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<snippet id="S_0NbePdE,S_RLXmKuG">The EU AI Act categorizes AI systems based on risk levels, with high-risk systems required to be "sufficiently transparent" under Article 13(1), allowing for differentiation based on the system's transparency levels</snippet>. <snippet id="S_0NbePdE,S_RLXmKuG">Article 13 mandates that high-risk AI systems must provide sufficient transparency mechanisms and include user instructions that are accessible and understandable, detailing the systems' characteristics, capabilities, and limitations</snippet>. <snippet id="S_TVBhkcK,S_WG0otDu">Article 14(3) requires human overseers to understand the AI system's capabilities and limitations to monitor its operation and detect anomalies, while Article 14(4) specifies that personnel must be able to interpret outputs correctly and have the authority to override or halt the system</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 not within the system but to the AI deployer through an order to disclose proportional evidence necessary</snippet>. <snippet id="S_iTsiprz,S_uIhZh72">High-risk AI systems face the most stringent documentation obligations, with users requiring clear and accessible instructions while authorities and conformity assessment bodies need comprehensive technical documentation to ensure compliance</snippet>. <snippet id="S_xQp268d">General-purpose AI providers face significant requirements including conformity assessments, human oversight, and detailed technical documentation about system architecture and training datasets, though open-source models may receive some exemptions</snippet>. <snippet id="S_vzKoGDA">The AI Act contains wide-ranging disclosure obligations under Article 11 and Annex IV that apply only to high-risk systems, though there are discussions about extending transparency duties to non-high-risk large generative AI models</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,S_ohmbRBN">Strava serves as a social network for amateur and professional athletes, enabling users to log, monitor, and share fitness accomplishments through status updates, comments, and photos</snippet>, with <snippet id="S_tyjIFLb">core gamification techniques including challenges that reward users with digital badges and trophies for completing specific distances</snippet>. <snippet id="S_ohmbRBN">The app is categorized as a persuasive technology designed to motivate users by tracking routes and providing performance feedback</snippet>, fostering competitive behaviors that can significantly influence user motivation. <snippet id="S_soikqKO">Strava features segments defined by users, allowing for performance comparisons, and highlights achievements with icons like bronze medals for personal records</snippet>, while <snippet id="S_soikqKO">users can view leaderboards to compare their results with others, including specific demographics if they have a premium subscription</snippet>. <snippet id="S_fdz63NK">However, research indicates that many users selectively share data, often withholding metrics like heart rate and wattage, opting instead for basic information such as segment times and elevation</snippet>, reflecting concerns about <snippet id="S_fdz63NK">self-validation and awareness of how others perceive their data</snippet>. <snippet id="S_5nbN41y">Limitations include reliance on a cross-sectional sample of one particular user type (cyclists), with future research needed to replicate findings across other populations and longitudinal tracking of app usage behaviors</snippet>. <snippet id="S_47DnIT5">Designers should support persuasive features such as Competition and Cooperation to foster intrinsic motivation and accountability among socially oriented users</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">The White House fact sheet announces a 25% additional tariff on imports from Canada and Mexico, with 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">The announcement specifies that these measures are being implemented to address the national emergency of illegal aliens and drugs, including fentanyl</snippet>. <snippet id="S_ixOFgje">The fact sheet notes that trade accounts for 67% of Canada's GDP, 73% of Mexico's GDP, and 37% of China's GDP, while the U.S. trade deficit in goods was over $1 trillion in 2023</snippet>. <snippet id="S_ixOFgje">The announcement references a previous Presidential Memorandum on American First Trade Policy where President Trump promised to charge Mexico and Canada 25% tariffs on all products entering the United States</snippet>. <snippet id="S_ixOFgje">The document also references the Opioid Crisis as a public health emergency and the use of tariffs to secure the border</snippet>. <snippet id="S_ixOFgje">The fact sheet includes statistics on fentanyl seizures and overdose deaths, noting 75,000 deaths per year attributed to fentanyl alone and 4 billion people worth of fentanyl seized</snippet>. <snippet id="S_ixOFgje">The announcement concludes that tariffs are a proven source of leverage for protecting the national interest and that the U.S. has one of the lowest average tariff rates in the world</snippet>.
