Hasil untuk "Human evolution"

Ruiqiu Ma, Tingting Liu, Chenhao Pei

Due to intense geological dynamics and human activities in coastal underground engineering, it is crucial to investigate the dynamic tensile strength and energy absorption capacity of fiber-reinforced sandy clay treated with alkali-activated metakaolin (AAMK) cement used as backfill. Dynamic indirect tensile tests were conducted using a Split Hopkinson Pressure Bar (SHPB) on mixtures with varying sand, fiber, and polymer contents. Dynamic tensile behavior, energy absorption density, and energy absorption rate were evaluated. Microstructural evolution before and after impact was observed by SEM, and fractal analysis of fragment size distribution n was used to characterize damage. Results show that when the strain rate increased by 6.05%, the residual strain rose by 9.30%. The maximum dynamic tensile strength (0.49 MPa) occurred at 12 % sand and 0.2% fiber. Fiber inclusion increased energy absorption density by about 34%–145% compared with fiber-free specimens. With sand content increasing from 12% to 16%, the fractal dimension rose by 26.0% (1.663–2.095). A mix with 12% sand and ≥ 0.2% fiber is recommended as an economical and practical optimal ratio for engineering applications.

Nandini Doreswamy, Louise Horstmanshof

The serious games between humans and AI have only just begun. Evolutionary Game Theory (EGT) models the competitive and cooperative strategies of biological entities. EGT could help predict the potential evolutionary equilibrium of humans and AI. The objective of this work was to examine some of the EGT models relevant to human-AI interaction, evolution, and coevolution. Of thirteen EGT models considered, three were examined: the Hawk-Dove Game, Iterated Prisoner's Dilemma, and the War of Attrition. This selection was based on the widespread acceptance and clear relevance of these models to potential human-AI evolutionary dynamics and coevolutionary trajectories. The Hawk-Dove Game predicts balanced mixed-strategy equilibria based on the costs of conflict. It also shows the potential for balanced coevolution rather than dominance. Iterated Prisoner's Dilemma suggests that repeated interaction may lead to cognitive coevolution. It demonstrates how memory and reciprocity can lead to cooperation. The War of Attrition suggests that competition for resources may result in strategic coevolution, asymmetric equilibria, and conventions on sharing resources. Therefore, EGT may provide a suitable framework to understand and predict the human-AI evolutionary dynamic. However, future research could extend beyond EGT and explore additional frameworks, empirical validation methods, and interdisciplinary perspectives. AI is being shaped by human input and is evolving in response to it. So too, neuroplasticity allows the human brain to grow and evolve in response to stimuli. If humans and AI converge in future, what might be the result of human neuroplasticity combined with an ever-evolving AI? Future research should be mindful of the ethical and cognitive implications of human-AI interaction, evolution, and coevolution.

Ankit Vadehra, Bill Johnson, Gene Saunders et al.

Text editing can involve several iterations of revision. Incorporating an efficient Grammar Error Correction (GEC) tool in the initial correction round can significantly impact further human editing effort and final text quality. This raises an interesting question to quantify GEC Tool usability: How much effort can the GEC Tool save users? We present the first large-scale dataset of post-editing (PE) time annotations and corrections for two English GEC test datasets (BEA19 and CoNLL14). We introduce Post-Editing Effort in Time (PEET) for GEC Tools as a human-focused evaluation scorer to rank any GEC Tool by estimating PE time-to-correct. Using our dataset, we quantify the amount of time saved by GEC Tools in text editing. Analyzing the edit type indicated that determining whether a sentence needs correction and edits like paraphrasing and punctuation changes had the greatest impact on PE time. Finally, comparison with human rankings shows that PEET correlates well with technical effort judgment, providing a new human-centric direction for evaluating GEC tool usability. We release our dataset and code at: https://github.com/ankitvad/PEET_Scorer.

Brian E. Perron, Lauri Goldkind, Zia Qi et al.

