<answer>
<snippet id="S_0NbePdE,S_RLXmKuG">The EU AI Act mandates that high-risk AI systems must be "sufficiently transparent" to enable users to interpret their outputs, with Article 13 requiring sufficient transparency mechanisms and user instructions detailing the system's characteristics, capabilities, and limitations</snippet>. <snippet id="S_TVBhkcK,S_WG0otDu">Article 14(3) mandates measures to enable effective human oversight, requiring personnel to understand the system's capabilities and limitations, correctly interpret outputs, and have authority to override or intervene in the system's operation</snippet>. <snippet id="S_UuXjGBn">Article 11(2) allows for a unified technical documentation file combining AI system details with existing EU MDR/IVDR documentation, including design, architecture, data requirements, training methodologies, and performance metrics</snippet>. <snippet id="S_3iocoPc">Article 4(2)(b) details that if an AI system is considered high-risk, opaque, and complex, explainability is mandated from an EU court to the AI deployer through disclosure of proportional evidence such as logs, documentation, and datasets</snippet>. <snippet id="S_E4eihUQ,S_JvLynMd">General-purpose AI systems (GPAI) are subject to high-risk obligations if they can be used in high-risk contexts, with the European Commission defining how these rules apply to GPAI systems including transparency obligations for training data provenance and intended use cases</snippet>. <snippet id="S_AsIfzah">Article 50 imposes a transparency duty on deployers of certain AI systems, requiring outputs to be 'watermarked' and users to be informed when interacting with chatbots</snippet>. <snippet id="S_7lZRMkh">The European Commission is responsible for setting information obligations along the AI value chain reflecting current technological standards, with guidance offered to ensure compliance with transparency requirements particularly for GPAI</snippet>.
</answer>

<answer>
The EU AI Act mandates high-risk AI systems to provide sufficient transparency mechanisms under Article 13, including user instructions detailing characteristics, capabilities, and limitations to ensure adequate user information <snippet id="S_0NbePdE, S_RLXmKuG">. To enable effective human oversight, Article 14 requires providers to implement measures allowing overseers to correctly interpret outputs, detect anomalies, and override decisions <snippet id="S_WG0otDu">. Oversight personnel must understand system capabilities and limitations to identify automation bias and intervene safely, ensuring outputs are not blindly followed <snippet id="S_TVBhkcK">. Comprehensive technical documentation under Article 11(2) and Annex IV supports these goals by recording design, data, and performance metrics for authorities and users <snippet id="S_UuXjGBn">. Specific transparency duties apply to deepfakes requiring AI-generated disclosure, while GPAI providers face duties for technical documentation summaries, with specific exemptions for open-source models <snippet id="S_JvLynMd, S_XQp268d">.
</answer>


<answer>
<snippet id="S_tyjIFLb,S_soikqKO">Strava serves as a social network for amateur and professional athletes where users log, monitor, and share fitness accomplishments with others via status updates, comments, and photos</snippet>, and <snippet id="S_tyjIFLb,S_soikqKO">it features social features such as leaderboards, segments, and challenges that enable users to compare performance with friends or local users</snippet>. <snippet id="S_tyjIFLb">Gamification techniques like challenges and digital badges are used to encourage repeated use, with users rewarded 25%, 50%, and 75% for completing monthly distance goals</snippet>. <snippet id="S_fdz63NK">However, users often selectively share data, withholding metrics like heart rate and wattage while opting for basic information such as segment times and elevation</snippet>, reflecting a desire for self-validation and awareness of how others perceive their data. <snippet id="S_ohmbRBN">Strava is categorized as a persuasive technology that motivates users through tracking routes and providing performance feedback, fostering competitive behaviors</snippet>. <snippet id="S_KfOy5t1">Social comparison is identified as a key psychological driver in Strava's social features, though most fitness apps overlook individual differences in comparison direction (upward vs downward)</snippet>. <snippet id="S_5nbN41y">Research on Strava's social dimension has been limited to cross-sectional samples of cyclists, with calls for longitudinal studies to validate models of user engagement and wellness outcomes</snippet>. <snippet id="S_fdz63NK">Users acknowledge data inaccuracies in Strava and engage in self-reflection through performance visualizations, though privacy and data control remain important considerations in the social dynamics</snippet>.
</answer>

<answer>
Strava employs social features such as challenges, leaderboards, and digital badges as core gamification techniques to encourage repeated use and foster a sense of community among its 36 million active users <snippet id="S_tyjIFLb">. The competitive nature of these persuasive technologies significantly influences user motivation by facilitating social comparison, though individual preferences for comparison direction vary <snippet id="S_ohmbRBN, S_KfOy5t1">. To manage privacy and self-presentation, users often selectively share data, withholding specific metrics like heart rate to avoid potential profile scrutiny <snippet id="S_fdz63NK">. Features like user-defined segments allow for performance comparisons while enabling users to control data visibility and foster localized community connections <snippet id="S_soikqKO">. Incorporating competitive and cooperative social features is recommended to increase user engagement and intrinsic motivation in fitness applications <snippet id="S_47DnIT5">. However, current implementations largely overlook nuanced psychological theories regarding social comparison, which could otherwise enhance engagement further <snippet id="S_KfOy5t1">.
</answer>


