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<snippet id="S_0NbePdE,S_RLXmKuG">The EU AI Act mandates that high-risk AI systems must provide sufficient transparency mechanisms and be "sufficiently transparent to enable users to interpret outputs," as outlined in Article 13</snippet>. <snippet id="S_TVBhkcK,S_WG0otDu">Article 14(3) requires human overseers to have the authority to decide against using the AI system, override its outputs, and intervene in its operation, including the ability to halt it safely</snippet>. <snippet id="S_UuXjGBn">Article 11(2) allows for a unified technical documentation file that combines 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) details 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_xQp268d">General-purpose AI (GPAI) systems are subject to high-risk obligations if they can be used in high-risk contexts, with Article 53 requiring technical documentation and transparency in the value chain</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, enabling users to log, monitor, and share fitness accomplishments through status updates, comments, photos, and leaderboards</snippet>. <snippet id="S_tyjIFLb,S_KfOy5t1">Core gamification techniques include challenges where users compete to complete distances, receiving digital badges, trophies, and special prizes for completion</snippet>. <snippet id="S_ohmbRBN,S_tyjIFLb">The app fosters competitive behaviors and motivation through tracking routes, providing performance feedback, and encouraging self-presentation and comparison with friends</snippet>. <snippet id="S_KfOy5t1,S_47DnIT5">Social comparison is a key psychological driver for engagement, with users participating in competitive challenges and cooperative features to foster intrinsic motivation and accountability</snippet>. <snippet id="S_fdz63NK">However, users often selectively share data, withholding metrics like heart rate and wattage while opting for basic information such as segment times and elevation</snippet>. <snippet id="S_fdz63NK,S_5nbN41y">This behavior reflects a desire for self-validation and awareness of how others perceive their data, with longitudinal tracking needed to validate causal relationships and understand user retention</snippet>. <snippet id="S_QiL3aAT,S_5nbN41y">Research relies on cross-sectional samples, with limitations including potential biases from self-reported data and need for longitudinal studies to capture user behaviors over time</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>, with energy resources from Canada subject to a lower 10% tariff rate. <snippet id="S_ixOFgje">These tariff rates are part of President Trump's action to address illegal immigration and fentanyl-related national emergency threats</snippet>, as declared under the International Emergency Economic Powers Act (IEEEPA). <snippet id="S_ixOFgje">The fact sheet references trade statistics showing Canada, Mexico, and China contribute significantly to U.S. trade deficits, with 2023 U.S. trade deficit in goods exceeding $1 trillion</snippet>. <snippet id="S_ixOFgje">The document also notes that previous administrations failed to leverage America's economic position as a tool to secure borders against illegal migration and combat fentanyl</snippet>. However, the snippet does not provide specific effective dates for these tariff announcements, nor does it detail EU-specific tariff rates or retaliation measures from Canada, China, or the EU Commission. <snippet id="S_ixOFgje">The fact sheet emphasizes that these tariffs are a "powerful, proven source of leverage for protecting the national interest" in trade policy</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">Scholarly analysis of Orwell's Nineteen Eighty-Four slogans ("War is Peace," "Freedom is Slavery," "Ignorance is Strength") emphasizes their role in discursive drift, where meanings and stances shift over time in public discourse</snippet>. <snippet id="S_lgOMCo4">The term "doubleplus unfree" is cited as a rare but legitimate formation derived from Orwell's Newspeak in Nineteen Eighty-Four, exemplifying the intensifying use of language through relexicalization</snippet>. <snippet id="S_iDUQY7x">Slogans are defined as brief, striking phrases that may include labeling and stereotyping, acting as emotional appeals that can function as conversation killers by discouraging critical thought</snippet>. <snippet id="S_tpXkvyW">Metaphoric slogans are deployed to project covert ideology by creating us versus them dichotomies and representing positive-self and negative representation of others</snippet>. <snippet id="S_WxyOc9V">The metaphor of the "heart" has evolved from a conventional positive connotation to critical views influenced by sarcastic reinterpretations, altering evaluative connotations associated with being at the "heart" of Europe</snippet>. However, the available snippets do not provide direct scholarly analysis of the specific slogans' instantiation of doublethink or their role in thought control, which would require more targeted sources.
