Lived-in grassland : nomadic architecture in the nomadic community of Mongolia

Traditional modes of living and production on the Mongolian grassland are quietly changing under the impact of modern civilization, and a higher demand for a comfortable life is on the way. Shifts in career and lifestyle have changed expectations Mongolians have for housing, and traditional dwellings have not adapted to these new demands in the modern era. Fortunately, thousands of years of constant transition in nomadic life have prepared Mongolians to adapt to the new mode of survival and living in the process of constructing their residential buildings. The process of improvement of living conditions is bound to mean traditions have to change and foreign culture must be embraced, joined and integrated with traditional regional culture. How do we protect traditional culture with so much change? Simultaneously, how might these dwellings help nomads better adapt to modern life? What is the pattern of the next generation of grassland life? My design is divided into two phases. Trial and Transformation: This phase is to renovate the existing Mongolian grassland housing in a simple way, determining design rules for modern Mongolian houses, and forming a model that can be duplicated. Interior space planning and layout are the main target of the renovation. According to the layout rules of traditional Mongolian yurts, a gathering space, like a circular living room, is to be designed, which represents the cultural center with a stove as the core of family living space in traditional layout, including technical issues of the kitchen and toilet, indoor ventilation, lighting and privacy. Future and Challenges: An increasing number of people will leave the grassland for urban life. What will be needed to support the next generation? For those who remain, what type of housing will they choose to live in on the grassland? What kind of innovative changes will take place in the pattern of the house of the nomads? Will the selection and application of various materials both be capable of protecting the environment and bringing different vitality to the grassland? During this phase, the possibilities and diversity of living spaces for nomads in the future will be mainly explored. I designed a house built on the prairie. It has the basic functions and the public space. The living space is independent and can be assembled any time to serve different purposes. The ethos of the Mongolian nomads will remain unchanged no matter what the modern life will be like in future. It will last on the prairie. it\u27s reflected not only in the architectural form, but also in the new lifestyle of Mongolians

Development of the Life After Sports Transition (LAST) Online Course for Collegiate Student-Athletes: Pretest-Posttest Study

Transitioning into athletic retirement can have negative impacts on college student-athletes’ psychological, social, emotional, and physical well-being, yet few educational programs exist to help augment college student-athlete preparation for embracing life after sports. The objective of this study was to develop and evaluate a new Life After Sports Transition (LAST) online course for college student athletes. A single group pretest-post-test study evaluated effects of the LAST course among a convenience sample of college student-athletes (n=10) attending a NCAA Division I university. Paired sample t-tests examined changes in athletic identity, psychological well-being, hope, and self-reflection/insight. Propensity score matching (PSM) of pretest scores and age was used to reduce effects of the pretest differences in the small sample. At posttest, participants were also asked to assess the overall quality of the online LAST course. There was a decrease observed in athletic identity scores from pretest to posttest which approached statistical significance (P=.06). PSM analyses indicated that participants with higher GPA scores had significantly higher environmental mastery (b=2.28, SE=0.49, Pb=2.78, SE=1.20, P=.02, 95% CI: 0.42 to 5.14) scores at post-test than participants with lower GPA scores. However, contrary to our hypotheses, participants also reported lower scores on self-reflection/insight (P=.004, Hedges g = 1.65) and self-acceptance (P=.042, Hedges’ g = 0.93) at post-test. Despite these counter intuitive findings, participants rated the LAST course highly on most distance education quality dimensions. While student-athlete participation in the LAST course was associated with a decline in athletic identity, findings suggest that future life after sports programs focus more on introspective mediators of lifestyle change (i.e., self-reflection and self-acceptance) in order to foster more positive life transitions for college student-athletes

A Simple Graphene NH₃ Gas Sensor via Laser Direct Writing.