<answer>
<snippet id="S_ixOFgje">The White House fact sheet announces a 25% additional tariff on imports from Canada and Mexico, and a 10% additional tariff on imports from China</snippet>. <snippet id="S_ixOFgje">Energy resources from Canada will have a lower 10% tariff rate</snippet>. <snippet id="S_ixOFgje">These tariff measures are part of President Trump's trade policy actions to address national security concerns including illegal immigration and drug trafficking</snippet>. <snippet id="S_ixOFgje">The fact sheet references a November 2024 Presidential Memorandum where Trump promised to charge Mexico and Canada 25% on all products entering the U.S.</snippet>. <snippet id="S_ixOFgje">The announcement cites that trade accounts for 67% of Canada's GDP, 73% of Mexico's GDP, and 37% of China's GDP, though the U.S. trade deficit in goods was the world's largest at over $1 trillion in 2023</snippet>. <snippet id="S_ixOFgje">The fact sheet emphasizes that these tariffs are being used to leverage national security outcomes, including stopping fentanyl and illegal alien flows</snippet>.
</answer>

<answer>
<snippet id="S_ixOFgje">The White House fact sheet specifies a 25% additional tariff on imports from Canada and Mexico, with energy resources from Canada receiving a lower 10% tariff, while China faces a 10% additional tariff</snippet>. <snippet id="S_ixOFgje">The fact sheet notes that in 2023 the U.S. trade deficit in goods was the world's largest at over $1 trillion</snippet>, and that trade accounts for 67% of Canada's GDP, 73% of Mexico's GDP, and 37% of China's GDP. However, <snippet id="S_ixOFgje">this fact sheet does not include specific dollar trade values covered by the tariffs, any retaliation measures from Canada, China, or the EU, or quantitative economic impact estimates (consumer cost, inflation, GDP)</snippet>. The tariff announcement is dated February 2025 and cites the International Emergency Economic Powers Act as the legal basis. <snippet id="S_ixOFgje">The tariffs remain in effect "until such time as Drugs, in particular Fentanyl, and all Illegal Aliens stop this Invasion of our Country"</snippet> as per the presidential directive.
</answer>


<answer>
<snippet id="S_7QgSGSe">The page discusses the interpretation of metaphors, particularly focusing on the slogans from George Orwell's "Nineteen Eighty-Four": "War is Peace," "Freedom is Slavery," and "Ignorance is Strength."</snippet> However, the available search results do not provide specific CDA analysis of these slogans as instances of doublethink or ideology. <snippet id="S_tpXkvyW">Charteris-Black (2004) examined metaphor deployment in political discourse to project covert ideology, showing how metaphors create us versus them dichotomies.</snippet> This provides relevant CDA framework grounding but does not specifically apply to Orwell's slogans. <snippet id="S_iDUQY7x">Slogans are defined as brief striking phrases that act as emotional appeals, while thought-terminating clichés are words that discourage critical thought.</snippet> These definitions help understand the function of slogans in discourse but lack the specific analysis of Orwell's paradoxical slogans. <snippet id="S_lgOMCo4">The term "unfreedom" is noted as a rare but legitimate formation, while "doubleplus unfree," derived from Orwell's Newspeak in Nineteen Eighty-Four, exemplifies the intensifying use of language.</snippet> This shows the linguistic control aspect but does not analyze the specific slogans or their doublethink mechanisms. The search results do not contain sufficient CDA scholarship specifically analyzing "War is Peace," "Freedom is Slavery," and "Ignorance is Strength" as discourse strategies of ideology and thought control.
</answer>

<answer>
<snippet id="S_7QgSGSe">The slogans from Orwell's Nineteen Eighty-Four—"War is Peace," "Freedom is Slavery," and "Ignorance is Strength"—are analyzed as metaphors that undergo discursive drift, where meaning and stance shift over time</snippet>, while <snippet id="S_iDUQY7x">slogans function as thought-terminating clichés that discourage critical thought by offering seemingly simple answers to complex questions</snippet>. <snippet id="S_15dkzZt">These phrases operate as brief, striking terms that act as emotional appeals and may include labeling and stereotyping</snippet>. <snippet id="S_lgOMCo4">Orwell's Newspeak language engineering is exemplified by terms like "doubleplus unfree," which demonstrates the intensifying use of language for ideological control</snippet>. However, these search results do not contain specific scholarly analysis on the "Who controls the past" formula, Ministry name rebranding, or explicit CDA frameworks (Fairclough/van Dijk/Foucault) applied to Orwell's text. <snippet id="S_7QgSGSe">A significant portion of references to these slogans in media are secondary uses (73%), indicating their evolution as discursive artifacts rather than original texts</snippet>.
</answer>


<answer>
<snippet id="S_gsbJVy0,S_5VWgqHF">Eric A. Stach from the University of Pennsylvania served as MRS Vice President beginning January 1, 2024</snippet>. <snippet id="S_gsbJVy0">He will lead the Board of Directors as MRS President in 2025</snippet>, which confirms his role as President-Elect for that year. <snippet id="S_ZPx3NY5">The Past MRS Presidents page lists Takao Someya (2024) in the context of service beginning in the position of vice president/president-elect</snippet>, though this may not specify the current year's leadership transition. <snippet id="S_Mcho4xl">The MRS website announces the Vice President/President Elect and new Board Members for 2025</snippet>, supporting the naming convention of the position.
</answer>

<answer>
Eric A. Stach from the University of Pennsylvania served as MRS Vice President beginning January 1, 2024 <snippet id="S_gsbJVy0, S_5VWgqHF">. He will lead the Board of Directors as MRS President in 2025, following the standard VP/President-Elect transition <snippet id="S_gsbJVy0, S_5VWgqHF">. This confirms that Eric A. Stach was the 2024 Vice President and President-Elect who was scheduled to assume the Presidency in 2025 <snippet id="S_gsbJVy0">.
</answer>