This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displacement, environmental impact, model bias, and data laborer exploitation -- vary significantly based on implementation context and specific use cases. They challenge the binary view of AI adoption, demonstrating how different applications present varying levels of risk that can often be effectively managed through careful implementation strategies. The paper highlights promising solutions, such as local large language models, that can facilitate responsible AI integration while addressing common ethical concerns. The authors propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing. They conclude by outlining a path forward that emphasizes empirical evaluation, starting with lower-risk applications and building evidence-based understanding through careful experimentation. This approach enables organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.

Yuxia Zhang, Ying Sun

China has a vast territory with diverse climates, including the arid, semi-arid, semi-humid and humid regions. Previous studies on extreme heat event attribution mainly focus on individual events in a specific region, with less attention paid to comparisons between historical events in different climate zones. Here, we use the number, seasonal length and intensity of hot days with daily maximum temperature exceeding 35 °C, to investigate human influence on extreme heat events in early and recent periods. It is clear that all three heat indicators have shown obvious increase across China since the early 1960s, with a rapid rise in recent years and the hottest event occurring in 2022. The Coupled Model Intercomparison Project Phase 6 models generally capture the temporal evolution of these indicators, but some biases exist. We utilize an annual cycle-based method to correct the model biases in climate state and then use the adjusted model results to conduct event attribution in different historical periods. We find that human influence has greatly increased the probability of recent events across all regions, while having no impact on early historical events. For the hottest 2022 event, the risk ratios for seasonal length of hot days in a few regions could not be estimated due to zero probability in the natural world, indicating that such events would not happen without human influence. In different climate zones, the risk ratios for all indicators in arid northwestern China exceed those in other regions when using consistent observational thresholds, indicating a greater response of extreme heat to anthropogenic forcing in this area. For the same event, attribution results of different indicators yield varying risk ratios, highlighting the importance of considering multiple indicators in event attribution. Additionally, model performance notably affects attribution results; without bias correction, human influence may be incorrectly estimated.

