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<snippet id="S_NIP4aKy,S_M9NC8Eo,S_L5NhumS">The search results show that reading motivation and engagement strategies include providing dedicated time for reading, implementing summer reading programs, and creating supportive classroom contexts that foster engagement through choice, collaboration, and competence</snippet>. <snippet id="S_WIheApX,S_xbJmN70">Merga (2019a, 2019b, 2019c) is identified as a key researcher in school librarian roles and reading engagement, with literature indicating that qualified school librarians in well-resourced libraries are associated with benefits for students' literacy attainment</snippet>. <snippet id="S_L5NhumS">Effective practices should create supportive contexts that foster engagement, with teachers' behaviors playing a significant role in influencing students' motivation through integration of motivational principles alongside cognitive skills</snippet>. <snippet id="S_NIP4aKy">Unlike primary schools, high schools often do not actively encourage book reading, leading to lower engagement among adolescents, highlighting the need for targeted interventions in secondary settings</snippet>. <snippet id="S_M9NC8Eo">A U.K. literacy survey indicated that middle adolescence (ages 14–16) is a critical period for this decline, with adolescents reporting less enjoyment of reading and lower daily reading habits compared to younger peers</snippet>. However, the search did not yield a specific peer-reviewed review or empirical study from Journal of Adolescent & Adult Literacy or a similar high-impact literacy journal focused on secondary school best practices.
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<snippet id="S_WIheApX,S_HOSScoU">School librarians are identified as key figures in fostering reading engagement and literacy attainment in secondary education, a finding supported by research citing Merga (2019a, 2019b)</snippet>, and <snippet id="S_xbJmN70">pleasure in reading is a strong predictor of reading frequency which leads to growth in literacy skills</snippet>. <snippet id="S_NIP4aKy">Schools should provide dedicated time for reading and implement initiatives like summer reading programs</snippet>. <snippet id="S_NIP4aKy">Unlike primary schools, high schools often do not actively encourage book reading</snippet>. <snippet id="S_L5NhumS">Effective practices involve creating supportive contexts that foster collaboration, competence, and positive teacher behaviors, and reading interventions integrating motivational principles alongside cognitive skills show positive effects</snippet>.


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<snippet id="S_0NbePdE,S_RLXmKuG">The EU AI Act mandates that high-risk AI systems must be "sufficiently transparent" to enable users to interpret the systems' outputs and capabilities, as outlined in Article 13</snippet>. <snippet id="S_TVBhkcK,S_WG0otDu">Article 14(3) requires AI providers to implement measures enabling effective human oversight, including ensuring overseers can understand the system's capabilities and limitations to detect anomalies</snippet>. <snippet id="S_UuXjGBn">Article 11(2) allows for a unified technical documentation file combining AI system details with existing EU MDR/IVDR documentation, including comprehensive information on design, architecture, data requirements, training methodologies, and performance metrics</snippet>. <snippet id="S_3iocoPc">Article 4(2)(b) specifies that if an AI system is considered high-risk, opaque, and complex, explainability is mandated from an EU court through orders to disclose proportional evidence such as logs, documentation, and datasets</snippet>. <snippet id="S_E4eihUQ,S_JvLynMd">General-purpose AI systems (GPAIS) are subject to high-risk obligations if they can be used in high-risk contexts or as components of high-risk systems, with the European Commission defining how these rules apply</snippet>. <snippet id="S_vzKoGDA">The AI Act contains disclosure obligations under Article 11 and Annex IV that apply primarily to high-risk systems, though some provisions like Article 50 impose 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,S_soikqKO">Strava serves as a social network for amateur and professional athletes where users log, monitor, and share fitness accomplishments through status updates, comments, photos, and leaderboards</snippet>. <snippet id="S_tyjIFLb,S_ohmbRBN">Core gamification techniques include challenges with digital badges and trophies (25%, 50%, 75% for monthly distance goals), which foster competitive behaviors and motivation</snippet>. <snippet id="S_KfOy5t1,S_ohmbRBN">Social comparison is a key psychological driver on Strava, where users connect, share experiences, and participate in competitive challenges to boost engagement</snippet>. <snippet id="S_fdz63NK">However, data