<answer>
<snippet id="S_3JECVB5,S_Xa6M06B">STIX 2.1 is a machine-readable, semi-structured framework for structuring and exchanging cyber threat intelligence (CTI) data, primarily using JavaScript Object Notation (JSON) with 12 STIX Domain Objects (SDOs) including 'indicator', 'malware', and 'report'</snippet>. <snippet id="S_JYIyMdS,S_3JECVB5">The STIX 2.1 format includes two main object types: STIX Domain Objects (SDOs) which describe characteristics of incidents, and STIX Relationship Objects (SROs) which define the relationships between those characteristics</snippet>. <snippet id="S_lIvobvB">The Indicator SDO specifically uses the 'pattern' property to detail malware indicators within the CTI framework</snippet>. <snippet id="S_06F1WZp">STIX objects such as Threat Actor, Malware, or Indicator belong to the set of SDOs, while Relationship and Sighting objects are SROs</snippet>. <snippet id="S_3TK690R">STIX uses a combination of observed data structures, indicator patterns, and relationship objects, which require UUIDs to establish connections between different objects</snippet>. <snippet id="S_3M0PARM">Real-world CTI datasets show malware entities and threat actor relationships are frequently mapped to ATT&CK Matrix tactics and techniques for automated analysis</snippet>. However, the provided snippets do not contain specific definitions of the Malware SDO or Indicator SDO structures, nor do they describe how to map malware indicators to these CTI data models for classification.
</answer>

<answer>
<snippet id="S_3JECVB5,S_Xa6M06B">The OASIS CTI technical committee maintains STIX 2.1 as a machine-readable standard for structuring and exchanging cyber threat intelligence using JSON serialization.</snippet> <snippet id="S_JYIyMdS,S_06F1WZp">The framework defines STIX Domain Objects (SDOs), including specific types such as 'malware' and 'indicator', to describe characteristics of incidents and threat indicators.</snippet> <snippet id="S_JYIyMdS,S_lIvobvB">The 'indicator' SDO includes a 'pattern' property for detailing malware indicators, while data quality metrics can assess the connectedness of these objects.</snippet> <snippet id="S_3JECVB5,S_06F1WZp">STIX uses STIX Relationship Objects (SROs) to link SDOs, enabling representations such as a malware exploiting a vulnerability or an indicator referencing a threat actor.</snippet> <snippet id="S_Xa6M06B">Integration of CybOX into STIX 2.1 allows for the representation of cyber observables as cyber observable objects within this flat structure.</snippet>
</answer>


<answer>
The search results do not contain specific information about newly formed counties in Kohgiluyeh and Boyer-Ahmad Province between 2020-2024. <snippet id=3d29ab62-0>Kohgiluyeh and Boyer-Ahmad province is one of the 31 provinces of Iran in the southwest</snippet>, but no details about county formation in recent years. <snippet id=3d29ab62-1>Kohgiluyeh County is located in Kohgiluyeh and Boyer-Ahmad province with Dehdasht as its capital</snippet>, though this does not indicate a newly formed county. <snippet id=3d29ab62-5>A 2024 FAO report mentions newly formed local and province level governments</snippet> but provides no specific county-level details for this province. <snippet id=3d29ab62-6,3d29ab62-7>Recent studies from 2024 focus on agricultural productivity and climate indices</snippet> without county formation information. The available snippets do not confirm any new county establishment in Kohgiluyeh and Boyer-Ahmad Province during the 2020-2024 period.
</answer>

<answer>
<snippet id=3d29ab62-5>The FAO report mentions "newly formed local and province level governments" in a general context, but does not specify particular counties formed in Kohgiluyeh and Boyer-Ahmad Province during 2020-2024</snippet>. The search results include information about Kohgiluyeh County with its capital at Dehdasht, but do not identify newly formed counties in the 2020-2024 timeframe</snippet>. No specific administrative changes or county formations in Kohgiluyeh and Boyer-Ahmad Province during this period are clearly documented in the available snippets</snippet>.
</answer>


<answer>
<snippet id="S_ywdgTkl">For the Trusted Computing Environment & Platform (可信计算环境与平台) research area, the project "CROWN" won the National Science and Technology Progress Award Second Prize (二等奖)</snippet>. <snippet id="S_UoGmAYk">For the Virtual Reality & Digital Media (虚拟现实与数字媒体) research area, the projects "BH-GRAPH" and "BH_RTI" along with the distributed virtual environment "DVENET" won the National Science and Technology Progress Award First Prize (一等奖) and Second Prize (二等奖)</snippet>. These projects are documented on the official Beihang University School of Computer Science website under the respective research area pages.
</answer>

<answer>
针对虚拟现实与数字媒体方向，北京航空航天大学计算机学院研制了实时三维图形平台BH-GRAPH、分布交互仿真运行支撑平台BH_RTI及分布式虚拟环境DVENET，这些成果获得国家科技进步一等奖和二等奖<snippet id="S_UoGmAYk">。在可信计算环境与平台方面，学院建立的CROWN高可信软件开发环境、Web服务中间件平台及网络环境运行平台，荣获国家科技进步二等奖<snippet id="S_ywdgTkl">。这些研究面向国家战略规划及航空航天行业应用需求展开<snippet id="S_ywdgTkl,S_UoGmAYk">。
</answer>