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<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" are central to the novel's discourse, with analysis noting that a significant portion of references are secondary uses rather than original</snippet>. <snippet id="S_7QgSGSe">The analysis suggests these slogans can evolve in their interpretation and application within public discourse, reflecting changing societal attitudes and contexts</snippet>. <snippet id="S_iDUQY7x">Slogans tend to act as emotional appeals and can function as conversation killers, discouraging critical thought and meaningful discussion about a given topic</snippet>. <snippet id="S_15dkzZt">In propaganda analysis, slogans are defined as brief and striking phrases that may include labeling and stereotyping, often used to persuade audiences to disapprove of an action or idea</snippet>. <snippet id="S_lgOMCo4">The term "doubleplus unfree" is noted as an example of intensifying language derived from Orwell's Newspeak in Nineteen Eighty-Four</snippet>. However, the provided snippets do not contain specific scholarly CDA analysis of these slogans through frameworks like Fairclough, van Dijk, or Foucault, nor detailed analysis of Newspeak linguistic engineering or memory/history control mechanisms.
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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">Eric A. Stach from the University of Pennsylvania will serve as MRS Vice President beginning January 1, 2024</snippet>. <snippet id="S_ZPx3NY5">He will begin his service in the position of vice president/president-elect</snippet>. <snippet id="S_gsbJVy0">He will lead the Board of Directors as MRS President in 2025</snippet>. <snippet id="S_5VWgqHF">He will finish his three-year term as Immediate Past President in 2026</snippet>. <snippet id="S_SNLHE9w">The 2024 election results for the 2025 MRS leadership team were announced</snippet>.
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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,S_Xa6M06B">The OASIS STIX 2.1 format is a machine-readable, semi-structured framework for structuring and exchanging cyber threat intelligence (CTI) using JavaScript Object Notation (JSON), with twelve STIX Domain Objects (SDOs) including 'indicator', 'malware', 'report', and 'vulnerability' that describe characteristics of incidents</snippet>. <snippet id="S_3JECVB5,S_Xa6M06B">STIX 2.1 introduced a flat structure where STIX Domain Objects (SDOs) are defined at the top level and relationships between them are managed through STIX Relationship Objects (SROs)</snippet>. <snippet id="S_JYIyMdS,S_3JECVB5">The Indicator SDO contains a 'pattern' property that is crucial for detailing malware indicators within the CTI framework, while SDOs contain common attributes like IDs and object types, with specific attributes relevant to the type such as attacker motivation or tool version</snippet>. <snippet id="S_3JECVB5">SROs come in two types: one that connects two SDOs to highlight relationships (e.g., malware exploiting a vulnerability) and another that identifies a specific SDO with evidential data</snippet>. <snippet id="S_3JECVB5">The Report object serves as the SDO that references these elements, with relevant SDOs and SROs encapsulated in a report</snippet>. <snippet id="S_3M0PARM">In practice, STIX bundles contain 36,100 entities and 13,600 relations with nine unique entity types and five unique relation types, featuring 75% of bundles including a Malware entity and 54% including a Threat Actor</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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<snippet id=3d29ab62-0>The search results do not contain specific information about newly formed counties in Kohgiluyeh and Boyer-Ahmad Province between 2020-2024</snippet>. <snippet id=3d29ab62-1>One snippet mentions Kohgiluyeh County with Dehdasht as its capital, but this refers to the county rather than a newly formed county</snippet>. <snippet id=3d29ab62-5>One snippet mentions "newly formed local and province level governments" but does not specify which counties were formed in this province</snippet>. <snippet id=3d29ab62-8>One snippet lists various locations including "Kokomian, Kokoumbo, Kolda, Koldaga, Kolia, Koloko" but these appear to be from a different region (likely Mali) rather than Iran</snippet>. The search results do not contain the specific information needed to identify newly formed counties in this province during the 2020-2024 period.