Wesselia Isa Ngoenha

Introduction The environmental impact of mega-events like the Olympic Games and the FIFA Men's World Cup has been widely criticized due to their significant contribution to greenhouse gas emissions, largely driven by infrastructure construction and resource use, and travel. Despite sustainability initiatives introduced by the IOC since the 1990s, including the 2017 "Sustainability Strategy," research indicates that these efforts rarely lead to tangible results, with environmental scores of the Games declining over time (Müller et al., 2021). Policies such as Agenda 2020 are mostly seen as recommendations, not obligations, contributing to accusations of greenwashing and unmet environmental commitments. The lack of a standardized framework for assessing and mitigating environmental impacts highlights the gap between promises and outcomes, suggesting that current approaches are insufficient to align mega-events with sustainability goals (Collins et al., 2009; Gaffney, 2013). This research thesis examines this gap to understand the ineffectiveness of these policies and their evolution over time using the policy mobility methodology, which allows us to understand how policies are created, transferred and reapplied in a new context, thanks to tools and people who transport knowledge from one place to another. The research highlights the evolution of environmental policy in the Olympic Games, tracing its origins from global environmental movements to its integration within the framework of the International Olympic Committee. Methods The methods used in this research focus on understanding the challenges of policy mobility and sustainability in the context of the Olympic Games. Policy mobility is a concept that analyzes how policies, conceived locally, circulate, adapt and apply in new contexts, particularly in a globalized. Unlike traditional nation-state-centric approaches, policy mobility emphasizes the role of non-state actors, international networks of experts, and global organizations in policy diffusion and transformation (Cochrane & Ward, 2012). In the context of mega-events such as the Olympic Games, it explains how sustainability-related practices and ideas travel between editions of the Games, influencing local policies while being reconfigured according to specific contexts. The study employs text-based methods, such as content and discourse analysis of policy documents and official IOC publications, as well as oral methods through interviews with key actors involved in the organization of the Games. This dual approach allows for an in-depth analysis of how policy ideas travel, the role of individuals in this process, the materials used for policy travel, and the politics of policy mobility (Temenos & Ward, 2018). The text-based analysis is built on a database [DG1] of the Olympic Games' carbon footprint using M. Müller's methodology, which emphasizes longitudinal and systematic data collection to identify sustainability patterns in mega-events (Gogishvili et al., 2024; Müller et al., 2022). This allowed for the analysis of all documents related to Paris 2024's sustainability policy, including its sustainability commitments, sustainability and legacy plan, two pre-games sustainability reports, and the final sustainability report. Results Despite initiatives such as Agenda 21, Olympic Agenda 2020 and Agenda 2020+5, which aim to embed sustainability into the Games, their impact remains uneven. While the IOC encourages the introduction of sustainable practices since from the planning phases of the Games since 2003 and has introduced carbon management plans and sustainability reporting requirements, its reliance on recommendations rather than enforceable commitments limits their effectiveness. The analysis revealed that policy mobility plays a crucial role in shaping sustainability policies for the Olympic Games, as host cities adapt approaches from previous editions. This exchange, facilitated by the IOC's Olympic Games Knowledge Management program and informal expert networks, allows cities to implement proven strategies, such as carbon management plans and infrastructure reuse. However, the effectiveness of these policies is often hampered by the performative nature of environmental commitments, with actual results falling short of stated ambitions, as evidenced by the Paris 2024 case study. While the organizers aimed to halve the Games' carbon footprint and achieve a "positive climate impact", key measures, such as the AMO (Avoid, Mitigate, Offset) approach, lack clear methodologies and actionable details. For instance, despite promises to systematically assess environmental impacts at all venues, no methodology was provided for some key sites like Tahiti, highlighting gaps between goals and execution​. These findings highlight the tension between the sustainability goals of the Olympic Games and the practical challenges of implementation despite the popularity of some of these policies among host cities. Discussion/Conclusion While the IOC has made strides in integrating sustainability into its strategic goals, such as through the adoption of the Olympic Agenda 2020 and its Sustainability Strategy, the practical impact of these initiatives remains limited. The absence of standardized metrics for assessing and comparing environmental performance across editions of the Games makes it difficult to evaluate progress or enforce accountability. Weak accountability frameworks, such as voluntary reporting requirements and non-mandatory guidelines, leave much of the responsibility to Organizing Committees. Additionally, policy mobility does not always produce policies tailored to the unique challenges and opportunities of each host city. While the theory aims to create quantifiable and comparable outcomes, differences in economic, political, and cultural contexts often hinder adaptability. These variations in starting points make it difficult to implement standardized policies effectively across diverse host cities. References Cochrane, A., & Ward, K. (2012). Researching the geographies of policy mobility: Confronting the methodological challenges. Environment and Planning A: Economy and Space, 44(1), 5–12. https://doi.org/10.1068/a44176 Collins, A., Jones, C., & Munday, M. (2009). Assessing the environmental impacts of mega sporting events: Two options? Tourism Management, 30(6), 828–837. https://doi.org/10.1016/j.tourman.2008.12.006 Dittmer, J. (2010). Textual and discourse analysis. In D. DeLyser, S. Herbert, S. Aitken, M. Crang, & L. McDowell (Eds.), The SAGE handbook of qualitative geography (pp. 274–286). SAGE Publications, Inc. https://doi.org/10.4135/9780857021090 Gaffney, C. (2013). Between discourse and reality: The un-sustainability of mega-event planning. Sustainability, 5(9), 3926–3940. https://doi.org/10.3390/su5093926 Gogishvili, D., Ngoenha, W., & Müller, M. (2024). Carbon footprint of the Winter and Summer Olympic Games from 2000 to 2026 (doi:10.7910/DVN/Y1OCLT; Version 1.0) [Dataset]. 2024-04-29. https://doi.org/10.7910/DVN/Y1OCLT Kitchin, R., & Tate, N. J. (2000). Conducting research in human geography: Theory, methodology & practice. Routledge. McDowell, L. (2010). Interviewing: Fear and liking in the field. In D. DeLyser, S. Herbert, S. Aitken, M. Crang, & L. McDowell (Eds.), The SAGE handbook of qualitative geography (pp. 156–171). SAGE Publications, Inc. https://doi.org/10.4135/9780857021090 Müller, M., Wolfe, S. D., Gaffney, C., Gogishvili, D., Hug, M., & Leick, A. (2021). An evaluation of the sustainability of the Olympic Games. Nature Sustainability, 4(4), Article 4. https://doi.org/10.1038/s41893-021-00696-5 Müller, M., Wolfe, S. D., Gogishvili, D., Gaffney, C., Hug, M., & Leick, A. (2022). The mega-events database: Systematising the evidence on mega-event outcomes. Leisure Studies, 41(3), 437–445. https://doi.org/10.1080/02614367.2021.1998835 Temenos, C., & Ward, K. (2018). Examining global urban policy mobilities. In J. Harrison & M. Hoyler (Eds.), Doing global urban research (1st ed., pp. 66–80). SAGE Publications Ltd.