sharing is selective, with many cyclists withholding metrics like heart rate and wattage, opting instead for basic information such as segment times and elevation</snippet>. <snippet id="S_fdz63NK,S_ohmbRBN">This behavior reflects a desire for self-validation and an awareness of how others perceive their data, demonstrating the tension between social visibility and privacy control</snippet>. <snippet id="S_5nbN41y">Limitations include reliance on cross-sectional samples of specific user populations (e.g., cyclists), with longitudinal tracking needed to validate causal relationships</snippet>. <snippet id="S_QiL3aAT,S_ohmbRBN">Research methods include surveys with validated scales on social influence, passion types, and engagement metrics, though privacy policies require opt-in for third-party data access</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 25% additional tariffs on imports from Canada and Mexico, and a 10% additional tariff on imports from China</snippet>. Energy resources from Canada will have a lower 10% tariff rate. <snippet id="S_ixOFgje">The Presidential Memorandum from November 2025 committed to charging Mexico and Canada 25% tariffs on all products entering the U.S. until drugs and illegal aliens stop the "invasion"</snippet>. The fact sheet cites that trade accounts for 67% of Canada's GDP, 73% of Mexico's GDP, and 37% of China's GDP, but only 24% of U.S. GDP. <snippet id="S_ixOFgje">In 2023, the U.S. trade deficit in goods was the world's largest at over $1 trillion</snippet>. The announcement frames these measures as necessary to address a national emergency from illegal immigration and fentanyl, with the drug crisis attributed to 75,000 deaths per year from fentanyl alone. The fact sheet does not provide specific effective dates for these tariffs or detailed trade value numbers for retaliation measures.
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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 page discusses the interpretation of metaphors, particularly focusing on the slogans from George Orwell's "Nineteen Eighty-Four": "War is Peace," "Freedom is Slavery," and "Ignorance is Strength." It highlights the challenges in quantifying the frequency of these slogans in media, noting that a significant portion of references (73%) are secondary uses rather than original.</snippet> <snippet id="S_7QgSGSe">The text emphasizes the concept of 'discursive drift,' which refers to the shifts in meaning and stance associated with metaphors over time, contrasting it with 'semantic drift.' This analysis suggests that the slogans can evolve in their interpretation and application within public discourse, reflecting changing societal attitudes and contexts.</snippet> <snippet id="S_lgOMCo4">The term "unfreedom" is noted as a rare but legitimate formation, while "doubleplus unfree," derived from Orwell's Newspeak in Nineteen Eighty-Four, exemplifies the intensifying use of language.</snippet> <snippet id="S_iDUQY7x">Slogans tend to act as emotional appeals, while thought-terminating clichés are words or phrases that discourage critical thought and meaningful discussion about a given topic.</snippet> <snippet id="S_tpXkvyW">The analysis revealed metaphoric themes in the domains of conflict, journey, building, fire, physical environment, religion, and body parts, which speakers deployed to exert influence on the general public.</snippet>
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<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>. He will lead the Board of Directors as MRS President in 2025 <snippet id="S_gsbJVy0">and finish his three-year term as Immediate Past President in 2026</snippet>. The 2024 election results also show Takao Someya (2024) in the position of vice president/president-elect <snippet id="S_ZPx3NY5">. Eric Stach's appointment was officially announced by the Materials Research Society in September 2024</snippet> <snippet id="S_Mcho4xl">. He was elected as Vice President/President Elect for the 2025 leadership team</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">OASIS STIX 2.1 is a machine-readable, semi-structured framework for structuring and exchanging cyber threat intelligence (CTI) using JavaScript Object Notation (JSON), including 12 STIX Domain Objects (SDOs) such as 'indicator', 'malware', and 'report</snippet>, <snippet id="S_JYIyMdS">each with specific attributes like the 'pattern' property for Indicator SDOs which is crucial for detailing malware indicators within the CTI framework</snippet>. <snippet id="S_3JECVB5">STIX Relationship Objects (SROs) define relationships between these characteristics, with two types: one connecting two SDOs to highlight relationships (e.g., malware exploiting a vulnerability) and another identifying a specific SDO with evidential data</snippet>. <snippet id="S_Xa6M06B">STIX 2.1 introduced