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<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 research area, the School of Computer Science at Beihang University won the National Science and Technology Progress Award Second Prize (二等奖) 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 research area, the school won the National Science and Technology Progress Award First Prize (一等奖) and Second Prize (二等奖) for developing real-time 3D graphics platform BH-GRAPH and distributed interactive simulation support platform BH_RTI, as well as building distributed virtual environment DVENET</snippet>. <snippet id="S_hR66r0h">The School of Computer Science at Beihang University is recognized as a national key laboratory for virtual reality technology and systems</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_ksMf31Q">Sports betting has gained popularity among university students in Nigeria, influenced by the accessibility of online platforms and smartphone applications</snippet>, with research indicating 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 id="S_sAPmmcf">Characteristics of past-30-day sports bettors compared to past-year sports bettors show that those who reported past-30-day sports betting were more likely to have a history of indebtedness (11 vs. 6%, p = 0.04)</snippet>, while also exhibiting higher levels of gambling problems. <snippet id="S_iL4llG9">Among young adults aged 16-24, esports bettors were more likely to be students (59%) and exhibit higher impulsivity scores</snippet>, with economic data showing esports bettors were less likely to be inactive (10% vs. 15% for non-gamblers). <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_zEaN2n7">Students aged 16-19 years old are at a higher risk for developing a gambling problem compared to younger adolescents when regularly engaging in sports-related gambling</snippet>, with regularly participating in daily fantasy sports being the strongest predictor of at-risk gambling behaviour in 13 to 15-year-old students. <snippet id="S_ksMf31Q">The impact of sports betting advertising has also been a focus of concern, with studies suggesting that such advertising may contribute to higher rates of gambling problems, especially among young males</snippet>. <snippet id="S_d3zPQEk">The study examines the determinants and prevalence of esports betting among emerging adults, focusing on socio-demographics, economic status, impulsivity, and gaming behaviors</snippet>, though specific data on that demographic is not detailed in this study.
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<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 hosted at lmarena.ai, which has accumulated over 3.5M votes</snippet>. <snippet id="S_6IdzsAE">The leaderboard uses an Elo rating system based on anonymous voting data collected between April 24 and May 22, 2023</snippet>. <snippet id="S_hhzNfal">A multimodal leaderboard was released on June 27, 2024, computed from battles containing images</snippet>. However, <snippet id="S_831cvJ0">none of the provided search snippets contain the current top model name, its Elo rating, or the timestamp/update note</snippet>. The agent will need to browse the official leaderboard page to capture this information.
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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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<snippet id="S_gu69vHf,S_Cn1GhvJ,S_DVeLkbI">DESI results indicate 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 suggests a ≃ 3σ deviation from ΛCDM, indicating a potential phantom crossing at z_c ≃ 0.45, where w(z) < -1</snippet>. <snippet id="S_ZCPd5ub">Recent findings from DESI Data Release 2 (DR2) favor a dynamical dark energy characterized by a phantom crossing feature</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">Latest DESI measurements suggest dark energy may be evolving into the phantom regime with w(z) < -1, indicating potential deviations from the ΛCDM model</snippet>. <snippet id="S_ZIWoomJ">However, there are various issues associated with using the w0wa model, as it is a phenomenological ansatz that is not based on a physical and selfconsistent model of dark energy, with no obstacle to the phantom regime w < -1</snippet>. <snippet id="S_9e2mrFy">This work contributes to the growing body of research aimed at unraveling the mysteries of dark energy and its role in the accelerated expansion of the universe</snippet>.
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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,S_NDPofwS,S_Tg2CFEg">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, calculated as LD1/ED99</snippet>. <snippet id="S_NDPofwS,S_Tg2CFEg">This represents the safety of a drug at high doses, with a higher margin of safety indicating lower risk of toxicity</snippet>. However, <snippet id="S_KtkXCqd">none of the retrieved snippets contain explicit discussion about when margin of safety cannot be calculated or when it fails to appear in a definitional sense</snippet>. The search results confirm the standard definition but do not address the specific condition where margin of safety becomes undefined or uncomputable.