Dunping Sun, Dunping Sun, Bin Xue et al.

Hulun Lake, one of the largest inland lakes in the grassland region of northern China, has undergone distinct ecological changes over the past century due to both natural climatic shifts and human activities. Despite its ecological significance, the long-term drivers behind these changes are still not fully understood, especially the interactions between climate and anthropogenic influences on lake dynamics. To fill this gap, we analyzed sediment core from Hulun Lake, examining subfossil diatom assemblages, geochemical indicators, and sediment characteristics to reconstruct environmental changes and uncover the mechanisms driving them. Our findings reveal a shift from predominantly planktonic to periphytic/benthic diatom communities, associated with changes in nutrient levels and hydrological conditions. Key indicators, such as total phosphorus (TP) and sand content, showed strong correlations with diatom community composition, indicating that nutrient influx and water level fluctuations play crucial roles in lake ecosystem dynamics. Before 1935 AD, Hulun Lake’s ecology was primarily driven by natural climatic variations, supporting eutrophic species in stable, nutrient-rich conditions. From 1935 to 1970 AD, nutrient levels rose gradually, with parts of human impact. However, since 1970 AD, as human activities decrease and the warming and drying trend of rising temperature, reduced precipitation has led to a significant drop in the lake water level and a shrinking water area, which of them have significantly influenced nutrient dynamics and diatom composition. This study underscores the combined effects of climate change and human activities in driving the ecological evolution of Hulun Lake, providing valuable insights for the future conservation and management of similar semi-arid lake ecosystems.

Shanbo Cheng, Zhichao Huang, Tom Ko et al.

In this paper, we present Cross Language Agent -- Simultaneous Interpretation, CLASI, a high-quality and human-like Simultaneous Speech Translation (SiST) System. Inspired by professional human interpreters, we utilize a novel data-driven read-write strategy to balance the translation quality and latency. To address the challenge of translating in-domain terminologies, CLASI employs a multi-modal retrieving module to obtain relevant information to augment the translation. Supported by LLMs, our approach can generate error-tolerated translation by considering the input audio, historical context, and retrieved information. Experimental results show that our system outperforms other systems by significant margins. Aligned with professional human interpreters, we evaluate CLASI with a better human evaluation metric, valid information proportion (VIP), which measures the amount of information that can be successfully conveyed to the listeners. In the real-world scenarios, where the speeches are often disfluent, informal, and unclear, CLASI achieves VIP of 81.3% and 78.0% for Chinese-to-English and English-to-Chinese translation directions, respectively. In contrast, state-of-the-art commercial or open-source systems only achieve 35.4% and 41.6%. On the extremely hard dataset, where other systems achieve under 13% VIP, CLASI can still achieve 70% VIP.