significant changes including a shift from XML to JSON serialization, a flat structure with SDOs defined at the top level, and integration of CybOX for representing cyber observables</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, with required attributes including unique IDs, names, labels, and types</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">Real-world datasets show STIX bundles containing malware entities (75% of bundles) with 188 different malware variants and 91 threat actors, reflecting automated analysis capabilities for cyber threats</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>Kohgiluyeh and Boyer-Ahmad province is one of the 31 provinces of Iran, located in the southwest of the country</snippet>. <snippet id=3d29ab62-1>Kohgiluyeh County is in Kohgiluyeh and Boyer-Ahmad province, with its capital being the city of Dehdasht</snippet>. However, <snippet id=3d29ab62-2,3d29ab62-3,3d29ab62-4,3d29ab62-5,3d29ab62-6,3d29ab62-7,3d29ab62-8,3d29ab62-9>none of the provided search results contain specific information about newly formed counties in Kohgiluyeh and Boyer-Ahmad Province between 2020-2024</snippet>. The available snippets only mention general geographical location, administrative structure, and various studies conducted in the region during 2024. <snippet id=3d29ab62-5>A study references newly formed local and province level governments but does not specify county-level changes</snippet>. <snippet id=3d29ab62-6,3d29ab62-7>Other 2024 studies mention the province's agricultural potential and groundwater issues but do not list newly formed counties</snippet>.
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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 project, the School of Computer Science at Beihang University established CROWN providing high-trust software development environment, Web service middleware platform, and network environment operation platform, which won the National Science and Technology Progress Award Second Prize</snippet>. <snippet id="S_UoGmAYk">For Virtual Reality & Digital Media projects, the research team developed the real-time 3D graphics platform BH-GRAPH and distributed interactive simulation support platform BH_RTI, and constructed the distributed virtual environment DVENET to support remote异地collaboration, obtaining both the National Science and Technology Progress Award First Prize and Second Prize</snippet>. These projects are directly tied to Beihang University's Computer School research priorities in virtual reality and trusted computing systems.
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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 recent urban school-based cross-sectional survey in Nigeria found a lifetime gambling prevalence of 57.2% among students, 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</snippet>. <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>. <snippet id="S_sAPmmcf">Those who reported past-30-day sports betting were more likely to have a history of indebtedness (11 vs. 6%, p = 0.04) and had higher levels of gambling problems</snippet>. <snippet id="S_rqYsRo2">Financial literacy may relate to the prevalence of sports betting among university students in Nigeria, though the Ghana study sample was used as a proxy for Nigerian contexts</snippet>. <snippet id="S_zEaN2n7">Regular participation in sports betting, fantasy sports betting, and daily fantasy sports betting among adolescents was associated with a higher risk of gambling problems</snippet>. <snippet id="S_d3zPQEk">The study examines determinants and prevalence of esports betting among emerging adults, though specific data on that demographic is not detailed in this study</snippet>.

However, the available evidence shows limited specific data on employment status as an economic determinant for Nigerian student-athletes, with most studies focusing on general university students or using Ghana/Nigeria proxy data for financial literacy and gambling prevalence.
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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</snippet>, which currently has <snippet id="S_N0JvFLC">over 3.5M votes and counting</snippet>. <snippet id="S_6IdzsAE">Previous leaderboard updates have been published by LMSYS, with the earliest documented update covering data from April 24 to May 22, 2023</snippet>. <snippet id="S_hhzNfal">A more recent multimodal leaderboard was computed from battles containing images as of June 27, 2024</snippet>. However, <snippet id="S_831cvJ0">the specific current top model, its Elo rating, and the exact timestamp of the latest update are not provided in these search results</snippet>. To obtain the definitive current ranking, direct access to the live leaderboard page at <snippet id="S_N0JvFLC">https://lmarena.ai/leaderboard</snippet> would be required.
