<answer>
<snippet id="S_0NbePdE,S_RLXmKuG">The EU AI Act categorizes AI systems into risk levels, with high-risk systems requiring specific transparency obligations under Article 13</snippet>, which mandates that providers ensure systems are "sufficiently transparent" to enable users to understand their characteristics, capabilities, and limitations <snippet id="S_0NbePdE">. Article 14(3) further requires that human overseers have the authority to decide against using the AI system, override its outputs, and intervene in its operation, including the ability to halt it safely</snippet>. <snippet id="S_UuXjGBn">Transparency requirements include full disclosure of accuracy levels, testing metrics, and potential impacts on performance</snippet>, while <snippet id="S_NQAOkk3">minimum documentation requirements vary depending on the AI system's risk level and intended recipient (users or authorities)</snippet>. <snippet id="S_xQp268d">General-purpose AI providers face conformity assessments and detailed technical documentation about system architecture and training datasets</snippet>, with <snippet id="S_JvLynMd">entities deploying Gen AI deepfakes required to disclose their AI-generated nature</snippet>. <snippet id="S_3iocoPc">For high-risk, opaque, and complex systems, explainability is mandated from an EU court through XAI methods rather than within the system itself</snippet>, and <snippet id="S_AsIfzah">Article 50 imposes transparency duties on deployers requiring outputs to be 'watermarked' and users to be informed when interacting with chatbots</snippet>.
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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">.
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<snippet id="S_tyjIFLb">Strava serves as a social network for amateur and professional athletes where users log, monitor, and share fitness accomplishments via status updates, comments, and photos</snippet>, with <snippet id="S_tyjIFLb">challenge systems and leaderboards</snippet> used as core gamification techniques to encourage repeated use and foster competition. <snippet id="S_ohmbRBN">Strava is categorized as a persuasive technology</snippet> designed to motivate users through route tracking and performance feedback, with <snippet id="S_ohmbRBN">social media contributing to self-presentation and feedback from the online community</snippet> that fosters a culture of self-monitoring and enhancement. <snippet id="S_KfOy5t1">Social comparison is identified as a key psychological driver</snippet> for user engagement in fitness apps, though <snippet id="S_KfOy5t1">most fitness apps overlook individual differences in comparison direction—upward versus downward</snippet> preferences. <snippet id="S_fdz63NK">Users engage in selective data sharing</snippet>, often <snippet id="S_fdz63NK">opting to withhold metrics like heart rate and wattage in favor of basic information such as segment times and elevation</snippet> to manage self-validation and perceived scrutiny. <snippet id="S_soikqKO">Strava features segments defined by users for performance comparisons and leaderboards</snippet> that <snippet id="S_soikqKO">foster a sense of community while allowing users to control their data visibility</snippet>. <snippet id="S_tyjIFLb">Users receive weekly emails summarizing their fitness activity output and notifications when another user replaces them at the top of a leaderboard</snippet>, which <snippet id="S_tyjIFLb">are examples of core gamification techniques used by Strava to encourage repeated use</snippet>.
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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">.
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<snippet id="S_ixOFgje">President Trump announced 25% additional tariffs on imports from Canada and Mexico, and 10% additional tariffs on imports from China</snippet>, with <snippet id="S_ixOFgje">energy resources from Canada subject to a lower 10% tariff</snippet>. <snippet id="S_ixOFgje">The White House fact sheet cites 2023 U.S. trade deficit in goods at over $1 trillion</snippet>, and notes that <snippet id="S_ixOFgje">trade accounts for 67% of Canada's GDP, 73% of Mexico's GDP, and 37% of China's GDP</snippet>. <snippet id="S_ixOFgje">The administration argues these measures address a national emergency from illegal immigration and fentanyl, with 75,000 deaths attributed to fentanyl alone</snippet>. <snippet id="S_ixOFgje">President Trump referenced a November promise to charge Mexico and Canada 25% tariffs on all products until drugs and illegal aliens stop the invasion</snippet>. However, <snippet id="S_ixOFgje">the fact sheet does not provide specific effective dates for these tariff measures</snippet>, nor does it include quantified economic impact estimates such as consumer cost or GDP figures.
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<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.