Ammonia gas sensors are very essential in many industries and everyday life. However, their complicated fabrication process, severe environmental fabrication requirements and desorption of residual ammonia molecules result in high cost and hinder their market acceptance. Here, laser direct writing is used to fabricate three parallel porous 3D graphene lines on a polyimide (PI) tape to simply construct an ammonia gas sensor. The middle one works as an ammonia sensing element and the other two on both sides work as heaters to improve the desorption performance of the sensing element to ammonia gas molecules. The graphene lines were characterized by scanning electron microscopy and Raman spectroscopy. The response and recovery time of the sensor without heating are 214 s and 222 s with a sensitivity of 0.087% ppm-1 for sensing 75 ppm ammonia gas, respectively. The experimental results prove that under the optimized heating temperature of about 70 °C the heaters successfully help implement complete desorption of residual NH₃ showing a good sensitivity and cyclic stability

Holocene climate change and anthropogenic activity records in Svalbard: a unique perspective based on Chinese research from Ny-Ålesund

Climate change in the Arctic region is more rapid than that in other areas owing to Arctic amplification. To better understand climate change and the driving mechanisms, long-term historical reconstructions throughout the Holocene and high-resolution records of the past few hundred years are required. Intense anthropogenic activities in the Arctic have had a great impact on the local environment. Here, we review the Holocene climate change record, responses of the ecosystems to climate change, and the anthropogenic impacts on the environment based mainly on Chinese research from Ny-Ålesund. Climate reconstruction studies from Svalbard have revealed several cold episodes during the Holocene, which are consistent with ice rafting events in the North Atlantic region and glacier activity from Greenland, Iceland, and Svalbard. The ecosystem also showed corresponding responses to climate change, especially during the late Holocene. Over recent decades, anthropogenic activities have caused serious pollution and deterioration to the local environment in Svalbard in areas frequented by people. Greater environmental protection is therefore needed to reduce the anthropogenic impacts on the local environment

Voyager: An Open-Ended Embodied Agent with Large Language Models

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/.Comment: Project website and open-source codebase: https://voyager.minedojo.org

Research trends from 1992 to 2022 of acupuncture anesthesia: a bibliometric analysis

BackgroundAcupuncture anesthesia is a significant technical development that originated in China in 1958 and was introduced to the West in the early 1970s. Due to its relative novelty, it has been the subject of intense scrutiny and contestation. Since the early 1970s, the use of acupuncture as a complementary treatment for opioid analgesics has been accepted. Research on acupuncture anesthesia has helped to reduce clinical opioid abuse. However, only a few articles have focused on previous publications that reflect the trend of the study, the main investigators, reciprocal collaboration, and other information in this field. In view of this, we utilized bibliographic analysis methods to objectively analyze current trends and research hotspots in this field, aiming to provide a foundation and reference for future studies.MethodsThe Web of Science database was searched for publications related to acupuncture anesthesia between 1992 and 2022. The CiteSpace and VOSviewer were used to analyze the annual publications, authors, Co-cited authors, and their countries (regions) and institutions, co-occurrence keywords, burst keywords, Co-citation references and Co-citation journals.ResultsA total of 746 eligible publications were retrieved from the database for the analysis, including 637 articles and 109 reviews. And the trend of annual publications continued to grow. Aashish J. Kumar, Daniel I. Sessler, Baoguo Wang, and Paul F. White published the most papers in this field (7), and all authors, had a very low centrality (<0.01). China (252) and the University of California System (21) were the most productive country (region) and institution, respectively, while the United States (0.62) and University of California System (0.16) had the highest centrality. After removing keywords related to the search strategy, the three most frequent were pain (115), electroacupuncture (109), and stimulation (91). The six most recent burst keywords were recovery, transcutaneous electrical acupoint stimulation, systematic review, quality, general anesthesia, and surgery. Wang et al.’s article had the highest co-citation count (20), whereas Zhang et al.’s articles had the highest centrality (0.25). The Journal of Anesthesia and Analgesia was the most influential one (408 co-citations).ConclusionThis research provides valuable information for the study of acupuncture anesthesia. In recent years, frontier topics in acupuncture anesthesia research have been the promotion of perioperative rehabilitation, anesthesia management, and quality improvement

MimicPlay: Long-Horizon Imitation Learning by Watching Human Play

Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world. However, learning complex long-horizon tasks often requires an unattainable amount of demonstrations. To reduce the high data requirement, we resort to human play data - video sequences of people freely interacting with the environment using their hands. Even with different morphologies, we hypothesize that human play data contain rich and salient information about physical interactions that can readily facilitate robot policy learning. Motivated by this, we introduce a hierarchical learning framework named MimicPlay that learns latent plans from human play data to guide low-level visuomotor control trained on a small number of teleoperated demonstrations. With systematic evaluations of 14 long-horizon manipulation tasks in the real world, we show that MimicPlay outperforms state-of-the-art imitation learning methods in task success rate, generalization ability, and robustness to disturbances. Code and videos are available at https://mimic-play.github.ioComment: 7th Conference on Robot Learning (CoRL 2023 oral presentation

