Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI

In this paper, we present "1001 Nights", an AI-native game that allows players lead in-game reality through co-created storytelling with the character driven by large language model. The concept is inspired by Wittgenstein's idea of the limits of one's world being determined by the bounds of their language. Using advanced AI tools like GPT-4 and Stable Diffusion, the second iteration of the game enables the protagonist, Shahrzad, to realize words and stories in her world. The player can steer the conversation with the AI King towards specific keywords, which then become battle equipment in the game. This blend of interactive narrative and text-to-image transformation challenges the conventional border between the game world and reality through a dual perspective. We focus on Shahrzad, who seeks to alter her fate compared to the original folklore, and the player, who collaborates with AI to craft narratives and shape the game world. We explore the technical and design elements of implementing such a game with an objective to enhance the narrative game genre with AI-generated content and to delve into AI-native gameplay possibilities

Grade-control outdoor turning flight of robo-pigeon with quantitative stimulus parameters

IntroductionThe robo-pigeon using homing pigeons as a motion carrier has great potential in search and rescue operations due to its superior weight-bearing capacity and sustained flight capabilities. However, before deploying such robo-pigeons, it is necessary to establish a safe, stable, and long-term effective neuro-electrical stimulation interface and quantify the motion responses to various stimuli.MethodsIn this study, we investigated the effects of stimulation variables such as stimulation frequency (SF), stimulation duration (SD), and inter-stimulus interval (ISI) on the turning flight control of robo-pigeons outdoors, and evaluated the efficiency and accuracy of turning flight behavior accordingly.ResultsThe results showed that the turning angle can be significantly controlled by appropriately increasing SF and SD. Increasing ISI can significantly control the turning radius of robotic pigeons. The success rate of turning flight control decreases significantly when the stimulation parameters exceed SF > 100 Hz or SD > 5 s. Thus, the robo-pigeon's turning angle from 15 to 55° and turning radius from 25 to 135 m could be controlled in a graded manner by selecting varying stimulus variables.DiscussionThese findings can be used to optimize the stimulation strategy of robo-pigeons to achieve precise control of their turning flight behavior outdoors. The results also suggest that robo-pigeons have potential for use in search and rescue operations where precise control of flight behavior is required

Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights Using Generative AI

Generative AI (GenAI), encompassing image generation and large language models (LLMs), has opened new avenues for gameplay experiences. This paper introduces "1001 Nights", a narrative game centered on GenAI. Drawing inspiration from Wittgenstein's note, "The limits of my language mean the limits of my world", the game exemplifies the concept of language as reality. The protagonist, Shahrzad, possesses a unique power: specific keywords, such as "sword" or "shield", when spoken by others in tales, materialize as tangible weapons, serving as battle equipment against the King. Players guide the LLM-driven King in co-creating narratives, with GPT-4 employing LLM reasoning methods to ensure story consistency. As these narratives progress, the depicted world is dynamically generated and visualized through Stable Diffusion, blurring the boundaries between narrative and in-game reality. This fusion of interactive storytelling combines gameplay paradigms and story together with dynamic content generation. Players not only aim to alter Shahrzad's fate from the original folklore, but also leverage the power of natural language to shape the game's world. With this example, we propose the term "AI-Native games" to categorize innovative games where GenAI is fundamental to the game's novel mechanics and very existence

Vulnerability assessment of urban remnant mountain ecosystems based on ecological sensitivity and ecosystem services

Urban remnant mountains (URMs) are precious natural green habitat patches that can provide a series of ecosystem services for multi-mountainous cities. The increase in ecological sensitivity and degradation of ecosystem services affected by urban expansion and climate change have led to an increasing vulnerability of urban remnant mountain ecosystems (URMEs). To explore the vulnerability of URMEs, taking the central urban built-up area of the Guiyang city as the study area and URMs as the research object, the vulnerability of URMEs under natural factors and human disturbance was analyzed based on the Pressure-State-Response (PSR) model. The results showed that: (1) Karst rocky desertification, human disturbance, and road density within the buffer zones around URMs were the most important factors affecting the vulnerability of URMEs. Karst rocky desertification was the most likely eco-environmental problem of URMs, and carbon storage was the most important ecosystem service of URMEs. (2) Characteristics of fragile karst habitats in URMs and unreasonable human activities led to high ecological vulnerability, mainly with moderate and severe vulnerability predominating, and the low vulnerability of URMEs when they had moderate park utilization. (3) The ecological vulnerability of small URMs and those distributed in the urban center is higher, and the invulnerable URMEs and the slightly vulnerable URMEs are mainly distributed in the urban edge. The results of this study could provide references for ecological restoration and protection of URMs, and offer a basis for improving the resilience of multi-mountainous cities

Table_1_Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma.docx

BackgroundThe coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma.MethodsTwo sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury.ResultsWe discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes.ConclusionWe identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.</p

Table_3_Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma.docx

BackgroundThe coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma.MethodsTwo sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury.ResultsWe discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes.ConclusionWe identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.</p

DataSheet_1_Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma.docx

BackgroundThe coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma.MethodsTwo sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury.ResultsWe discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes.ConclusionWe identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.</p

Table_2_Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma.docx

BackgroundThe coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma.MethodsTwo sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury.ResultsWe discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes.ConclusionWe identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.</p

Presentation_1_Grade-control outdoor turning flight of robo-pigeon with quantitative stimulus parameters.pdf

IntroductionThe robo-pigeon using homing pigeons as a motion carrier has great potential in search and rescue operations due to its superior weight-bearing capacity and sustained flight capabilities. However, before deploying such robo-pigeons, it is necessary to establish a safe, stable, and long-term effective neuro-electrical stimulation interface and quantify the motion responses to various stimuli.MethodsIn this study, we investigated the effects of stimulation variables such as stimulation frequency (SF), stimulation duration (SD), and inter-stimulus interval (ISI) on the turning flight control of robo-pigeons outdoors, and evaluated the efficiency and accuracy of turning flight behavior accordingly.ResultsThe results showed that the turning angle can be significantly controlled by appropriately increasing SF and SD. Increasing ISI can significantly control the turning radius of robotic pigeons. The success rate of turning flight control decreases significantly when the stimulation parameters exceed SF > 100 Hz or SD > 5 s. Thus, the robo-pigeon's turning angle from 15 to 55° and turning radius from 25 to 135 m could be controlled in a graded manner by selecting varying stimulus variables.DiscussionThese findings can be used to optimize the stimulation strategy of robo-pigeons to achieve precise control of their turning flight behavior outdoors. The results also suggest that robo-pigeons have potential for use in search and rescue operations where precise control of flight behavior is required.</p