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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>. <snippet id="S_gsbJVy0">He will lead the Board of Directors as MRS President in 2025</snippet>, which indicates he held the concurrent title of President-Elect during the 2024 term. <snippet id="S_ZPx3NY5">Past MRS Presidents page also shows Takao Someya (2024) in the vice president/president-elect context</snippet>, though Eric Stach's appointment is confirmed for the 2024 Vice President position with the 2025 presidential transition.
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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">OASIS STIX 2.1 is a machine-readable, semi-structured framework for structuring and exchanging cyber threat intelligence (CTI) data, primarily using JavaScript Object Notation (JSON) instead of XML</snippet>. <snippet id="S_JYIyMdS,S_3JECVB5">The STIX 2.1 format defines 12 STIX Domain Objects (SDOs), including 'indicator', 'malware', 'report', and 'vulnerability', each with specific attributes</snippet>, while <snippet id="S_JYIyMdS">STIX Relationship Objects (SROs) enable the linking of multiple SDOs to facilitate complex representations of CTI</snippet>. <snippet id="S_lIvobvB">For malware-specific representation, the indicator SDO's pattern property can contain malware indicators, with mapping rules converting threat article values into formal STIX vocabulary</snippet>. <snippet id="S_3M0PARM">In practice, STIX bundles from real-world sources can contain 36,100 entities and 13,600 relations, featuring nine unique entity types and five unique relation types</snippet>. <snippet id="S_3TK690R">STIX uses UUIDs to establish connections between different objects through relationships, though this can lead to complex documents compared to simpler formats like MISP</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=3d29ab62-0,3d29ab62-1>The search results do not contain information about newly formed counties in Kohgiluyeh and Boyer-Ahmad Province during 2020-2024</snippet>. <snippet id=3d29ab62-0>The available snippets only provide general information that the province is one of Iran's 31 provinces in the southwest</snippet>. <snippet id=3d29ab62-1>Kohgiluyeh County is identified as being in the province with its capital at Dehdasht</snippet>. <snippet id=3d29ab62-2,3d29ab62-3,3d29ab62-4,3d29ab62-5,3d29ab62-6,3d29ab62-7,3d29ab62-8,3d29ab62-9>The remaining search results focus on various studies about language distribution, climate indices, groundwater, and agricultural productivity rather than county formation</snippet>. <snippet id=3d29ab62-5>The term "newly formed" appears only in the context of local and province level governments without specifying any county creations</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 the Virtual Reality & Digital Media project, the school developed real-time 3D graphics platform BH-GRAPH and distributed interactive simulation running support platform BH_RTI, constructed a distributed virtual environment DVENET supporting 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 research priorities in virtual reality, trusted computing, and aerospace industry applications.
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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">Research indicates that demographic factors such as being young, male, single, educated, and engaged in full-time studies or employment are associated with higher risks of problem gambling among university students in Nigeria</snippet>. <snippet id="S_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_C7vME7Z">An urban school-based cross-sectional survey involving 507 students in Nigeria also found a lifetime gambling prevalence of 57.2%, out of which 77.6% had gambled in the previous 12 months</snippet>. <snippet id="S_ksMf31Q">Studies from various countries, including Australia and Germany, highlight that typical sports bettors tend to be male, often with lower household incomes but a strong interest in sports</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>. However, <snippet id="S_d3zPQEk">specific data on university students in Nigeria is not detailed in the esports betting study, which instead uses data from Great Britain</snippet>. <snippet id="S_rqYsRo2">A study involving 5,000 college students from 12 universities in Ghana explored the role of financial literacy in predicting financial behavior, which may relate to the prevalence of sports betting among university students in Nigeria</snippet>.