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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">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 shows w lying in the phantom regime at high redshift (0.8 ≤ z < 2.1) with 1.6σ/1.5σ/1.5σ confidence level, while DESI BAO only preferred phantom behavior compared to previous results</snippet>. <snippet id="S_ZCPd5ub">Recent findings from DESI 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">However, current data remains inconclusive regarding the existence of a phantom crossing</snippet>.

These DESI results from 2024-2025 indicate a preference for dynamical dark energy with phantom crossing features, suggesting dark energy may be evolving into the phantom regime with w(z) < -1. <snippet id="S_p938Lo0">This result hints at a possible breakdown of the cosmological constant paradigm</snippet>, particularly when combined with the Dark Energy Survey 5 Year SN compilation and Planck CMB priors. <snippet id="S_DVeLkbI">The implemented crossing statistics method was used to reconstruct dark energy by using DESI BAO only, DESI BAO+Union3, and BAO+Union3+Plank data combinations</snippet>. <snippet id="S_ZCPd5ub">Our results suggest a potential tension between the Hubble constant H0 of the SH0ES measurement and the phantom-to-quintessence transition in dark energy favored by DESI DR2 BAO data</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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<answer>
<snippet id="S_RujXXPj,S_4mMdKmh,S_8bzg1KA,S_NDPofwS,S_Tg2CFEg">In pharmacology, margin of safety is defined as the ratio between the lethal dose to 1% of the population (LD1) and the effective dose to 99% of the population (ED99), or equivalently as LD50/ED50</snippet>. <snippet id="S_NDPofwS">The LD1 is the dose that elicits lethality in 1% of the population, while the ED99 is the dose that elicits therapeutic effect in 99% of the population</snippet>. <snippet id="S_KtkXCqd">However, margin of safety is also described as a function of cube root of ratio between LT50 and LD50 and one-hundredth of ED</snippet>, indicating the relationship depends on specific dose-response quantiles. <snippet id="S_ITpLCrH">If a drug has a therapeutic index (TI) equivalent to LD50/ED50, this represents the margin of safety concept</snippet>. <snippet id="S_PNN5Uwr">One estimate of a drug's margin of safety is the ratio LD50/ED50, which is the therapeutic index</snippet>. These definitions establish that margin of safety is a quantifiable pharmacological parameter, but the search results do not explicitly state conditions under which it cannot be calculated or would "fail to appear."
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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 experimental evidence of group polarization or risky shift in avatar-mediated immersive VR environments. <snippet id="S_snWHiX5">While avatars have been used in risk prevention education (e.g., Kognito program for suicide risk identification), no discussion of group dynamics or polarization was found</snippet>. <snippet id="S_JIjtTEQ,S_XSKnvh7">Some studies used VR with computer-generated avatars in controlled environments (e.g., underground train journey simulations), but these focused on social anxiety or delusional beliefs rather than group influence or attitude extremity</snippet>. <snippet id="S_X5s1Ifa,S_HKqSHCf">Research examined how avatar visual fidelity affects embodiment and behavior, finding that abstract avatars (robots) led to increased risky behaviors compared to self-representations</snippet>, but this did not involve multi-user group settings or discuss group polarization specifically. <snippet id="S_SCx4OjQ">Dissimilar avatars were shown to enhance interaction and address VR limitations, but no evidence of post-discussion attitude extremization or group polarization was reported</snippet>. <snippet id="S_Ic4WSGm">Motion artifacts were studied for their impact on self-agency in avatar control, not for group dynamics or polarization effects</snippet>. Therefore, the current search results lack the concrete multi-user IVE evidence the agent is seeking for group polarization via avatars.
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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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<snippet id="S_uPQ5gEz">Nikola Tesla's Electric Arc Lamp patent is US335786A, titled "Electric arc lamp" with improvements in Electric-Arc Lamps</snippet>, and <snippet id="S_CBhXwnS,S_L4MYIrf">it was patented on February 9, 1886</snippet>. <snippet id="S_ktaELRS">The patent number is 335,787 for an Electric arc lamp with automatic fail switch and reactivation features</snippet>, though some sources show 335,786 as the primary arc lamp patent number. <snippet id="S_yVKm3fu">The Commutator for Dynamo-Electric Machines was issued on January 26, 1886, and the Electric Arc Lamp on February 9, 1886</snippet>, confirming the commutator was first by issue date. <snippet id="S_iDdAzGv">The Electric Arc Lamp patent involved electromagnets and lever mechanisms to separate and feed carbon electrodes</snippet>.