<answer>
<snippet id="S_ksMf31Q">Research on sports betting among university students in Nigeria indicates that demographic factors such as being young, male, single, educated, and engaged in full-time studies or employment are associated with higher risks of problem gambling</snippet>. <snippet id="S_C7vME7Z">An urban school-based cross-sectional survey in Nigeria found a lifetime gambling prevalence of 57.2%, with 77.6% of students having gambled in the previous 12 months</snippet>. <snippet id="S_sAPmmcf">A study on online gambling during the pandemic found that past-30-day sports bettors were more likely to have a history of indebtedness (11 vs 6%) and higher levels of gambling problems compared to non-betters</snippet>. <snippet id="S_rqYsRo2">A study of 5,000 college students in Ghana explored financial literacy's role in predicting financial behavior, which may relate to sports betting prevalence among Nigerian university students</snippet>. <snippet id="S_zEaN2n7">USA research shows regular participation in sports betting among adolescents aged 16-19 is associated with higher risk of gambling problems, with males participating more frequently</snippet>. However, <snippet id="S_d3zPQEk">specific data on esports bettors or detailed economic determinants like employment status for Nigerian university students is not detailed in available studies</snippet>. <snippet id="S_iL4llG9">Global data shows esports bettors were more likely to be students (59%) and exhibit higher impulsivity scores, but this does not provide Nigeria-specific prevalence or determinants</snippet>. The available evidence suggests employment status and financial stress may be determinants, but <snippet id="S_XKaoZhm">the studies rely on self-report data which can be affected by biases, and samples may not be representative of all online sports bettors</snippet>. There is a noted gap in <snippet id="S_ksMf31Q">Nigeria-specific literature on economic determinants, with most research coming from other countries like Australia and Germany</snippet>.
</answer>

<answer>
<snippet id="S_C7vME7Z">An urban school-based survey of 507 Nigerian students found a lifetime gambling prevalence of 57.2%, with 77.6% having gambled in the previous 12 months</snippet>, indicating high rates of gambling participation among Nigerian youth. <snippet id="S_ksMf31Q">Sports betting has gained popularity among university students in Nigeria, with demographic factors including being young, male, single, educated, and engaged in full-time studies or employment associated with higher risks of problem gambling</snippet>. However, specific evidence on employment status as an economic determinant for Nigerian student-athletes remains sparse in the available literature. <snippet id="S_sAPmmcf">Among recent sports bettors, those active in work or studies showed lower percentages (68% for online casino, 59% for horse betting) compared to non-gamblers, while past-30-day sports bettors had higher history of indebtedness (11% vs 6%)</snippet>. <snippet id="S_iL4llG9">Research on emerging adults shows students comprised 59% of esports bettors in a Great Britain sample, with bettors more likely to live in deprived areas</snippet>, suggesting economic strain may be a relevant pathway. Overall, while employment and financial stress mechanisms appear connected to gambling behaviors broadly, Nigeria-specific quantitative evidence linking employment status directly to sports betting participation among student-athletes is limited and requires further research.
</answer>


<answer>
The search results do not contain the current top model name or its Elo rating from the Chatbot Arena Leaderboard. <snippet id="S_N0JvFLC">The official LMArena URL is https://lmarena.ai/ with over 3.5M votes</snippet>, but the snippets do not show the current leaderboard rankings. <snippet id="S_6IdzsAE">Previous leaderboard data from May 2023 shows Elo ratings based on 27K anonymous votes</snippet>, but this is outdated information. <snippet id="S_hhzNfal">A multimodal leaderboard was released in June 2024</snippet>, but the top model details are not provided in these search results. The search results confirm the existence of the Chatbot Arena Leaderboard but do not contain the specific current top model information needed to complete the query.
</answer>

<answer>
The official Chatbot Arena Leaderboard is hosted at https://lmarena.ai/ <snippet id="S_N0JvFLC">where over 3.5M votes have been collected for model rankings. The leaderboard uses an Elo rating system based on anonymous voting data <snippet id="S_6IdzsAE">to rank large language models. A snapshot of the leaderboard is also available on Hugging Face at https://huggingface.co/spaces/lmarena-ai/lmarena-leaderboard <snippet id="S_831cvJ0">. However, the current search results do not contain the specific name of the current top model on the leaderboard. A direct visit to the official lmarena.ai leaderboard page would be needed to identify the #1 ranked model and its current Elo score.
</answer>


<answer>
<snippet id="S_gu69vHf,S_Cn1GhvJ,S_ZCPd5ub">DESI DR2 BAO measurements indicate a potential phantom crossing at z_c ≈ 0.45, where w(z) < -1, suggesting evolving dark energy models that deviate from w = -1</snippet>. <snippet id="S_ZIWoomJ">DESI initial results favor a phantom behavior of dark energy (w < -1) over a significant redshift range when using the w0wa parametrization</snippet>. <snippet id="S_DVeLkbI">Gaussian process reconstructions from DESI BAO data show quintom-B behavior with w lying in the phantom regime at high redshift (0.8 ≤ z < 2.1)</snippet>. However, <snippet id="S_ZCPd5ub">incorporating SH0ES prior with CMB, DESI DR2 BAO and Pantheon Plus data reduces the preference to dynamical dark energy to 1.5σ/1.4σ/2.4σ level</snippet>, suggesting a potential tension between the Hubble constant of the SH0ES measurement and the phantom-to-quintessence transition favored by DESI DR2 BAO data. <snippet id="S_ZIWoomJ">The w0wa model generalizes the standard ΛCDM model but is a phenomenological ansatz where there is no obstacle to the phantom regime w < -1, which is unphysical in general relativity</snippet>. <snippet id="S_otM3Qb8">Current DESI measurements suggest dark energy may be evolving into the phantom regime with w(z) < -1, though data remains inconclusive regarding the existence of a phantom crossing</snippet>. This tension and preference for phantom crossing motivate investigation of non-minimal coupling frameworks that can realize stable phantom crossing without ghosts.
</answer>

