it was aimed to transfer Piri Reis’s 1513 world map into an interactive and dynamic environment to reflect social participation and map literacy skills of pre-service social studies teachers, to determine the reflection of the skills from the views of the pre-service teachers and the information they have structured in the database. In this sense, the virtualized map was integrated into a geographical information system application developed by the researcher. This research, in which a qualitative research approach was adopted, was carried out as a case study. The views of the pre-service teacher’s skills and environment were evaluated with content analysis, and their reflection skills were evaluated with descriptive analysis. Eighteen students determined with the maximum diversity sampling method participated in the study, which lasted for two and a half months. It was concluded through the findings obtained from the views of pre-service teachers that social participation and map literacy skills could be reflected in various dimensions, the map could be interpreted collectively with social participation and map literacy skills, and cultural heritage could be transformed into a form of instructional technology. It was determined that the general views of pre-service teachers regarding this environment were mostly positive and the environment beneficially enabled them to discover cultural heritage, developed various skills and gave different perspectives. Still, some pre-service teachers remained passive in the environment. In the findings obtained from the database of the virtualized map, it was determined that the sub-dimensions of map literacy with social participation skill were mostly reflected in the form of understandinginterpreting symbols, finding location - coordinate, measuring distance, finding direction, reading - interpreting a map, but not reflected in using scales.