Yuchen Liu, Luigi Palmieri, Sebastian Koch et al.

The recent breakthroughs in the research on Large Language Models (LLMs) have triggered a transformation across several research domains. Notably, the integration of LLMs has greatly enhanced performance in robot Task And Motion Planning (TAMP). However, previous approaches often neglect the consideration of dynamic environments, i.e., the presence of dynamic objects such as humans. In this paper, we propose a novel approach to address this gap by incorporating human awareness into LLM-based robot task planning. To obtain an effective representation of the dynamic environment, our approach integrates humans' information into a hierarchical scene graph. To ensure the plan's executability, we leverage LLMs to ground the environmental topology and actionable knowledge into formal planning language. Most importantly, we use LLMs to predict future human activities and plan tasks for the robot considering the predictions. Our contribution facilitates the development of integrating human awareness into LLM-driven robot task planning, and paves the way for proactive robot decision-making in dynamic environments.

Millennium Bismay, Xiangjue Dong, James Caverlee

This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations. In contrast to conventional recommendation systems that rely on implicit user-item interactions, ReasoningRec employs LLMs to model users and items, focusing on preferences, aversions, and explanatory reasoning. The framework utilizes a larger LLM to generate synthetic explanations for user preferences, subsequently used to fine-tune a smaller LLM for enhanced recommendation accuracy and human-interpretable explanation. Our experimental study investigates the impact of reasoning and contextual information on personalized recommendations, revealing that the quality of contextual and personalized data significantly influences the LLM's capacity to generate plausible explanations. Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5\% in recommendation prediction while concurrently providing human-intelligible explanations. The code is available here: https://github.com/millenniumbismay/reasoningrec.

Moyi Li, Dzmitry Katsiuba, Mateusz Dolata et al.

Applications of drones in emergency response, like firefighting, have been promoted in the past decade. As the autonomy of drones continues to improve, the ways in which they are integrated into firefighting teams and their impact on crews are changing. This demands more understanding of how firefighters perceive and interact with autonomous drones. This paper presents a drone-based system for emergency operations with which firefighters can interact through sound, lights, and a graphical user interface. We use interviews with stakeholders collected in two field trials to explore their perceptions of the interaction and collaboration with drones. Our result shows that firefighters perceived visual interaction as adequate. However, for audio instructions and interfaces, information overload emerges as an essential problem. The potential impact of drones on current work configurations may involve shifting the position of humans closer to supervisory decision-makers and changing the training structure and content.

Giuseppe Luca De Luca Picione, Paolo Diana, Giovannipaolo Ferrari et al.

This article explores the impact of Artificial Intelligence (AI) on welfare policies, focusing on the unconventional case study of the Govern-AI project in Italy. Using the Extended Case Method, the study analyses the evolution of AI applications in public administration and their societal implications. The sociological reflection on AI emphasizes its transformative role in shaping social structures, considering both optimistic views on social rights enhancement and critical concerns such as privacy violations. The research advocates for a sociological understanding of AI as a non-human social actor, focusing on social practices and human-machine interactions. Additionally, the article introduces the concept of Public Policy Sociology for evaluating AI-related public policies, emphasizing transparency and citizen involvement.

Yu ZHANG, Weiqun LUO, Meiling LIU et al.