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<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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<snippet id="S_X5s1Ifa,S_HKqSHCf">The study found that abstract avatars, particularly robots, led to increased risky behaviors while self-representations fostered cautious behavior</snippet>, with half of participants reporting altered reactions and strategies based on the controlled character. <snippet id="S_X5s1Ifa,S_HKqSHCf">Ownership perceptions favored doppelgangers over robots, and abstract representations allowed users to adopt more risky behaviors</snippet>. However, <snippet id="S_JIjtTEQ,S_XSKnvh7">none of the provided snippets contain explicit evidence of group polarization or risky shift in multi-user virtual environments with avatars</snippet>. The search results discuss avatar visual fidelity, embodiment, and risky behaviors in single-user contexts rather than group discussion or social influence effects. <snippet id="S_snWHiX5">Avatar coaches have been implemented in immersive virtual reality situations for various applications including risk prevention education</snippet>, but this does not address the specific group polarization construct. Additional searches may be needed to find studies on group polarization in avatar-mediated immersive VR environments.
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<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,S_s2HVIjN,S_CBhXwnS,S_L4MYIrf,S_7dWRH62,S_iDdAzGv,S_yVKm3fu,S_ktaELRS">The Electric Arc Lamp patent was issued to Nikola Tesla on February 9, 1886, with patent number 335,786</snippet>. <snippet id="S_L4MYIrf">This patent was granted to Nikola Tesla of Smiljan Lika, Austria-Hungary</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_yVKm3fu">This patent was issued after the Commutator for Dynamo-Electric Machines on January 26, 1886</snippet>. <snippet id="S_ktaELRS">The patent is listed in the Wikipedia list of Nikola Tesla patents as U.S. patent 335,787 for Electric arc lamp in 1886</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,S_IhbWqXF">The episode "Rhino Rocket with Tina Munjal, MD" is part of "Stories from the World of Medicine" Season 3, Episode 2, released on February 18, 2020</snippet>. <snippet id="S_cGRsAZI,S_IhbWqXF">The guest is Otolaryngologist Tina Munjal, MD</snippet>. <snippet id="S_cGRsAZI">The episode is hosted by The Nocturnists Podcast</snippet>. <snippet id="S_NZ9gUn7,S_eavn8We">The story focuses on learning to be comfortable outside of her comfort zone</snippet>. <snippet id="S_Q1JiXuV">The episode is also listed as S3 E2: Rhino Rocket</snippet>. <snippet id="S_C2IKgBh">The episode is sponsored by The Nocturnists</snippet>. <snippet id="S_bWx7wAc">Tina Munjal shared highlights of her medical school and residency experience with a live audience</snippet>.
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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 concept of de-extinction is discussed in the context of functional proxies for species driven to extinction by humans, with potential benefits 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>. <snippet id="S_aQHGJDJ,S_BVGS7oK">Evolutionary potential (EP) serves as a proxy for extinction risk, with its disregard leading to misdirected conservation prioritization and missed recovery opportunities</snippet>. <snippet id="S_yJ0bKZt">Extinction-risk assessments that include genetic factors focus on inbreeding depression and rarely integrate EP, creating uncertainty in decision-making</snippet>. <snippet id="S_btUw1xy">Genomic modifications including gene drives raise ethical and regulatory concerns, while chromosome-level reference genomes remain scarce for over 95% of animal species</snippet>. <snippet id="S_btUw1xy">Functional proxies of recently extinct species could be beneficial for ecosystems, though the field of conservation paleobiology remains defining its identity and practical engagement</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. <snippet id="S_exyOPhA">The neutron critical chemical potential, which indicates the transition to a quark phase, lies between 1050 MeV and 1400 MeV at zero temperature</snippet>. <snippet id="S_BQ1tanr">Baryon chemical potential values in the context of beta equilibrium typically fall within the range of several hundred MeV to a few GeV</snippet>, depending on the specific conditions and models used. <snippet id="S_jARyMl0">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">Neutron stars reach beta equilibrium involving neutrons, protons, and electrons, characterized by the relationship µp = µn - µe</snippet>, with additional baryons such as Λ hyperons emerging when their chemical potential condition is satisfied.