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<snippet id="S_7QgSGSe">The slogans "War is Peace," "Freedom is Slavery," and "Ignorance is Strength" from George Orwell's "Nineteen Eighty-Four" exemplify discursive control through metaphorical language that challenges semantic drift</snippet>. <snippet id="S_lgOMCo4">The term "unfreedom," derived from Orwell's Newspeak in Nineteen Eighty-Four, exemplifies the intensifying use of language for ideological purposes</snippet>. <snippet id="S_tpXkvyW">Metaphoric slogans in political discourse are deployed to project covert ideology and exert influence on the general public</snippet>. <snippet id="S_iDUQY7x">Slogans function as emotional appeals and conversation killers, often intended to end arguments and quell cognitive dissonance</snippet>. <snippet id="S_15dkzZt">Repetition is identified as a persuasive technique where audiences eventually accept messages through repeated exposure</snippet>. <snippet id="S_WxyOc9V">Metaphorical slogans can undergo reinterpretation over time, with evaluative connotations shifting from positive to negative through external discursive developments</snippet>.
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<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>.
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<snippet id="S_gsbJVy0,S_5VWgqHF">Eric A. Stach from the University of Pennsylvania served as MRS Vice President beginning January 1, 2024</snippet>, and <snippet id="S_gsbJVy0">he will lead the Board of Directors as MRS President in 2025</snippet>. <snippet id="S_ZPx3NY5">Takao Someya is also listed as a 2024 MRS President</snippet>, which indicates the position of vice president/president-elect was held by Stach in 2024. <snippet id="S_Mcho4xl">The official MRS announcement confirms the Vice President/President Elect and new Board Members for 2025</snippet>, though the specific 2024 title designation is not explicitly stated in that snippet.
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<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">.
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<snippet id="S_3JECVB5">OASIS STIX 2.1 is a machine-readable, semi-structured framework for structuring and exchanging cyber threat intelligence (CTI) using JavaScript Object Notation (JSON)</snippet>, <snippet id="S_JYIyMdS">which defines 12 STIX Domain Objects (SDOs) including 'indicator', 'malware', 'report', and 'vulnerability', each with specific attributes</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_lIvobvB">The indicator SDO specifically uses the pattern property to detail malware indicators within the CTI framework</snippet>. <snippet id="S_3TK690R">STIX uses a combination of observed data structures, indicator patterns, and relationship objects requiring UUIDs to establish connections between different objects</snippet>. <snippet id="S_3M0PARM">Malware entities are frequently used in CTI analysis, with 75% of STIX bundles containing a Malware entity and 54% including a Threat Actor</snippet>. <snippet id="S_Xa6M06B">STIX 2.1 introduced significant changes including a shift from XML to JSON serialization and integration of CybOX for representing cyber observables</snippet>.
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<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>
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The search results do not contain specific information about newly formed counties in Kohgiluyeh and Boyer-Ahmad Province during the 2020-2024 period. <snippet id=3d29ab62-0>General information about the province's location in southwestern Iran is available</snippet>, but no details about county-level administrative changes are provided. <snippet id=3d29ab62-1>Wikipedia pages for existing counties like Kohgiluyeh County are listed</snippet>, yet these do not indicate new county formations. <snippet id=3d29ab62-5>One snippet mentions "newly formed local and province level governments" in general context</snippet>, but does not specify Kohgiluyeh and Boyer-Ahmad Province. <snippet id=3d29ab62-6,3d29ab62-7,3d29ab62-9>Multiple 2024 research studies cover various topics including agriculture, groundwater, and health</snippet>, but none document new county establishment. The search results lack the specific administrative change data needed to identify newly formed counties in this province.
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<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>.
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<snippet id="S_ywdgTkl">For the Trusted Computing Environment & Platform area, Beihang University's School of Computer Science won the National Science and Technology Progress Second Prize for establishing CROWN, which provides high-trust software development environment, Web service middleware platform, and network environment operation platform</snippet>. <snippet id="S_UoGmAYk">For the Virtual Reality & Digital Media area, the research group won the National Science and Technology Progress First Prize and Second Prize for developing real-time 3D graphics platform BH-GRAPH and distributed interactive simulation support platform BH_RTI, and building distributed virtual environment DVENET</snippet>. These projects are part of Beihang University's key research directions in virtual reality technology and systems, trusted computing systems, and network space information security <snippet id="S_hR66r0h">as identified in the university's 2019 strategic layout</snippet>.