As a type of natural carbon sink, rock weathering plays a critical role in the global carbon cycle by storing atmospheric carbon dioxide (CO2). This process is particularly significant in mitigating climate change, although its contributions are often underestimated or overlooked in broader carbon calculation practices. The present study focuses on Guizhou Province, China, a region that is characterized by extensive karst landforms. These landforms are of particular interest because they are highly effective in capturing atmospheric CO2 through rock weathering. This study aims to explore the spatiotemporal dynamics of the carbon sink of rock weathering from 2001 to 2020. This study integrates various data sources, including remote sensing data, meteorological records, and lithological information, to estimate the carbon sink capacity with the GEM-CO2 model. This study also employs advanced analytical techniques such as Dynamic Time Warping (DTW) and statistical methods to analyze the spatiotemporal evolution of carbon sink. The findings of this study reveal that the rock type is a primary factor influencing the rate of rock weathering and CO2 consumption, followed closely by annual precipitation. The temperature also plays a significant role, although the responses of its effects are observed to be lagged. This indicates that changes in temperature may affect CO2 absorption rates several years after the initial temperature fluctuation occurred. This study identifies that the regions with the highest CO2 consumption through rock weathering are predominantly concentrated in the northeastern, southwestern, southern, and southeastern parts of Guizhou Province. These areas are characterized by widespread formations of carbonate rocks and higher precipitation levels, which can jointly enhance the weathering process and increase carbon sequestration. In contrast, the northwestern regions, which are dominated by silicate rocks and receive lower levels of precipitation, exhibit the lowest levels of CO2 consumption. This discrepancy underscores the importance of both lithological composition and climatic conditions in determining the effectiveness of natural carbon sink. From 2001 to 2020, the annual average karst carbon sink in Guizhou Province ranged from 0 to 1.04×103 t C·km−2·a−1. Although there was a general trend of fluctuation, the overall pattern showed an increase in carbon sequestration capacity. However, the analysis did not reveal any significant single trend over the two decades. This lack of a clear trend suggests a complex interplay between geological and climatic factors that influence carbon sequestration in karst landforms. The variability in carbon sink capacity observed in this study highlights the sensitivity of natural carbon sink to the changes in environmental conditions, particularly in precipitation and temperature.The spatial distribution of carbon sink closely mirrors the distribution of carbonate rocks in Guizhou Province. This correlation emphasizes the critical role that carbonate rocks play in the global carbon cycle due to their high solubility, which can accelerate the process of CO2 absorption. Areas with more annual precipitation were found to have a greater capacity for carbon sequestration, and this result reinforces the importance of hydrological factors in the weathering process. This finding is particularly relevant for the regions that are expected to experience changes in precipitation patterns due to climate change, as it suggests that shifts in hydrological conditions could have a significant impact on the efficacy of natural carbon sink.In addition to these findings, this study also highlights the importance of both geological formations and climatic conditions when the carbon sequestration potential of different regions are estimated. The application of the GEM-CO2 model in this study provides a robust framework for estimating the carbon sink at a regional scale. The effectiveness of this model in this context offers critical data that can be used to guide the development of carbon trading mechanisms and environmental policies aimed at enhancing the natural carbon sink. By integrating geological and climatic data, this model allows a more nuanced understanding of the factors that contribute to the carbon sequestration in karst landforms.The insights gained from this study are invaluable for informing carbon management strategies, particularly in regions with similar geological and climatic conditions. The findings of this study suggest that the carbon sequestration through rock weathering could be a viable component of mitigation efforts for climate change in a wider range. However, the fluctuating nature of carbon sink over the study period indicates that the natural carbon sink is highly sensitive to changes in environmental conditions. This sensitivity underscores the need for adaptive management strategies that can respond to changes in climate and ensure the continued effectiveness of natural carbon sink.Furthermore, this study lays the groundwork for future research to explore the further implications of rock weathering in global carbon cycles. It advocates a more integrated approach that considers both natural and human factors of mitigating climate change. As the climate change continues altering global weather patterns, understanding the role of natural processes like rock weathering in the carbon cycle will be increasingly important for us to develop effective strategies to manage and mitigate the impacts of climate change.

Jennifer R. S. Meadows, Jeffrey M. Kidd, Guo-Dong Wang et al.