<answer>
DESI results indicate a preference for phantom behaviour (w < -1) over a significant redshift range, with a crossing to the non-phantom region at lower redshift <snippet id=S_ZIWoomJ>. DESI DR2 data suggests a potential phantom crossing at z_c ≃ 0.45, representing a ≃ 3σ deviation from ΛCDM that challenges standard models <snippet id=S_Cn1GhvJ>. The phantom regime w < -1 is considered unphysical in general relativity, motivating alternative theoretical frameworks <snippet id=S_ZIWoomJ>. Recent work explicitly matches these DESI constraints with nonminimally coupled dark energy models that can accommodate evolving dark energy and phantom crossing <snippet id=S_otM3Qb8>. Such frameworks provide mechanisms to realize w<-1 and crossing phenomena that DESI hints towards in coupled dark matter and dark energy scenarios <snippet id=S_gu69vHf>. This directly addresses DESI's findings by offering stable alternatives to minimal coupling that support the observed phantom crossing tendency <snippet id=S_p938Lo0>.
</answer>


<answer>
<snippet id="S_RujXXPj,S_4mMdKmh,S_8bzg1KA,S_NDPofwS,S_Tg2CFEg">The margin of safety in pharmacology is defined as LD1/ED99, representing the ratio between the dose lethal to 1% of the population and the dose effective in 99% of the population</snippet>. However, <snippet id="S_RujXXPj,S_4mMdKmh,S_8bzg1KA,S_NDPofwS,S_Tg2CFEg">none of the retrieved snippets explicitly discuss when this margin of safety cannot be calculated or is considered undefined</snippet>. The available literature focuses on the definition and calculation of margin of safety using dose–response quantiles, but does not address conditions under which these values may not be observable or meaningful <snippet id="S_NDPofwS">. This suggests the user's query about "margin of safety fail to appear" may point to a specific scenario where LD1 or ED99 points are not computable from available data</snippet>.
</answer>

<answer>
<snippet id="S_RujXXPj,S_4mMdKmh,S_NDPofwS,S_Tg2CFEg">The margin of safety in pharmacology is defined as the ratio of LD1 (lethal dose to 1% of population) to ED99 (effective dose to 99% of population)</snippet>. <snippet id="S_8bzg1KA">This measure is also referred to as the Certain Safety Factor</snippet>. However, the provided search results do not contain information about conditions under which margin of safety "fails to appear" or cannot be calculated <snippet id="S_KtkXCqd">, and no snippet addresses when this definition is undefined or inapplicable</snippet>. <snippet id="S_ITpLCrH,S_gWkoXUl,S_PNN5Uwr">Alternative measures such as the therapeutic index using LD50/ED50 are also mentioned</snippet>. <answer>
</answer>


<answer>
The search results do not provide explicit evidence of group polarization or risky shift phenomena in avatar-mediated immersive VR environments. <snippet id="S_snWHiX5">While avatars are used in risk prevention education (e.g., Kognito program), this does not demonstrate group discussion-driven attitude extremity</snippet>. <snippet id="S_JIjtTEQ,S_XSKnvh7">Virtual reality environments with computer-generated avatars have been used to simulate social contexts, but these studies focused on individual psychological responses rather than group dynamics</snippet>. <snippet id="S_X5s1Ifa,S_HKqSHCf">Research on avatar visual fidelity found that abstract representations allowed users to adopt more risky behaviors, while self-representations encouraged cautious behavior, but this involved single-user control rather than group interaction</snippet>. <snippet id="S_SCx4OjQ">Dissimilar avatars can enhance user interaction and social behaviors, but no studies in these results document group polarization or risky shift in multi-user VR</snippet>. <snippet id="S_Ic4WSGm">Motion artifacts and self-agency studies focus on individual user experiences with avatars, not group discussion effects</snippet>. None of the retrieved snippets provide concrete experimental evidence of group polarization (post-discussion extremitization) in avatar-mediated immersive VR with multi-user interaction.
</answer>

<answer>
<snippet id="S_X5s1Ifa, S_HKqSHCf">The search results indicate that abstract avatars (such as robots) can lead to increased risky behaviors compared to self-representations that foster cautious behavior</snippet>, which relates to the Proteus Effect where avatar appearance influences user conduct <snippet id="S_X5s1Ifa">. However, these findings demonstrate individual behavioral changes based on avatar type rather than classic group polarization or risky shift effects where group discussion leads to attitude extremitization</snippet>. The available studies do not contain concrete experimental evidence of post-discussion extremitization in multi-user IVEs where avatars are involved <snippet id="S_JIjtTEQ">specifically notes that "risky shift" findings were not detailed</snippet>. While avatar-mediated environments can influence risk-related behavior, direct evidence of group polarization through avatar-mediated discussion remains absent from these search results <snippet id="S_snWHiX5">. The research focuses more on how visual fidelity and avatar similarity affect embodiment and individual actions rather than group dynamics leading to polarization</snippet>.
</answer>


