The rapid evolution and self-improving nature of artificial intelligence (AI) present severe societal, security, and governance challenges that traditional, slow-moving legal frameworks are ill-equipped to manage. Focusing heavily on high-risk domains like military operations (e.g., Lethal Autonomous Weapons Systems and Aided Target Recognition) and cybersecurity (e.g., AI-driven spear-phishing and model data poisoning), this paper highlights the urgent need to address the "pace regulation" problem before critical threshold crises occur.

To reconcile the need for safety with the desire to foster technical innovation, the paper advocates for "soft law" instruments—such as voluntary guidelines, codes of conduct, and international frameworks like the OECD AI Principles. These non-binding, adaptable mechanisms offer the flexibility needed to provide immediate regulatory updates, encourage corporate accountability, and promote international cooperation without crippling technological growth.

This profile examines the life of Xiao Wu, a 55-year-old Chinese immigrant co-owning a beauty and massage shop in Newtown, USA. After entering the country undocumented to seek higher earning potential, she secured legal status through humanitarian relief related to China's one-child policy and brought her family to the United States. Despite benefiting from age-inclusive employment and reliable health insurance, Wu faces significant personal and financial strain: her husband and son both returned to China due to employment and cultural integration struggles, leaving her working 12.5-hour shifts seven days a week to afford her mortgage. To supplement her income, she sublets her spare room to visiting Chinese mothers. Ultimately, the piece illustrates the complex trade-offs of the immigrant experience, highlighting how economic opportunity and property ownership come at the cost of grueling labor, financial precarity, and family dispersion, driving Wu's ultimate goal to retire back in China.

This paper examines the political and statistical dynamics of Zohran Mamdani’s mayoral campaign, analyzing how his democratic socialist strategy disrupted traditional New York City electoral logic. Moving beyond conventional class-based progressive appeals, Mamdani implemented a localized socialist framework centered on immediate cost-of-living concerns, such as rent freezes and expanded public transit. Statistical election returns reveal an unexpected coalition realignment: Mamdani achieved broad-based support by outperforming expectations in majority-white precincts, expanding margins among Hispanic and Asian communities, and securing key support from middle-class Black voters, wealthy Manhattanites, and Orthodox/Hasidic Jewish precincts historically aligned with establishment figures. Crucially, his campaign centered on a high-variance strategy targeting historically low-turnout young voters under the unifying slogan "A City We Can Afford." The study contrasts Mamdani's decentralized, coalition-driven movement with traditional legacy-based power structures, while evaluating the long-term governance risks—specifically the potential alienation of private capital and the challenge of sustaining momentum through volatile grassroots activism rather than established party infrastructure.

This study investigates the relationship between occupational earnings, skill classifications, and the likelihood of technological automation across 490 occupations in California. Merging 2025 Occupational Employment and Wage Statistics (OEWS) data with Frey & Osborne's automation probability estimates by SOC code, the analysis evaluates how annual wages and skill types correspond to automation vulnerability.

The findings show a statistically significant, moderate negative linear relationship between mean annual wage and automation risk ($r = -0.6036$, $p < 0.0001$), with wage explaining roughly 36% of the variance in automation probability. A one-way ANOVA further reveals substantial variation in automation risk across broad skill groups ($F = 114.05$, $p < 0.0001$). Routine cognitive, clerical, sales, and manual occupations face the highest risk of automation, whereas high-skill analytical and interpersonal/care/creative occupations exhibit very low risk. The study concludes that non-routine interpersonal and analytical skills—rather than salary alone—serve as the primary protective buffer against automation

This paper examines the shift from the utopian vision of a borderless cyberspace to an era of geopolitical struggle over digital sovereignty and narrative control. As global tech platforms like Meta, Google, TikTok, and X replace traditional media as modern gatekeepers, their recommendation algorithms systematically flatten cultural diversity, favor commercially viable formats, and marginalize underrepresented languages and local contexts. In response, nation-states are asserting jurisdiction through judicial enforcement (Brazil and Kenya), regulatory mandates (the EU and India), and strict legislative control (China). Ultimately, the document argues that digital sovereignty is no longer just a question of data security or censorship, but a fundamental contest over who holds the authority to shape national identity and public discourse in the platform age.