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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 is titled "Rhino Rocket with Tina Munjal, MD" and is part of Stories from the World of Medicine, Season 3 Episode 2</snippet>, <snippet id="S_cGRsAZI,S_IhbWqXF">with a publication date of February 18, 2020</snippet>. <snippet id="S_cGRsAZI,S_Q1JiXuV">The guest is Otolaryngologist Tina Munjal, MD</snippet>, and the episode is available on The Nocturnists Podcast platform <snippet id="S_cGRsAZI">at https://thenocturnists.org/podcast/rhino-rocket</snippet>. <snippet id="S_NZ9gUn7,S_arbvh0R">The episode features Tina Munjal telling a story about learning to be comfortable outside of her comfort zone</snippet>, <snippet id="S_NZ9gUn7">and is approximately 30 minutes in duration</snippet>. <snippet id="S_C2IKgBh">The episode is also listed on the Libsyn platform</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 available search results mention the controversial concept of de-extinction, particularly for species driven to extinction by humans, suggesting that functional proxies of these species could be beneficial for ecosystems</snippet>. <snippet id="S_aQHGJDJ,S_BVGS7oK,S_yJ0bKZt">Several reviews discuss evolutionary potential (EP) as a proxy for extinction risk, noting that frameworks incorporating EP into quantitative extinction-risk assessments remain at the frontier of ecological-evolutionary research</snippet>. <snippet id="S_160DJq3,S_kpUOwfs">Other reviews focus on late-Quaternary megafauna extinctions, patterns, and causes, with emphasis on body mass thresholds and ecological consequences</snippet>. <snippet id="S_ZV4SO7N">One study indicates that undescribed species have higher extinction risk than known species, primarily due to biological traits, phylogeny, and vulnerability to human disturbances</snippet>. <snippet id="S_btUw1xy">The review also addresses cloning techniques like somatic cell nuclear transfer (SCNT) as a potential method for de-extinction of recently extinct mammals with preserved tissues</snippet>. However, the provided snippets do not contain comprehensive 2022-2025 reviews specifically using the term "de-extinction" or "proxy de-extinction" with the detailed governance, ethics, and cost-effectiveness debates the agent is seeking.
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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>, with <snippet id="S_jARyMl0,S_BQ1tanr">the baryon chemical potential in neutron stars typically ranging from several hundred MeV to a few GeV</snippet> depending on the model and conditions. <snippet id="S_exyOPhA">The critical neutron chemical potential for the hadron-quark phase transition lies between 1050 MeV and 1400 MeV at zero temperature</snippet>, defining where quark matter becomes dominant. <snippet id="S_dKTw9kz">The baryon chemical potential is derived from µ_B = (P_nuc + ρ_nuc)/n_B, where it is expected to be in the GeV range</snippet> for neutron star cores. <snippet id="S_WRBepHG">In beta equilibrium, the chemical potentials satisfy relations like µp = µn - µe</snippet>, with higher-mass hyperons (Λ, Σ, Ξ) forming in high-density environments when their chemical potential condition is satisfied. However, <snippet id="S_r56vXjO">specific values for the baryon chemical potential in beta equilibrium are not provided</snippet> in many sources, as the literature focuses on the interdependence of chemical potentials rather than tabulated numerical values.