<answer>
<snippet id="S_uPQ5gEz,S_CBhXwnS,S_L4MYIrf">Nikola Tesla's Electric Arc Lamp patent was issued on February 9, 1886, with patent number US335786A</snippet>. <snippet id="S_iDdAzGv">The patent describes an improved electric arc lamp using electromagnets and lever mechanisms to precisely separate and feed carbon electrodes</snippet>. <snippet id="S_ktaELRS">This patent is listed on Wikipedia under the title "U.S. patent 335,787 - Electric arc lamp - 1886 February 9"</snippet>. <snippet id="S_yVKm3fu">Multiple sources confirm the Electric Arc Lamp was issued on February 9, 1886, following the Commutator patent issued on January 26, 1886</snippet>. <snippet id="S_7dWRH62">Tesla's 1886 patents were for improved control of the feed of the carbon rods</snippet>.
</answer>

<answer>
Nikola Tesla's Electric Arc Lamp patent, US 335,786, was granted on February 9, 1886 <snippet id="S_uPQ5gEz,S_L4MYIrf">. A second related patent, US 335,787, was also issued on February 9, 1886 <snippet id="S_ktaELRS,S_s2HVIjN">. The "Commutator for Dynamo-Electric Machines" was issued on January 26, 1886 <snippet id="S_yVKm3fu">. Because the Commutator patent's date precedes the Arc Lamp patent by nearly two weeks, it stands as Tesla's first U.S. patent by issue date <snippet id="S_yVKm3fu">. These arc lamp patents involved improvements to control carbon rod feed using electromagnets <snippet id="S_iDdAzGv">.
</answer>


<answer>
<snippet id="S_cGRsAZI">The episode is titled "Rhino Rocket with Tina Munjal, MD" and is part of Stories from the World of Medicine, Season 3 Episode 2</snippet>, published on <snippet id="S_cGRsAZI,S_IhbWqXF">February 18, 2020</snippet>. <snippet id="S_cGRsAZI,S_NZ9gUn7">The guest is Otolaryngologist Tina Munjal, MD</snippet>, who tells a story about learning to be comfortable outside of her comfort zone <snippet id="S_NZ9gUn7">. The episode is available on The Nocturnists Podcast website at https://thenocturnists.org/podcast/rhino-rocket</snippet>, and is also listed on platforms like Libsyn and Spotify under the same title <snippet id="S_C2IKgBh">. Additional information about the episode can be found on the official Nocturnists site or through podcast platforms like Apple Podcasts and Spotify</snippet>.
</answer>

<answer>
<snippet id="S_cGRsAZI,S_IhbWqXF">The episode "Rhino Rocket with Tina Munjal, MD" is Season 3, Episode 2 of Stories from the World of Medicine podcast by The Nocturnists, released on February 18, 2020</snippet>. <snippet id="S_cGRsAZI,S_IhbWqXF">The guest is Tina Munjal, MD, an Otolaryngologist who shares her medical story</snippet>. <snippet id="S_NZ9gUn7,S_arbvh0R">The story is about learning to be comfortable outside of her comfort zone</snippet>. <snippet id="S_cGRsAZI,S_IhbWqXF">The episode page is available at thenocturnists.org/podcast/rhino-rocket with illustrations by Lindsay Mound</snippet>. Runtime information is not available in the search results.
</answer>


<answer>
<snippet id="S_btUw1xy">The search results include one snippet discussing de-extinction, particularly for species driven to extinction by humans, suggesting that functional proxies of these species could be beneficial for ecosystems</snippet>. However, this snippet does not explicitly use the term "de-extinction" in its title or abstract, and focuses on genomic modifications and cloning techniques rather than recent reviews on the concept. <snippet id="S_aQHGJDJ,S_BVGS7oK,S_yJ0bKZt">Other results discuss evolutionary potential (EP) as a proxy for extinction risk, but these are not de-extinction-specific and focus on conservation risk assessments rather than revival technologies</snippet>. <snippet id="S_160DJq3,S_kpUOwfs">Additional snippets cover late-Quaternary megafauna extinctions and trophic rewilding, but do not address de-extinction terminology or recent reviews</snippet>. <snippet id="S_hlWQ6qg,S_ZV4SO7N,S_zN6EiNI,S_pIULMfn">The remaining results discuss general conservation challenges, biodiversity shortfalls, and conservation paleobiology, with no mention of de-extinction or proxy species</snippet>. The available snippets do not provide the specific 2022-2025 reviews on de-extinction with proxy/functional terminology that the agent is seeking.
</answer>

<answer>
Based on the search results, explicit "de‑extinction" research from 2022‑2025 is limited in the provided snippets. <snippet id=S_btUw1xy>The controversial concept of de‑extinction, particularly for species driven to extinction by humans, suggests that functional proxies of these species could be beneficial for ecosystems</snippet>. <snippet id=S_btUw1xy>Cloning techniques like somatic cell nuclear transfer (SCNT) could enable the de‑extinction of recently extinct mammals with preserved tissues</snippet>. While direct governance/ethics debates are not detailed in the snippets, genomic modifications including gene drives to enhance species resilience raise "ethical and regulatory concerns." <snippet id=S_160DJq3,S_kpUOwfs>Trophic rewilding represents a related conservation strategy through ecosystem management that focuses on megafauna extinction implications</snippet>. The search results indicate that functional proxies and evolutionary potential remain important concepts for assessing extinction risk in applied conservation science. <snippet id=S_aQHGJDJ,S_yJ0bKZT>Integration of proxies into conservation decision‑making remains an important area for innovation in applied conservation science</snippet>.
</answer>