Large sporting events create significant economic value but generate large environmental footprints. To address this, we developed the Environmental Sustainability Score (ESS)—a data-driven decision framework that evaluates and optimizes host selection based on ecological impact.

• Baseline Footprint (Problem 1): Using Super Bowl LIV as a Life Cycle Assessment baseline, we quantified event impacts at 8,750 metric tons of $\text{CO}_2\text{e}$ (62% travel-related), 12.3 GWh of energy, and 142 tons of waste.
• ESS Framework (Problem 2): We built a 6-dimension scoring model adjusting for local climate, geography, and infrastructure. Combining AHP and Entropy Weighting, the model matched historical host performance with high stability (94.7% top-rank consistency in Monte Carlo trials).
• 2029 Super Bowl Selection (Problem 3): Evaluating 9 candidate cities via regression and TOPSIS, Seattle ranked first (80.7) due to its 68% renewable energy grid and Platinum LEED stadium, followed by Los Angeles (77.0) and Denver (70.4).
• Olympic Extension & Optimization (Problem 4): Extended to 8 dimensions (adding SROI-based legacy value and multi-venue coordination efficiency), the model correlated strongly with IOC historical data ($\rho = 0.92$). A two-level optimization applied to Paris 2024 demonstrated how targeted investments (€1.8B) could raise sustainability scores from 68.5 to 85.2.
• NFL Recommendation (Problem 5): We formally advocate for Seattle as the 2029 Super Bowl host based on its superior environmental infrastructure and climate resilience.

Keywords: Life Cycle Assessment, Monte Carlo simulation, Two-level Optimization Model, Entropy Weight Method, Sustainability

This paper explores why sustained economic growth was rare prior to the late 18th century and examines the factors that catalyzed this shift. Using the "cake theory" as an analytical framework—where expanding total output enlarges the cake and policy/structural efficiency determines its distribution—the paper identifies two main drivers behind modern, steady economic growth: technological innovation and political/economic reform.

Key Takeaways:

• Technological Advancement: Driven by the Enlightenment and the practical application of natural philosophy (e.g., academic journals, scientific institutions, and inventions like the Newcomen steam engine), agricultural productivity and industrial manufacturing increased dramatically.
• Policy & Market Shifts: The decline of mercantilism, central state control, and feudalism led toward free-market capitalism, as seen in Adam Smith’s economic theories.
• Global Examples:
• Britain: Shifted toward free-market principles alongside extensive colonial expansion.
• Russia: Abolished serfdom during the Emancipation Reform to expand free labor and private enterprise.
• Japan: Transitioned from the Sakoku isolation policy and feudal structures toward proto-capitalism and large-scale industrialization.

This paper examines the contrasting democratic transitions in Chile and Argentina following military rule in the late 20th century.

• Argentina's Transition: Characterized as an abrupt regime collapse triggered by the defeat in the Falklands/Malvinas War, severe economic crisis, and loss of legitimacy. This provided President Raúl Alfonsín the opportunity to establish democratic legitimacy through immediate moral condemnation of the junta and key transitional justice measures (e.g., CONADEP's Nunca Más report and the 1985 Trial of the Juntas). However, this moral clarity was coupled with political instability and institutional weakness, leading to military rebellions and subsequent legislative concessions.
• Chile's Transition: Characterized by a negotiated withdrawal within a framework of strong institutional continuity. Augusto Pinochet's regime retained substantial power through the 1980 Constitution, appointed senators, and military prerogatives even after losing the 1988 plebiscite. Guided by the Concertación coalition, Chile pursued democratization incrementally under constrained conditions, balancing truth-telling with gradual accountability to preserve political stability.
• Core Conclusion: The mechanics of regime exit—replacement in Argentina versus transplacement in Chile—fundamentally shaped subsequent civil-military relations, party politics, labor mobilization, and the deployment of public memory in defining post-authoritarian democratic legitimacy.

The Eventown and Oddtown problems are classics in Extremal Combinatorics that analyze set systems under specific membership constraints. Focusing on finding the maximum number of even-sized clubs that 32 residents can form, this paper uses abstract set theory, linear algebra, and combinatorial techniques to bridge intuitive concepts with rigorous mathematical formalism.