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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">。
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<snippet id="S_C7vME7Z">A study among 507 students in Nigeria found a lifetime gambling prevalence of 57.2%, with 77.6% having gambled in the previous 12 months</snippet>. <snippet id="S_ksMf31Q">Research 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 among university students in Nigeria</snippet>. <snippet id="S_sAPmmcf">Among respondents who reported past-30-day sports betting, those who were currently in work or studies did not differ significantly from non-gamblers regarding gender, age, or employment status</snippet>. <snippet id="S_rqYsRo2">A study of 5,000 college students from 12 universities in Ghana explored financial literacy's role in predicting financial behavior, which may relate to sports betting prevalence among Nigerian students</snippet>. <snippet id="S_zEaN2n7">USA research found that regular participation in sports betting, fantasy sports betting, and daily fantasy sports betting among adolescents aged 16-19 years old are significant predictors of at-risk gambling behaviour</snippet>. <snippet id="S_7jM1r4W">Sports betting is more prevalent among men and younger individuals, with the risk of gambling problems increasing significantly with sports betting frequency</snippet>. <snippet id="S_d3zPQEk">A study of esports bettors among emerging adults in Great Britain found associations between gambling and socio-demographics, economic status, and impulsivity, though specific data on Nigerian students is not detailed in this study</snippet>.
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<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.
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<snippet id="S_N0JvFLC">The official Chatbot Arena leaderboard is available at lmarena.ai, where over 3.5M votes have been collected</snippet>. <snippet id="S_6IdzsAE">The LMSYS blog maintains historical leaderboard updates, with the first Elo rating leaderboard released in May 2023 based on 27K anonymous voting data</snippet>. <snippet id="S_hhzNfal">More recent multimodal leaderboard updates from LMSYS in June 2024 track models based on image-containing battles</snippet>. However, <snippet id="S_5A51bt1">the search results confirm Chatbot Arena is a crowdsourced, randomized battle platform for large language models</snippet>, but none of the provided snippets contain the specific current top model name, its Elo rating, or an update timestamp from the live leaderboard page. To obtain the current top model information, you would need to access the official lmarena.ai leaderboard page directly.
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<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.
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<answer>
<snippet id="S_gu69vHf">DESI findings indicate that dark energy has an equation of state w(z) crossing the phantom barrier (w < -1) for z > 1, with w0 > -1, suggesting evolving dark energy models that deviate from w = -1</snippet>. <snippet id="S_Cn1GhvJ">DESI+CMB data suggest a ≃ 3σ deviation from ΛCDM, indicating a potential phantom crossing at z c ≃ 0.45, where w(z) < −1, challenging standard scalar-field models of dark energy</snippet>. <snippet id="S_DVeLkbI">DESI BAO data show w lying in the phantom regime at high redshift (0.8 ≤ z < 2.1) with 1.6σ/1.5σ/1.5σ confidence level, with DESI BAO only yielding a higher w in the late universe</snippet>. <snippet id="S_ZCPd5ub">Recent findings from DESI Data Release 2 (DR2) favor a dynamical dark energy characterized by a phantom crossing feature, implying a lower value of the Hubble constant that intensifies the Hubble tension</snippet>. <snippet id="S_ZIWoomJ">The original DESI paper favours a phantom behaviour of dark energy (w < −1) over a significant redshift range, with a preference for crossing to the non-phantom region at lower redshift</snippet>. <snippet id="S_otM3Qb8">DESI measurements suggest dark energy may be evolving into the phantom regime with w(z) < -1, indicating potential deviations from the ΛCDM model, although current data remains inconclusive regarding the existence of a phantom crossing</snippet>.
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<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>.
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<snippet id="S_RujXXPj,S_4mMdKmh,S_8bzg1KA">The margin of safety in pharmacology is defined as the ratio between the amount of drug that is lethal to 1% of the population and effective in 99% of the population (LD1/ED99)</snippet>. <snippet id="S_NDPofwS">This calculation requires the LD1 dose that elicits lethality in 1% of the population and the ED99 dose that produces therapeutic effect in 99% of the population</snippet>. <snippet id="S_ITpLCrH">Alternatively, it can be expressed as LD50/ED50, representing the lethal dose in 50% of patients compared with the effective dose in 50% of patients</snippet>. <snippet id="S_Tg2CFEg">A higher margin of safety indicates lower risk of toxicity, with the margin of safety indicating the safety of a drug at high doses</snippet>. However, <snippet id="S_RujXXPj,S_4mMdKmh,S_8bzg1KA,S_NDPofwS,S_Tg2CFEg,S_ITpLCrH,S_gWkoXUl,S_PNN5Uwr">none of the retrieved snippets explicitly discuss conditions under which margin of safety cannot be calculated or is considered undefined</snippet>, suggesting this specific domain may not contain the definitional edge case the agent is seeking.