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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 can be accessed at lmarena.ai, which has collected over 3.5M votes</snippet>. <snippet id="S_6IdzsAE">Previous leaderboard updates have been published by LMSYS, including an Elo rating system based on anonymous voting data</snippet>. However, <snippet id="S_831cvJ0,S_5A51bt1">the provided search snippets do not contain the specific current top model name, Elo rating, or timestamp/update note from the live leaderboard page</snippet>. <snippet id="S_hhzNfal">The leaderboard has also expanded to include multimodal rankings based on image-containing battles</snippet>. To obtain the current top model information, direct access to the live leaderboard at lmarena.ai is 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,S_Cn1GhvJ,S_p938Lo0">DESI observations indicate that dark energy has an equation of state w(z) crossing the phantom barrier (w < -1) for z > 1, with DESI DR2 BAO data suggesting a potential crossing at z_c ≃ 0.45</snippet>, <snippet id="S_ZCPd5ub">which favors a dynamical dark energy characterized by a phantom crossing feature</snippet> and <snippet id="S_ZIWoomJ">implies a lower value of the Hubble constant, thereby intensifying the so-called Hubble tension</snippet>. <snippet id="S_ZIWoomJ">However, the w0wa parametrization is a phenomenological ansatz that is not based on a physical and selfconsistent model of dark energy</snippet>, <snippet id="S_ZIWoomJ">where there is no obstacle to the phantom regime w < -1, which is unphysical in general relativity</snippet>. <snippet id="S_otM3Qb8">While DESI measurements suggest dark energy may be evolving into the phantom regime with w(z) < -1, current data remains inconclusive regarding the existence of a phantom crossing</snippet>. <snippet id="S_p938Lo0">Most subsequent works assessed this issue, where most of them showed that the z = 0.51 and z = 0.71 BAO data points could be responsible for this result</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">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 represents the dose that elicits lethality in 1% of the population, while the ED99 represents the dose that elicits therapeutic effect in 99% of the population</snippet>. <snippet id="S_KtkXCqd">Some formulations express margin of safety as a function of cube root of ratio between LT50 and LD50 and one-hundredth of ED</snippet>. However, <snippet id="S_ITpLCrH">none of the provided search results discuss conditions under which margin of safety cannot be calculated or when it fails to appear as a meaningful value</snippet>. <snippet id="S_PNN5Uwr">The therapeutic index (LD50/ED50) is commonly used as a measure of drug safety</snippet>. The search results confirm the standard definition but do not address scenarios where this metric would be 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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The search results do not contain explicit demonstrations of group polarization or risky shift in avatar-mediated immersive VR environments. While some studies discuss avatar visual fidelity and its effects on behavior, they do not report group discussion contexts or pre/post-discussion attitude comparisons <snippet id="S_X5s1Ifa">avatar visual fidelity did not significantly affect self-location or agency, but abstract avatars led to increased risky behaviors</snippet>. One study notes that dissimilar avatars can enhance social interactions but does not detail group polarization outcomes <snippet id="S_SCx4OjQ">research indicates that the diverse range of avatar appearances can enhance user interaction, lead to perceptual and behavioral changes, address VR limitations, and improve social interactions</snippet>. Another snippet mentions participants controlling avatars in a virtual environment but explicitly states that "specific findings related to 'risky shift' in virtual reality avatars were not detailed in the provided text" <snippet id="S_JIjtTEQ">the aim was to explore the differential prediction of social anxiety and persecutory ideation in an experimental context, although specific findings related to "risky shift" in virtual reality avatars were not detailed in the provided text</snippet>. No snippets provide concrete evidence of multi-user IVEs with group discussion cues where attitude extremity increases relative to pre-discussion baselines.