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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,S_37bk94m">The Bond et al. (2012) experiment involved 61 million Facebook users during the 2010 U.S. Congressional Election who were shown messages at the top of their News Feeds encouraging them to vote</snippet>, with results showing that <snippet id="S_e9zzIKc">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_37bk94m">This approach led to approximately 60,000 individuals voting directly and an additional 280,000 influenced indirectly</snippet>. <snippet id="S_mXK3pZh,S_8SBonkH">The 2012 replication experiment during the U.S. Presidential Election showed similar effects, with total voting increases of 270,000 people and 280,000 influenced indirectly through close friends</snippet>. <snippet id="S_gGLgZLn">However, the study found very small effects from this information treatment, which the authors acknowledged as a limitation</snippet>, though the paper's abstract and conclusion emphasized the success of influencing voter behavior through Facebook.
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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 the launch date as November 23, 2004</snippet>, with the article explicitly stating this is the date for North America, Australia, and New Zealand. <snippet id="S_4am1dTv">Another IGN article notes the game first launched in North America on November 23, 2004</snippet>. <snippet id="S_Iyt7Mpt">A December 2004 IGN article also references the November 23 release date</snippet>. This fourth independent source from IGN corroborates the November 23, 2004 release date for World of Warcraft.
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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 promotes axillary bud outgrowth by counteracting auxin-mediated inhibition, while auxin suppresses bud outgrowth through the promotion of systemic and local strigolactone (SL) synthesis and upregulation of MAX genes in Arabidopsis</snippet>. <snippet id="S_jYWskoE,S_gqi212y">In this regulatory network, auxin and SL act as inducers while CK act as repressors of BRC1 expression</snippet>. <snippet id="S_nFj5ih3,S_Uuo070s">Strigolactones are synthesized by carotenoid cleavage dioxygenases (CCD7/CCD8, also known as MAX3/MAX4 in Arabidopsis) and function as endogenous suppressors of axillary bud outgrowth by upregulating BRANCHED1 (BRC1), a gene encoding a repressor of bud outgrowth</snippet>. <snippet id="S_jLTk85S,S_XRhJBhI">The interactions among auxin, cytokinin, and strigolactone are crucial for axillary bud outgrowth, with auxin inhibiting cytokinin biosynthesis through an AXR1-dependent pathway while promoting strigolactone biosynthesis</snippet>. <snippet id="S_gLbtlMj,S_XRhJBhI">Auxin-mediated inhibition of bud outgrowth is linked to increased SL synthesis, and auxin can indirectly promote BRC1 expression in the bud through control of these antagonistic factors</snippet>. <snippet id="S_NtlEzla">BRC1 is a key regulatory hub for shoot branching in Arabidopsis, interacting with various endogenous and environmental factors including hormones, nutrients, and light to determine the branching phenotype</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,S_Gk4sG8w,S_5WLXO2p">Perplexity AI offers three pricing tiers: Free, Pro ($20/month or $200/year), and Enterprise Pro ($40/month or $400/year) for AI Copilot services</snippet>. <snippet id="S_Y8Mojus,S_Gk4sG8w,S_5WLXO2p">The Pro version includes access to advanced AI models like GPT-4o and Claude 3.7 Sonnet, file analysis for PDFs and images, image generation, and 300 daily Pro searches</snippet>. <snippet id="S_Y8Mojus,S_Gk4sG8w,S_5WLXO2p">It also provides unlimited Copilot queries, real-time web access for research, and faster response times</snippet>. <snippet id="S_Y8Mojus">The Enterprise Pro tier offers unlimited queries, 500 daily deep research capabilities, organizational file repositories, advanced security options, and dedicated support</snippet>. <snippet id="S_yvm9XpN">Users report the Pro subscription is beneficial for research and casual searches, though there are limitations including restricted programming support and challenges with long-form content generation</snippet>. <snippet id="S_yvm9XpN">Some users find it superior to alternatives like Google for search capabilities, while others note drawbacks in file handling and custom bot functionality</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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