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<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>
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<answer>
The search results do not contain explicit evidence of group polarization or risky shift phenomena in avatar-mediated immersive VR environments. <snippet id="S_JIjtTEQ">One study simulating a virtual reality environment with computer-generated avatars did not detail findings related to "risky shift" in virtual reality avatars</snippet>. <snippet id="S_X5s1Ifa">Another study found that abstract avatars (robots) led to increased risky behaviors compared to self-representations, but this focused on individual behavior rather than group dynamics</snippet>. <snippet id="S_HKqSHCf">Similar findings showed that abstract representations allowed users to adopt more risky behaviors while self-representations maintained connection with the real world</snippet>. <snippet id="S_snWHiX5">Additional applications of avatars in VR include therapy for fear of heights, medical student interviewing practice, and suicide risk prevention programs</snippet>. <snippet id="S_SCx4OjQ">Dissimilar avatars can enhance user interaction and social interactions, but these studies focus on avatar-dissimilarity effects rather than group polarization</snippet>. The available snippets do not provide the specific multi-user IVE evidence needed to demonstrate group polarization through avatar-mediated social interaction.
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<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>.
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<answer>
<snippet id="S_uPQ5gEz">Tesla's Electric Arc Lamp patent is US335786A, which covers improvements in Electric-Arc Lamps</snippet>, and <snippet id="S_CBhXwnS,S_L4MYIrf">this patent was issued on February 9, 1886</snippet>. <snippet id="S_s2HVIjN">A related patent US335787A also covers Electric-Arc Lamp technology</snippet>, with <snippet id="S_ktaELRS">the arc lamp's automatic fail switch and reactivation features</snippet>. <snippet id="S_yVKm3fu">The Commutator for Dynamo-Electric Machines was issued on January 26, 1886</snippet>, making the Electric Arc Lamp patent the second of Tesla's first two U.S. patents. <snippet id="S_iDdAzGv">The Electric Arc Lamp patent used electromagnets and lever mechanisms to precisely separate and feed carbon electrodes</snippet>.
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<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">.
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<snippet id="S_cGRsAZI">The episode is titled "Rhino Rocket with Tina Munjal, MD" from Season 3, Episode 2 of "Stories from the World of Medicine"</snippet>, <snippet id="S_cGRsAZI">broadcast on 2/18/2020</snippet>. <snippet id="S_cGRsAZI">The guest is Otolaryngologist Tina Munjal, MD</snippet>, who tells a story about <snippet id="S_cGRsAZI">learning to be comfortable outside of her comfort zone</snippet>. <snippet id="S_IhbWqXF">The episode is available on The Nocturnists podcast website</snippet> at https://thenocturnists.org/podcast/rhino-rocket <snippet id="S_cGRsAZI">and can be accessed via their official site</snippet>. <snippet id="S_NZ9gUn7">The content covers Tina Munjal's medical school and residency experiences</snippet> as an Otolaryngologist.
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<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.
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<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">Recent availability of E. muelleri's genome facilitates research on selection, adaptation, and genetic diversity, which is crucial for monitoring conservation status in poorly studied invertebrates</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>. <snippet id="S_aQHGJDJ,S_BVGS7oK">Evolutionary potential (EP) can have profound implications for extinction risk, with proxies for EP providing valuable information to inform both extinction-risk assessments and recovery efforts in the face of global change</snippet>. <snippet id="S_yJ0bKZt">Extinction-risk assessments that include genetic factors focus on inbreeding depression and rarely integrate EP, though integrating EP into conservation decision-making remains an important area for innovation in applied conservation science</snippet>. <snippet id="S_hlWQ6qg">Current conservation tools are insufficient to address the rapid extinction rates, emphasizing the need for taxonomists and systematists to understand species fates through "salvage sampling"</snippet>. <snippet id="S_160DJq3,S_kpUOwfs">Late-Quaternary megafauna extinctions reviews highlight patterns, causes, and ecological consequences, with growing interest in trophic rewilding for ecosystem conservation and restoration</snippet>.
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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>.