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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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<snippet id="S_uPQ5gEz,S_CBhXwnS,S_L4MYIrf">Nikola Tesla's Electric Arc Lamp patent was US 335,786, issued on February 9, 1886</snippet>. <snippet id="S_s2HVIjN,S_ktaELRS">The patent (US 335,787) was for an electric arc lamp with two magnets in the main and shunt circuits, respectively, along with an armature-lever and feed-mechanism</snippet>. <snippet id="S_yVKm3fu">This was issued on the same day (February 9, 1886) as Tesla's second patent, which was for an improved electric arc lamp using electromagnets and lever mechanisms</snippet>. <snippet id="S_L4MYIrf">The Electric-Arc Lamp patent (US 335786) was granted to Nikola Tesla of Smiljan Lika, Austria-Hungary</snippet>. <snippet id="S_ktaELRS">The patent included an automatic fail switch when arc possesses abnormal behavior and automatic reactivation features</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>, <snippet id="S_NZ9gUn7,S_arbvh0R">who tells a story about learning to be comfortable outside of her comfort zone</snippet>. <snippet id="S_cGRsAZI">The episode is available on The Nocturnists Podcast website at thenocturnists.org/podcast/rhino-rocket</snippet>, <snippet id="S_IhbWqXF">and is also listed on the official Stories From The World Of Medicine page</snippet>. <snippet id="S_NZ9gUn7">The episode runtime is approximately 30 minutes</snippet>, <snippet id="S_C2IKgBh">and is sponsored by The Nocturnists podcast network</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 do not contain explicit 2022-2025 dated reviews or perspectives using the term "de-extinction" or "proxy de-extinction"</snippet>. <snippet id="S_aQHGJDJ,S_BVGS7oK,S_yJ0bKZt">The closest matching concepts appear in reviews titled "Linking evolutionary potential to extinction risk" which discuss proxies for evolutionary potential rather than de-extinction specifically</snippet>. <snippet id="S_btUw1xy">One snippet mentions "the controversial concept of de-extinction" but only in the context of genomic modifications for species driven to extinction by humans</snippet>. <snippet id="S_160DJq3,S_kpUOwfs">Other results focus on late-Quaternary megafauna extinctions and their ecological consequences rather than de-extinction technology or governance</snippet>. <snippet id="S_hlWQ6qg,S_ZV4SO7N,S_zN6EiNI,S_pIULMfn">The remaining snippets address general conservation topics including extinction risk assessments, biodiversity shortfalls, and conservation paleobiology without de-extinction terminology</snippet>. <answer>
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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_exyOPhA">the critical neutron chemical potential for the hadron-quark phase transition lying between 1050 MeV and 1400 MeV at zero temperature</snippet>. In beta-equilibrated hadronic matter, <snippet id="S_dKTw9kz">the baryon chemical potential is expected to be in the GeV range</snippet>, and <snippet id="S_jARyMl0,S_BQ1tanr">specific values for the neutron chemical potential in beta equilibrium are not provided in the text</snippet> but the overall framework suggests the baryon chemical potential in neutron stars is typically in the range of several hundred MeV to a few GeV. <snippet id="S_WRBepHG">Neutron stars reach beta equilibrium involving neutrons, protons, and electrons, characterized by the relationship µp = µn - µe</snippet>, where the chemical potentials of the baryons must satisfy specific relations at high densities. <snippet id="S_in5xo5t">The density dependence of the neutron and proton chemical potentials from different models are presented in figures</snippet>, showing that at all densities the neutron chemical potentials of the two models agree.
