Peking University (PKU; Chinese: 北京大学; lit. 'University of Beijing') is a public research university in Beijing, China. The university is funded by the Ministry of Education. Peking University was established as the Imperial University of Peking in 1898 when it received its royal charter by the Guangxu Emperor. A successor of the older Guozijian Imperial College, the university's romanized name 'Peking' retains the older transliteration of 'Beijing' that has been superseded in most other contexts. Ranked as one of the top academic institutions in China, as of 2021 Peking University was ranked 16th globally and 1st in the Asia-Pacific & emerging countries by Times Higher Education, while as of 2022 it was ranked 12th globally and 1st in China by QS World University Rankings.Throughout its history, Peking University has had an important role \"at the center of major intellectual movements\" in China. Abolished of its status as a royal institution after the fall of the Qing dynasty and the Xinhai Revolution; from the early 1920s, the university became a center for China's emerging, progressive, and republican movements. Faculty and students held important roles in originating the New Culture Movement, the May Fourth Movement protests, and other significant cultural and sociopolitical events, to the extent that the university's history has been closely tied to that of modern China. Peking University has educated and hosted many prominent modern Chinese figures, including Mao Zedong, Lu Xun, Gu Hongming, Hu Shih, Mao Dun, Li Dazhao, Chen Duxiu, and current Premier Li Keqiang.Peking University is a member of the C9 League, Double First Class University Plan, Project 985 and Project 211. The university library contains over 8 million volumes. The university also operates the PKU Hall, a professional performing arts center, and the Arthur M. Sackler Museum of Arts and Archaeology. The university is also renowned for its campus grounds and the beauty of its traditional Chinese architecture. Additionally, it hosts one of the only undergraduate liberal arts colleges in Asia, and is a Class A institution under the Chinese Double First Class University program. Peking University's staff include 76 members of the Chinese Academy of Sciences, 19 members of the Chinese Academy of Engineering and 25 members of the World Academy of Sciences.
Variational quantum algorithms (VQAs) have established themselves as a central computational paradigm in the Noisy Intermediate-Scale Quantum (NISQ) era. By coupling parameterized quantum circuits (PQCs) with classical optimization, they operate effectively under strict hardware limitations. However, as quantum architectures transition toward early fault-tolerant (EFT) and ultimate fault-tolerant (FT) regimes, the foundational principles and long-term viability of VQAs require systematic reassessment. This review offers an insightful analysis of VQAs and their progression toward the fault-tolerant regime. We deconstruct the core algorithmic framework by examining ansatz design and classical optimization strategies, including cost function formulation, gradient computation, and optimizer selection. Concurrently, we evaluate critical training bottlenecks, notably barren plateaus (BPs), alongside established mitigation strategies. The discussion then explores the EFT phase, detailing how the integration of quantum error mitigation and partial error correction can sustain algorithmic performance. Addressing the FT phase, we analyze the inherent challenges confronting current hybrid VQA models. Furthermore, we synthesize recent VQA applications across diverse domains, including many-body physics, quantum chemistry, machine learning, and mathematical optimization. Ultimately, this review outlines a theoretical roadmap for adapting quantum algorithms to future hardware generations, elucidating how variational principles can be systematically refined to maintain their relevance and efficiency within an error-corrected computational environment.
IZA DP No. 14839 NOVEMBER 2021 Formalized Employee Search and Labor Demand* Firms often use social networks to find workers, limiting the pool of potential applicants. We conduct a field experiment subsidizing firms’ formal vacancy posting. The subsidies increase non-network employee search and shift vacancies towards high-skilled positions. Post-treatment, firms continue searching for high-skilled workers despite reverting to network-based search. This change in skill requirements does not increase vacancy posting or hiring, suggesting substitutability between workers of different skill levels. Finally, we experimentally show that information asymmetries about applicants’ skills do not limit firms’ formal search. Our results highlight that exposure to different labor market segments can permanently change firms’ labor demand. JEL Classification: D22, J23, J46, C93
Helium (He)-induced degradation of structural materials remains a critical challenge for advanced nuclear energy systems, yet the optimal microstructural strategy to mitigate such damage is unclear. Here, we investigate the microstructural evolution of an oxide dispersion-strengthened (ODS) steel containing high-density oxide particles with misfit interfaces, using single Fe2+ (15 dpa) and dual-beam Fe2 + + He+ irradiation at 450 degrees C (15-50 dpa, 10 appm He/dpa). Single ion irradiation at 15 dpa produces no obvious voids, whereas He markedly promotes cavity nucleation. Under dual ion beam irradiation, cavities preferentially nucleate at misfit oxide particle interfaces rather than at grain boundaries, thereby suppressing He migration toward grain boundaries. On individual oxide particles, cavities tend to form at regions with the most severe lattice misfit, suggesting that misfit heterogeneity governs preferred nucleation sites. With increasing dose, cavities at oxide interfaces exhibit a broader size distribution, while grain-boundary cavities remain small. This indicates that the defect trapping capability of oxide particles is significantly stronger than that of grain boundaries. The synergistic trapping of defects by oxide particles and grain boundaries effectively inhibits large cavity formation, contributing to excellent swelling resistance in ODS steels. These findings elucidate the role of misfit oxide particle interfaces in defect trapping and He management, providing guidance for the design of radiation-tolerant nuclear materials. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Reconstructing causal dynamic networks from multivariate time series is a foundational problem in complex systems science. Yet, the key scientific issue is not simply causality detection, but causal interpretability. Interpretable causality is the foundation for testable mechanistic hypotheses, transferable forecasting, and principled decision-making for intervention and control. In real-world complex systems, causal inference is often compromised by noise, missing data, high dimensionality, nonlinearity, time delays, heterogeneity, and partial observability. Classic approaches to interpretable causality yield explicit, inspectable quantities such as causal graphs, coefficients, and governing equations. However, this methodological shift has heightened expectations: AI-extended approaches are increasingly required to recover explicit causal mechanisms rather than opaque predictive dependencies, thereby preserving interpretability. This review summarizes four classic methods and their AI-extended counterparts based on time series data, including Granger frameworks, information-theoretic measures, nonlinear state–space/manifold reconstruction, and mechanistic differential-equation learning. Next, we elucidate their motivations, core principles, the origins of interpretability, and the assumptions required for meaningful conclusions. Finally, we highlight representative applications across climate studies, neuroscience, epidemiology, finance, social science, ecology and molecular biology, followed by a discussion of comparative analysis, open challenges, and future research directions.
Effective tourism management requires understanding how attraction attributes and supporting factors shape tourists’ perceived attractiveness, and how these in turn translate into visitation. We develop a tourist-centered framework using 610,136 reviews across 2128 heritage attractions in 143 cities, measuring multi-scale perceived attractiveness with a large language model pipeline and visitation with review volume, then identifying the factors that influence both metrics using XGBoost and SHAP. Results show that (1) perceived attractiveness and visitation share a multi-core spatial structure, while visitation displays a polarized long-tail distribution; (2) attraction attributes, especially official designation, visual quality, and ticket pricing, dominate both metrics but shape them differently; (3) positively perceived social, economic, scientific, historic and aesthetical values increase visitation, whereas political value is negatively associated; and (4) accessibility and spatial agglomeration also contribute positively. Practically, multi-scale benchmarking identifies underperforming attractions, while attraction-scale value profiles and the identified influencing factors guide targeted strategies.