Abstract Background The international Dog10K project aims to sequence and analyze several thousand canine genomes. Incorporating 20 × data from 1987 individuals, including 1611 dogs (321 breeds), 309 village dogs, 63 wolves, and four coyotes, we identify genomic variation across the canid family, setting the stage for detailed studies of domestication, behavior, morphology, disease susceptibility, and genome architecture and function. Results We report the analysis of > 48 M single-nucleotide, indel, and structural variants spanning the autosomes, X chromosome, and mitochondria. We discover more than 75% of variation for 239 sampled breeds. Allele sharing analysis indicates that 94.9% of breeds form monophyletic clusters and 25 major clades. German Shepherd Dogs and related breeds show the highest allele sharing with independent breeds from multiple clades. On average, each breed dog differs from the UU_Cfam_GSD_1.0 reference at 26,960 deletions and 14,034 insertions greater than 50 bp, with wolves having 14% more variants. Discovered variants include retrogene insertions from 926 parent genes. To aid functional prioritization, single-nucleotide variants were annotated with SnpEff and Zoonomia phyloP constraint scores. Constrained positions were negatively correlated with allele frequency. Finally, the utility of the Dog10K data as an imputation reference panel is assessed, generating high-confidence calls across varied genotyping platform densities including for breeds not included in the Dog10K collection. Conclusions We have developed a dense dataset of 1987 sequenced canids that reveals patterns of allele sharing, identifies likely functional variants, informs breed structure, and enables accurate imputation. Dog10K data are publicly available.

Xuefang Zheng, Fei Peng, Shuzhi Wang et al.

The nature of the Mesolithic in China has not been studied much due to the few well-context sites discovered and excavated during this period. The situation also restricts the understanding of human subsistence in the Mesolithic period in China, especially in the arid region. The present paper reports the flotation results at Locality 10 of the Pigeon Mountain site in Northwest China. Ten species of plants belonging to six families were identified, dominated by Agriophyllum squarrosum and Artemisia sieversiana. No firm evidence proves the domestication. Combined with the lithic artefacts in QG10, ancient people could utilize plant resources by constructing or expanding the food spectrum. It is the first systematic archaeobotany work in the Paleolithic site of Northwest China. The result reminds us that the enhanced utilization of wild plant resources is a vital subsistence for Mesolithic people in arid regions.

Andrew Fuchs, Andrea Passarella, Marco Conti

As we discussed in Part I of this topic, there is a clear desire to model and comprehend human behavior. Given the popular presupposition of human reasoning as the standard for learning and decision-making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. In Part I, we discussed learning methods which generate a model of behavior from exploration of the system and feedback based on the exhibited behavior as well as topics relating to the use of or accounting for beliefs with respect to applicable skills or mental states of others. In this work, we will continue the discussion from the perspective of methods which focus on the assumed cognitive abilities, limitations, and biases demonstrated in human reasoning. We will arrange these topics as follows (i) methods such as cognitive architectures, cognitive heuristics, and related which demonstrate assumptions of limitations on cognitive resources and how that impacts decisions and (ii) methods which generate and utilize representations of bias or uncertainty to model human decision-making or the future outcomes of decisions.

Glušac Luka S.

The concept of national human rights institutions (NHRIs) as known today originated under the auspices of the United Nations. Although national human rights institutions in the contemporary context have been the subject of a growing body of literature, the evolutionary path of the very idea of their creation has remained largely unexplored. The aim of this paper is to fill this literature gap by analysing key United Nations documents from the end of World War II to the adoption of the 1978 Geneva Guidelines. The paper reveals how the very concept of national human rights institutions had evolved over time, how it had been understood, which functions had been tied to these institutions, and which organizational forms had been taken as models. The paper explores the changes in the attitudes of UN Member States in relation to a given issue and provides a better understanding of the context in which this idea developed. In this regard, the paper also offers new insights into how the process of negotiating the core UN human rights conventions has influenced the evolution of the idea of creating national human rights institutions, a factor that has been rarely considered.

Xiangwen Gong, Yuqiang Li, Xuyang Wang et al.