<answer>
<snippet id="S_4iZB0OB">The central baryon chemical potential of a neutron star is predicted to be 1319 MeV at zero temperature</snippet>, which is below the limits set by perturbative quantum chromodynamics. <snippet id="S_exyOPhA">The critical neutron chemical potential, which indicates the transition to a quark phase, is model-dependent and defined where the quark chemical potential equals the baryon chemical potential at the same pressure</snippet>, with current models suggesting values between 1050 MeV and 1400 MeV at zero temperature. <snippet id="S_jARyMl0,S_BQ1tanr">The baryon chemical potential in neutron stars is typically in the range of several hundred MeV to a few GeV</snippet>, reflecting the extreme conditions present in such dense astrophysical objects. <snippet id="S_dKTw9kz">The baryon chemical potential in this context is expected to be in the GeV range</snippet>, though specific numerical values are not provided in the text. <snippet id="S_WRBepHG">In high-density environments, additional baryons, such as Λ hyperons, can emerge through weak interactions, replacing energetic neutrons when their chemical potential condition (µΛ = µn = µp + µe) is satisfied</snippet>. However, none of the available snippets provide explicit tabular values of μ_B as a function of density in units of n0 or radius/mass for β-equilibrated hadronic matter where μ_B ≈ μ_n.
</answer>

<answer>
At zero temperature, the central baryon chemical potential of a neutron star is predicted to be 1319 MeV <snippet id="S_4iZB0OB">. Models of the hadron-quark phase transition indicate the critical neutron chemical potential lies between 1050 MeV and 1400 MeV at zero temperature <snippet id="S_exyOPhA">. In the context of beta equilibrium, baryon chemical potential values typically fall within the range of several hundred MeV to a few GeV <snippet id="S_BQ1tanr">. This scale reflects the extreme conditions present in such dense astrophysical objects <snippet id="S_jARyMl0">.
</answer>


<answer>
<snippet id="S_e9zzIKc,S_GveNEie,S_37bk94m">Bond et al. (2012) conducted a landmark experiment in 2010 involving 61 million Facebook users during the U.S. Congressional Election, demonstrating that social proof messages (showing images of friends who had voted) significantly increased voter turnout</snippet>. <snippet id="S_e9zzIKc,S_Z2mbNNa,S_mXK3pZh,S_8SBonkH">The study found approximately 60,000 additional votes directly attributed to the message, with an additional 280,000 votes from friends of those who received the treatment, for a total increase of 340,000 votes</snippet>. <snippet id="S_Z2mbNNa,S_mXK3pZh">This effect was replicated in the 2012 U.S. Presidential Election, where the total number of people directly mobilized was 90,000, and the treatment effects spread through the network to cause an additional 180,000 close friends of the treated to vote</snippet>. <snippet id="S_gGLgZLn">However, some analyses found very small effects from the information treatment, suggesting the large sample size may have led to overinterpretation of the statistical significance</snippet>. <snippet id="S_37bk94m">The manipulation exploited human heuristics of imitation, leading to increased voter participation through social proof rather than direct algorithmic recommendations</snippet>. <snippet id="S_NkyJ9df">Bond et al. (2012) is frequently cited alongside Taylor et al (2013) as one of the most ambitious field experiments in network science for measuring social influence</snippet>.
</answer>

<answer>
The Bond et al. 2012 study conducted a large-scale experiment involving 61 million Facebook users during the 2010 U.S. Congressional Election, where users were shown messages with images of friends who had already voted <snippet id="S_e9zzIKc,S_GveNEie,S_37bk94m">to assess social influence on voting behavior</snippet>. The results demonstrated that this social messaging approach increased turnout, with approximately 60,000 additional votes directly attributed to the message in 2010 <snippet id="S_e9zzIKc,S_mXK3pZh">and effects replicated in the 2012 U.S. Presidential Election with about 90,000 directly mobilized voters</snippet> <snippet id="S_Z2mbNNa,S_8SBonkH">. Social influence spread through the network as friends of treated users were also indirectly influenced, adding approximately 180,000-280,000 additional votes in total across both elections</snippet> <snippet id="S_Z2mbNNa,S_mXK3pZh,S_37bk94m">. However, some researchers note that while the effect sizes were statistically significant, they were relatively small relative to the massive sample size <snippet id="S_gGLgZLn">. Despite this limitation, the study remains widely cited as evidence that online social networks can impact offline political behavior through interpersonal influence mechanisms</snippet> <snippet id="S_8SBonkH,S_I0UxhZS">.
</answer>


<answer>
<snippet id="S_4LDk9pb">IGN explicitly confirms the launch date for World of Warcraft as November 23, 2004</snippet>, providing a fourth independent confirmation from a major game outlet. <snippet id="S_mhE8bKy">GamesIndustry.biz corroborates that the game will be in stores in North America on November 23, 2004</snippet>, with simultaneous launch in Australia and New Zealand. <snippet id="S_3ft58Qe">Wikipedia states the game was released for the 10th anniversary of the Warcraft franchise on November 23, 2004</snippet>. <snippet id="S_K0CkkAv">Wowpedia also documents the release date as November 23, 2004</snippet>. Multiple authoritative sources consistently confirm this November 23, 2004 launch date.
</answer>

<answer>
<snippet id="S_4LDk9pb, S_4am1dTv, S_Iyt7Mpt">World of Warcraft was officially released on November 23, 2004</snippet>, with the launch date confirmed for North America, Australia, and New Zealand <snippet id="S_4LDk9pb, S_mhE8bKy">simultaneously across these regions</snippet>. IGN explicitly reported that November 23, 2004 was the launch date in multiple articles from late 2004 <snippet id="S_4LDk9pb, S_4am1dTv, S_Iyt7Mpt">. This date is further corroborated by Wikipedia, which states the game was released on November 23, 2004</snippet> <snippet id="S_3ft58Qe, S_K0CkkAv>, and by Blizzard's own press announcements</snippet> <snippet id="S_6wNkGtZ, S_mn2djgd>. The release marked the 10th anniversary of the Warcraft franchise</snippet> <snippet id="S_3ft58Qe>.
</answer>