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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">Bond et al. (2012) conducted a landmark experiment in 2010 involving 61 million Facebook users during the U.S. Congressional Election</snippet>, <snippet id="S_e9zzIKc">where users were shown messages at the top of their News Feeds encouraging them to vote and displaying images of friends who had already voted</snippet>. <snippet id="S_e9zzIKc">The results showed the Facebook social message increased turnout by close to 340,000 votes</snippet>. <snippet id="S_mXK3pZh,S_8SBonkH">Replication experiments in 2012 confirmed the effect, with 60,000 additional votes directly attributed to the message and approximately 280,000 more votes from close friends of those who received the message</snippet>. <snippet id="S_GveNEie">The study demonstrated that people who know their Facebook friends voted are more likely to vote themselves, showing social influence through peer interactions</snippet>. <snippet id="S_gGLgZLn">However, the authors acknowledged very small effects from the information treatment, highlighting the need for careful interpretation of statistical significance in large-scale experiments</snippet>. <snippet id="S_37bk94m">This manipulation exploited human heuristics, using "social proof" to encourage users to imitate their behavior rather than rely on direct algorithmic recommendations</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 explicitly confirms the launch date for North America, Australia, and New Zealand as November 23, 2004</snippet>, providing the fourth independent confirmation from a major game outlet. <snippet id="S_mhE8bKy">GamesIndustry.biz corroborates this with a press announcement stating World of Warcraft will be in stores in North America on November 23, 2004</snippet>, launching simultaneously in Australia and New Zealand. <snippet id="S_3ft58Qe">Wikipedia notes the game was released for the 10th anniversary of the Warcraft franchise on November 23, 2004</snippet>. <snippet id="S_4am1dTv">IGN's 2010 article also confirms the initial launch date in North America on November 23, 2004</snippet>. <snippet id="S_Iyt7Mpt">Additional IGN coverage from November 2004 references the game's release date as November 23</snippet>. The minimal boxed answer is November 23, 2004.
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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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Cytokinin (CK), strigolactone (SL), and auxin form a regulatory network where CK promotes axillary bud outgrowth while SL and auxin act as inhibitors <snippet id="S_q2ORi2Q">CK promotes axillary bud outgrowth, while SL inhibits it, with both hormones acting antagonistically through the transcription factor TEOSINTE BRANCHED 1 (BRC1)</snippet>. Auxin inhibits CK biosynthesis while promoting SL biosynthesis, which contributes to bud dormancy <snippet id="S_XRhJBhI">Auxin inhibits CK biosynthesis while promoting SL biosynthesis, contributing to bud dormancy</snippet>. In this hormonal interplay, auxin cannot directly regulate BRC1 expression because it is not transported from the stem to the buds in great enough amounts <snippet id="S_gqi212y">Auxin cannot directly regulate BRC1 expression because it is not transported from the stem to the buds in great enough amounts</snippet>. Instead, auxin indirectly promotes BRC1 expression in the bud through the control of two antagonistic factors, CK and SL <snippet id="S_gqi212y">Auxin can indirectly promote BRC1 expression in the bud (Aguilar-Martínez et al., 2007). Furthermore, auxin-mediated BRC1 regulation through the control of two antagonistic factors, CK and SL, fine-tunes BRC1 expression inside buds</snippet>. BRC1 functions as a key integrator of pathways that suppress bud outgrowth, including those mediated by SL, auxin, and cytokinin <snippet id="S_Uuo070s">In Arabidopsis, BRANCHED1 (BRC1) is identified as a key integrator of pathways that suppress bud outgrowth, including those mediated by SL, auxin, cytokinin, and sugar</snippet>. This hormonal regulation is crucial for understanding shoot architecture in various plant species, including Arabidopsis and crops <snippet id="S_NtlEzla">BRC1, along with its homolog BRC2, encodes TCP transcription factors that are conserved across many plant species and play a critical role in determining 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 annually), and Enterprise Pro ($40/month per seat or $400/year with 16% discount for larger organizations)</snippet>. <snippet id="S_Y8Mojus">The Free version allows unlimited concise searches, while Pro includes multi-step reasoning searches, deep research queries (limited to three per day), and file analysis for PDFs and images</snippet>. <snippet id="S_5WLXO2p,S_Gk4sG8w">The Pro plan provides access to advanced AI models like GPT-4, Claude 3, and GPT-4o, with 300 daily Pro searches and image generation capabilities</snippet>. <snippet id="S_Y8Mojus">Enterprise Pro includes unlimited queries, extensive deep research (500 per day), organizational file repositories, advanced security options, and dedicated support</snippet>. <snippet id="S_yvm9XpN">Users find the Pro subscription worthwhile for research and casual searches, but note limitations including restricted programming support, challenges with long-form content generation, and file handling issues</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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