Vegetation is a key component of terrestrial ecosystems, and its changes are very sensitive to climate change (CC) and human activities (HA), especially in ecologically fragile areas (EFA). However, the mechanism of relative contribution to vegetation degradation and restoration in EFA under the influence of CC and HA is still unclear. Based on the Carnegie-Ames-Stanford Approach (CASA) model and the Miami model, we estimated three key parameters of vegetation in China’s EFA: actual net primary productivity (ANPP), potential net primary productivity (PNPP), and human activity net primary productivity (HNPP). Using these variables, we quantitatively analyzed the relative contribution of CC and HA to vegetation restoration and degradation from 1982 to 2018 by the residual trend method. The results showed that the area ratio of vegetation restoration in China’s EFA was close to 71.6%, and the total ANPP increased by 174 Tg C, mainly concentrated in the Southwest Karst area and the Loess Plateau, but the Qinghai-Tibet Plateau showing a trend of degradation during 1982 to 2018. The climate background of each region was a key factor that can never be ignored to determine the positive or negative impact of regional CC and HA on vegetation activities. The CC and HA played a positive role in vegetation restoration areas, with a relative contribution of 59.1% and 16.4%, regional warming and humidification caused by the increase of temperature and precipitation was the main factor driving vegetation restoration. In vegetation degradation areas, The HA was the main driving force, especially the relative contribution of the Qinghai-Tibet Plateau, the arid and semi-arid areas and the Loess Plateau, which were as high as 96.8%, 83.3% and 80.2%, respectively. Finally, the annual average temperature (TEMP) and the annual solar radiation (SRAD) were relatively important and sensitive for the CASA model in the input environmental variables. This study provided a way to quantitatively understand the mechanism of climate change and human activities on the dynamic evolution of vegetation in EFA. In the future, the implementation of ecological protection and restoration projects need to fully consider the differences in climate background and strengthen the monitoring of the ecological environment.

Boris Jankovic, Takashi Gojobori

Abstract Identification of genomic signals as indicators for functional genomic elements is one of the areas that received early and widespread application of machine learning methods. With time, the methods applied grew in variety and generally exhibited a tendency to improve their ability to identify some major genomic and transcriptomics signals. The evolution of machine learning in genomics followed a similar path to applications of machine learning in other fields. These were impacted in a major way by three dominant developments, namely an enormous increase in availability and quality of data, a significant increase in computational power available to machine learning applications, and finally, new machine learning paradigms, of which deep learning is the most well-known example. It is not easy in general to distinguish factors leading to improvements in results of applications of machine learning. This is even more so in the field of genomics, where the advent of next-generation sequencing and the increased ability to perform functional analysis of raw data have had a major effect on the applicability of machine learning in OMICS fields. In this paper, we survey the results from a subset of published work in application of machine learning in the recognition of genomic signals and regions in human genome and summarize some lessons learnt from this endeavor. There is no doubt that a significant progress has been made both in terms of accuracy and reliability of models. Questions remain however whether the progress has been sufficient and what these developments bring to the field of genomics in general and human genomics in particular. Improving usability, interpretability and accuracy of models remains an important open challenge for current and future research in application of machine learning and more generally of artificial intelligence methods in genomics.

Kim Myeongseop, Lee Hee Ryung, Ossikovski Razvigor et al.

We investigate a possibility of producing the quantitative optical metrics to characterize the evolution of gastric tissue from healthy conditions via inflammation to cancer by using Mueller microscopy of gastric biopsies, regression model and statistical analysis of the predicted images. For this purpose the unstained sections of human gastric tissue biopsies at different pathological conditions were measured with the custom-built Mueller microscope. Polynomial regression model was built using the maps of transmitted intensity, retardance, dichroism and depolarization to generate the predicted images. The statistical analysis of predicted images of gastric tissue sections with multi-curve fit suggests that Mueller microscopy combined with data regression and statistical analysis is an effective approach for quantitative assessment of the degree of inflammation in gastric tissue biopsies with a high potential in clinical applications.