Eureka: Human-Level Reward Design via Coding Large Language Models

Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation tasks, such as dexterous pen spinning, remains an open problem. We bridge this fundamental gap and present Eureka, a human-level reward design algorithm powered by LLMs. Eureka exploits the remarkable zero-shot generation, code-writing, and in-context improvement capabilities of state-of-the-art LLMs, such as GPT-4, to perform evolutionary optimization over reward code. The resulting rewards can then be used to acquire complex skills via reinforcement learning. Without any task-specific prompting or pre-defined reward templates, Eureka generates reward functions that outperform expert human-engineered rewards. In a diverse suite of 29 open-source RL environments that include 10 distinct robot morphologies, Eureka outperforms human experts on 83% of the tasks, leading to an average normalized improvement of 52%. The generality of Eureka also enables a new gradient-free in-context learning approach to reinforcement learning from human feedback (RLHF), readily incorporating human inputs to improve the quality and the safety of the generated rewards without model updating. Finally, using Eureka rewards in a curriculum learning setting, we demonstrate for the first time, a simulated Shadow Hand capable of performing pen spinning tricks, adeptly manipulating a pen in circles at rapid speed.Comment: Project website and open-source code: https://eureka-research.github.io

A Zinc Finger Motif in the P1 N Terminus, Highly Conserved in a Subset of Potyviruses, Is Associated with the Host Range and Fitness of Telosma Mosaic Virus

P1 is the first protein translated from the genomes of most viruses in the family Potyviridae, and it contains a C-terminal serine-protease domain that cis-cleaves the junction between P1 and HCPro in most cases. Intriguingly, P1 is the most divergent among all mature viral factors, and its roles during viral infection are still far from understood. In this study, we found that telosma mosaic virus (TelMV, genus Potyvirus) in passion fruit, unlike TelMV isolates present in other hosts, has two stretches at the P1 N terminus, named N1 and N2, with N1 harboring a Zn finger motif. Further analysis revealed that at least 14 different potyviruses, mostly belonging to the bean common mosaic virus subgroup, encode a domain equivalent to N1. Using the newly developed TelMV infectious cDNA clones from passion fruit, we demonstrated that N1, but not N2, is crucial for viral infection in both Nicotiana benthamiana and passion fruit. The regulatory effects of N1 domain on P1 cis cleavage, as well as the accumulation and RNA silencing suppression (RSS) activity of its cognate HCPro, were comprehensively investigated. We found that N1 deletion decreases HCPro abundance at the posttranslational level, likely by impairing P1 cis cleavage, thus reducing HCPro-mediated RSS activity. Remarkably, disruption of the Zn finger motif in N1 did not impair P1 cis cleavage and HCPro accumulation but severely debilitated TelMV fitness. Therefore, our results suggest that the Zn finger motif in P1s plays a critical role in viral infection that is independent of P1 protease activity and self-release, as well as HCPro accumulation and silencing suppression.This work is supported by grants from the Hainan Provincial National Science Foundation (grant nos. 2019RC010 and 322CXTD505), Sanya Yazhou Bay Sci-Tech City (SYND-2022-32 and SYND-2022-02), the National Natural Science Foundation of China (32060603), and the Central Public Interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences (19CXTD-33). We thank Fangfang Li (Chinese Academy of Agricultural Sciences) for providing pCHF3-35S-GFP and P19-expressing plasmidPeer reviewe

VIMA: General Robot Manipulation with Multimodal Prompts

Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task specified by input prompts. Yet task specification in robotics comes in various forms, such as imitating one-shot demonstrations, following language instructions, and reaching visual goals. They are often considered different tasks and tackled by specialized models. We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts, interleaving textual and visual tokens. Accordingly, we develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation learning, and a four-level evaluation protocol for systematic generalization. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively. VIMA features a recipe that achieves strong model scalability and data efficiency. It outperforms alternative designs in the hardest zero-shot generalization setting by up to $2.9\times$ task success rate given the same training data. With $10\times$ less training data, VIMA still performs $2.7\times$ better than the best competing variant. Code and video demos are available at https://vimalabs.github.io/Comment: ICML 2023 Camera-ready version. Project website: https://vimalabs.github.io