<answer>
<snippet id="S_jYWskoE,S_q2ORi2Q">Cytokinin promotes axillary bud outgrowth while strigolactone (SL) inhibits it, with both hormones acting antagonistically through the transcription factor BRANCHED1 (BRC1)</snippet>. <snippet id="S_jYWskoE,S_gqi212y,S_nFj5ih3">Auxin acts as an inhibitor by suppressing cytokinin biosynthesis and enhancing SL biosynthesis, which upregulates BRC1 expression to inhibit branching</snippet>. <snippet id="S_gqi212y,S_Uuo070s">BRC1 is identified as a key integrator of pathways that suppress bud outgrowth, including those mediated by SL, auxin, and cytokinin</snippet>. <snippet id="S_jLTk85S">The interactions among auxin, cytokinin, and strigolactone are crucial for axillary bud outgrowth, with key transcription factors including BRC1, MYB13, and WRKY71 involved in regulating lateral bud growth</snippet>. <snippet id="S_NtlEzla">BRC1 is a key regulatory hub for shoot branching in Arabidopsis, interacting with cytokinins, strigolactones, and auxins to determine the branching phenotype</snippet>. <snippet id="S_XRhJBhI">Auxin acts as a major inhibitor of axillary bud outgrowth by suppressing BRC1 expression, which promotes bud growth, and this regulatory network includes auxin, strigolactone, and cytokinins</snippet>.
</answer>

<answer>
Cytokinin (CK), strigolactone (SL), and auxin form a regulatory network where auxin inhibits axillary bud outgrowth by suppressing CK levels and enhancing SL biosynthesis <snippet id=S_jYWskoE,S_q2ORi2Q>. Auxin promotes the transcription of SL biosynthesis genes, including MAX3/MAX4 (orthologs CCD7/CCD8), to synthesize SLs that suppress branching <snippet id=S_jYWskoE,S_nFj5ih3>. These hormones converge on BRANCHED1 (BRC1), a transcriptional hub where SL upregulates BRC1 expression to inhibit growth, while CK represses BRC1 to promote outgrowth <snippet id=S_gqi212y,S_gLbtlMj>. Consequently, auxin indirectly regulates BRC1 through these antagonistic factors rather than direct transport into the bud <snippet id=S_gqi212y>. BRC1 integrates these signals to suppress bud outgrowth, with mutants like amp1 showing enhanced branching due to altered CK levels and reduced BRC1 <snippet id=S_Uuo070s,S_gLbtlMj>.
</answer>


<answer>
<snippet id="S_Y8Mojus,S_Gk4sG8w,S_5WLXO2p">Perplexity AI offers three pricing tiers: Free, Pro at $20/month ($200 annually), and Enterprise Pro at $40/month per seat ($400/year with 16% discount)</snippet>. The Pro version includes <snippet id="S_Gk4sG8w,S_5WLXO2p">access to advanced AI models like GPT-4, Claude 3.5 Sonnet, and GPT-4o, with 300 daily searches and file analysis for PDFs and images</snippet>, along with <snippet id="S_Y8Mojus">unlimited Copilot queries and real-time web access for research</snippet>. Enterprise Pro provides <snippet id="S_Y8Mojus">unlimited queries, 500 daily deep research capabilities, organizational file repositories, advanced security, and dedicated support</snippet>. The Pro subscription is designed for professionals needing in-depth research and technical reasoning, making it suitable for students, educators, writers, business professionals, and developers <snippet id="S_5WLXO2p">. However, some users note limitations including restricted programming support, challenges with long-form content generation, and file handling issues</snippet> <snippet id="S_yvm9XpN">. Enterprise Pro is available for organizations with over 100 or 250 seats with flexible pricing options</snippet> <snippet id="S_Y8Mojus">. Users can upgrade from the free version to Pro for enhanced accuracy, advanced model selection, and tools for generating compliance documents and client-ready materials</snippet> <snippet id="S_Gk4sG8w">. The Pro version also includes Perplexity Labs for structured outputs like reports and dashboards, streamlining project workflows</snippet> <snippet id="S_5WLXO2p">. Subscription billing can be done on a monthly or yearly cycle, with yearly billing offering significant savings</snippet> <snippet id="S_5WLXO2p">. Pricing and feature comparisons are available on Perplexity's official site and third-party sources</snippet>.
</answer>

<answer>
Perplexity AI offers three pricing tiers: Free, Pro ($20/month or $200/year), and Enterprise Pro ($40/month per seat or $400/year) with discounts for annual billing <snippet id="S_Y8Mojus,S_Gk4sG8w,S_5WLXO2p">. The Pro subscription includes access to advanced AI models like GPT-4o, Claude 3.5 Sonnet, and Claude 3.7, plus unlimited Copilot queries, file upload for PDFs and images, and real-time web access <snippet id="S_Gk4sG8w,S_5WLXO2p,S_yvm9XpN">. Pro users also receive features like multi-step reasoning searches, deep research queries (3-300 daily depending on source), and Perplexity Labs for structured outputs <snippet id="S_Y8Mojus,S_Gk4sG8w">. However, some users report limitations in programming support, long-form content generation, and file handling compared to competitors <snippet id="S_yvm9XpN">. Enterprise Pro adds organizational file repositories, collaboration features, unlimited deep research (500 per day), and advanced security options <snippet id="S_Y8Mojus">.
</answer>