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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 received get-out-the-vote messages</snippet>, with <snippet id="S_e9zzIKc">results showing the Facebook social message increased turnout by close to 340,000 votes</snippet>. <snippet id="S_GveNEie,S_37bk94m">Participants in the "Social message" group saw a voting prompt that included images of friends who had already voted, while the "informational message" group received the same prompt without this social context</snippet>, and <snippet id="S_GveNEie">results showed that those exposed to the social message were more likely to vote</snippet>. <snippet id="S_mXK3pZh,S_8SBonkH">The study found that people who know that their Facebook friends voted are more likely to vote themselves, with approximately 60,000 individuals voting directly and an additional 280,000 influenced indirectly through close friends with strong offline relationships</snippet>. <snippet id="S_Z2mbNNa">Replication data from the 2012 U.S. Presidential Election showed a total increase of 270,000 people voting, with treatment effects spreading through the network to cause an additional 180,000 close friends of the treated to vote</snippet>. <snippet id="S_gGLgZLn">The study underscores the need for researchers to adapt their reporting practices in the context of big data, ensuring that findings are accurately contextualized and not overstated</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 needed. <snippet id="S_4am1dTv">Another IGN article states World of Warcraft first launched in North America on November 23, 2004 with several expansion add-ons being released for the game since</snippet>. <snippet id="S_Iyt7Mpt">A subsequent IGN report also references the November 23 launch date when discussing Blizzard's reporting on game sales</snippet>. This date is consistent across all sources including Wikipedia, Activision's investor press release, GamesIndustry.biz, and multiple IGN articles. The minimal boxed answer for the official release date 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) promotes axillary bud outgrowth, while auxin and strigolactone (SL) act as inhibitors by suppressing CK biosynthesis and upregulating SL biosynthesis genes <snippet id=S_jYWskoE>Auxin inhibits bud outgrowth through the promotion of systemic and local strigolactone (SL) synthesis by upregulating SL biosynthesis genes, MAXs (more axillary growth) in Arabidopsis</snippet><snippet id=S_q2ORi2Q>Auxin, produced in the main shoot tip, suppresses the growth of axillary buds by reducing cytokinin (CK) levels and enhancing strigolactone (SL) biosynthesis</snippet>. The key transcription factor BRANCHED1 (BRC1) functions as a repressor of bud outgrowth that is regulated by auxin, CK, and SL <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><snippet id=S_gqi212y>Auxin, cytokinins (CK), and strigolactones (SL) are implicated in the hormonal regulation of BRC1 expression. In this regulation network, auxin and SL act as inducers while CK act as repressors</snippet>. Auxin cannot directly regulate BRC1 expression because it is not transported from the stem to the buds in great enough amounts, but it indirectly promotes BRC1 expression through control of antagonistic factors CK and SL <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><snippet id=S_jLTk85S>Auxin inhibits cytokinin biosynthesis through an AXR1-dependent pathway, suppressing axillary bud outgrowth</snippet>. Strigolactones function as endogenous inhibitors of shoot branching, with SL mutants displaying increased branching <snippet id=S_nFj5ih3>Strigolactones (SLs), a group of plant hormones synthesized from carotenoids, have been identified as suppressors of axillary bud outgrowth</snippet><snippet id=S_gLbtlMj>Strigolactones (SL), which are derived from carotenoids, function as endogenous inhibitors of shoot branching</snippet>. BRC1 is a conserved TCP transcription factor that interacts with hormones, nutrients, and light to determine branching phenotype <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 for its AI Copilot services: Free, Pro, and Enterprise Pro, with the Pro version costing $20/month or $200 annually</snippet>. <snippet id="S_Y8Mojus">The Pro tier includes additional features like 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">Users also have access to advanced AI models including GPT-4o, Claude 3.5 Sonnet, and DeepSeek R1</snippet>. <snippet id="S_Y8Mojus">The Enterprise Pro tier is priced at $40/month per seat or $400/year (16% discount) and provides unlimited queries, extensive deep research capabilities (500 per day), and enhanced collaboration features</snippet>. <snippet id="S_yvm9XpN">Some users note limitations including restricted programming support, challenges with long-form content generation, and issues with file handling and custom bots</snippet>.
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Perplexity AI offers three pricing tiers: Free, Pro ($20/month or $200/year), and Enterprise Pro ($40/month per seat or $400/year) with discounts for annual billing <snippet id="S_Y8Mojus,S_Gk4sG8w,S_5WLXO2p">. The Pro subscription includes access to advanced AI models like GPT-4o, Claude 3.5 Sonnet, and Claude 3.7, plus unlimited Copilot queries, file upload for PDFs and images, and real-time web access <snippet id="S_Gk4sG8w,S_5WLXO2p,S_yvm9XpN">. Pro users also receive features like multi-step reasoning searches, deep research queries (3-300 daily depending on source), and Perplexity Labs for structured outputs <snippet id="S_Y8Mojus,S_Gk4sG8w">. However, some users report limitations in programming support, long-form content generation, and file handling compared to competitors <snippet id="S_yvm9XpN">. Enterprise Pro adds organizational file repositories, collaboration features, unlimited deep research (500 per day), and advanced security options <snippet id="S_Y8Mojus">.
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