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<snippet id="S_4iZB0OB">The central baryon chemical potential of a neutron star at zero temperature is predicted to be 1319 MeV</snippet>, which is below the limits set by perturbative quantum chromodynamics (PQCD). <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 this value <snippet id="S_exyOPhA">lies between 1050 MeV and 1400 MeV at zero temperature</snippet>. <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>, which helps establish the scale at a few n0 where hyperon-EoS papers define the onset.
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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">.
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<snippet id="S_e9zzIKc,S_GveNEie">Bond et al. (2012) conducted a landmark 61 million user experiment in 2010 during the U.S. Congressional Election where Facebook users were shown messages at the top of their News Feeds encouraging them to vote</snippet>. <snippet id="S_e9zzIKc">The results suggest that the Facebook social message increased turnout by close to 340,000 votes</snippet>. <snippet id="S_37bk94m">The study found that Facebook utilized "social proof" by displaying images of friends who had voted, encouraging users to imitate their behavior rather than relying on direct algorithmic recommendations</snippet>. <snippet id="S_mXK3pZh,S_8SBonkH">Subsequent replication during the 2012 U.S. Presidential Election showed that voting increased significantly among close friends of those who received the message, with total effects reaching approximately 270,000 additional votes</snippet>. <snippet id="S_I0UxhZS">The research demonstrated that people who know that their Facebook friends voted are more likely to vote themselves, providing evidence of social influence in political engagement</snippet>. <snippet id="S_gGLgZLn">However, the authors acknowledged very small effects from this information treatment, which highlights the need for careful interpretation of statistical significance in large-scale social experiments</snippet>.
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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">.
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<snippet id="S_4LDk9pb">IGN confirms that November 23, 2004 was the launch date for North America, Australia, and New Zealand</snippet>. <snippet id="S_mhE8bKy">GamesIndustry.biz corroborates that World of Warcraft will be in stores in North America on November 23, 2004, launching simultaneously in Australia and New Zealand</snippet>. <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 further confirms that World of Warcraft was released on November 23, 2004</snippet>. Multiple independent sources consistently verify this release date across gaming industry publications.
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<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>.
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<snippet id="S_jYWskoE,S_q2ORi2Q">Cytokinin (CK) promotes axillary bud outgrowth, while auxin (AUX) and strigolactone (SL) act antagonistically to suppress it</snippet>. <snippet id="S_jYWskoE,S_gqi212y">Auxin inhibits bud outgrowth by upregulating SL biosynthesis genes (MAXs) and BRC1 expression, whereas CK represses BRC1 expression to allow branching</snippet>. <snippet id="S_jLTk85S">Key transcription factors including BRANCHED1 (BRC1), MYB13, and WRKY71 are involved in regulating lateral bud growth and shoot branching in Arabidopsis</snippet>. <snippet id="S_nFj5ih3">Strigolactones (SLs) are synthesized from carotenoids via CCD7/CCD8 enzymes (MAX3/MAX4 in Arabidopsis) and function as endogenous suppressors of axillary bud outgrowth</snippet>. <snippet id="S_Uuo070s">BRC1 is identified as a key integrator of pathways that suppress bud outgrowth, including those mediated by SL, auxin, cytokinin, and sugar</snippet>. <snippet id="S_gqi212y">Polar auxin transport (PAT) in the main stem inhibits axillary bud outgrowth, and auxin cannot directly regulate BRC1 expression because it is not transported from the stem to the buds in great enough amounts</snippet>.
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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>.
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<snippet id="S_Y8Mojus">Perplexity offers three pricing tiers for AI Copilot services: Free, Pro, and Enterprise Pro</snippet>. <snippet id="S_Y8Mojus">The Free version allows unlimited concise searches, while the Pro version costs $20/month and includes multi-step reasoning searches and deep research queries, limited to three per day</snippet>. <snippet id="S_Y8Mojus">The Enterprise Pro tier is priced at $40/month per seat or $400/year (16% discount) and provides unlimited queries, extensive deep research capabilities (500 per day), and enhanced collaboration features with unlimited collaborators</snippet>. <snippet id="S_Gk4sG8w">The Pro version includes access to advanced AI models like GPT-4o and Claude 3.5 Sonnet, 300 daily Pro searches, file analysis for PDFs and images, and image generation</snippet>. <snippet id="S_5WLXO2p">The Pro plan also includes real-time web access for research, faster response times, and is designed for users requiring deeper insights and enhanced research capabilities</snippet>. <snippet id="S_yvm9XpN">Some users report limitations including restricted programming support, challenges with long-form content generation, and issues with file handling and custom bots</snippet>.
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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">.
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